├── .Rbuildignore ├── .gitignore ├── .travis.yml ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── NEWS.md ├── R ├── data.R └── sgf_sfdf.R ├── README.Rmd ├── README.md ├── data-raw ├── carto_changements_geo_departements.R ├── clean_data.R ├── crea_carto.R └── script_dev.R ├── data ├── data_SGF.rda ├── geo_ARR_SGF_histo.rda ├── geo_DEP_SGF_histo.rda └── indicateurs_SGF.rda ├── data_source ├── ENSP_T116.dbf ├── ENSP_T118.dbf ├── ENSP_T18.dbf ├── ENSP_T24.dbf ├── ENSP_T31.dbf ├── ENSP_T32.dbf ├── ENSP_T33.dbf ├── ENSP_T34.dbf ├── ENSP_T35.dbf ├── ENSP_T36.dbf ├── ENSP_T37.dbf ├── ENSP_T38.dbf ├── ENSP_T51.dbf ├── ENSP_T52.dbf ├── ENSP_T53.dbf ├── ENSP_T54.dbf ├── ENSP_T55.dbf ├── ENSP_T56.dbf ├── ENSP_T57.dbf ├── ENSP_T58.dbf ├── ENSP_T59.dbf ├── ENSP_T77.dbf ├── ENSP_T78.dbf ├── ENSP_T79.dbf ├── ENSP_T80.dbf ├── ENSP_T81.dbf ├── ENSP_T82.dbf ├── ENSP_T83.dbf ├── ENSP_T84.dbf ├── MVTPOP_T100.dbf ├── MVTPOP_T101.dbf ├── MVTPOP_T102.dbf ├── MVTPOP_T103.dbf ├── MVTPOP_T104.dbf ├── MVTPOP_T105.dbf ├── MVTPOP_T106.dbf ├── MVTPOP_T107.dbf ├── MVTPOP_T108.dbf ├── MVTPOP_T109.dbf ├── MVTPOP_T110.dbf ├── MVTPOP_T111.dbf ├── MVTPOP_T112.dbf ├── MVTPOP_T113.dbf ├── MVTPOP_T114.dbf ├── MVTPOP_T115.dbf ├── MVTPOP_T116.dbf ├── MVTPOP_T119.dbf ├── MVTPOP_T120.dbf ├── MVTPOP_T121.dbf ├── MVTPOP_T122.dbf ├── MVTPOP_T123.dbf ├── MVTPOP_T124.dbf ├── MVTPOP_T125.dbf ├── MVTPOP_T126.dbf ├── MVTPOP_T127.dbf ├── MVTPOP_T128.dbf ├── MVTPOP_T129.dbf ├── MVTPOP_T130.dbf ├── MVTPOP_T131.dbf ├── MVTPOP_T132.dbf ├── MVTPOP_T133.dbf ├── MVTPOP_T134.dbf ├── MVTPOP_T135.dbf ├── MVTPOP_T136.dbf ├── MVTPOP_T137.dbf ├── MVTPOP_T138.dbf ├── MVTPOP_T139.dbf ├── MVTPOP_T140.dbf ├── MVTPOP_T141.dbf ├── MVTPOP_T142.dbf ├── MVTPOP_T143.dbf ├── MVTPOP_T144.dbf ├── MVTPOP_T145.dbf ├── MVTPOP_T146.dbf ├── MVTPOP_T147.dbf ├── MVTPOP_T148.dbf ├── MVTPOP_T149.dbf ├── MVTPOP_T150.dbf ├── MVTPOP_T151.dbf ├── MVTPOP_T152.dbf ├── MVTPOP_T153.dbf ├── MVTPOP_T154.dbf ├── MVTPOP_T155.dbf ├── MVTPOP_T156.dbf ├── MVTPOP_T157.dbf ├── MVTPOP_T158.dbf ├── MVTPOP_T159.dbf ├── MVTPOP_T160.dbf ├── MVTPOP_T161.dbf ├── MVTPOP_T170.dbf ├── MVTPOP_T173.dbf ├── MVTPOP_T174.dbf ├── MVTPOP_T210.dbf ├── MVTPOP_T213.dbf ├── MVTPOP_T214.dbf ├── MVTPOP_T263.dbf ├── MVTPOP_T264.dbf ├── MVTPOP_T272.dbf ├── MVTPOP_T329.dbf ├── MVTPOP_T330.dbf ├── MVTPOP_T331.dbf ├── MVTPOP_T332.dbf ├── MVTPOP_T333.dbf ├── MVTPOP_T74.dbf ├── MVTPOP_T75.dbf ├── MVTPOP_T76.dbf ├── MVTPOP_T87.dbf ├── MVTPOP_T88.dbf ├── MVTPOP_T89.dbf ├── MVTPOP_T90.dbf ├── MVTPOP_T91.dbf ├── MVTPOP_T92.dbf ├── MVTPOP_T93.dbf ├── MVTPOP_T94.dbf ├── MVTPOP_T95.dbf ├── MVTPOP_T96.dbf ├── MVTPOP_T97.dbf ├── MVTPOP_T98.dbf ├── MVTPOP_T99.dbf ├── REC_T01.dbf ├── REC_T02.dbf ├── REC_T03.dbf ├── REC_T04.dbf ├── REC_T05.dbf ├── REC_T06.dbf ├── REC_T07.dbf ├── REC_T08.dbf ├── REC_T09.dbf ├── REC_T10.dbf ├── REC_T11.dbf ├── REC_T12.dbf ├── REC_T13.dbf ├── REC_T14.dbf ├── REC_T15.dbf ├── REC_T16.dbf ├── REC_T17.dbf ├── REC_T18.dbf ├── REC_T19.dbf ├── REC_T20.dbf ├── REC_T21.dbf ├── REC_T214.dbf ├── REC_T217.dbf ├── REC_T218.dbf ├── REC_T22.dbf ├── REC_T225.dbf ├── REC_T226.dbf ├── REC_T229.dbf ├── REC_T23.dbf ├── REC_T234.dbf ├── REC_T235.dbf ├── REC_T237.dbf ├── REC_T238.dbf ├── REC_T239.dbf ├── REC_T24.dbf ├── REC_T25.dbf ├── REC_T256.dbf ├── REC_T257.dbf ├── REC_T259.dbf ├── REC_T26.dbf ├── REC_T27.dbf ├── REC_T28.dbf ├── REC_T29.dbf ├── REC_T30.dbf ├── REC_T39.dbf ├── REC_T40.dbf ├── REC_T41.dbf ├── REC_T42.dbf ├── REC_T43.dbf ├── REC_T44.dbf ├── REC_T45.dbf ├── REC_T46.dbf ├── REC_T47.dbf ├── REC_T48.dbf ├── REC_T49.dbf ├── REC_T50.dbf ├── REC_T59.dbf ├── REC_T60.dbf ├── REC_T61.dbf ├── REC_T62.dbf ├── REC_T63.dbf ├── REC_T64.dbf ├── REC_T65.dbf ├── REC_T66.dbf ├── REC_T67.dbf ├── REC_T68.dbf ├── REC_T69.dbf ├── REC_T70.dbf ├── REC_T71.dbf ├── REC_T72.dbf ├── REC_T73.dbf ├── TERR_T116.dbf ├── TERR_T117.dbf ├── TERR_T85.dbf ├── TERR_T86.dbf ├── liste_datasets.xlsx └── liste_variables.xlsx ├── geo └── geo_DEP_SGF_histo.rds ├── geo_source ├── arrondissements1801_S.dbf ├── arrondissements1801_S.prj ├── arrondissements1801_S.shp ├── arrondissements1801_S.shx ├── arrondissements1826_S.dbf ├── arrondissements1826_S.prj ├── arrondissements1826_S.shp ├── arrondissements1826_S.shx ├── arrondissements1851_S.dbf ├── arrondissements1851_S.prj ├── arrondissements1851_S.shp ├── arrondissements1851_S.shx ├── arrondissements1876_S.dbf ├── arrondissements1876_S.prj ├── arrondissements1876_S.shp └── arrondissements1876_S.shx ├── man ├── data_SGF.Rd ├── figures │ ├── README-plot_exemple_carto-1.png │ ├── README-plot_exemple_carto_arr-1.png │ ├── README-plot_geographie_departement-1.png │ └── README-recherche_indics_2-1.png ├── geo_ARR_SGF_histo.Rd ├── geo_DEP_SGF_histo.Rd ├── indicateurs_SGF.Rd └── sgf_sfdf.Rd └── sgf.Rproj /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^data-raw$ 2 | ^.*\.Rproj$ 3 | ^\.Rproj\.user$ 4 | ^\.travis\.yml$ 5 | ^LICENSE\.md$ 6 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | # R for travis: see documentation at https://docs.travis-ci.com/user/languages/r 2 | 3 | language: R 4 | cache: packages 5 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: sgf 2 | Title: sgf 3 | Version: 0.1.1 4 | Authors@R: person('Observatoire', 'des Territoires',email='observatoire@cget.gouv.fr', role=c('cre','aut')) 5 | Description: Statistiques sur la France du XIXème siécle. 6 | License: MIT + file LICENSE 7 | LazyData: true 8 | URL: https://github.com/observatoire-territoires/sgf 9 | BugReports: https://github.com/observatoire-territoires/sgf/issues 10 | Encoding: UTF-8 11 | Depends: 12 | dplyr, 13 | tidyr, 14 | janitor, 15 | sf 16 | RoxygenNote: 6.1.1 17 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2019 2 | COPYRIGHT HOLDER: Insee - SGF - ICPSR - Observatoire des territoires 3 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # MIT License 2 | 3 | Copyright (c) 2019 Insee - SGF - ICPSR - Observatoire des territoires 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(sgf_sfdf) 4 | import(sf) 5 | importFrom(dplyr,bind_rows) 6 | importFrom(dplyr,case_when) 7 | importFrom(dplyr,distinct) 8 | importFrom(dplyr,filter) 9 | importFrom(dplyr,group_by) 10 | importFrom(dplyr,left_join) 11 | importFrom(dplyr,mutate) 12 | importFrom(dplyr,mutate_if) 13 | importFrom(dplyr,pull) 14 | importFrom(dplyr,rename) 15 | importFrom(dplyr,select) 16 | importFrom(dplyr,summarise) 17 | importFrom(dplyr,tribble) 18 | importFrom(dplyr,ungroup) 19 | importFrom(janitor,clean_names) 20 | importFrom(tidyr,gather) 21 | importFrom(tidyr,spread) 22 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # sgf 0.1.1 2 | 3 | * Ajout de la maille arrondissements. 4 | 5 | # sgf 0.1.0 6 | 7 | * Version exploitable avec datasets et fonction. 8 | 9 | # sgf 0.0.0.9000 10 | 11 | * Initialisation. 12 | -------------------------------------------------------------------------------- /R/data.R: -------------------------------------------------------------------------------- 1 | #' Liste des indicateurs disponibles 2 | #' 3 | #' Description des plus de 40 000 indicateurs de la SGF publiées par l'Insee. 4 | #' 5 | #' @format Table (format data.frame) de 43 702 lignes et 7 variables. 6 | #' \describe{ 7 | #' \item{TABLEAU}{Identifiant du tableau de donnée d'origine} 8 | #' \item{NOM_TABLEAU}{Nom en clair du tableau de donnée d'origine} 9 | #' \item{VAR_COD}{Identifiant de l'indicateur (1 id unique par tableau de donnée d'origine)} 10 | #' \item{VAR_LIB}{Nom en clair de l'indicateur} 11 | #' \item{ANNEE_DONNEE}{Millésime de la donnée statistique} 12 | #' \item{ECHELLES_GEO}{Echelle(s) géographique(s) disponibles (DEP pour Département, 13 | #' ARR pour Arrondissement, DEP_ARR pour Département+Arrondissement, CHL_VILLE pour Chef-lieu d'arrondissement)} 14 | #' \item{ANNEE_GEOGRAPHIE}{Millésime des contours géographiques correspondants (1801, 1826, 1866, 1876 ou 1918)} 15 | #' } 16 | #' @source \url{https://www.insee.fr/fr/statistiques/2591397} 17 | "indicateurs_SGF" 18 | 19 | #' Table des données de la SGF 20 | #' 21 | #' Données de la SGF (au format long) publiées par l'Insee. 22 | #' 23 | #' @format Table (format data.frame) de 4 627 598 lignes et 9 variables. 24 | #' \describe{ 25 | #' \item{NIVGEO}{Nom de l'échelle géographique (FR pour France entière, DEP pour Département, 26 | #' ARR pour Arrondissement, CHL pour Chef-lieu d'arrondissement, VIL pour Ville)} 27 | #' \item{CODGEO}{Identifiant du territoire} 28 | #' \item{LIBGEO}{Libellé du territoire} 29 | #' \item{SRC_DATA}{Identifiant du tableau de donnée d'origine} 30 | #' \item{VAR_COD}{Identifiant de l'indicateur (1 id unique par tableau de donnée d'origine)} 31 | #' \item{VAR_LIB}{Nom en clair de l'indicateur} 32 | #' \item{ANNEE_DONNEE}{Millésime de la donnée} 33 | #' \item{ANNEE_GEOGRAPHIE}{Millésime des contours géographiques correspondants (1801, 1826, 1866, 1876 ou 1918)} 34 | #' \item{VAL}{Valeur de l'indicateur} 35 | #' } 36 | #' @source \url{https://www.insee.fr/fr/statistiques/2591397} 37 | "data_SGF" 38 | 39 | #' Géographies des départements 40 | #' 41 | #' Contours et libellés des départements de France métropolitaine à plusieurs millésimes. 42 | #' 43 | #' @format Table géographique (format sf data.frame) de 437 lignes et 3 variables. 44 | #' \describe{ 45 | #' \item{CODGEO}{Identifiant du territoire} 46 | #' \item{LIBGEO}{Nom du territoire} 47 | #' \item{ANNEE_GEOGRAPHIE}{Millésime du contour géographique (1801, 1826, 1866, 1876 ou 1918)} 48 | #' \item{geometry}{Description des contours géographiques} 49 | #' } 50 | "geo_DEP_SGF_histo" 51 | 52 | #' Géographies des arrondissements 53 | #' 54 | #' Contours et libellés des arrondissements de France métropolitaine à plusieurs millésimes. 55 | #' 56 | #' @format Table géographique (format sf data.frame) de 1084 lignes et 3 variables. 57 | #' \describe{ 58 | #' \item{CODGEO}{Identifiant du territoire} 59 | #' \item{LIBGEO}{Nom du territoire} 60 | #' \item{ANNEE_GEOGRAPHIE}{Millésime du contour géographique (1801, 1826 ou 1876)} 61 | #' \item{geometry}{Description des contours géographiques} 62 | #' } 63 | "geo_ARR_SGF_histo" -------------------------------------------------------------------------------- /R/sgf_sfdf.R: -------------------------------------------------------------------------------- 1 | #' @name sgf_sfdf 2 | #' 3 | #' @title Créer une table de données géographiques avec des indicateurs de la SGF 4 | #' 5 | #' @description Générer un sf dataframe contenant les indicateurs de la SGF préalablement sélectionnés. 6 | #' 7 | #' @param TYPE_NIVGEO Nom court du niveau géographique ('DEP' pour département, "ARR" pour arrondissement). 8 | #' @param SRC Nom du tableau de données dans lequel se trouvent les indicateurs à récupérer (cf. champ 'TABLEAU' dans la table 'indicateurs_SGF'). 9 | #' @param LISTE_VAR_COD Liste des codes des variables à récupérer (cf. champ 'VAR_COD' dans la table 'indicateurs_SGF'). 10 | #' 11 | #' @return Renvoie un sf dataframe contenant code et nom du territoire ainsi que les indicateurs sélectionnés. 12 | #' 13 | #' @importFrom dplyr tribble filter distinct pull mutate select left_join case_when group_by summarise ungroup rename mutate_if bind_rows 14 | #' @importFrom janitor clean_names 15 | #' @importFrom tidyr spread gather 16 | #' @import sf 17 | #' 18 | #' @examples 19 | #' \dontrun{ 20 | #' # Creation du sf dataframe contenant les indicateurs de cheptels (bovins, ovins, etc...) 21 | #' issus du recensement de 1872 22 | #' DEP_cheptels_1872 <- 23 | #' sgf_sfdf(TYPE_NIVGEO = "DEP", 24 | #' SRC ="REC_T17", 25 | #' LISTE_VAR_COD = c(52, 59, 65, 70, 83, 87, 94)) 26 | #' 27 | #'} 28 | #' 29 | #' @details 30 | #' Deux niveaux géographiques sont disponibles : département (TYPE_NIVGEO = 'DEP') et arrondissement ("ARR"). \cr 31 | #' 32 | #' 33 | #' @export 34 | 35 | sgf_sfdf <- function(TYPE_NIVGEO, SRC, LISTE_VAR_COD) { 36 | 37 | df_data <- data_SGF %>% 38 | filter(NIVGEO %in% TYPE_NIVGEO) %>% 39 | filter(SRC_DATA %in% SRC & VAR_COD %in% LISTE_VAR_COD) %>% 40 | select(CODGEO,ANNEE_GEOGRAPHIE,VAR_LIB, VAL) %>% 41 | spread(VAR_LIB, VAL) %>% 42 | clean_names() %>% 43 | ungroup() 44 | 45 | 46 | annee_geo <- df_data %>% distinct(annee_geographie) %>% pull() 47 | 48 | if(TYPE_NIVGEO == "DEP") { 49 | data <- geo_DEP_SGF_histo %>% 50 | filter(ANNEE_GEOGRAPHIE %in% annee_geo) %>% 51 | left_join(df_data %>% select(-annee_geographie), 52 | by = c("CODGEO" = "codgeo")) %>% 53 | ungroup() 54 | } 55 | 56 | else { 57 | data <- geo_ARR_SGF_histo %>% 58 | filter(ANNEE_GEOGRAPHIE %in% annee_geo) %>% 59 | left_join(df_data %>% select(-annee_geographie), 60 | by = c("CODGEO" = "codgeo")) %>% 61 | ungroup() 62 | } 63 | 64 | } 65 | 66 | -------------------------------------------------------------------------------- /README.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: github_document 3 | always_allow_html: yes 4 | 5 | --- 6 | 7 | 8 | 9 | ```{r setup, include = F} 10 | knitr::opts_chunk$set( 11 | collapse = TRUE, 12 | comment = "#>", 13 | fig.path = "man/figures/README-", 14 | out.width = "100%" 15 | ) 16 | ``` 17 | 18 | # SGF 19 | 20 | La SGF (pour _Statistique Générale de la France_) est [l'ancêtre de l'Insee](https://www.insee.fr/fr/information/1300622), à savoir un service ministériel chargé de collecter et analyser des statistiques pour le gouvernement français entre 1833 et 1940. 21 | 22 | Une partie de ces données a été saisie dans les années 1980 par une équipe de chercheurs franco-américains à l'Inter-University Consortium for Political and Social Research (ICPSR), situé à Ann Arbor, Michigan, États-Unis. L'Insee a [publié ici](https://www.insee.fr/fr/statistiques/2591397) 200 tableurs au format dbf portant sur les recensements de la population, la démographie et l'enseignement primaire, entre 1800 et 1925. 23 | 24 | Le package `sgf` regroupe, normalise et documente ces indicateurs disponibles dans la plupart des cas à l'échelon géographique du département (des arrondissements voire des chefs-lieux d'arrondissements dans quelques rares cas). Il permet également de cartographier ces indicateurs au millésime géographique du département ou de l'arrondissement correspondant au millésime de la donnée. 