├── .DS_Store ├── .Procfile.swp ├── .ipynb_checkpoints ├── data_engineering-checkpoint.ipynb ├── database_engineering-checkpoint.ipynb ├── dmgp_query-checkpoint.ipynb ├── feature_engineering-checkpoint.ipynb ├── ml_data_query-checkpoint.ipynb ├── nielsen_div_coordinate_query-checkpoint.ipynb └── test_data_for_bi-checkpoint.ipynb ├── Juputer_Notebook ├── .ipynb_checkpoints │ ├── bi_data_query-checkpoint.ipynb │ ├── data_engineering-checkpoint.ipynb │ ├── database_engineering-checkpoint.ipynb │ └── nielsen_div_coordinate_query-checkpoint.ipynb ├── age_label_reindex.ipynb ├── bi_data_query.ipynb ├── data_engineering.ipynb ├── feature_engineering.ipynb ├── ml_data_query.ipynb └── nielsen_div_coordinate_query.ipynb ├── ML ├── .DS_Store ├── .ipynb_checkpoints │ └── data_cleaning_r_ml-checkpoint.ipynb ├── Python │ ├── .ipynb_checkpoints │ │ ├── RF_model_python_with_prob-checkpoint.ipynb │ │ └── RF_model_python_with_scale-checkpoint.ipynb │ ├── RF_model_python_with_prob.ipynb │ ├── RF_model_python_with_scale.ipynb │ ├── all_possible_prediction.csv │ ├── prob_model_RF.pkl │ └── scale_model_RF.pkl ├── R │ ├── .RData │ ├── .Rhistory │ └── age_class_based_on_scale.Rmd ├── README.md └── images │ ├── graph1.png │ ├── graph10.png │ ├── graph11.png │ ├── graph2.png │ ├── graph3.png │ ├── graph4.png │ ├── graph5.png │ ├── graph6.png │ ├── graph7.png │ ├── graph8.png │ └── graph9.png ├── Procfile ├── README.md ├── Survey └── Project Survey.docx ├── app.py ├── bi_data └── bi_data.csv ├── clean_data ├── .ipynb_checkpoints │ └── cleaning_all_year-checkpoint.ipynb ├── all_possible_prediction.csv ├── all_region_prob_2013_2017.csv ├── category_index_reference.txt ├── clean_all_year_EC.csv ├── clean_all_year_EC_withCAT.csv ├── clean_all_year_EC_withIndex.csv ├── clean_data_ec_2013_to_2017.csv ├── clean_data_ne_2013_to_2017.csv ├── clean_data_p_2013_to_2017.csv ├── clean_data_s_2013_to_2017.csv ├── clean_data_se_2013_to_2017.csv ├── clean_data_sw_2013_to_2017.csv ├── clean_data_wc_2013_to_2017.csv ├── clean_demography_data.csv ├── ec_prob_2013_2017.csv ├── nielsen_div_coordinate.csv ├── prob.csv ├── r_data_18_35_55.csv └── r_data_18_35_55_scaled.csv ├── database.sqlite ├── database_engineering.ipynb ├── images ├── .DS_Store ├── Behavior Comperison Based on Generation Group.png ├── Frankenselfie-Logomoose.png ├── Gen Z + M.png ├── Geography.png ├── Linear.png ├── Population per Category per Generation DB.png ├── Project Flow Chart.jpg ├── behavior_prediction.jpg ├── data_engineering_1.png ├── database_engineering.png ├── david.png ├── feature_engineering.png ├── frontend_meet_the_team.png ├── graph1.png ├── graph10.png ├── graph11.png ├── graph2.png ├── graph3.png ├── graph4.png ├── graph5.png ├── graph6.png ├── graph7.png ├── graph8.png ├── graph9.png ├── income.jpg ├── loader.gif ├── machinelearning.png ├── map.png ├── math.jpg ├── merge_table.png ├── pref.png ├── project.jpg ├── project_background.jpg ├── python loop_1.png ├── query_avg_coordinate.png ├── query_avg_coordinate_2.png ├── reference.png ├── scaling process.png ├── sean.png ├── shutterstock_1017804253.jpg ├── shutterstock_1065761984.jpg ├── shutterstock_766093927.jpg ├── team.jpg ├── weighted_and_unweighted_explaination.png ├── weijing.png ├── xiangyu.png ├── yinling.png └── yoyo.png ├── raw_data ├── east_central_data │ ├── East Central │ │ ├── Movie Purcharser behavior EC 2017.cdct │ │ ├── Movie Purcharser behavior EC 2017.xlsx │ │ ├── Movie Purchaser Behavior 2013 CE.cdct │ │ ├── Movie Purchaser Behavior 2013 CE.xlsx │ │ ├── Movie Purchaser Behavior 2014.cdct │ │ ├── Movie Purchaser Behavior 2014.xlsx │ │ ├── Movie Purchaser Behavior 2015 CE.cdct │ │ ├── Movie Purchaser Behavior 2015 CE.xlsx │ │ ├── movie purchaser behavior 2016 EC.cdct │ │ └── movie purchaser behavior 2016 EC.xlsx │ ├── Movie Purchaser Behavior EC 2013.csv │ ├── Movie Purchaser Behavior EC 2014.csv │ ├── Movie Purchaser Behavior EC 2015.csv │ ├── Movie Purchaser Behavior EC 2016.csv │ └── Movie Purchaser Behavior EC 2017.csv ├── nielsen_division.csv ├── north_east_data │ ├── Movie Purchaser Behavior NE 2013.csv │ ├── Movie Purchaser Behavior NE 2014.csv │ ├── Movie Purchaser Behavior NE 2015.csv │ ├── Movie Purchaser Behavior NE 2016.csv │ └── Movie Purchaser Behavior NE 2017.csv ├── pacific_data │ ├── Movie Purchaser Behavior P 2013.csv │ ├── Movie Purchaser Behavior P 2014.csv │ ├── Movie Purchaser Behavior P 2015.csv │ ├── Movie Purchaser Behavior P 2016.csv │ └── Movie Purchaser Behavior P 2017.csv ├── south_data │ ├── Movie Purchaser Behavior S 2013.csv │ ├── Movie Purchaser Behavior S 2014.csv │ ├── Movie Purchaser Behavior S 2015.csv │ ├── Movie Purchaser Behavior S 2016.csv │ └── Movie Purchaser Behavior S 2017.csv ├── south_east_data │ ├── Movie Purchaser Behavior SE 2013.csv │ ├── Movie Purchaser Behavior SE 2014.csv │ ├── Movie Purchaser Behavior SE 2015.csv │ ├── Movie Purchaser Behavior SE 2016.csv │ └── Movie Purchaser Behavior SE 2017.csv ├── south_west_data │ ├── Movie Purchaser Behavior SW 2013.csv │ ├── Movie Purchaser Behavior SW 2014.csv │ ├── Movie Purchaser Behavior SW 2015.csv │ ├── Movie Purchaser Behavior SW 2016.csv │ └── Movie Purchaser Behavior SW 2017.csv ├── statelatlong.csv └── west_central_data │ ├── Movie Purchaser Behavior WC 2013.csv │ ├── Movie Purchaser Behavior WC 2014.csv │ ├── Movie Purchaser Behavior WC 2015.csv │ ├── Movie Purchaser Behavior WC 2016.csv │ ├── Movie Purchaser Behavior WC 2017.csv │ └── east_central_data │ ├── East Central │ ├── Movie Purcharser behavior EC 2017.cdct │ ├── Movie Purcharser behavior EC 2017.xlsx │ ├── Movie Purchaser Behavior 2013 CE.cdct │ ├── Movie Purchaser Behavior 2013 CE.xlsx │ ├── Movie Purchaser Behavior 2014.cdct │ ├── Movie Purchaser Behavior 2014.xlsx │ ├── Movie Purchaser Behavior 2015 CE.cdct │ ├── Movie Purchaser Behavior 2015 CE.xlsx │ ├── movie purchaser behavior 2016 EC.cdct │ └── movie purchaser behavior 2016 EC.xlsx │ ├── Movie Purchaser Behavior EC 2013.csv │ ├── Movie Purchaser Behavior EC 2014.csv │ ├── Movie Purchaser Behavior EC 2015.csv │ ├── Movie Purchaser Behavior EC 2016.csv │ └── Movie Purchaser Behavior EC 2017.csv ├── requirements.txt ├── static ├── .DS_Store ├── css │ ├── .DS_Store │ ├── animate.css │ ├── bootstrap.css │ ├── bootstrap.css.map │ ├── flexslider.css │ ├── icomoon.css │ ├── magnific-popup.css │ ├── style.css │ └── style.css.map ├── fonts │ ├── .DS_Store │ ├── bootstrap │ │ ├── glyphicons-halflings-regular.eot │ │ ├── glyphicons-halflings-regular.svg │ │ ├── glyphicons-halflings-regular.ttf │ │ ├── glyphicons-halflings-regular.woff │ │ └── glyphicons-halflings-regular.woff2 │ └── icomoon │ │ ├── icomoon.eot │ │ ├── icomoon.svg │ │ ├── icomoon.ttf │ │ └── icomoon.woff └── js │ ├── area_chart.js │ ├── bootstrap.min.js │ ├── bubble.js │ ├── jquery.countTo.js │ ├── jquery.easing.1.3.js │ ├── jquery.flexslider-min.js │ ├── jquery.magnific-popup.min.js │ ├── jquery.min.js │ ├── jquery.waypoints.min.js │ ├── magnific-popup-options.js │ ├── main.js │ ├── modernizr-2.6.2.min.js │ └── respond.min.js ├── tableau_work ├── Behavior Analytics.twbx └── debug.log └── templates ├── .DS_Store ├── index.html ├── machine_learning.html ├── meet_the_team.html ├── output.html ├── projects.html └── tableau_dashboard.html /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/.DS_Store -------------------------------------------------------------------------------- /.Procfile.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/.Procfile.swp -------------------------------------------------------------------------------- /ML/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/ML/.DS_Store -------------------------------------------------------------------------------- /ML/Python/prob_model_RF.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/ML/Python/prob_model_RF.pkl -------------------------------------------------------------------------------- /ML/Python/scale_model_RF.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/ML/Python/scale_model_RF.pkl -------------------------------------------------------------------------------- /ML/R/.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/ML/R/.RData -------------------------------------------------------------------------------- /ML/R/.Rhistory: -------------------------------------------------------------------------------- 1 | knitr::opts_chunk$set(echo = TRUE) 2 | library(rpart) 3 | library(caret) 4 | d_class = read.csv("r_data_18_35_55_scaled.csv") 5 | d_class$age_label = as.factor(d_class$age_label) 6 | head(d_class) 7 | knitr::opts_chunk$set(echo = TRUE) 8 | library(rpart) 9 | library(caret) 10 | --- 11 | title: "model_5_age_d_class" 12 | author: "XIANGYU ZHANG" 13 | date: "July 26, 2018" 14 | output: word_document 15 | --- 16 | ```{r setup, include=FALSE} 17 | knitr::opts_chunk$set(echo = TRUE) 18 | library(rpart) 19 | library(caret) 20 | ``` 21 | ```{r} 22 | # set working directory 23 | # Load csv file with with all category rescaled to index 1 to 5 24 | d_class = read.csv("../../clean_data/r_data_18_35_55_scaled.csv") 25 | ``` 26 | ```{r} 27 | # convert age_labels to factor 28 | d_class$age_label = as.factor(d_class$age_label) 29 | head(d_class) 30 | ``` 31 | ```{r} 32 | # Run algorithms using 10-fold cross validation 33 | control <- trainControl(method="cv", number=10) 34 | metric <- "Accuracy" 35 | ``` 36 | ```{r} 37 | # 70% and 30%, best model: RF, Accuracy with Validation: 75% 38 | # 80% and 20%, best model: RF, Accuracy wiht validation: 68% 39 | # 60% and 40%, best model: RF, Accuracy with validation: 81% 40 | ``` 41 | ```{r} 42 | # use 70% of data to training and testing the models 43 | # and use the remaining 30% as validation dataset 44 | set.seed(1) 45 | n = nrow(d_class) 46 | train = sample(1:n, n*0.7) 47 | test = (-train) 48 | dataset = d_class[train, ] 49 | validation = d_class[test, ] 50 | ``` 51 | ```{r} 52 | # a) linear algorithms 53 | set.seed(1) 54 | fit.lda <- train(age_label~., data=dataset, method="lda", metric=metric, trControl=control) 55 | # b) nonlinear algorithms 56 | # CART 57 | set.seed(1) 58 | fit.cart <- train(age_label~., data=dataset, method="rpart", metric=metric, trControl=control) 59 | # kNN 60 | set.seed(1) 61 | fit.knn <- train(age_label~., data=dataset, method="knn", metric=metric, trControl=control) 62 | # c) advanced algorithms 63 | # SVM 64 | set.seed(1) 65 | fit.svm <- train(age_label~., data=dataset, method="svmRadial", metric=metric, trControl=control) 66 | # Random Forest 67 | set.seed(1) 68 | fit.rf <- train(age_label~., data=dataset, method="rf", metric=metric, trControl=control) 69 | ``` 70 | ```{r} 71 | # summarize accuracy of models 72 | results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf)) 73 | summary(results) 74 | ``` 75 | ```{r} 76 | dotplot(results) # Random Forest has the highest accuracy 77 | ``` 78 | ```{r} 79 | print(fit.rf) 80 | ``` 81 | ```{r} 82 | # feature selection, importance of each factor 83 | importance = varImp(fit.rf, scale=FALSE) 84 | print(importance) 85 | plot(importance) 86 | ``` 87 | ```{r} 88 | # choose the model with best performance to do prediction on test dataset and generate a confusion matrix 89 | # to calculate the accuracy 90 | predictions <- predict(fit.rf, validation) 91 | print(predictions) 92 | confusionMatrix(predictions, validation$age_label) 93 | ``` 94 | ```{r} 95 | # change the feature selection and compare accuracy 96 | set.seed(1) 97 | fit.rf2 <- train(age_label~tv+social_media+video_game+tablet_owner+magazine+all_live, data=dataset, method="rf", metric=metric, trControl=control) 98 | predictions <- predict(fit.rf2, validation) 99 | confusionMatrix(predictions, validation$age_label) 100 | #without "movie_goer",86.79% 101 | # - last 3, 86.79% 102 | # - last 4, 88.68% 103 | # - last 5, 84.91% 104 | ``` 105 | ```{r} 106 | # 70% and 30%, accuracy: 81.13% 107 | # 80% and 20%, a = 83% 108 | # 60% and 40%, a = 82.86% 109 | # 50%, a= 86% 110 | ``` 111 | --- 112 | title: "model_5_age_d_class" 113 | author: "XIANGYU ZHANG" 114 | date: "July 26, 2018" 115 | output: word_document 116 | --- 117 | ```{r setup, include=FALSE} 118 | knitr::opts_chunk$set(echo = TRUE) 119 | library(rpart) 120 | library(caret) 121 | ``` 122 | ```{r} 123 | # set working directory 124 | # Load csv file with with all category rescaled to index 1 to 5 125 | d_class = read.csv("../../clean_data/r_data_18_35_55_scaled.csv") 126 | ``` 127 | ```{r} 128 | # convert age_labels to factor 129 | d_class$age_label = as.factor(d_class$age_label) 130 | head(d_class) 131 | ``` 132 | ```{r} 133 | # Run algorithms using 10-fold cross validation 134 | control <- trainControl(method="cv", number=10) 135 | metric <- "Accuracy" 136 | ``` 137 | ```{r} 138 | # 70% and 30%, best model: RF, Accuracy with Validation: 75% 139 | # 80% and 20%, best model: RF, Accuracy wiht validation: 68% 140 | # 60% and 40%, best model: RF, Accuracy with validation: 81% 141 | ``` 142 | ```{r} 143 | # use 70% of data to training and testing the models 144 | # and use the remaining 30% as validation dataset 145 | set.seed(1) 146 | n = nrow(d_class) 147 | train = sample(1:n, n*0.7) 148 | test = (-train) 149 | dataset = d_class[train, ] 150 | validation = d_class[test, ] 151 | ``` 152 | ```{r} 153 | # a) linear algorithms 154 | set.seed(1) 155 | fit.lda <- train(age_label~., data=dataset, method="lda", metric=metric, trControl=control) 156 | # b) nonlinear algorithms 157 | # CART 158 | set.seed(1) 159 | fit.cart <- train(age_label~., data=dataset, method="rpart", metric=metric, trControl=control) 160 | # kNN 161 | set.seed(1) 162 | fit.knn <- train(age_label~., data=dataset, method="knn", metric=metric, trControl=control) 163 | # c) advanced algorithms 164 | # SVM 165 | set.seed(1) 166 | fit.svm <- train(age_label~., data=dataset, method="svmRadial", metric=metric, trControl=control) 167 | # Random Forest 168 | set.seed(1) 169 | fit.rf <- train(age_label~., data=dataset, method="rf", metric=metric, trControl=control) 170 | ``` 171 | ```{r} 172 | # summarize accuracy of models 173 | results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf)) 174 | summary(results) 175 | ``` 176 | ```{r} 177 | dotplot(results) # Random Forest has the highest accuracy 178 | ``` 179 | ```{r} 180 | print(fit.rf) 181 | ``` 182 | ```{r} 183 | # feature selection, importance of each factor 184 | importance = varImp(fit.rf, scale=FALSE) 185 | print(importance) 186 | plot(importance) 187 | ``` 188 | ```{r} 189 | # choose the model with best performance to do prediction on test dataset and generate a confusion matrix 190 | # to calculate the accuracy 191 | predictions <- predict(fit.rf, validation) 192 | print(predictions) 193 | confusionMatrix(predictions, validation$age_label) 194 | ``` 195 | ```{r} 196 | # change the feature selection and compare accuracy 197 | set.seed(1) 198 | fit.rf2 <- train(age_label~tv+social_media+video_game+tablet_owner+magazine+all_live, data=dataset, method="rf", metric=metric, trControl=control) 199 | predictions <- predict(fit.rf2, validation) 200 | confusionMatrix(predictions, validation$age_label) 201 | #without "movie_goer",86.79% 202 | # - last 3, 86.79% 203 | # - last 4, 88.68% 204 | # - last 5, 84.91% 205 | ``` 206 | ```{r} 207 | # 70% and 30%, accuracy: 81.13% 208 | # 80% and 20%, a = 83% 209 | # 60% and 40%, a = 82.86% 210 | # 50%, a= 86% 211 | ``` 212 | install.packages("caret") 213 | install.packages("rpart") 214 | knitr::opts_chunk$set(echo = TRUE) 215 | library(rpart) 216 | library(caret) 217 | -------------------------------------------------------------------------------- /ML/R/age_class_based_on_scale.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "model_5_age_d_class" 3 | author: "XIANGYU ZHANG" 4 | date: "July 26, 2018" 5 | output: word_document 6 | --- 7 | 8 | ```{r setup, include=FALSE} 9 | knitr::opts_chunk$set(echo = TRUE) 10 | library(rpart) 11 | library(caret) 12 | ``` 13 | 14 | ```{r} 15 | # set working directory 16 | # Load csv file with with all category rescaled to index 1 to 5 17 | d_class = read.csv("../../clean_data/r_data_18_35_55_scaled.csv") 18 | ``` 19 | 20 | 21 | ```{r} 22 | # convert age_labels to factor 23 | d_class$age_label = as.factor(d_class$age_label) 24 | head(d_class) 25 | ``` 26 | 27 | ```{r} 28 | # Run algorithms using 10-fold cross validation 29 | control <- trainControl(method="cv", number=10) 30 | metric <- "Accuracy" 31 | ``` 32 | 33 | ```{r} 34 | # 70% and 30%, best model: RF, Accuracy with Validation: 75% 35 | # 80% and 20%, best model: RF, Accuracy wiht validation: 68% 36 | # 60% and 40%, best model: RF, Accuracy with validation: 81% 37 | ``` 38 | 39 | 40 | 41 | ```{r} 42 | # use 70% of data to training and testing the models 43 | # and use the remaining 30% as validation dataset 44 | set.seed(1) 45 | n = nrow(d_class) 46 | train = sample(1:n, n*0.7) 47 | test = (-train) 48 | dataset = d_class[train, ] 49 | validation = d_class[test, ] 50 | 51 | ``` 52 | 53 | 54 | ```{r} 55 | # a) linear algorithms 56 | set.seed(1) 57 | fit.lda <- train(age_label~., data=dataset, method="lda", metric=metric, trControl=control) 58 | # b) nonlinear algorithms 59 | # CART 60 | set.seed(1) 61 | fit.cart <- train(age_label~., data=dataset, method="rpart", metric=metric, trControl=control) 62 | # kNN 63 | set.seed(1) 64 | fit.knn <- train(age_label~., data=dataset, method="knn", metric=metric, trControl=control) 65 | # c) advanced algorithms 66 | # SVM 67 | set.seed(1) 68 | fit.svm <- train(age_label~., data=dataset, method="svmRadial", metric=metric, trControl=control) 69 | # Random Forest 70 | set.seed(1) 71 | fit.rf <- train(age_label~., data=dataset, method="rf", metric=metric, trControl=control) 72 | ``` 73 | 74 | ```{r} 75 | # summarize accuracy of models 76 | results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf)) 77 | summary(results) 78 | ``` 79 | 80 | ```{r} 81 | dotplot(results) # Random Forest has the highest accuracy 82 | ``` 83 | 84 | ```{r} 85 | print(fit.rf) 86 | ``` 87 | 88 | ```{r} 89 | # feature selection, importance of each factor 90 | importance = varImp(fit.rf, scale=FALSE) 91 | print(importance) 92 | plot(importance) 93 | ``` 94 | 95 | 96 | ```{r} 97 | # choose the model with best performance to do prediction on test dataset and generate a confusion matrix 98 | # to calculate the accuracy 99 | predictions <- predict(fit.rf, validation) 100 | print(predictions) 101 | confusionMatrix(predictions, validation$age_label) 102 | ``` 103 | 104 | ```{r} 105 | # change the feature selection and compare accuracy 106 | set.seed(1) 107 | fit.rf2 <- train(age_label~tv+social_media+video_game+tablet_owner+magazine+all_live, data=dataset, method="rf", metric=metric, trControl=control) 108 | predictions <- predict(fit.rf2, validation) 109 | confusionMatrix(predictions, validation$age_label) 110 | 111 | #without "movie_goer",86.79% 112 | # - last 3, 86.79% 113 | # - last 4, 88.68% 114 | # - last 5, 84.91% 115 | 116 | ``` 117 | 118 | 119 | ```{r} 120 | # 70% and 30%, accuracy: 81.13% 121 | # 80% and 20%, a = 83% 122 | # 60% and 40%, a = 82.86% 123 | # 50%, a= 86% 124 | ``` 125 | 126 | 127 | -------------------------------------------------------------------------------- /ML/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Step by Step 2 | 3 | ## Step 1 4 | 5 | Category “Totals” refers to the sample group of our project which is the total population 6 | of _**DVD movie purchasers**_ with given division (region), year, age and gender. Population under 7 | all other categories refer to the “Cross Region” with our study group. For example: 8 | 9 | ![graph1](images/graph1.png) 10 | 11 | ![graph2](images/graph2.png) 12 | 13 | 14 | ## Step 2 15 | We generate another column named “share” which is the proportion in _**DVD purchaser**_ people who also 16 | choose given category **(e.g. share = 15/31 = 0.484)**, and there are 48.4% people from the sample group 17 | who also choose social media, who are male at age 18-24 from east central region . So, there are 18 | absolutely some people who will choose more than one category. 19 | 20 | ![graph3](images/graph3.png) 21 | 22 | 23 | ## Step 3 24 | 25 | We generate a share table with average share of each category between two genders each division and year. 26 | (using Pivot Table in Excel) 27 | 28 | ![graph4](images/graph4.png) 29 | 30 | ## Step 4 31 | 32 | We replace each text age label with index, where **age 18-35 was defined as class 1, age 35-55 was 33 | defined as class 2, and 55+ was defined as class 3**. And we used Excel to rescale all share decimal 34 | numbers to 5 levels, indexing 1 through 5. 35 | 36 | ![graph5](images/graph5.png) 37 | 38 | 39 | ## Step 5 40 | 41 | We used **R** to do model comparison and feature selections. There are 5 candidate ML models, and 42 | Random Forest Classifier has the highest Accuracy and Kappa, so we choose to build RF Classification 43 | Model and make prediction in python. And according to the “Variable Importance Table”, we have tested 44 | several models with different feature selection. And we found the one with features [ 'tv','social_media', 45 | 'magazine','all_live','video_game','tablet_owner'] has the highest accuracy. 46 | 47 | ![graph6](images/graph6.png) 48 | 49 | ![graph8](images/graph8.png) 50 | 51 | ![graph7](images/graph7.png) 52 | 53 | 54 | ## Step 6 55 | 56 | We rebuilt a **Random Forest Classification Model** using **Python** since it is more front-end friendly. 57 | The following table were used as validation dataset, and we have calculated a probability array, 58 | where each one array inside represents a case in the validation dataset, and each of the three numbers 59 | in this array is the probability of the model predicted class. For instance, in first array, it shows 60 | [1., 0., 0.] which mean the model predicted that this case has 100% probability belongs to class 1, and 61 | zero probability belongs to other two classes. And then we generated a **confusion matrix**, and numbers on 62 | the diagonal means it was corrected predicted, and numbers on all other position means it was not correctly 63 | predicted. So the **accuracy rate = (11 + 19 + 13)/(11 + 2 + 1 + 19 + 1 + 1 + 13) = 89.6%**. 64 | 65 | ![graph9](images/graph9.png) 66 | 67 | ![graph10](images/graph10.png) 68 | 69 | ![graph11](images/graph11.png) -------------------------------------------------------------------------------- /ML/images/graph1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/ML/images/graph1.png -------------------------------------------------------------------------------- /ML/images/graph10.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/ML/images/graph10.png -------------------------------------------------------------------------------- /ML/images/graph11.png: 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-------------------------------------------------------------------------------- 1 | web: gunicorn app:app 2 | -------------------------------------------------------------------------------- /Survey/Project Survey.