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Generating the Île-de-France population

The following sections describe how to generate a synthetic population for Île-de-France using the pipeline. First all necessary data must be gathered. Afterwards, the pipeline can be run to create a synthetic population in CSV and GPKG format. These outputs can be used for analysis, or serve as input to run a transport simulation in MATSim. Also, this guide is the basis for creating populations and simulations of other regions and cities such as Toulouse or Lyon.

This guide will cover the following steps:

Gathering the data

To create the scenario, a couple of data sources must be collected. It is best to start with an empty folder, e.g. /data. All data sets need to be named in a specific way and put into specific sub-directories. The following paragraphs describe this process.

1) Census data (RP 2019)

Census data containing the socio-demographic information of people living in France is available from INSEE:

  • Census data
  • Download the data set in csv format by clicking the link under Individus localisés au canton-ou-ville.
  • Copy the zip file into the folder data/rp_2019

2) Population totals (RP 2019)

We also make use of more aggregated population totals available from INSEE:

  • Population data
  • Download the data for France hors Mayotte in xlsx format.
  • Copy the zip file into the folder data/rp_2019.

3) Origin-destination data (RP-MOBPRO / RP-MOBSCO 2019)

Origin-destination data is available from INSEE (at two locations):

4) Income tax data (Filosofi 2019)

The tax data set is available from INSEE:

  • Income tax data
  • Download the munipality data (first link): Base niveau communes en 2019 in xlsx format
  • Copy the zip file into the folder data/filosofi_2019
  • Download the administrative level data (second link): Base niveau administratif en 2019 in xlsx format
  • Copy the second zip file into data/filosofi_2019

5) Service and facility census (BPE 2021)

The census of services and facilities in France is available from INSEE:

  • Service and facility census
  • Download the uppermost data set in csv format. It contains all available services while the lower data sets only contain observations for specific sectors.
  • Copy the zip file into the folder data/bpe_2021.

6a) National household travel survey (ENTD 2008)

The national household travel survey is available from the Ministry of Ecology:

  • National household travel survey
  • Scroll all the way down the website to the Table des donnés (a clickable pop-down menu).
  • You can either download all the available csv files in the list, but only a few are actually relevant for the pipeline. Those are:
    • Données socio-démographiques des ménages (Q_tcm_menage_0.csv)
    • Données socio-démographiques des individus (Q_tcm_individu.csv)
    • Logement, stationnement, véhicules à disposition des ménages (Q_menage.csv)
    • Données trajets domicile-travail, domicile-étude, accidents (Q_individu.csv)
    • Données mobilité contrainte, trajets vers lieu de travail (Q_ind_lieu_teg.csv)
    • Données mobilité déplacements locaux (K_deploc.csv)
  • Put the downloaded csv files in to the folder data/entd_2008.

6b) (Optional) Regional household travel survey (EGT)

Usually, you do not have access to the regional household travel survey, which is not available publicly. In case you have access (but we cannot guarantee that you have exactly the correct format), you should make sure that the following files are accessible in the folder data/egt_2010: Menages_semaine.csv, Personnes_semaine.csv, Deplacements_semaine.csv.

7) IRIS zoning system (2021)

The IRIS zoning system is available from IGN:

  • IRIS data
  • Download the 2021 edition.
  • Copy the 7z file into the folder data/iris_2021

8) Zoning registry (2021)

We make use of a zoning registry by INSEE that establishes a connection between the identifiers of IRIS, municipalities, departments and regions:

  • Zoning data
  • Download the 2021 edition as a zip file.
  • Copy the zip file into data/codes_2021.

9) Enterprise census (SIRENE)

The enterprise census of France is available on data.gouv.fr:

  • Enterprise census
  • Scroll down and click on the blue download button on the right for the two following data sets:
    • Sirene : Fichier StockUniteLegale (followed by a date), the database of enterprises
    • Sirene : Fichier StockEtablissement (followed by a date), the database of enterprise facilities
  • The files are updated monthly and are rather large. After downloading, you should have two files:
    • StockEtablissement_utf8.zip
    • StockUniteLegale_utf8.zip
  • Move both zip files into data/sirene.

