{"id":19070292,"url":"https://github.com/ropenspain/spanishoddata","last_synced_at":"2025-04-28T14:13:46.349Z","repository":{"id":243859331,"uuid":"813670716","full_name":"rOpenSpain/spanishoddata","owner":"rOpenSpain","description":"Access national high-quality and open-access datasets on movement patterns derived from mobile telephone datasets / Accede y usa datos nacionales abiertos sobre movimientos basados en teléfonos 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spanishoddata: Get Spanish Origin-Destination Data \u003ca href=\"https://rOpenSpain.github.io/spanishoddata/\"\u003e\u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"200\" alt=\"spanishoddata website\" /\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n\n[![Project Status:\nActive](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n\u003ca href=\"https://lifecycle.r-lib.org/articles/stages.html#stable\"\ntarget=\"_blank\"\u003e\u003cimg\nsrc=\"https://img.shields.io/badge/lifecycle-stable-brightgreen.svg\"\nalt=\"Lifecycle: stable\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=spanishoddata\"\ntarget=\"_blank\"\u003e\u003cimg\nsrc=\"https://www.r-pkg.org/badges/version/spanishoddata\"\nalt=\"CRAN status\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=spanishoddata\"\ntarget=\"_blank\"\u003e\u003cimg\nsrc=\"https://cranlogs.r-pkg.org/badges/grand-total/spanishoddata?color=blue\"\nalt=\"CRAN/METACRAN Total downloads\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://CRAN.R-project.org/package=spanishoddata\"\ntarget=\"_blank\"\u003e\u003cimg\nsrc=\"https://cranlogs.r-pkg.org/badges/spanishoddata?color=blue\"\nalt=\"CRAN/METACRAN Downloads per month\" /\u003e\u003c/a\u003e\n[![R-CMD-check](https://github.com/rOpenSpain/spanishoddata/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rOpenSpain/spanishoddata/actions/workflows/R-CMD-check.yaml)\n[![DOI](https://zenodo.org/badge/DOI/10.32614/CRAN.package.spanishoddata.svg)](https://doi.org/10.32614/CRAN.package.spanishoddata)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14516104.svg)](https://doi.org/10.5281/zenodo.14516104)\n\u003c!-- 10.5281/zenodo.14516104 --\u003e \u003c!-- badges: end --\u003e\n\n**spanishoddata** is an R package that provides functions for\ndownloading and formatting Spanish open mobility data released by the\nSpanish government (Ministerio de Transportes y Movilidad Sostenible\nMITMS 2024).\n\nIt supports the two versions of the Spanish mobility data. [The first\nversion (2020 to\n2021)](https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/estudios-de-movilidad-anteriores/covid-19/opendata-movilidad),\ncovering the period of the COVID-19 pandemic, contains tables detailing\ntrip numbers and distances, broken down by origin, destination,\nactivity, residence province, time interval, distance interval, and\ndate. It also provides tables of individual counts by location and trip\nfrequency. [The second version (2022\nonwards)](https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/opendata-movilidad)\nimproves spatial resolution, adds trips to and from Portugal and France,\nand introduces new fields for study-related activities and\nsociodemographic factors (income, age, and sex) in the\norigin-destination tables, along with additional tables showing\nindividual counts by overnight stay location, residence, and date. See\nthe [package website](https://rOpenSpain.github.io/spanishoddata/) and\nvignettes for\n[v1](https://rOpenSpain.github.io/spanishoddata/articles/v1-2020-2021-mitma-data-codebook)\nand\n[v2](https://rOpenSpain.github.io/spanishoddata/articles/v2-2022-onwards-mitma-data-codebook)\ndata for more details.\n\n**spanishoddata** is designed to save time by providing the data in\nanalysis-ready formats. Automating the process of downloading, cleaning,\nand importing the data can also reduce the risk of errors in the\nlaborious process of data preparation. It also reduces computational\nresources by using computationally efficient packages behind the scenes.