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/Survey/Project Survey.docx -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | 5 | import sqlalchemy 6 | from sqlalchemy.ext.automap import automap_base 7 | from sqlalchemy.orm import Session 8 | from sqlalchemy import create_engine, func 9 | import json 10 | 11 | from flask import Flask, flash, jsonify, render_template, request 12 | 13 | # messagebox.showinfo("Hello", "a Tk MessageBox") 14 | 15 | # Database Setup 16 | engine = create_engine("sqlite:///database.sqlite") 17 | 18 | # Flask Setup 19 | app = Flask(__name__) 20 | app.config['SECRET_KEY'] = '123456' 21 | 22 | # Flask Routes 23 | 24 | # Website Home Page 25 | 26 | 27 | @app.route('/') 28 | def welcome(): 29 | return render_template('index.html') 30 | 31 | 32 | # Tableau 33 | @app.route("/tableau") 34 | def projectmainpage1(): 35 | return render_template("tableau_dashboard.html") 36 | 37 | 38 | # Project Main Page 39 | @app.route("/projects", methods=['GET', 'POST']) 40 | def projectmainpage2(): 41 | if request.method == 'GET': 42 | pass 43 | # Collect all input from HTML forms 44 | if request.method == 'POST': 45 | tv = request.form["tv"] 46 | social = request.form["social_media"] 47 | magazine = request.form["magazine"] 48 | all_lives = request.form["all_lives"] 49 | video_games = request.form["video_games"] 50 | tablet = request.form['tablet'] 51 | 52 | # print(tv) 53 | input_x = str(tv) + str(social) + str(magazine) + str(all_lives) + str(video_games) + str(tablet) 54 | print(input_x) 55 | # print(type(input_x)) 56 | 57 | df3 = pd.read_csv("clean_data/all_possible_prediction.csv") 58 | df4 = pd.DataFrame(df3) 59 | key_list = list(df4['pref_ID']) 60 | value_list = list(df4['result']) 61 | test_dict = {} 62 | for i in range(0, len(key_list)): 63 | test_dict[str(key_list[i])] = str(value_list[i]) 64 | 65 | output_y = test_dict[str(input_x)] 66 | print("Result:", output_y) 67 | #render_template(output.html, result=output_y) 68 | return 'Result: ' + output_y 69 | # render_template(output.html, result=output_y) 70 | return render_template("projects.html") 71 | 72 | 73 | # ML Knowleage 74 | @app.route("/ml_knowledge") 75 | def knowledge(): 76 | return render_template("machine_learning.html") 77 | 78 | 79 | # Acknowledgements 80 | @app.route("/acknowledgments") 81 | def acknowledgments(): 82 | return render_template("acknowledgments.html") 83 | 84 | 85 | # Team Infos 86 | @app.route("/meet_the_team") 87 | def team(): 88 | return render_template("meet_the_team.html") 89 | 90 | 91 | if __name__ == '__main__': 92 | app.run(debug=True) 93 | 94 | 95 | # ORM Process 96 | # Stacked Chart (test_data) 97 | # query = """ 98 | # SELECT * 99 | # FROM test_table 100 | """ 101 | 102 | 103 | @app.route("/api/stacked_chart") 104 | def summary_data(): 105 | # Return the data 106 | df_1 = pd.read_sql_query(query, engine) 107 | return jsonify(df_1.to_dict(orient="records")) 108 | 109 | 110 | @app.route("/api/bubble_chart") 111 | def bubble_chart_data(): 112 | # Return the data 113 | df_2 = pd.read_sql_query(query_1, engine) 114 | return jsonify(df_2.to_dict(orient="records")) 115 | 116 | 117 | # ML Library 118 | ''' 119 | query_3 = """ 120 | # SELECT result, pref_ID 121 | # from prediction_table 122 | """ 123 | 124 | @app.route("/api/library") 125 | def ml_library(): 126 | # Return the data 127 | df3 = pd.read_csv("all_possible_prediction.csv") 128 | df4 = pd.DataFrame(df3) 129 | key_list = list(df4['pref_ID']) 130 | value_list = list(df4['result']) 131 | test_dict = {} 132 | for i in range(0, len(key_list)): 133 | test_dict[key_list[i]] = value_list[i] 134 | 135 | # print("Result:", test_dict[str(input_x)]) 136 | 137 | return jsonify(test_dict) 138 | # return jsonify(df4.to_dict(orient="records")) 139 | 140 | 141 | ''' 142 | def get_time(): 143 | time = strftime("%Y-%m-%dT%H:%M") 144 | return time 145 | 146 | 147 | def write_to_disk(TV, Social_Media, Magazine, All_Lives, Video_Games, Tablet): 148 | data = open('file.csv') 149 | timestamp = get_time() 150 | data.write('DateStamp={}, TV={}, Social_Media={}, Magazine={}, All_Lives={}, Video_Games={}, Tablet={} '.format(timestamp, tv, social_media, magazine, all_lives, video_games, tablet)) 151 | data.close() 152 | 153 | 154 | @app.route("/", methods=['GET', 'POST']) 155 | def get_post(): 156 | form = ReusableForm(request.form) 157 | 158 | # print(form.errors) 159 | if request.method == 'POST': 160 | tv = request.form['tv'] 161 | social_media = request.form['social_media'] 162 | magazine = request.form['magazine'] 163 | all_lives = request.form['all_lives'] 164 | video_games = request.form['video_games'] 165 | tablet = request.form['tablet'] 166 | 167 | if form.validate(): 168 | write_to_disk(tv, social_media, magazine, all_lives, video_games, tablet) 169 | flash('You Picked {} {}'.format(tv, social_media, magazine, all_lives, video_games, tablet)) 170 | 171 | else: 172 | flash('Error: All Fields are Required') 173 | 174 | return render_template('projects.html', form=form) 175 | ''' 176 | """ 177 | -------------------------------------------------------------------------------- /clean_data/category_index_reference.txt: -------------------------------------------------------------------------------- 1 | { 2 | 'totals': '0', 3 | 'video_game': '1', 4 | 'tv': '2', 5 | 'radio': '3', 6 | 'magazine': '4', 7 | 'movie_goers': '5', 8 | 'supermarket_goer_last_4_weeks': '6', 9 | 'tablet_owner': '7', 10 | 'all_live_theater/concerts/dance-attended_last_12_months': '8', 11 | 'social_media_[social_media_user]': '9', 12 | 'digital_music': '10', 13 | 'streaming_video': '11' 14 | } 15 | 16 | { 17 | '18-24': '18', 18 | '25-34': '25', 19 | '35-44': '35', 20 | '45-54': '45', 21 | '55+': '55'} 22 | 23 | { 24 | 'east_central': '1', 25 | 'north_east': '2', 26 | 'pacific': '3', 27 | 'south': '4', 28 | 'south_east': '5', 29 | 'south_west': '6', 30 | 'west_central': '7'} -------------------------------------------------------------------------------- /clean_data/nielsen_div_coordinate.csv: -------------------------------------------------------------------------------- 1 | ,division,Latitude,Longitude 2 | 0,east_central,41.885151300000004,-86.97143566 3 | 1,north_east,42.23324158888889,-72.81556558888889 4 | 2,pacific,40.41512574000001,-126.62062017999999 5 | 3,south,35.918663744444444,-79.6929642111111 6 | 4,south_east,34.709732974999994,-87.076198725 7 | 5,south_west,33.05110145,-95.588672275 8 | 6,west_central,42.623573914285714,-96.82040301428572 9 | -------------------------------------------------------------------------------- /clean_data/r_data_18_35_55_scaled.csv: -------------------------------------------------------------------------------- 1 | age_label,all_live,magazine,movie_goers,radio,social_media,supermarket,tablet_owner,tv,video_game 2 | 1,4,5,4,3,4,4,1,3,3 3 | 1,3,4,4,4,4,4,2,3,3 4 | 1,4,3,4,4,4,4,2,3,3 5 | 1,3,4,4,4,4,4,2,3,3 6 | 1,4,4,4,4,5,4,2,4,4 7 | 1,3,4,4,4,4,4,1,4,3 8 | 1,3,4,4,4,4,4,2,4,3 9 | 1,4,4,4,4,4,4,2,3,3 10 | 1,3,3,4,4,4,4,2,4,3 11 | 1,3,3,4,4,5,4,3,3,4 12 | 2,3,4,4,4,4,4,2,4,3 13 | 2,3,4,4,4,4,4,2,4,3 14 | 2,4,4,4,4,4,4,2,4,3 15 | 2,3,4,4,4,4,4,3,4,3 16 | 2,3,4,4,4,4,4,3,4,4 17 | 2,3,4,4,4,4,4,2,4,2 18 | 2,3,4,4,4,4,4,2,4,2 19 | 2,4,4,4,4,4,4,2,4,2 20 | 2,3,4,4,4,4,4,2,4,2 21 | 2,4,4,4,4,4,4,3,4,4 22 | 3,3,4,4,4,3,4,1,4,1 23 | 3,3,4,4,4,3,4,2,4,1 24 | 3,4,4,4,4,3,4,2,4,1 25 | 3,3,4,4,4,3,4,2,4,1 26 | 3,3,4,4,4,4,4,3,4,4 27 | 1,4,4,4,4,4,4,2,4,3 28 | 1,4,4,4,4,4,4,2,4,3 29 | 1,5,5,4,4,3,2,3,3,3 30 | 1,4,4,4,4,4,4,2,4,3 31 | 1,4,4,4,4,5,4,2,3,4 32 | 1,3,4,4,4,4,4,2,4,3 33 | 1,3,4,4,4,4,4,2,4,3 34 | 1,5,5,4,4,4,2,3,4,3 35 | 1,4,4,4,4,4,4,2,4,2 36 | 1,4,4,4,4,4,4,3,4,4 37 | 2,4,4,4,4,4,4,2,4,3 38 | 2,4,4,4,4,4,4,3,4,3 39 | 2,5,5,4,4,4,3,3,4,3 40 | 2,4,4,4,4,4,4,3,4,2 41 | 2,3,4,4,4,4,4,3,4,4 42 | 2,3,4,4,4,4,4,2,4,2 43 | 2,3,4,4,4,4,4,2,4,2 44 | 2,5,5,4,5,4,3,3,4,2 45 | 2,3,3,4,3,4,4,2,4,4 46 | 2,4,4,4,4,4,4,3,4,4 47 | 3,3,4,4,4,3,4,1,4,1 48 | 3,3,4,4,4,3,4,2,4,1 49 | 3,4,5,4,5,4,2,3,4,1 50 | 3,3,4,4,4,4,4,2,4,2 51 | 3,3,4,4,4,4,4,3,4,3 52 | 1,3,4,4,3,4,4,1,4,3 53 | 1,3,4,4,4,4,4,2,4,2 54 | 1,4,3,4,3,4,4,2,3,3 55 | 1,4,4,4,3,4,4,2,3,3 56 | 1,4,4,4,3,5,4,1,3,4 57 | 1,3,4,4,4,4,4,2,4,3 58 | 1,3,4,4,4,4,4,2,4,3 59 | 1,4,4,4,4,4,4,3,4,3 60 | 1,3,4,4,4,4,4,2,3,3 61 | 1,4,3,4,4,5,4,2,3,4 62 | 2,3,4,4,4,4,4,2,4,3 63 | 2,3,4,4,4,4,4,3,4,3 64 | 2,4,4,4,4,4,4,3,4,2 65 | 2,3,4,4,4,4,4,2,4,2 66 | 2,4,4,4,4,5,4,3,4,4 67 | 2,3,4,4,4,4,4,2,4,2 68 | 2,3,4,4,4,4,4,2,4,2 69 | 2,4,4,4,4,4,4,3,4,2 70 | 2,3,4,4,4,4,4,3,4,2 71 | 2,4,4,5,5,5,5,4,4,4 72 | 3,3,4,4,4,3,4,1,4,1 73 | 3,3,4,4,4,3,4,2,4,1 74 | 3,4,4,4,4,4,4,2,4,1 75 | 3,3,4,4,4,3,4,2,4,1 76 | 3,4,5,5,5,5,5,4,5,4 77 | 1,3,4,4,4,4,4,1,3,3 78 | 1,3,4,4,4,4,4,2,3,3 79 | 1,4,3,4,4,4,4,2,4,3 80 | 1,3,3,4,4,4,4,2,3,2 81 | 1,3,3,4,3,4,4,2,3,4 82 | 1,3,4,4,4,4,4,2,4,3 83 | 1,3,4,4,4,4,4,2,4,3 84 | 1,4,3,4,4,4,4,2,4,3 85 | 1,3,4,4,4,4,4,2,4,3 86 | 1,3,3,4,4,4,4,3,4,4 87 | 2,3,4,4,4,4,4,2,4,3 88 | 2,3,4,4,4,4,4,2,4,2 89 | 2,4,4,4,4,4,4,3,4,2 90 | 2,3,3,4,4,4,4,3,4,2 91 | 2,3,4,4,4,4,4,3,4,4 92 | 2,3,4,4,4,4,4,2,4,2 93 | 2,3,4,4,4,4,4,2,4,2 94 | 2,4,4,4,4,4,4,2,4,2 95 | 2,3,4,4,4,4,4,3,4,2 96 | 2,3,4,4,4,4,4,3,4,4 97 | 3,3,4,4,4,3,4,2,4,1 98 | 3,3,4,4,4,3,4,2,4,1 99 | 3,4,4,4,4,3,4,2,4,1 100 | 3,3,4,4,4,4,4,2,4,1 101 | 3,2,4,4,4,4,4,3,4,4 102 | 1,3,4,4,4,4,4,2,3,3 103 | 1,3,4,4,4,4,4,2,4,3 104 | 1,4,3,4,3,4,4,2,3,3 105 | 1,3,4,4,3,4,4,2,3,3 106 | 1,3,3,4,3,4,4,2,3,4 107 | 1,3,4,4,4,4,4,2,4,3 108 | 1,3,4,4,4,4,4,2,4,3 109 | 1,4,3,4,4,4,4,2,4,3 110 | 1,3,4,4,4,4,4,2,4,3 111 | 1,3,4,4,4,5,4,3,4,4 112 | 2,3,4,4,4,4,4,2,4,3 113 | 2,3,4,4,4,4,4,2,4,2 114 | 2,4,4,4,4,4,4,3,4,2 115 | 2,3,3,4,4,4,4,3,4,2 116 | 2,3,4,4,4,5,4,3,4,4 117 | 2,3,4,4,4,4,4,2,4,2 118 | 2,3,4,4,4,4,4,2,4,2 119 | 2,4,4,4,4,4,4,2,4,2 120 | 2,3,4,4,4,4,4,3,4,2 121 | 2,3,4,5,4,4,5,3,4,4 122 | 3,3,4,4,4,3,4,2,4,1 123 | 3,3,4,4,4,3,4,2,4,1 124 | 3,4,4,4,4,3,4,2,4,1 125 | 3,3,4,4,4,3,4,2,4,1 126 | 3,4,5,5,4,5,5,4,5,4 127 | 1,3,5,4,4,4,4,1,4,3 128 | 1,3,3,4,4,4,4,2,3,3 129 | 1,4,3,4,4,4,4,2,4,3 130 | 1,3,3,4,4,4,4,2,3,2 131 | 1,3,3,4,4,4,4,2,3,4 132 | 1,3,4,4,4,4,4,2,4,3 133 | 1,3,4,4,4,4,4,2,4,3 134 | 1,4,3,4,4,4,4,2,4,3 135 | 1,3,4,4,4,4,4,2,3,2 136 | 1,3,3,4,4,5,5,3,4,4 137 | 2,3,4,4,4,4,4,2,4,3 138 | 2,3,4,4,4,4,4,2,4,2 139 | 2,4,4,4,4,4,4,3,4,2 140 | 2,3,3,4,4,4,4,3,4,2 141 | 2,3,4,4,4,5,5,3,4,4 142 | 2,3,4,4,4,4,4,2,4,2 143 | 2,3,4,4,4,4,4,2,4,2 144 | 2,4,4,4,4,4,4,2,4,2 145 | 2,3,4,4,4,4,4,3,4,2 146 | 2,4,4,5,5,5,5,3,5,4 147 | 3,2,4,4,4,3,4,1,4,1 148 | 3,3,4,4,4,3,4,2,4,1 149 | 3,4,4,4,4,3,4,2,4,1 150 | 3,3,4,4,4,4,4,2,4,1 151 | 3,3,5,5,5,5,5,3,5,4 152 | 1,3,4,4,3,4,4,2,4,3 153 | 1,4,4,4,4,4,4,2,4,3 154 | 1,4,3,4,3,4,4,1,3,3 155 | 1,4,4,4,4,4,4,2,3,2 156 | 1,3,4,4,3,4,4,2,4,4 157 | 1,3,4,4,4,4,4,2,4,3 158 | 1,3,4,4,4,4,4,2,4,3 159 | 1,4,4,4,4,4,4,2,4,3 160 | 1,4,4,4,4,4,4,3,4,3 161 | 1,4,4,4,4,4,4,3,3,4 162 | 2,3,5,4,4,4,4,2,4,3 163 | 2,3,4,4,4,4,4,2,4,3 164 | 2,4,4,4,4,4,4,3,4,3 165 | 2,3,4,4,4,4,4,2,4,2 166 | 2,4,4,4,4,4,4,3,4,4 167 | 2,3,4,4,4,4,4,2,4,2 168 | 2,3,4,4,4,4,4,2,4,2 169 | 2,4,4,4,4,4,4,3,4,2 170 | 2,3,4,4,4,4,4,3,4,2 171 | 2,3,4,4,4,4,4,3,4,4 172 | 3,3,4,4,4,3,4,1,4,1 173 | 3,3,4,4,4,3,4,2,4,1 174 | 3,4,4,4,4,3,4,2,4,1 175 | 3,3,4,4,4,3,4,2,4,1 176 | 3,3,4,4,4,4,4,3,4,4 177 | -------------------------------------------------------------------------------- /database.sqlite: 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https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/raw_data/east_central_data/East Central/movie purchaser behavior 2016 EC.xlsx -------------------------------------------------------------------------------- /raw_data/east_central_data/Movie Purchaser Behavior EC 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser East Central,GenralMovie Purchaser East Central Male,Genral Movie Purchaser East Central Female,Movie Purchaser EC 18-24,"Movie Purchaser EC 18-24 male 3 | ","Movie Purchaser EC 18-24 Female 4 | ",Movie Purchaser EC 25-34,Movie Purchaser EC 25-34 male,Movie Purchaser EC 25-34 Female,Movie Purchaser EC 35-44,Movie Purchaser EC 35-44 Male,Movie Purchaser EC 35-44 Female,Movie Purchaser EC 45-54,Movie Purchaser EC 45-54 Male,Movie Purchaser EC 45-54 Female,Movie Purchaser EC 55+,Movie Purchaser EC 55+ Male,Movie Purchaser EC 55+ Female 5 | Totals,Unwgt,23689,6041,702,295,407,71,31,40,124,51,73,137,71,66,165,64,101,205,78,127 6 | ,(000s),231709,65066,8005,3795,4209,1277,490*,787*,1840,980*,859,1610,889,722,1515,702,813,1763,734,1029 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,3.45,1.64,1.82,0.55,0.21,0.34,0.79,0.42,0.37,0.69,0.38,0.31,0.65,0.3,0.35,0.76,0.32,0.44 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,324,156,168,50,26,24,83,37,46,84,49,35,62,31,31,45,13,32 11 | ,(000s),66903,29479,3960,2111,1849,820*,358#,462#,1208,694*,513*,1046,638*,409*,533,300*,232*,354*,121#,233* 12 | ,Vert%,28.87,45.31,49.47,55.63,43.93,64.21,73.06,58.7,65.65,70.82,59.72,64.97,71.77,56.65,35.18,42.74,28.54,20.08,16.49,22.64 13 | ,Horz%,100,44.06,5.92,3.16,2.76,1.23,0.54,0.69,1.81,1.04,0.77,1.56,0.95,0.61,0.8,0.45,0.35,0.53,0.18,0.35 14 | ,Index,100,157,171,193,152,222,253,203,227,245,207,225,249,196,122,148,99,70,57,78 15 | TV,Unwgt,20633,5376,631,258,373,53,23,30,103,38,65,125,63,62,158,62,96,192,72,120 16 | ,(000s),209096,59689,7341,3478,3863,981*,356#,626#,1614,840*,774,1559,867,691,1476,700,777,1710,716,995 17 | ,Vert%,90.24,91.74,91.71,91.65,91.78,76.82,72.65,79.54,87.72,85.71,90.1,96.83,97.53,95.71,97.43,99.72,95.57,96.99,97.55,96.7 18 | ,Horz%,100,28.55,3.51,1.66,1.85,0.47,0.17,0.3,0.77,0.4,0.37,0.75,0.41,0.33,0.71,0.33,0.37,0.82,0.34,0.48 19 | ,Index,100,102,102,102,102,85,81,88,97,95,100,107,108,106,108,110,106,107,108,107 20 | RADIO,Unwgt,16599,4637,550,223,327,48,17,31,100,39,61,108,52,56,134,53,81,160,62,98 21 | ,(000s),171433,51520,6604,3050,3555,980*,350#,631*,1477,750*,728,1315,699*,616*,1346,643*,703,1486,609,878 22 | ,Vert%,73.99,79.18,82.5,80.37,84.46,76.74,71.43,80.18,80.27,76.53,84.75,81.68,78.63,85.32,88.84,91.6,86.47,84.29,82.97,85.33 23 | ,Horz%,100,30.05,3.85,1.78,2.07,0.57,0.2,0.37,0.86,0.44,0.42,0.77,0.41,0.36,0.79,0.38,0.41,0.87,0.36,0.51 24 | ,Index,100,107,112,109,114,104,97,108,108,103,115,110,106,115,120,124,117,114,112,115 25 | MAGAZINE,Unwgt,22182,5824,684,288,396,71,31,40,121,49,72,133,69,64,160,64,96,199,75,124 26 | ,(000s),219735,62794,7803,3688,4115,1277,490*,787*,1832,973*,859,1555,838,717,1439,702,738,1700,685,1015 27 | ,Vert%,94.83,96.51,97.48,97.18,97.77,100,100,100,99.57,99.29,100,96.58,94.26,99.31,94.98,100,90.77,96.43,93.32,98.64 28 | ,Horz%,100,28.58,3.55,1.68,1.87,0.58,0.22,0.36,0.83,0.44,0.39,0.71,0.38,0.33,0.65,0.32,0.34,0.77,0.31,0.46 29 | ,Index,100,102,103,102,103,105,105,105,105,105,105,102,99,105,100,105,96,102,98,104 30 | MOVIE GOERS,Unwgt,23689,6041,702,295,407,71,31,40,124,51,73,137,71,66,165,64,101,205,78,127 31 | ,(000s),231709,65066,8005,3795,4209,1277,490*,787*,1840,980*,859,1610,889,722,1515,702,813,1763,734,1029 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,3.45,1.64,1.82,0.55,0.21,0.34,0.79,0.42,0.37,0.69,0.38,0.31,0.65,0.3,0.35,0.76,0.32,0.44 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,694,293,401,69,30,39,124,51,73,135,71,64,163,64,99,203,77,126 36 | ,(000s),224335,64291,7951,3780,4171,1262,480#,782*,1840,980*,859,1605,889,717,1511,702,809,1733,729,1004 37 | ,Vert%,96.82,98.81,99.33,99.6,99.1,98.83,97.96,99.36,100,100,100,99.69,100,99.31,99.74,100,99.51,98.3,99.32,97.57 38 | ,Horz%,100,28.66,3.54,1.68,1.86,0.56,0.21,0.35,0.82,0.44,0.38,0.72,0.4,0.32,0.67,0.31,0.36,0.77,0.32,0.45 39 | ,Index,100,102,103,103,102,102,101,103,103,103,103,103,103,103,103,103,103,102,103,101 40 | TABLET OWNER,Unwgt,5929,2018,200,75,125,13,3,10,35,10,25,49,26,23,57,21,36,46,15,31 41 | ,(000s),58910,22321,2205,965,1240,291#,44#,247#,516*,246#,270#,452*,282#,170#,594*,283#,312*,352*,110#,242* 42 | ,Vert%,25.42,34.31,27.55,25.43,29.46,22.79,8.98,31.39,28.04,25.1,31.43,28.07,31.72,23.55,39.21,40.31,38.38,19.97,14.99,23.52 43 | ,Horz%,100,37.89,3.74,1.64,2.1,0.49,0.07,0.42,0.88,0.42,0.46,0.77,0.48,0.29,1.01,0.48,0.53,0.6,0.19,0.41 44 | ,Index,100,135,108,100,116,90,35,123,110,99,124,110,125,93,154,159,151,79,59,93 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,370,137,233,43,17,26,61,21,40,61,27,34,94,29,65,111,43,68 46 | ,(000s),100764,34457,4032,1712,2320,772*,229#,542#,914,481#,433*,496,225#,271*,848,313#,535,1003,464*,539 47 | ,Vert%,43.49,52.96,50.37,45.11,55.12,60.45,46.73,68.87,49.67,49.08,50.41,30.81,25.31,37.53,55.97,44.59,65.81,56.89,63.22,52.38 48 | ,Horz%,100,34.2,4,1.7,2.3,0.77,0.23,0.54,0.91,0.48,0.43,0.49,0.22,0.27,0.84,0.31,0.53,1,0.46,0.53 49 | ,Index,100,122,116,104,127,139,107,158,114,113,116,71,58,86,129,103,151,131,145,120 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,571,225,346,67,29,38,116,46,70,117,57,60,135,51,84,136,42,94 51 | ,(000s),159830,54775,6526,2879,3647,1254,475#,779*,1712,871*,841,1403,723*,680*,1218,509*,710,938,301*,637 52 | ,Vert%,68.98,84.18,81.52,75.86,86.65,98.2,96.94,98.98,93.04,88.88,97.9,87.14,81.33,94.18,80.4,72.51,87.33,53.2,41.01,61.9 53 | ,Horz%,100,34.27,4.08,1.8,2.28,0.78,0.3,0.49,1.07,0.54,0.53,0.88,0.45,0.43,0.76,0.32,0.44,0.59,0.19,0.4 54 | ,Index,100,122,118,110,126,142,141,143,135,129,142,126,118,137,117,105,127,77,59,90 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Wednesday, July 18, 2018 / 6:29 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/east_central_data/Movie Purchaser Behavior EC 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser East Central,GenralMovie Purchaser East Central Male,Genral Movie Purchaser East Central Female,Movie Purchaser EC 18-24,"Movie Purchaser EC 18-24 male 3 | ","Movie Purchaser EC 18-24 Female 4 | ",Movie Purchaser EC 25-34,Movie Purchaser EC 25-34 male,Movie Purchaser EC 25-34 Female,Movie Purchaser EC 35-44,Movie Purchaser EC 35-44 Male,Movie Purchaser EC 35-44 Female,Movie Purchaser EC 45-54,Movie Purchaser EC 45-54 Male,Movie Purchaser EC 45-54 Female,Movie Purchaser EC 55+,Movie Purchaser EC 55+ Male,Movie Purchaser EC 55+ Female 5 | Totals,Unwgt,27446,6622,764,313,451,77,28,49,141,51,90,182,77,105,176,81,95,188,76,112 6 | ,(000s),234034,59760,7342,3459,3884,1202,509#,692*,2004,1106*,898,1377,632,745,1497,744,753,1263,469,795 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,3.14,1.48,1.66,0.51,0.22,0.3,0.86,0.47,0.38,0.59,0.27,0.32,0.64,0.32,0.32,0.54,0.2,0.34 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,345,159,186,48,22,26,92,39,53,110,52,58,69,36,33,26,10,16 11 | ,(000s),65410,27699,3667,1988,1679,818*,413#,405#,1249,729*,519*,893,446*,446*,538,345*,193*,170#,54#,116# 12 | ,Vert%,27.95,46.35,49.95,57.47,43.23,68.05,81.14,58.53,62.33,65.91,57.8,64.85,70.57,59.87,35.94,46.37,25.63,13.46,11.51,14.59 13 | ,Horz%,100,42.35,5.61,3.04,2.57,1.25,0.63,0.62,1.91,1.11,0.79,1.37,0.68,0.68,0.82,0.53,0.3,0.26,0.08,0.18 14 | ,Index,100,166,179,206,155,243,290,209,223,236,207,232,252,214,129,166,92,48,41,52 15 | Digital Music,Unwgt,27446,6622,764,313,451,77,28,49,141,51,90,182,77,105,176,81,95,188,76,112 16 | ,(000s),234034,59760,7342,3459,3884,1202,509#,692*,2004,1106*,898,1377,632,745,1497,744,753,1263,469,795 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,3.14,1.48,1.66,0.51,0.22,0.3,0.86,0.47,0.38,0.59,0.27,0.32,0.64,0.32,0.32,0.54,0.2,0.34 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,663,264,399,58,18,40,118,42,76,157,66,91,164,73,91,166,65,101 21 | ,(000s),207976,54297,6503,3020,3483,907*,402#,505*,1645,862*,783,1323,608,716,1430,699,731,1199,449,749 22 | ,Vert%,88.87,90.86,88.57,87.31,89.68,75.46,78.98,72.98,82.09,77.94,87.19,96.08,96.2,96.11,95.52,93.95,97.08,94.93,95.74,94.21 23 | ,Horz%,100,26.11,3.13,1.45,1.67,0.44,0.19,0.24,0.79,0.41,0.38,0.64,0.29,0.34,0.69,0.34,0.35,0.58,0.22,0.36 24 | ,Index,100,102,100,98,101,85,89,82,92,88,98,108,108,108,107,106,109,107,108,106 25 | RADIO,Unwgt,19045,5069,596,231,365,55,15,40,117,40,77,143,52,91,143,67,76,138,57,81 26 | ,(000s),169892,47452,5855,2688,3167,862*,375#,487*,1609,900*,709,1055,386*,669,1343,668,674,986,359*,627 27 | ,Vert%,72.59,79.4,79.75,77.71,81.54,71.71,73.67,70.38,80.29,81.37,78.95,76.62,61.08,89.8,89.71,89.78,89.51,78.07,76.55,78.87 28 | ,Horz%,100,27.93,3.45,1.58,1.86,0.51,0.22,0.29,0.95,0.53,0.42,0.62,0.23,0.39,0.79,0.39,0.4,0.58,0.21,0.37 29 | ,Index,100,109,110,107,112,99,101,97,111,112,109,106,84,124,124,124,123,108,105,109 30 | MAGAZINE,Unwgt,21921,5698,668,266,402,59,20,39,113,39,74,158,63,95,158,71,87,180,73,107 31 | ,(000s),190820,51486,6167,2782,3385,796*,401#,395*,1646,878*,769,1125,426,698,1356,610,746,1244,466,778 32 | ,Vert%,81.54,86.15,84,80.43,87.15,66.22,78.78,57.08,82.14,79.39,85.63,81.7,67.41,93.69,90.58,81.99,99.07,98.5,99.36,97.86 33 | ,Horz%,100,26.98,3.23,1.46,1.77,0.42,0.21,0.21,0.86,0.46,0.4,0.59,0.22,0.37,0.71,0.32,0.39,0.65,0.24,0.41 34 | ,Index,100,106,103,99,107,81,97,70,101,97,105,100,83,115,111,101,122,121,122,120 35 | MOVIE GOERS,Unwgt,27446,6622,764,313,451,77,28,49,141,51,90,182,77,105,176,81,95,188,76,112 36 | ,(000s),234034,59760,7342,3459,3884,1202,509#,692*,2004,1106*,898,1377,632,745,1497,744,753,1263,469,795 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,3.14,1.48,1.66,0.51,0.22,0.3,0.86,0.47,0.38,0.59,0.27,0.32,0.64,0.32,0.32,0.54,0.2,0.34 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,753,304,449,74,25,49,140,50,90,180,75,105,173,79,94,186,75,111 41 | ,(000s),227019,59228,7220,3349,3870,1171,478#,692*,1957,1059*,898,1358,612,745,1479,732,747,1255,467,788 42 | ,Vert%,97,99.11,98.34,96.82,99.64,97.42,93.91,100,97.65,95.75,100,98.62,96.84,100,98.8,98.39,99.2,99.37,99.57,99.12 43 | ,Horz%,100,26.09,3.18,1.48,1.7,0.52,0.21,0.3,0.86,0.47,0.4,0.6,0.27,0.33,0.65,0.32,0.33,0.55,0.21,0.35 44 | ,Index,100,102,101,100,103,100,97,103,101,99,103,102,100,103,102,101,102,102,103,102 45 | TABLET OWNER,Unwgt,8845,2791,310,114,196,23,6,17,70,20,50,92,35,57,67,29,38,58,24,34 46 | ,(000s),75009,25141,3227,1268,1959,647#,166#,481#,920,478#,442*,628,200*,428*,732,359#,373*,299*,64#,235* 47 | ,Vert%,32.05,42.07,43.95,36.66,50.44,53.83,32.61,69.51,45.91,43.22,49.22,45.61,31.65,57.45,48.9,48.25,49.54,23.67,13.65,29.56 48 | ,Horz%,100,33.52,4.3,1.69,2.61,0.86,0.22,0.64,1.23,0.64,0.59,0.84,0.27,0.57,0.98,0.48,0.5,0.4,0.09,0.31 49 | ,Index,100,131,137,114,157,168,102,217,143,135,154,142,99,179,153,151,155,74,43,92 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,394,154,240,33,10,23,70,28,42,103,38,65,97,46,51,91,32,59 51 | ,(000s),97217,30518,3405,1432,1972,572*,134#,438#,849,499#,351*,721,256*,465,782,395*,387*,481,149*,332* 52 | ,Vert%,41.54,51.07,46.38,41.4,50.77,47.59,26.33,63.29,42.37,45.12,39.09,52.36,40.51,62.42,52.24,53.09,51.39,38.08,31.77,41.76 53 | ,Horz%,100,31.39,3.5,1.47,2.03,0.59,0.14,0.45,0.87,0.51,0.36,0.74,0.26,0.48,0.8,0.41,0.4,0.49,0.15,0.34 54 | ,Index,100,123,112,100,122,115,63,152,102,109,94,126,98,150,126,128,124,92,76,101 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,644,252,392,73,25,48,138,50,88,164,67,97,148,64,84,121,46,75 56 | ,(000s),168880,51870,6483,3142,3341,1174,484#,690*,1934,1101*,833,1237,586,651,1233,622,611,905,349*,556 57 | ,Vert%,72.16,86.8,88.3,90.84,86.02,97.67,95.09,99.71,96.51,99.55,92.76,89.83,92.72,87.38,82.36,83.6,81.14,71.65,74.41,69.94 58 | ,Horz%,100,30.71,3.84,1.86,1.98,0.7,0.29,0.41,1.15,0.65,0.49,0.73,0.35,0.39,0.73,0.37,0.36,0.54,0.21,0.33 59 | ,Index,100,120,122,126,119,135,132,138,134,138,129,124,128,121,114,116,112,99,103,97 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 63 | ,,,,,,,,,,,,,,,,,,,,, 64 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 65 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 66 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 67 | ,,,,,,,,,,,,,,,,,,,,, 68 | "Wednesday, July 18, 2018 / 6:14 PM",,,,,,,,,,,,,,,,,,,,, 69 | -------------------------------------------------------------------------------- /raw_data/east_central_data/Movie Purchaser Behavior EC 2017.csv: -------------------------------------------------------------------------------- 1 | All Respondents and VIDEO GAMES-DO YOU OWN OR PLAY? [YES],,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,GeECral Movie Purchaser,GECral Movie Purchaser East Central,GECral Movie Purchaser East Central Male,GECral Movie Purchaser East Central Female,Movie Purchaser EC 18-24,"Movie Purchaser EC 18-24 male 3 | ","Movie Purchaser EC 18-24 Female 4 | ",Movie Purchaser EC 25-34,Movie Purchaser EC 25-34 male,Movie Purchaser EC 25-34 Female,Movie Purchaser EC 35-44,Movie Purchaser EC 35-44 Male,Movie Purchaser EC 35-44 Female,Movie Purchaser EC 45-54,Movie Purchaser EC 45-54 Male,Movie Purchaser EC 45-54 Female,Movie Purchaser EC 55+,Movie Purchaser EC 55+ Male,Movie Purchaser EC 55+ Female 5 | Totals,Unwgt,6088,1877,194,99,95,22,15,7,51,22,29,53,28,25,33,15,18,35,19,16 6 | ,(000s),71575,23088,3023,1656,1367,411#,367#,44#,1037*,473#,564#,910*,496#,414#,337*,106#,231#,328*,214#,114# 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,32.26,4.22,2.31,1.91,0.57,0.51,0.06,1.45,0.66,0.79,1.27,0.69,0.58,0.47,0.15,0.32,0.46,0.3,0.16 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,4991,1708,180,93,87,21,15,6,48,22,26,53,28,25,30,13,17,28,15,13 11 | ,(000s),61255,21650,2851,1588,1263,395#,367#,29#,979*,473#,506#,910*,496#,414#,327#,99#,229#,240#,153#,87# 12 | ,Vert%,85.58,93.77,94.31,95.89,92.39,96.11,100,65.91,94.41,100,89.72,100,100,100,97.03,93.4,99.13,73.17,71.5,76.32 13 | ,Horz%,100,35.34,4.65,2.59,2.06,0.64,0.6,0.05,1.6,0.77,0.83,1.49,0.81,0.68,0.53,0.16,0.37,0.39,0.25,0.14 14 | ,Index,100,110,110,112,108,112,117,77,110,117,105,117,117,117,113,109,116,85,84,89 15 | Streaming Video,Unwgt,4956,1613,168,86,82,21,14,7,45,19,26,47,26,21,27,12,15,28,15,13 16 | ,(000s),58741,20176,2657,1461,1196,402#,357#,44#,890*,387#,503#,776*,439#,337#,321#,96#,225#,269#,182#,87# 17 | ,Vert%,82.07,87.39,87.89,88.22,87.49,97.81,97.28,100,85.82,81.82,89.18,85.27,88.51,81.4,95.25,90.57,97.4,82.01,85.05,76.32 18 | ,Horz%,100,34.35,4.52,2.49,2.04,0.68,0.61,0.07,1.52,0.66,0.86,1.32,0.75,0.57,0.55,0.16,0.38,0.46,0.31,0.15 19 | ,Index,100,106,107,108,107,119,119,122,105,100,109,104,108,99,116,110,119,100,104,93 20 | Digital Music,Unwgt,6088,1877,194,99,95,22,15,7,51,22,29,53,28,25,33,15,18,35,19,16 21 | ,(000s),71575,23088,3023,1656,1367,411#,367#,44#,1037*,473#,564#,910*,496#,414#,337*,106#,231#,328*,214#,114# 22 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 23 | ,Horz%,100,32.26,4.22,2.31,1.91,0.57,0.51,0.06,1.45,0.66,0.79,1.27,0.69,0.58,0.47,0.15,0.32,0.46,0.3,0.16 24 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 25 | TV,Unwgt,4770,1538,161,82,79,19,13,6,29,11,18,50,27,23,30,13,17,33,18,15 26 | ,(000s),56475,18971,2488,1395,1093,347#,306#,41#,640#,306#,335#,865*,486#,380#,315#,85#,230#,321*,214#,108# 27 | ,Vert%,78.9,82.17,82.3,84.24,79.96,84.43,83.38,93.18,61.72,64.69,59.4,95.05,97.98,91.79,93.47,80.19,99.57,97.87,100,94.74 28 | ,Horz%,100,33.59,4.41,2.47,1.94,0.61,0.54,0.07,1.13,0.54,0.59,1.53,0.86,0.67,0.56,0.15,0.41,0.57,0.38,0.19 29 | ,Index,100,104,104,107,101,107,106,118,78,82,75,120,124,116,118,102,126,124,127,120 30 | RADIO,Unwgt,4089,1386,154,75,79,15,10,5,38,13,25,45,25,20,27,13,14,29,14,15 31 | ,(000s),48424,16720,2435,1235,1200,251#,235#,16#,793*,325#,468#,816*,421#,394#,291#,81#,210#,284#,172#,112# 32 | ,Vert%,67.65,72.42,80.55,74.58,87.78,61.07,64.03,36.36,76.47,68.71,82.98,89.67,84.88,95.17,86.35,76.42,90.91,86.59,80.37,98.25 33 | ,Horz%,100,34.53,5.03,2.55,2.48,0.52,0.49,0.03,1.64,0.67,0.97,1.69,0.87,0.81,0.6,0.17,0.43,0.59,0.36,0.23 34 | ,Index,100,107,119,110,130,90,95,54,113,102,123,133,125,141,128,113,134,128,119,145 35 | MAGAZINE,Unwgt,4249,1456,151,71,80,18,12,6,32,11,21,41,18,23,28,12,16,32,18,14 36 | ,(000s),48801,17437,2357,1189,1168,341#,300#,41#,761*,323#,438#,668*,258#,410#,268#,96#,172#,320*,213#,107# 37 | ,Vert%,68.18,75.52,77.97,71.8,85.44,82.97,81.74,93.18,73.38,68.29,77.66,73.41,52.02,99.03,79.53,90.57,74.46,97.56,99.53,93.86 38 | ,Horz%,100,35.73,4.83,2.44,2.39,0.7,0.61,0.08,1.56,0.66,0.9,1.37,0.53,0.84,0.55,0.2,0.35,0.66,0.44,0.22 39 | ,Index,100,111,114,105,125,122,120,137,108,100,114,108,76,145,117,133,109,143,146,138 40 | MOVIE GOERS,Unwgt,6088,1877,194,99,95,22,15,7,51,22,29,53,28,25,33,15,18,35,19,16 41 | ,(000s),71575,23088,3023,1656,1367,411#,367#,44#,1037*,473#,564#,910*,496#,414#,337*,106#,231#,328*,214#,114# 42 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 43 | ,Horz%,100,32.26,4.22,2.31,1.91,0.57,0.51,0.06,1.45,0.66,0.79,1.27,0.69,0.58,0.47,0.15,0.32,0.46,0.3,0.16 44 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 45 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,5741,1823,189,96,93,21,14,7,48,20,28,53,28,25,32,15,17,35,19,16 46 | ,(000s),67039,22222,2822,1512,1310,352#,308#,44#,914*,388#,526#,910*,496#,414#,318*,106#,213#,328*,214#,114# 47 | ,Vert%,93.66,96.25,93.35,91.3,95.83,85.64,83.92,100,88.14,82.03,93.26,100,100,100,94.36,100,92.21,100,100,100 48 | ,Horz%,100,33.15,4.21,2.26,1.95,0.53,0.46,0.07,1.36,0.58,0.78,1.36,0.74,0.62,0.47,0.16,0.32,0.49,0.32,0.17 49 | ,Index,100,103,100,97,102,91,90,107,94,88,100,107,107,107,101,107,98,107,107,107 50 | TABLET OWNER,Unwgt,2876,1107,117,54,63,8,6,2,31,14,17,36,16,20,22,11,11,20,7,13 51 | ,(000s),33165,12938,1843,912*,931,209#,196#,13#,612*,306#,305#,605*,262#,343#,271#,91#,180#,146#,56#,90# 52 | ,Vert%,46.34,56.04,60.97,55.07,68.11,50.85,53.41,29.55,59.02,64.69,54.08,66.48,52.82,82.85,80.42,85.85,77.92,44.51,26.17,78.95 53 | ,Horz%,100,39.01,5.56,2.75,2.81,0.63,0.59,0.04,1.85,0.92,0.92,1.82,0.79,1.03,0.82,0.27,0.54,0.44,0.17,0.27 54 | ,Index,100,121,132,119,147,110,115,64,127,140,117,143,114,179,174,185,168,96,56,170 55 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,2863,1007,101,51,50,15,10,5,21,7,14,28,14,14,21,12,9,16,8,8 56 | ,(000s),32744,12012,1536,820*,715*,295#,261#,34#,406#,182#,224#,614#,299#,315#,132#,51#,82#,88#,27#,61# 57 | ,Vert%,45.75,52.03,50.81,49.52,52.3,71.78,71.12,77.27,39.15,38.48,39.72,67.47,60.28,76.09,39.17,48.11,35.5,26.83,12.62,53.51 58 | ,Horz%,100,36.68,4.69,2.5,2.18,0.9,0.8,0.1,1.24,0.56,0.68,1.88,0.91,0.96,0.4,0.16,0.25,0.27,0.08,0.19 59 | ,Index,100,114,111,108,114,157,155,169,86,84,87,147,132,166,86,105,78,59,28,117 60 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,5434,1779,186,94,92,22,15,7,51,22,29,52,27,25,31,14,17,30,16,14 61 | ,(000s),65448,22299,2985,1626,1358,411#,367#,44#,1037*,473#,564#,900*,486#,414#,329*,101#,228#,308#,200#,108# 62 | ,Vert%,91.44,96.58,98.74,98.19,99.34,100,100,100,100,100,100,98.9,97.98,100,97.63,95.28,98.7,93.9,93.46,94.74 63 | ,Horz%,100,34.07,4.56,2.48,2.07,0.63,0.56,0.07,1.58,0.72,0.86,1.38,0.74,0.63,0.5,0.15,0.35,0.47,0.31,0.17 64 | ,Index,100,106,108,107,109,109,109,109,109,109,109,108,107,109,107,104,108,103,102,104 65 | ,,,,,,,,,,,,,,,,,,,,, 66 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 67 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 68 | ,,,,,,,,,,,,,,,,,,,,, 69 | Source: Simmons Fall 2017 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 70 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 71 | (C) 2017 Simmons Research LLC. All Rights Reserved,,,,,,,,,,,,,,,,,,,,, 72 | ,,,,,,,,,,,,,,,,,,,,, 73 | "Thursday, July 19, 2018 / 10:02 AM",,,,,,,,,,,,,,,,,,,,, 74 | -------------------------------------------------------------------------------- /raw_data/nielsen_division.csv: -------------------------------------------------------------------------------- 1 | state,division 2 | Alabama,south_east 3 | Alaska,pacific 4 | Arizona,mountain 5 | Arkansas,south_west 6 | California,pacific 7 | Colorado,mountain 8 | Connecticut,north_east 9 | Delaware,south 10 | District of Columbia,south 11 | Florida,south 12 | Georgia,south 13 | Hawaii,pacific 14 | Idaho,mountain 15 | Illinois,east_central 16 | Indiana,east_central 17 | Iowa,west_central 18 | Kansas,west_central 19 | Kentucky,south_east 20 | Louisiana,south_west 21 | Maine,north_east 22 | Maryland,south 23 | Massachusetts,north_east 24 | Michigan,east_central 25 | Minnesota,west_central 26 | Mississippi,south_east 27 | Missouri,west_central 28 | Montana,mountain 29 | Nebraska,west_central 30 | Nevada,mountain 31 | New Hampshire,north_east 32 | New Jersey,north_east 33 | New Mexico,mountain 34 | New York,north_east 35 | North Carolina,south 36 | North Dakota,west_central 37 | Ohio,east_central 38 | Oklahoma,south_west 39 | Oregon,pacific 40 | Pennsylvania,north_east 41 | Rhode Island,north_east 42 | South Carolina,south 43 | South Dakota,west_central 44 | Tennessee,south_east 45 | Texas,south_west 46 | Utah,mountain 47 | Vermont,north_east 48 | Virginia,south 49 | Washington,pacific 50 | West Virginia,south 51 | Wisconsin,east_central 52 | Wyoming,mountain 53 | -------------------------------------------------------------------------------- /raw_data/north_east_data/Movie Purchaser Behavior NE 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Gneral Movie Purchaser North East,Gneral Movie Purchaser North East Male,Gneral Movie Purchaser North East Female,Movie Purchaser NE 18-24,"Movie Purchaser NE 18-24 male 3 | ","Movie Purchaser NE 18-24 Female 4 | ",Movie Purchaser NE 25-34,Movie Purchaser NE 25-34 male,Movie Purchaser NE 25-34 Female,Movie Purchaser NE 35-44,Movie Purchaser NE 35-44 Male,Movie Purchaser NE 35-44 Female,Movie Purchaser NE 45-54,Movie Purchaser NE 45-54 Male,Movie Purchaser NE 45-54 Female,Movie Purchaser NE 55+,Movie Purchaser NE 55+ Male,Movie Purchaser NE 55+ Female 5 | Totals,Unwgt,23689,6041,1225,516,672,126,53,73,180,63,117,219,93,126,297,131,166,366,176,190 6 | ,(000s),231709,65066,10227,5236,6945,1764,650*,1114,2446,954,1493,2456,1048,1407,3227,1457,1770,2288,1127,1161 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,4.37,2.26,3,0.76,0.28,0.48,1.06,0.41,0.64,1.06,0.45,0.61,1.39,0.63,0.76,0.99,0.49,0.5 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,485,234,252,82,47,35,92,41,51,119,56,63,109,51,58,84,39,45 11 | ,(000s),66903,29479,4791,2500,2523,932,530*,402*,1253,669*,584*,1314,617*,697,1062,479*,583*,463,206*,257* 12 | ,Vert%,28.87,45.31,46.85,47.75,36.33,52.83,81.54,36.09,51.23,70.13,39.12,53.5,58.87,49.54,32.91,32.88,32.94,20.24,18.28,22.14 13 | ,Horz%,100,44.06,7.32,3.74,3.77,1.39,0.79,0.6,1.87,1,0.87,1.96,0.92,1.04,1.59,0.72,0.87,0.69,0.31,0.38 14 | ,Index,100,157,168,165,126,183,282,125,177,243,135,185,204,172,114,114,114,70,63,77 15 | TV,Unwgt,20633,5376,1225,466,612,106,44,62,159,53,106,201,88,113,264,115,149,348,166,182 16 | ,(000s),209096,59689,10227,4945,6460,1637,625*,1013,2264,825*,1439,2365,1022,1343,2971,1378,1594,2167,1096,1071 17 | ,Vert%,90.24,91.74,100,94.44,93.02,92.8,96.15,90.93,92.56,86.48,96.38,96.29,97.52,95.45,92.07,94.58,90.06,94.71,97.25,92.25 18 | ,Horz%,100,28.55,4.37,2.36,3.09,0.78,0.3,0.48,1.08,0.39,0.69,1.13,0.49,0.64,1.42,0.66,0.76,1.04,0.52,0.51 19 | ,Index,100,102,100,105,103,103,107,101,103,96,107,107,108,106,102,105,100,105,108,102 20 | RADIO,Unwgt,16599,4637,1097,384,536,88,29,59,126,38,88,173,70,103,244,110,134,289,137,152 21 | ,(000s),171433,51520,9468,4073,5982,1464,443#,1021*,2021,682*,1339,2000,839,1161,2732,1260,1472,1838,849,989 22 | ,Vert%,73.99,79.18,92.58,77.79,86.13,82.99,68.15,91.65,82.62,71.49,89.69,81.43,80.06,82.52,84.66,86.48,83.16,80.33,75.33,85.19 23 | ,Horz%,100,30.05,4.55,2.38,3.49,0.85,0.26,0.6,1.18,0.4,0.78,1.17,0.49,0.68,1.59,0.73,0.86,1.07,0.5,0.58 24 | ,Index,100,107,104,105,116,112,92,124,112,97,121,110,108,112,114,117,112,109,102,115 25 | MAGAZINE,Unwgt,22182,5824,950,492,643,121,52,69,173,59,114,208,87,121,284,126,158,349,168,181 26 | ,(000s),219735,62794,8509,4945,6681,1753,650*,1104,2392,904*,1488,2293,897,1396,3097,1437,1660,2091,1058,1034 27 | ,Vert%,94.83,96.51,83.2,94.44,96.2,99.38,100,99.1,97.79,94.76,99.67,93.36,85.59,99.22,95.97,98.63,93.79,91.39,93.88,89.06 28 | ,Horz%,100,28.58,5.01,2.25,3.04,0.8,0.3,0.5,1.09,0.41,0.68,1.04,0.41,0.64,1.41,0.65,0.76,0.95,0.48,0.47 29 | ,Index,100,102,115,100,101,105,105,105,103,100,105,98,90,105,101,104,99,96,99,94 30 | MOVIE GOERS,Unwgt,23689,6041,1090,516,672,126,53,73,180,63,117,219,93,126,297,131,166,366,176,190 31 | ,(000s),231709,65066,9296,5236,6945,1764,650*,1114,2446,954,1493,2456,1048,1407,3227,1457,1770,2288,1127,1161 32 | ,Vert%,100,100,90.9,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,4.87,2.26,3,0.76,0.28,0.48,1.06,0.41,0.64,1.06,0.45,0.61,1.39,0.63,0.76,0.99,0.49,0.5 34 | ,Index,100,100,111,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,1225,499,663,119,48,71,175,59,116,217,93,124,293,128,165,358,171,187 36 | ,(000s),224335,64291,10227,5161,6837,1730,639*,1091,2406,914*,1492,2455,1048,1407,3190,1440,1750,2217,1120,1097 37 | ,Vert%,96.82,98.81,100,98.57,98.44,98.07,98.31,97.94,98.36,95.81,99.93,99.96,100,100,98.85,98.83,98.87,96.9,99.38,94.49 38 | ,Horz%,100,28.66,4.37,2.3,3.05,0.77,0.28,0.49,1.07,0.41,0.67,1.09,0.47,0.63,1.42,0.64,0.78,0.99,0.5,0.49 39 | ,Index,100,102,100,102,102,101,102,101,102,99,103,103,103,103,102,102,102,100,103,98 40 | TABLET OWNER,Unwgt,5929,2018,1212,162,249,42,13,29,73,22,51,104,41,63,98,43,55,94,43,51 41 | ,(000s),58910,22321,10115,1680,2710,736*,173#,562#,1103,327#,776*,1060,436*,624,967,488*,478*,525,255*,270* 42 | ,Vert%,25.42,34.31,98.9,32.09,39.02,41.72,26.62,50.45,45.09,34.28,51.98,43.16,41.6,44.35,29.97,33.49,27.01,22.95,22.63,23.26 43 | ,Horz%,100,37.89,4.46,2.85,4.6,1.25,0.29,0.95,1.87,0.56,1.32,1.8,0.74,1.06,1.64,0.83,0.81,0.89,0.43,0.46 44 | ,Index,100,135,102,126,153,164,105,198,177,135,204,170,164,174,118,132,106,90,89,91 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,526,286,418,80,28,52,106,33,73,131,54,77,175,75,100,212,96,116 46 | ,(000s),100764,34457,4265,2702,4202,1124,328#,796*,1379,425*,954,1487,627*,861,1726,834,892,1188,489,698 47 | ,Vert%,43.49,52.96,41.7,51.6,60.5,63.72,50.46,71.45,56.38,44.55,63.9,60.55,59.83,61.19,53.49,57.24,50.4,51.92,43.39,60.12 48 | ,Horz%,100,34.2,5.69,2.68,4.17,1.12,0.33,0.79,1.37,0.42,0.95,1.48,0.62,0.85,1.71,0.83,0.89,1.18,0.49,0.69 49 | ,Index,100,122,130,119,139,147,116,164,130,102,147,139,138,141,123,132,116,119,100,138 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,723,383,572,122,50,72,169,56,113,194,78,116,241,99,142,229,100,129 51 | ,(000s),159830,54775,5513,3917,6159,1728,614*,1114,2216,797*,1419,2182,850,1332,2664,1105,1559,1288,553,735 52 | ,Vert%,68.98,84.18,53.91,74.81,88.68,97.96,94.46,100,90.6,83.54,95.04,88.84,81.11,94.67,82.55,75.84,88.08,56.29,49.07,63.31 53 | ,Horz%,100,34.27,5.67,2.45,3.85,1.08,0.38,0.7,1.39,0.5,0.89,1.37,0.53,0.83,1.67,0.69,0.98,0.81,0.35,0.46 54 | ,Index,100,122,130,108,129,142,137,145,131,121,138,129,118,137,120,110,128,82,71,92 55 | ,,,,1027,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,8808,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,86.12,,,,,,,,,,,,,,,,, 58 | ,,,,5.22,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,119,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Wednesday, July 18, 2018 / 5:01 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/north_east_data/Movie Purchaser Behavior NE 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Gneral Movie Purchaser North East,Gneral Movie Purchaser North East Male,Gneral Movie Purchaser North East Female,Movie Purchaser NE 18-24,"Movie Purchaser NE 18-24 male 3 | ","Movie Purchaser NE 18-24 Female 4 | ",Movie Purchaser NE 25-34,Movie Purchaser NE 25-34 male,Movie Purchaser NE 25-34 Female,Movie Purchaser NE 35-44,Movie Purchaser NE 35-44 Male,Movie Purchaser NE 35-44 Female,Movie Purchaser NE 45-54,Movie Purchaser NE 45-54 Male,Movie Purchaser NE 45-54 Female,Movie Purchaser NE 55+,Movie Purchaser NE 55+ Male,Movie Purchaser NE 55+ Female 5 | Totals,Unwgt,27446,6622,1225,519,706,133,54,79,192,70,122,222,98,124,273,114,159,405,183,222 6 | ,(000s),234034,59760,10227,5089,5138,1702,808*,894,2385,1033,1352,1937,1004,933,2173,1123,1050,2030,1121,909 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,4.37,2.17,2.2,0.73,0.35,0.38,1.02,0.44,0.58,0.83,0.43,0.4,0.93,0.48,0.45,0.87,0.48,0.39 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,485,230,255,80,46,34,111,55,56,124,56,68,100,48,52,70,25,45 11 | ,(000s),65410,27699,4791,2598,2194,1108,732*,376*,1560,903*,657*,1117,488*,628,698,350*,348*,309,124#,185* 12 | ,Vert%,27.95,46.35,46.85,51.05,42.7,65.1,90.59,42.06,65.41,87.42,48.59,57.67,48.61,67.31,32.12,31.17,33.14,15.22,11.06,20.35 13 | ,Horz%,100,42.35,7.32,3.97,3.35,1.69,1.12,0.57,2.38,1.38,1,1.71,0.75,0.96,1.07,0.54,0.53,0.47,0.19,0.28 14 | ,Index,100,166,168,183,153,233,324,150,234,313,174,206,174,241,115,112,119,54,40,73 15 | Digital Music,Unwgt,27446,6622,1225,519,706,133,54,79,192,70,122,222,98,124,273,114,159,405,183,222 16 | ,(000s),234034,59760,10227,5089,5138,1702,808*,894,2385,1033,1352,1937,1004,933,2173,1123,1050,2030,1121,909 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,4.37,2.17,2.2,0.73,0.35,0.38,1.02,0.44,0.58,0.83,0.43,0.4,0.93,0.48,0.45,0.87,0.48,0.39 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,1097,451,646,110,43,67,153,50,103,201,86,115,251,99,152,382,173,209 21 | ,(000s),207976,54297,9468,4608,4859,1470,652*,817,2094,837*,1258,1833,937,896,2108,1080,1028,1963,1103,861 22 | ,Vert%,88.87,90.86,92.58,90.55,94.57,86.37,80.69,91.39,87.8,81.03,93.05,94.63,93.33,96.03,97.01,96.17,97.9,96.7,98.39,94.72 23 | ,Horz%,100,26.11,4.55,2.22,2.34,0.71,0.31,0.39,1.01,0.4,0.6,0.88,0.45,0.43,1.01,0.52,0.49,0.94,0.53,0.41 24 | ,Index,100,102,104,102,106,97,91,103,99,91,105,106,105,108,109,108,110,109,111,107 25 | RADIO,Unwgt,19045,5069,950,396,554,94,34,60,146,50,96,176,78,98,225,94,131,309,140,169 26 | ,(000s),169892,47452,8509,4231,4279,1283,580*,703*,2051,887*,1164,1631,869,762,1843,954,888,1703,941,762 27 | ,Vert%,72.59,79.4,83.2,83.14,83.28,75.38,71.78,78.64,86,85.87,86.09,84.2,86.55,81.67,84.81,84.95,84.57,83.89,83.94,83.83 28 | ,Horz%,100,27.93,5.01,2.49,2.52,0.76,0.34,0.41,1.21,0.52,0.69,0.96,0.51,0.45,1.08,0.56,0.52,1,0.55,0.45 29 | ,Index,100,109,115,115,115,104,99,108,118,118,119,116,119,113,117,117,117,116,116,115 30 | MAGAZINE,Unwgt,21921,5698,1090,444,646,110,44,66,173,62,111,185,79,106,243,95,148,379,164,215 31 | ,(000s),190820,51486,9296,4619,4677,1536,759*,777,2122,921,1202,1768,918,850,1978,997,981,1892,1024,868 32 | ,Vert%,81.54,86.15,90.9,90.76,91.03,90.25,93.94,86.91,88.97,89.16,88.91,91.28,91.43,91.1,91.03,88.78,93.43,93.2,91.35,95.49 33 | ,Horz%,100,26.98,4.87,2.42,2.45,0.8,0.4,0.41,1.11,0.48,0.63,0.93,0.48,0.45,1.04,0.52,0.51,0.99,0.54,0.45 34 | ,Index,100,106,111,111,112,111,115,107,109,109,109,112,112,112,112,109,115,114,112,117 35 | MOVIE GOERS,Unwgt,27446,6622,1225,519,706,133,54,79,192,70,122,222,98,124,273,114,159,405,183,222 36 | ,(000s),234034,59760,10227,5089,5138,1702,808*,894,2385,1033,1352,1937,1004,933,2173,1123,1050,2030,1121,909 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,4.37,2.17,2.2,0.73,0.35,0.38,1.02,0.44,0.58,0.83,0.43,0.4,0.93,0.48,0.45,0.87,0.48,0.39 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,1212,511,701,131,52,79,192,70,122,220,97,123,269,113,156,400,179,221 41 | ,(000s),227019,59228,10115,5016,5098,1666,772*,894,2385,1033,1352,1929,997,932,2146,1122,1025,1989,1093,896 42 | ,Vert%,97,99.11,98.9,98.57,99.22,97.88,95.54,100,100,100,100,99.59,99.3,99.89,98.76,99.91,97.62,97.98,97.5,98.57 43 | ,Horz%,100,26.09,4.46,2.21,2.25,0.73,0.34,0.39,1.05,0.46,0.6,0.85,0.44,0.41,0.95,0.49,0.45,0.88,0.48,0.39 44 | ,Index,100,102,102,102,102,101,98,103,103,103,103,103,102,103,102,103,101,101,101,102 45 | TABLET OWNER,Unwgt,8845,2791,526,196,330,45,15,30,101,32,69,122,53,69,118,43,75,140,53,87 46 | ,(000s),75009,25141,4265,1926,2340,693*,351#,342#,1142,468*,673,942,440*,502,864,371*,493,625,295*,329 47 | ,Vert%,32.05,42.07,41.7,37.85,45.54,40.72,43.44,38.26,47.88,45.3,49.78,48.63,43.82,53.8,39.76,33.04,46.95,30.79,26.32,36.19 48 | ,Horz%,100,33.52,5.69,2.57,3.12,0.92,0.47,0.46,1.52,0.62,0.9,1.26,0.59,0.67,1.15,0.49,0.66,0.83,0.39,0.44 49 | ,Index,100,131,130,118,142,127,136,119,149,141,155,152,137,168,124,103,146,96,82,113 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,723,280,443,88,34,54,109,31,78,136,55,81,156,63,93,234,97,137 51 | ,(000s),97217,30518,5513,2301,3212,988,385*,603*,1167,330*,837,1249,567*,681,1032,471,561,1078,549,529 52 | ,Vert%,41.54,51.07,53.91,45.22,62.51,58.05,47.65,67.45,48.93,31.95,61.91,64.48,56.47,72.99,47.49,41.94,53.43,53.1,48.97,58.2 53 | ,Horz%,100,31.39,5.67,2.37,3.3,1.02,0.4,0.62,1.2,0.34,0.86,1.28,0.58,0.7,1.06,0.48,0.58,1.11,0.56,0.54 54 | ,Index,100,123,130,109,150,140,115,162,118,77,149,155,136,176,114,101,129,128,118,140 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,1027,418,609,130,52,78,183,66,117,214,92,122,224,92,132,276,116,160 56 | ,(000s),168880,51870,8808,4182,4627,1676,784*,892,2283,999,1284,1875,947,928,1744,848,896,1231,603,628 57 | ,Vert%,72.16,86.8,86.12,82.18,90.05,98.47,97.03,99.78,95.72,96.71,94.97,96.8,94.32,99.46,80.26,75.51,85.33,60.64,53.79,69.09 58 | ,Horz%,100,30.71,5.22,2.48,2.74,0.99,0.46,0.53,1.35,0.59,0.76,1.11,0.56,0.55,1.03,0.5,0.53,0.73,0.36,0.37 59 | ,Index,100,120,119,114,125,136,134,138,133,134,132,134,131,138,111,105,118,84,75,96 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 63 | ,,,,,,,,,,,,,,,,,,,,, 64 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 65 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 66 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 67 | ,,,,,,,,,,,,,,,,,,,,, 68 | "Wednesday, July 18, 2018 / 4:56 PM",,,,,,,,,,,,,,,,,,,,, 69 | -------------------------------------------------------------------------------- /raw_data/pacific_data/Movie Purchaser Behavior P 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser Pacific,Genral Movie Purchaser Pacific Male,Genral Movie Purchaser Pacific Female,Movie Purchaser Pacific 18-24,"Movie Purchaser Pacific 18-24 male 3 | ","Movie Purchaser Pacific 18-24 Female 4 | ",Movie Purchaser Pacific 25-34,Movie Purchaser Pacific 25-34 male,Movie Purchaser Pacific 25-34 Female,Movie Purchaser Pacific 35-44,Movie Purchaser Pacific 35-44 Male,Movie Purchaser Pacific 35-44 Female,Movie Purchaser Pacific 45-54,Movie Purchaser Pacific 45-54 Male,Movie Purchaser Pacific 45-54 Female,Movie Purchaser Pacific 55+,Movie Purchaser Pacific 55+ Male,Movie Purchaser Pacific 55+ Female 5 | Totals,Unwgt,23689,6041,1232,531,701,165,78,87,210,89,121,257,111,146,283,112,171,317,141,176 6 | ,(000s),231709,65066,13405,6147,7257,2004,1152,851,2817,1148,1669,3031,1227,1804,2593,1260,1333,2960,1360,1600 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,5.79,2.65,3.13,0.86,0.5,0.37,1.22,0.5,0.72,1.31,0.53,0.78,1.12,0.54,0.58,1.28,0.59,0.69 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,504,253,251,104,62,42,115,54,61,133,71,62,92,37,55,60,29,31 11 | ,(000s),66903,29479,5699,2993,2706,1298,1024,273*,1254,512*,742,1833,865,968,879,393*,486*,435*,198#,237* 12 | ,Vert%,28.87,45.31,42.51,48.69,37.29,64.77,88.89,32.08,44.52,44.6,44.46,60.48,70.5,53.66,33.9,31.19,36.46,14.7,14.56,14.81 13 | ,Horz%,100,44.06,8.52,4.47,4.04,1.94,1.53,0.41,1.87,0.77,1.11,2.74,1.29,1.45,1.31,0.59,0.73,0.65,0.3,0.35 14 | ,Index,100,157,147,169,129,224,308,111,154,154,154,209,244,186,117,108,126,51,50,51 15 | TV,Unwgt,20633,5376,1077,449,628,128,55,73,167,66,101,225,97,128,258,102,156,299,129,170 16 | ,(000s),209096,59689,11928,5333,6595,1482,855*,627,2315,886,1430,2778,1090,1687,2487,1228,1259,2867,1275,1592 17 | ,Vert%,90.24,91.74,88.98,86.76,90.88,73.95,74.22,73.68,82.18,77.18,85.68,91.65,88.83,93.51,95.91,97.46,94.45,96.86,93.75,99.5 18 | ,Horz%,100,28.55,5.7,2.55,3.15,0.71,0.41,0.3,1.11,0.42,0.68,1.33,0.52,0.81,1.19,0.59,0.6,1.37,0.61,0.76 19 | ,Index,100,102,99,96,101,82,82,82,91,86,95,102,98,104,106,108,105,107,104,110 20 | RADIO,Unwgt,16599,4637,914,371,543,106,41,65,162,60,102,206,83,123,229,92,137,211,95,116 21 | ,(000s),171433,51520,10406,4382,6023,1246,658*,587,2341,800*,1541,2479,988,1491,2177,1054,1124,2163,882,1280 22 | ,Vert%,73.99,79.18,77.63,71.29,83,62.18,57.12,68.98,83.1,69.69,92.33,81.79,80.52,82.65,83.96,83.65,84.32,73.07,64.85,80 23 | ,Horz%,100,30.05,6.07,2.56,3.51,0.73,0.38,0.34,1.37,0.47,0.9,1.45,0.58,0.87,1.27,0.61,0.66,1.26,0.51,0.75 24 | ,Index,100,107,105,96,112,84,77,93,112,94,125,111,109,112,113,113,114,99,88,108 25 | MAGAZINE,Unwgt,22182,5824,1191,510,681,159,76,83,204,86,118,249,107,142,273,104,169,306,137,169 26 | ,(000s),219735,62794,12823,5832,6991,1873,1055,818,2697,1135,1562,2835,1118,1717,2492,1169,1323,2926,1356,1571 27 | ,Vert%,94.83,96.51,95.66,94.88,96.33,93.46,91.58,96.12,95.74,98.87,93.59,93.53,91.12,95.18,96.1,92.78,99.25,98.85,99.71,98.19 28 | ,Horz%,100,28.58,5.84,2.65,3.18,0.85,0.48,0.37,1.23,0.52,0.71,1.29,0.51,0.78,1.13,0.53,0.6,1.33,0.62,0.71 29 | ,Index,100,102,101,100,102,99,97,101,101,104,99,99,96,100,101,98,105,104,105,104 30 | MOVIE GOERS,Unwgt,23689,6041,1232,531,701,165,78,87,210,89,121,257,111,146,283,112,171,317,141,176 31 | ,(000s),231709,65066,13405,6147,7257,2004,1152,851,2817,1148,1669,3031,1227,1804,2593,1260,1333,2960,1360,1600 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,5.79,2.65,3.13,0.86,0.5,0.37,1.22,0.5,0.72,1.31,0.53,0.78,1.12,0.54,0.58,1.28,0.59,0.69 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,1209,516,693,160,74,86,207,86,121,254,110,144,279,109,170,309,137,172 36 | ,(000s),224335,64291,13241,6056,7186,1990,1139,851,2809,1141,1669,3027,1226,1801,2492,1193,1299,2923,1357,1566 37 | ,Vert%,96.82,98.81,98.78,98.52,99.02,99.3,98.87,100,99.72,99.39,100,99.87,99.92,99.83,96.1,94.68,97.45,98.75,99.78,97.88 38 | ,Horz%,100,28.66,5.9,2.7,3.2,0.89,0.51,0.38,1.25,0.51,0.74,1.35,0.55,0.8,1.11,0.53,0.58,1.3,0.6,0.7 39 | ,Index,100,102,102,102,102,103,102,103,103,103,103,103,103,103,99,98,101,102,103,101 40 | TABLET OWNER,Unwgt,5929,2018,428,171,257,42,12,30,77,29,48,113,48,65,111,46,65,85,36,49 