The geolocated enterprise census is available on data.gouv.fr:

  • Geolocated enterprise census
  • Scroll down and click on the blue download button on the right for the following data set:
    • Sirene : Fichier GeolocalisationEtablissement_Sirene_pour_etudes_statistiques (followed by a date),
  • Put the downloaded zip file into data/sirene

10) Buildings database (BD TOPO)

The French Buildings database is available from IGN:

  • Buildings database
  • In the sidebar on the right, under Téléchargement anciennes éditions, click on BD TOPO® 2022 GeoPackage Départements to go to the saved data publications from 2022.
  • The data is split by department and they are identified with a number. For the Île-de-France region, download:
    • Paris (75)
    • Seine-et-Marne (77)
    • Yvelines (78)
    • Essonne (91)
    • Hauts-de-Seine (92)
    • Seine-Saint-Denis (93)
    • Val-de-Marne (94)
    • Val-d'Oise (95)
  • Copy the eight 7z files into data/bdtopo_idf.

11) Adresses database (BAN)

The French adresses database is available on data.gouv.fr :

  • Adresses database
  • Click on the link adresses-xx.csv.gz where xx = departments codes (75, 77, 78, 91, 92, 93, 94, 95)
  • Copy the gz files into data/ban_idf.

Overview

Your folder structure should now have at least the following files:

  • data/rp_2019/RP2019_INDCVI_csv.zip
  • data/rp_2019/RP2019_MOBPRO_csv.zip
  • data/rp_2019/RP2019_MOBSCO_csv.zip
  • data/rp_2019/base-ic-evol-struct-pop-2019.zip
  • data/filosofi_2019/indic-struct-distrib-revenu-2019-COMMUNES.zip
  • data/filosofi_2019/indic-struct-distrib-revenu-2019-SUPRA.zip
  • data/bpe_2021/bpe21_ensemble_xy_csv.zip
  • data/entd_2008/Q_individu.csv
  • data/entd_2008/Q_tcm_individu.csv
  • data/entd_2008/Q_menage.csv
  • data/entd_2008/Q_tcm_menage_0.csv
  • data/entd_2008/K_deploc.csv
  • data/entd_2008/Q_ind_lieu_teg.csv
  • data/iris_2021/CONTOURS-IRIS_2-1__SHP__FRA_2021-01-01.7z
  • data/codes_2021/reference_IRIS_geo2021.zip
  • data/sirene/StockEtablissement_utf8.csv
  • data/sirene/StockUniteLegale_utf8.zip
  • data/sirene/GeolocalisationEtablissement_Sirene_pour_etudes_statistiques_utf8.zip
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D075_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D077_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D078_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D091_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D092_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D093_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D094_2022-03-15.7z
  • data/bdtopo_idf/BDTOPO_3-0_TOUSTHEMES_GPKG_LAMB93_D095_2022-03-15.7z
  • data/ban_idf/adresses-75.csv.gz
  • data/ban_idf/adresses-77.csv.gz
  • data/ban_idf/adresses-78.csv.gz
  • data/ban_idf/adresses-91.csv.gz
  • data/ban_idf/adresses-92.csv.gz
  • data/ban_idf/adresses-93.csv.gz
  • data/ban_idf/adresses-94.csv.gz

In case you are using the regional household travel survey (EGT), the following files should also be in place:

  • data/egt_2010/Menages_semaine.csv
  • data/egt_2010/Personnes_semaine.csv
  • data/egt_2010/Deplacements_semaine.csv

Running the pipeline

The pipeline code is available in this repository. To use the code, you have to clone the repository with git:

git clone https://github.com/eqasim-org/ile-de-france

which will create the ile-de-france folder containing the pipeline code. To set up all dependencies, especially the synpp package, which is the code of the pipeline code, we recommend setting up a Python environment using Anaconda:

cd ile-de-france
conda env create -f environment.yml

This will create a new Anaconda environment with the name ile-de-france.