\nTo effectively work with multiple data files, it’s recommended you set\nup a data directory where the package can search for the data and\ndownload only the files that are not already present.\n\n## Examples of available data\n\n\u003cdiv id=\"fig-barcelona-flows\"\u003e\n\n\u003cimg src=\"vignettes/media/flows_plot_barcelona.svg\"\nstyle=\"width:100.0%\" /\u003e\n\nFigure 1: Example of the data available through the package: daily flows\nin Barcelona on 7 April 2021\n\n\u003c/div\u003e\n\nTo create static maps like that see our vignette\n[here](https://ropenspain.github.io/spanishoddata/articles/flowmaps-static.html).\n\n------------------------------------------------------------------------\n\n\u003cdiv id=\"fig-spain-flows\"\u003e\n\n![](https://ropenspain.github.io/spanishoddata/media/spain-folding-flows.gif)\n\nFigure 2: Example of the data available through the package: interactive\ndaily flows in Spain\n\n\u003c/div\u003e\n\n\u003cdiv id=\"fig-spain-flows\"\u003e\n\n![](https://ropenspain.github.io/spanishoddata/media/barcelona-time.gif)\n\nFigure 3: Example of the data available through the package: interactive\ndaily flows in Barcelona with time filter\n\n\u003c/div\u003e\n\nTo create interactive maps see our vignette\n[here](https://ropenspain.github.io/spanishoddata/articles/flowmaps-interactive.html).\n\n## Install the package\n\nInstall from CRAN:\n\n``` r\ninstall.packages(\"spanishoddata\")\n```\n\n\u003cdetails\u003e\n\n\u003csummary\u003e\n\nAlternative installation and developemnt\n\u003c/summary\u003e\n\nYou can also install the latest development version of the package from\nrOpenSpain R universe:\n\n``` r\ninstall.packages(\"spanishoddata\",\n  repos = c(\"https://ropenspain.r-universe.dev\",\n    \"https://cloud.r-project.org\"))\n```\n\nAlternative way to install the development version from GitHub:\n\n``` r\nif (!require(\"remotes\")) install.packages(\"remotes\")\n\nremotes::install_github(\"rOpenSpain/spanishoddata\",\n  force = TRUE, dependencies = TRUE)\n```\n\n**For Developers**\n\nTo load the package locally, clone it and navigate to the root of the\npackage in the terminal, e.g. with the following:\n\n``` bash\ngh repo clone rOpenSpain/spanishoddata\ncode spanishoddata\n# with rstudio:\nrstudio spanishoddata/spanishoddata.Rproj\n```\n\nThen run the following command from the R console:\n\n``` r\ndevtools::load_all()\n```\n\n\u003c/details\u003e\n\nLoad it as follows:\n\n``` r\nlibrary(spanishoddata)\n```\n\n## Set the data directory\n\nChoose where `{spanishoddata}` should download (and convert) the data by\nsetting the data directory following command:\n\n``` r\nspod_set_data_dir(data_dir = \"~/spanish_od_data\")\n```\n\nThe function above will also ensure that the directory is created and\nthat you have sufficient permissions to write to it.\n\n\u003cdetails\u003e\n\n\u003csummary\u003e\n\nSetting data directory for advanced users\n\u003c/summary\u003e\n\nYou can also set the data directory with an environment variable:\n\n``` r\nSys.setenv(SPANISH_OD_DATA_DIR = \"~/spanish_od_data\")\n```\n\nThe package will create this directory if it does not exist on the first\nrun of any function that downloads the data.