41 | ,(000s),58910,22321,4709,1899,2810,365*,110#,255#,936,301#,635*,1415,551*,864,1170,591*,579,823,346*,476* 42 | ,Vert%,25.42,34.31,35.13,30.89,38.72,18.21,9.55,29.96,33.23,26.22,38.05,46.68,44.91,47.89,45.12,46.9,43.44,27.8,25.44,29.75 43 | ,Horz%,100,37.89,7.99,3.22,4.77,0.62,0.19,0.43,1.59,0.51,1.08,2.4,0.94,1.47,1.99,1,0.98,1.4,0.59,0.81 44 | ,Index,100,135,138,122,152,72,38,118,131,103,150,184,177,188,177,184,171,109,100,117 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,634,253,381,87,37,50,110,42,68,137,53,84,153,55,98,147,66,81 46 | ,(000s),100764,34457,6976,2712,4264,1029,457*,572*,1665,542*,1123,1724,613*,1111,1291,581*,710,1268,520,748 47 | ,Vert%,43.49,52.96,52.04,44.12,58.76,51.35,39.67,67.22,59.11,47.21,67.29,56.88,49.96,61.59,49.79,46.11,53.26,42.84,38.24,46.75 48 | ,Horz%,100,34.2,6.92,2.69,4.23,1.02,0.45,0.57,1.65,0.54,1.11,1.71,0.61,1.1,1.28,0.58,0.7,1.26,0.52,0.74 49 | ,Index,100,122,120,101,135,118,91,155,136,109,155,131,115,142,114,106,122,99,88,108 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,1024,427,597,159,75,84,190,74,116,236,98,138,235,90,145,204,90,114 51 | ,(000s),159830,54775,10986,4685,6302,1917,1094,823,2396,799,1598,2642,942,1700,2189,1070,1119,1842,781,1061 52 | ,Vert%,68.98,84.18,81.95,76.22,86.84,95.66,94.97,96.71,85.06,69.6,95.75,87.17,76.77,94.24,84.42,84.92,83.95,62.23,57.43,66.31 53 | ,Horz%,100,34.27,6.87,2.93,3.94,1.2,0.68,0.51,1.5,0.5,1,1.65,0.59,1.06,1.37,0.67,0.7,1.15,0.49,0.66 54 | ,Index,100,122,119,110,126,139,138,140,123,101,139,126,111,137,122,123,122,90,83,96 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Thursday, July 19, 2018 / 6:44 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/pacific_data/Movie Purchaser Behavior P 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser Pacific,Genral Movie Purchaser Pacific Male,Genral Movie Purchaser Pacific Female,Movie Purchaser Pacific 18-24,"Movie Purchaser Pacific 18-24 male 3 | ","Movie Purchaser Pacific 18-24 Female 4 | ",Movie Purchaser Pacific 25-34,Movie Purchaser Pacific 25-34 male,Movie Purchaser Pacific 25-34 Female,Movie Purchaser Pacific 35-44,Movie Purchaser Pacific 35-44 Male,Movie Purchaser Pacific 35-44 Female,Movie Purchaser Pacific 45-54,Movie Purchaser Pacific 45-54 Male,Movie Purchaser Pacific 45-54 Female,Movie Purchaser Pacific 55+,Movie Purchaser Pacific 55+ Male,Movie Purchaser Pacific 55+ Female 5 | Totals,Unwgt,27446,6622,1189,499,690,1189,499,83,224,87,137,224,87,153,238,94,144,324,151,173 6 | ,(000s),234034,59760,12195,5582,6613,12195,5582,1249,2871,1242,1628,2871,1242,1402,2100,866,1234,2601,1501,1100 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,5.21,2.39,2.83,5.21,2.39,0.53,1.23,0.53,0.7,1.23,0.53,0.6,0.9,0.37,0.53,1.11,0.64,0.47 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,494,228,266,494,228,37,123,62,61,123,62,77,83,32,51,68,28,40 11 | ,(000s),65410,27699,5773,2992,2781,5773,2992,587*,1554,875,679,1554,875,842,694,268*,426*,697,449#,248* 12 | ,Vert%,27.95,46.35,47.34,53.6,42.05,47.34,53.6,47,54.13,70.45,41.71,54.13,70.45,60.06,33.05,30.95,34.52,26.8,29.91,22.55 13 | ,Horz%,100,42.35,8.83,4.57,4.25,8.83,4.57,0.9,2.38,1.34,1.04,2.38,1.34,1.29,1.06,0.41,0.65,1.07,0.69,0.38 14 | ,Index,100,166,169,192,150,169,192,168,194,252,149,194,252,215,118,111,124,96,107,81 15 | Digital Music,Unwgt,27446,6622,1189,499,690,1189,499,83,224,87,137,224,87,153,238,94,144,324,151,173 16 | ,(000s),234034,59760,12195,5582,6613,12195,5582,1249,2871,1242,1628,2871,1242,1402,2100,866,1234,2601,1501,1100 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,5.21,2.39,2.83,5.21,2.39,0.53,1.23,0.53,0.7,1.23,0.53,0.6,0.9,0.37,0.53,1.11,0.64,0.47 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,1030,438,592,1030,438,63,176,71,105,176,71,135,214,80,134,293,138,155 21 | ,(000s),207976,54297,11066,4851,6215,11066,4851,1151,2594,1092,1503,2594,1092,1296,1972,777,1195,2494,1423,1071 22 | ,Vert%,88.87,90.86,90.74,86.9,93.98,90.74,86.9,92.15,90.35,87.92,92.32,90.35,87.92,92.44,93.9,89.72,96.84,95.89,94.8,97.36 23 | ,Horz%,100,26.11,5.32,2.33,2.99,5.32,2.33,0.55,1.25,0.53,0.72,1.25,0.53,0.62,0.95,0.37,0.57,1.2,0.68,0.51 24 | ,Index,100,102,102,98,106,102,98,104,102,99,104,102,99,104,106,101,109,108,107,110 25 | RADIO,Unwgt,19045,5069,905,363,542,905,363,64,168,61,107,168,61,128,194,76,118,232,107,125 26 | ,(000s),169892,47452,9619,4125,5494,9619,4125,940,2206,883,1323,2206,883,1191,1873,755,1118,2052,1130,922 27 | ,Vert%,72.59,79.4,78.88,73.9,83.08,78.88,73.9,75.26,76.84,71.1,81.27,76.84,71.1,84.95,89.19,87.18,90.6,78.89,75.28,83.82 28 | ,Horz%,100,27.93,5.66,2.43,3.23,5.66,2.43,0.55,1.3,0.52,0.78,1.3,0.52,0.7,1.1,0.44,0.66,1.21,0.67,0.54 29 | ,Index,100,109,109,102,114,109,102,104,106,98,112,106,98,117,123,120,125,109,104,115 30 | MAGAZINE,Unwgt,21921,5698,1022,393,629,1022,393,72,185,62,123,185,62,137,209,74,135,290,128,162 31 | ,(000s),190820,51486,10471,4416,6055,10471,4416,1135,2433,872,1561,2433,872,1276,1664,604,1061,2331,1309,1022 32 | ,Vert%,81.54,86.15,85.86,79.11,91.56,85.86,79.11,90.87,84.74,70.21,95.88,84.74,70.21,91.01,79.24,69.75,85.98,89.62,87.21,92.91 33 | ,Horz%,100,26.98,5.49,2.31,3.17,5.49,2.31,0.59,1.28,0.46,0.82,1.28,0.46,0.67,0.87,0.32,0.56,1.22,0.69,0.54 34 | ,Index,100,106,105,97,112,105,97,111,104,86,118,104,86,112,97,86,105,110,107,114 35 | MOVIE GOERS,Unwgt,27446,6622,1189,499,690,1189,499,83,224,87,137,224,87,153,238,94,144,324,151,173 36 | ,(000s),234034,59760,12195,5582,6613,12195,5582,1249,2871,1242,1628,2871,1242,1402,2100,866,1234,2601,1501,1100 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,5.21,2.39,2.83,5.21,2.39,0.53,1.23,0.53,0.7,1.23,0.53,0.6,0.9,0.37,0.53,1.11,0.64,0.47 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,1181,495,686,1181,495,83,223,86,137,223,86,151,238,94,144,319,148,171 41 | ,(000s),227019,59228,12141,5556,6586,12141,5556,1249,2868,1240,1628,2868,1240,1379,2100,866,1234,2573,1477,1096 42 | ,Vert%,97,99.11,99.56,99.53,99.59,99.56,99.53,100,99.9,99.84,100,99.9,99.84,98.36,100,100,100,98.92,98.4,99.64 43 | ,Horz%,100,26.09,5.35,2.45,2.9,5.35,2.45,0.55,1.26,0.55,0.72,1.26,0.55,0.61,0.93,0.38,0.54,1.13,0.65,0.48 44 | ,Index,100,102,103,103,103,103,103,103,103,103,103,103,103,101,103,103,103,102,101,103 45 | TABLET OWNER,Unwgt,8845,2791,525,203,322,525,203,31,121,49,72,121,49,95,90,30,60,106,42,64 46 | ,(000s),75009,25141,5372,2452,2920,5372,2452,474*,1498,797*,701,1498,797*,828,707,242#,466*,889,438*,451 47 | ,Vert%,32.05,42.07,44.05,43.93,44.16,44.05,43.93,37.95,52.18,64.17,43.06,52.18,64.17,59.06,33.67,27.94,37.76,34.18,29.18,41 48 | ,Horz%,100,33.52,7.16,3.27,3.89,7.16,3.27,0.63,2,1.06,0.93,2,1.06,1.1,0.94,0.32,0.62,1.19,0.58,0.6 49 | ,Index,100,131,137,137,138,137,137,118,163,200,134,163,200,184,105,87,118,107,91,128 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,628,239,389,628,239,51,126,44,82,126,44,88,123,45,78,163,73,90 51 | ,(000s),97217,30518,6541,2770,3771,6541,2770,642*,1618,648*,970,1618,648*,885,1155,381*,775,1174,674,500 52 | ,Vert%,41.54,51.07,53.64,49.62,57.02,53.64,49.62,51.4,56.36,52.17,59.58,56.36,52.17,63.12,55,44,62.8,45.14,44.9,45.45 53 | ,Horz%,100,31.39,6.73,2.85,3.88,6.73,2.85,0.66,1.66,0.67,1,1.66,0.67,0.91,1.19,0.39,0.8,1.21,0.69,0.51 54 | ,Index,100,123,129,119,137,129,119,124,136,126,143,136,126,152,132,106,151,109,108,109 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,1015,405,610,1015,405,83,212,78,134,212,78,142,196,73,123,228,100,128 56 | ,(000s),168880,51870,10518,4508,6011,10518,4508,1249,2596,1062,1533,2596,1062,1322,1724,666,1058,1766,917,849 57 | ,Vert%,72.16,86.8,86.25,80.76,90.9,86.25,80.76,100,90.42,85.51,94.16,90.42,85.51,94.29,82.1,76.91,85.74,67.9,61.09,77.18 58 | ,Horz%,100,30.71,6.23,2.67,3.56,6.23,2.67,0.74,1.54,0.63,0.91,1.54,0.63,0.78,1.02,0.39,0.63,1.05,0.54,0.5 59 | ,Index,100,120,120,112,126,120,112,139,125,118,130,125,118,131,114,107,119,94,85,107 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 64 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 65 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 66 | ,,,,,,,,,,,,,,,,,,,,, 67 | "Thursday, July 19, 2018 / 6:50 PM",,,,,,,,,,,,,,,,,,,,, 68 | -------------------------------------------------------------------------------- /raw_data/south_data/Movie Purchaser Behavior S 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South,Genral Movie Purchaser South Male,Genral Movie Purchaser South Female,Movie Purchaser South 18-24,"Movie Purchaser South 18-24 male 3 | ","Movie Purchaser South 18-24 Female 4 | ",Movie Purchaser South 25-34,Movie Purchaser South 25-34 male,Movie Purchaser South 25-34 Female,Movie Purchaser South 35-44,Movie Purchaser South 35-44 Male,Movie Purchaser South 35-44 Female,Movie Purchaser South 45-54,Movie Purchaser South 45-54 Male,Movie Purchaser South 45-54 Female,Movie Purchaser South 55+,Movie Purchaser South 55+ Male,Movie Purchaser South 55+ Female 5 | Totals,Unwgt,23689,6041,2013,853,1160,212,78,134,400,160,240,457,198,259,412,170,242,532,247,285 6 | ,(000s),231709,65066,20937,10771,10166,2790,1465,1325,5276,2744,2532,4319,2445,1875,4141,1809,2331,4412,2308,2103 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,9.04,4.65,4.39,1.2,0.63,0.57,2.28,1.18,1.09,1.86,1.06,0.81,1.79,0.78,1.01,1.9,1,0.91 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,844,413,431,130,61,69,241,120,121,255,125,130,122,61,61,96,46,50 11 | ,(000s),66903,29479,10247,5987,4260,1905,1113,791,3463,2076,1387,2598,1556,1042,1256,653,603,1025,589*,436* 12 | ,Vert%,28.87,45.31,48.94,55.58,41.9,68.28,75.97,59.7,65.64,75.66,54.78,60.15,63.64,55.57,30.33,36.1,25.87,23.23,25.52,20.73 13 | ,Horz%,100,44.06,15.32,8.95,6.37,2.85,1.66,1.18,5.18,3.1,2.07,3.88,2.33,1.56,1.88,0.98,0.9,1.53,0.88,0.65 14 | ,Index,100,157,170,193,145,236,263,207,227,262,190,208,220,192,105,125,90,80,88,72 15 | TV,Unwgt,20633,5376,1791,765,1026,158,58,100,340,135,205,420,182,238,380,157,223,493,233,260 16 | ,(000s),209096,59689,19047,9751,9296,2229,1106*,1123,4651,2467,2185,4015,2220,1794,3977,1767,2210,4175,2191,1984 17 | ,Vert%,90.24,91.74,90.97,90.53,91.44,79.89,75.49,84.75,88.15,89.91,86.3,92.96,90.8,95.68,96.04,97.68,94.81,94.63,94.93,94.34 18 | ,Horz%,100,28.55,9.11,4.66,4.45,1.07,0.53,0.54,2.22,1.18,1.04,1.92,1.06,0.86,1.9,0.85,1.06,2,1.05,0.95 19 | ,Index,100,102,101,100,101,89,84,94,98,100,96,103,101,106,106,108,105,105,105,105 20 | RADIO,Unwgt,16599,4637,1550,634,916,152,48,104,312,120,192,377,158,219,324,137,187,385,171,214 21 | ,(000s),171433,51520,16055,7831,8224,1738,672*,1066,3987,2013,1974,3530,1965,1565,3347,1510,1837,3454,1671,1783 22 | ,Vert%,73.99,79.18,76.68,72.7,80.9,62.29,45.87,80.45,75.57,73.36,77.96,81.73,80.37,83.47,80.83,83.47,78.81,78.29,72.4,84.78 23 | ,Horz%,100,30.05,9.37,4.57,4.8,1.01,0.39,0.62,2.33,1.17,1.15,2.06,1.15,0.91,1.95,0.88,1.07,2.01,0.97,1.04 24 | ,Index,100,107,104,98,109,84,62,109,102,99,105,110,109,113,109,113,107,106,98,115 25 | MAGAZINE,Unwgt,22182,5824,1937,817,1120,207,76,131,388,158,230,440,187,253,389,160,229,513,236,277 26 | ,(000s),219735,62794,20227,10427,9800,2674,1351,1324,5104,2714,2390,4217,2353,1865,4010,1768,2243,4221,2241,1979 27 | ,Vert%,94.83,96.51,96.61,96.81,96.4,95.84,92.22,99.92,96.74,98.91,94.39,97.64,96.24,99.47,96.84,97.73,96.22,95.67,97.1,94.1 28 | ,Horz%,100,28.58,9.21,4.75,4.46,1.22,0.61,0.6,2.32,1.24,1.09,1.92,1.07,0.85,1.82,0.8,1.02,1.92,1.02,0.9 29 | ,Index,100,102,102,102,102,101,97,105,102,104,100,103,101,105,102,103,101,101,102,99 30 | MOVIE GOERS,Unwgt,23689,6041,2013,853,1160,212,78,134,400,160,240,457,198,259,412,170,242,532,247,285 31 | ,(000s),231709,65066,20937,10771,10166,2790,1465,1325,5276,2744,2532,4319,2445,1875,4141,1809,2331,4412,2308,2103 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,9.04,4.65,4.39,1.2,0.63,0.57,2.28,1.18,1.09,1.86,1.06,0.81,1.79,0.78,1.01,1.9,1,0.91 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,1995,843,1152,210,77,133,395,157,238,453,196,257,410,169,241,527,244,283 36 | ,(000s),224335,64291,20803,10684,10118,2751,1454,1297,5266,2735,2531,4269,2396,1873,4123,1807,2317,4393,2293,2101 37 | ,Vert%,96.82,98.81,99.36,99.19,99.53,98.6,99.25,97.89,99.81,99.67,99.96,98.84,98,99.89,99.57,99.89,99.4,99.57,99.35,99.9 38 | ,Horz%,100,28.66,9.27,4.76,4.51,1.23,0.65,0.58,2.35,1.22,1.13,1.9,1.07,0.83,1.84,0.81,1.03,1.96,1.02,0.94 39 | ,Index,100,102,103,102,103,102,103,101,103,103,103,102,101,103,103,103,103,103,103,103 40 | TABLET OWNER,Unwgt,5929,2018,685,274,411,57,19,38,142,48,94,188,82,106,147,64,83,151,61,90 41 | ,(000s),58910,22321,7556,3732,3824,537*,301#,236*,2201,1009*,1191,1905,1103,803,1556,616,940,1357,704,653 42 | ,Vert%,25.42,34.31,36.09,34.65,37.62,19.25,20.55,17.81,41.72,36.77,47.04,44.11,45.11,42.83,37.58,34.05,40.33,30.76,30.5,31.05 43 | ,Horz%,100,37.89,12.83,6.34,6.49,0.91,0.51,0.4,3.74,1.71,2.02,3.23,1.87,1.36,2.64,1.05,1.6,2.3,1.2,1.11 44 | ,Index,100,135,142,136,148,76,81,70,164,145,185,173,177,168,148,134,159,121,120,122 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,953,393,560,113,43,70,188,78,110,233,98,135,197,73,124,222,101,121 46 | ,(000s),100764,34457,10877,5381,5496,1565,758*,807,3143,1952,1191,2483,1296,1187,1709,477,1232,1978,899,1078 47 | ,Vert%,43.49,52.96,51.95,49.96,54.06,56.09,51.74,60.91,59.57,71.14,47.04,57.49,53.01,63.31,41.27,26.37,52.85,44.83,38.95,51.26 48 | ,Horz%,100,34.2,10.79,5.34,5.45,1.55,0.75,0.8,3.12,1.94,1.18,2.46,1.29,1.18,1.7,0.47,1.22,1.96,0.89,1.07 49 | ,Index,100,122,119,115,124,129,119,140,137,164,108,132,122,146,95,61,122,103,90,118 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,1684,683,1001,203,74,129,371,144,227,406,173,233,336,127,209,368,165,203 51 | ,(000s),159830,54775,18239,9308,8931,2659,1446,1213,5132,2629,2503,3718,2088,1630,3567,1488,2079,3162,1657,1505 52 | ,Vert%,68.98,84.18,87.11,86.42,87.85,95.3,98.7,91.55,97.27,95.81,98.85,86.08,85.4,86.93,86.14,82.26,89.19,71.67,71.79,71.56 53 | ,Horz%,100,34.27,11.41,5.82,5.59,1.66,0.9,0.76,3.21,1.64,1.57,2.33,1.31,1.02,2.23,0.93,1.3,1.98,1.04,0.94 54 | ,Index,100,122,126,125,127,138,143,133,141,139,143,125,124,126,125,119,129,104,104,104 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Thursday, July 19, 2018 / 6:44 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/south_data/Movie Purchaser Behavior S 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South,Genral Movie Purchaser South Male,Genral Movie Purchaser South Female,Movie Purchaser South 18-24,"Movie Purchaser South 18-24 male 3 | ","Movie Purchaser South 18-24 Female 4 | ",Movie Purchaser South 25-34,Movie Purchaser South 25-34 male,Movie Purchaser South 25-34 Female,Movie Purchaser South 35-44,Movie Purchaser South 35-44 Male,Movie Purchaser South 35-44 Female,Movie Purchaser South 45-54,Movie Purchaser South 45-54 Male,Movie Purchaser South 45-54 Female,Movie Purchaser South 55+,Movie Purchaser South 55+ Male,Movie Purchaser South 55+ Female 5 | Totals,Unwgt,27446,6622,2479,1059,1420,302,127,175,489,199,290,548,226,322,513,210,303,627,297,330 6 | ,(000s),234034,59760,19298,9077,10221,3045,1565,1480,4451,1965,2486,4017,1948,2069,3320,1455,1865,4465,2144,2321 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,8.25,3.88,4.37,1.3,0.67,0.63,1.9,0.84,1.06,1.72,0.83,0.88,1.42,0.62,0.8,1.91,0.92,0.99 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,1011,493,518,197,105,92,273,131,142,257,124,133,155,69,86,129,64,65 11 | ,(000s),65410,27699,8436,4525,3910,2042,1325,717,2346,1173,1172,2023,1073,950,983,474,508,1043,481,562 12 | ,Vert%,27.95,46.35,43.71,49.85,38.25,67.06,84.66,48.45,52.71,59.69,47.14,50.36,55.08,45.92,29.61,32.58,27.24,23.36,22.43,24.21 13 | ,Horz%,100,42.35,12.9,6.92,5.98,3.12,2.03,1.1,3.59,1.79,1.79,3.09,1.64,1.45,1.5,0.72,0.78,1.59,0.74,0.86 14 | ,Index,100,166,156,178,137,240,303,173,189,214,169,180,197,164,106,117,97,84,80,87 15 | Digital Music,Unwgt,27446,6622,2479,1059,1420,302,127,175,489,199,290,548,226,322,513,210,303,627,297,330 16 | ,(000s),234034,59760,19298,9077,10221,3045,1565,1480,4451,1965,2486,4017,1948,2069,3320,1455,1865,4465,2144,2321 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,8.25,3.88,4.37,1.3,0.67,0.63,1.9,0.84,1.06,1.72,0.83,0.88,1.42,0.62,0.8,1.91,0.92,0.99 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,2181,918,1263,224,93,131,405,159,246,487,197,290,468,187,281,597,282,315 21 | ,(000s),207976,54297,17380,7959,9421,2407,1147,1260,4009,1658,2351,3625,1755,1870,3128,1356,1772,4211,2044,2168 22 | ,Vert%,88.87,90.86,90.06,87.68,92.17,79.05,73.29,85.14,90.07,84.38,94.57,90.24,90.09,90.38,94.22,93.2,95.01,94.31,95.34,93.41 23 | ,Horz%,100,26.11,8.36,3.83,4.53,1.16,0.55,0.61,1.93,0.8,1.13,1.74,0.84,0.9,1.5,0.65,0.85,2.02,0.98,1.04 24 | ,Index,100,102,101,99,104,89,82,96,101,95,106,102,101,102,106,105,107,106,107,105 25 | RADIO,Unwgt,19045,5069,1877,763,1114,212,81,131,385,142,243,428,164,264,414,167,247,438,209,229 26 | ,(000s),169892,47452,14477,6151,8326,2049,805,1244,3631,1432,2198,3047,1273,1775,2592,1057,1535,3158,1583,1575 27 | ,Vert%,72.59,79.4,75.02,67.76,81.46,67.29,51.44,84.05,81.58,72.88,88.42,75.85,65.35,85.79,78.07,72.65,82.31,70.73,73.83,67.86 28 | ,Horz%,100,27.93,8.52,3.62,4.9,1.21,0.47,0.73,2.14,0.84,1.29,1.79,0.75,1.04,1.53,0.62,0.9,1.86,0.93,0.93 29 | ,Index,100,109,103,93,112,93,71,116,112,100,122,104,90,118,108,100,113,97,102,93 30 | MAGAZINE,Unwgt,21921,5698,2072,834,1238,228,86,142,385,136,249,450,174,276,438,173,265,571,265,306 31 | ,(000s),190820,51486,15963,7103,8860,2345,1092,1253,3457,1270,2188,3438,1603,1835,2782,1264,1518,3940,1874,2066 32 | ,Vert%,81.54,86.15,82.72,78.25,86.68,77.01,69.78,84.66,77.67,64.63,88.01,85.59,82.29,88.69,83.8,86.87,81.39,88.24,87.41,89.01 33 | ,Horz%,100,26.98,8.37,3.72,4.64,1.23,0.57,0.66,1.81,0.67,1.15,1.8,0.84,0.96,1.46,0.66,0.8,2.06,0.98,1.08 34 | ,Index,100,106,101,96,106,94,86,104,95,79,108,105,101,109,103,107,100,108,107,109 35 | MOVIE GOERS,Unwgt,27446,6622,2479,1059,1420,302,127,175,489,199,290,548,226,322,513,210,303,627,297,330 36 | ,(000s),234034,59760,19298,9077,10221,3045,1565,1480,4451,1965,2486,4017,1948,2069,3320,1455,1865,4465,2144,2321 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,8.25,3.88,4.37,1.3,0.67,0.63,1.9,0.84,1.06,1.72,0.83,0.88,1.42,0.62,0.8,1.91,0.92,0.99 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,2447,1039,1408,299,126,173,480,192,288,538,221,317,508,207,301,622,293,329 41 | ,(000s),227019,59228,19157,8975,10182,3044,1565,1479,4387,1902,2485,3986,1918,2068,3278,1446,1831,4462,2143,2319 42 | ,Vert%,97,99.11,99.27,98.88,99.62,99.97,100,99.93,98.56,96.79,99.96,99.23,98.46,99.95,98.73,99.38,98.18,99.93,99.95,99.91 43 | ,Horz%,100,26.09,8.44,3.95,4.49,1.34,0.69,0.65,1.93,0.84,1.09,1.76,0.84,0.91,1.44,0.64,0.81,1.97,0.94,1.02 44 | ,Index,100,102,102,102,103,103,103,103,102,100,103,102,102,103,102,102,101,103,103,103 45 | TABLET OWNER,Unwgt,8845,2791,1039,404,635,105,41,64,226,82,144,276,117,159,239,82,157,193,82,111 46 | ,(000s),75009,25141,7834,3200,4634,762,210*,553,1802,492,1310,2240,1193,1046,1492,523,969,1539,783,755 47 | ,Vert%,32.05,42.07,40.59,35.25,45.34,25.02,13.42,37.36,40.49,25.04,52.7,55.76,61.24,50.56,44.94,35.95,51.96,34.47,36.52,32.53 48 | ,Horz%,100,33.52,10.44,4.27,6.18,1.02,0.28,0.74,2.4,0.66,1.75,2.99,1.59,1.39,1.99,0.7,1.29,2.05,1.04,1.01 49 | ,Index,100,131,127,110,141,78,42,117,126,78,164,174,191,158,140,112,162,108,114,101 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,1187,443,744,146,54,92,244,86,158,253,89,164,263,95,168,281,119,162 51 | ,(000s),97217,30518,9235,3776,5459,1344,611*,733,2142,787,1355,1942,798,1144,1772,773,999,2035,807,1229 52 | ,Vert%,41.54,51.07,47.85,41.6,53.41,44.14,39.04,49.53,48.12,40.05,54.51,48.34,40.97,55.29,53.37,53.13,53.57,45.58,37.64,52.95 53 | ,Horz%,100,31.39,9.5,3.88,5.62,1.38,0.63,0.75,2.2,0.81,1.39,2,0.82,1.18,1.82,0.8,1.03,2.09,0.83,1.26 54 | ,Index,100,123,115,100,129,106,94,119,116,96,131,116,99,133,128,128,129,110,91,127 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,2114,865,1249,293,122,171,466,185,281,493,194,299,442,168,274,420,196,224 56 | ,(000s),168880,51870,16935,7802,9133,3015,1544,1472,4177,1824,2353,3684,1710,1974,2890,1209,1681,3169,1515,1654 57 | ,Vert%,72.16,86.8,87.76,85.95,89.36,99.01,98.66,99.46,93.84,92.82,94.65,91.71,87.78,95.41,87.05,83.09,90.13,70.97,70.66,71.26 58 | ,Horz%,100,30.71,10.03,4.62,5.41,1.79,0.91,0.87,2.47,1.08,1.39,2.18,1.01,1.17,1.71,0.72,1,1.88,0.9,0.98 59 | ,Index,100,120,122,119,124,137,137,138,130,129,131,127,122,132,121,115,125,98,98,99 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 64 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 65 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 66 | ,,,,,,,,,,,,,,,,,,,,, 67 | "Thursday, July 19, 2018 / 6:50 PM",,,,,,,,,,,,,,,,,,,,, 68 | -------------------------------------------------------------------------------- /raw_data/south_east_data/Movie Purchaser Behavior SE 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South East,Genral Movie Purchaser South East Male,Genral Movie Purchaser South East Female,Movie Purchaser South East 18-24,"Movie Purchaser South East 18-24 male 3 | ","Movie Purchaser South East 18-24 Female 4 | ",Movie Purchaser South East 25-34,Movie Purchaser South East 25-34 male,Movie Purchaser South East 25-34 Female,Movie Purchaser South East 35-44,Movie Purchaser South East 35-44 Male,Movie Purchaser South East 35-44 Female,Movie Purchaser South East 45-54,Movie Purchaser South East 45-54 Male,Movie Purchaser South East 45-54 Female,Movie Purchaser South East 55+,Movie Purchaser South East 55+ Male,Movie Purchaser South East 55+ Female 5 | Totals,Unwgt,23689,6041,920,394,526,77,27,50,163,72,91,191,76,115,204,74,130,285,145,140 6 | ,(000s),231709,65066,13101,6777,6324,1762,962#,800*,3125,1710,1415,2736,1505,1231,2682,1111,1571,2796,1489,1307 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,5.65,2.92,2.73,0.76,0.42,0.35,1.35,0.74,0.61,1.18,0.65,0.53,1.16,0.48,0.68,1.21,0.64,0.56 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,374,183,191,54,23,31,100,58,42,109,50,59,58,23,35,53,29,24 11 | ,(000s),66903,29479,6462,3995,2467,1436*,892#,544*,1997,1351*,646*,1546,913*,634*,746*,360#,386*,738*,480#,258# 12 | ,Vert%,28.87,45.31,49.32,58.95,39.01,81.5,92.72,68,63.9,79.01,45.65,56.51,60.66,51.5,27.82,32.4,24.57,26.39,32.24,19.74 13 | ,Horz%,100,44.06,9.66,5.97,3.69,2.15,1.33,0.81,2.98,2.02,0.97,2.31,1.36,0.95,1.12,0.54,0.58,1.1,0.72,0.39 14 | ,Index,100,157,171,204,135,282,321,236,221,274,158,196,210,178,96,112,85,91,112,68 15 | TV,Unwgt,20633,5376,820,357,463,53,20,33,138,61,77,172,68,104,190,68,122,267,140,127 16 | ,(000s),209096,59689,11861,6081,5780,1298*,635#,663*,2691,1528,1163,2545,1358,1188,2626,1087,1539,2701,1474,1228 17 | ,Vert%,90.24,91.74,90.54,89.73,91.4,73.67,66.01,82.88,86.11,89.36,82.19,93.02,90.23,96.51,97.91,97.84,97.96,96.6,98.99,93.96 18 | ,Horz%,100,28.55,5.67,2.91,2.76,0.62,0.3,0.32,1.29,0.73,0.56,1.22,0.65,0.57,1.26,0.52,0.74,1.29,0.7,0.59 19 | ,Index,100,102,100,99,101,82,73,92,95,99,91,103,100,107,109,108,109,107,110,104 20 | RADIO,Unwgt,16599,4637,685,282,403,52,19,33,121,51,70,151,58,93,160,58,102,201,96,105 21 | ,(000s),171433,51520,9972,4845,5127,1014*,414#,600*,2402,1287*,1115,2217,1202*,1015,2165,925*,1240,2176,1018,1157 22 | ,Vert%,73.99,79.18,76.12,71.49,81.07,57.55,43.04,75,76.86,75.26,78.8,81.03,79.87,82.45,80.72,83.26,78.93,77.83,68.37,88.52 23 | ,Horz%,100,30.05,5.82,2.83,2.99,0.59,0.24,0.35,1.4,0.75,0.65,1.29,0.7,0.59,1.26,0.54,0.72,1.27,0.59,0.67 24 | ,Index,100,107,103,97,110,78,58,101,104,102,107,110,108,111,109,113,107,105,92,120 25 | MAGAZINE,Unwgt,22182,5824,880,375,505,73,26,47,157,72,85,187,73,114,188,67,121,275,137,138 26 | ,(000s),219735,62794,12520,6494,6027,1648,849#,798*,2988,1710,1278,2651,1420,1231,2586,1090,1496,2648,1424,1224 27 | ,Vert%,94.83,96.51,95.57,95.82,95.3,93.53,88.25,99.75,95.62,100,90.32,96.89,94.35,100,96.42,98.11,95.23,94.71,95.63,93.65 28 | ,Horz%,100,28.58,5.7,2.96,2.74,0.75,0.39,0.36,1.36,0.78,0.58,1.21,0.65,0.56,1.18,0.5,0.68,1.21,0.65,0.56 29 | ,Index,100,102,101,101,100,99,93,105,101,105,95,102,99,105,102,103,100,100,101,99 30 | MOVIE GOERS,Unwgt,23689,6041,920,394,526,77,27,50,163,72,91,191,76,115,204,74,130,285,145,140 31 | ,(000s),231709,65066,13101,6777,6324,1762,962#,800*,3125,1710,1415,2736,1505,1231,2682,1111,1571,2796,1489,1307 