25 | 26 | `sgf` est composé de : 27 | 28 | - 2 tables de données (format dataframe) : 29 | - __indicateurs_SGF__ : description des 40 000 indicateurs disponibles (libellé, identifiant, millésime de la donnée, échelles géographiques disponibles et millésime de la géographie) 30 | - __data_SGF__ : table contenant l'ensemble des données en format long (libellés, identifiants, millésime de la donnée et millésime de la géographie) 31 | 32 | 33 | - 2 tables de données géographiques (format sf dataframe) : 34 | - __geo_DEP_SGF_histo__ : contours géographiques des départements (avec leur code et libellé) selon le millésime de la géographie (1801/1826/1866/1876/1918, cf. "géographie ci-dessous) 35 | - __geo_ARR_SGF_histo__ : contours géographiques des arrondissements (avec leur code et libellé) selon le millésime de la géographie (1801/1826/1876, cf. "géographie ci-dessous) 36 | 37 | - 1 fonction : 38 | - __sgf_sfdf__ : après avoir identifié les indicateurs de son choix dans la table __indicateurs_SGF__, cette fonction permet de créer un sf dataframe avec les indicateurs en format large 39 | 40 | 41 | ## Sources et crédits 42 | 43 | Données statistiques : SGF, ICPSR, Insee, [insee.fr/fr/information/1300622](https://www.insee.fr/fr/information/1300622) 44 | 45 | Fonds cartographiques : F. Salmon, 2019, [fondsdecarte.free.fr/](http://fondsdecarte.free.fr/) 46 | 47 | Documentation, géoréférencement : Observatoire des Territoires. 48 | 49 | 50 | ## Installation 51 | 52 | Le package `sgf` peut être installé depuis [github](https://github.com/observatoire-territoires/sgf) via la commande suivante : 53 | 54 | ``` {r eval=F} 55 | remotes::install_github("observatoire-territoires/sgf") 56 | ``` 57 | 58 | Pour accéder à la documentation décrivant les tables de données, par exemple celle listant les indicateurs : 59 | ``` {r eval=F, echo=T, message=F, warning=F} 60 | ?sgf::indicateurs_SGF 61 | ``` 62 | 63 | 64 | ## Géographie 65 | 66 | Les contours des départements de France métropolitaine ont évolué sur la période que couvre les données de la SGF. 67 | Les 5 millésimes de cette géographie départementale sont disponibles dans la table `geo_DEP_SGF_histo` (idem pour les contours des arrondissements dans la table `geo_ARR_SGF_histo`) : 68 | 69 | ```{r plot_geographie_departement, eval=T, echo=F, message=F, warning=F , dpi= 500} 70 | 71 | library(tidyverse) 72 | library(sf) 73 | 74 | load( file = "./data/geo_DEP_SGF_histo.rda") 75 | 76 | geo_DEP_SGF_histo_modifs <- 77 | geo_DEP_SGF_histo %>% 78 | mutate(modif = case_when(CODGEO %in% '82' & ANNEE_GEOGRAPHIE == 1826 ~ "création", 79 | CODGEO %in% '73' & ANNEE_GEOGRAPHIE == 1866 ~ "création", 80 | CODGEO %in% '74' & ANNEE_GEOGRAPHIE == 1866 ~ "création", 81 | CODGEO %in% '06' & ANNEE_GEOGRAPHIE == 1866 ~ "création", 82 | CODGEO %in% '83' & ANNEE_GEOGRAPHIE == 1866 ~ "modification", 83 | CODGEO %in% '54' & ANNEE_GEOGRAPHIE == 1876 ~ "création", 84 | CODGEO %in% '90' & ANNEE_GEOGRAPHIE == 1876 ~ "création", 85 | CODGEO %in% '88' & ANNEE_GEOGRAPHIE == 1876 ~ "modification", 86 | CODGEO %in% '57' & ANNEE_GEOGRAPHIE == 1918 ~ "création", 87 | CODGEO %in% '67' & ANNEE_GEOGRAPHIE == 1918 ~ "création", 88 | CODGEO %in% '68' & ANNEE_GEOGRAPHIE == 1918 ~ "création")) %>% 89 | # ajout des départements supprimés en 1876 90 | rbind.data.frame(geo_DEP_SGF_histo %>% 91 | filter(ANNEE_GEOGRAPHIE == 1866 & CODGEO %in% c('57','67','68')) %>% 92 | mutate(ANNEE_GEOGRAPHIE = 1876) %>% 93 | mutate(modif = "suppression")) 94 | 95 | ggplot(data = geo_DEP_SGF_histo_modifs ) + 96 | geom_sf(aes(fill = modif), 97 | color = "grey70", lwd = 0.2) + 98 | geom_sf_text(aes(label = CODGEO), color = "grey20", size = 1.5, fontface = "bold") + 99 | scale_fill_manual(name = "Modifications successives\ndepuis 1801", 100 | values =c("green", "orange", "red"), na.translate = F, na.value = "grey") + 101 | facet_wrap(~ ANNEE_GEOGRAPHIE, ncol = 3) + 102 | theme(axis.text = element_blank(), 103 | axis.title = element_blank(), 104 | text = element_text(size = 10,face = "bold"), 105 | strip.text.x = element_text(size = 10, colour = "white"), 106 | strip.background = element_rect(fill="grey20", colour=NA,size=0), 107 | legend.position = c(0.85,0.3), 108 | legend.key.size = unit(0.2, "cm"), 109 | legend.key.width = unit(0.4,"cm") , 110 | legend.title = element_text(size = 7), 111 | legend.text = element_text(size = 5), 112 | axis.ticks = element_blank()) + 113 | labs( 114 | title = "Contours des départements de France métropolitaine" 115 | ) + 116 | coord_sf(crs = st_crs(2154), datum = NA) 117 | 118 | ``` 119 | 120 | 121 | ## Démo 122 | 123 | La recherche des indicateurs se fait directement dans la table __indicateurs_SGF__ : 124 | 125 | ```{r lib, eval=T , echo=T, message=F, warning=F} 126 | library(sgf) 127 | ``` 128 | 129 | ```{r view, results="hide"} 130 | View(indicateurs_SGF) 131 | ``` 132 | 133 | ```{r recherche_indics, eval=F , echo=F, message=F, warning=F} 134 | indicateurs_SGF %>% slice(1:3) %>% 135 | kableExtra::kable() %>% 136 | kableExtra::kable_styling(font_size = 6.5, bootstrap_options = c("striped", "hover", "condensed", "responsive")) 137 | ``` 138 | 139 | ```{r recherche_indics_2, eval=F , echo=F, message=F, warning=F} 140 | DT::datatable(indicateurs_SGF %>% slice(1:2000)) 141 | ``` 142 | 143 | Une fois le niveau géographique (variable 'TYPE_NIVGEO'), la source (variable 'TABLEAU') et l'identifiant (variable 'VAR_COD') des indicateurs sélectionnés, la fonction __sgf_sfdf__ permet de générer un sf dataframe des territoires avec les indicateurs correspondants, au format large : 144 | 145 | ```{r plot_exemple_data , eval=T , warning=F, message=F} 146 | DEP_pop_1866 <- 147 | sgf_sfdf(TYPE_NIVGEO = "DEP", 148 | SRC ="REC_T25", 149 | LISTE_VAR_COD = c(215, 177, 183)) 150 | 151 | glimpse(DEP_pop_1866) 152 | ``` 153 | 154 | Puis on peut cartographier le tout avec `ggplot` par exemple : 155 | 156 | ```{r plot_exemple_carto, eval=T, echo=T, message=F, warning=F , dpi= 500, fig.height=6} 157 | library(ggplot2) 158 | library(scales) 159 | library(hrbrthemes) 160 | 161 | ggplot() + 162 | geom_sf(data = DEP_pop_1866 %>% 163 | mutate(personnes_sachant_lire_et_ecrire_total_1866_pct = 164 | personnes_sachant_lire_et_ecrire_total_1866 / total_general_de_la_population_total_1866), 165 | aes(fill= personnes_sachant_lire_et_ecrire_total_1866_pct), 166 | color = "grey20", 167 | lwd = 0.15) + 168 | scale_fill_distiller(name = "", palette = "RdBu", 169 | labels = percent_format(accuracy =1), 170 | direction = 1) + 171 | theme_ipsum() + 172 | theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank()) + 173 | labs( 174 | title = "Part de la population sachant lire et écrire", 175 | subtitle = "En 1866, par département", 176 | caption = "Sources : Insee - SGF - ICPSR - F.Salmon - OT" 177 | ) 178 | 179 | ``` 180 | 181 | 182 | 183 | 184 | Autre exemple avec une cartographie d'un indicateur à la maille arrondissement en symboles proportionnels : 185 | 186 | 187 | 188 | ```{r plot_exemple_carto_arr, eval=T, echo=T, message=F, warning=F , dpi= 500, fig.height=8} 189 | 190 | ARR_pop_sexe_1851 <- 191 | sgf_sfdf(TYPE_NIVGEO = "ARR", 192 | SRC ="REC_T01", 193 | LISTE_VAR_COD = c(11,12,13,14)) %>% 194 | mutate(ratio_femmes_hommes_1851 = (total_du_sexe_feminin_1851 / total_du_sexe_masculin_1851) *100 ) 195 | 196 | 197 | 198 | ggplot() + 199 | geom_sf(data = ARR_pop_sexe_1851, fill="grey80", color = "grey90" , lwd = 0.35) + 200 | geom_sf(data = geo_DEP_SGF_histo %>% filter(ANNEE_GEOGRAPHIE == 1826), 201 | fill=NA, color = "grey95", lwd = 0.75 ) + 202 | geom_sf(data = ARR_pop_sexe_1851 %>% st_centroid(of_largest_polygon=T), 203 | aes(fill= ratio_femmes_hommes_1851, 204 | size = total_du_sexe_masculin_1851 + total_du_sexe_feminin_1851 ), 205 | color = "grey40", shape = 21) + 206 | scale_fill_gradientn(colours = c("#1b7837", "white", "#762a83"), 207 | name = "Nombre de femmes\npour 100 hommes", 208 | breaks = c(70,80,90,100,105,110,115), 209 | values = scales::rescale(c(70, 92, 100, 104, 115))) + 210 | scale_size_continuous(labels=function(x) format(x, big.mark = " ", scientific = FALSE), 211 | range = c(1,12), 212 | name = "Population totale") + 213 | guides(fill = guide_legend(reverse=T)) + 214 | theme_ipsum() + 215 | theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank()) + 216 | labs( 217 | title = "Sex-ratio en 1851", 218 | subtitle = "Par arrondissement", 219 | caption = "Sources : Insee - SGF - ICPSR - F.Salmon - OT" 220 | ) + 221 | theme(legend.position = c(0.95,0.5), 222 | axis.line=element_blank(), 223 | axis.title=element_blank(), 224 | axis.text=element_blank() ) 225 | 226 | 227 | 228 | ``` 229 | 230 | 231 | 232 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # SGF 5 | 6 | La SGF (pour *Statistique Générale de la France*) est [l’ancêtre de 7 | l’Insee](https://www.insee.fr/fr/information/1300622), à savoir un 8 | service ministériel chargé de collecter et analyser des statistiques 9 | pour le gouvernement français entre 1833 et 1940. 10 | 11 | Une partie de ces données a été saisie dans les années 1980 par une 12 | équipe de chercheurs franco-américains à l’Inter-University Consortium 13 | for Political and Social Research (ICPSR), situé à Ann Arbor, Michigan, 14 | États-Unis. L’Insee a [publié 15 | ici](https://www.insee.fr/fr/statistiques/2591397) 200 tableurs au 16 | format dbf portant sur les recensements de la population, la démographie 17 | et l’enseignement primaire, entre 1800 et 1925. 18 | 19 | Le package `sgf` regroupe, normalise et documente ces indicateurs 20 | disponibles dans la plupart des cas à l’échelon géographique du 21 | département (des arrondissements voire des chefs-lieux 22 | d’arrondissements dans quelques rares cas). Il permet également de 23 | cartographier ces indicateurs au millésime géographique du département 24 | ou de l’arrondissement correspondant au millésime de la donnée. 25 | 26 | `sgf` est composé de : 27 | 28 | - 2 tables de données (format dataframe) : 29 | - **indicateurs\_SGF** : description des 40 000 indicateurs 30 | disponibles (libellé, identifiant, millésime de la donnée, 31 | échelles géographiques disponibles et millésime de la 32 | géographie) 33 | - **data\_SGF** : table contenant l’ensemble des données en format 34 | long (libellés, identifiants, millésime de la donnée et 35 | millésime de la géographie) 36 | - 2 tables de données géographiques (format sf dataframe) : 37 | - **geo\_DEP\_SGF\_histo** : contours géographiques des 38 | départements (avec leur code et libellé) selon le millésime de 39 | la géographie (1801/1826/1866/1876/1918, cf. "géographie 40 | ci-dessous) 41 | - **geo\_ARR\_SGF\_histo** : contours géographiques des 42 | arrondissements (avec leur code et libellé) selon le millésime 43 | de la géographie (1801/1826/1876, cf. "géographie ci-dessous) 44 | - 1 fonction : 45 | - **sgf\_sfdf** : après avoir identifié les indicateurs de son 46 | choix dans la table **indicateurs\_SGF**, cette fonction permet 47 | de créer un sf dataframe avec les indicateurs en format large 48 | 49 | ## Sources et crédits 50 | 51 | Données statistiques : SGF, ICPSR, Insee, 52 | [insee.fr/fr/information/1300622](https://www.insee.fr/fr/information/1300622) 53 | 54 | Fonds cartographiques : F. Salmon, 2019, 55 | [fondsdecarte.free.fr/](http://fondsdecarte.free.fr/) 56 | 57 | Documentation, géoréférencement : Observatoire des Territoires. 58 | 59 | ## Installation 60 | 61 | Le package `sgf` peut être installé depuis 62 | [github](https://github.com/observatoire-territoires/sgf) via la 63 | commande suivante : 64 | 65 | ``` r 66 | remotes::install_github("observatoire-territoires/sgf") 67 | ``` 68 | 69 | Pour accéder à la documentation décrivant les tables de données, par 70 | exemple celle listant les indicateurs : 71 | 72 | ``` r 73 | ?sgf::indicateurs_SGF 74 | ``` 75 | 76 | ## Géographie 77 | 78 | Les contours des départements de France métropolitaine ont évolué sur la 79 | période que couvre les données de la SGF. Les 5 millésimes de cette 80 | géographie départementale sont disponibles dans la table 81 | `geo_DEP_SGF_histo` (idem pour les contours des arrondissements dans la 82 | table `geo_ARR_SGF_histo`) 83 | : 84 | 85 | 86 | 87 | ## Démo 88 | 89 | La recherche des indicateurs se fait directement dans la table 90 | **indicateurs\_SGF** : 91 | 92 | ``` r 93 | library(sgf) 94 | ``` 95 | 96 | ``` r 97 | View(indicateurs_SGF) 98 | ``` 99 | 100 | Une fois le niveau géographique (variable ‘TYPE\_NIVGEO’), la source 101 | (variable ‘TABLEAU’) et l’identifiant (variable ‘VAR\_COD’) des 102 | indicateurs sélectionnés, la fonction **sgf\_sfdf** permet de générer un 103 | sf dataframe des territoires avec les indicateurs correspondants, au 104 | format large : 105 | 106 | ``` r 107 | DEP_pop_1866 <- 108 | sgf_sfdf(TYPE_NIVGEO = "DEP", 109 | SRC ="REC_T25", 110 | LISTE_VAR_COD = c(215, 177, 183)) 111 | 112 | glimpse(DEP_pop_1866) 113 | #> Observations: 89 114 | #> Variables: 7 115 | #> $ CODGEO "01", "02", "03", ... 116 | #> $ LIBGEO "AIN", "AISNE", "A... 117 | #> $ ANNEE_GEOGRAPHIE 1866, 1866, 1866, ... 118 | #> $ geometry POLYGON (... 119 | #> $ aveugles_total_1866 318, 659, 219, 168... 120 | #> $ personnes_sachant_lire_et_ecrire_total_1866 178797, 324069, 10... 121 | #> $ total_general_de_la_population_total_1866 365895, 561620, 37... 