To activate the environment, run:

conda activate ile-de-france

Now have a look at config.yml which is the configuration of the pipeline code. Have a look at synpp in case you want to get a more general understanding of what it does. For the moment, it is important to adjust two configuration values inside of config.yml:

  • working_directory: This should be an existing (ideally empty) folder where the pipeline will put temporary and cached files during runtime.
  • data_path: This should be the path to the folder where you were collecting and arranging all the raw data sets as described above.
  • output_path: This should be the path to the folder where the output data of the pipeline should be stored. It must exist and should ideally be empty for now.
  • output_formats: This should specify the formats of outputs. Available formats are csv, gpkg, parquet and geoparquet. Default value is csv and gpkg: ["csv", "gpkg"].

To set up the working/output directory, create, for instance, a cache and a output directory. These are already configured in config.yml:

mkdir cache
mkdir output

Everything is set now to run the pipeline. The way config.yml is configured it will create the relevant output files in the output folder.

To run the pipeline, call the synpp runner:

python3 -m synpp

It will automatically deshptect the config.yml, process all the pipeline code and eventually create the synthetic population. You should see a couple of stages running one after another. Most notably, first, the pipeline will read all the raw data sets to filter them and put them into the correct internal formats.

After running, you should be able to see a couple of files in the output folder:

  • meta.json contains some meta data, e.g. with which random seed or sampling rate the population was created and when.
  • persons.csv and households.csv contain all persons and households in the population with their respective sociodemographic attributes.
  • activities.csv and trips.csv contain all activities and trips in the daily mobility patterns of these people including attributes on the purposes of activities.
  • activities.gpkg and trips.gpkg represent the same trips and activities, but in the spatial GPKG format. Activities contain point geometries to indicate where they happen and the trips file contains line geometries to indicate origin and destination of each trip.

Warning

Windows users :

The cache file paths can get very long and may break the 256 characters limit in the Microsoft Windows OS. In order to avoid any issue make sure the following regitry entry is set to 1 : HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem\LongPathsEnabled

You should also set git into long path mode by calling : git config --system core.longpaths true

Mode choice

The synthetic data generated by the pipeine so far does not include transport modes (car, bike, walk, pt, ...) for the individual trips as assigning them consistently is a more computation-heavy process (including routing the individual trips for the modes). To add modes to the trip table, a light-weight MATSim simulation needs to be performed. For that, please configure the additional data requirements as described in the procedure to run a MATSim simulation:

After that, you can change the mode_choice entry in the pipeline configuration file config.yml to true:

config:
  mode_choice: true

Running the pipeline again will add the mode colum to the trips.csv file and its spatial equivalent.

Population projections

The pipeline allows to make use of population projections from INSEE up to 2070. The same methodology can also be used to scale down the population. The process takes into account the marginal distribution of sex, age, their combination, and the total number of persons. The census data for the base year (see above) is reweighted according to those marginals using Iterative Proportional Updating.

  • To make use of the scaling, download the projection data from INSEE. There are various scenarios in Excel format that you can choose from. The default is the Scénario centrale, the central scenario.
  • Put the downloaded file into data/projection_2021, so you will have the file data/projection_2021/00_central.xlsx

Then, activate the projection procedure by defining the projection year in the configuration:

config: 
  # [...]
  projection_year: 2030

You may choose any year (past or future) that is contained in the projection scenario Excel file. In case you want to use a different scenario, download the corresponding file, put it into the folder mentioned above, and choose the scenario name via configuration:

config: 
  # [...]
  projection_scenario: 00_central

Urban type

The pipeline allows to work with INSEE's urban type classification (unité urbaine) that distinguishes municipalities in center cities, suburbs, isolated cities, and unclassified ones. To impute the data (currently only for some HTS), activate it via the configuration:

config:
  # [...]
  use_urban_type: true

In order to make use of it for activity chain matching, you can set a custom list of matching attributes like so:

config:
  # [...]
  matching_attributes: ["urban_type", "*default*"]

The *default* trigger will be replaced by the default list of matching attributes.

Note that not all HTS implement the urban type, so matching may not work with some implementations. Most of them, however, contain the data, we just need to update the code to read them in.