\n\nTo permanently set the directory for all projects, you can specify the\ndata directory globally by setting the `SPANISH_OD_DATA_DIR` environment\nvariable, e.g. with the following command:\n\n``` r\nusethis::edit_r_environ()\n# Then set the data directory globally, by typing this line in the file:\n```\n\n    SPANISH_OD_DATA_DIR = \"~/spanish_od_data\"\n\nYou can also set the data directory locally, just for the current\nproject. Set the ‘envar’ in the working directory by editing `.Renviron`\nfile in the root of the project:\n\n``` r\nfile.edit(\".Renviron\")\n```\n\n\u003c/details\u003e\n\n## Overall approach to accessing the data\n\nIf you only need flows data aggregated by day at municipal level, you\ncan use the `spod_quick_get_od()` function. This will download the data\ndirectly from the web API and let you analyse it in-memory. More on this\nin the [Quickly get daily\ndata](https://ropenspain.github.io/spanishoddata/articles/quick-get.html)\nvignette.\n\nIf you only want to analyse the data for a few days, you can use the\n`spod_get()` function. It will download the raw data in CSV format and\nlet you analyse it in-memory. This is what we cover in the steps on this\npage.\n\nIf you need longer periods (several months or years), you should use the\n`spod_convert()` and `spod_connect()` functions, which will convert the\ndata into special format which is much faster for analysis, for this see\nthe [Download and convert OD\ndatasets](https://ropenspain.github.io/spanishoddata/articles/convert.html)\nvignette. `spod_get_zones()` will give you spatial data with zones that\ncan be matched with the origin-destination flows from the functions\nabove using zones ’id’s. Please see a simple example below, and also\nconsult the vignettes with detailed data description and instructions in\nthe package vignettes with `spod_codebook(ver = 1)` and\n`spod_codebook(ver = 2)`, or simply visit the package website at\n\u003chttps://ropenspain.github.io/spanishoddata/\u003e. The\n\u003ca href=\"#fig-overall-flow\" class=\"quarto-xref\"\u003eFigure 4\u003c/a\u003e presents\nthe overall approach to accessing the data in the `spanishoddata`\npackage.\n\n\u003cdiv id=\"fig-overall-flow\"\u003e\n\n\u003cimg src=\"man/figures/package-functions-overview.svg\"\nstyle=\"width:78.0%\" /\u003e\n\nFigure 4: The overview of package functions to get the data\n\n\u003c/div\u003e\n\n## Showcase\n\nTo run the code in this README we will use the following setup:\n\n``` r\nlibrary(tidyverse)\ntheme_set(theme_minimal())\nsf::sf_use_s2(FALSE)\n```\n\nGet metadata for the datasets as follows (we are using version 2 data\ncovering years 2022 and onwards):\n\n``` r\nmetadata \u003c- spod_available_data(ver = 2) # for version 2 of the data\nmetadata\n```\n\n    # A tibble: 9,442 × 6\n       target_url           pub_ts              file_extension data_ym data_ymd  \n       \u003cchr\u003e                \u003cdttm\u003e              \u003cchr\u003e          \u003cdate\u003e  \u003cdate\u003e    \n     1 https://movilidad-o… 2024-07-30 10:54:08 gz             NA      2022-10-23\n     2 https://movilidad-o… 2024-07-30 10:51:07 gz             NA      2022-10-22\n     3 https://movilidad-o… 2024-07-30 10:47:52 gz             NA      2022-10-20\n     4 https://movilidad-o… 2024-07-30 10:14:55 gz             NA      2022-10-18\n     5 https://movilidad-o… 2024-07-30 10:11:58 gz             NA      2022-10-17\n     6 https://movilidad-o… 2024-07-30 10:09:03 gz             NA      2022-10-12\n     7 https://movilidad-o… 2024-07-30 10:05:57 gz             NA      2022-10-07\n     8 https://movilidad-o… 2024-07-30 10:02:12 gz             NA      2022-08-07\n     9 https://movilidad-o… 2024-07-30 09:58:34 gz             NA      2022-08-06\n    10 https://movilidad-o… 2024-07-30 09:54:30 gz             NA      2022-08-05\n    # ℹ 9,432 more rows\n    # ℹ 1 more variable: local_path \u003cchr\u003e\n\n### Zones\n\nZones can be downloaded as follows:\n\n``` r\ndistritos \u003c- spod_get_zones(\"distritos\", ver = 2)\ndistritos_wgs84 \u003c- distritos |\u003e\n  sf::st_simplify(dTolerance = 200) |\u003e\n  sf::st_transform(4326)\nplot(sf::st_geometry(distritos_wgs84), lwd = 0.2)\n```\n\n![](man/figures/README-distritos-1.png)\n\n### OD data\n\n``` r\nod_db \u003c- spod_get(\n  type = \"origin-destination\",\n  