122 | ``` 123 | 124 | Puis on peut cartographier le tout avec `ggplot` par exemple : 125 | 126 | ``` r 127 | library(ggplot2) 128 | library(scales) 129 | library(hrbrthemes) 130 | 131 | ggplot() + 132 | geom_sf(data = DEP_pop_1866 %>% 133 | mutate(personnes_sachant_lire_et_ecrire_total_1866_pct = 134 | personnes_sachant_lire_et_ecrire_total_1866 / total_general_de_la_population_total_1866), 135 | aes(fill= personnes_sachant_lire_et_ecrire_total_1866_pct), 136 | color = "grey20", 137 | lwd = 0.15) + 138 | scale_fill_distiller(name = "", palette = "RdBu", 139 | labels = percent_format(accuracy =1), 140 | direction = 1) + 141 | theme_ipsum() + 142 | theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank()) + 143 | labs( 144 | title = "Part de la population sachant lire et écrire", 145 | subtitle = "En 1866, par département", 146 | caption = "Sources : Insee - SGF - ICPSR - F.Salmon - OT" 147 | ) 148 | ``` 149 | 150 | 151 | 152 | Autre exemple avec une cartographie d’un indicateur à la maille 153 | arrondissement en symboles proportionnels : 154 | 155 | ``` r 156 | 157 | ARR_pop_sexe_1851 <- 158 | sgf_sfdf(TYPE_NIVGEO = "ARR", 159 | SRC ="REC_T01", 160 | LISTE_VAR_COD = c(11,12,13,14)) %>% 161 | mutate(ratio_femmes_hommes_1851 = (total_du_sexe_feminin_1851 / total_du_sexe_masculin_1851) *100 ) 162 | 163 | 164 | 165 | ggplot() + 166 | geom_sf(data = ARR_pop_sexe_1851, fill="grey80", color = "grey90" , lwd = 0.35) + 167 | geom_sf(data = geo_DEP_SGF_histo %>% filter(ANNEE_GEOGRAPHIE == 1826), 168 | fill=NA, color = "grey95", lwd = 0.75 ) + 169 | geom_sf(data = ARR_pop_sexe_1851 %>% st_centroid(of_largest_polygon=T), 170 | aes(fill= ratio_femmes_hommes_1851, 171 | size = total_du_sexe_masculin_1851 + total_du_sexe_feminin_1851 ), 172 | color = "grey40", shape = 21) + 173 | scale_fill_gradientn(colours = c("#1b7837", "white", "#762a83"), 174 | name = "Nombre de femmes\npour 100 hommes", 175 | breaks = c(70,80,90,100,105,110,115), 176 | values = scales::rescale(c(70, 92, 100, 104, 115))) + 177 | scale_size_continuous(labels=function(x) format(x, big.mark = " ", scientific = FALSE), 178 | range = c(1,12), 179 | name = "Population totale") + 180 | guides(fill = guide_legend(reverse=T)) + 181 | theme_ipsum() + 182 | theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank()) + 183 | labs( 184 | title = "Sex-ratio en 1851", 185 | subtitle = "Par arrondissement", 186 | caption = "Sources : Insee - SGF - ICPSR - F.Salmon - OT" 187 | ) + 188 | theme(legend.position = c(0.95,0.5), 189 | axis.line=element_blank(), 190 | axis.title=element_blank(), 191 | axis.text=element_blank() ) 192 | ``` 193 | 194 | 195 | -------------------------------------------------------------------------------- /data-raw/carto_changements_geo_departements.R: -------------------------------------------------------------------------------- 1 | library(scales) 2 | library(hrbrthemes) 3 | 4 | # fonts 5 | library(extrafont) 6 | 7 | library(showtext) 8 | font_add_google("Roboto", "Roboto") 9 | 10 | myFont2 <- "Roboto" 11 | 12 | # liste changements contours départements 13 | 14 | geo_DEP_SGF_histo_modifs <- 15 | geo_DEP_SGF_histo %>% 16 | mutate(modif = case_when(CODGEO %in% '82' & ANNEE_GEOGRAPHIE == 1826 ~ "création", 17 | CODGEO %in% '73' & ANNEE_GEOGRAPHIE == 1866 ~ "création", 18 | CODGEO %in% '74' & ANNEE_GEOGRAPHIE == 1866 ~ "création", 19 | CODGEO %in% '06' & ANNEE_GEOGRAPHIE == 1866 ~ "création", 20 | CODGEO %in% '83' & ANNEE_GEOGRAPHIE == 1866 ~ "modification", 21 | CODGEO %in% '54' & ANNEE_GEOGRAPHIE == 1876 ~ "création", 22 | CODGEO %in% '90' & ANNEE_GEOGRAPHIE == 1876 ~ "création", 23 | CODGEO %in% '88' & ANNEE_GEOGRAPHIE == 1876 ~ "modification", 24 | CODGEO %in% '57' & ANNEE_GEOGRAPHIE == 1918 ~ "création", 25 | CODGEO %in% '67' & ANNEE_GEOGRAPHIE == 1918 ~ "création", 26 | CODGEO %in% '68' & ANNEE_GEOGRAPHIE == 1918 ~ "création")) %>% 27 | # ajout des départements supprimés en 1876 28 | rbind.data.frame(geo_DEP_SGF_histo %>% 29 | filter(ANNEE_GEOGRAPHIE == 1866 & CODGEO %in% c('57','67','68')) %>% 30 | mutate(ANNEE_GEOGRAPHIE = 1876) %>% 31 | mutate(modif = "suppression")) 32 | 33 | 34 | 35 | # choroplèthe 36 | ggplot(data = geo_DEP_SGF_histo %>% 37 | filter(ANNEE_GEOGRAPHIE == 1801)) + 38 | geom_sf(fill = "grey90", 39 | color = "grey70") + 40 | geom_sf_text(aes(label = CODGEO), color = "grey20", size = 3.5, fontface = "bold", family = "Times") + 41 | #guides(fill = guide_legend(reverse=T)) + 42 | theme_ipsum_tw() + 43 | theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank()) + 44 | labs( 45 | title = "Contours des départements de France métropolitaine"#, 46 | #subtitle = "1881", 47 | )+ 48 | theme(legend.position = c(0.95,0.5), 49 | axis.line=element_blank(), 50 | axis.title=element_blank(), 51 | axis.text=element_blank() )+ 52 | coord_sf(crs = st_crs(2154), datum = NA) 53 | 54 | 55 | 56 | # planche de cartes 57 | 58 | ggplot(data = geo_DEP_SGF_histo_modifs ) + 59 | geom_sf(aes(fill = modif), 60 | color = "grey70") + 61 | geom_sf_text(aes(label = CODGEO), color = "grey20", size = 2.5, fontface = "bold") + 62 | #guides(fill = guide_legend(reverse=T)) + 63 | #theme_ipsum() + 64 | scale_fill_manual(name = "Modifications successives\ndepuis 1801", 65 | values =c("green", "orange", "red"), na.translate = F, na.value = "grey") + 66 | facet_wrap(~ ANNEE_GEOGRAPHIE, ncol = 3) + 67 | theme(axis.text = element_blank(), 68 | axis.title = element_blank(), 69 | text = element_text(size = 10,face = "bold"), 70 | strip.text.x = element_text(size = 10, colour = "white"), 71 | strip.background = element_rect(fill="grey20", colour=NA,size=0), 72 | legend.position = c(0.85,0.3), 73 | axis.ticks = element_blank()) + 74 | labs( 75 | title = "Contours des départements de France métropolitaine"#, 76 | #subtitle = "1881", 77 | )+ 78 | coord_sf(crs = st_crs(2154), datum = NA) 79 | -------------------------------------------------------------------------------- /data-raw/clean_data.R: -------------------------------------------------------------------------------- 1 | library(tidyverse) 2 | library(foreign) 3 | library(janitor) 4 | 5 | #### fonction pour import simple d'un dataset 6 | 7 | f_dl_import_dataset <- function(id_theme,name_theme, id_dataset){ 8 | 9 | # dl 10 | # curl_download(paste0("https://www.insee.fr/fr/statistiques/fichier/",id_theme,"/",name_theme,"_",id_dataset,".dbf"), 11 | # paste0("./data/",name_theme,"_",id_dataset,".dbf")) 12 | 13 | # import 14 | data = read.dbf(paste0("./data/",name_theme,"_",id_dataset,".dbf"), as.is = T) %>% 15 | mutate_if(is.integer, as.numeric) 16 | 17 | } 18 | 19 | test <- f_dl_import_dataset(id_theme <- "2653233", 20 | name_theme <- "REC", 21 | id_dataset <- "T02") 22 | 23 | 24 | 25 | #### fonction pour import d'un dataset avec son année de millésime géographique correspondant 26 | 27 | f_dl_import_md_dataset <- function(id_theme,name_theme, id_dataset, annee_recensement, annee_geographie){ 28 | 29 | # dl 30 | # curl_download(paste0("https://www.insee.fr/fr/statistiques/fichier/",id_theme,"/",name_theme,"_",id_dataset,".dbf"), 31 | # paste0("./data/",name_theme,"_",id_dataset,".dbf")) 32 | # 33 | # import 34 | data = read.dbf(paste0("./data_source/",name_theme,"_",id_dataset,".dbf"), as.is = T) %>% 35 | clean_names(case = "upper_camel") %>% 36 | mutate_if(is.integer, as.numeric) %>% 37 | mutate(ANNEE_GEOGRAPHIE = annee_geographie) %>% 38 | mutate_at(.vars = vars(-c(V1,V3,V4,V5,V6)), as.numeric) 39 | 40 | # long format 41 | data_lg <- 42 | data %>% 43 | gather(VAR,VAL, -c(V1,V3,V4,V5,V6,ANNEE_GEOGRAPHIE)) 44 | 45 | # récup noms variables 46 | data_lg <- 47 | data_lg %>% 48 | left_join(liste_variables %>% 49 | filter(TABLEAU %in% c("Tous les tableaux",paste0(name_theme,"_", id_dataset) )) %>% 50 | select(VAR,VAR_LIB = LIB, ANNEE_DONNEE), 51 | by = "VAR") %>% 52 | rename(VAR_COD = VAR) 53 | 54 | # nettoyage nivgeo 55 | data_lg <- 56 | data_lg %>% 57 | mutate(NIVGEO = case_when(V1 %in% "0" ~ "FR", 58 | V1 %in% "1" ~ "DEP", 59 | V1 %in% "2" ~ "ARR", 60 | V1 %in% "3" ~ "CHL", 61 | V1 %in% "4" ~ "VIL", 62 | V3 %in% '00' & V4 %in% '00' & V5 %in% '00' ~ "FR", 63 | !V3 %in% '00' & V4 %in% '00' & V5 %in% '00' ~ "DEP", 64 | !V3 %in% '00' & !V4 %in% '00' & V5 %in% '00' ~ "ARR", 65 | !V3 %in% '00' & !V4 %in% '00' & !V5 %in% '00' ~ "CHL")) %>% 66 | mutate(CODGEO = case_when(NIVGEO %in% "FR" ~ "FR", 67 | NIVGEO %in% "DEP" ~ V3, 68 | NIVGEO %in% "ARR" ~ paste0(V3,V4), 69 | NIVGEO %in% "CHL" ~ paste0(V3,V5), 70 | NIVGEO %in% "VIL" ~ paste0(V3,V5))) %>% 71 | rename(LIBGEO = V6) %>% 72 | select(-c(V1:V5)) %>% 73 | mutate(SRC_DATA = paste0(name_theme,"_",id_dataset)) %>% 74 | select(NIVGEO, CODGEO, LIBGEO,SRC_DATA, VAR_COD, VAR_LIB, ANNEE_DONNEE,ANNEE_GEOGRAPHIE, VAL) 75 | 76 | } 77 | 78 | 79 | 80 | ######### 81 | library(readxl) 82 | 83 | liste_datasets <- read_xlsx("./data_source/liste_datasets.xlsx", sheet = "REC") %>% 84 | rbind.data.frame(read_xlsx("./data_source/liste_datasets.xlsx", sheet = "MVTPOP")) %>% 85 | rbind.data.frame(read_xlsx("./data_source/liste_datasets.xlsx", sheet = "TERR")) %>% 86 | rbind.data.frame(read_xlsx("./data_source/liste_datasets.xlsx", sheet = "ENSP")) 87 | 88 | 89 | liste_variables <- read_xlsx("./data_source/liste_variables.xlsx", sheet = "REC") %>% 90 | rbind.data.frame(read_xlsx("./data_source/liste_variables.xlsx", sheet = "MVTPOP")) %>% 91 | rbind.data.frame(read_xlsx("./data_source/liste_variables.xlsx", sheet = "TERR")) %>% 92 | rbind.data.frame(read_xlsx("./data_source/liste_variables.xlsx", sheet = "ENSP")) 93 | 94 | 95 | indicateurs_SGF <- liste_variables %>% 96 | left_join(liste_datasets %>% 97 | mutate(TABLEAU = paste0(name_theme,"_",id_dataset)) %>% 98 | select(TABLEAU, 99 | ECHELLES_GEO = type_geo, 100 | NOM_TABLEAU = label_file, 101 | ANNEE_GEOGRAPHIE = annee_geographie_DEP), 102 | by = "TABLEAU") %>% 103 | filter(!TABLEAU %in% 'Tous les tableaux') %>% 104 | select(TABLEAU, NOM_TABLEAU, VAR_COD = VAR_N, VAR_LIB = LIB, ANNEE_DONNEE, ECHELLES_GEO, ANNEE_GEOGRAPHIE) 105 | 106 | # imports et aggrégation de tous les datasets du thème REC 107 | 108 | 109 | argList <- list(liste_datasets$id_theme, 110 | liste_datasets$name_theme, 111 | liste_datasets$id_dataset, 112 | liste_datasets$annee_recensement, 113 | liste_datasets$annee_geographie_DEP) 114 | 115 | data_SGF <- pmap_dfr(argList, 116 | f_dl_import_md_dataset) %>% 117 | mutate(VAR_COD = as.numeric(str_replace_all(VAR_COD, "V",""))) 118 | 119 | 120 | # # Save an object to a file 121 | # saveRDS(data_SGF, file = "./data/data_SGF.rds") 122 | # # Restore the object 123 | # data_SGF <- readRDS(file = "./data/data_SGF.rds") 124 | 125 | 126 | 127 | 128 | 129 | ################################ 130 | #### modifs pour intégration data arrondissements 131 | ################################ 132 | 133 | # modifs année géographie 134 | indicateurs_SGF_NEW <- 135 | indicateurs_SGF %>% 136 | mutate(ANNEE_GEOGRAPHIE = case_when(TABLEAU %in% 'MVTPOP_T91' ~ 1826, TRUE ~ ANNEE_GEOGRAPHIE)) 137 | 138 | 139 | 140 | # modifs année géographie 141 | data_SGF_NEW <- data_SGF %>% 142 | mutate(NIVGEO = case_when(SRC_DATA %in% 'MVTPOP_T91' ~ "ARR", TRUE ~ as.character(NIVGEO))) %>% 143 | mutate(ANNEE_GEOGRAPHIE = case_when(SRC_DATA %in% 'MVTPOP_T91' ~ 1826, TRUE ~ ANNEE_GEOGRAPHIE)) 144 | 145 | # corrections arrondissements PARIS 146 | data_SGF_NEW <- data_SGF_NEW %>% 147 | ungroup() %>% 148 | mutate(CODGEO_NEW = case_when(substr(CODGEO,1,3) %in% c('754','755','756') ~ "7501", TRUE ~ as.character(CODGEO))) %>% 149 | group_by(NIVGEO, CODGEO_NEW , SRC_DATA ,VAR_COD , VAR_LIB , ANNEE_DONNEE , ANNEE_GEOGRAPHIE) %>% 150 | summarise(LIBGEO = first(LIBGEO), 151 | VAL = sum(VAL, na.rm = T)) %>% 152 | select("NIVGEO", CODGEO = "CODGEO_NEW" , "LIBGEO" , "SRC_DATA" ,"VAR_COD" , "VAR_LIB" , "ANNEE_DONNEE" , "ANNEE_GEOGRAPHIE", "VAL") %>% 153 | mutate(LIBGEO = case_when(LIBGEO %in% 'LOUVRE' ~ "PARIS", TRUE ~ as.character(LIBGEO))) 154 | 155 | 156 | data_SGF <- data_SGF_NEW 157 | data_SGF <- data_SGF %>% ungroup() %>% as.data.frame() 158 | 159 | 160 | ## rectifs manuelles indicateurs_SGF 161 | # suppr data/indicateurs_sgf.rda 162 | indicateurs_SGF <- indicateurs_SGF %>% 163 | mutate(ECHELLES_GEO = case_when(TABLEAU %in% "MVTPOP_T76" ~ "CHL_VILLE", 164 | TRUE ~ as.character(ECHELLES_GEO))) 165 | usethis::use_data( indicateurs_SGF, overwrite = T) 166 | 167 | 168 | date_SGF <- data_SGF -------------------------------------------------------------------------------- /data-raw/crea_carto.R: -------------------------------------------------------------------------------- 1 | library(tidyverse) 2 | library(sf) 3 | library(rmapshaper) 4 | 5 | # nettoyage puis création des mailles DEP 6 | # par millésime 7 | 8 | # 1801 9 | geo_ARR_1801 <- st_read("./geo_source/arrondissements1801_S.shp") %>% 10 | st_transform(2154) %>% 11 | mutate(CODGEO = str_pad(IDEN, side = "left", width = 4, pad = "0")) %>% 12 | select(CODGEO) 13 | 14 | geo_DEP_1801 <- geo_ARR_1801 %>% 15 | mutate(CODGEO = substr(CODGEO,1,2)) %>% 16 | group_by(CODGEO) %>% 17 | summarise() 18 | 19 | # 1826 20 | geo_ARR_1826 <- st_read("./geo_source/arrondissements1826_S.shp") %>% 21 | st_transform(2154) %>% 22 | mutate(CODGEO = str_pad(IDEN, side = "left", width = 4, pad = "0")) %>% 23 | select(CODGEO) 24 | 25 | geo_DEP_1826 <- geo_ARR_1826 %>% 26 | mutate(CODGEO = substr(CODGEO,1,2)) %>% 27 | group_by(CODGEO) %>% 28 | summarise() 29 | 30 | # 1866 31 | geo_DEP_1866 <- geo_DEP_1876 %>% 32 | filter(!CODGEO %in% c('90','57','54','88')) %>% 33 | rbind(geo_DEP_1826 %>% filter(CODGEO %in% c('57', '99','67','68','88'))) 34 | 35 | 36 | # 1876 37 | geo_ARR_1876 <- st_read("./geo_source/arrondissements1876_S.shp") %>% 38 | st_transform(2154) %>% 39 | mutate(CODGEO = str_pad(IDEN, side = "left", width = 4, pad = "0")) %>% 40 | select(CODGEO) 41 | 42 | geo_DEP_1876 <- geo_ARR_1876 %>% 43 | mutate(CODGEO = substr(CODGEO,1,2)) %>% 44 | group_by(CODGEO) %>% 45 | summarise() 46 | 47 | 48 | # 1918 49 | 50 | geo_DEP_1918 <- st_read("https://raw.githubusercontent.com/gregoiredavid/france-geojson/master/departements.geojson") %>% 51 | rename(CODGEO = code, LIBGEO = nom) %>% 52 | st_transform(2154) %>% 53 | mutate(CODGEO = case_when(CODGEO %in% c('2A','2B') ~ '20', 54 | #CODGEO %in% c('90') ~ '68', 55 | CODGEO %in% c('75','92','93','94') ~ '75', 56 | CODGEO %in% c('95','78','91') ~ '78', 57 | TRUE ~ as.character(CODGEO))) %>% 58 | group_by(CODGEO) %>% 59 | summarise() %>% 60 | ms_simplify(keep = 0.01) 61 | 62 | 63 | 64 | ################# 65 | ## fichier unique de l'historique des geometries des départements 66 | 67 | geo_DEP_SGF_histo <- 68 | geo_DEP_1801 %>% mutate(ANNEE_GEOGRAPHIE = 1801) %>% 69 | rbind.data.frame(geo_DEP_1826 %>% mutate(ANNEE_GEOGRAPHIE = 1826)) %>% 70 | rbind.data.frame(geo_DEP_1866 %>% mutate(ANNEE_GEOGRAPHIE = 1866)) %>% 71 | rbind.data.frame(geo_DEP_1876 %>% mutate(ANNEE_GEOGRAPHIE = 1876)) %>% 72 | rbind.data.frame(geo_DEP_1918 %>% mutate(ANNEE_GEOGRAPHIE = 1918)) 73 | 74 | # libellés départements 75 | 76 | ref_lib_dep_1 <- data_SGF %>% filter(NIVGEO %in% 'DEP') %>% filter(SRC_DATA %in% 'REC_T01') %>% filter(VAR_COD %in% 10) %>% 77 | select(CODGEO, LIBGEO) %>% 78 | mutate(LIBGEO = case_when(CODGEO %in% '04' ~ "BASSES-ALPES", 79 | CODGEO %in% '05' ~ "HAUTES-ALPES", 80 | CODGEO %in% '31' ~ "HAUTE-GARONNE", 81 | CODGEO %in% '43' ~ "HAUTE-LOIRE", 82 | CODGEO %in% '52' ~ "HAUTE-MARNE", 83 | CODGEO %in% '64' ~ "BASSES-PYRENEES", 84 | CODGEO %in% '65' ~ "HAUTES-PYRENEES", 85 | CODGEO %in% '67' ~ "BAS-RHIN", 86 | CODGEO %in% '68' ~ "HAUT-RHIN", 87 | CODGEO %in% '70' ~ "HAUTE-SAONE", 88 | CODGEO %in% '74' ~ "HAUTE-SAVOIE", 89 | CODGEO %in% '79' ~ "DEUX-SEVRES", 90 | CODGEO %in% '87' ~ "HAUTE-VIENNE", 91 | TRUE ~ as.character(LIBGEO))) 92 | 93 | 94 | geo_DEP_SGF_histo <- 95 | geo_DEP_SGF_histo %>% 96 | left_join(ref_lib_dep_1, by = "CODGEO") %>% 97 | mutate(LIBGEO = case_when(CODGEO %in% '54' ~ "MEURTHE-ET-MOSELLE", 98 | CODGEO %in% '90' ~ "BELFORT", 99 | TRUE ~ as.character(LIBGEO))) %>% 100 | select(CODGEO, LIBGEO, ANNEE_GEOGRAPHIE, geometry) 101 | 102 | 103 | class(geo_DEP_SGF_histo) <- c("sf", "data.frame") 104 | 105 | # save carto deps 106 | #saveRDS(geo_DEP_SGF_histo, file = "./geo/geo_DEP_SGF_histo.rds") 107 | 108 | 109 | ############################## 110 | #### fonds ARRONDISSEMENTS 111 | ############################# 112 | 113 | 114 | geo_ARR_SGF_histo <- 115 | geo_ARR_1801 %>% mutate(ANNEE_GEOGRAPHIE = 1801) %>% 116 | rbind.data.frame(geo_ARR_1826 %>% mutate(ANNEE_GEOGRAPHIE = 1826)) %>% 117 | rbind.data.frame(geo_ARR_1876 %>% mutate(ANNEE_GEOGRAPHIE = 1876)) 118 | 119 | 120 | # correction des arrondissements avec iles 121 | 122 | geo_ARR_SGF_histo <- geo_ARR_SGF_histo %>% group_by(CODGEO, LIBGEO, ANNEE_GEOGRAPHIE) %>% summarise() 123 | 124 | 125 | # rectification du code et de l'arrondissement de Toul en 1876 126 | 127 | geo_ARR_SGF_histo_2 <- geo_ARR_SGF_histo 128 | class(geo_ARR_SGF_histo_2) <- c('sf','data.frame') 129 | 130 | geo_ARR_SGF_histo_2 <-geo_ARR_SGF_histo_2 %>% 131 | ungroup() %>% 132 | mutate(CODGEO = case_when(CODGEO %in% '5404' & ANNEE_GEOGRAPHIE %in% 1876 ~ "5405", 133 | TRUE ~ as.character(CODGEO))) %>% 134 | mutate(LIBGEO = case_when(CODGEO %in% '5405' & ANNEE_GEOGRAPHIE == 1876 ~ "TOUL", 135 | TRUE ~ as.character(LIBGEO))) 136 | 137 | # changement de format DEP sf 138 | geo_DEP_SGF_histo_2 <- geo_DEP_SGF_histo 139 | class(geo_DEP_SGF_histo_2) <- c('sf','data.frame') 140 | 141 | # libellés départements 142 | 143 | ref_lib_arr_1 <- data_SGF %>% 144 | filter(NIVGEO %in% 'ARR') %>% 145 | # filter(SRC_DATA %in% 'REC_T01') %>% 146 | # filter(VAR_COD %in% 10) %>% 147 | distinct(CODGEO, LIBGEO) %>% 148 | filter(!LIBGEO %in% c('VILLEFR-DE-ROUERG','TARASCON',"PONT L'EVEQUE", "CHATILLON-SUR-SEI", 149 | "NOGENT LE ROTROU","VILLEFRANC DE LAU","ISSODUN","SAVENAY","VILLENEUV-SUR-LOT", 150 | "SCHELESTADT","GUEBWILLER","VILLE-FR-SUR-SAON", "VILLEFR-SUR-SAONE", 151 | "SAINT-JEAN-DE-MAU","ST-JEAN-DE-MAUR","SAINT-JULLIEN","NEUFCHATEL-EN-BRA", 152 | "LAROCHE-SUR-YON","LES SABLES-D'OLONNE","MOULINS-SUR-ALLIE") ) %>% 153 | mutate(LIBGEO = case_when(CODGEO %in% '6802' ~ 'BELFORT', TRUE ~ as.character(LIBGEO))) %>% 154 | filter(!substr(CODGEO,1,3) %in% c('754','755','756')) %>% 155 | distinct(CODGEO, LIBGEO) 156 | 157 | 158 | 159 | 160 | geo_ARR_SGF_histo <- 161 | geo_ARR_SGF_histo %>% 162 | left_join(ref_lib_arr_1, by = "CODGEO") %>% 163 | select(CODGEO, LIBGEO, ANNEE_GEOGRAPHIE, geometry) 164 | 165 | 166 | class(geo_ARR_SGF_histo) <- c("sf", "data.frame") 167 | -------------------------------------------------------------------------------- /data-raw/script_dev.R: -------------------------------------------------------------------------------- 1 | library(usethis) 2 | library(devtools) 3 | 4 | #https://speakerdeck.com/colinfay/building-a-package-that-lasts-erum-2018-workshop 5 | #https://www.hvitfeldt.me/blog/usethis-workflow-for-package-development/ 6 | 7 | unlink("DESCRIPTION") 8 | my_desc <- desc::description$new("!new") 9 | my_desc$set("Package","sgf") 10 | my_desc$set("Title","sgf") 11 | my_desc$set("Authors@R", "person('Observatoire des Territoires',email='observatoire@☺cget.gouv.fr', role=c('cre','aut'))") 12 | #my_desc$set_authors() 13 | my_desc$del("Maintainer") 14 | my_desc$set_version("0.0.0.9000") 15 | my_desc$set(Description = "Old French statistics") 16 | my_desc$set("URL","https://github.com/observatoire-territoires/sgf") 17 | my_desc$set("BugReports","https://github.com/observatoire-territoires/sgf/issues") 18 | 19 | #use_gpl3_license() 20 | use_mit_license(name = "Insee - SGF - ICPSR - Observatoire des territoires") 21 | 22 | # save 23 | my_desc$write(file = "DESCRIPTION") 24 | 25 | # A FINIR 26 | use_travis() 27 | 28 | # ✔ Setting active project to 'C:/Users/mgarnier/Documents/diffusion_donnees/sgf' 29 | # ✔ Writing '.travis.yml' 30 | # ✔ Adding '^\\.travis\\.yml$' to '.Rbuildignore' 31 | # ● Turn on travis for your repo at https://travis-ci.org/profile/observatoire-territoires 32 | # ● Copy and paste the following lines into 'C:/Users/mgarnier/Documents/diffusion_donnees/sgf/README.Rmd': 33 | # 34 | # [![Travis build status](https://travis-ci.org/observatoire-territoires/sgf.svg?branch=master)](https://travis-ci.org/observatoire-territoires/sgf) 35 | # 36 | # [Copied to clipboard] 37 | 38 | # data-raw dir 39 | devtools::use_data_raw() 40 | 41 | # sauvegarde des dataframes dans dossier data 42 | usethis::use_data(geo_DEP_SGF_histo, indicateurs_SGF, data_SGF, overwrite = T) 43 | usethis::use_data( indicateurs_SGF, overwrite = T) 44 | usethis::use_data( geo_DEP_SGF_histo, overwrite = T) 45 | 46 | # rectifs des tables 47 | 48 | usethis::use_data( indicateurs_SGF, data_SGF, overwrite = T) 49 | 50 | usethis::use_data( data_SGF, overwrite = T) 51 | 52 | usethis::use_data( geo_ARR_SGF_histo, overwrite = T) 53 | 54 | 55 | use_news_md() 56 | 57 | use_version() 58 | 59 | # fonction 60 | use_r("sgf_sfdf") 61 | 62 | # documentation des datasets 63 | use_r("data") 64 | 65 | # dependances 66 | use_package("dplyr") 67 | use_package("dplyr") 68 | use_package("tidyr") 69 | use_package("janitor") 70 | use_package("sf") 71 | 72 | 73 | # doc 74 | 75 | library(pkgdown) 76 | library(roxygen2) 77 | 78 | #générer doc .Rd 79 | roxygen2::roxygenise() 80 | 81 | # vérification de l'intégrité du package 82 | 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-------------------------------------------------------------------------------- /data_source/MVTPOP_T119.dbf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/data_source/MVTPOP_T119.dbf -------------------------------------------------------------------------------- /data_source/MVTPOP_T120.dbf: -------------------------------------------------------------------------------- 1 |  2 |  Z! NV6CV1CV3CV4CV5CV7NV8NV9NV10NV11NV12NV13NV14NV15NV16NV17NV18NV19NV20NV21NV22NV23NV24NV25NV26NV27NV28NV29NV30NV31NV32NV33NV34NV35NV36NV37NV38NV39NV40NV41NV42NV43NV44NV45NV46NV47NV48NV49NV50NV51NV52NV53NV54NV55NV56NV57NV58NV59NV60NV61NV62NV63NV64NV65NV66NV67NV68NV69NV70NV71NV72NV73N FRANCE 00000001.6538 16.3631.7648.9217.1635.0311.01104563111352756611117461921370957311105859301372263680.380.360.000.0263342418875250361764680042851486577140571374543149281760668853225.26.47 6.3028.5024.833.6735.3237.5011.6820.661.853.08 7.564.36105.12.282.951.116.8222.0717.9330.412.336.4263.226.951.655859.9 AIN 10100001.6666 22.2821.6747.8326.1634.76 8.6 103 23 297 26 90 23 303 26 91 22 306 260.200.100.000.10 5 2 7 2 0 2 2 0 2 2 5 7 3 2 554025.05.93 7.0729.0024.334.6732.7541.4210.9518.871.182.87 4.854.02102.42.212.461.167.4820.2217.2741.911.038.9262.4 1.701.666270.4 AISNE 10200001.6584 18.4823.7134.0710.3634.7621.4 102 144 210 70 108 135 219 67 110 134 224 700.200.010.000.19 10 1 11 12 1 13 19 17 36 8 6 14 12 6 1868729.19.01 8.4125.7522.503.2521.3840.5011.3616.353.042.7810.024.83105.32.252.551.106.5426.2821.9635.323.038.5862.4 1.981.658862.9 ALLIER 10300001.6472 11.7470.2881.6311.3531.89 4.5 106 23 127 36 102 23 125 35 104 27 129 360.891.110.220.00 0 6 6 0 1 1 5 0 5 1 2 3 2 2 477821.29.70 9.8427.0823.084.0063.3434.4212.6620.211.442.83 4.603.64105.92.012.481.126.2515.5014.2426.5 6.033.0066.9 5.761.633261.5 ALPES(BASSES-) 10400001.6476 12.6028.8445.4816.6431.4812.4 54 35 21 20 56 35 19 21 53 35 21 210.040.460.000.42 22 4 26 14 3 17 25 7 32 53 1 54 28 4 3256724.96.10 7.7928.1723.584.5839.9937.3312.5921.700.513.20 1.734.35103.42.723.791.157.7123.2027.7833.815.735.1755.2 2.511.652853.9 ALPES(HAUTES-) 10500001.6452 10.2510.1521.7311.5832.44 8.6 20 12 24 9 21 13 22 9 20 13 22 90.010.480.000.49 34 7 41 17 10 27 22 17 39 17 4 21 23 9 3243624.24.99 5.2928.5024.833.6719.6833.8313.4127.700.654.08 2.574.45104.32.943.971.458.4927.1223.1530.110.731.3355.5 0.851.644243.9 ALPES-MARITIMES 1060000 0.210.440.230.00 25 7 32 36 9 45 64 11 75 32 11 43 39 10 4948826.55.69 6.0028.6724.084.5848.9836.0812.1222.401.113.55 4.425.31105.72.563.491.347.0020.2118.1038.1 9.234.08 3.751.656466.2 ARDECHE 10700001.6526 15.6145.5963.5417.9529.94 4.7 76 14 75 24 78 15 74 24 69 19 72 280.480.070.000.55 36 9 45 3 8 11 0 11 11 11 5 16 13 8 2160623.66.12 5.6729.5825.424.1746.7331.8313.9528.510.723.94 2.711.59104.72.593.731.276.3226.2124.6927.6 6.029.9261.0 3.701.647866.0 ARDENNES 10800001.6596 18.9012.2421.11 8.8737.6212.3 63 47 109 37 64 45 116 37 61 47 111 370.430.140.000.57 0 17 17 0 2 2 0 20 20 0 20 20 0 15 1557528.07.04 7.3027.4224.502.92 8.8642.3310.8317.651.572.83 6.164.46103.01.932.150.896.0117.0614.7634.916.242.0861.9 0.911.663044.0 ARIEGE 10900001.6518 13.8257.0167.8210.8135.16 2.0 63 45 141 41 64 44 141 41 65 45 170 410.600.110.000.71 84 50 134 23 41 64 2 32 34 14 28 42 31 38 6949724.26.55 5.9328.6724.833.8370.1935.9211.9522.701.133.45 4.732.91106.02.192.900.976.5514.4712.9925.5 4.035.0059.3 5.451.645858.8 AUBE 11000001.6576 21.52 9.3725.2415.8739.2317.7 81 59 141 31 82 60 143 31 77 58 138 310.060.060.000.00 20 3 23 6 8 14 6 11 17 5 19 24 9 10 1959832.87.61 7.7927.0823.253.8311.4849.08 9.0913.441.622.51 6.544.46107.12.102.900.776.1030.3425.6830.919.342.4266.3 0.601.671866.6 AUDE 11100001.6444 11.4131.4053.7222.3234.36 5.3 122 51 133 69 131 50 134 67 123 49 136 720.370.350.000.02 1 70 71 0 41 41 0 46 46 0 27 27 0 46 4666527.69.06 9.0827.1723.253.9250.9537.7511.7619.651.123.14 4.062.93106.02.293.150.957.9717.7314.8624.9 5.335.8358.7 2.861.644657.6 AVEYRON 11200001.6468 13.6130.4852.5622.0834.87 2.3 66 20 74 72 164 19 72 71 159 18 78 700.810.200.000.61 40 7 47 50 10 60 12 4 16 3 0 3 26 5 3149722.06.08 5.7230.5025.584.9245.5932.5013.9428.870.953.68 3.633.27105.92.363.260.986.7620.8616.7226.1 2.734.0864.2 2.371.648657.6 BOUCHES-DU-RHONE 11300001.6552 13.6631.4851.1919.7130.4710.4 247 122 198 119 256 134 217 126 275 110 218 1300.181.611.430.00 222 374 596 236 57 293 240 41 281 318 57 375 257 132 38946225.34.57 5.6929.5824.674.9238.8532.8314.6327.012.713.59 9.455.93105.22.864.921.385.5826.7514.7931.313.228.7556.6 2.371.658658.7 CALVADOS 11400001.6526 16.0017.2528.6211.3744.79 9.6 143 107 150 139 144 93 137 130 143 90 137 1350.270.250.020.00 7 0 7 9 0 9 8 0 8 3 2 5 7 0 746428.86.62 5.4228.6725.083.5810.7449.83 8.9715.581.852.46 9.924.20103.12.292.431.086.7819.6817.1638.010.945.4271.3 1.451.653258.8 CANTAL 11500001.6428 12.9533.5649.8116.2539.23 2.7 106 16 28 35 110 14 26 36 108 12 34 270.280.210.000.49 205 5 210 207 5 212 22 0 22 3 0 3 109 3 11244122.66.56 4.0629.1726.083.0836.3638.7511.0224.971.143.18 6.022.22104.02.312.591.077.6721.6717.8020.4 3.739.9265.0 2.171.650650.7 CHARENTE 11600001.6350 8.8837.5061.5024.0041.1711.5 121 55 95 40 121 64 120 43 125 65 110 480.110.050.000.16 40 6 46 61 11 72 15 12 27 16 11 27 33 10 4366627.68.14 8.9127.0824.252.8347.2545.75 9.4414.560.922.46 3.683.45106.22.082.371.056.9518.2114.8326.213.441.0070.9 3.281.644059.7 CHARENTE-INFERIEURE 11700001.6512 16.5230.4453.3822.9439.6811.0 153 60 173 63 156 55 181 64 155 47 195 630.200.060.000.26 22 6 28 22 8 30 10 13 23 12 5 17 17 8 2563128.57.65 7.6727.0823.923.1742.7445.4210.0715.880.912.57 3.503.97105.42.002.450.935.8822.2417.4227.013.741.0066.5 2.381.648859.1 CHER 11800001.6462 11.6362.5779.3116.7429.25 7.3 83 15 95 35 79 14 95 35 82 13 96 361.090.820.000.27 0 0 0 8 4 12 6 0 6 3 9 12 4 3 769023.28.47 8.5227.4223.084.3364.7633.4213.5922.022.013.27 6.232.60106.01.912.221.046.4416.9414.7730.9 8.233.3363.7 5.491.650254.3 CORREZE 11900001.6312 8.7165.9481.6215.6830.53 4.2 144 29 101 17 151 23 97 31 134 23 105 310.630.050.000.58 30 24 54 32 10 42 5 9 14 6 5 11 18 12 3063221.38.09 7.0927.6724.423.2567.1832.3313.4324.921.443.32 5.232.14109.42.472.871.527.2920.1216.2418.3 4.332.7562.7 5.901.659849.9 CORSE 12000001.6514 12.4338.0044.85 6.8531.50 1.4 34 195 1 65 33 194 3 67 37 171 9 580.590.550.000.04 0 20 20 0 56 56 0 25 25 0 34 34 0 34 3440123.24.75 4.5129.8325.254.5857.6233.7512.5925.991.764.08 7.721.61105.92.383.051.495.7219.6718.0820.5 2.429.1764.1 3.331.650870.5 COTE-D'OR 12100001.6686 24.9410.9129.3418.4337.7311.9 175 71 357 51 163 68 346 49 168 68 340 520.060.070.000.13 48 26 74 9 7 16 17 19 36 12 6 18 21 15 3653929.76.82 6.8928.5024.084.42 9.7246.3310.1216.621.492.67 6.344.06104.82.092.640.895.8526.8820.9127.313.941.2564.0 0.601.669660.5 COTES-DU-NORD 12200001.6408 10.0660.8374.0113.1833.70 5.1 78 73 65 35 79 72 66 35 83 69 67 350.670.400.000.27 53 2 55 3 0 3 3 0 3 0 1 1 15 1 1641821.45.40 4.1430.2527.173.0866.7332.4213.3833.020.804.04 3.645.53105.42.422.931.287.4719.5816.8027.2 4.534.4261.8 5.601.648665.8 CREUSE 12300001.6502 15.0442.5367.2124.6836.14 4.5 82 31 56 20 80 27 55 19 79 21 54 190.600.300.000.30 0 0 0 11 0 11 0 0 0 0 0 0 3 0 369922.79.96 6.5926.7523.922.8354.8740.7510.0618.141.382.66 6.592.20107.71.851.741.007.2811.1110.8716.2 4.840.9266.6 2.721.655259.3 DORDOGNE 12400001.6394 9.5359.6577.5217.8735.26 7.6 171 91 124 57 162 94 130 53 167 88 128 600.340.040.000.30 0 10 10 0 0 0 0 0 0 0 0 0 0 2 266724.88.57 8.2228.0024.253.7569.8037.3311.7319.271.102.93 3.883.31107.82.343.021.127.1018.6715.4921.3 8.836.5061.4 5.051.635451.0 DOUBS 12500001.6734 26.76 3.7814.6510.8737.13 9.2 93 64 214 34 90 63 246 32 89 63 258 310.470.120.000.35 145 15 160 41 10 51 27 3 30 23 1 24 59 7 6642523.14.75 4.7330.0827.172.92 4.5437.1712.1725.892.023.29 9.524.89105.32.222.361.167.2719.4316.5740.710.038.5866.3 0.301.668867.0 DROME 12600001.6484 15.4227.3954.4327.0438.5213.4 76 43 136 36 99 38 145 48 105 34 134 500.190.150.000.34 12 50 62 11 12 23 5 36 41 8 31 39 9 32 4151227.65.79 6.2028.5023.924.5828.1438.8311.3119.911.373.21 5.644.05103.62.393.221.067.7926.2019.7838.113.036.8362.8 2.111.649057.6 EURE 12700001.6586 20.2626.4842.7216.2442.1616.2 87 76 67 108 101 75 73 124 88 72 67 1060.320.210.110.00 13 2 15 7 0 7 1 4 5 17 0 17 10 1 1160432.28.33 8.4826.9223.253.6716.4150.33 9.5213.452.362.34 8.794.07104.72.313.010.886.5326.1625.6043.317.943.5867.8 2.081.661850.2 EURE-ET-LOIR 12800001.6614 19.8120.1735.4615.2935.8316.7 63 38 127 33 66 36 131 33 68 35 133 330.000.020.020.00 17 0 17 16 0 16 15 0 15 5 1 6 13 0 1362930.48.49 7.9926.5823.173.4216.6542.8310.7116.131.482.90 5.113.52104.72.333.400.876.1227.5329.8740.321.736.0866.3 1.481.657265.5 FINISTERE 12900001.6402 7.8562.8180.1317.3229.51 7.9 97 32 82 36 79 25 70 39 80 26 62 390.901.120.220.