To make use of the urban type, the following data is needed:

  • Download the urban type data from INSEE. The pipeline is currently compatible with the 2023 data set (referencing 2020 boundaries).
  • Put the downloaded zip file into data/urban_type, so you will have the file data/urban_type/UU2020_au_01-01-2023.zip

Then, you should be able to run the pipeline with the configuration explained above.

Exclude entreprise with no employee

The pipeline allows to exclude all entreprise without any employee (trancheEffectifsEtablissement is NA, "NN" or "00") indicated in Sirene data for working place distribution. It can be activate via this configuration :

config:
  # [...]
  exclude_no_employee: true

INSEE 200m tiles data

The pipeline allows to use INSEE 200m tiles data in order to locate population instead of using BAN or BDTOPO data. Population is located in the center of the tiles with the INSEE population weight for each tile.

  • In order to use of this location,download the 200m grid data from INSEE. The pipeline is currently compatible with 2019 data set.
  • Put the downloaded zip file into data/tiles_2019, so you will have the file data/tiles_2019/Filosofi2019_carreaux_200m_gpkg.zip

Then, activate it via the configuration :

config:
  # [...]
  home_location_source: tiles

This parameter can also activate use of BDTOPO data only or with BAN data to locate population with respectively building and addresses values.

Education activities locations

The synthetic data generated by the pipeline so far distribute population to education locations without any distinction of age or type of educational institution. To avoid to send yound children to high school for example, a matching of educational institution and person by age range can be activated via configuration :

config:
  # [...]
  education_location_source: weighted

For each type of institution, a weight is attributed by default in the pipeline. To realise a matching weighted with known student numbers by educational institution, the pipeline can also work with a list of educational institution from external geojson or geopackage file with addresses as parameter value. This file must include TYPEQU, commune_id,weightand geometry as column with weight number of student and TYPEQU type of educational institution code similar as BPE ones.

config:
  # [...]
  education_location_source: adresses
  education_file: education/education_addresses.geojson

Income

This pipeline allows using the Bhepop2 package for income assignation.

By default, Eqasim infers income from the global income distribution by municipality from the Filosofi data set. An income value is drawn from this distribution, independent of the household characteristics. This method is called uniform.

Bhepop2 uses income distributions on subpopulations. For instance, Filosofi provides distributions depending on household size. Bhepop2 tries to match all the available distributions, instead of just the global one. This results in more accurate income assignation on subpopulations, but also on the global synthetic population. See the documentation for more information on the affectation algorithm.

To use the bhepop2 method, provide the following config:

config:
  income_assignation_method: bhepop2

Caution, this method will fail on communes where the Filosofi subpopulation distributions are missing. In this case, we fall back to the uniform method.

Analysing synthetic population

In addition to creating synthetic populations, it is possible to output files for analysis.

Comparison population on grid

Using the comparison_flow_volume pipeline in the Analysis directory, you can generate grids comparing the volumes of two synthetic populations on a grid of 1km² squares for each age group and each purpose of their trips. Like with population creation, the pipeline is run with the synpp runner and all parameters needed must be included in the config.yml file.

To be able to use this pipeline, you must already have create at least one synthetic population (1 for volume visualization and 2 for comparison) and download France grid from INSEE. From this zip file, you need to extract grille200m_metropole.gpkg and put it into data/grid.

Then you need to open the config.yml and add the analysis.grid.comparison_flow_volume stage in the run section. To proprely use the comparison_flow_volume pipeline,you'd have to provide the following config:

config:
  output_prefix: name_output_studied_
  comparison_file_prefix: name_output_compared_
  analysis_from_file: true

Before running it, make sur that populations have same format of file. After running, you should find all grids for each age group and each trips' purpose in the output folder as: {output_prefix}_{age group}_{trip pupose}.html

Note: With analysis_from_file at False, the last synthetic population is studied by default. Also if output_prefix and comparison_file_prefix refer to the same outputs, or comparison_file_prefix is not specified, then only a volume visualisation of this particular population is produced.