Code: Diese Web-App wurde mit Shiny gebaut. Den Code für die Shiny-App findet man hier.
33 | Daten: Die Daten kommen von der Deutschen Bahn. Die Karte berücksichtigt alle Fahrten zwischen dem 01.06.16 und 30.06.16. Auf der Karte werden nur Streckenabschnitte mit mindestens fünf Fahrten abgebildet.
'), 34 | value="about") 35 | ) 36 | ) 37 | ) 38 | ) -------------------------------------------------------------------------------- /server.R: -------------------------------------------------------------------------------- 1 | # load packages 2 | require(dplyr) 3 | require(leaflet) 4 | require(rgdal) 5 | require(RColorBrewer) 6 | require(shiny) 7 | 8 | # server fuction 9 | shinyServer( 10 | function(input, output, session){ 11 | 12 | # setwd 13 | # setwd("C:/Users/akruse/Documents/Projekte_Weitere/stadtrad") 14 | 15 | # load hamburg shape for map 16 | hhshape <- readOGR(dsn = ".", layer = "HH_ALKIS_Landesgrenze") 17 | 18 | # load stations for markers on map 19 | station <- read.csv("HACKATHON_RENTAL_ZONE_CALL_A_BIKE.csv", sep = ";", encoding = "UTF-8") 20 | station <- filter(station, CITY == "Hamburg") 21 | station <- select(station, RENTAL_ZONE_GROUP, RENTAL_ZONE_X_COORDINATE, RENTAL_ZONE_Y_COORDINATE) 22 | station$RENTAL_ZONE_X_COORDINATE <- gsub(",",".",station$RENTAL_ZONE_X_COORDINATE) 23 | station$RENTAL_ZONE_Y_COORDINATE <- gsub(",",".",station$RENTAL_ZONE_Y_COORDINATE) 24 | station <- filter(station, RENTAL_ZONE_X_COORDINATE != "0.000000000000000") 25 | station <- filter(station, RENTAL_ZONE_Y_COORDINATE != "0.000000000000000") 26 | station <- filter(station, RENTAL_ZONE_Y_COORDINATE != "") 27 | station <- filter(station, RENTAL_ZONE_X_COORDINATE != "") 28 | station$RENTAL_ZONE_X_COORDINATE = as.numeric(station$RENTAL_ZONE_X_COORDINATE) 29 | station$RENTAL_ZONE_Y_COORDINATE = as.numeric(station$RENTAL_ZONE_Y_COORDINATE) 30 | 31 | # get pre-processed sp file 32 | sp_plot <- readOGR(dsn = "sp_files", layer = "june16") 33 | 34 | # color palette 35 | qpal <- colorQuantile(rev(brewer.pal(4, "YlGnBu")), NULL, n = 4) 36 | 37 | # create map 38 | output$stadtrad.map <- renderLeaflet({ 39 | withProgress(message = 'Erstelle interaktive Karte...', 40 | 41 | stadtrad.map <- leaflet(sp_plot) %>% 42 | setView(lng = 9.992924, lat = 53.55100, zoom = 12) %>% 43 | addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png', 44 | attribution='Map tiles by Stamen Design, CC BY 3.0 — Map data © OpenStreetMap') %>% 45 | addPolygons(data = hhshape, stroke = T, smoothFactor = 0.05, fillOpacity = 0.05, color = "red", weight = 1, layerId = "notfoo") %>% 46 | addPolylines(popup = paste("Fahrten:",sp_plot@data$count),color = qpal(sp_plot@data$count),opacity = 1,weight = 1.5) %>% 47 | addCircleMarkers(lng = station$RENTAL_ZONE_X_COORDINATE, lat = station$RENTAL_ZONE_Y_COORDINATE, popup=station$RENTAL_ZONE_GROUP, fillOpacity = 100, color = "red", stroke = F, radius = 3, group="markers") %>% 48 | addLegend(position = 'bottomleft',colors = rev(brewer.pal(5, "YlGnBu")),labels = c("sehr schwach","schwach","mittel","stark","sehr stark"),title = 'Frequentierung') 49 | ) 50 | }) 51 | 52 | # observer to view/hide markers 53 | observeEvent(input$show, { 54 | proxy <- leafletProxy('stadtrad.map') 55 | if (input$show) proxy %>% showGroup('markers') 56 | else proxy %>% hideGroup('markers') 57 | }) 58 | }) 59 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Read an article on the usage of open data for bicycle traffic planning on my [Medium](https://medium.com/@alex_kruse/nutzung-von-open-data-im-rahmen-der-radverkehrsstrategie-9cf85a813c48). 2 | 3 | ## Visualization of usage of bike sharing network in Hamburg (StadtRAD) 4 | + Data: http://data.deutschebahn.com/dataset/data-call-a-bike 5 | + Use the map here: http://207.154.245.18/shiny/stadtrad/ or https://alexkruse.shinyapps.io/stadtrad/ 6 | + I created a [Poster](https://github.com/kruse-alex/bike_sharing/blob/master/Kruse_poster-session.pdf) for [useR 2017](https://user2017.brussels/posters) Poster Session and done a [workshop](https://github.com/kruse-alex/osm_brussels) for [OpenStreetMap](https://www.eventbrite.com/e/open-bike-data-mapping-with-openstreetmap-registration-34806438996). 