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 052518.76.22 5.7828.2525.253.0066.9828.8315.0234.290.803.88 3.064.75105.22.563.351.517.1118.4215.7732.9 9.328.7560.5 5.751.639259.7 GARD 13000001.6454 15.0626.0542.6816.6329.56 8.6 154 88 195 93 155 82 195 98 169 90 197 920.260.360.100.00 16 20 36 26 1 27 24 7 31 36 13 49 26 10 3654927.76.78 7.2427.5023.174.3338.5633.0013.3022.710.843.84 2.713.38105.32.764.671.106.9028.9122.2133.5 8.929.8354.2 2.181.646856.5 GARONNE(HAUTE-) 13100001.6534 14.1536.3860.0223.6438.80 4.3 193 165 250 145 193 158 246 140 192 167 247 1370.200.500.300.00 340 40 380 271 25 296 162 39 201 165 52 217 235 39 27446428.15.79 5.5128.5824.753.8349.5146.58 8.8816.011.312.65 7.174.60104.91.952.480.867.0924.0814.0523.2 4.841.3366.1 3.011.648867.8 GERS 13200001.6490 14.4233.9654.4020.4444.92 4.1 195 210 214 94 121 211 201 95 124 205 201 900.110.220.000.11 105 46 151 37 12 49 32 22 54 30 10 40 51 22 7355427.16.68 7.5129.5024.425.0847.7752.25 8.6614.780.922.16 4.723.23107.62.022.280.867.2016.8114.5220.2 5.046.2570.2 2.581.647652.9 GIRONDE 13300001.6456 11.0532.1148.2116.1041.13 8.7 285 190 331 156 287 178 334 144 286 173 310 1600.181.040.860.00 412 26 438 376 10 386 377 2 379 328 2 330 373 10 38366726.38.51 9.3528.3323.335.0034.4243.75 9.9614.042.932.3010.794.49106.52.102.451.037.4419.9816.5529.5 9.841.3366.9 2.651.644456.5 HERAULT 13400001.6552 16.3125.7744.0018.2329.71 6.0 262 101 194 116 271 84 184 111 262 94 197 680.350.870.520.00 51 20 71 52 41 93 22 25 47 10 30 40 34 29 6353526.06.66 6.8227.0823.583.5040.6836.6711.5821.351.003.09 4.164.69106.32.374.501.025.4727.5816.7627.3 6.530.9259.1 2.431.648058.5 ILLE-ET-VILAINE 13500001.6430 12.7845.2263.0617.8434.33 4.5 105 131 38 65 105 124 34 64 108 112 37 650.370.260.000.11 30 10 40 15 0 15 16 0 16 3 9 12 16 5 2144021.95.28 4.5930.6727.832.8351.5436.6711.7828.230.623.45 3.155.80103.92.352.991.147.3720.4018.1639.8 6.435.7563.7 3.671.641249.4 INDRE 13600001.6524 15.0462.5078.5716.0730.76 6.9 91 42 78 39 62 30 45 22 79 33 77 360.890.580.000.31 3 0 3 1 0 1 0 0 0 4 0 4 2 0 270622.89.62 9.0726.9223.423.5061.5936.5012.3019.861.662.81 5.612.88102.91.851.961.036.2213.0512.5023.9 6.736.4265.4 5.581.649256.1 INDRE-ET-LOIRE 13700001.6488 14.3345.7968.5422.7541.6314.1 88 72 176 38 100 71 187 41 101 63 175 420.060.100.040.00 17 4 21 4 0 4 5 5 10 0 0 0 7 2 964629.38.31 8.2327.3324.003.3348.9850.08 9.0413.911.342.29 5.763.67105.71.941.940.916.4616.1714.9835.215.945.1769.6 3.651.657452.8 ISERE 13800001.6512 16.9715.7252.1536.4333.30 8.2 127 37 213 65 131 48 233 65 123 38 239 550.170.130.000.04 41 26 67 23 58 81 34 18 52 24 16 40 31 29 6057722.97.09 6.5930.5025.584.9242.4736.6712.2023.150.833.18 3.365.37101.32.562.821.338.0321.2820.1530.3 8.138.3364.8 1.751.673060.3 JURA 13900001.6674 24.30 7.1716.18 9.0136.75 8.9 75 47 206 24 92 65 202 31 87 55 210 320.040.030.000.01 24 7 31 21 10 31 12 20 32 18 5 23 18 11 2950722.45.86 5.8529.9225.674.2510.6339.9211.1922.731.052.84 4.995.36105.92.462.751.307.7019.4116.1436.9 9.939.4265.5 0.531.670856.1 LANDES 14000001.6348 10.7251.9067.6015.7032.93 8.7 103 126 124 41 99 128 118 40 97 112 118 380.790.390.000.40 54 0 54 55 4 59 15 0 15 4 0 4 32 1 3356524.17.14 6.7229.2523.835.4259.7836.1711.8220.152.243.15 8.842.93108.21.982.341.027.2116.3114.4019.6 6.035.3359.2 4.701.636658.0 LOIR-ET-CHER 14100001.6516 14.1540.3957.4817.0932.6310.7 67 21 100 30 69 22 107 29 63 21 104 260.470.500.030.00 7 7 14 9 0 9 2 0 2 1 0 1 5 2 767426.28.76 8.2327.0023.833.1743.6838.9211.7318.901.592.91 5.643.45105.72.102.850.906.9023.2021.9728.712.638.9264.1 3.281.648448.7 LOIRE 14200001.6442 13.9934.5250.5916.0729.14 4.2 104 23 146 47 104 20 163 53 105 21 163 510.790.750.000.04 27 13 40 38 13 51 30 27 57 33 6 39 32 15 4752023.86.19 5.6928.7525.003.7538.4031.8313.3724.911.223.94 4.444.12104.72.343.091.227.3720.5118.9335.5 6.131.4264.9 3.001.640062.0 LOIRE(HAUTE-) 14300001.6548 10.7055.7568.8713.1233.26 2.7 43 22 20 18 44 10 25 19 44 9 27 190.530.470.000.06 41 3 44 9 5 14 9 1 10 11 0 11 18 2 2046422.16.21 4.6529.4225.923.5038.4834.4212.4327.620.813.71 3.672.69106.32.372.721.277.6821.7018.6024.8 2.935.0065.1 4.521.640653.4 LOIRE-INFERIEURE 14400001.6482 16.1443.7763.7619.9936.83 5.3 167 79 144 65 167 79 144 65 168 77 134 640.740.630.000.11 85 15 100 6 10 16 13 12 25 29 8 37 33 11 4443723.85.31 4.7629.8326.922.9248.9037.4211.6925.860.913.45 4.454.32105.11.932.181.006.3815.2213.6832.4 9.938.5868.3 3.991.651067.6 LOIRET 14500001.6624 20.6329.8249.2319.4132.8613.8 102 57 118 54 100 57 122 53 103 54 125 520.440.250.000.19 13 0 13 12 14 26 7 2 9 11 0 11 11 4 1566426.29.03 7.8127.4223.833.5832.9936.6712.3519.502.223.11 7.413.32104.32.293.131.016.2522.3823.0633.617.133.1760.1 2.631.657063.5 LOT 14600001.6404 9.1744.7270.6725.9540.02 4.8 105 39 81 48 106 39 88 49 94 35 76 430.080.450.000.53 10 22 32 14 18 32 17 18 35 16 6 22 14 16 3053327.87.34 6.5530.1724.335.8356.2442.1710.2918.350.742.93 3.272.94105.52.293.040.976.8921.8616.6922.2 5.340.1765.8 3.621.644455.1 LOT-ET-GARONNE 14700001.6460 12.3536.4362.2225.7939.62 6.1 136 72 130 80 140 67 127 82 130 59 120 790.280.250.030.00 33 8 41 23 0 23 11 1 12 12 0 12 20 2 2269330.79.5010.0627.0022.334.6751.1553.17 8.6112.260.852.15 3.163.99107.02.162.500.946.6120.7317.7826.2 7.146.0868.3 2.781.658457.5 LOZERE 14800001.6418 10.7832.7453.5420.8035.14 3.9 42 12 26 11 42 10 24 10 41 10 26 110.580.010.000.59 1 18 19 1 3 4 0 10 10 0 11 11 0 11 1142423.15.07 5.0229.6724.505.1739.8733.4213.9630.421.084.02 4.333.43101.62.412.951.197.0018.7116.6533.7 4.935.1760.5 3.141.644258.9 MAINE-ET-LOIRE 14900001.6526 14.6638.6059.6621.0641.70 9.3 162 100 192 75 155 78 202 63 144 89 172 640.160.240.080.00 12 16 28 1 15 16 2 1 3 12 2 14 6 9 1552725.56.48 6.1829.0825.923.1746.3046.33 9.4817.370.902.54 4.564.54105.31.992.031.036.8918.8114.9130.0 9.643.0071.7 3.251.657663.9 MANCHE 15000001.6532 16.0215.6126.3710.7643.75 4.3 164 37 86 114 158 38 88 114 156 41 93 1140.040.590.000.63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 039126.85.17 4.4430.9227.173.75 7.5145.7510.1621.801.153.05 6.374.47104.32.141.991.166.7214.1812.9949.9 5.144.0871.7 1.161.654066.7 MARNE 15100001.6608 19.5411.1821.8710.6934.6730.0 101 100 165 42 110 96 169 41 107 98 170 430.170.280.110.00 25 20 45 24 18 42 15 8 23 17 12 29 20 15 3557330.96.98 7.5026.9223.003.9210.9342.6710.6815.662.662.84 9.575.11104.52.173.070.906.0029.9824.4641.432.437.0859.8 0.831.662266.9 MARNE(HAUTE-) 15200001.6674 25.38 5.3315.4210.0941.5410.6 98 40 242 24 98 40 242 24 99 40 246 240.240.050.000.19 1 38 39 6 7 13 3 7 10 2 19 21 3 18 2153329.97.10 6.3828.0024.253.75 4.0644.4210.2617.791.013.41 4.434.27107.72.012.470.836.8324.2819.8137.210.741.5867.1 0.321.668060.2 MAYENNE 15300001.6458 11.9940.4864.8624.3840.30 5.3 58 49 115 42 47 44 113 38 60 42 107 400.160.390.000.55 0 2 2 0 2 2 0 5 5 0 6 6 0 4 448422.35.71 5.6229.7526.333.4246.6441.2510.9122.450.912.89 4.484.98106.02.262.361.167.7718.6015.6632.2 6.240.0069.1 3.781.644864.8 MEUSE 15500001.6646 22.47 5.3112.77 7.4631.1014.0 82 49 251 40 70 34 250 37 80 36 249 350.180.250.000.43 3 4 7 11 17 28 3 0 3 1 0 1 5 5 1053130.97.24 6.5327.4223.923.50 2.3946.2510.0616.781.162.86 4.984.31103.01.982.190.896.0322.2818.3139.115.542.5860.0 0.351.662267.2 MORBIHAN 15600001.6436 13.1662.4374.8812.4533.69 5.7 53 24 69 26 57 22 73 25 57 22 79 250.860.600.000.26 27 7 34 7 3 10 1 1 2 0 0 0 9 3 1245520.25.69 4.3028.9226.422.5065.8032.2513.0730.400.733.79 3.264.66106.72.252.651.207.9316.6314.6030.0 5.734.8364.1 5.731.640872.3 MOSELLE 15700001.6662 23.51 7.6225.7418.1233.80 9.2 101 29 285 55 91 25 266 54 83 24 269 530.670.350.000.32 59 14 73 114 101 215 96 16 112 97 4 101 91 34 12541726.24.90 4.7629.7526.083.67 6.4035.9212.7026.121.453.87 6.214.37107.12.122.551.996.5720.6016.2430.510.236.9259.2 0.721.665648.2 NIEVRE 15800001.6482 14.2152.6678.0925.4929.86 7.0 104 18 67 35 92 18 64 33 89 19 63 330.750.600.000.15 11 5 16 20 5 25 7 5 12 9 5 14 12 5 1779023.19.84 9.8927.1722.584.5852.0934.8313.0320.481.573.01 4.732.86107.02.122.511.126.9514.5116.6724.9 8.033.9265.5 4.321.650058.9 NORD 15900001.6676 22.9436.6942.68 5.9929.8710.1 218 287 172 169 222 291 179 170 213 287 172 1700.951.360.410.00 0 15 15 0 10 10 0 1 1 0 10 10 0 9 949622.75.45 5.7928.0825.332.7538.5130.4214.9328.282.713.82 9.494.67105.22.343.341.086.6921.7017.6927.711.030.5856.7 3.111.667069.6 OISE 16000001.6684 23.5815.5329.0313.5038.2926.1 87 113 115 69 89 109 112 69 93 107 110 600.070.010.060.00 6 2 8 13 1 14 8 1 9 10 0 10 9 1 1068630.69.49 9.6125.7521.923.8315.5343.6710.7315.012.262.59 7.064.17104.92.363.021.016.5927.5324.1835.131.840.1760.6 1.431.657055.8 ORNE 16100001.6630 22.1620.4739.3418.8744.67 6.5 100 64 46 78 103 60 49 78 101 59 48 760.210.410.000.20 1 0 1 5 0 5 5 0 5 2 7 9 3 2 546830.26.50 5.4229.0025.253.7520.0750.50 8.4816.200.812.51 4.773.91105.12.112.290.966.6222.9418.6430.2 6.445.1776.0 1.531.663655.2 PAS-DE-CALAIS 16200001.6534 21.0431.1837.57 6.3938.0612.2 109 277 173 111 99 299 175 112 98 253 178 1040.720.700.000.02 1 3 4 0 1 1 0 4 4 0 27 27 0 9 950124.16.09 5.9828.0025.172.8328.9833.6713.8624.952.683.52 9.623.80104.72.242.871.036.3817.2915.6532.414.535.3362.5 2.791.662069.4 PUY-DE-DOME 16300001.6418 11.7545.9871.3025.3238.32 4.1 168 63 275 53 190 67 284 54 178 62 275 590.110.160.000.27 39 5 44 13 7 20 10 6 16 14 7 21 19 6 2553225.56.97 6.3128.5824.923.6747.5941.6710.2419.170.602.88 2.833.98106.42.292.641.206.9816.1517.1022.7 5.140.2564.6 3.791.636063.1 PYRENEES(BASSES-) 1640000 164.9634.9445.0710.1341.87 5.1 139 112 159 67 139 109 164 72 129 102 156 690.400.050.000.451573 4516181448 3314811339 3313721027 2710541347 34138138924.15.27 4.2230.2526.254.0044.1539.0811.0924.791.543.37 8.192.20106.22.152.351.096.6811.6012.2431.9 6.140.1769.8 2.871.655655.7 PYRENEES(HAUTES-) 16500001.6518 13.3620.9042.1921.2940.66 3.2 96 158 185 72 80 144 174 66 96 144 169 590.210.010.000.20 417 61 478 215 48 263 253 43 296 209 51 260 273 51 32436927.24.90 3.9431.2527.174.0841.3743.50 9.8921.751.353.25 7.922.36109.32.092.430.956.9115.3013.2719.3 3.640.5869.9 1.411.650857.4 PYRENEES-ORIENTALES 16600001.6498 13.3548.9463.1414.2029.14 5.5 62 125 156 41 62 126 158 45 64 123 159 420.680.850.170.00 156 122 278 115 65 180 13 33 46 5 51 56 72 68 14057122.96.45 7.1128.0824.833.2563.5830.2514.7227.001.623.68 5.223.55105.32.624.271.086.7817.6914.7626.1 5.628.9254.3 4.341.648467.3 RHIN(BAS-) 16700001.6616 22.25 5.1811.77 6.5929.69 7.7 169 50 521 82 177 54 500 68 103 38 466 530.840.390.000.45 222 31 253 131 22 153 171 25 196 301 36 337 206 29 23544622.65.84 4.5729.1726.752.42 2.3329.8314.7132.052.664.1510.694.58105.32.503.251.257.5128.6222.1124.0 9.531.8357.7 0.421.665072.1 RHIN(HAUT-) 16800001.6534 16.55 9.7721.2511.4829.34 9.2 118 25 399 60 112 22 388 64 113 22 396 650.930.560.000.37 321 82 403 358 63 421 348 29 377 342 40 382 342 54 39648119.95.95 4.9029.5027.082.42 5.5428.5014.7733.152.533.9310.285.33105.62.573.321.328.3026.9820.7524.5 9.830.5060.7 0.621.659063.6 RHONE 16900001.6562 17.4614.3035.3421.0434.8911.4 239 46 239 130 265 49 283 143 239 46 281 1420.340.490.150.00 33 54 87 14 18 32 30 39 69 69 31 100 37 35 7247524.15.26 5.2029.6725.753.9220.9437.6710.3418.192.832.7614.026.03104.82.302.901.357.6725.2813.9331.311.236.8359.5 1.021.656865.0 SAONE(HAUTE-) 17000001.6620 19.93 9.5222.9013.3836.73 6.7 79 67 220 34 85 67 213 33 83 67 215 340.500.030.000.47 108 90 198 71 118 189 59 100 159 66 69 135 76 94 17049725.86.36 5.6029.2526.332.9211.0139.0810.9720.061.703.11 7.694.39105.22.062.380.937.8219.8216.1628.9 8.339.6761.3 0.761.673270.5 SAONE-ET-LOIRE 17100001.6582 18.7732.3658.3225.9629.80 9.7 165 31 296 66 153 34 285 62 163 33 281 610.620.610.000.01 0 16 16 0 13 13 0 12 12 0 6 6 0 12 1270723.78.78 8.6127.2523.254.0039.0634.6713.0420.981.463.08 4.564.29104.92.272.901.086.7922.1618.4030.4 8.934.6762.5 2.761.661464.8 SARTHE 17200001.6514 13.5442.0860.1718.0941.6210.8 93 44 122 65 95 43 113 63 93 42 109 640.050.110.000.06 13 6 19 6 1 7 6 3 9 7 5 12 8 4 1264227.39.36 7.4027.4224.502.9242.5247.42 9.4915.221.582.35 6.994.75105.22.152.440.976.6022.3520.4429.810.241.6769.8 3.461.653861.0 SAVOIE 1730000 0.270.250.000.52 216 78 294 162 1 163 118 4 122 67 4 71 141 22 16338722.74.52 4.2130.5026.583.9231.9634.0812.9330.330.944.05 3.846.64104.02.663.351.268.2423.4320.3741.7 5.534.50 1.971.652255.8 SAVOIE(HAUTE-) 1740000 0.590.470.000.12 101 0 101 37 2 39 21 3 24 18 4 22 44 2 4638422.24.51 4.2731.8327.754.0837.3235.0812.6329.951.243.84 5.925.61106.12.262.671.167.6219.6216.7837.4 4.036.00 1.441.660661.2 SEINE 17500001.6622 17.58 8.3416.57 8.2330.8835.7 1204 225 605 519 1224 230 639 575 1259 233 684 5790.462.021.560.00 154 429 583 199 449 648 238 326 564 284 338 622 219 385 60450423.04.89 6.4830.0825.924.17 8.0933.1711.0014.956.322.4426.316.85103.42.554.421.437.4239.0716.1357.639.231.7557.6 0.711.657466.5 SEINE-INFERIEURE 17600001.6596 19.1831.9242.5410.6233.9917.9 190 177 82 239 197 169 75 241 192 157 67 2360.280.070.000.21 123 11 134 103 19 122 68 2 70 73 12 85 92 11 10347126.05.88 5.3728.4225.253.1728.7234.0012.3823.172.832.3512.154.42103.02.664.361.117.0937.3226.2733.419.232.8357.6 2.931.655446.6 SEINE-ET-MARNE 17700001.6592 21.3713.4523.4610.0133.9429.5 101 49 76 53 98 55 87 49 94 50 89 550.190.120.000.07 5 0 5 2 0 2 8 2 10 8 0 8 6 0 668128.58.2410.2426.0821.834.2515.4341.0011.1117.041.652.82 5.733.64105.22.263.100.926.0724.9224.7543.631.736.7564.6 1.121.650860.9 SEINE-ET-OISE 17800001.6678 23.2012.5630.6418.0837.3027.5 177 67 165 100 175 73 159 98 184 75 158 1070.020.800.780.00 0 24 24 0 3 3 0 10 10 0 6 6 0 11 1160128.26.96 9.0926.8322.674.1711.9242.1711.2916.752.212.68 7.384.19101.72.393.341.046.3427.2523.0747.529.238.4265.0 0.941.656660.1 SEVRES(DEUX-) 17900001.6598 19.2129.6152.2122.6038.10 8.3 107 21 91 29 141 22 118 43 107 21 91 270.450.260.000.19 5 2 7 3 4 7 6 3 9 4 2 6 4 3 760825.77.53 7.2528.7525.922.8349.9741.0010.8718.561.212.79 4.952.65107.11.992.190.986.7415.2913.3720.7 8.940.6772.5 2.751.646664.5 SOMME 18000001.6694 21.7123.1445.3522.2135.0114.4 86 215 92 71 84 219 81 70 88 213 77 690.090.000.000.09 6 11 17 8 12 20 3 0 3 1 2 3 5 6 1154828.97.64 6.7326.7523.753.6022.9142.1710.7916.942.582.8510.264.33106.62.282.970.996.7627.1022.7226.115.638.2562.6 2.051.662264.9 TARN 18100001.6478 11.4247.9364.8816.9535.13 4.6 155 37 92 65 148 34 90 70 166 28 93 690.490.110.000.38 30 57 87 9 31 40 12 51 63 4 31 35 14 42 5657525.57.39 7.1628.3323.754.5856.5337.6711.6620.500.733.11 2.813.95106.02.163.040.856.7718.8215.5024.5 3.437.0061.1 4.001.639458.3 TARN-ET-GARONNE 18200001.6516 15.6847.3466.6619.3240.88 6.6 100 35 66 58 98 34 64 59 97 33 61 590.110.310.000.20 67 6 73 15 5 20 5 5 10 3 7 10 22 6 2866029.09.51 9.0426.7522.754.0056.1647.83 9.4814.490.722.35 2.774.29106.52.212.940.846.4326.1918.6327.8 6.043.6761.4 3.621.644262.5 VAR 18300001.6516 15.9337.3356.6519.3232.3315.1 115 93 181 103 154 87 207 98 161 92 200 8460.280.440.000.16 21 8 29 21 10 31 14 5 19 13 1 14 17 6 2344726.93.81 7.5029.2524.504.7545.1341.0811.8219.541.343.01 4.804.60105.02.713.741.596.7425.4517.4726.917.836.33 3.071.655269.2 VAUCLUSE 18400001.6518 15.7634.0753.9219.8529.9612.1 118 73 126 55 113 74 134 57 106 66 118 500.190.160.000.35 20 47 67 11 41 52 20 47 67 11 31 42 15 42 5750728.45.81 6.3628.0823.834.2545.4636.0012.5721.891.193.63 4.254.89103.62.593.961.066.6530.4021.9329.915.733.2553.6 2.751.655066.7 VENDEE 18500001.6470 12.0642.7269.8427.1234.27 5.1 116 49 181 29 115 54 195 28 119 48 194 290.740.440.000.30 3 0 3 0 0 0 0 0 0 0 0 0 1 0 153923.36.55 6.1128.5025.752.7555.5837.3312.0424.980.653.21 