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,5.65,2.92,2.73,0.76,0.42,0.35,1.35,0.74,0.61,1.18,0.65,0.53,1.16,0.48,0.68,1.21,0.64,0.56 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,913,390,523,75,26,49,163,72,91,188,74,114,204,74,130,283,144,139 36 | ,(000s),224335,64291,13006,6712,6294,1723,951#,772*,3125,1710,1415,2687,1456,1231,2682,1111,1571,2789,1485,1305 37 | ,Vert%,96.82,98.81,99.27,99.04,99.53,97.79,98.86,96.5,100,100,100,98.21,96.74,100,100,100,100,99.75,99.73,99.85 38 | ,Horz%,100,28.66,5.8,2.99,2.81,0.77,0.42,0.34,1.39,0.76,0.63,1.2,0.65,0.55,1.2,0.5,0.7,1.24,0.66,0.58 39 | ,Index,100,102,103,102,103,101,102,100,103,103,103,101,100,103,103,103,103,103,103,103 40 | TABLET OWNER,Unwgt,5929,2018,333,131,202,28,9,19,55,21,34,82,32,50,79,28,51,89,41,48 41 | ,(000s),58910,22321,4765,2300,2465,330#,213#,117#,1221*,538#,683*,1291,694*,597*,990,338#,653*,932,518*,415* 42 | ,Vert%,25.42,34.31,36.37,33.94,38.98,18.73,22.14,14.63,39.07,31.46,48.27,47.19,46.11,48.5,36.91,30.42,41.57,33.33,34.79,31.75 43 | ,Horz%,100,37.89,8.09,3.9,4.18,0.56,0.36,0.2,2.07,0.91,1.16,2.19,1.18,1.01,1.68,0.57,1.11,1.58,0.88,0.7 44 | ,Index,100,135,143,133,153,74,87,58,154,124,190,186,181,191,145,120,163,131,137,125 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,466,190,276,46,15,31,80,34,46,109,42,67,102,32,70,129,67,62 46 | ,(000s),100764,34457,6905,3352,3553,995*,431#,563*,1882,1225*,658*,1807,876*,931,964,197*,767,1257,623,634 47 | ,Vert%,43.49,52.96,52.71,49.46,56.18,56.47,44.8,70.38,60.22,71.64,46.5,66.05,58.21,75.63,35.94,17.73,48.82,44.96,41.84,48.51 48 | ,Horz%,100,34.2,6.85,3.33,3.53,0.99,0.43,0.56,1.87,1.22,0.65,1.79,0.87,0.92,0.96,0.2,0.76,1.25,0.62,0.63 49 | ,Index,100,122,121,114,129,130,103,162,138,165,107,152,134,174,83,41,112,103,96,112 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,780,325,455,74,26,48,159,68,91,169,67,102,175,63,112,203,101,102 51 | ,(000s),159830,54775,11408,5943,5465,1648,949#,700*,3089,1674,1415,2270,1222,1048,2323,945,1379,2077,1153,924 52 | ,Vert%,68.98,84.18,87.08,87.69,86.42,93.53,98.65,87.5,98.85,97.89,100,82.97,81.2,85.13,86.61,85.06,87.78,74.28,77.43,70.7 53 | ,Horz%,100,34.27,7.14,3.72,3.42,1.03,0.59,0.44,1.93,1.05,0.89,1.42,0.76,0.66,1.45,0.59,0.86,1.3,0.72,0.58 54 | ,Index,100,122,126,127,125,136,143,127,143,142,145,120,118,123,126,123,127,108,112,102 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Thursday, July 19, 2018 / 6:44 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/south_east_data/Movie Purchaser Behavior SE 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South East,Genral Movie Purchaser South East Male,Genral Movie Purchaser South East Female,Movie Purchaser South East 18-24,"Movie Purchaser South East 18-24 male 3 | ","Movie Purchaser South East 18-24 Female 4 | ",Movie Purchaser South East 25-34,Movie Purchaser South East 25-34 male,Movie Purchaser South East 25-34 Female,Movie Purchaser South East 35-44,Movie Purchaser South East 35-44 Male,Movie Purchaser South East 35-44 Female,Movie Purchaser South East 45-54,Movie Purchaser South East 45-54 Male,Movie Purchaser South East 45-54 Female,Movie Purchaser South East 55+,Movie Purchaser South East 55+ Male,Movie Purchaser South East 55+ Female 5 | Totals,Unwgt,27446,6622,955,395,560,98,34,64,161,64,97,191,71,120,218,85,133,287,141,146 6 | ,(000s),234034,59760,11936,5411,6525,1765,834*,931,2569,1092,1477,2433,1099,1335,2134,821,1314,3034,1566,1468 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,5.1,2.31,2.79,0.75,0.36,0.4,1.1,0.47,0.63,1.04,0.47,0.57,0.91,0.35,0.56,1.3,0.67,0.63 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,376,170,206,63,30,33,88,41,47,88,39,49,72,28,44,65,32,33 11 | ,(000s),65410,27699,5306,2740,2566,1244,801#,443*,1456,678*,779*,1219,627*,592*,576,222#,354*,811,412*,400* 12 | ,Vert%,27.95,46.35,44.45,50.64,39.33,70.48,96.04,47.58,56.68,62.09,52.74,50.1,57.05,44.34,26.99,27.04,26.94,26.73,26.31,27.25 13 | ,Horz%,100,42.35,8.11,4.19,3.92,1.9,1.22,0.68,2.23,1.04,1.19,1.86,0.96,0.91,0.88,0.34,0.54,1.24,0.63,0.61 14 | ,Index,100,166,159,181,141,252,344,170,203,222,189,179,204,159,97,97,96,96,94,97 15 | Digital Music,Unwgt,27446,6622,955,395,560,98,34,64,161,64,97,191,71,120,218,85,133,287,141,146 16 | ,(000s),234034,59760,11936,5411,6525,1765,834*,931,2569,1092,1477,2433,1099,1335,2134,821,1314,3034,1566,1468 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,5.1,2.31,2.79,0.75,0.36,0.4,1.1,0.47,0.63,1.04,0.47,0.57,0.91,0.35,0.56,1.3,0.67,0.63 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,842,345,497,75,27,48,133,49,84,168,63,105,196,74,122,270,132,138 21 | ,(000s),207976,54297,10784,4805,5979,1461,644#,817*,2290,866*,1424,2241,1074,1167,2000,749,1251,2793,1473,1320 22 | ,Vert%,88.87,90.86,90.35,88.8,91.63,82.78,77.22,87.76,89.14,79.3,96.41,92.11,97.73,87.42,93.72,91.23,95.21,92.06,94.06,89.92 23 | ,Horz%,100,26.11,5.19,2.31,2.87,0.7,0.31,0.39,1.1,0.42,0.68,1.08,0.52,0.56,0.96,0.36,0.6,1.34,0.71,0.63 24 | ,Index,100,102,102,100,103,93,87,99,100,89,108,104,110,98,105,103,107,104,106,101 25 | RADIO,Unwgt,19045,5069,693,273,420,69,21,48,117,40,77,148,52,96,168,64,104,191,96,95 26 | ,(000s),169892,47452,8981,3591,5390,1231,400#,831*,2083,768*,1316,1830,710*,1120,1674,575,1099,2163,1139,1024 27 | ,Vert%,72.59,79.4,75.24,66.36,82.61,69.75,47.96,89.26,81.08,70.33,89.1,75.22,64.6,83.9,78.44,70.04,83.64,71.29,72.73,69.75 28 | ,Horz%,100,27.93,5.29,2.11,3.17,0.72,0.24,0.49,1.23,0.45,0.77,1.08,0.42,0.66,0.99,0.34,0.65,1.27,0.67,0.6 29 | ,Index,100,109,104,91,114,96,66,123,112,97,123,104,89,116,108,96,115,98,100,96 30 | MAGAZINE,Unwgt,21921,5698,821,332,489,81,27,54,133,48,85,160,58,102,183,70,113,264,129,135 31 | ,(000s),190820,51486,10220,4541,5679,1485,704#,781*,2124,775*,1349,2116,950*,1166,1759,711,1048,2736,1400,1336 32 | ,Vert%,81.54,86.15,85.62,83.92,87.03,84.14,84.41,83.89,82.68,70.97,91.33,86.97,86.44,87.34,82.43,86.6,79.76,90.18,89.4,91.01 33 | ,Horz%,100,26.98,5.36,2.38,2.98,0.78,0.37,0.41,1.11,0.41,0.71,1.11,0.5,0.61,0.92,0.37,0.55,1.43,0.73,0.7 34 | ,Index,100,106,105,103,107,103,104,103,101,87,112,107,106,107,101,106,98,111,110,112 35 | MOVIE GOERS,Unwgt,27446,6622,955,395,560,98,34,64,161,64,97,191,71,120,218,85,133,287,141,146 36 | ,(000s),234034,59760,11936,5411,6525,1765,834*,931,2569,1092,1477,2433,1099,1335,2134,821,1314,3034,1566,1468 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,5.1,2.31,2.79,0.75,0.36,0.4,1.1,0.47,0.63,1.04,0.47,0.57,0.91,0.35,0.56,1.3,0.67,0.63 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,948,390,558,98,34,64,159,62,97,189,69,120,217,85,132,285,140,145 41 | ,(000s),227019,59228,11824,5335,6489,1765,834*,931,2519,1042,1477,2407,1072,1335,2101,821,1280,3032,1566,1465 42 | ,Vert%,97,99.11,99.06,98.6,99.45,100,100,100,98.05,95.42,100,98.93,97.54,100,98.45,100,97.41,99.93,100,99.8 43 | ,Horz%,100,26.09,5.21,2.35,2.86,0.78,0.37,0.41,1.11,0.46,0.65,1.06,0.47,0.59,0.93,0.36,0.56,1.34,0.69,0.65 44 | ,Index,100,102,102,102,103,103,103,103,101,98,103,102,101,103,101,103,100,103,103,103 45 | TABLET OWNER,Unwgt,8845,2791,403,147,256,32,10,22,75,25,50,98,37,61,104,33,71,94,42,52 46 | ,(000s),75009,25141,5010,1991,3019,383*,96#,287#,1111,247#,864*,1409,725*,684,960,283*,677,1146,640*,506* 47 | ,Vert%,32.05,42.07,41.97,36.8,46.27,21.7,11.51,30.83,43.25,22.62,58.5,57.91,65.97,51.24,44.99,34.47,51.52,37.77,40.87,34.47 48 | ,Horz%,100,33.52,6.68,2.65,4.02,0.51,0.13,0.38,1.48,0.33,1.15,1.88,0.97,0.91,1.28,0.38,0.9,1.53,0.85,0.67 49 | ,Index,100,131,131,115,144,68,36,96,135,71,183,181,206,160,140,108,161,118,128,108 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,463,169,294,51,15,36,78,26,52,87,25,62,114,43,71,133,60,73 51 | ,(000s),97217,30518,5830,2345,3485,851*,342#,508*,1262,466#,796*,1211,433#,778,1172,477*,695,1336,627*,709 52 | ,Vert%,41.54,51.07,48.84,43.34,53.41,48.22,41.01,54.56,49.12,42.67,53.89,49.77,39.4,58.28,54.92,58.1,52.89,44.03,40.04,48.3 53 | ,Horz%,100,31.39,6,2.41,3.58,0.88,0.35,0.52,1.3,0.48,0.82,1.25,0.45,0.8,1.21,0.49,0.71,1.37,0.64,0.73 54 | ,Index,100,123,118,104,129,116,99,131,118,103,130,120,95,140,132,140,127,106,96,116 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,817,320,497,93,32,61,154,60,94,178,62,116,194,73,121,198,93,105 56 | ,(000s),168880,51870,10561,4683,5878,1751,827*,923,2356,964*,1391,2284,996,1288,1940,746,1194,2231,1149,1082 57 | ,Vert%,72.16,86.8,88.48,86.55,90.08,99.21,99.16,99.14,91.71,88.28,94.18,93.88,90.63,96.48,90.91,90.86,90.87,73.53,73.37,73.71 58 | ,Horz%,100,30.71,6.25,2.77,3.48,1.04,0.49,0.55,1.4,0.57,0.82,1.35,0.59,0.76,1.15,0.44,0.71,1.32,0.68,0.64 59 | ,Index,100,120,123,120,125,137,137,137,127,122,131,130,126,134,126,126,126,102,102,102 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 64 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 65 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 66 | ,,,,,,,,,,,,,,,,,,,,, 67 | "Thursday, July 19, 2018 / 6:50 PM",,,,,,,,,,,,,,,,,,,,, 68 | -------------------------------------------------------------------------------- /raw_data/south_west_data/Movie Purchaser Behavior SW 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South West,Genral Movie Purchaser South West Male,Genral Movie Purchaser South West Female,Movie Purchaser South West 18-24,"Movie Purchaser South West 18-24 male 3 | ","Movie Purchaser South West 18-24 Female 4 | ",Movie Purchaser South West 25-34,Movie Purchaser South West 25-34 male,Movie Purchaser South West 25-34 Female,Movie Purchaser South West 35-44,Movie Purchaser South West 35-44 Male,Movie Purchaser South West 35-44 Female,Movie Purchaser South West 45-54,Movie Purchaser South West 45-54 Male,Movie Purchaser South West 45-54 Female,Movie Purchaser South West 55+,Movie Purchaser South West 55+ Male,Movie Purchaser South West 55+ Female 5 | Totals,Unwgt,23689,6041,1093,459,634,135,51,84,237,88,149,266,122,144,208,96,112,247,102,145 6 | ,(000s),231709,65066,7836,3994,3842,1028,503*,525,2151,1034,1117,1584,940,644,1459,699,760,1615,819,796 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,3.38,1.72,1.66,0.44,0.22,0.23,0.93,0.45,0.48,0.68,0.41,0.28,0.63,0.3,0.33,0.7,0.35,0.34 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,470,230,240,76,38,38,141,62,79,146,75,71,64,38,26,43,17,26 11 | ,(000s),66903,29479,3785,1991,1793,469,222*,247*,1466,724,742,1052,644,408,510,293*,217#,288*,109#,179# 12 | ,Vert%,28.87,45.31,48.3,49.85,46.67,45.62,44.14,47.05,68.15,70.02,66.43,66.41,68.51,63.35,34.96,41.92,28.55,17.83,13.31,22.49 13 | ,Horz%,100,44.06,5.66,2.98,2.68,0.7,0.33,0.37,2.19,1.08,1.11,1.57,0.96,0.61,0.76,0.44,0.32,0.43,0.16,0.27 14 | ,Index,100,157,167,173,162,158,153,163,236,243,230,230,237,219,121,145,99,62,46,78 15 | TV,Unwgt,20633,5376,971,408,563,105,38,67,202,74,128,248,114,134,190,89,101,226,93,133 16 | ,(000s),209096,59689,7186,3670,3516,931,471*,460,1961,939,1021,1469,862,607,1352,680,672,1474,718,756 17 | ,Vert%,90.24,91.74,91.7,91.89,91.51,90.56,93.64,87.62,91.17,90.81,91.41,92.74,91.7,94.25,92.67,97.28,88.42,91.27,87.67,94.97 18 | ,Horz%,100,28.55,3.44,1.76,1.68,0.45,0.23,0.22,0.94,0.45,0.49,0.7,0.41,0.29,0.65,0.33,0.32,0.7,0.34,0.36 19 | ,Index,100,102,102,102,101,100,104,97,101,101,101,103,102,104,103,108,98,101,97,105 20 | RADIO,Unwgt,16599,4637,865,352,513,100,29,71,191,69,122,226,100,126,164,79,85,184,75,109 21 | ,(000s),171433,51520,6083,2986,3097,724,258#,466,1585,726,859,1313,764,550,1182,585,597,1278,653,625 22 | ,Vert%,73.99,79.18,77.63,74.76,80.61,70.43,51.29,88.76,73.69,70.21,76.9,82.89,81.28,85.4,81.01,83.69,78.55,79.13,79.73,78.52 23 | ,Horz%,100,30.05,3.55,1.74,1.81,0.42,0.15,0.27,0.92,0.42,0.5,0.77,0.45,0.32,0.69,0.34,0.35,0.75,0.38,0.36 24 | ,Index,100,107,105,101,109,95,69,120,100,95,104,112,110,115,109,113,106,107,108,106 25 | MAGAZINE,Unwgt,22182,5824,1057,442,615,134,50,84,231,86,145,253,114,139,201,93,108,238,99,139 26 | ,(000s),219735,62794,7706,3933,3774,1027,502*,525,2116,1004,1112,1566,932,634,1424,677,747,1572,817,755 27 | ,Vert%,94.83,96.51,98.34,98.47,98.23,99.9,99.8,100,98.37,97.1,99.55,98.86,99.15,98.45,97.6,96.85,98.29,97.34,99.76,94.85 28 | ,Horz%,100,28.58,3.51,1.79,1.72,0.47,0.23,0.24,0.96,0.46,0.51,0.71,0.42,0.29,0.65,0.31,0.34,0.72,0.37,0.34 29 | ,Index,100,102,104,104,104,105,105,105,104,102,105,104,105,104,103,102,104,103,105,100 30 | MOVIE GOERS,Unwgt,23689,6041,1093,459,634,135,51,84,237,88,149,266,122,144,208,96,112,247,102,145 31 | ,(000s),231709,65066,7836,3994,3842,1028,503*,525,2151,1034,1117,1584,940,644,1459,699,760,1615,819,796 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,3.38,1.72,1.66,0.44,0.22,0.23,0.93,0.45,0.48,0.68,0.41,0.28,0.63,0.3,0.33,0.7,0.35,0.34 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,1082,453,629,135,51,84,232,85,147,265,122,143,206,95,111,244,100,144 36 | ,(000s),224335,64291,7797,3972,3825,1028,503*,525,2141,1025,1116,1582,940,643,1441,696,745,1604,808,796 37 | ,Vert%,96.82,98.81,99.5,99.45,99.56,100,100,100,99.54,99.13,99.91,99.87,100,99.84,98.77,99.57,98.03,99.32,98.66,100 38 | ,Horz%,100,28.66,3.48,1.77,1.71,0.46,0.22,0.23,0.95,0.46,0.5,0.71,0.42,0.29,0.64,0.31,0.33,0.72,0.36,0.35 39 | ,Index,100,102,103,103,103,103,103,103,103,102,103,103,103,103,102,103,101,103,102,103 40 | TABLET OWNER,Unwgt,5929,2018,352,143,209,29,10,19,87,27,60,106,50,56,68,36,32,62,20,42 41 | ,(000s),58910,22321,2791,1432,1359,207#,87#,119#,980,472#,508*,615,409*,206*,566,279*,287*,425,186#,239* 42 | ,Vert%,25.42,34.31,35.62,35.85,35.37,20.14,17.3,22.67,45.56,45.65,45.48,38.83,43.51,31.99,38.79,39.91,37.76,26.32,22.71,30.03 43 | ,Horz%,100,37.89,4.74,2.43,2.31,0.35,0.15,0.2,1.66,0.8,0.86,1.04,0.69,0.35,0.96,0.47,0.49,0.72,0.32,0.41 44 | ,Index,100,135,140,141,139,79,68,89,179,180,179,153,171,126,153,157,149,104,89,118 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,487,203,284,67,28,39,108,44,64,124,56,68,95,41,54,93,34,59 46 | ,(000s),100764,34457,3972,2029,1943,570,326#,244*,1260,727*,533,676,420*,256,745,280*,465*,720,276*,444* 47 | ,Vert%,43.49,52.96,50.69,50.8,50.57,55.45,64.81,46.48,58.58,70.31,47.72,42.68,44.68,39.75,51.06,40.06,61.18,44.58,33.7,55.78 48 | ,Horz%,100,34.2,3.94,2.01,1.93,0.57,0.32,0.24,1.25,0.72,0.53,0.67,0.42,0.25,0.74,0.28,0.46,0.71,0.27,0.44 49 | ,Index,100,122,117,117,116,128,149,107,135,162,110,98,103,91,117,92,141,103,77,128 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,904,358,546,129,48,81,212,76,136,237,106,131,161,64,97,165,64,101 51 | ,(000s),159830,54775,6831,3365,3466,1011,498*,513,2043,955,1088,1448,866,582,1244,543,701,1085,504,581 52 | ,Vert%,68.98,84.18,87.17,84.25,90.21,98.35,99.01,97.71,94.98,92.36,97.4,91.41,92.13,90.37,85.26,77.68,92.24,67.18,61.54,72.99 53 | ,Horz%,100,34.27,4.27,2.11,2.17,0.63,0.31,0.32,1.28,0.6,0.68,0.91,0.54,0.36,0.78,0.34,0.44,0.68,0.32,0.36 54 | ,Index,100,122,126,122,131,143,144,142,138,134,141,133,134,131,124,113,134,97,89,106 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Thursday, July 19, 2018 / 6:44 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/south_west_data/Movie Purchaser Behavior SW 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South West,Genral Movie Purchaser South West Male,Genral Movie Purchaser South West Female,Movie Purchaser South West 18-24,"Movie Purchaser South West 18-24 male 3 | ","Movie Purchaser South West 18-24 Female 4 | ",Movie Purchaser South West 25-34,Movie Purchaser South West 25-34 male,Movie Purchaser South West 25-34 Female,Movie Purchaser South West 35-44,Movie Purchaser South West 35-44 Male,Movie Purchaser South West 35-44 Female,Movie Purchaser South West 45-54,Movie Purchaser South West 45-54 Male,Movie Purchaser South West 45-54 Female,Movie Purchaser South West 55+,Movie Purchaser South West 55+ Male,Movie Purchaser South West 55+ Female 5 | Totals,Unwgt,27446,6622,1524,664,860,204,93,111,328,135,193,357,155,202,295,125,170,156,184,340 6 | ,(000s),234034,59760,7363,3666,3697,1280,731,549,1882,873,1009,1584,850,734,1186,634,551,578,854,1431 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,3.15,1.57,1.58,0.55,0.31,0.23,0.8,0.37,0.43,0.68,0.36,0.31,0.51,0.27,0.24,0.25,0.36,0.61 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,635,323,312,134,75,59,185,90,95,169,85,84,83,41,42,32,32,64 11 | ,(000s),65410,27699,3130,1786,1344,798,524,275*,889,496,394,804,445,359,407,252*,155*,69*,163*,232 12 | ,Vert%,27.95,46.35,42.51,48.72,36.35,62.34,71.68,50.09,47.24,56.82,39.05,50.76,52.35,48.91,34.32,39.75,28.13,11.94,19.09,16.21 13 | ,Horz%,100,42.35,4.79,2.73,2.05,1.22,0.8,0.42,1.36,0.76,0.6,1.23,0.68,0.55,0.62,0.39,0.24,0.11,0.25,0.35 14 | ,Index,100,166,152,174,130,223,256,179,169,203,140,182,187,175,123,142,101,43,68,58 15 | Digital Music,Unwgt,27446,6622,1524,664,860,204,93,111,328,135,193,357,155,202,295,125,170,156,184,340 16 | ,(000s),234034,59760,7363,3666,3697,1280,731,549,1882,873,1009,1584,850,734,1186,634,551,578,854,1431 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,3.15,1.57,1.58,0.55,0.31,0.23,0.8,0.37,0.43,0.68,0.36,0.31,0.51,0.27,0.24,0.25,0.36,0.61 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,1339,573,766,149,66,83,272,110,162,319,134,185,272,113,159,150,177,327 21 | ,(000s),207976,54297,6596,3154,3442,946,503,443,1719,792,927,1384,681,703,1128,607,521,571,848,1419 22 | ,Vert%,88.87,90.86,89.58,86.03,93.1,73.91,68.81,80.69,91.34,90.72,91.87,87.37,80.12,95.78,95.11,95.74,94.56,98.79,99.3,99.16 23 | ,Horz%,100,26.11,3.17,1.52,1.65,0.45,0.24,0.21,0.83,0.38,0.45,0.67,0.33,0.34,0.54,0.29,0.25,0.27,0.41,0.68 24 | ,Index,100,102,101,97,105,83,77,91,103,102,103,98,90,108,107,108,106,111,112,112 25 | RADIO,Unwgt,19045,5069,1184,490,694,143,60,83,268,102,166,280,112,168,246,103,143,113,134,247 26 | ,(000s),169892,47452,5496,2560,2936,818,405*,412,1547,665,883,1217,563,655,918,482,436,444,551,995 27 | ,Vert%,72.59,79.4,74.64,69.83,79.42,63.91,55.4,75.05,82.2,76.17,87.51,76.83,66.24,89.24,77.4,76.03,79.13,76.82,64.52,69.53 28 | ,Horz%,100,27.93,3.23,1.51,1.73,0.48,0.24,0.24,0.91,0.39,0.52,0.72,0.33,0.39,0.54,0.28,0.26,0.26,0.32,0.59 29 | ,Index,100,109,103,96,109,88,76,103,113,105,121,106,91,123,107,105,109,106,89,96 30 | MAGAZINE,Unwgt,21921,5698,1251,502,749,147,59,88,252,88,164,290,116,174,255,103,152,136,171,307 31 | ,(000s),190820,51486,5743,2563,3181,860,388*,472,1333,494,839,1323,653,669,1023,553,470,474,730,1204 32 | ,Vert%,81.54,86.15,78,69.91,86.04,67.19,53.08,85.97,70.83,56.59,83.15,83.52,76.82,91.14,86.26,87.22,85.3,82.01,85.48,84.14 33 | ,Horz%,100,26.98,3.01,1.34,1.67,0.45,0.2,0.25,0.7,0.26,0.44,0.69,0.34,0.35,0.54,0.29,0.25,0.25,0.38,0.63 34 | ,Index,100,106,96,86,106,82,65,105,87,69,102,102,94,112,106,107,105,101,105,103 35 | MOVIE GOERS,Unwgt,27446,6622,1524,664,860,204,93,111,328,135,193,357,155,202,295,125,170,156,184,340 36 | ,(000s),234034,59760,7363,3666,3697,1280,731,549,1882,873,1009,1584,850,734,1186,634,551,578,854,1431 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,3.15,1.57,1.58,0.55,0.31,0.23,0.8,0.37,0.43,0.68,0.36,0.31,0.51,0.27,0.24,0.25,0.36,0.61 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,1499,649,850,201,92,109,321,130,191,349,152,197,291,122,169,153,184,337 41 | ,(000s),227019,59228,7333,3640,3693,1279,731,548,1868,860,1008,1579,846,733,1177,626,551,577,854,1431 42 | ,Vert%,97,99.11,99.59,99.29,99.89,99.92,100,99.82,99.26,98.51,99.9,99.68,99.53,99.86,99.24,98.74,100,99.83,100,100 43 | ,Horz%,100,26.09,3.23,1.6,1.63,0.56,0.32,0.24,0.82,0.38,0.44,0.7,0.37,0.32,0.52,0.28,0.24,0.25,0.38,0.63 44 | ,Index,100,102,103,102,103,103,103,103,102,102,103,103,103,103,102,102,103,103,103,103 45 | TABLET OWNER,Unwgt,8845,2791,636,257,379,73,31,42,151,57,94,178,80,98,135,49,86,40,59,99 46 | ,(000s),75009,25141,2824,1209,1615,379,113*,265*,690,244*,446,831,469,362,532,240*,292,143*,249*,392 47 | ,Vert%,32.05,42.07,38.35,32.98,43.68,29.61,15.46,48.27,36.66,27.95,44.2,52.46,55.18,49.32,44.86,37.85,52.99,24.74,29.16,27.39 48 | ,Horz%,100,33.52,3.76,1.61,2.15,0.51,0.15,0.35,0.92,0.33,0.59,1.11,0.63,0.48,0.71,0.32,0.39,0.19,0.33,0.52 49 | ,Index,100,131,120,103,136,92,48,151,114,87,138,164,172,154,140,118,165,77,91,85 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,724,274,450,95,39,56,166,60,106,166,64,102,149,52,97,59,89,148 51 | ,(000s),97217,30518,3405,1430,1974,493,269*,224*,880,321*,559,732,365,367,600,296*,304,180*,520,700 52 | ,Vert%,41.54,51.07,46.24,39.01,53.39,38.52,36.8,40.8,46.76,36.77,55.4,46.21,42.94,50,50.59,46.69,55.17,31.14,60.89,48.92 53 | ,Horz%,100,31.39,3.5,1.47,2.03,0.51,0.28,0.23,0.91,0.33,0.58,0.75,0.38,0.38,0.62,0.3,0.31,0.19,0.53,0.72 54 | ,Index,100,123,111,94,129,93,89,98,113,89,133,111,103,120,122,112,133,75,147,118 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,1297,545,752,200,90,110,312,125,187,315,132,183,248,95,153,103,119,222 56 | ,(000s),168880,51870,6374,3119,3256,1265,716,548,1821,860,962,1400,714,686,950,462,488,367,572,938 57 | ,Vert%,72.16,86.8,86.57,85.08,88.07,98.83,97.95,99.82,96.76,98.51,95.34,88.38,84,93.46,80.1,72.87,88.57,63.49,66.98,65.55 58 | ,Horz%,100,30.71,3.77,1.85,1.93,0.75,0.42,0.32,1.08,0.51,0.57,0.83,0.42,0.41,0.56,0.27,0.29,0.22,0.34,0.56 59 | ,Index,100,120,120,118,122,137,136,138,134,137,132,122,116,130,111,101,123,88,93,91 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 64 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 65 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 66 | ,,,,,,,,,,,,,,,,,,,,, 67 | "Thursday, July 19, 2018 / 6:50 PM",,,,,,,,,,,,,,,,,,,,, 68 | -------------------------------------------------------------------------------- /raw_data/south_west_data/Movie Purchaser Behavior SW 2017.csv: -------------------------------------------------------------------------------- 1 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser South West,Genral Movie Purchaser South West Male,Genral Movie Purchaser South West Female,Movie Purchaser South West 18-24,"Movie Purchaser South West 18-24 male 2 | ","Movie Purchaser South West 18-24 Female 3 | ",Movie Purchaser South West 25-34,Movie Purchaser South West 25-34 male,Movie Purchaser South West 25-34 Female,Movie Purchaser South West 35-44,Movie Purchaser South West 35-44 Male,Movie Purchaser South West 35-44 Female,Movie Purchaser South West 45-54,Movie Purchaser South West 45-54 Male,Movie Purchaser South West 45-54 Female,Movie Purchaser South West 55+,Movie Purchaser South West 55+ Male,Movie Purchaser South West 55+ Female 4 | Totals,Unwgt,6088,1877,398,214,184,60,37,23,114,62,52,88,52,36,74,28,34,74,35,39 5 | ,(000s),71575,23088,2410,1452,958,371*,243*,128#,973,540,433*,543,370*,173*,303,119#,102*,303,181*,122* 6 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 7 | ,Horz%,100,32.26,3.37,2.03,1.34,0.52,0.34,0.18,1.36,0.75,0.6,0.76,0.52,0.24,0.42,0.17,0.14,0.42,0.25,0.17 8 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 9 | Video Game ,Unwgt,4991,1708,358,205,153,59,36,23,108,62,46,83,51,32,56,24,28,56,32,24 10 | ,(000s),61255,21650,2196,1405,791,371*,243*,128#,882,540,342*,523,370*,153*,236*,103#,82#,236*,150*,86# 11 | ,Vert%,85.58,93.77,91.12,96.76,82.57,100,100,100,90.65,100,78.98,96.32,100,88.44,77.89,86.55,80.39,77.89,82.87,70.49 12 | ,Horz%,100,35.34,3.59,2.29,1.29,0.61,0.4,0.21,1.44,0.88,0.56,0.85,0.6,0.25,0.39,0.17,0.13,0.39,0.24,0.14 13 | ,Index,100,110,106,113,96,117,117,117,106,117,92,113,117,103,91,101,94,91,97,82 14 | Streaming Video,Unwgt,4956,1613,340,183,157,53,32,21,103,54,49,72,44,28,59,24,29,59,29,30 15 | ,(000s),58741,20176,2152,1284,868,332*,207*,125#,940,508*,431*,447,332*,115#,239*,104#,90#,239*,132#,106# 16 | ,Vert%,82.07,87.39,89.29,88.43,90.61,89.49,85.19,97.66,96.61,94.07,99.54,82.32,89.73,66.47,78.88,87.39,88.24,78.88,72.93,86.89 17 | ,Horz%,100,34.35,3.66,2.19,1.48,0.57,0.35,0.21,1.6,0.86,0.73,0.76,0.57,0.2,0.41,0.18,0.15,0.41,0.22,0.18 18 | ,Index,100,106,109,108,110,109,104,119,118,115,121,100,109,81,96,106,108,96,89,106 19 | Digital Music,Unwgt,6088,1877,398,214,184,60,37,23,114,62,52,88,52,36,74,28,34,74,35,39 20 | ,(000s),71575,23088,2410,1452,958,371*,243*,128#,973,540,433*,543,370*,173*,303,119#,102*,303,181*,122* 21 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 22 | ,Horz%,100,32.26,3.37,2.03,1.34,0.52,0.34,0.18,1.36,0.75,0.6,0.76,0.52,0.24,0.42,0.17,0.14,0.42,0.25,0.17 23 