zones = \"districts\",\n  dates = c(start = \"2024-03-01\", end = \"2024-03-07\")\n)\nclass(od_db)\n```\n\n    [1] \"tbl_duckdb_connection\" \"tbl_dbi\"               \"tbl_sql\"              \n    [4] \"tbl_lazy\"              \"tbl\"                  \n\n``` r\ncolnames(od_db)\n```\n\n     [1] \"full_date\"                   \"hour\"                  \n     [3] \"id_origin\"                   \"id_destination\"             \n     [5] \"distance\"                    \"activity_origin\"            \n     [7] \"activity_destination\"        \"study_possible_origin\"      \n     [9] \"study_possible_destination\"  \"residence_province_ine_code\"\n    [11] \"residence_province\"          \"income\"                     \n    [13] \"age\"                         \"sex\"                        \n    [15] \"n_trips\"                     \"trips_total_length_km\"      \n    [17] \"year\"                        \"month\"                      \n    [19] \"day\"                        \n\nThe result is an R database interface object (`tbl_dbi`) that can be\nused with dplyr functions and SQL queries ‘lazily’, meaning that the\ndata is not loaded into memory until it is needed. Let’s do an\naggregation to find the total number trips per hour over the 7 days:\n\n``` r\nn_per_hour \u003c- od_db |\u003e\n  group_by(date, hour) |\u003e\n  summarise(n = n(), Trips = sum(n_trips)) |\u003e\n  collect() |\u003e\n  mutate(Time = lubridate::ymd_h(paste0(date, hour, sep = \" \"))) |\u003e\n  mutate(Day = lubridate::wday(Time, label = TRUE))\nn_per_hour |\u003e\n  ggplot(aes(x = Time, y = Trips)) +\n  geom_line(aes(colour = Day)) +\n  labs(title = \"Number of trips per hour over 7 days\")\n```\n\n![](man/figures/README-trips-per-hour-1.png)\n\nThe figure above summarises 925,874,012 trips over the 7 days associated\nwith 135,866,524 records.\n\n### `spanishoddata` advantage over accessing the data yourself\n\nAs we demonstrated above, you can perform very quick analysis using just\na few lines of code.\n\nTo highlight the benefits of the package, here is how you would do this\nmanually:\n\n- download the [xml](https://movilidad-opendata.mitma.es/RSS.xml) file\n  with the download links\n\n- parse this xml to extract the download links\n\n- write a script to download the files and locate them on disk in a\n  logical manner\n\n- figure out the data structure of the downloaded files, read the\n  codebook\n\n- translate the data (columns and values) into English, if you are not\n  familiar with Spanish\n\n- write a script to load the data into the database or figure out a way\n  to claculate summaries on multiple files\n\n- and much more…\n\nWe did all of that for you and present you with a few simple functions\nthat get you straight to the data in one line of code, and you are ready\nto run any analysis on it.\n\n## Desire lines\n\nWe’ll use the same input data to pick-out the most important flows in\nSpain, with a focus on longer trips for visualisation:\n\n``` r\nod_national_aggregated \u003c- od_db |\u003e\n  group_by(id_origin, id_destination) |\u003e\n  summarise(Trips = sum(n_trips), .groups = \"drop\") |\u003e\n  filter(Trips \u003e 500) |\u003e\n  collect() |\u003e\n  arrange(desc(Trips))\nod_national_aggregated\n```\n\n    # A tibble: 96,404 × 3\n       id_origin id_destination    Trips\n       \u003cfct\u003e     \u003cfct\u003e             \u003cdbl\u003e\n     1 2807908   2807908        2441404.\n     2 0801910   0801910        2112188.\n     3 0801902   0801902        2013618.\n     4 2807916   2807916        1821504.\n     5 2807911   2807911        1785981.\n     6 04902     04902          1690606.\n     7 2807913   2807913        1504484.\n     8 2807910   2807910        1299586.\n     9 0704004   0704004        1287122.\n    10 28106     28106          1286058.