3.643.22105.81.942.111.097.5316.6413.5128.9 5.637.1768.2 4.211.651266.0 VIENNE 18600001.6488 14.8650.5467.4816.9438.77 9.0 119 52 182 37 118 53 183 34 117 44 171 330.380.160.000.22 0 1 1 0 8 8 0 0 0 1 11 12 0 5 566125.28.55 7.9027.9224.423.5057.3641.3310.8718.281.222.67 4.853.31107.32.032.311.036.5115.4214.5224.0 9.738.9269.6 4.381.659058.7 VIENNE(HAUTE-) 18700001.6354 9.7267.4581.7814.3330.03 8.7 115 44 101 37 117 42 100 38 105 34 102 370.710.400.000.31 43 2 45 14 7 21 3 6 9 3 7 10 16 5 2172820.78.79 8.8526.6723.083.5872.1932.4212.7921.061.792.99 6.013.34105.02.382.960.978.0815.2913.1419.4 8.532.4261.6 6.211.640047.1 VOSGES 18800001.6504 16.66 7.6726.4518.7837.56 8.8 94 23 263 37 93 24 273 40 93 22 281 380.560.170.000.39 36 23 59 18 25 43 30 69 99 16 12 28 25 32 5757223.67.82 5.9328.9226.252.67 2.9144.8311.8521.732.192.97 9.376.06102.82.202.551.107.1220.3917.3331.311.637.0865.2 0.481.662857.5 YONNE 18900001.6556 20.1218.3041.1622.8635.6014.0 134 80 114 37 137 81 120 39 136 79 112 380.100.120.020.00 5 0 5 7 2 9 0 1 1 0 0 0 3 1 468529.09.25 9.0826.8323.503.3321.3345.3310.4316.051.242.60 4.533.53104.52.112.910.805.7626.1226.1232.717.038.0067.6 1.511.651454.7 MEURTHE 19900001.6610 20.24 6.6423.1516.5135.4014.3 96 38 355 55 96 38 355 55 99 37 424 530.380.010.000.39 70 73 143 26 39 65 51 33 84 54 32 86 50 44 9450626.06.61 5.4929.0825.833.25 4.0540.5810.8320.121.842.91 8.625.50104.52.092.391.056.4220.3017.5736.714.039.7561.9 0.451.663059.8 -------------------------------------------------------------------------------- /data_source/MVTPOP_T122.dbf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/data_source/MVTPOP_T122.dbf 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-------------------------------------------------------------------------------- /data_source/MVTPOP_T90.dbf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/data_source/MVTPOP_T90.dbf -------------------------------------------------------------------------------- /data_source/MVTPOP_T92.dbf: -------------------------------------------------------------------------------- 1 |  2 |  W! V6CV1CV3CV4CV5CV7NV8NV9NV10NV11NV12NV13NV14NV15NV16NV17NV18NV19NV20NV21NV22NV23NV24NV25NV26NV27NV28NV29NV30NV31NV32NV33NV34NV35NV36NV37NV38NV39NV40NV41NV42NV43NV44NV45NV46NV47NV48NV49NV50NV51NV52NV53NV54NV55NV56NV57NV58NV59NV60NV61NV62NV63NV64NV65NV66NV67NV68NV69NV70NV71NV72NV73NV74NV75NV76NV77NV78NV79NV80NV81NV82NV83NV84NV85NV86NV87NV88NV89NV90NV91NV92NV93NV94NV95NV96NV97NV98NV99NV100NV101NV102NV103NV104NV105N FRANCE 00000008174714981614105429087016982615400832383450623802620158833652331295664947933968131244065212132954355962833354863394540109403495095074664489759553095666562 126.72121.9639.9437.8538.3138.4243.0541.96103.95 87.92 91.292.963.112.94 7.11 7.17 7.544.044.114.26105.59105.31105.23105.59103.23105.2339.9239.0838.6865917343789193474817471682006791193267271201588234117102282824510745283335120118348266201607381446750336212498174761495587762716360521794914004624425972015887350742258933652125260932075496063846283335 AIN 1010000 394 697 566 1263 968 836 1804 0 541 2192 3767 3516 7283 4166 4122 8288 0 1005 2586 4464 4082 8546 5134 4958 10092 0 1546 139.13139.5545.09 44.92 118.79 98.30107.27 103.32109.78102.24 298 19 58 19 394 1756 115 213 108 2192 2054 134 271 127 2586 18 99 123 73 21 37 14 9 394 11 354 750 510 238 194 86 49 2192 29 453 873 583 259 231 100 58 2586 AISNE 1020000 683 1434 1330 2764 1716 1440 3156 0 392 3972 5640 5253 10893 6226 5778 12004 0 1111 4655 7074 6583 13657 7942 7218 15160 0 1503 8.60 8.644.735.365.38 557 27 56 43 683 3248 93 373 258 3972 3805 120 429 301 4655 18 219 250 85 36 30 33 12 683 148 1811 1076 407 174 182 127 47 3972 166 2030 1326 492 210 212 160 59 4655 ALLIER 1030000 548 985 892 1877 1068 911 1979 0 102 2743 4087 3885 7972 3515 2841 6356 1616 0 3291 5072 4777 9849 4583 3752 8335 1514 0 111.11101.63 44.33 84.00 77.29 83.52 102.18 119.99111.19105.7672.3373.5865.61 450 13 59 26 548 2300 118 245 80 2743 2750 131 304 106 3291 1 171 201 74 44 30 22 5 548 256 919 778 386 181 143 63 17 2743 257 1090 979 460 225 173 85 22 3291 ALPES(BASSES-) 1040000 206 366 331 697 471 362 833 0 136 1156 1752 1604 3356 1956 1661 3617 0 261 1362 2118 1935 4053 2427 2023 4450 0 397 110.96113.82 35.0135.3338.17 99.04 3.70 1.96 1.83 1.58 108.72 99.81101.97119.68 104.86 175 4 20 7 206 941 65 116 34 1156 1116 69 136 41 1362 4 60 74 34 13 15 5 1 206 114 273 320 201 109 85 33 21 1156 118 333 394 235 122 100 38 22 1362 ALPES(HAUTES-) 1050000 123 309 264 573 502 293 795 0 222 848 1687 1597 3284 1641 1501 3142 142 0 971 1996 1861 3857 2143 1794 3937 0 80 144.76150.1335.1133.5436.0034.3734.3234.28 97.72105.00 4.224.08 2.63 2.20 2.17 4.66 102.65118.42 106.00 96 3 18 6 123 671 30 113 34 848 767 33 131 40 971 1 24 40 28 11 12 4 3 123 15 153 253 199 102 70 25 31 848 16 177 293 227 113 82 29 34 971 ARDECHE 1070000 438 904 860 1764 899 795 1694 70 0 2857 5502 5323 10825 4917 4500 9417 1408 0 3295 6406 6183 12589 5816 5295 11111 1478 0 118.87 31.1931.6132.9035.43 88.05 78.57 78.993.663.673.88 2.37 2.35 2.641.740.951.54103.45108.22 107.71108.30 352 22 43 21 438 2437 90 233 97 2857 2789 112 276 118 3295 10 82 163 88 42 32 15 6 438 94 569 951 661 282 198 77 25 2857 104 651 1114 749 324 230 92 31 3295 ARDENNES 1080000 340 800 776 1576 765 720 1485 91 0 2241 2991 2816 5807 2968 2908 5876 0 69 2581 3791 3592 7383 3733 3628 7361 22 0 45.76 45.9152.4049.56 85.26 4.66 107.81 101.12 96.36 94.2010.3411.8110.67 284 10 34 12 340 1866 58 209 108 2241 2150 68 243 120 2581 2 98 114 56 30 20 12 8 340 46 686 814 329 146 113 69 38 2241 48 784 928 385 176 133 81 46 2581 ARIEGE 1090000 482 687 604 1291 766 671 1437 0 146 1741 2564 2402 4966 2524 2220 4744 222 0 2223 3251 3006 6257 3290 2891 6181 76 0 2.072.222.69 112.50117.39105.7569.8670.1171.36 387 13 72 10 482 1413 33 236 59 1741 1800 46 308 69 2223 2 105 177 83 42 48 19 6 482 30 351 484 406 238 185 36 11 1741 32 456 661 489 280 233 55 17 2223 AUBE 1100000 501 797 723 1520 798 807 1605 0 85 1827 2124 1923 4047 2465 2177 4642 0 595 2328 2921 2646 5567 3263 2984 6247 0 680 49.1643.3746.1543.59 112.78 2.13 2.26 4.90108.20108.18107.92 13.7513.9316.00 364 15 81 41 501 1513 63 163 88 1827 1877 78 244 129 2328 7 131 171 72 32 39 34 15 501 74 682 576 242 91 79 57 26 1827 81 813 747 314 123 118 91 41 2328 AUDE 1110000 575 938 877 1815 959 852 1811 4 0 1835 3044 2765 5809 2032 1956 3988 1821 0 2410 3982 3642 7624 2991 2808 5799 1825 0 114.36112.60 50.6439.53 75.41 97.56 2.69 3.48 2.81 109.46 486 18 55 16 575 1611 41 124 59 1835 2097 59 179 75 2410 11 201 178 81 37 49 12 6 575 107 626 576 274 122 84 33 13 1835 118 827 754 355 159 133 45 19 2410 AVEYRON 1120000 508 1113 1021 2134 1373 1076 2449 0 315 2176 4685 4425 9110 4762 4145 8907 203 0 2684 5798 5446 11244 6135 5221 11356 0 112 35.85 35.4934.2238.61 100.80 3.963.73 3.35 4.00 3.442.282.57 109.27 116.10108.36105.19 416 26 51 15 508 1789 122 190 75 2176 2205 148 241 90 2684 3 111 146 112 66 47 18 5 508 58 334 543 544 366 210 78 43 2176 61 445 689 656 432 257 96 48 2684 BOUCHES-DU-RHONE 1130000 2830 5861 5493 11354 7964 5920 13884 0 2530 717 1397 1408 2805 1420 1313 2733 72 0 3547 7258 6901 14159 9384 7233 16617 0 2458 121.13 35.5832.5530.6830.0336.4034.43118.47 3.42 9.78 9.5711.786.036.206.03 100.29102.50129.80110.55108.18 2187 238 272 133 2830 603 41 55 18 717 2790 279 327 151 3547 20 532 911 635 362 235 102 33 2830 5 224 239 129 63 33 15 9 717 25 756 1150 764 425 268 117 42 3547 CALVADOS 1140000 688 1194 1205 2399 1589 1640 3229 0 830 2759 3580 3400 6980 4487 4407 8894 0 1914 3447 4774 4605 9379 6076 6047 12123 0 2744 53.2549.7750.89 49.2646.96130.54101.04108.372.322.452.2810.82 9.6710.67 4.65102.93 99.84 89.50 90.1812.5016.8913.52 544 41 82 21 688 2357 67 254 81 2759 2901 108 336 102 3447 14 154 220 105 70 64 37 24 688 43 808 855 462 202 195 110 84 2759 57 962 1075 567 272 259 147 108 3447 CANTAL 1150000 133 361 332 693 352 387 739 0 46 1370 2737 2595 5332 2585 2406 4991 341 0 1503 3098 2927 6025 2937 2793 5730 295 0 45.1144.42 94.98 105.663.70 2.562.291.66 101.99108.69 93.69 91.77 109 5 17 2 133 1152 30 159 29 1370 1261 35 176 31 1503 3 29 41 22 16 14 6 2 133 32 259 439 274 174 123 46 23 1370 35 288 480 296 190 137 52 25 1503 CHARENTE 1160000 379 694 633 1327 951 1014 1965 0 638 2952 3677 3611 7288 4741 4730 9471 0 2183 3331 4371 4244 8615 5692 5744 11436 0 2821 149.55153.1645.31 33.88 46.83 84.39 2.402.342.21 3.36 101.90 98.25 98.22 313 12 39 15 379 2285 161 334 172 2952 2598 173 373 187 3331 2 108 123 71 34 22 13 6 379 62 842 899 497 296 228 93 35 2952 64 950 1022 568 330 250 106 41 3331 CHARENTE-INFERIEURE 1170000 1014 1677 1609 3286 1878 1713 3591 0 305 2997 4251 4013 8264 3874 3671 7545 719 0 4011 5928 5622 11550 5752 5384 11136 414 0 116.44101.81 45.07 44.1551.1451.57 2.482.43 3.61 811 41 109 53 1014 2562 92 234 109 2997 3373 133 343 162 4011 6 258 351 175 89 78 41 16 1014 108 996 987 410 199 142 92 63 2997 114 1254 1338 585 288 220 133 79 4011 CHER 1180000 586 1171 1101 2272 1288 1189 2477 0 205 1894 3664 3517 7181 3403 3021 6424 757 0 2480 4835 4618 9453 4691 4210 8901 552 0 140.77 34.2833.7033.90 37.41 93.97 3.92 2.612.48103.10 108.02 105.8581.0760.1561.14 483 20 65 18 586 1575 85 142 92 1894 2058 105 207 110 2480 6 198 197 82 50 36 15 2 586 24 746 598 238 127 90 51 20 1894 30 944 795 320 177 126 66 22 2480 CORREZE 1190000 280 615 557 1172 619 528 1147 25 0 2128 4177 3834 8011 4006 3576 7582 429 0 2408 4792 4391 9183 4625 4104 8729 454 0 145.49 34.8434.48 35.7134.75 105.573.564.01 1.561.591.87107.98 111.58 106.3873.9275.5973.52 211 11 44 14 280 1674 71 270 113 2128 1885 82 314 127 2408 4 57 89 67 25 23 11 4 280 21 504 721 428 194 160 77 23 2128 25 561 810 495 219 183 88 27 2408 CORSE 1200000 299 753 789 1542 1260 871 2131 0 589 1306 2823 2634 5457 3141 2591 5732 0 275 1605 3576 3423 6999 4401 3462 7863 0 864 139.4034.8232.4734.6330.94 79.46 78.354.03 3.81 1.441.361.34103.36 126.23 58.4460.3356.70 258 6 28 7 299 1155 57 66 28 1306 1413 63 94 35 1605 11 68 97 57 39 18 7 2 299 84 335 356 219 134 116 46 16 1306 95 403 453 276 173 134 53 18 1605 COTE-D'OR 1210000 566 1027 986 2013 1125 1105 2230 0 217 2713 3605 3260 6865 3644 3621 7265 0 400 3279 4632 4246 8878 4769 4726 9495 0 617 109.62107.79 2.412.682.52 108.08101.46109.46 98.41 14.0914.6513.20 471 20 56 19 566 2213 79 296 125 2713 2684 99 352 144 3279 1 148 183 108 45 46 22 13 566 51 711 925 522 188 169 95 52 2713 52 859 1108 630 233 215 117 65 3279 COTES-DU-NORD 1220000 277 788 732 1520 972 1020 1992 0 472 3571 8183 7683 15866 8994 7600 16594 0 728 3848 8971 8415 17386 9966 8620 18586 0 1200 139.77 33.6533.3734.9337.4035.70 4.143.904.03 3.61 3.87 114.13 81.3757.9967.46 216 14 38 9 277 2840 188 428 115 3571 3056 202 466 124 3848 2 31 86 82 36 23 15 2 277 167 704 959 785 426 350 125 55 3571 169 735 1045 867 462 373 140 57 3848 CREUSE 1230000 159 271 241 512 211 298 509 3 0 2165 2921 2767 5688 2664 2783 5447 241 0 2324 3192 3008 6200 2875 3081 5956 244 0 152.07144.0545.8246.1247.1447.7247.93 112.572.47 1.822.122.12 110.31 92.08 93.45 91.55 58.1060.85 136 7 12 4 159 1823 54 202 86 2165 1959 61 214 90 2324 2 61 48 22 14 6 5 1 159 90 794 676 292 114 118 64 17 2165 92 855 724 314 128 124 69 18 2324 DORDOGNE 1240000 290 560 489 1049 637 657 1294 0 245 3562 6403 5793 12196 6282 5660 11942 254 0 3852 6963 6282 13245 6919 6317 13236 9 0 38.11 108.01 3.40 3.98 2.572.782.42109.22 103.10 107.60103.8360.3673.3371.77 232 14 30 14 290 2985 91 318 168 3562 3217 105 348 182 3852 0 57 104 52 26 35 9 7 290 111 992 1166 590 282 261 114 46 3562 111 1049 1270 642 308 296 123 53 3852 DOUBS 1250000 410 747 737 1484 983 913 1896 0 412 1522 2984 2840 5824 3171 2802 5973 0 149 1932 3731 3577 7308 4154 3715 7869 0 561 84.95 8.86 9.3710.554.945.015.40102.24 111.08 5.44 7.52 6.64 321 22 50 17 410 1278 48 161 35 1522 1599 70 211 52 1932 3 64 130 87 50 36 34 6 410 18 196 367 380 281 174 70 36 1522 21 260 497 467 331 210 104 42 1932 DROME 1260000 803 1016 947 1963 1260 1165 2425 0 462 1837 3475 3158 6633 3346 3094 6440 193 0 2640 4491 4105 8596 4606 4259 8865 0 269 116.40 2.73 108.85108.34 96.51 594 100 66 43 803 1399 184 150 104 1837 1993 284 216 147 2640 19 144 209 168 141 77 33 12 803 21 348 537 366 332 154 63 16 1837 40 492 746 534 473 231 96 28 2640 EURE 1270000 466 698 695 1393 884 820 1704 0 311 2697 3362 3175 6537 4775 4273 9048 0 2511 3163 4060 3870 7930 5659 5093 10752 0 2822 140.07 53.1952.2051.52 59.00 137.10111.41125.032.212.462.28 8.36 8.44 8.95 102.83 107.83 98.32 18.7021.8618.04 367 23 41 35 466 2325 63 215 94 2697 2692 86 256 129 3163 5 151 136 65 35 32 27 15 466 90 1016 766 364 171 138 100 52 2697 95 1167 902 429 206 170 127 67 3163 EURE-ET-LOIR 1280000 283 523 502 1025 598 637 1235 0 210 2032 3178 2974 6152 3419 3328 6747 0 595 2315 3701 3476 7177 4017 3965 7982 0 805 99.73109.98100.38 99.28 225 8 33 17 283 1700 51 187 94 2032 1925 59 220 111 2315 1 91 103 35 19 17 10 7 283 15 818 640 227 124 93 82 33 2032 16 909 743 262 143 110 92 40 2315 FINISTERE 1290000 833 1768 1644 3412 2701 2099 4800 0 1388 4071 7974 7541 15515 8800 7599 16399 0 884 4904 9742 9185 18927 11501 9698 21199 0 2272 116.51105.6533.5830.0228.9829.8331.6034.10112.58 84.993.553.703.50 4.574.895.17 107.34117.01 79.0273.9077.61 604 46 120 63 833 3288 201 472 110 4071 3892 247 592 173 4904 7 132 189 200 177 92 31 5 833 162 1112 1302 758 385 273 66 13 4071 169 1244 1491 958 562 365 97 18 4904 GARD 1300000 1677 3443 3208 6651 3479 3122 6601 50 0 1887 3463 3124 6587 3185 2853 6038 549 0 3564 6906 6332 13238 6664 5975 12639 599 0 32.7231.7131.2134.3235.2236.86 84.673.493.703.98 3.10 2.462.80108.37 101.59 103.47 1406 46 131 94 1677 1649 45 146 47 1887 3055 91 277 141 3564 47 561 524 264 112 94 58 17 1677 110 580 621 325 115 103 20 13 1887 157 1141 1145 589 227 197 78 30 3564 GARONNE(HAUTE-) 1310000 1112 1939 1785 3724 2159 2158 4317 0 593 2512 3662 3405 7067 4234 4144 8378 0 1311 3624 5601 5190 10791 6393 6302 12695 0 1904 138.2946.9645.5646.52 47.8447.74118.58 2.66 5.034.84 109.81100.19 99.75 98.78 93.1658.66 946 25 104 37 1112 2264 35 166 47 2512 3210 60 270 84 3624 42 281 359 179 94 87 56 14 1112 100 758 835 398 208 148 39 26 2512 142 1039 1194 577 302 235 95 40 3624 GERS 1320000 526 696 607 1303 815 766 1581 0 278 1873 2611 2221 4832 2407 2430 4837 0 5 2399 3307 2828 6135 3222 3196 6418 0 283 51.0053.6055.3148.7055.2553.17 97.01104.032.402.182.07 3.67 2.692.352.34115.52112.61107.89 99.17 94.93 447 45 48 16 526 1620 52 182 19 1873 2067 67 230 35 2399 11 103 156 124 56 50 18 8 526 49 294 615 389 332 135 39 20 1873 60 397 771 513 388 185 57 28 2399 GIRONDE 1330000 2075 2952 2789 5741 3125 3077 6202 0 461 3288 4280 3796 8076 4365 4005 8370 0 