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 24 | TV,Unwgt,4770,1538,316,165,151,37,22,15,86,44,42,70,42,28,68,26,29,68,31,37 25 | ,(000s),56475,18971,1986,1143,844,256*,136#,120#,807,456*,351*,422,264*,159#,292,115#,94#,292,172*,119* 26 | ,Vert%,78.9,82.17,82.41,78.72,88.1,69,55.97,93.75,82.94,84.44,81.06,77.72,71.35,91.91,96.37,96.64,92.16,96.37,95.03,97.54 27 | ,Horz%,100,33.59,3.52,2.02,1.49,0.45,0.24,0.21,1.43,0.81,0.62,0.75,0.47,0.28,0.52,0.2,0.17,0.52,0.3,0.21 28 | ,Index,100,104,104,100,112,87,71,119,105,107,103,98,90,116,122,122,117,122,120,124 29 | RADIO,Unwgt,4089,1386,292,144,148,39,20,19,79,42,37,66,35,31,60,20,28,60,27,33 30 | ,(000s),48424,16720,1700,900,800,257*,135#,122#,588,283*,305*,416,249*,166*,246*,95#,97#,246*,138#,108* 31 | ,Vert%,67.65,72.42,70.54,61.98,83.51,69.27,55.56,95.31,60.43,52.41,70.44,76.61,67.3,95.95,81.19,79.83,95.1,81.19,76.24,88.52 32 | ,Horz%,100,34.53,3.51,1.86,1.65,0.53,0.28,0.25,1.21,0.58,0.63,0.86,0.51,0.34,0.51,0.2,0.2,0.51,0.28,0.22 33 | ,Index,100,107,104,92,123,102,82,141,89,77,104,113,99,142,120,118,141,120,113,131 34 | MAGAZINE,Unwgt,4249,1456,290,150,140,36,21,15,73,39,34,64,40,24,66,21,30,66,29,37 35 | ,(000s),48801,17437,1712,1003,710,228*,136#,92#,584,328*,255*,451,305*,146#,260,91#,99#,260,143#,117* 36 | ,Vert%,68.18,75.52,71.04,69.08,74.11,61.46,55.97,71.88,60.02,60.74,58.89,83.06,82.43,84.39,85.81,76.47,97.06,85.81,79.01,95.9 37 | ,Horz%,100,35.73,3.51,2.06,1.45,0.47,0.28,0.19,1.2,0.67,0.52,0.92,0.62,0.3,0.53,0.19,0.2,0.53,0.29,0.24 38 | ,Index,100,111,104,101,109,90,82,105,88,89,86,122,121,124,126,112,142,126,116,141 39 | MOVIE GOERS,Unwgt,6088,1877,398,214,184,60,37,23,114,62,52,88,52,36,74,28,34,74,35,39 40 | ,(000s),71575,23088,2410,1452,958,371*,243*,128#,973,540,433*,543,370*,173*,303,119#,102*,303,181*,122* 41 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 42 | ,Horz%,100,32.26,3.37,2.03,1.34,0.52,0.34,0.18,1.36,0.75,0.6,0.76,0.52,0.24,0.42,0.17,0.14,0.42,0.25,0.17 43 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 44 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,5741,1823,386,207,179,57,35,22,113,61,52,87,51,36,70,26,33,70,34,36 45 | ,(000s),67039,22222,2372,1424,947,354*,228*,126#,965,532,433*,542,369*,173*,292,118#,101*,292,178*,114* 46 | ,Vert%,93.66,96.25,98.42,98.07,98.85,95.42,93.83,98.44,99.18,98.52,100,99.82,99.73,100,96.37,99.16,99.02,96.37,98.34,93.44 47 | ,Horz%,100,33.15,3.54,2.12,1.41,0.53,0.34,0.19,1.44,0.79,0.65,0.81,0.55,0.26,0.44,0.18,0.15,0.44,0.27,0.17 48 | ,Index,100,103,105,105,106,102,100,105,106,105,107,107,106,107,103,106,106,103,105,100 49 | TABLET OWNER,Unwgt,2876,1107,210,99,111,21,8,13,64,28,36,55,30,25,33,14,23,33,19,14 50 | ,(000s),33165,12938,1157,534,623,159#,79#,80#,484,177#,307*,241*,130#,111#,156*,31#,85#,156*,117#,40# 51 | ,Vert%,46.34,56.04,48.01,36.78,65.03,42.86,32.51,62.5,49.74,32.78,70.9,44.38,35.14,64.16,51.49,26.05,83.33,51.49,64.64,32.79 52 | ,Horz%,100,39.01,3.49,1.61,1.88,0.48,0.24,0.24,1.46,0.53,0.93,0.73,0.39,0.33,0.47,0.09,0.26,0.47,0.35,0.12 53 | ,Index,100,121,104,79,140,92,70,135,107,71,153,96,76,138,111,56,180,111,140,71 54 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,2863,1007,192,101,91,31,18,13,57,29,28,48,29,19,23,16,17,23,9,14 55 | ,(000s),32744,12012,1144,620,524,206*,109#,97#,430*,188#,242#,324*,227#,98#,80#,76#,28#,80#,21#,59# 56 | ,Vert%,45.75,52.03,47.47,42.7,54.7,55.53,44.86,75.78,44.19,34.81,55.89,59.67,61.35,56.65,26.4,63.87,27.45,26.4,11.6,48.36 57 | ,Horz%,100,36.68,3.49,1.89,1.6,0.63,0.33,0.3,1.31,0.57,0.74,0.99,0.69,0.3,0.24,0.23,0.09,0.24,0.06,0.18 58 | ,Index,100,114,104,93,120,121,98,166,97,76,122,130,134,124,58,140,60,58,25,106 59 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,5434,1779,373,198,175,57,34,23,111,59,52,84,49,35,64,25,32,64,31,33 60 | ,(000s),65448,22299,2345,1404,941,350*,222*,128#,971,538*,433*,538,365*,173*,273,112#,101*,273,167*,106* 61 | ,Vert%,91.44,96.58,97.3,96.69,98.23,94.34,91.36,100,99.79,99.63,100,99.08,98.65,100,90.1,94.12,99.02,90.1,92.27,86.89 62 | ,Horz%,100,34.07,3.58,2.15,1.44,0.53,0.34,0.2,1.48,0.82,0.66,0.82,0.56,0.26,0.42,0.17,0.15,0.42,0.26,0.16 63 | ,Index,100,106,106,106,107,103,100,109,109,109,109,108,108,109,99,103,108,99,101,95 64 | ,,,,,,,,,,,,,,,,,,,,, 65 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 66 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 67 | ,,,,,,,,,,,,,,,,,,,,, 68 | Source: Simmons Fall 2017 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 69 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 70 | (C) 2017 Simmons Research LLC. All Rights Reserved,,,,,,,,,,,,,,,,,,,,, 71 | ,,,,,,,,,,,,,,,,,,,,, 72 | "Thursday, July 19, 2018 / 7:08 PM",,,,,,,,,,,,,,,,,,,,, 73 | -------------------------------------------------------------------------------- /raw_data/statelatlong.csv: -------------------------------------------------------------------------------- 1 | abbreviation,Latitude,Longitude,state 2 | AL,32.6010112,-86.6807365,Alabama 3 | AK,61.3025006,-158.7750198,Alaska 4 | AZ,34.1682185,-111.930907,Arizona 5 | AR,34.7519275,-92.1313784,Arkansas 6 | CA,37.2718745,-119.2704153,California 7 | CO,38.9979339,-105.550567,Colorado 8 | CT,41.5187835,-72.757507,Connecticut 9 | DE,39.145251,-75.4189206,Delaware 10 | DC,38.8993487,-77.0145666,District of Columbia 11 | FL,27.9757279,-83.8330166,Florida 12 | GA,32.6781248,-83.2229757,Georgia 13 | HI,20.46,-157.505,Hawaii 14 | ID,45.4945756,-114.1424303,Idaho 15 | IL,39.739318,-89.504139,Illinois 16 | IN,39.7662195,-86.441277,Indiana 17 | IA,41.9383166,-93.389798,Iowa 18 | KS,38.4987789,-98.3200779,Kansas 19 | KY,37.8222935,-85.7682399,Kentucky 20 | LA,30.9733766,-91.4299097,Louisiana 21 | ME,45.2185133,-69.0148656,Maine 22 | MD,38.8063524,-77.2684162,Maryland 23 | MA,42.0629398,-71.718067,Massachusetts 24 | MI,44.9435598,-86.4158049,Michigan 25 | MN,46.4418595,-93.3655146,Minnesota 26 | MS,32.5851062,-89.8772196,Mississippi 27 | MO,38.3046615,-92.437099,Missouri 28 | MT,46.6797995,-110.044783,Montana 29 | NE,41.5008195,-99.680902,Nebraska 30 | NV,38.502032,-117.0230604,Nevada 31 | NH,44.0012306,-71.5799231,New Hampshire 32 | NJ,40.1430058,-74.7311156,New Jersey 33 | NM,34.1662325,-106.0260685,New Mexico 34 | NY,40.7056258,-73.97968,New York 35 | NC,35.2145629,-79.8912675,North Carolina 36 | ND,47.4678819,-100.3022655,North Dakota 37 | OH,40.1903624,-82.6692525,Ohio 38 | OK,35.3097654,-98.7165585,Oklahoma 39 | OR,44.1419049,-120.5380993,Oregon 40 | PA,40.9945928,-77.6046984,Pennsylvania 41 | RI,41.5827282,-71.5064508,Rhode Island 42 | SC,33.62505,-80.9470381,South Carolina 43 | SD,44.2126995,-100.2471641,South Dakota 44 | TN,35.830521,-85.9785989,Tennessee 45 | TX,31.1693363,-100.0768425,Texas 46 | UT,39.4997605,-111.547028,Utah 47 | VT,43.8717545,-72.4477828,Vermont 48 | VA,38.0033855,-79.4587861,Virginia 49 | WA,38.8993487,-77.0145665,Washington 50 | WV,38.9201705,-80.1816905,West Virginia 51 | WI,44.7862968,-89.8267049,Wisconsin 52 | WY,43.000325,-107.5545669,Wyoming 53 | -------------------------------------------------------------------------------- /raw_data/west_central_data/Movie Purchaser Behavior WC 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,Genearl Movie Purchaser,General Movie Purchaser West Central,General Movie Purchaser West Central Male,General Movie Purchaser West Central Female,Movie Purchaser WC 18-24,"Movie Purchaser WC 18-24 male 3 | ","Movie Purchaser WC 18-24 Female 4 | ",Movie Purchaser WC 25-34,Movie Purchaser WC 25-34 male,Movie Purchaser WC 25-34 Female,Movie Purchaser WC 35-44,Movie Purchaser WC 35-44 Male,Movie Purchaser WC 35-44 Female,Movie Purchaser WC 45-54,Movie Purchaser WC 45-54 Male,Movie Purchaser WC 45-54 Female,Movie Purchaser WC 55+,Movie Purchaser WC 55+ Male,Movie Purchaser WC 55+ Female 5 | Totals,Unwgt,23689,6041,906,414,492,117,55,62,179,78,101,178,85,93,182,87,95,250,109,141 6 | ,(000s),231709,65066,10540,5183,5357,2072,1157*,915,2455,1039,1415,2046,899,1148,1677,1004,673,2290,1085,1206 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,4.55,2.24,2.31,0.89,0.5,0.39,1.06,0.45,0.61,0.88,0.39,0.5,0.72,0.43,0.29,0.99,0.47,0.52 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,397,217,180,75,43,32,122,65,57,106,58,48,60,30,30,34,21,13 11 | ,(000s),66903,29479,4549,2609,1941,1252,910*,342*,1696,773,923*,887,505*,382*,483*,283#,200#,232*,138#,94# 12 | ,Vert%,28.87,45.31,43.16,50.34,36.23,60.42,78.65,37.38,69.08,74.4,65.23,43.35,56.17,33.28,28.8,28.19,29.72,10.13,12.72,7.79 13 | ,Horz%,100,44.06,6.8,3.9,2.9,1.87,1.36,0.51,2.54,1.16,1.38,1.33,0.75,0.57,0.72,0.42,0.3,0.35,0.21,0.14 14 | ,Index,100,157,149,174,125,209,272,129,239,258,226,150,195,115,100,98,103,35,44,27 15 | TV,Unwgt,20633,5376,799,358,441,92,41,51,154,63,91,158,76,82,162,77,85,233,101,132 16 | ,(000s),209096,59689,9969,4825,5145,1821,991*,830*,2298,909,1389,1977,880,1097,1619,981,637,2254,1063,1192 17 | ,Vert%,90.24,91.74,94.58,93.09,96.04,87.89,85.65,90.71,93.6,87.49,98.16,96.63,97.89,95.56,96.54,97.71,94.65,98.43,97.97,98.84 18 | ,Horz%,100,28.55,4.77,2.31,2.46,0.87,0.47,0.4,1.1,0.43,0.66,0.95,0.42,0.52,0.77,0.47,0.3,1.08,0.51,0.57 19 | ,Index,100,102,105,103,106,97,95,101,104,97,109,107,108,106,107,108,105,109,109,110 20 | RADIO,Unwgt,16599,4637,703,315,388,74,31,43,151,63,88,144,64,80,154,76,78,180,81,99 21 | ,(000s),171433,51520,8400,4153,4247,1577,882*,695*,1872,832,1040,1650,701,949,1440,841,599,1862,897,965 22 | ,Vert%,73.99,79.18,79.7,80.13,79.28,76.11,76.23,75.96,76.25,80.08,73.5,80.65,77.98,82.67,85.87,83.76,89,81.31,82.67,80.02 23 | ,Horz%,100,30.05,4.9,2.42,2.48,0.92,0.51,0.41,1.09,0.49,0.61,0.96,0.41,0.55,0.84,0.49,0.35,1.09,0.52,0.56 24 | ,Index,100,107,108,108,107,103,103,103,103,108,99,109,105,112,116,113,120,110,112,108 25 | MAGAZINE,Unwgt,22182,5824,877,399,478,114,52,62,175,75,100,176,85,91,175,84,91,237,103,134 26 | ,(000s),219735,62794,10315,5023,5292,2021,1106*,915,2434,1037,1397,2020,899,1122,1639,976,663,2199,1004,1196 27 | ,Vert%,94.83,96.51,97.87,96.91,98.79,97.54,95.59,100,99.14,99.81,98.73,98.73,100,97.74,97.73,97.21,98.51,96.03,92.53,99.17 28 | ,Horz%,100,28.58,4.69,2.29,2.41,0.92,0.5,0.42,1.11,0.47,0.64,0.92,0.41,0.51,0.75,0.44,0.3,1,0.46,0.54 29 | ,Index,100,102,103,102,104,103,101,105,105,105,104,104,105,103,103,103,104,101,98,105 30 | MOVIE GOERS,Unwgt,23689,6041,906,414,492,117,55,62,179,78,101,178,85,93,182,87,95,250,109,141 31 | ,(000s),231709,65066,10540,5183,5357,2072,1157*,915,2455,1039,1415,2046,899,1148,1677,1004,673,2290,1085,1206 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,4.55,2.24,2.31,0.89,0.5,0.39,1.06,0.45,0.61,0.88,0.39,0.5,0.72,0.43,0.29,0.99,0.47,0.52 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,895,406,489,114,52,62,178,77,101,176,83,93,181,86,95,246,108,138 36 | ,(000s),224335,64291,10298,4971,5327,2059,1144*,915,2353,938,1415,2040,892,1148,1654,980,673,2192,1017,1175 37 | ,Vert%,96.82,98.81,97.7,95.91,99.44,99.37,98.88,100,95.85,90.28,100,99.71,99.22,100,98.63,97.61,100,95.72,93.73,97.43 38 | ,Horz%,100,28.66,4.59,2.22,2.37,0.92,0.51,0.41,1.05,0.42,0.63,0.91,0.4,0.51,0.74,0.44,0.3,0.98,0.45,0.52 39 | ,Index,100,102,101,99,103,103,102,103,99,93,103,103,102,103,102,101,103,99,97,101 40 | TABLET OWNER,Unwgt,5929,2018,294,123,171,38,16,22,70,26,44,65,25,40,65,28,37,56,28,28 41 | ,(000s),58910,22321,3461,1627,1835,509*,346#,163#,1044,306#,738*,599,251#,348*,689,370#,319*,621*,354#,266# 42 | ,Vert%,25.42,34.31,32.84,31.39,34.25,24.57,29.9,17.81,42.53,29.45,52.16,29.28,27.92,30.31,41.09,36.85,47.4,27.12,32.63,22.06 43 | ,Horz%,100,37.89,5.88,2.76,3.11,0.86,0.59,0.28,1.77,0.52,1.25,1.02,0.43,0.59,1.17,0.63,0.54,1.05,0.6,0.45 44 | ,Index,100,135,129,123,135,97,118,70,167,116,205,115,110,119,162,145,186,107,128,87 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,468,193,275,66,25,41,97,39,58,93,39,54,95,44,51,117,46,71 46 | ,(000s),100764,34457,5669,2473,3196,1261,618#,644*,1627,656*,972*,902,361*,541*,1037,614*,423*,841,224*,617 47 | ,Vert%,43.49,52.96,53.79,47.71,59.66,60.86,53.41,70.38,66.27,63.14,68.69,44.09,40.16,47.13,61.84,61.16,62.85,36.72,20.65,51.16 48 | ,Horz%,100,34.2,5.63,2.45,3.17,1.25,0.61,0.64,1.61,0.65,0.96,0.9,0.36,0.54,1.03,0.61,0.42,0.83,0.22,0.61 49 | ,Index,100,122,124,110,137,140,123,162,152,145,158,101,92,108,142,141,145,84,47,118 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,736,321,415,111,51,60,170,70,100,156,66,90,141,69,72,158,65,93 51 | ,(000s),159830,54775,8947,4224,4723,2040,1127*,913*,2400,1000,1400,1669,627,1042,1349,816,533,1489,654,835 52 | ,Vert%,68.98,84.18,84.89,81.5,88.17,98.46,97.41,99.78,97.76,96.25,98.94,81.57,69.74,90.77,80.44,81.27,79.2,65.02,60.28,69.24 53 | ,Horz%,100,34.27,5.6,2.64,2.96,1.28,0.71,0.57,1.5,0.63,0.88,1.04,0.39,0.65,0.84,0.51,0.33,0.93,0.41,0.52 54 | ,Index,100,122,123,118,128,143,141,145,142,140,143,118,101,132,117,118,115,94,87,100 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Thursday, July 19, 2018 / 5:21 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/west_central_data/Movie Purchaser Behavior WC 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,Genearl Movie Purchaser,General Movie Purchaser West Central,General Movie Purchaser West Central Male,General Movie Purchaser West Central Female,Movie Purchaser WC 18-24,"Movie Purchaser WC 18-24 male 3 | ","Movie Purchaser WC 18-24 Female 4 | ",Movie Purchaser WC 25-34,Movie Purchaser WC 25-34 male,Movie Purchaser WC 25-34 Female,Movie Purchaser WC 35-44,Movie Purchaser WC 35-44 Male,Movie Purchaser WC 35-44 Female,Movie Purchaser WC 45-54,Movie Purchaser WC 45-54 Male,Movie Purchaser WC 45-54 Female,Movie Purchaser WC 55+,Movie Purchaser WC 55+ Male,Movie Purchaser WC 55+ Female 5 | Totals,Unwgt,27446,6622,965,382,583,95,27,68,176,65,111,196,87,109,216,85,131,282,118,164 6 | ,(000s),234034,59760,10697,4937,5760,1389,573#,817,2797,1358,1439,2151,1048,1103,2209,983,1226,2151,976,1175 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,4.57,2.11,2.46,0.59,0.24,0.35,1.2,0.58,0.61,0.92,0.45,0.47,0.94,0.42,0.52,0.92,0.42,0.5 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,411,180,231,49,25,24,112,45,67,111,55,56,83,36,47,56,19,37 11 | ,(000s),65410,27699,5032,2591,2441,823*,552#,271#,1843,866*,977,1162,671*,490*,850,437*,413*,354*,65#,289* 12 | ,Vert%,27.95,46.35,47.04,52.48,42.38,59.25,96.34,33.17,65.89,63.77,67.89,54.02,64.03,44.42,38.48,44.46,33.69,16.46,6.66,24.6 13 | ,Horz%,100,42.35,7.69,3.96,3.73,1.26,0.84,0.41,2.82,1.32,1.49,1.78,1.03,0.75,1.3,0.67,0.63,0.54,0.1,0.44 14 | ,Index,100,166,168,188,152,212,345,119,236,228,243,193,229,159,138,159,121,59,24,88 15 | Digital Music,Unwgt,27446,6622,965,382,583,95,27,68,176,65,111,196,87,109,216,85,131,282,118,164 16 | ,(000s),234034,59760,10697,4937,5760,1389,573#,817,2797,1358,1439,2151,1048,1103,2209,983,1226,2151,976,1175 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,4.57,2.11,2.46,0.59,0.24,0.35,1.2,0.58,0.61,0.92,0.45,0.47,0.94,0.42,0.52,0.92,0.42,0.5 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,843,321,522,74,20,54,146,50,96,172,74,98,194,72,122,257,105,152 21 | ,(000s),207976,54297,9880,4521,5360,1240,520#,720*,2556,1273*,1283,1958,919,1038,2050,871,1179,2077,938,1139 22 | ,Vert%,88.87,90.86,92.36,91.57,93.06,89.27,90.75,88.13,91.38,93.74,89.16,91.03,87.69,94.11,92.8,88.61,96.17,96.56,96.11,96.94 23 | ,Horz%,100,26.11,4.75,2.17,2.58,0.6,0.25,0.35,1.23,0.61,0.62,0.94,0.44,0.5,0.99,0.42,0.57,1,0.45,0.55 24 | ,Index,100,102,104,103,105,100,102,99,103,105,100,102,99,106,104,100,108,109,108,109 25 | RADIO,Unwgt,19045,5069,741,273,468,70,17,53,128,43,85,162,67,95,172,62,110,209,84,125 26 | ,(000s),169892,47452,8992,3918,5074,1069,380#,689*,2376,1123*,1253,1888,891,997,1921,755,1166,1738,768,970 27 | ,Vert%,72.59,79.4,84.06,79.36,88.09,76.96,66.32,84.33,84.95,82.7,87.07,87.77,85.02,90.39,86.96,76.81,95.11,80.8,78.69,82.55 28 | ,Horz%,100,27.93,5.29,2.31,2.99,0.63,0.22,0.41,1.4,0.66,0.74,1.11,0.52,0.59,1.13,0.44,0.69,1.02,0.45,0.57 29 | ,Index,100,109,116,109,121,106,91,116,117,114,120,121,117,125,120,106,131,111,108,114 30 | MAGAZINE,Unwgt,21921,5698,846,312,534,78,20,58,141,47,94,166,67,99,191,68,123,270,110,160 31 | ,(000s),190820,51486,9590,4215,5375,1154,384#,770*,2415,1144*,1271,1960,938,1022,2002,846,1155,2060,904,1156 32 | ,Vert%,81.54,86.15,89.65,85.38,93.32,83.08,67.02,94.25,86.34,84.24,88.33,91.12,89.5,92.66,90.63,86.06,94.21,95.77,92.62,98.38 33 | ,Horz%,100,26.98,5.03,2.21,2.82,0.6,0.2,0.4,1.27,0.6,0.67,1.03,0.49,0.54,1.05,0.44,0.61,1.08,0.47,0.61 34 | ,Index,100,106,110,105,114,102,82,116,106,103,108,112,110,114,111,106,116,117,114,121 35 | MOVIE GOERS,Unwgt,27446,6622,965,382,583,95,27,68,176,65,111,196,87,109,216,85,131,282,118,164 36 | ,(000s),234034,59760,10697,4937,5760,1389,573#,817,2797,1358,1439,2151,1048,1103,2209,983,1226,2151,976,1175 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,4.57,2.11,2.46,0.59,0.24,0.35,1.2,0.58,0.61,0.92,0.45,0.47,0.94,0.42,0.52,0.92,0.42,0.5 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,949,375,574,91,26,65,171,61,110,192,85,107,216,85,131,279,118,161 41 | ,(000s),227019,59228,10596,4890,5706,1342,572#,770,2767,1328,1439,2133,1031,1102,2209,983,1226,2146,976,1170 42 | ,Vert%,97,99.11,99.06,99.05,99.06,96.62,99.83,94.25,98.93,97.79,100,99.16,98.38,99.91,100,100,100,99.77,100,99.57 43 | ,Horz%,100,26.09,4.67,2.15,2.51,0.59,0.25,0.34,1.22,0.58,0.63,0.94,0.45,0.49,0.97,0.43,0.54,0.95,0.43,0.52 44 | ,Index,100,102,102,102,102,100,103,97,102,101,103,102,101,103,103,103,103,103,103,103 45 | TABLET OWNER,Unwgt,8845,2791,391,134,257,33,9,24,72,17,55,99,39,60,86,37,49,101,32,69 46 | ,(000s),75009,25141,4444,1439,3005,556*,131#,424#,1126,291#,835*,1081,429*,652*,860,322*,538*,821,266*,555 47 | ,Vert%,32.05,42.07,41.54,29.15,52.17,40.03,22.86,51.9,40.26,21.43,58.03,50.26,40.94,59.11,38.93,32.76,43.88,38.17,27.25,47.23 48 | ,Horz%,100,33.52,5.92,1.92,4.01,0.74,0.17,0.57,1.5,0.39,1.11,1.44,0.57,0.87,1.15,0.43,0.72,1.09,0.35,0.74 49 | ,Index,100,131,130,91,163,125,71,162,126,67,181,157,128,184,121,102,137,119,85,147 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,518,185,333,63,14,49,95,36,59,109,41,68,124,51,73,127,43,84 51 | ,(000s),97217,30518,5824,2396,3429,900,296#,604*,1537,703*,834*,1295,532*,763,1038,466*,572,1054,399*,655 52 | ,Vert%,41.54,51.07,54.45,48.53,59.53,64.79,51.66,73.93,54.95,51.77,57.96,60.2,50.76,69.17,46.99,47.41,46.66,49,40.88,55.74 53 | ,Horz%,100,31.39,5.99,2.46,3.53,0.93,0.3,0.62,1.58,0.72,0.86,1.33,0.55,0.78,1.07,0.48,0.59,1.08,0.41,0.67 54 | ,Index,100,123,131,117,143,156,124,178,132,125,140,145,122,167,113,114,112,118,98,134 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,801,297,504,91,24,67,164,59,105,170,69,101,183,70,113,193,75,118 56 | ,(000s),168880,51870,9125,3955,5170,1296,480#,815,2581,1185*,1396,1843,878,966,1904,828,1076,1501,584,917 57 | ,Vert%,72.16,86.8,85.3,80.11,89.76,93.3,83.77,99.76,92.28,87.26,97.01,85.68,83.78,87.58,86.19,84.23,87.77,69.78,59.84,78.04 58 | ,Horz%,100,30.71,5.4,2.34,3.06,0.77,0.28,0.48,1.53,0.7,0.83,1.09,0.52,0.57,1.13,0.49,0.64,0.89,0.35,0.54 59 | ,Index,100,120,118,111,124,129,116,138,128,121,134,119,116,121,119,117,122,97,83,108 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 63 | ,,,,,,,,,,,,,,,,,,,,, 64 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 65 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 66 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 67 | ,,,,,,,,,,,,,,,,,,,,, 68 | "Thursday, July 19, 2018 / 5:15 PM",,,,,,,,,,,,,,,,,,,,, 69 | -------------------------------------------------------------------------------- /raw_data/west_central_data/Movie Purchaser Behavior WC 2017.csv: -------------------------------------------------------------------------------- 1 | All Respondents and VIDEO GAMES-DO YOU OWN OR PLAY? [YES],,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,Genearl Movie Purchaser,General Movie Purchaser West Central,General Movie Purchaser West Central Male,General Movie Purchaser West Central Female,Movie Purchaser WC 18-24,"Movie Purchaser WC 18-24 male 3 | ","Movie Purchaser WC 18-24 Female 4 | ",Movie Purchaser WC 25-34,Movie Purchaser WC 25-34 male,Movie Purchaser WC 25-34 Female,Movie Purchaser WC 35-44,Movie Purchaser WC 35-44 Male,Movie Purchaser WC 35-44 Female,Movie Purchaser WC 45-54,Movie Purchaser WC 45-54 Male,Movie Purchaser WC 45-54 Female,Movie Purchaser WC 55+,Movie Purchaser WC 55+ Male,Movie Purchaser WC 55+ Female 5 | Totals,Unwgt,6088,1877,254,128,126,30,17,13,61,37,24,64,26,38,54,28,26,45,20,25 6 | ,(000s),71575,23088,4205,2371,1834,573#,297#,276#,1718,1066*,652#,865,410#,455*,697*,400#,297#,351*,197#,154# 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,32.26,5.87,3.31,2.56,0.8,0.41,0.39,2.4,1.49,0.91,1.21,0.57,0.64,0.97,0.56,0.41,0.49,0.28,0.22 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,4991,1708,231,121,110,30,17,13,60,37,23,62,26,36,41,21,20,38,20,18 11 | ,(000s),61255,21650,4059,2329,1730,573#,297#,276#,1716*,1066*,650#,824,410#,414*,634*,358#,276#,312*,197#,115# 12 | ,Vert%,85.58,93.77,96.53,98.23,94.33,100,100,100,99.88,100,99.69,95.26,100,90.99,90.96,89.5,92.93,88.89,100,74.68 13 | ,Horz%,100,35.34,6.63,3.8,2.82,0.94,0.48,0.45,2.8,1.74,1.06,1.35,0.67,0.68,1.04,0.58,0.45,0.51,0.32,0.19 14 | ,Index,100,110,113,115,110,117,117,117,117,117,116,111,117,106,106,105,109,104,117,87 15 | Streaming Video,Unwgt,4956,1613,211,108,103,27,15,12,55,32,23,55,24,31,42,22,20,32,15,17 16 | ,(000s),58741,20176,3550,1905,1645,555#,288#,267#,1507*,856*,651#,755*,343#,412*,464*,251#,213#,269*,167#,102# 17 | ,Vert%,82.07,87.39,84.42,80.35,89.69,96.86,96.97,96.74,87.72,80.3,99.85,87.28,83.66,90.55,66.57,62.75,71.72,76.64,84.77,66.23 18 | ,Horz%,100,34.35,6.04,3.24,2.8,0.94,0.49,0.45,2.57,1.46,1.11,1.29,0.58,0.7,0.79,0.43,0.36,0.46,0.28,0.17 19 | ,Index,100,106,103,98,109,118,118,118,107,98,122,106,102,110,81,76,87,93,103,81 20 | Digital Music,Unwgt,6088,1877,254,128,126,30,17,13,61,37,24,64,26,38,54,28,26,45,20,25 21 | ,(000s),71575,23088,4205,2371,1834,573#,297#,276#,1718,1066*,652#,865,410#,455*,697*,400#,297#,351*,197#,154# 22 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 23 | ,Horz%,100,32.26,5.87,3.31,2.56,0.8,0.41,0.39,2.4,1.49,0.91,1.21,0.57,0.64,0.97,0.56,0.41,0.49,0.28,0.22 24 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 25 | TV,Unwgt,4770,1538,211,104,107,24,13,11,43,27,16,55,21,34,46,24,22,43,19,24 26 | ,(000s),56475,18971,3293,1822,1471,421#,228#,193#,1249*,818#,431#,668*,253#,416*,610*,332#,278#,345*,192#,154# 27 | ,Vert%,78.9,82.17,78.31,76.85,80.21,73.47,76.77,69.93,72.7,76.74,66.1,77.23,61.71,91.43,87.52,83,93.6,98.29,97.46,100 28 | ,Horz%,100,33.59,5.83,3.23,2.6,0.75,0.4,0.34,2.21,1.45,0.76,1.18,0.45,0.74,1.08,0.59,0.49,0.61,0.34,0.27 29 | ,Index,100,104,99,97,102,93,97,89,92,97,84,98,78,116,111,105,119,125,124,127 30 | RADIO,Unwgt,4089,1386,190,92,98,16,8,8,44,27,17,52,22,30,45,21,24,33,14,19 31 | ,(000s),48424,16720,2924,1555,1368,290#,96#,194#,1208*,734#,474#,645*,330#,316#,519*,254#,265#,262*,142#,121# 32 | ,Vert%,67.65,72.42,69.54,65.58,74.59,50.61,32.32,70.29,70.31,68.86,72.7,74.57,80.49,69.45,74.46,63.5,89.23,74.64,72.08,78.57 33 | ,Horz%,100,34.53,6.04,3.21,2.83,0.6,0.2,0.4,2.49,1.52,0.98,1.33,0.68,0.65,1.07,0.52,0.55,0.54,0.29,0.25 34 | ,Index,100,107,103,97,110,75,48,104,104,102,107,110,119,103,110,94,132,110,107,116 35 | MAGAZINE,Unwgt,4249,1456,209,101,108,25,15,10,45,26,19,51,20,31,45,21,24,43,19,24 36 | ,(000s),48801,17437,3168,1664,1505,479#,229#,250#,1222*,733#,489#,557*,229#,328*,569*,283#,286#,341*,189#,152# 37 | ,Vert%,68.18,75.52,75.34,70.18,82.06,83.6,77.1,90.58,71.13,68.76,75,64.39,55.85,72.09,81.64,70.75,96.3,97.15,95.94,98.7 38 | ,Horz%,100,35.73,6.49,3.41,3.08,0.98,0.47,0.51,2.5,1.5,1,1.14,0.47,0.67,1.17,0.58,0.59,0.7,0.39,0.31 39 | ,Index,100,111,110,103,120,123,113,133,104,101,110,94,82,106,120,104,141,142,141,145 40 | MOVIE GOERS,Unwgt,6088,1877,254,128,126,30,17,13,61,37,24,64,26,38,54,28,26,45,20,25 41 | ,(000s),71575,23088,4205,2371,1834,573#,297#,276#,1718,1066*,652#,865,410#,455*,697*,400#,297#,351*,197#,154# 42 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 43 | ,Horz%,100,32.26,5.87,3.31,2.56,0.8,0.41,0.39,2.4,1.49,0.91,1.21,0.57,0.64,0.97,0.56,0.41,0.49,0.28,0.22 44 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 45 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,5741,1823,251,126,125,30,17,13,61,37,24,62,25,37,53,27,26,45,20,25 46 | ,(000s),67039,22222,4151,2330,1821,573#,297#,276#,1718,1066*,652#,818,376#,442*,691*,394#,297#,351*,197#,154# 47 | ,Vert%,93.66,96.25,98.72,98.27,99.29,100,100,100,100,100,100,94.57,91.71,97.14,99.14,98.5,100,100,100,100 48 | ,Horz%,100,33.15,6.19,3.48,2.72,0.85,0.44,0.41,2.56,1.59,0.97,1.22,0.56,0.66,1.03,0.59,0.44,0.52,0.29,0.23 49 | ,Index,100,103,105,105,106,107,107,107,107,107,107,101,98,104,106,105,107,107,107,107 50 | TABLET OWNER,Unwgt,2876,1107,156,71,85,12,5,7,34,19,15,49,19,30,35,18,17,26,10,16 51 | ,(000s),33165,12938,2137,1094,1043,159#,43#,116#,816*,498#,318#,587*,219#,368#,370*,210#,161#,205#,124#,81# 52 | ,Vert%,46.34,56.04,50.82,46.14,56.87,27.75,14.48,42.03,47.5,46.72,48.77,67.86,53.41,80.88,53.08,52.5,54.21,58.4,62.94,52.6 53 | ,Horz%,100,39.01,6.44,3.3,3.14,0.48,0.13,0.35,2.46,1.5,0.96,1.77,0.66,1.11,1.12,0.63,0.49,0.62,0.37,0.24 54 | ,Index,100,121,110,100,123,60,31,91,103,101,105,146,115,175,115,113,117,126,136,114 55 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,2863,1007,145,69,76,13,8,5,35,18,17,43,20,23,31,14,17,23,9,14 56 | ,(000s),32744,12012,2606,1381,1225,260#,154#,106#,1210*,640#,570#,634*,339#,295#,357*,149#,208#,145#,99#,46# 57 | ,Vert%,45.75,52.03,61.97,58.25,66.79,45.38,51.85,38.41,70.43,60.04,87.42,73.29,82.68,64.84,51.22,37.25,70.03,41.31,50.25,29.87 58 | ,Horz%,100,36.68,7.96,4.22,3.74,0.79,0.47,0.32,3.7,1.95,1.74,1.94,1.04,0.9,1.09,0.46,0.64,0.44,0.3,0.14 59 | ,Index,100,114,135,127,146,99,113,84,154,131,191,160,181,142,112,81,153,90,110,65 60 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,5434,1779,235,115,120,29,16,13,59,35,24,62,24,38,49,25,24,36,15,21 61 | ,(000s),65448,22299,4000,2203,1796,562#,286#,276#,1665*,1013*,652#,825,371#,455*,639*,368#,270#,309*,166#,143# 62 | ,Vert%,91.44,96.58,95.12,92.91,97.93,98.08,96.3,100,96.92,95.03,100,95.38,90.49,100,91.68,92,90.91,88.03,84.26,92.86 63 | ,Horz%,100,34.07,6.11,3.37,2.74,0.86,0.44,0.42,2.54,1.55,1,1.26,0.57,0.7,0.98,0.56,0.41,0.47,0.25,0.22 64 | ,Index,100,106,104,102,107,107,105,109,106,104,109,104,99,109,100,101,99,96,92,102 65 | ,,,,,,,,,,,,,,,,,,,,, 66 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 67 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 68 | ,,,,,,,,,,,,,,,,,,,,, 69 | Source: Simmons Fall 2017 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 70 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 71 | (C) 2017 Simmons Research LLC. All Rights Reserved,,,,,,,,,,,,,,,,,,,,, 72 | ,,,,,,,,,,,,,,,,,,,,, 73 | "Thursday, July 19, 2018 / 4:48 PM",,,,,,,,,,,,,,,,,,,,, 74 | -------------------------------------------------------------------------------- /raw_data/west_central_data/east_central_data/East Central/Movie Purcharser behavior EC 2017.cdct: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/raw_data/west_central_data/east_central_data/East Central/Movie Purcharser behavior EC 2017.cdct -------------------------------------------------------------------------------- /raw_data/west_central_data/east_central_data/East Central/Movie Purcharser behavior EC 2017.xlsx: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/raw_data/west_central_data/east_central_data/East Central/movie purchaser behavior 2016 EC.xlsx -------------------------------------------------------------------------------- /raw_data/west_central_data/east_central_data/Movie Purchaser Behavior EC 2013.