7 | 8 | My interactive map shows the bike sharing usage of StadtRAD, the bike sharing system in Hamburg – Germany. The data is available on the open data platform from Deutsche Bahn, the public railway company in Germany. The last new StadtRAD station was put into operation in May 2016, that is why a have chosen to display the usage of June 2016. The brighter the lines, the more bikes have been cycled along that street. 9 | 10 |  11 | 12 | From data processing and spatial analysis to visualization the whole project was done in R. I have used [leaflet](https://rstudio.github.io/leaflet/) and [shiny](https://shiny.rstudio.com/) to display the data interactively. The bikes themselves don’t have GPS, so the routes are estimated on a shortest route basis using the awesome [CycleStreets API](https://www.cyclestreets.net/api/). The biggest challenge has been the aggregation of overlapping routes. I found the overline function from the [stplanr package](https://github.com/ropensci/stplanr) very helpful. It converts a series of overlaying lines and aggregates their values for overlapping segments. The raw data file from Deutsche Bahn is quite huge so I struggled to import the data into R to process it. In the end the read.csv.sql function from the [sqldf package](https://cran.r-project.org/web/packages/sqldf/sqldf.pdf) did the job. 13 | 14 | To further analyze the StadtRAD data I also took the full booking data from 2016 and did some diagrams. The first one is a calendar heatmap where you can see the amount of rented bikes aggregated on a daily basis. On the top of the graph you can see a barplot representing the rented bikes aggregated by the day of week. On the right you see a barplot to display the StadtRAD usage for each calendar week of 2016. The idea to use calendar heatmaps to display bike sharing usage comes from [Via Velox](http://infovis-mannheim.de/viavelox/). The code to create this heatmap is also in this Repo. 15 | 16 |  17 | 18 | I also created joyplots with ggplot to display and analyze the data. The first one shows the daily usage of every weekday. You can see big differences between working days and the weekend. 19 | 20 |  21 | 22 | The next diagram shows the daily usage per month. You can see that people renting bikes earlier in the summer. 23 | 24 |  25 | 26 | The last diagram shows the differences between the months on a daily basis. You can see that people were not using StadtRAD a lot during chrismas. 27 | 28 |  29 | -------------------------------------------------------------------------------- /superheat_processing.R: -------------------------------------------------------------------------------- 1 | ############################################################################################################################################# 2 | # PACKAGES 3 | ############################################################################################################################################# 4 | 5 | # load packages 6 | library(sqldf) 7 | require(dplyr) 8 | require(reshape2) 9 | require(superheat) 10 | 11 | # set locale to get weekdays in English 12 | Sys.setlocale("LC_TIME", "C") 13 | 14 | ############################################################################################################################################# 15 | # LOAD DATA 16 | ############################################################################################################################################# 17 | 18 | # setwd 19 | # your wd 20 | 21 | # load data (download data from Deutsche Bahn) 22 | mydata = read.csv.sql("OPENDATA_BOOKING_CALL_A_BIKE.csv", sql = "select * from file where CITY_RENTAL_ZONE = '\"Hamburg\"' ", sep = ";") 23 | 24 | ############################################################################################################################################# 25 | # PROCESS DATA 26 | ############################################################################################################################################# 27 | 28 | # processing 29 | mydata = select(mydata, DATE_FROM) 30 | mydata = as.data.frame(sapply(mydata, function(x) gsub("\"", "", x))) 31 | mydata$DATE_FROM = as.POSIXct(strptime(mydata$DATE_FROM, "%Y-%m-%d %H:%M:%S")) 32 | 33 | # filter on 2016 (Note: full KWs needed) 34 | mydata = filter(mydata, DATE_FROM >= "2015-12-28 00:00:00" & DATE_FROM <= "2017-01-01 23:59:59") 35 | 36 | # time formatting day of week and KW 37 | mydata$week = strftime(mydata$DATE_FROM,format="%W") 38 | mydata$week[mydata$DATE_FROM >= "2015-12-28 