294 5363 7232 6585 13817 7490 7082 14572 0 755 120.28104.3648.5146.0746.0245.8847.6049.40 2.362.392.0710.17 8.25 8.87 98.09109.02 99.61 95.80 1769 67 181 58 2075 2937 72 208 71 3288 4706 139 389 129 5363 26 482 715 408 181 155 81 27 2075 249 1196 1035 411 171 140 64 22 3288 275 1678 1750 819 352 295 145 49 5363 HERAULT 1340000 1617 3053 2989 6042 3782 3143 6925 0 883 1711 2551 2487 5038 2496 2337 4833 205 0 3328 5604 5476 11080 6278 5480 11758 0 678 35.7136.85 97.75 3.55 3.44 3.754.91 101.50103.68 113.89 97.04 1360 41 171 45 1617 1475 49 146 41 1711 2835 90 317 86 3328 47 488 485 291 128 127 44 7 1617 123 497 489 347 130 95 28 2 1711 170 985 974 638 258 222 72 9 3328 ILLE-ET-VILAINE 1350000 743 1495 1388 2883 1955 1948 3903 0 1020 2726 6468 6250 12718 6268 5340 11608 1110 0 3469 7963 7638 15601 8223 7288 15511 90 0 182.56 83.344.114.723.41 3.05 3.05 3.015.845.396.02102.12109.30102.04 95.70 95.16 526 49 139 29 743 2129 162 357 78 2726 2655 211 496 107 3469 6 89 191 191 76 127 52 11 743 44 362 710 625 457 336 124 68 2726 50 451 901 816 533 463 176 79 3469 INDRE 1360000 737 1201 1146 2347 1312 1165 2477 0 130 1913 2985 2752 5737 2399 2222 4621 1116 0 2650 4186 3898 8084 3711 3387 7098 986 0 34.68 38.2237.69 87.50 106.04 3.68 2.462.492.76 102.10101.03 93.3974.6071.5074.70 629 11 74 23 737 1653 44 163 53 1913 2282 55 237 76 2650 49 243 234 93 47 34 28 9 737 172 641 567 290 113 87 35 8 1913 221 884 801 383 160 121 63 17 2650 INDRE-ET-LOIRE 1370000 526 703 763 1466 771 842 1613 0 147 2189 2685 2540 5225 2350 2387 4737 488 0 2715 3388 3303 6691 3121 3229 6350 341 0 49.3048.0349.5152.0552.7347.49 94.50 104.252.252.562.28 100.10100.33 93.98 99.64 94.23 414 13 80 19 526 1863 63 170 93 2189 2277 76 250 112 2715 4 150 182 74 43 42 21 10 526 3 737 734 345 124 116 78 52 2189 7 887 916 419 167 158 99 62 2715 ISERE 1380000 669 1166 1173 2339 1400 1327 2727 0 388 3587 6433 5906 12339 7476 6556 14032 0 1693 4256 7599 7079 14678 8876 7883 16759 0 2081 142.17139.02 33.97 36.0432.98114.88 3.964.00 2.81 2.304.71 111.44108.01 83.36 509 33 100 27 669 2995 126 346 120 3587 3504 159 446 147 4256 4 135 206 135 76 57 39 17 669 130 854 1078 714 378 292 103 38 3587 134 989 1284 849 454 349 142 55 4256 JURA 1390000 355 514 530 1044 842 717 1559 0 515 1998 3159 2871 6030 3335 3092 6427 0 397 2353 3673 3401 7074 4177 3809 7986 0 912 113.51 97.92105.93 4.605.11 110.80 13.7912.2011.66 243 20 56 36 355 1562 64 284 88 1998 1805 84 340 124 2353 1 46 90 81 48 50 20 19 355 8 395 549 435 251 229 98 33 1998 9 441 639 516 299 279 118 52 2353 LANDES 1400000 479 729 637 1366 749 703 1452 0 86 2048 3966 3669 7635 3967 3651 7618 17 0 2527 4695 4306 9001 4716 4354 9070 0 69 144.31 34.9139.20 3.5311.08 8.83 8.342.162.472.43108.06108.68112.08 113.77115.7865.7568.7468.84 424 7 43 5 479 1776 50 172 50 2048 2200 57 215 55 2527 26 88 148 94 64 44 12 3 479 39 551 674 392 174 149 53 16 2048 65 639 822 486 238 193 65 19 2527 LOIR-ET-CHER 1410000 530 821 872 1693 1096 1027 2123 0 430 1657 2752 2556 5308 2493 2382 4875 433 0 2187 3573 3428 7001 3589 3409 6998 3 0 2.89103.33 436 13 57 24 530 1407 49 144 57 1657 1843 62 201 81 2187 8 207 157 69 32 32 19 6 530 55 670 509 193 89 71 55 15 1657 63 877 666 262 121 103 74 21 2187 LOIRE 1420000 1830 3474 2947 6421 2926 2315 52411180 0 2544 5148 4878 10026 4532 4051 8583 1443 0 4374 8622 7825 16447 7458 6366 13824 2623 0 117.37 31.8529.3729.96 38.7336.31 83.47 75.80 82.533.493.843.76 3.97 108.34 115.14111.58104.75 1540 75 171 44 1830 1960 153 317 114 2544 3500 228 488 158 4374 30 419 659 349 173 153 39 8 1830 14 462 853 608 288 242 63 14 2544 44 881 1512 957 461 395 102 22 4374 LOIRE(HAUTE-) 1430000 293 597 551 1148 612 640 1252 0 104 1740 3593 3441 7034 3430 3237 6667 367 0 2033 4190 3992 8182 4042 3877 7919 263 0 138.96137.37 82.21 3.823.803.51 2.88 3.52 3.672.362.512.63 108.61 226 13 39 15 293 1503 50 139 48 1740 1729 63 178 63 2033 0 58 95 49 37 37 9 8 293 22 360 563 395 200 135 44 21 1740 22 418 658 444 237 172 53 29 2033 LOIRE-INFERIEURE 1440000 959 1636 1559 3195 1699 1809 3508 0 313 2687 6065 5541 11606 4723 4146 8869 2737 0 3646 7701 7100 14801 6422 5955 12377 2424 0 47.57 82.82 80.81 3.79 2.08 4.66 4.96 61.44 762 41 132 24 959 2201 93 321 72 2687 2963 134 453 96 3646 6 168 289 235 130 80 40 11 959 67 390 814 614 347 311 107 37 2687 73 558 1103 849 477 391 147 48 3646 LOIRET 1450000 819 1674 1565 3239 1754 1742 3496 0 257 2176 3617 3354 6971 3349 2906 6255 716 0 2995 5291 4919 10210 5103 4648 9751 459 0 35.0933.24 38.41 79.75 9.35 9.03 107.66101.36 659 22 96 42 819 1774 109 218 75 2176 2433 131 314 117 2995 4 232 273 135 77 59 28 11 819 106 767 702 299 128 104 47 23 2176 110 999 975 434 205 163 75 34 2995 LOT 1460000 219 439 374 813 430 483 913 0 100 1763 3182 2896 6078 3086 2862 5948 130 0 1982 3621 3270 6891 3516 3345 6861 30 0 147.60 44.16 43.77 97.01106.86 2.47 2.39 3.092.18 2.50109.03 101.44 72.8166.13 180 9 22 8 219 1534 84 119 26 1763 1714 93 141 34 1982 4 41 80 48 20 17 7 2 219 65 342 649 368 186 99 34 20 1763 69 383 729 416 206 116 41 22 1982 LOT-ET-GARONNE 1470000 755 856 817 1673 1207 1065 2272 0 599 2115 2634 2359 4993 2905 2801 5706 0 713 2870 3490 3176 6666 4112 3866 7978 0 1312 52.4254.1154.3143.60 120.23116.50118.142.182.182.14 3.27 2.93 3.032.69 108.53107.74111.37 51.89 641 22 70 22 755 1835 69 156 55 2115 2476 91 226 77 2870 48 149 278 134 59 45 27 15 755 94 758 644 311 125 109 43 31 2115 142 907 922 445 184 154 70 46 2870 LOZERE 1480000 86 279 247 526 226 207 433 93 0 889 1955 1892 3847 1820 1723 3543 304 0 975 2234 2139 4373 2046 1930 3976 397 0 149.33144.1332.9832.4633.75 38.86 90.91 4.284.344.08 3.70 2.352.232.66103.82 102.57 105.47 66 7 8 5 86 787 29 56 17 889 853 36 64 22 975 0 22 22 25 7 3 7 0 86 4 247 300 173 78 67 17 3 889 4 269 322 198 85 70 24 3 975 MAINE-ET-LOIRE 1490000 835 1296 1265 2561 1464 1602 3066 0 505 3184 4595 4382 8977 4127 3851 7978 999 0 4019 5891 5647 11538 5591 5453 11044 494 0 47.2045.8447.5249.6451.2549.64 2.55 3.45 103.18103.58102.03101.28 97.00 94.65 689 27 90 29 835 2695 132 282 75 3184 3384 159 372 104 4019 15 191 278 153 95 60 30 13 835 230 632 998 607 370 204 116 27 3184 245 823 1276 760 465 264 146 40 4019 MANCHE 1500000 847 1456 1467 2923 1803 1727 3530 0 607 3099 5326 4734 10060 5093 4870 9963 97 0 3946 6782 6201 12983 6896 6597 13493 0 510 138.4847.9344.9246.0246.0449.1949.81 109.05102.30 94.55 94.50 7.87 11.57 687 41 93 26 847 2647 100 279 73 3099 3334 141 372 99 3946 6 158 247 184 107 79 46 20 847 55 546 933 690 362 341 137 35 3099 61 704 1180 874 469 420 183 55 3946 MARNE 1510000 840 1702 1653 3355 1697 1574 3271 84 0 2485 3312 3066 6378 3075 2900 5975 403 0 3325 5014 4719 9733 4772 4474 9246 487 0 94.73 9.00 8.78 5.11 6.03 101.16 110.30 90.8314.4412.9211.62 633 44 89 74 840 1957 81 250 197 2485 2590 125 339 271 3325 6 219 308 126 48 70 37 26 840 62 988 733 312 149 120 86 35 2485 68 1207 1041 438 197 190 123 61 3325 MARNE(HAUTE-) 1520000 243 493 441 934 518 481 999 0 65 2335 2571 2322 4893 2443 2275 4718 175 0 2578 3064 2763 5827 2961 2756 5717 110 0 109.48 46.00 46.93 48.60 2.052.49 108.43108.32101.29 111.20108.29 6.60 6.34 164 15 42 22 243 1661 128 358 188 2335 1825 143 400 210 2578 1 47 84 42 24 23 19 3 243 50 543 794 394 185 206 125 38 2335 51 590 878 436 209 229 144 41 2578 MAYENNE 1530000 349 673 611 1284 969 1025 1994 0 710 2214 3995 3773 7768 4013 3476 7489 279 0 2563 4668 4384 9052 4982 4501 9483 0 431 46.75 4.93 102.88 276 18 49 6 349 1853 101 218 42 2214 2129 119 267 48 2563 2 61 100 88 42 37 14 5 349 13 382 681 495 311 224 86 22 2214 15 443 781 583 353 261 100 27 2563 MEUSE 1550000 364 593 582 1175 657 584 1241 0 66 2385 3198 2910 6108 2917 2802 5719 389 0 2749 3791 3492 7283 3574 3386 6960 323 0 45.9551.0748.15 2.43 3.77 102.50108.10 4.44 250 15 68 31 364 1713 120 366 186 2385 1963 135 434 217 2749 4 66 118 65 46 27 25 13 364 16 727 750 388 191 166 105 42 2385 20 793 868 453 237 193 130 55 2749 MORBIHAN 1560000 537 1069 978 2047 1436 1233 2669 0 622 2698 5614 5438 11052 6182 5560 11742 0 690 3235 6683 6416 13099 7618 6793 14411 0 1312 152.44 34.2434.0434.4830.3533.91 112.80 3.744.353.68 2.67 2.42 3.295.094.594.70101.82101.29 60.0063.1470.01 425 36 56 20 537 2250 75 289 84 2698 2675 111 345 104 3235 1 99 156 128 62 66 18 7 537 13 526 880 660 278 248 77 16 2698 14 625 1036 788 340 314 95 23 3235 MOSELLE 1570000 638 1247 1291 2538 1486 1344 2830 0 292 2137 4737 4336 9073 4308 4042 8350 723 0 2775 5984 5627 11611 5794 5386 11180 431 0 143.22140.33 48.6252.04 78.64 73.403.77 3.45 112.55 9.64 9.89 509 24 90 15 638 1777 64 243 53 2137 2286 88 333 68 2775 5 90 230 143 72 61 26 11 638 33 430 759 441 224 165 62 23 2137 38 520 989 584 296 226 88 34 2775 NIEVRE 1580000 480 800 729 1529 819 698 1517 12 0 2289 4002 3619 7621 3536 3003 6539 1082 0 2769 4802 4348 9150 4355 3701 8056 1094 0 112.83 87.65 81.55 2.791.72107.94 114.95120.67106.2662.6158.9462.08 397 18 46 19 480 1906 69 189 125 2289 2303 87 235 144 2769 3 143 168 86 36 25 17 2 480 19 831 721 341 140 140 62 35 2289 22 974 889 427 176 165 79 37 2769 NORD 1590000 4456 10783 10263 21046 9676 8914 185902456 0 4398 9076 8507 17583 7697 7291 14988 2595 0 8854 19859 18770 38629 17373 16205 33578 5051 0 114.3232.8930.7929.19 86.30 69.83 69.223.823.663.54 8.32 8.08 9.644.584.634.75 3632 176 458 190 4456 3750 115 422 111 4398 7382 291 880 301 8854 68 890 1536 941 484 356 131 50 4456 187 900 1416 978 475 305 122 15 4398 255 1790 2952 1919 959 661 253 65 8854 OISE 1600000 469 875 789 1664 1116 1143 2259 0 595 3006 3954 3669 7623 4618 4379 8997 0 1374 3475 4829 4458 9287 5734 5522 11256 0 1969 43.69 122.15 105.472.392.69 102.25101.69102.18 19.1220.7818.62 362 23 50 34 469 2541 89 227 149 3006 2903 112 277 183 3475 6 174 135 53 30 38 21 12 469 133 1498 741 252 132 136 78 36 3006 139 1672 876 305 162 174 99 48 3475 ORNE 1610000 481 787 707 1494 939 1084 2023 0 529 2623 3532 3237 6769 4220 4001 8221 0 1452 3104 4319 3944 8263 5159 5085 10244 0 1981 148.42138.2553.8552.6254.2443.1553.3149.64124.80 98.70109.272.472.702.43 99.87 98.72 94.98 390 24 49 18 481 2252 64 224 83 2623 2642 88 273 101 3104 21 102 149 81 52 44 21 11 481 174 696 666 447 241 212 145 42 2623 195 798 815 528 293 256 166 53 3104 PAS-DE-CALAIS 1620000 1229 3010 2667 5677 4082 2730 6812 0 1135 3933 7320 6662 13982 6894 6411 13305 677 0 5162 10330 9329 10659 10976 9141 20117 0 458 32.38 75.28 71.19 3.60 8.92 8.99 9.03 109.02109.88 118.63 99.62 989 41 165 34 1229 3301 143 421 68 3933 4290 184 586 102 5162 17 377 407 190 108 80 39 11 1229 116 1136 1161 739 333 326 96 26 3933 133 1513 1568 929 441 406 135 37 5162 PUY-DE-DOME 1630000 980 1352 1313 2665 1806 1612 3418 0 753 3815 5888 5482 11370 6042 5785 11827 0 457 4795 7240 6795 14035 7848 7397 15245 0 1210 44.93 99.70108.57 3.23 3.31 3.36 102.40 110.11 802 37 101 40 980 3303 77 320 115 3815 4105 114 421 155 4795 12 274 329 157 98 56 40 14 980 12 1034 1326 836 289 195 96 27 3815 24 1308 1655 993 387 251 136 41 4795 PYRENEES(BASSES-) 1640000 348 800 727 1527 1408 1596 3004 0 1477 2262 4902 4435 9337 6750 7049 13799 0 4462 2610 5702 5162 10864 8158 8645 16803 0 5939 154.49152.92 26.2339.95 155.52106.11 3.77 8.00 8.09 8.081.541.531.83110.14107.96 94.08 95.71 291 11 39 7 348 1955 49 225 33 2262 2246 60 264 40 2610 0 59 120 73 42 34 20 0 348 44 350 655 544 325 253 73 18 2262 44 409 775 617 367 287 93 18 2610 PYRENEES(HAUTES-) 1650000 261 662 567 1229 719 623 1342 0 113 1179 2266 2055 4321 2418 2150 4568 0 247 1440 2928 2622 5550 3137 2773 5910 0 360 165.01170.8545.9943.4048.32 36.91 99.62 8.19 8.53 2.61 109.70 111.73 226 6 27 2 261 1034 34 95 16 1179 1260 40 122 18 1440 10 52 93 45 31 18 9 3 261 8 159 322 313 197 120 47 13 1179 18 211 415 358 228 138 56 16 1440 PYRENEES-ORIENTALES 1660000 402 772 696 1468 635 552 1187 281 0 1325 2261 2099 4360 2053 1805 3858 502 0 1727 3033 2795 5828 2688 2357 5045 783 0 137.4332.4632.3535.70 36.3138.89 3.643.67 107.86 112.61107.23103.3356.37 68.77 306 12 54 30 402 1024 56 186 59 1325 1330 68 240 89 1727 12 103 116 66 38 47 16 4 402 62 329 377 241 129 113 58 16 1325 74 432 493 307 167 160 74 20 1727 RHIN(BAS-) 1670000 1339 3240 3090 6330 3498 3559 7057 0 727 1917 5004 4720 9724 4917 4477 9394 330 0 3256 8244 7810 16054 8415 8036 16451 0 397 135.93 32.2732.36 86.11 74.04 4.303.893.79 8.59 9.34 9.74 4.895.29103.63103.72 102.58 94.50 2.89 3.44 2.93 1033 39 229 38 1339 1572 60 268 17 1917 2605 99 497 55 3256 11 167 414 342 177 151 59 18 1339 64 329 654 445 223 169 25 8 1917 75 496 1068 787 400 320 84 26 3256 RHIN(HAUT-) 1680000 1389 3422 3146 6568 4908 4683 9591 0 3023 1778 4156 3893 8049 5323 4719 10042 0 1993 3167 7578 7039 14617 10231 9402 19633 0 5016 29.6632.4626.62 136.50 69.25 76.933.983.90 8.46 8.9710.255.984.955.76103.64107.63 4.83 5.0510.55 1079 43 217 50 1389 1474 46 223 35 1778 2553 89 440 85 3167 4 234 436 314 196 137 55 13 1389 17 303 563 458 215 157 49 16 1778 21 537 999 772 411 294 104 29 3167 RHONE 1690000 3829 6240 5844 12084 6950 5931 12881 0 797 1468 2572 2288 4860 2459 2242 4701 159 0 5297 8812 8132 16944 9409 8173 17582 0 638 104.63107.32 73.77 2.602.5313.5012.9913.205.795.996.20 113.91 24.42 3162 128 405 134 3829 1300 38 108 22 1468 4462 166 513 156 5297 33 594 1290 895 426 337 187 67 3829 22 384 513 277 144 81 30 17 1468 55 978 1803 1172 570 418 217 84 5297 SAONE(HAUTE-) 1700000 314 480 479 959 623 564 1187 0 228 2498 3443 3290 6733 3954 3754 7708 0 975 2812 3923 3769 7692 4577 4318 8895 0 1203 111.97110.78 47.5049.66116.27 81.69 2.452.65 8.21 8.65 102.91 14.8316.6517.27 214 13 55 32 314 1837 91 396 174 2498 2051 104 451 206 2812 1 45 100 68 37 30 25 8 314 47 532 695 521 268 231 133 71 2498 48 577 795 589 305 261 158 79 2812 SAONE-ET-LOIRE 1710000 746 1623 1580 3203 1623 1446 3069 134 0 4305 6827 6545 13372 5902 5298 11200 2172 0 5051 8450 8125 16575 7525 6744 14269 2306 0 117.61114.7536.21 85.48 79.36 76.31 102.96 575 28 108 35 746 3620 111 411 163 4305 4195 139 519 198 5051 7 174 260 152 63 51 28 11 746 58 1366 1506 683 284 243 98 67 4305 65 1540 1766 835 347 294 126 78 5051 SARTHE 1720000 562 777 733 1510 965 999 1964 0 454 3346 4589 4329 8918 5001 4445 9446 0 528 3908 5366 5062 10428 5966 5444 11410 0 982 115.8746.9046.3546.35 53.28 2.382.502.35 5.09 95.69 422 21 81 38 562 2807 78 336 125 3346 3229 99 417 163 3908 4 126 169 116 55 44 35 13 562 69 1037 1133 505 219 209 98 76 3346 73 1163 1302 621 274 253 133 89 3908 SEINE 175000015918 26359 24937 51296 26161 24953 51114 182 0 456 811 800 1611 738 733 1471 140 0 16374 27170 25737 52907 26899 25686 52585 322 0 96.71 95.1534.7631.6830.7734.99 36.47 77.19 84.582.242.252.2626.3726.1926.816.096.496.52 99.41 9.2910.16 9.8912895 7751608 64015918 360 24 53 