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser East Central,GenralMovie Purchaser East Central Male,Genral Movie Purchaser East Central Female,Movie Purchaser EC 18-24,"Movie Purchaser EC 18-24 male 3 | ","Movie Purchaser EC 18-24 Female 4 | ",Movie Purchaser EC 25-34,Movie Purchaser EC 25-34 male,Movie Purchaser EC 25-34 Female,Movie Purchaser EC 35-44,Movie Purchaser EC 35-44 Male,Movie Purchaser EC 35-44 Female,Movie Purchaser EC 45-54,Movie Purchaser EC 45-54 Male,Movie Purchaser EC 45-54 Female,Movie Purchaser EC 55+,Movie Purchaser EC 55+ Male,Movie Purchaser EC 55+ Female 5 | Totals,Unwgt,23689,6041,702,295,407,71,31,40,124,51,73,137,71,66,165,64,101,205,78,127 6 | ,(000s),231709,65066,8005,3795,4209,1277,490*,787*,1840,980*,859,1610,889,722,1515,702,813,1763,734,1029 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,28.08,3.45,1.64,1.82,0.55,0.21,0.34,0.79,0.42,0.37,0.69,0.38,0.31,0.65,0.3,0.35,0.76,0.32,0.44 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,5867,2555,324,156,168,50,26,24,83,37,46,84,49,35,62,31,31,45,13,32 11 | ,(000s),66903,29479,3960,2111,1849,820*,358#,462#,1208,694*,513*,1046,638*,409*,533,300*,232*,354*,121#,233* 12 | ,Vert%,28.87,45.31,49.47,55.63,43.93,64.21,73.06,58.7,65.65,70.82,59.72,64.97,71.77,56.65,35.18,42.74,28.54,20.08,16.49,22.64 13 | ,Horz%,100,44.06,5.92,3.16,2.76,1.23,0.54,0.69,1.81,1.04,0.77,1.56,0.95,0.61,0.8,0.45,0.35,0.53,0.18,0.35 14 | ,Index,100,157,171,193,152,222,253,203,227,245,207,225,249,196,122,148,99,70,57,78 15 | TV,Unwgt,20633,5376,631,258,373,53,23,30,103,38,65,125,63,62,158,62,96,192,72,120 16 | ,(000s),209096,59689,7341,3478,3863,981*,356#,626#,1614,840*,774,1559,867,691,1476,700,777,1710,716,995 17 | ,Vert%,90.24,91.74,91.71,91.65,91.78,76.82,72.65,79.54,87.72,85.71,90.1,96.83,97.53,95.71,97.43,99.72,95.57,96.99,97.55,96.7 18 | ,Horz%,100,28.55,3.51,1.66,1.85,0.47,0.17,0.3,0.77,0.4,0.37,0.75,0.41,0.33,0.71,0.33,0.37,0.82,0.34,0.48 19 | ,Index,100,102,102,102,102,85,81,88,97,95,100,107,108,106,108,110,106,107,108,107 20 | RADIO,Unwgt,16599,4637,550,223,327,48,17,31,100,39,61,108,52,56,134,53,81,160,62,98 21 | ,(000s),171433,51520,6604,3050,3555,980*,350#,631*,1477,750*,728,1315,699*,616*,1346,643*,703,1486,609,878 22 | ,Vert%,73.99,79.18,82.5,80.37,84.46,76.74,71.43,80.18,80.27,76.53,84.75,81.68,78.63,85.32,88.84,91.6,86.47,84.29,82.97,85.33 23 | ,Horz%,100,30.05,3.85,1.78,2.07,0.57,0.2,0.37,0.86,0.44,0.42,0.77,0.41,0.36,0.79,0.38,0.41,0.87,0.36,0.51 24 | ,Index,100,107,112,109,114,104,97,108,108,103,115,110,106,115,120,124,117,114,112,115 25 | MAGAZINE,Unwgt,22182,5824,684,288,396,71,31,40,121,49,72,133,69,64,160,64,96,199,75,124 26 | ,(000s),219735,62794,7803,3688,4115,1277,490*,787*,1832,973*,859,1555,838,717,1439,702,738,1700,685,1015 27 | ,Vert%,94.83,96.51,97.48,97.18,97.77,100,100,100,99.57,99.29,100,96.58,94.26,99.31,94.98,100,90.77,96.43,93.32,98.64 28 | ,Horz%,100,28.58,3.55,1.68,1.87,0.58,0.22,0.36,0.83,0.44,0.39,0.71,0.38,0.33,0.65,0.32,0.34,0.77,0.31,0.46 29 | ,Index,100,102,103,102,103,105,105,105,105,105,105,102,99,105,100,105,96,102,98,104 30 | MOVIE GOERS,Unwgt,23689,6041,702,295,407,71,31,40,124,51,73,137,71,66,165,64,101,205,78,127 31 | ,(000s),231709,65066,8005,3795,4209,1277,490*,787*,1840,980*,859,1610,889,722,1515,702,813,1763,734,1029 32 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 33 | ,Horz%,100,28.08,3.45,1.64,1.82,0.55,0.21,0.34,0.79,0.42,0.37,0.69,0.38,0.31,0.65,0.3,0.35,0.76,0.32,0.44 34 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 35 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,22818,5955,694,293,401,69,30,39,124,51,73,135,71,64,163,64,99,203,77,126 36 | ,(000s),224335,64291,7951,3780,4171,1262,480#,782*,1840,980*,859,1605,889,717,1511,702,809,1733,729,1004 37 | ,Vert%,96.82,98.81,99.33,99.6,99.1,98.83,97.96,99.36,100,100,100,99.69,100,99.31,99.74,100,99.51,98.3,99.32,97.57 38 | ,Horz%,100,28.66,3.54,1.68,1.86,0.56,0.21,0.35,0.82,0.44,0.38,0.72,0.4,0.32,0.67,0.31,0.36,0.77,0.32,0.45 39 | ,Index,100,102,103,103,102,102,101,103,103,103,103,103,103,103,103,103,103,102,103,101 40 | TABLET OWNER,Unwgt,5929,2018,200,75,125,13,3,10,35,10,25,49,26,23,57,21,36,46,15,31 41 | ,(000s),58910,22321,2205,965,1240,291#,44#,247#,516*,246#,270#,452*,282#,170#,594*,283#,312*,352*,110#,242* 42 | ,Vert%,25.42,34.31,27.55,25.43,29.46,22.79,8.98,31.39,28.04,25.1,31.43,28.07,31.72,23.55,39.21,40.31,38.38,19.97,14.99,23.52 43 | ,Horz%,100,37.89,3.74,1.64,2.1,0.49,0.07,0.42,0.88,0.42,0.46,0.77,0.48,0.29,1.01,0.48,0.53,0.6,0.19,0.41 44 | ,Index,100,135,108,100,116,90,35,123,110,99,124,110,125,93,154,159,151,79,59,93 45 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,9912,3129,370,137,233,43,17,26,61,21,40,61,27,34,94,29,65,111,43,68 46 | ,(000s),100764,34457,4032,1712,2320,772*,229#,542#,914,481#,433*,496,225#,271*,848,313#,535,1003,464*,539 47 | ,Vert%,43.49,52.96,50.37,45.11,55.12,60.45,46.73,68.87,49.67,49.08,50.41,30.81,25.31,37.53,55.97,44.59,65.81,56.89,63.22,52.38 48 | ,Horz%,100,34.2,4,1.7,2.3,0.77,0.23,0.54,0.91,0.48,0.43,0.49,0.22,0.27,0.84,0.31,0.53,1,0.46,0.53 49 | ,Index,100,122,116,104,127,139,107,158,114,113,116,71,58,86,129,103,151,131,145,120 50 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,15702,4970,571,225,346,67,29,38,116,46,70,117,57,60,135,51,84,136,42,94 51 | ,(000s),159830,54775,6526,2879,3647,1254,475#,779*,1712,871*,841,1403,723*,680*,1218,509*,710,938,301*,637 52 | ,Vert%,68.98,84.18,81.52,75.86,86.65,98.2,96.94,98.98,93.04,88.88,97.9,87.14,81.33,94.18,80.4,72.51,87.33,53.2,41.01,61.9 53 | ,Horz%,100,34.27,4.08,1.8,2.28,0.78,0.3,0.49,1.07,0.54,0.53,0.88,0.45,0.43,0.76,0.32,0.44,0.59,0.19,0.4 54 | ,Index,100,122,118,110,126,142,141,143,135,129,142,126,118,137,117,105,127,77,59,90 55 | ,,,,,,,,,,,,,,,,,,,,, 56 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 57 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 58 | ,,,,,,,,,,,,,,,,,,,,, 59 | Source: Simmons Fall 2013 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 60 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 61 | "(C) 2013 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 62 | ,,,,,,,,,,,,,,,,,,,,, 63 | "Wednesday, July 18, 2018 / 6:29 PM",,,,,,,,,,,,,,,,,,,,, 64 | -------------------------------------------------------------------------------- /raw_data/west_central_data/east_central_data/Movie Purchaser Behavior EC 2014.csv: -------------------------------------------------------------------------------- 1 | All Respondents,,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,General Movie Purchaser,Genral Movie Purchaser East Central,GenralMovie Purchaser East Central Male,Genral Movie Purchaser East Central Female,Movie Purchaser EC 18-24,"Movie Purchaser EC 18-24 male 3 | ","Movie Purchaser EC 18-24 Female 4 | ",Movie Purchaser EC 25-34,Movie Purchaser EC 25-34 male,Movie Purchaser EC 25-34 Female,Movie Purchaser EC 35-44,Movie Purchaser EC 35-44 Male,Movie Purchaser EC 35-44 Female,Movie Purchaser EC 45-54,Movie Purchaser EC 45-54 Male,Movie Purchaser EC 45-54 Female,Movie Purchaser EC 55+,Movie Purchaser EC 55+ Male,Movie Purchaser EC 55+ Female 5 | Totals,Unwgt,27446,6622,764,313,451,77,28,49,141,51,90,182,77,105,176,81,95,188,76,112 6 | ,(000s),234034,59760,7342,3459,3884,1202,509#,692*,2004,1106*,898,1377,632,745,1497,744,753,1263,469,795 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,25.53,3.14,1.48,1.66,0.51,0.22,0.3,0.86,0.47,0.38,0.59,0.27,0.32,0.64,0.32,0.32,0.54,0.2,0.34 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,6623,2746,345,159,186,48,22,26,92,39,53,110,52,58,69,36,33,26,10,16 11 | ,(000s),65410,27699,3667,1988,1679,818*,413#,405#,1249,729*,519*,893,446*,446*,538,345*,193*,170#,54#,116# 12 | ,Vert%,27.95,46.35,49.95,57.47,43.23,68.05,81.14,58.53,62.33,65.91,57.8,64.85,70.57,59.87,35.94,46.37,25.63,13.46,11.51,14.59 13 | ,Horz%,100,42.35,5.61,3.04,2.57,1.25,0.63,0.62,1.91,1.11,0.79,1.37,0.68,0.68,0.82,0.53,0.3,0.26,0.08,0.18 14 | ,Index,100,166,179,206,155,243,290,209,223,236,207,232,252,214,129,166,92,48,41,52 15 | Digital Music,Unwgt,27446,6622,764,313,451,77,28,49,141,51,90,182,77,105,176,81,95,188,76,112 16 | ,(000s),234034,59760,7342,3459,3884,1202,509#,692*,2004,1106*,898,1377,632,745,1497,744,753,1263,469,795 17 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 18 | ,Horz%,100,25.53,3.14,1.48,1.66,0.51,0.22,0.3,0.86,0.47,0.38,0.59,0.27,0.32,0.64,0.32,0.32,0.54,0.2,0.34 19 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 20 | TV,Unwgt,23478,5814,663,264,399,58,18,40,118,42,76,157,66,91,164,73,91,166,65,101 21 | ,(000s),207976,54297,6503,3020,3483,907*,402#,505*,1645,862*,783,1323,608,716,1430,699,731,1199,449,749 22 | ,Vert%,88.87,90.86,88.57,87.31,89.68,75.46,78.98,72.98,82.09,77.94,87.19,96.08,96.2,96.11,95.52,93.95,97.08,94.93,95.74,94.21 23 | ,Horz%,100,26.11,3.13,1.45,1.67,0.44,0.19,0.24,0.79,0.41,0.38,0.64,0.29,0.34,0.69,0.34,0.35,0.58,0.22,0.36 24 | ,Index,100,102,100,98,101,85,89,82,92,88,98,108,108,108,107,106,109,107,108,106 25 | RADIO,Unwgt,19045,5069,596,231,365,55,15,40,117,40,77,143,52,91,143,67,76,138,57,81 26 | ,(000s),169892,47452,5855,2688,3167,862*,375#,487*,1609,900*,709,1055,386*,669,1343,668,674,986,359*,627 27 | ,Vert%,72.59,79.4,79.75,77.71,81.54,71.71,73.67,70.38,80.29,81.37,78.95,76.62,61.08,89.8,89.71,89.78,89.51,78.07,76.55,78.87 28 | ,Horz%,100,27.93,3.45,1.58,1.86,0.51,0.22,0.29,0.95,0.53,0.42,0.62,0.23,0.39,0.79,0.39,0.4,0.58,0.21,0.37 29 | ,Index,100,109,110,107,112,99,101,97,111,112,109,106,84,124,124,124,123,108,105,109 30 | MAGAZINE,Unwgt,21921,5698,668,266,402,59,20,39,113,39,74,158,63,95,158,71,87,180,73,107 31 | ,(000s),190820,51486,6167,2782,3385,796*,401#,395*,1646,878*,769,1125,426,698,1356,610,746,1244,466,778 32 | ,Vert%,81.54,86.15,84,80.43,87.15,66.22,78.78,57.08,82.14,79.39,85.63,81.7,67.41,93.69,90.58,81.99,99.07,98.5,99.36,97.86 33 | ,Horz%,100,26.98,3.23,1.46,1.77,0.42,0.21,0.21,0.86,0.46,0.4,0.59,0.22,0.37,0.71,0.32,0.39,0.65,0.24,0.41 34 | ,Index,100,106,103,99,107,81,97,70,101,97,105,100,83,115,111,101,122,121,122,120 35 | MOVIE GOERS,Unwgt,27446,6622,764,313,451,77,28,49,141,51,90,182,77,105,176,81,95,188,76,112 36 | ,(000s),234034,59760,7342,3459,3884,1202,509#,692*,2004,1106*,898,1377,632,745,1497,744,753,1263,469,795 37 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 38 | ,Horz%,100,25.53,3.14,1.48,1.66,0.51,0.22,0.3,0.86,0.47,0.38,0.59,0.27,0.32,0.64,0.32,0.32,0.54,0.2,0.34 39 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 40 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,26457,6542,753,304,449,74,25,49,140,50,90,180,75,105,173,79,94,186,75,111 41 | ,(000s),227019,59228,7220,3349,3870,1171,478#,692*,1957,1059*,898,1358,612,745,1479,732,747,1255,467,788 42 | ,Vert%,97,99.11,98.34,96.82,99.64,97.42,93.91,100,97.65,95.75,100,98.62,96.84,100,98.8,98.39,99.2,99.37,99.57,99.12 43 | ,Horz%,100,26.09,3.18,1.48,1.7,0.52,0.21,0.3,0.86,0.47,0.4,0.6,0.27,0.33,0.65,0.32,0.33,0.55,0.21,0.35 44 | ,Index,100,102,101,100,103,100,97,103,101,99,103,102,100,103,102,101,102,102,103,102 45 | TABLET OWNER,Unwgt,8845,2791,310,114,196,23,6,17,70,20,50,92,35,57,67,29,38,58,24,34 46 | ,(000s),75009,25141,3227,1268,1959,647#,166#,481#,920,478#,442*,628,200*,428*,732,359#,373*,299*,64#,235* 47 | ,Vert%,32.05,42.07,43.95,36.66,50.44,53.83,32.61,69.51,45.91,43.22,49.22,45.61,31.65,57.45,48.9,48.25,49.54,23.67,13.65,29.56 48 | ,Horz%,100,33.52,4.3,1.69,2.61,0.86,0.22,0.64,1.23,0.64,0.59,0.84,0.27,0.57,0.98,0.48,0.5,0.4,0.09,0.31 49 | ,Index,100,131,137,114,157,168,102,217,143,135,154,142,99,179,153,151,155,74,43,92 50 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,11617,3450,394,154,240,33,10,23,70,28,42,103,38,65,97,46,51,91,32,59 51 | ,(000s),97217,30518,3405,1432,1972,572*,134#,438#,849,499#,351*,721,256*,465,782,395*,387*,481,149*,332* 52 | ,Vert%,41.54,51.07,46.38,41.4,50.77,47.59,26.33,63.29,42.37,45.12,39.09,52.36,40.51,62.42,52.24,53.09,51.39,38.08,31.77,41.76 53 | ,Horz%,100,31.39,3.5,1.47,2.03,0.59,0.14,0.45,0.87,0.51,0.36,0.74,0.26,0.48,0.8,0.41,0.4,0.49,0.15,0.34 54 | ,Index,100,123,112,100,122,115,63,152,102,109,94,126,98,150,126,128,124,92,76,101 55 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,19439,5601,644,252,392,73,25,48,138,50,88,164,67,97,148,64,84,121,46,75 56 | ,(000s),168880,51870,6483,3142,3341,1174,484#,690*,1934,1101*,833,1237,586,651,1233,622,611,905,349*,556 57 | ,Vert%,72.16,86.8,88.3,90.84,86.02,97.67,95.09,99.71,96.51,99.55,92.76,89.83,92.72,87.38,82.36,83.6,81.14,71.65,74.41,69.94 58 | ,Horz%,100,30.71,3.84,1.86,1.98,0.7,0.29,0.41,1.15,0.65,0.49,0.73,0.35,0.39,0.73,0.37,0.36,0.54,0.21,0.33 59 | ,Index,100,120,122,126,119,135,132,138,134,138,129,124,128,121,114,116,112,99,103,97 60 | ,,,,,,,,,,,,,,,,,,,,, 61 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 62 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 63 | ,,,,,,,,,,,,,,,,,,,,, 64 | Source: Simmons Fall 2014 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 65 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 66 | "(C) 2014 SMRB, Inc. All Rights Reserved",,,,,,,,,,,,,,,,,,,,, 67 | ,,,,,,,,,,,,,,,,,,,,, 68 | "Wednesday, July 18, 2018 / 6:14 PM",,,,,,,,,,,,,,,,,,,,, 69 | -------------------------------------------------------------------------------- /raw_data/west_central_data/east_central_data/Movie Purchaser Behavior EC 2017.csv: -------------------------------------------------------------------------------- 1 | All Respondents and VIDEO GAMES-DO YOU OWN OR PLAY? [YES],,,,,,,,,,,,,,,,,,,,, 2 | ,,Totals,GeECral Movie Purchaser,GECral Movie Purchaser East Central,GECral Movie Purchaser East Central Male,GECral Movie Purchaser East Central Female,Movie Purchaser EC 18-24,"Movie Purchaser EC 18-24 male 3 | ","Movie Purchaser EC 18-24 Female 4 | ",Movie Purchaser EC 25-34,Movie Purchaser EC 25-34 male,Movie Purchaser EC 25-34 Female,Movie Purchaser EC 35-44,Movie Purchaser EC 35-44 Male,Movie Purchaser EC 35-44 Female,Movie Purchaser EC 45-54,Movie Purchaser EC 45-54 Male,Movie Purchaser EC 45-54 Female,Movie Purchaser EC 55+,Movie Purchaser EC 55+ Male,Movie Purchaser EC 55+ Female 5 | Totals,Unwgt,6088,1877,194,99,95,22,15,7,51,22,29,53,28,25,33,15,18,35,19,16 6 | ,(000s),71575,23088,3023,1656,1367,411#,367#,44#,1037*,473#,564#,910*,496#,414#,337*,106#,231#,328*,214#,114# 7 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 8 | ,Horz%,100,32.26,4.22,2.31,1.91,0.57,0.51,0.06,1.45,0.66,0.79,1.27,0.69,0.58,0.47,0.15,0.32,0.46,0.3,0.16 9 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 10 | Video Game ,Unwgt,4991,1708,180,93,87,21,15,6,48,22,26,53,28,25,30,13,17,28,15,13 11 | ,(000s),61255,21650,2851,1588,1263,395#,367#,29#,979*,473#,506#,910*,496#,414#,327#,99#,229#,240#,153#,87# 12 | ,Vert%,85.58,93.77,94.31,95.89,92.39,96.11,100,65.91,94.41,100,89.72,100,100,100,97.03,93.4,99.13,73.17,71.5,76.32 13 | ,Horz%,100,35.34,4.65,2.59,2.06,0.64,0.6,0.05,1.6,0.77,0.83,1.49,0.81,0.68,0.53,0.16,0.37,0.39,0.25,0.14 14 | ,Index,100,110,110,112,108,112,117,77,110,117,105,117,117,117,113,109,116,85,84,89 15 | Streaming Video,Unwgt,4956,1613,168,86,82,21,14,7,45,19,26,47,26,21,27,12,15,28,15,13 16 | ,(000s),58741,20176,2657,1461,1196,402#,357#,44#,890*,387#,503#,776*,439#,337#,321#,96#,225#,269#,182#,87# 17 | ,Vert%,82.07,87.39,87.89,88.22,87.49,97.81,97.28,100,85.82,81.82,89.18,85.27,88.51,81.4,95.25,90.57,97.4,82.01,85.05,76.32 18 | ,Horz%,100,34.35,4.52,2.49,2.04,0.68,0.61,0.07,1.52,0.66,0.86,1.32,0.75,0.57,0.55,0.16,0.38,0.46,0.31,0.15 19 | ,Index,100,106,107,108,107,119,119,122,105,100,109,104,108,99,116,110,119,100,104,93 20 | Digital Music,Unwgt,6088,1877,194,99,95,22,15,7,51,22,29,53,28,25,33,15,18,35,19,16 21 | ,(000s),71575,23088,3023,1656,1367,411#,367#,44#,1037*,473#,564#,910*,496#,414#,337*,106#,231#,328*,214#,114# 22 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 23 | ,Horz%,100,32.26,4.22,2.31,1.91,0.57,0.51,0.06,1.45,0.66,0.79,1.27,0.69,0.58,0.47,0.15,0.32,0.46,0.3,0.16 24 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 25 | TV,Unwgt,4770,1538,161,82,79,19,13,6,29,11,18,50,27,23,30,13,17,33,18,15 26 | ,(000s),56475,18971,2488,1395,1093,347#,306#,41#,640#,306#,335#,865*,486#,380#,315#,85#,230#,321*,214#,108# 27 | ,Vert%,78.9,82.17,82.3,84.24,79.96,84.43,83.38,93.18,61.72,64.69,59.4,95.05,97.98,91.79,93.47,80.19,99.57,97.87,100,94.74 28 | ,Horz%,100,33.59,4.41,2.47,1.94,0.61,0.54,0.07,1.13,0.54,0.59,1.53,0.86,0.67,0.56,0.15,0.41,0.57,0.38,0.19 29 | ,Index,100,104,104,107,101,107,106,118,78,82,75,120,124,116,118,102,126,124,127,120 30 | RADIO,Unwgt,4089,1386,154,75,79,15,10,5,38,13,25,45,25,20,27,13,14,29,14,15 31 | ,(000s),48424,16720,2435,1235,1200,251#,235#,16#,793*,325#,468#,816*,421#,394#,291#,81#,210#,284#,172#,112# 32 | ,Vert%,67.65,72.42,80.55,74.58,87.78,61.07,64.03,36.36,76.47,68.71,82.98,89.67,84.88,95.17,86.35,76.42,90.91,86.59,80.37,98.25 33 | ,Horz%,100,34.53,5.03,2.55,2.48,0.52,0.49,0.03,1.64,0.67,0.97,1.69,0.87,0.81,0.6,0.17,0.43,0.59,0.36,0.23 34 | ,Index,100,107,119,110,130,90,95,54,113,102,123,133,125,141,128,113,134,128,119,145 35 | MAGAZINE,Unwgt,4249,1456,151,71,80,18,12,6,32,11,21,41,18,23,28,12,16,32,18,14 36 | ,(000s),48801,17437,2357,1189,1168,341#,300#,41#,761*,323#,438#,668*,258#,410#,268#,96#,172#,320*,213#,107# 37 | ,Vert%,68.18,75.52,77.97,71.8,85.44,82.97,81.74,93.18,73.38,68.29,77.66,73.41,52.02,99.03,79.53,90.57,74.46,97.56,99.53,93.86 38 | ,Horz%,100,35.73,4.83,2.44,2.39,0.7,0.61,0.08,1.56,0.66,0.9,1.37,0.53,0.84,0.55,0.2,0.35,0.66,0.44,0.22 39 | ,Index,100,111,114,105,125,122,120,137,108,100,114,108,76,145,117,133,109,143,146,138 40 | MOVIE GOERS,Unwgt,6088,1877,194,99,95,22,15,7,51,22,29,53,28,25,33,15,18,35,19,16 41 | ,(000s),71575,23088,3023,1656,1367,411#,367#,44#,1037*,473#,564#,910*,496#,414#,337*,106#,231#,328*,214#,114# 42 | ,Vert%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 43 | ,Horz%,100,32.26,4.22,2.31,1.91,0.57,0.51,0.06,1.45,0.66,0.79,1.27,0.69,0.58,0.47,0.15,0.32,0.46,0.3,0.16 44 | ,Index,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100 45 | SUPERMARKET GOER LAST 4 WEEKS,Unwgt,5741,1823,189,96,93,21,14,7,48,20,28,53,28,25,32,15,17,35,19,16 46 | ,(000s),67039,22222,2822,1512,1310,352#,308#,44#,914*,388#,526#,910*,496#,414#,318*,106#,213#,328*,214#,114# 47 | ,Vert%,93.66,96.25,93.35,91.3,95.83,85.64,83.92,100,88.14,82.03,93.26,100,100,100,94.36,100,92.21,100,100,100 48 | ,Horz%,100,33.15,4.21,2.26,1.95,0.53,0.46,0.07,1.36,0.58,0.78,1.36,0.74,0.62,0.47,0.16,0.32,0.49,0.32,0.17 49 | ,Index,100,103,100,97,102,91,90,107,94,88,100,107,107,107,101,107,98,107,107,107 50 | TABLET OWNER,Unwgt,2876,1107,117,54,63,8,6,2,31,14,17,36,16,20,22,11,11,20,7,13 51 | ,(000s),33165,12938,1843,912*,931,209#,196#,13#,612*,306#,305#,605*,262#,343#,271#,91#,180#,146#,56#,90# 52 | ,Vert%,46.34,56.04,60.97,55.07,68.11,50.85,53.41,29.55,59.02,64.69,54.08,66.48,52.82,82.85,80.42,85.85,77.92,44.51,26.17,78.95 53 | ,Horz%,100,39.01,5.56,2.75,2.81,0.63,0.59,0.04,1.85,0.92,0.92,1.82,0.79,1.03,0.82,0.27,0.54,0.44,0.17,0.27 54 | ,Index,100,121,132,119,147,110,115,64,127,140,117,143,114,179,174,185,168,96,56,170 55 | All LIVE THEATER/CONCERTS/DANCE-ATTENDED LAST 12 MONTHS,Unwgt,2863,1007,101,51,50,15,10,5,21,7,14,28,14,14,21,12,9,16,8,8 56 | ,(000s),32744,12012,1536,820*,715*,295#,261#,34#,406#,182#,224#,614#,299#,315#,132#,51#,82#,88#,27#,61# 57 | ,Vert%,45.75,52.03,50.81,49.52,52.3,71.78,71.12,77.27,39.15,38.48,39.72,67.47,60.28,76.09,39.17,48.11,35.5,26.83,12.62,53.51 58 | ,Horz%,100,36.68,4.69,2.5,2.18,0.9,0.8,0.1,1.24,0.56,0.68,1.88,0.91,0.96,0.4,0.16,0.25,0.27,0.08,0.19 59 | ,Index,100,114,111,108,114,157,155,169,86,84,87,147,132,166,86,105,78,59,28,117 60 | SOCIAL MEDIA [SOCIAL MEDIA USER],Unwgt,5434,1779,186,94,92,22,15,7,51,22,29,52,27,25,31,14,17,30,16,14 61 | ,(000s),65448,22299,2985,1626,1358,411#,367#,44#,1037*,473#,564#,900*,486#,414#,329*,101#,228#,308#,200#,108# 62 | ,Vert%,91.44,96.58,98.74,98.19,99.34,100,100,100,100,100,100,98.9,97.98,100,97.63,95.28,98.7,93.9,93.46,94.74 63 | ,Horz%,100,34.07,4.56,2.48,2.07,0.63,0.56,0.07,1.58,0.72,0.86,1.38,0.74,0.63,0.5,0.15,0.35,0.47,0.31,0.17 64 | ,Index,100,106,108,107,109,109,109,109,109,109,109,108,107,109,107,104,108,103,102,104 65 | ,,,,,,,,,,,,,,,,,,,,, 66 | * Proj relatively unstable due to small base-use with caution.,,,,,,,,,,,,,,,,,,,,, 67 | # Proj too small for reliability-shown for consistency only.,,,,,,,,,,,,,,,,,,,,, 68 | ,,,,,,,,,,,,,,,,,,,,, 69 | Source: Simmons Fall 2017 NCS Adult Full Year Study,,,,,,,,,,,,,,,,,,,,, 70 | Weighted by: Population,,,,,,,,,,,,,,,,,,,,, 71 | (C) 2017 Simmons Research LLC. All Rights Reserved,,,,,,,,,,,,,,,,,,,,, 72 | ,,,,,,,,,,,,,,,,,,,,, 73 | "Thursday, July 19, 2018 / 10:02 AM",,,,,,,,,,,,,,,,,,,,, 74 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.2.2 