00:00:00" & mydata$DATE_FROM <= "2016-01-03 23:59:59"] = "00" 39 | mydata$week[mydata$DATE_FROM >= "2016-12-26 00:00:00" & mydata$DATE_FROM <= "2017-01-01 23:59:59"] = "53" 40 | mydata$weekday = weekdays(mydata$DATE_FROM) 41 | 42 | # grouing 43 | mydata = mydata %>% group_by(week, weekday) %>% summarise(count = n()) 44 | 45 | # change order of factor levels for plotting 46 | mydata$weekday = as.factor(mydata$weekday) 47 | mydata$weekday = factor(mydata$weekday, levels = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")) 48 | mydata$week= as.factor(mydata$week) 49 | mydata$week = factor(mydata$week, levels = rev(levels(mydata$week))) 50 | 51 | # create matrix for heatmap 52 | mydata = acast(mydata, week~weekday, value.var="count") 53 | mydata = as.data.frame(mydata) 54 | 55 | ############################################################################################################################################# 56 | # CREATE SUPERHEAT 57 | ############################################################################################################################################# 58 | 59 | # prevent scientific notation of numbers for plotting 60 | options(scipen=999) 61 | 62 | # save plot 63 | png("superheat.png", height = 900, width = 800) 64 | 65 | # plot 66 | superheat(mydata, 67 | 68 | # main plot 69 | title = "StadtRAD Usage 2016 (Calendar Heatmap)", 70 | row.title = "Number of Week", 71 | left.label.text.size = 3, 72 | bottom.label.text.size = 4, 73 | 74 | # y axis bar 75 | yr = rowSums(mydata), 76 | yr.axis.name = "", 77 | yr.plot.type = "bar", 78 | 79 | # x axis bar 80 | yt = colSums(mydata), 81 | yt.plot.type = "bar", 82 | yt.axis.name = "", 83 | 84 | # legend 85 | legend.breaks = c(4000, 8000, 12000)) 86 | dev.off() 87 | -------------------------------------------------------------------------------- /stadtrad_processing.R: -------------------------------------------------------------------------------- 1 | ############################################################################################################################################# 2 | # PACKAGES 3 | ############################################################################################################################################# 4 | 5 | # load packages 6 | library(sqldf) 7 | require(dplyr) 8 | require(data.table) 9 | require(sp) 10 | require(rgdal) 11 | require(stplanr) 12 | require(reshape2) 13 | require(rmapshaper) 14 | 15 | # check for cs key 16 | Sys.getenv("CYCLESTREET") 17 | 18 | ############################################################################################################################################# 19 | # LOAD DATA 20 | ############################################################################################################################################# 21 | 22 | # setwd 23 | setwd("K:/Consulting/13_Alex_Data_Analyst/Datenanalyse_Projekte/Weitere/stadtrad") 24 | 25 | # load data (download data from Deutsche Bahn) 26 | mydata <- read.csv.sql("HACKATHON_BOOKING_CALL_A_BIKE.csv", sql = "select * from file where CITY_RENTAL_ZONE = '\"Hamburg\"' ", sep = ";") 27 | 28 | ############################################################################################################################################# 29 | # PROCESS DATA 30 | ############################################################################################################################################# 31 | 32 | # processing 33 | mydata <- select(mydata, DATE_FROM, TRIP_LENGTH_MINUTES, START_RENTAL_ZONE_GROUP, END_RENTAL_ZONE_GROUP) 34 | mydata <- as.data.frame(sapply(mydata, function(x) gsub("\"", "", x))) 35 | mydata$DATE_FROM <- gsub(".0000000","",mydata$DATE_FROM) 36 | mydata$DATE_FROM <- as.POSIXct(strptime(mydata$DATE_FROM, "%Y-%m-%d %H:%M:%S")) 37 | 38 | # filter on june 39 | mydata <- filter(mydata, DATE_FROM >= "2016-06-01 00:00:00" & DATE_FROM <= "2016-06-30 23:59:59") 40 | 41 | # aggregate doubles 42 | mydata <- transform(mydata, min = pmin(as.character(START_RENTAL_ZONE_GROUP), as.character(END_RENTAL_ZONE_GROUP))) 43 | mydata <- transform(mydata, max = pmax(as.character(START_RENTAL_ZONE_GROUP), as.character(END_RENTAL_ZONE_GROUP))) 44 | 45 | # get lan/lat from stations 46 | station <- read.csv("HACKATHON_RENTAL_ZONE_CALL_A_BIKE.csv", sep = ";", quote = "", stringsAsFactors = T) 47 | station <- as.data.frame(sapply(station, function(x) gsub("\"", "", x))) 48 | station <- filter(station, X.CITY. == "Hamburg") 49 | station <- select(station, X.RENTAL_ZONE_GROUP., X.RENTAL_ZONE_X_COORDINATE., X.RENTAL_ZONE_Y_COORDINATE.) 