19 456 13255 799 1661 659 16374 65 3006 5430 344416511421 660 24115918 13 165 138 70 22 27 15 6 456 78 3171 5568 3514 1673 1448 675 247 16374 SEINE-INFERIEURE 1760000 2604 5616 5277 10893 5634 5624 11258 0 365 3271 5897 5523 11420 6086 5657 11743 0 323 5875 11513 10800 22313 11720 11281 23001 0 688 36.28 33.9135.14 36.96 13.0611.5111.984.954.774.97 102.27 59.96 2099 139 232 134 2604 2897 117 168 89 3271 4996 256 400 223 5875 138 757 711 456 227 152 106 57 2604 95 1011 1105 532 223 174 95 36 3271 233 1768 1816 988 450 326 201 93 5875 SEINE-ET-MARNE 1770000 489 859 777 1636 1116 927 2043 0 407 2706 3693 3452 7145 3893 3554 7447 0 302 3195 4552 4229 8781 5009 4481 9490 0 709 102.80 110.29110.3422.6418.94 372 28 55 34 489 2305 95 198 108 2706 2677 123 253 142 3195 5 141 173 72 37 37 15 9 489 120 1250 778 265 105 96 60 32 2706 125 1391 951 337 142 133 75 41 3195 SEINE-ET-OISE 1780000 1082 1663 1587 3250 1979 1804 3783 0 533 3310 4381 4048 8429 4996 4522 9518 0 1089 4392 6044 5635 11679 6975 6326 13301 0 1622 106.20 114.40 2.38 2.37 109.22 107.83 15.1415.3014.20 878 46 92 66 1082 2888 85 216 121 3310 3766 131 308 187 4392 33 323 362 185 62 50 47 20 1082 125 1517 993 327 143 86 75 44 3310 158 1840 1355 512 205 136 122 64 4392 SEVRES(DEUX-) 1790000 386 653 583 1236 652 539 1191 45 0 2003 3454 3238 6692 2717 2621 5338 1354 0 2389 4107 3821 7928 3369 3160 6529 1399 0 145.64 43.47 51.6855.0551.08 81.93 78.98 85.26 2.342.862.59 326 19 31 10 386 1701 63 176 63 2003 2027 82 207 73 2389 4 99 136 50 53 27 14 3 386 56 505 654 341 215 140 73 19 2003 60 604 790 391 268 167 87 22 2389 SOMME 1800000 882 1659 1611 3270 1818 1769 3587 0 317 3699 5497 5037 10534 5829 5585 11414 0 880 4581 7156 6648 13804 7647 7354 15001 0 1197 49.23 81.76 8.22 8.64 8.82 107.13102.76 103.45 736 21 93 32 882 3248 62 284 105 3699 3984 83 377 137 4581 13 284 302 128 61 48 38 8 882 102 1401 1163 526 221 176 84 26 3699 115 1685 1465 654 282 224 122 34 4581 TARN 1810000 621 1035 1102 2137 1345 1293 2638 0 501 1828 3258 3002 6260 3755 3348 7103 0 843 2449 4293 4104 8397 5100 4641 9741 0 1344 149.02138.77 45.27 116.51102.01104.53 2.71 2.86 3.65 102.56 62.7564.94 502 19 73 27 621 1601 41 147 39 1828 2103 60 220 66 2449 4 143 211 119 58 54 25 7 621 42 433 608 403 166 123 37 16 1828 46 576 819 522 224 177 62 23 2449 TARN-ET-GARONNE 1820000 784 1013 906 1919 1243 1206 2449 0 530 1212 1437 1359 2796 1788 1540 3328 0 532 1996 2450 2265 4715 3031 2746 5777 0 1062 117.1553.2249.4548.87 123.53108.11116.612.192.392.33 2.99 2.40 2.83 4.74 107.88112.38 107.14 76.4558.9060.35 694 14 61 15 784 1072 19 107 14 1212 1766 33 168 29 1996 3 233 242 182 39 60 17 8 784 48 387 369 208 103 62 25 10 1212 51 620 611 390 142 122 42 18 1996 VAR 1830000 1681 2598 2476 5074 5639 3110 8749 0 3675 1327 1777 1628 3405 1930 1938 3868 0 463 3008 4375 4104 8479 7569 5048 12617 0 4138 45.67 30.28 150.82101.68 103.03148.94119.98 1342 77 154 108 1681 1123 41 121 42 1327 2465 118 275 150 3008 51 385 525 291 147 173 84 25 1681 32 397 412 232 105 87 40 22 1327 83 782 937 523 252 260 124 47 3008 VAUCLUSE 1840000 1256 2507 2305 4812 2341 2167 4508 304 0 1045 1764 1609 3373 1625 1540 3165 208 0 2301 4271 3914 8185 3966 3707 7673 512 0 136.4034.6233.6433.29 37.05 93.41 79.33 3.603.92 5.074.654.76 98.99 111.50 996 44 156 60 1256 850 37 98 60 1045 1846 81 254 120 2301 42 364 369 201 102 105 46 27 1256 64 320 313 165 69 62 30 22 1045 106 684 682 366 171 167 76 49 2301 VENDEE 1850000 260 524 466 990 546 396 942 48 0 2491 4517 4490 9007 4049 3507 7556 1451 0 2751 5041 4956 9997 4595 3903 8498 1499 0 47.4249.7146.60 84.57 75.42 83.39 3.14 2.48 3.052.81 2.92101.12 117.38 61.0961.3662.90 210 10 34 6 260 2083 92 223 93 2491 2293 102 257 99 2751 9 75 58 64 18 21 11 4 260 41 604 816 431 259 235 88 17 2491 50 679 874 495 277 256 99 21 2751 VIENNE 1860000 487 858 817 1675 876 938 1814 0 139 2106 3355 2912 6267 2625 2456 5081 1186 0 2593 4213 3729 7942 3501 3394 6895 1047 0 118.17117.79 48.33 47.54 86.44 81.80 2.56 2.772.74 111.33 101.12 68.3065.4466.86 388 14 65 20 487 1798 43 193 72 2106 2186 57 258 92 2593 5 132 175 77 40 33 21 4 487 150 646 685 282 136 123 57 27 2106 155 778 860 359 176 156 78 31 2593 VIENNE(HAUTE-) 1870000 600 1153 1109 2262 1334 1295 2629 0 367 2337 3826 3416 7242 3891 3486 7377 0 135 2937 4979 4525 9504 5225 4781 10006 0 502 118.26 34.7432.70 32.9435.4632.70 115.25 108.77 109.56 96.93 75.2678.7075.48 471 11 72 46 600 1865 68 259 145 2337 2336 79 331 191 2937 16 155 197 108 45 41 15 23 600 47 878 733 285 144 155 69 26 2337 63 1033 930 393 189 196 84 49 2937 VOSGES 1880000 373 619 573 1192 676 692 1368 0 176 2795 4683 4116 8799 4733 4098 8831 0 32 3168 5302 4689 9991 5409 4790 10199 0 208 115.42107.70 85.48 2.672.60 8.56 9.175.995.676.14110.23102.45107.18 92.88 8.50 5.46 3.85 286 11 57 19 373 2163 86 398 148 2795 2449 97 455 167 3168 5 79 127 57 40 36 22 7 373 96 578 868 553 304 232 123 41 2795 101 657 995 610 344 268 145 48 3168 YONNE 1890000 511 795 756 1551 907 866 1773 0 222 2772 3638 3358 6996 3751 3549 7300 0 304 3283 4433 4114 8547 4658 4415 9073 0 526 44.9545.79 2.42 3.96 3.97 99.18100.17 388 16 78 29 511 2297 71 245 159 2772 2685 87 323 188 3283 4 141 165 92 34 36 29 10 511 77 1009 932 385 136 100 84 49 2772 81 1150 1097 477 170 136 113 59 3283 MEURTHE 1990000 774 1332 1271 2603 1866 1730 3596 0 993 2367 3952 3527 7479 4589 4312 8901 0 1422 3141 5284 4798 10082 6455 6042 12497 0 2415 114.73 35.5651.8248.01125.37 2.41 7.85 9.4410.125.595.355.70 102.37 96.69 90.93 5.59 7.63 4.43 691 26 26 31 774 2037 137 136 57 2367 2728 163 162 88 3141 9 40 197 242 99 79 83 25 774 26 94 678 748 279 234 201 107 2367 35 134 875 990 378 313 284 132 3141 -------------------------------------------------------------------------------- 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PROJCS["RGF93_Lambert_93",GEOGCS["GCS_RGF_1993",DATUM["D_RGF_1993",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",700000.0],PARAMETER["False_Northing",6600000.0],PARAMETER["Central_Meridian",3.0],PARAMETER["Standard_Parallel_1",44.0],PARAMETER["Standard_Parallel_2",49.0],PARAMETER["Latitude_Of_Origin",46.5],UNIT["Meter",1.0]] -------------------------------------------------------------------------------- /geo_source/arrondissements1851_S.shp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/geo_source/arrondissements1851_S.shp -------------------------------------------------------------------------------- /geo_source/arrondissements1851_S.shx: -------------------------------------------------------------------------------- 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6405344 6405345 6401346 4003347 4001348 4002349 8301350 8302351 8304352 8304353 8304354 8304355 7403356 7401357 7404358 7402359 7303360 7304361 7301362 7302363 602 364 601 365 603 366 5403367 5404368 5401369 5402370 8804371 8805372 8803373 8802374 8801375 9002376 -------------------------------------------------------------------------------- /geo_source/arrondissements1876_S.prj: -------------------------------------------------------------------------------- 1 | PROJCS["RGF93_Lambert_93",GEOGCS["GCS_RGF_1993",DATUM["D_RGF_1993",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",700000.0],PARAMETER["False_Northing",6600000.0],PARAMETER["Central_Meridian",3.0],PARAMETER["Standard_Parallel_1",44.0],PARAMETER["Standard_Parallel_2",49.0],PARAMETER["Latitude_Of_Origin",46.5],UNIT["Meter",1.0]] -------------------------------------------------------------------------------- /geo_source/arrondissements1876_S.shp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/geo_source/arrondissements1876_S.shp -------------------------------------------------------------------------------- /geo_source/arrondissements1876_S.shx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/geo_source/arrondissements1876_S.shx -------------------------------------------------------------------------------- /man/data_SGF.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{data_SGF} 5 | \alias{data_SGF} 6 | \title{Table des données de la SGF} 7 | \format{Table (format data.frame) de 4 627 598 lignes et 9 variables. 8 | \describe{ 9 | \item{NIVGEO}{Nom de l'échelle géographique (FR pour France entière, DEP pour Département, 10 | ARR pour Arrondissement, CHL pour Chef-lieu d'arrondissement, VIL pour Ville)} 11 | \item{CODGEO}{Identifiant du territoire} 12 | \item{LIBGEO}{Libellé du territoire} 13 | \item{SRC_DATA}{Identifiant du tableau de donnée d'origine} 14 | \item{VAR_COD}{Identifiant de l'indicateur (1 id unique par tableau de donnée d'origine)} 15 | \item{VAR_LIB}{Nom en clair de l'indicateur} 16 | \item{ANNEE_DONNEE}{Millésime de la donnée} 17 | \item{ANNEE_GEOGRAPHIE}{Millésime des contours géographiques correspondants (1801, 1826, 1866, 1876 ou 1918)} 18 | \item{VAL}{Valeur de l'indicateur} 19 | }} 20 | \source{ 21 | \url{https://www.insee.fr/fr/statistiques/2591397} 22 | } 23 | \usage{ 24 | data_SGF 25 | } 26 | \description{ 27 | Données de la SGF (au format long) publiées par l'Insee. 28 | } 29 | \keyword{datasets} 30 | -------------------------------------------------------------------------------- /man/figures/README-plot_exemple_carto-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/man/figures/README-plot_exemple_carto-1.png -------------------------------------------------------------------------------- /man/figures/README-plot_exemple_carto_arr-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/man/figures/README-plot_exemple_carto_arr-1.png -------------------------------------------------------------------------------- /man/figures/README-plot_geographie_departement-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/man/figures/README-plot_geographie_departement-1.png -------------------------------------------------------------------------------- /man/figures/README-recherche_indics_2-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/observatoire-territoires/sgf/dade25ff0a5af50d30768019bfedd39fbcc20769/man/figures/README-recherche_indics_2-1.png -------------------------------------------------------------------------------- /man/geo_ARR_SGF_histo.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{geo_ARR_SGF_histo} 5 | \alias{geo_ARR_SGF_histo} 6 | \title{Géographies des arrondissements} 7 | \format{Table géographique (format sf data.frame) de 1084 lignes et 3 variables. 8 | \describe{ 9 | \item{CODGEO}{Identifiant du territoire} 10 | \item{LIBGEO}{Nom du territoire} 11 | \item{ANNEE_GEOGRAPHIE}{Millésime du contour géographique (1801, 1826 ou 1876)} 12 | \item{geometry}{Description des contours géographiques} 13 | }} 14 | \usage{ 15 | geo_ARR_SGF_histo 16 | } 17 | \description{ 18 | Contours et libellés des arrondissements de France métropolitaine à plusieurs millésimes. 19 | } 20 | \keyword{datasets} 21 | -------------------------------------------------------------------------------- /man/geo_DEP_SGF_histo.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{geo_DEP_SGF_histo} 5 | \alias{geo_DEP_SGF_histo} 6 | \title{Géographies des départements} 7 | \format{Table géographique (format sf data.frame) de 437 lignes et 3 variables. 8 | \describe{ 9 | \item{CODGEO}{Identifiant du territoire} 10 | \item{LIBGEO}{Nom du territoire} 11 | \item{ANNEE_GEOGRAPHIE}{Millésime du contour géographique (1801, 1826, 1866, 1876 ou 1918)} 12 | \item{geometry}{Description des contours géographiques} 13 | }} 14 | \usage{ 15 | geo_DEP_SGF_histo 16 | } 17 | \description{ 18 | Contours et libellés des départements de France métropolitaine à plusieurs millésimes. 19 | } 20 | \keyword{datasets} 21 | -------------------------------------------------------------------------------- /man/indicateurs_SGF.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{indicateurs_SGF} 5 | \alias{indicateurs_SGF} 6 | \title{Liste des indicateurs disponibles} 7 | \format{Table (format data.frame) de 43 702 lignes et 7 variables. 8 | \describe{ 9 | \item{TABLEAU}{Identifiant du tableau de donnée d'origine} 10 | \item{NOM_TABLEAU}{Nom en clair du tableau de donnée d'origine} 11 | \item{VAR_COD}{Identifiant de l'indicateur (1 id unique par tableau de donnée d'origine)} 12 | \item{VAR_LIB}{Nom en clair de l'indicateur} 13 | \item{ANNEE_DONNEE}{Millésime de la donnée statistique} 14 | \item{ECHELLES_GEO}{Echelle(s) géographique(s) disponibles (DEP pour Département, 15 | ARR pour Arrondissement, DEP_ARR pour Département+Arrondissement, CHL_VILLE pour Chef-lieu d'arrondissement)} 16 | \item{ANNEE_GEOGRAPHIE}{Millésime des contours géographiques correspondants (1801, 1826, 1866, 1876 ou 1918)} 17 | }} 18 | \source{ 19 | \url{https://www.insee.fr/fr/statistiques/2591397} 20 | } 21 | \usage{ 22 | indicateurs_SGF 23 | } 24 | \description{ 25 | Description des plus de 40 000 indicateurs de la SGF publiées par l'Insee. 26 | } 27 | \keyword{datasets} 28 | -------------------------------------------------------------------------------- /man/sgf_sfdf.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/sgf_sfdf.R 3 | \name{sgf_sfdf} 4 | \alias{sgf_sfdf} 5 | \title{Créer une table de données géographiques avec des indicateurs de la SGF} 6 | \usage{ 7 | sgf_sfdf(TYPE_NIVGEO, SRC, LISTE_VAR_COD) 8 | } 9 | \arguments{ 10 | \item{TYPE_NIVGEO}{Nom court du niveau géographique ('DEP' pour département, "ARR" pour arrondissement).} 11 | 12 | \item{SRC}{Nom du tableau de données dans lequel se trouvent les indicateurs à récupérer (cf. champ 'TABLEAU' dans la table 'indicateurs_SGF').} 13 | 14 | \item{LISTE_VAR_COD}{Liste des codes des variables à récupérer (cf. champ 'VAR_COD' dans la table 'indicateurs_SGF').} 15 | } 16 | \value{ 17 | Renvoie un sf dataframe contenant code et nom du territoire ainsi que les indicateurs sélectionnés. 18 | } 19 | \description{ 20 | Générer un sf dataframe contenant les indicateurs de la SGF préalablement sélectionnés. 21 | } 22 | \details{ 23 | Deux niveaux géographiques sont disponibles : département (TYPE_NIVGEO = 'DEP') et arrondissement ("ARR"). \cr 24 | } 25 | \examples{ 26 | \dontrun{ 27 | # Creation du sf dataframe contenant les indicateurs de cheptels (bovins, ovins, etc...) 28 | issus du recensement de 1872 29 | DEP_cheptels_1872 <- 30 | sgf_sfdf(TYPE_NIVGEO = "DEP", 31 | SRC ="REC_T17", 32 | LISTE_VAR_COD = c(52, 59, 65, 70, 83, 87, 94)) 33 | 34 | } 35 | 36 | } 37 | -------------------------------------------------------------------------------- /sgf.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | BuildType: Package 16 | PackageUseDevtools: Yes 17 | PackageInstallArgs: --no-multiarch --with-keep.source 18 | --------------------------------------------------------------------------------