2 | alabaster==0.7.11 3 | appdirs==1.4.3 4 | appnope==0.1.0 5 | appscript==1.0.1 6 | asn1crypto==0.24.0 7 | astor==0.7.1 8 | astroid==1.6.5 9 | astropy==3.0.3 10 | atomicwrites==1.1.5 11 | attrs==18.1.0 12 | Automat==0.7.0 13 | Babel==2.6.0 14 | backcall==0.1.0 15 | backports.shutil-get-terminal-size==1.0.0 16 | beautifulsoup4==4.6.0 17 | bitarray==0.8.3 18 | bkcharts==0.2 19 | blaze==0.11.3 20 | bleach==2.1.3 21 | bokeh==0.13.0 22 | boto==2.49.0 23 | Bottleneck==1.2.1 24 | certifi==2018.4.16 25 | cffi==1.11.5 26 | chardet==3.0.4 27 | click==6.7 28 | cloudpickle==0.5.3 29 | clyent==1.2.2 30 | colorama==0.3.9 31 | conda-build==3.12.0 32 | conda-verify==3.1.0 33 | constantly==15.1.0 34 | contextlib2==0.5.5 35 | cryptography==2.2.2 36 | cycler==0.10.0 37 | Cython==0.28.4 38 | cytoolz==0.9.0.1 39 | dask==0.18.2 40 | datashape==0.5.4 41 | decorator==4.3.0 42 | distributed==1.22.0 43 | docutils==0.14 44 | entrypoints==0.2.3 45 | et-xmlfile==1.0.1 46 | fastcache==1.0.2 47 | filelock==3.0.4 48 | Flask==1.0.2 49 | Flask-Cors==3.0.6 50 | Flask-SQLAlchemy==2.3.2 51 | future==0.16.0 52 | gast==0.2.0 53 | gevent==1.3.5 54 | glob2==0.6 55 | gmpy2==2.0.8 56 | greenlet==0.4.14 57 | grpcio==1.13.0 58 | h5py==2.8.0 59 | heapdict==1.0.0 60 | html5lib==1.0.1 61 | hyperlink==18.0.0 62 | idna==2.7 63 | imageio==2.3.0 64 | imagesize==1.0.0 65 | imutils==0.4.6 66 | incremental==17.5.0 67 | ipykernel==4.8.2 68 | ipython==6.4.0 69 | ipython-genutils==0.2.0 70 | ipywidgets==7.3.0 71 | isort==4.3.4 72 | itsdangerous==0.24 73 | jdcal==1.4 74 | jedi==0.12.1 75 | Jinja2==2.10 76 | jsonschema==2.6.0 77 | jupyter==1.0.0 78 | jupyter-client==5.2.3 79 | jupyter-console==5.2.0 80 | jupyter-core==4.4.0 81 | jupyterlab==0.32.1 82 | jupyterlab-launcher==0.10.5 83 | Keras==2.1.3 84 | Keras-Applications==1.0.2 85 | Keras-Preprocessing==1.0.1 86 | keyring==13.2.1 87 | kiwisolver==1.0.1 88 | lazy-object-proxy==1.3.1 89 | llvmlite==0.24.0 90 | locket==0.2.0 91 | lxml==4.2.3 92 | Markdown==2.6.11 93 | MarkupSafe==1.0 94 | matplotlib==2.2.2 95 | mccabe==0.6.1 96 | mistune==0.8.3 97 | mkl-fft==1.0.2 98 | mkl-random==1.0.1 99 | more-itertools==4.2.0 100 | mpmath==1.0.0 101 | msgpack==0.5.6 102 | msgpack-python==0.5.6 103 | multipledispatch==0.5.0 104 | navigator-updater==0.2.1 105 | nbconvert==5.3.1 106 | nbformat==4.4.0 107 | networkx==2.1 108 | nltk==3.3 109 | nose==1.3.7 110 | notebook==5.6.0 111 | numba==0.39.0 112 | numexpr==2.6.5 113 | numpy==1.14.5 114 | numpydoc==0.8.0 115 | odo==0.5.1 116 | olefile==0.45.1 117 | opencv-python==3.4.1.15 118 | openpyxl==2.5.4 119 | packaging==17.1 120 | pandas==0.23.3 121 | pandocfilters==1.4.2 122 | parso==0.3.1 123 | partd==0.3.8 124 | path.py==11.0.1 125 | pathlib2==2.3.2 126 | patsy==0.5.0 127 | pdfkit==0.6.1 128 | pep8==1.7.1 129 | pexpect==4.6.0 130 | pickleshare==0.7.4 131 | Pillow==5.1.0 132 | pkginfo==1.4.2 133 | pluggy==0.6.0 134 | ply==3.11 135 | prometheus-client==0.3.0 136 | prompt-toolkit==1.0.15 137 | protobuf==3.6.0 138 | psutil==5.4.6 139 | ptyprocess==0.6.0 140 | py==1.5.4 141 | py4j==0.10.7 142 | pyasn1==0.4.3 143 | pyasn1-modules==0.2.2 144 | pycodestyle==2.4.0 145 | pycosat==0.6.3 146 | pycparser==2.18 147 | pycrypto==2.6.1 148 | pycurl==7.43.0.2 149 | pyflakes==2.0.0 150 | Pygments==2.2.0 151 | pylint==1.9.2 152 | pymongo==3.7.1 153 | pyodbc==4.0.23 154 | pyOpenSSL==18.0.0 155 | pyparsing==2.2.0 156 | PySocks==1.6.8 157 | pyspark==2.3.1 158 | pytest==3.6.3 159 | pytest-arraydiff==0.2 160 | pytest-astropy==0.4.0 161 | pytest-doctestplus==0.1.3 162 | pytest-openfiles==0.3.0 163 | pytest-remotedata==0.3.0 164 | python-dateutil==2.7.3 165 | pytz==2018.5 166 | PyWavelets==0.5.2 167 | PyYAML==3.13 168 | pyzmq==17.0.0 169 | QtAwesome==0.4.4 170 | qtconsole==4.3.1 171 | QtPy==1.4.2 172 | requests==2.19.1 173 | rope==0.10.7 174 | ruamel-yaml==0.15.42 175 | scikit-image==0.14.0 176 | scikit-learn==0.19.1 177 | scipy==1.1.0 178 | seaborn==0.9.0 179 | Send2Trash==1.5.0 180 | service-identity==17.0.0 181 | simplegeneric==0.8.1 182 | singledispatch==3.4.0.3 183 | six==1.11.0 184 | snowballstemmer==1.2.1 185 | sortedcollections==1.0.1 186 | sortedcontainers==2.0.4 187 | Sphinx==1.7.6 188 | sphinxcontrib-websupport==1.1.0 189 | spyder==3.3.0 190 | spyder-kernels==0.2.4 191 | SQLAlchemy==1.2.10 192 | statsmodels==0.9.0 193 | sympy==1.2 194 | tables==3.4.4 195 | tblib==1.3.2 196 | tensorboard==1.9.0 197 | tensorflow==1.9.0 198 | tensorflow-gpu==1.1.0 199 | termcolor==1.1.0 200 | terminado==0.8.1 201 | testpath==0.3.1 202 | toolz==0.9.0 203 | tornado==5.0.2 204 | traitlets==4.3.2 205 | Twisted==17.5.0 206 | typing==3.6.4 207 | unicodecsv==0.14.1 208 | urllib3==1.23 209 | wcwidth==0.1.7 210 | webencodings==0.5.1 211 | Werkzeug==0.14.1 212 | widgetsnbextension==3.3.0 213 | wrapt==1.10.11 214 | WTForms==2.2.1 215 | xlrd==1.1.0 216 | XlsxWriter==1.0.5 217 | xlwings==0.11.8 218 | xlwt==1.2.0 219 | zict==0.1.3 220 | zope.interface==4.5.0 221 | -------------------------------------------------------------------------------- /static/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/static/.DS_Store -------------------------------------------------------------------------------- /static/css/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/david880110/Media-Behavior-Trends-Analytics/a1f0b16bb79e5c5443f9024e5eb1c6f2fcee2f43/static/css/.DS_Store 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[]; 26 | for (var i = 0 ; i < allAges.length ; i++){ 27 | if ( allAges[i] === chosenAge ) { 28 | currentPopulation.push(allPopulation[i]); 29 | currentYear.push(allYear[i]); 30 | } 31 | } 32 | }; 33 | 34 | // Default Age Data 35 | setBubblePlot('18-24'); 36 | 37 | function setBubblePlot(chosenAge) { 38 | getAgeData(chosenAge); 39 | 40 | var trace1 = { 41 | x: currentYear, 42 | y: currentPopulation, 43 | mode: 'lines+markers', 44 | marker: { 45 | size: 12, 46 | opacity: 0.5 47 | } 48 | }; 49 | 50 | var data = [trace1]; 51 | 52 | var layout = { 53 | title:'Line and Scatter Plot', 54 | height: 600, 55 | width: 840 56 | }; 57 | 58 | Plotly.newPlot('plotdiv', data, layout); 59 | }; 60 | 61 | var innerContainer = document.querySelector('[data-num="0"'), 62 | plotEl = innerContainer.querySelector('.plot'), 63 | ageSelector = innerContainer.querySelector('.agedata'); 64 | 65 | function assignOptions(textArray, selector) { 66 | for (var i = 0; i < textArray.length; i++) { 67 | var currentOption = document.createElement('option'); 68 | currentOption.text = textArray[i]; 69 | selector.appendChild(currentOption); 70 | } 71 | } 72 | 73 | assignOptions(listsofAges, ageSelector); 74 | 75 | function updateAge(){ 76 | setBubblePlot(ageSelector.value); 77 | } 78 | 79 | ageSelector.addEventListener('change', updateAge, false); 80 | }); 81 | -------------------------------------------------------------------------------- /static/js/bubble.js: -------------------------------------------------------------------------------- 1 | let data_url = "/api/bubble_chart"; 2 | 3 | Plotly.d3.json(data_url, function (err, data) { 4 | // Create a lookup table to sort and regroup the columns of data, 5 | // first by year, then by population: 6 | let lookup = {}; 7 | function getData(year, population) { 8 | let byYear, trace; 9 | if (!(byYear = lookup[year])) {; 10 | byYear = lookup[year] = {}; 11 | } 12 | // If a container for this year + population doesn't exist yet, 13 | // then create one: 14 | if (!(trace = byYear[population])) { 15 | trace = byYear[population] = { 16 | x: [], 17 | y: [], 18 | id: [], 19 | text: [], 20 | marker: {size: []} 21 | }; 22 | } 23 | return trace; 24 | } 25 | 26 | // Go through each row, get the right trace, and append the data: 27 | for (let i = 0; i < data.length; i++) { 28 | let datum = data[i]; 29 | let trace = getData(datum.year, datum.population); 30 | trace.text.push(`Category: ${datum.category}
population: ${datum.population}
Age: ${datum.age} `); 31 | trace.id.push(datum.category); 32 | trace.x.push(datum.age); 33 | trace.y.push(datum.population); 34 | trace.marker.size.push(datum.population*100); 35 | } 36 | 37 | // Get the group names: 38 | let years = Object.keys(lookup); 39 | // In this case, every year includes every studio, so we 40 | // can just infer the studios from the *first* year: 41 | let firstYear = lookup[years[0]]; 42 | let categories = Object.keys(firstYear); 43 | 44 | // Create the main traces, one for each studio: 45 | let traces = []; 46 | 47 | for (i = 0; i < categories.length; i++) { 48 | let data = firstYear[categories[i]]; 49 | // One small note. We're creating a single trace here, to which 50 | // the frames will pass data for the different year_id. It's 51 | // subtle, but to avoid data reference problems, we'll slice 52 | // the arrays to ensure we never write any new data into our 53 | // lookup table: 54 | traces.push({ 55 | name: categories[i], 56 | x: data.x.slice(), 57 | y: data.y.slice(), 58 | id: data.id.slice(), 59 | text: data.text.slice(), 60 | mode: 'markers', 61 | marker: { 62 | size: data.marker.size.slice(), 63 | sizemode: 'area', 64 | sizeref: 20 65 | } 66 | }); 67 | } 68 | 69 | // Create a frame for each year. Frames are effectively just 70 | // traces, except they don't need to contain the *full* trace 71 | // definition (for example, appearance). The frames just need 72 | // the parts the traces that change (here, the data). 73 | let frames = []; 74 | for (i = 0; i < years.length; i++) { 75 | frames.push({ 76 | name: years[i], 77 | data: categories.map(function (population) { 78 | return getData(years[i], population); 79 | }) 80 | }) 81 | } 82 | 83 | // Now create slider steps, one for each frame. The slider 84 | // executes a plotly.js API command (here, Plotly.animate). 85 | // In this example, we'll animate to one of the named frames 86 | // created in the above loop. 87 | let sliderSteps = []; 88 | for (i = 0; i < years.length; i++) { 89 | sliderSteps.push({ 90 | method: 'animate', 91 | label: years[i], 92 | args: [[years[i]], { 93 | mode: 'immediate', 94 | transition: {duration: 800}, 95 | frame: {duration: 800, redraw: false}, 96 | }] 97 | }); 98 | } 99 | 100 | let layout = { 101 | xaxis: { 102 | title: 'Age', 103 | //range: [0, 100] 104 | }, 105 | yaxis: { 106 | title: 'population', 107 | //range: [0, 100] 108 | }, 109 | 110 | // paper_bgcolor:'#D1D6E7', 111 | // plot_bgcolor:'#D1D6E7', 112 | 113 | hovermode: 'closest', 114 | // We'll use updatemenus (whose functionality includes menus as 115 | // well as buttons) to create a play button and a pause button. 116 | // The play button works by passing `null`, which indicates that 117 | // Plotly should animate all frames. The pause button works by 118 | // passing `[null]`, which indicates we'd like to interrupt any 119 | // currently running animations with a new list of frames. Here 120 | // The new list of frames is empty, so it halts the animation. 121 | updatemenus: [{ 122 | x: 0, 123 | y: 0, 124 | yanchor: 'top', 125 | xanchor: 'left', 126 | showactive: false, 127 | direction: 'left', 128 | type: 'buttons', 129 | pad: {t: 87, r: 10}, 130 | buttons: [{ 131 | method: 'animate', 132 | args: [null, { 133 | mode: 'immediate', 134 | fromcurrent: true, 135 | transition: {duration: 300}, 136 | frame: {duration: 800, redraw: false} 137 | }], 138 | label: 'Play' 139 | }, { 140 | method: 'animate', 141 | args: [[null], { 142 | mode: 'immediate', 143 | transition: {duration: 0}, 144 | frame: {duration: 0, redraw: false} 145 | }], 146 | label: 'Pause' 147 | }] 148 | }], 149 | // Finally, add the slider and use `pad` to position it 150 | // nicely next to the buttons. 151 | sliders: [{ 152 | pad: {l: 130, t: 55}, 153 | currentvalue: { 154 | visible: true, 155 | prefix: 'Year:', 156 | xanchor: 'right', 157 | font: {size: 20, color: '#666'} 158 | }, 159 | steps: sliderSteps 160 | }] 161 | }; 162 | 163 | // Create the plot: 164 | Plotly.plot('myDiv', { 165 | data: traces, 166 | layout: layout, 167 | frames: frames, 168 | }); 169 | }); 170 | -------------------------------------------------------------------------------- /static/js/jquery.countTo.js: -------------------------------------------------------------------------------- 1 | (function (factory) { 2 | if (typeof define === 'function' && define.amd) { 3 | // AMD 4 | define(['jquery'], factory); 5 | } else if (typeof exports === 'object') { 6 | // CommonJS 7 | factory(require('jquery')); 8 | } else { 9 | // Browser globals 10 | factory(jQuery); 11 | } 12 | }(function ($) { 13 | var CountTo = function (element, options) { 14 | this.$element = $(element); 15 | this.options = $.extend({}, CountTo.DEFAULTS, this.dataOptions(), options); 16 | this.init(); 17 | }; 18 | 19 | CountTo.DEFAULTS = { 20 | from: 0, // the number the element should start at 21 | to: 0, // the number the element should end at 22 | speed: 1000, // how long it should take to count between the target numbers 23 | refreshInterval: 100, // how often the element should be updated 24 | decimals: 0, // the number of decimal places to show 25 | formatter: formatter, // handler for formatting the value before rendering 26 | onUpdate: null, // callback method for every time the element is updated 27 | onComplete: null // callback method for when the element finishes updating 28 | }; 29 | 30 | CountTo.prototype.init = function () { 31 | this.value = this.options.from; 32 | this.loops = Math.ceil(this.options.speed / this.options.refreshInterval); 33 | this.loopCount = 0; 34 | this.increment = (this.options.to - this.options.from) / this.loops; 35 | }; 36 | 37 | CountTo.prototype.dataOptions = function () { 38 | var options = { 39 | from: this.$element.data('from'), 40 | to: this.$element.data('to'), 41 | speed: this.$element.data('speed'), 42 | refreshInterval: this.$element.data('refresh-interval'), 43 | decimals: this.$element.data('decimals') 44 | }; 45 | 46 | var keys = Object.keys(options); 47 | 48 | for (var i in keys) { 49 | var key = keys[i]; 50 | 51 | if (typeof(options[key]) === 'undefined') { 52 | delete options[key]; 53 | } 54 | } 55 | 56 | return options; 57 | }; 58 | 59 | CountTo.prototype.update = function () { 60 | this.value += this.increment; 61 | this.loopCount++; 62 | 63 | this.render(); 64 | 65 | if (typeof(this.options.onUpdate) == 'function') { 66 | this.options.onUpdate.call(this.$element, this.value); 67 | } 68 | 69 | if (this.loopCount >= this.loops) { 70 | clearInterval(this.interval); 71 | this.value = this.options.to; 72 | 73 | if (typeof(this.options.onComplete) == 'function') { 74 | this.options.onComplete.call(this.$element, this.value); 75 | } 76 | } 77 | }; 78 | 79 | CountTo.prototype.render = function () { 80 | var formattedValue = this.options.formatter.call(this.$element, this.value, this.options); 81 | this.$element.text(formattedValue); 82 | }; 83 | 84 | CountTo.prototype.restart = function () { 85 | this.stop(); 86 | this.init(); 87 | this.start(); 88 | }; 89 | 90 | CountTo.prototype.start = function () { 91 | this.stop(); 92 | this.render(); 93 | this.interval = setInterval(this.update.bind(this), this.options.refreshInterval); 94 | }; 95 | 96 | CountTo.prototype.stop = function () { 97 | if (this.interval) { 98 | clearInterval(this.interval); 99 | } 100 | }; 101 | 102 | CountTo.prototype.toggle = function () { 103 | if (this.interval) { 104 | this.stop(); 105 | } else { 106 | this.start(); 107 | } 108 | }; 109 | 110 | function formatter(value, options) { 111 | return value.toFixed(options.decimals); 112 | } 113 | 114 | $.fn.countTo = function (option) { 115 | return this.each(function () { 116 | var $this = $(this); 117 | var data = $this.data('countTo'); 118 | var init = !data || typeof(option) === 'object'; 119 | var options = typeof(option) === 'object' ? option : {}; 120 | var method = typeof(option) === 'string' ? option : 'start'; 121 | 122 | if (init) { 123 | if (data) data.stop(); 124 | $this.data('countTo', data = new CountTo(this, options)); 125 | } 126 | 127 | data[method].call(data); 128 | }); 129 | }; 130 | })); -------------------------------------------------------------------------------- /static/js/magnific-popup-options.js: -------------------------------------------------------------------------------- 1 | $(document).ready(function() { 2 | // MagnificPopup 3 | var magnifPopup = function() { 4 | $('.image-popup').magnificPopup({ 5 | type: 'image', 6 | removalDelay: 300, 7 | mainClass: 'mfp-with-zoom', 8 | gallery:{ 9 | enabled:true 10 | }, 11 | zoom: { 12 | enabled: true, // By default it's false, so don't forget to enable it 13 | 14 | duration: 300, // duration of the effect, in milliseconds 15 | easing: 'ease-in-out', // CSS transition easing function 16 | 17 | // The "opener" function should return the element from which popup will be zoomed in 18 | // and to which popup will be scaled down 19 | // By defailt it looks for an image tag: 20 | opener: function(openerElement) { 21 | // openerElement is the element on which popup was initialized, in this case its tag 22 | // you don't need to add "opener" option if this code matches your needs, it's defailt one. 23 | return openerElement.is('img') ? openerElement : openerElement.find('img'); 24 | } 25 | } 26 | }); 27 | }; 28 | 29 | var magnifVideo = function() { 30 | $('.popup-youtube, .popup-vimeo, .popup-gmaps').magnificPopup({ 31 | disableOn: 700, 32 | type: 'iframe', 33 | mainClass: 'mfp-fade', 34 | removalDelay: 160, 35 | preloader: false, 36 | 37 | fixedContentPos: false 38 | }); 39 | }; 40 | 41 | 42 | 43 | 44 | // Call the functions 45 | magnifPopup(); 46 | magnifVideo(); 47 | 48 | }); -------------------------------------------------------------------------------- /static/js/main.js: -------------------------------------------------------------------------------- 1 | ;(function () { 2 | 3 | 'use strict'; 4 | 5 | var isMobile = { 6 | Android: function() { 7 | return navigator.userAgent.match(/Android/i); 8 | }, 9 | BlackBerry: function() { 10 | return navigator.userAgent.match(/BlackBerry/i); 11 | }, 12 | iOS: function() { 13 | return navigator.userAgent.match(/iPhone|iPad|iPod/i); 14 | }, 15 | Opera: function() { 16 | return navigator.userAgent.match(/Opera Mini/i); 17 | }, 18 | Windows: function() { 19 | return navigator.userAgent.match(/IEMobile/i); 20 | }, 21 | any: function() { 22 | return (isMobile.Android() || isMobile.BlackBerry() || isMobile.iOS() || isMobile.Opera() || isMobile.Windows()); 23 | } 24 | }; 25 | 26 | var mobileMenuOutsideClick = function() { 27 | 28 | $(document).click(function (e) { 29 | var container = $("#fh5co-offcanvas, .js-fh5co-nav-toggle"); 30 | if (!container.is(e.target) && container.has(e.target).length === 0) { 31 | 32 | if ( $('body').hasClass('offcanvas') ) { 33 | 34 | $('body').removeClass('offcanvas'); 35 | $('.js-fh5co-nav-toggle').removeClass('active'); 36 | 37 | } 38 | 39 | 40 | } 41 | }); 42 | 43 | }; 44 | 45 | 46 | var offcanvasMenu = function() { 47 | 48 | $('#page').prepend('
'); 49 | $('#page').prepend(''); 50 | var clone1 = $('.menu-1 > ul').clone(); 51 | $('#fh5co-offcanvas').append(clone1); 52 | var clone2 = $('.menu-2 > ul').clone(); 53 | $('#fh5co-offcanvas').append(clone2); 54 | 55 | $('#fh5co-offcanvas .has-dropdown').addClass('offcanvas-has-dropdown'); 56 | $('#fh5co-offcanvas') 57 | .find('li') 58 | .removeClass('has-dropdown'); 59 | 60 | // Hover dropdown menu on mobile 61 | $('.offcanvas-has-dropdown').mouseenter(function(){ 62 | var $this = $(this); 63 | 64 | $this 65 | .addClass('active') 66 | .find('ul') 67 | .slideDown(500, 'easeOutExpo'); 68 | }).mouseleave(function(){ 69 | 70 | var $this = $(this); 71 | $this 72 | .removeClass('active') 73 | .find('ul') 74 | .slideUp(500, 'easeOutExpo'); 75 | }); 76 | 77 | 78 | $(window).resize(function(){ 79 | 80 | if ( $('body').hasClass('offcanvas') ) { 81 | 82 | $('body').removeClass('offcanvas'); 83 | $('.js-fh5co-nav-toggle').removeClass('active'); 84 | 85 | } 86 | }); 87 | }; 88 | 89 | 90 | var burgerMenu = function() { 91 | 92 | $('body').on('click', '.js-fh5co-nav-toggle', function(event){ 93 | var $this = $(this); 94 | 95 | 96 | if ( $('body').hasClass('overflow offcanvas') ) { 97 | $('body').removeClass('overflow offcanvas'); 98 | } else { 99 | $('body').addClass('overflow offcanvas'); 100 | } 101 | $this.toggleClass('active'); 102 | event.preventDefault(); 103 | 104 | }); 105 | }; 106 | 107 | 108 | var contentWayPoint = function() { 109 | var i = 0; 110 | $('.animate-box').waypoint( function( direction ) { 111 | 112 | if( direction === 'down' && !$(this.element).hasClass('animated-fast') ) { 113 | 114 | i++; 115 | 116 | $(this.element).addClass('item-animate'); 117 | setTimeout(function(){ 118 | 119 | $('body .animate-box.item-animate').each(function(k){ 120 | var el = $(this); 121 | setTimeout( function () { 122 | var effect = el.data('animate-effect'); 123 | if ( effect === 'fadeIn') { 124 | el.addClass('fadeIn animated-fast'); 125 | } else if ( effect === 'fadeInLeft') { 126 | el.addClass('fadeInLeft animated-fast'); 127 | } else if ( effect === 'fadeInRight') { 128 | el.addClass('fadeInRight animated-fast'); 129 | } else { 130 | el.addClass('fadeInUp animated-fast'); 131 | } 132 | 133 | el.removeClass('item-animate'); 134 | }, k * 200, 'easeInOutExpo' ); 135 | }); 136 | 137 | }, 100); 138 | 139 | } 140 | 141 | } , { offset: '85%' } ); 142 | }; 143 | 144 | 145 | var dropdown = function() { 146 | 147 | $('.has-dropdown').mouseenter(function(){ 148 | 149 | var $this = $(this); 150 | $this 151 | .find('.dropdown') 152 | .css('display', 'block') 153 | .addClass('animated-fast fadeInUpMenu'); 154 | 155 | }).mouseleave(function(){ 156 | var $this = $(this); 157 | 158 | $this 159 | .find('.dropdown') 160 | .css('display', 'none') 161 | .removeClass('animated-fast fadeInUpMenu'); 162 | }); 163 | 164 | }; 165 | 166 | 167 | var goToTop = function() { 168 | 169 | $('.js-gotop').on('click', function(event){ 170 | 171 | event.preventDefault(); 172 | 173 | $('html, body').animate({ 174 | scrollTop: $('html').offset().top 175 | }, 500, 'easeInOutExpo'); 176 | 177 | return false; 178 | }); 179 | 180 | $(window).scroll(function(){ 181 | 182 | var $win = $(window); 183 | if ($win.scrollTop() > 200) { 184 | $('.js-top').addClass('active'); 185 | } else { 186 | $('.js-top').removeClass('active'); 187 | } 188 | 189 | }); 190 | 191 | }; 192 | 193 | 194 | // Loading page 195 | var loaderPage = function() { 196 | $(".fh5co-loader").fadeOut("slow"); 197 | }; 198 | 199 | var counter = function() { 200 | $('.js-counter').countTo({ 201 | formatter: function (value, options) { 202 | return value.toFixed(options.decimals); 203 | }, 204 | }); 205 | }; 206 | 207 | 208 | var counterWayPoint = function() { 209 | if ($('#fh5co-counter').length > 0 ) { 210 | $('#fh5co-counter').waypoint( function( direction ) { 211 | 212 | if( direction === 'down' && !$(this.element).hasClass('animated') ) { 213 | setTimeout( counter , 400); 214 | $(this.element).addClass('animated'); 215 | } 216 | } , { offset: '90%' } ); 217 | } 218 | }; 219 | 220 | var sliderMain = function() { 221 | 222 | $('#fh5co-hero .flexslider').flexslider({ 223 | animation: "fade", 224 | slideshowSpeed: 5000, 225 | directionNav: true, 226 | start: function(){ 227 | setTimeout(function(){ 228 | $('.slider-text').removeClass('animated fadeInUp'); 229 | $('.flex-active-slide').find('.slider-text').addClass('animated fadeInUp'); 230 | }, 500); 231 | }, 232 | before: function(){ 233 | setTimeout(function(){ 234 | $('.slider-text').removeClass('animated fadeInUp'); 235 | $('.flex-active-slide').find('.slider-text').addClass('animated fadeInUp'); 236 | }, 500); 237 | } 238 | 239 | }); 240 | 241 | }; 242 | 243 | 244 | 245 | $(function(){ 246 | mobileMenuOutsideClick(); 247 | offcanvasMenu(); 248 | burgerMenu(); 249 | contentWayPoint(); 250 | sliderMain(); 251 | dropdown(); 252 | goToTop(); 253 | loaderPage(); 254 | counterWayPoint(); 255 | }); 256 | 257 | 258 | }()); -------------------------------------------------------------------------------- /static/js/respond.min.js: -------------------------------------------------------------------------------- 1 | 2 | /*! 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11 | 12 | 13 | 14 | --------------------------------------------------------------------------------