50 | colnames(station) <- c("RENTAL_ZONE_GROUP", "RENTAL_ZONE_X_COORDINATE", "RENTAL_ZONE_Y_COORDINATE") 51 | 52 | mydata <- merge(mydata, station, by.x = "min", by.y = "RENTAL_ZONE_GROUP", all.x = T) 53 | mydata <- merge(mydata, station, by.x = "max", by.y = "RENTAL_ZONE_GROUP", all.x = T) 54 | mydata <- mydata[complete.cases(mydata),] 55 | mydata <- filter(mydata, TRIP_LENGTH_MINUTES != "") 56 | 57 | # some more processing 58 | mydata$start <- paste(mydata$RENTAL_ZONE_Y_COORDINATE.x, mydata$RENTAL_ZONE_X_COORDINATE.x, sep = " ") 59 | mydata$dest <- paste(mydata$RENTAL_ZONE_Y_COORDINATE.y, mydata$RENTAL_ZONE_X_COORDINATE.y, sep = " ") 60 | mydata$date <- as.Date(mydata$DATE_FROM) 61 | mydata <- mydata %>% group_by(start, dest) %>% summarise(count = n()) 62 | mydata$start <- gsub(",",".",mydata$start) 63 | mydata$dest <- gsub(",",".",mydata$dest) 64 | mydata$start <- as.character(mydata$start) 65 | mydata$dest <- as.character(mydata$dest) 66 | mydata <- as.data.frame(mydata) 67 | 68 | mydata$check <- mydata$start == mydata$dest 69 | mydata <- filter(mydata, check == "FALSE") 70 | mydata <- filter(mydata, start != "0.000000000000000 0.000000000000000") 71 | mydata <- filter(mydata, dest != "0.000000000000000 0.000000000000000") 72 | mydata <- filter(mydata, dest != " ") 73 | mydata <- filter(mydata, start != " ") 74 | 75 | # take sample for testing 76 | #mydata <- mydata[1:50,] 77 | 78 | ############################################################################################################################################# 79 | # GET ROUTES 80 | ############################################################################################################################################# 81 | 82 | # some processing 83 | mydata$check <- NULL 84 | mydata$id <- rownames(mydata) 85 | mydata <- melt(mydata, id.vars = c("id","count")) 86 | test <- data.frame(do.call('rbind', strsplit(as.character(mydata$value),' ',fixed=TRUE))) 87 | mydata <- cbind(mydata,test) 88 | mydata <- select(mydata, X1, X2, id, count) 89 | rm(test) 90 | colnames(mydata) <- c("lat","lon","id","count") 91 | dt <- mydata 92 | dt$lat <- as.numeric(as.character(dt$lat)) 93 | dt$lon <- as.numeric(as.character(dt$lon)) 94 | dt$id <- as.factor(dt$id) 95 | 96 | # create spdf 97 | dt <- as.data.table(dt) 98 | lst_lines <- lapply(unique(dt$id), function(x){ 99 | Lines(Line(dt[id == x, .(lon, lat)]), ID = x) 100 | }) 101 | spl_lst <- SpatialLines(lst_lines) 102 | spl_df <- SpatialLinesDataFrame(spl_lst, data.frame(mydata$count)) 103 | 104 | # get routes from cyclestreet (needs API key) 105 | spl_df <- line2route(spl_df, "route_cyclestreet", plan = "fastest") 106 | mydata$lat <- NULL 107 | mydata$lon <- NULL 108 | mydata <- mydata[!duplicated(mydata), ] 109 | spl_df@data$count <- mydata$count 110 | spl_df@data <- select(spl_df@data, count) 111 | 112 | # remove rare tracks 113 | spl_df <- spl_df[spl_df@data$count >= 5, ] 114 | 115 | # overline overlaps 116 | spl_df <- ms_simplify(input = spl_df, keep = 0.01) 117 | spl_df <- overline(spl_df, attrib = "count", fun = sum) 118 | 119 | ############################################################################################################################################# 120 | # SAVE OBJECT 121 | ############################################################################################################################################# 122 | 123 | # save objects for leaflet map 124 | writeOGR(obj=spl_df, dsn="sp_files", layer="june16", driver="ESRI Shapefile") 125 | 126 | # remove objects 127 | rm(spl_lst,mydata,dt,lst_lines, spl_df, station) 128 | -------------------------------------------------------------------------------- /sp_files/june16.dbf: -------------------------------------------------------------------------------- 1 | u 2 | > A 3 | W count N 909 374 424 25 399 624 285 56 206 276 124 316 20 114 94 45 25 31 25 139 94 310 285 79 33 23 102 79 23 49 45 82 42 660 33 79 47 32 500 160 23 45 509 585 42 467 33 60 564 75 513 82 32 36 81 477 74 10 23 70 62 42 20 9 33 444 106 441 999 152 12 322 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