\n    # ℹ 96,394 more rows\n\nThe results show that the largest flows are intra-zonal. Let’s keep only\nthe inter-zonal flows:\n\n``` r\nod_national_interzonal \u003c- od_national_aggregated |\u003e\n  filter(id_origin != id_destination)\n```\n\nWe can convert these to geographic data with the {od} package (Lovelace\nand Morgan 2024):\n\n``` r\nod_national_sf \u003c- od::od_to_sf(\n  od_national_interzonal,\n  z = distritos_wgs84\n)\ndistritos_wgs84 |\u003e\n  ggplot() +\n  geom_sf(fill = \"grey\") +\n  geom_sf(data = spData::world, fill = NA, colour = \"black\") +\n  geom_sf(aes(linewidth = Trips), colour = \"blue\", data = od_national_sf) +\n  coord_sf(xlim = c(-10, 5), ylim = c(35, 45)) +\n  theme_void() +\n  scale_linewidth_continuous(range = c(0.2, 3))\n```\n\n![](man/figures/README-desire-lines-1.png)\n\nLet’s focus on trips in and around a particular area (Salamanca):\n\n``` r\nsalamanca_zones \u003c- zonebuilder::zb_zone(\"Salamanca\")\ndistritos_salamanca \u003c- distritos_wgs84[salamanca_zones, ]\nplot(distritos_salamanca)\n```\n\n![](man/figures/README-salamanca-zones-1.png)\n\nWe will use this information to subset the rows, to capture all movement\nwithin the study area:\n\n``` r\nids_salamanca \u003c- distritos_salamanca$id\nod_salamanca \u003c- od_national_sf |\u003e\n  filter(id_origin %in% ids_salamanca) |\u003e\n  filter(id_destination %in% ids_salamanca) |\u003e\n  arrange(Trips)\n```\n\nLet’s plot the results:\n\n``` r\nod_salamanca_sf \u003c- od::od_to_sf(\n  od_salamanca,\n  z = distritos_salamanca\n)\nggplot() +\n  geom_sf(fill = \"grey\", data = distritos_salamanca) +\n  geom_sf(aes(colour = Trips), size = 1, data = od_salamanca_sf) +\n  scale_colour_viridis_c() +\n  theme_void()\n```\n\n![](man/figures/README-salamanca-plot-1.png)\n\n## Further information\n\nFor more information on the package, see:\n\n- The [pkgdown site](https://rOpenSpain.github.io/spanishoddata/)\n  - [Functions\n    reference](https://rOpenSpain.github.io/spanishoddata/reference/index.html)\n  - [v1 data (2020-2021)\n    codebook](https://rOpenSpain.github.io/spanishoddata/articles/v1-2020-2021-mitma-data-codebook.html)\n  - [v2 data (2022 onwards) codebook (work in\n    progress)](https://rOpenSpain.github.io/spanishoddata/articles/v2-2022-onwards-mitma-data-codebook.html)\n  - [Download and convert\n    data](https://rOpenSpain.github.io/spanishoddata/articles/convert.html)\n  - The [OD disaggregation\n    vignette](https://rOpenSpain.github.io/spanishoddata/articles/disaggregation.html)\n    showcases flows disaggregation\n  - [Making static\n    flowmaps](https://rOpenSpain.github.io/spanishoddata/articles/flowmaps-static.html)\n    vignette shows how to create flowmaps using the data acquired with\n    `{spanishoddata}`\n  - [Making interactive\n    flowmaps](https://rOpenSpain.github.io/spanishoddata/articles/flowmaps-interactive.html)\n    shows how to create an interactive flowmap using the data acquired\n    with `{spanishoddata}`\n  - [Quickly getting daily aggregated 2022+ data at municipality\n    level](https://ropenspain.github.io/spanishoddata/articles/quick-get.html)\n\n### Citation\n\nTo cite the `spanishoddata` R package use:\n\nKotov E, Lovelace R, Vidal-Tortosa E (2024). *spanishoddata*.\ndoi:10.32614/CRAN.package.spanishoddata\n\u003chttps://doi.org/10.32614/CRAN.package.spanishoddata\u003e,\n\u003chttps://github.com/rOpenSpain/spanishoddata\u003e.\n\nTo cite the official website of the mobility study use:\n\nMinisterio de Transportes y Movilidad Sostenible (MITMS) (2024).\n“Estudio de la movilidad con Big Data (Study of mobility with Big\nData).”\n\u003chttps://www.transportes.gob.es/ministerio/proyectos-singulares/estudio-de-movilidad-con-big-data\u003e.\n\nTo cite the methodology for 2022 and onwards data use:\n\nMinisterio de Transportes y Movilidad Sostenible (MITMS) (2024).\n*Estudio de movilidad de viajeros de ámbito nacional aplicando la\ntecnología Big Data. Informe metodológico (Study of National Traveler\nmobility Using Big Data Technology. Methodological Report)*.\n\u003chttps://www.transportes.gob.es/recursos_mfom/paginabasica/recursos/a3_informe_metodologico_estudio_movilidad_mitms_v8.pdf\u003e.\n\nTo cite the methodology for 2020-2021 data use:\n\nMinisterio de Transportes, Movilidad y Agenda Urbana (MITMA) (2021).\n*Análisis de la movilidad en España con tecnología Big Data durante el\nestado de alarma para la gestión de la crisis del COVID-19 (Analysis of\nmobility in Spain with Big Data technology during the state of alarm for\nCOVID-19 crisis management)*.\n\u003chttps://cdn.mitma.gob.es/portal-web-drupal/covid-19/bigdata/mitma_-_estudio_movilidad_covid-19_informe_metodologico_v3.pdf\u003e.\n\nSee package website for more details:\nhttps://ropenspain.github.io/spanishoddata/\n\nBibTeX:\n\n    @Manual{r-spanishoddata,\n      title = {spanishoddata},\n      author = {Egor Kotov and Robin Lovelace and Eugeni Vidal-Tortosa},\n      year = {2024},\n      url = {https://github.com/rOpenSpain/spanishoddata},\n      doi = {10.32614/CRAN.package.spanishoddata},\n    }\n\n    @Misc{mitms_mobility_web,\n      title = {Estudio de la movilidad con Big Data (Study of mobility with Big Data)},\n      author = {{Ministerio de Transportes y Movilidad Sostenible (MITMS)}},\n      year = {2024},\n      url = {https://www.transportes.gob.es/ministerio/proyectos-singulares/estudio-de-movilidad-con-big-data},\n    }\n\n    @Manual{mitms_methodology_2022_v8,\n      title = {Estudio de movilidad de viajeros de ámbito nacional aplicando la tecnología Big Data. Informe metodológico (Study of National Traveler mobility Using Big Data Technology. Methodological Report)},\n      author = {{Ministerio de Transportes y Movilidad Sostenible (MITMS)}},\n      year = {2024},\n      url = {https://www.transportes.gob.es/recursos_mfom/paginabasica/recursos/a3_informe_metodologico_estudio_movilidad_mitms_v8.pdf},\n    }\n\n    @Manual{mitma_methodology_2020_v3,\n      title = {Análisis de la movilidad en España con tecnología Big Data durante el estado de alarma para la gestión de la crisis del COVID-19 (Analysis of mobility in Spain with Big Data technology during the state of alarm for COVID-19 crisis management)},\n      author = {{Ministerio de Transportes, Movilidad y Agenda Urbana (MITMA)}},\n      year = {2021},\n      url = {https://cdn.mitma.gob.es/portal-web-drupal/covid-19/bigdata/mitma_-_estudio_movilidad_covid-19_informe_metodologico_v3.pdf},\n    }\n\n## References\n\n\u003c!-- metadata for better search engine indexing --\u003e\n\n\u003c!-- should be picked up by pkgdown --\u003e\n\n\u003c!-- update metadata before release with  --\u003e\n\n\u003c!-- cffr::cff_write() --\u003e\n\n\u003c!-- codemetar::write_codemeta(write_minimeta = T) --\u003e\n\n\u003cdiv id=\"refs\" class=\"references csl-bib-body hanging-indent\"\nentry-spacing=\"0\"\u003e\n\n\u003cdiv id=\"ref-lovelace_od_2024\" class=\"csl-entry\"\u003e\n\nLovelace, Robin, and Malcolm Morgan. 2024. “Od: Manipulate and Map\nOrigin-Destination Data,” August.\n\u003chttps://doi.org/10.32614/CRAN.package.od\u003e.\n\n\u003c/div\u003e\n\n\u003cdiv id=\"ref-mitms_mobility_web\" class=\"csl-entry\"\u003e\n\nMinisterio de Transportes y Movilidad Sostenible MITMS. 2024. “Estudio\nde La Movilidad Con Big Data (Study of Mobility with Big Data).”\n\u003chttps://www.transportes.gob.es/ministerio/proyectos-singulares/estudio-de-movilidad-con-big-data\u003e.\n\n\u003c/div\u003e\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fropenspain%2Fspanishoddata","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fropenspain%2Fspanishoddata","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fropenspain%2Fspanishoddata/lists"}