spanishoddata

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 móviles.

https://github.com/ropenspain/spanishoddata

Keywords

cdr data data-package mobile-telephone-data mobility origin-destination rstats

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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 móviles.

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Host: GitHub
Owner: rOpenSpain
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Homepage: https://ropenspain.github.io/spanishoddata/
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cdr data data-package mobile-telephone-data mobility origin-destination rstats

Created over 1 year ago · Last pushed 5 months ago

Metadata Files

Readme Changelog License Citation Codemeta

README.md

spanishoddata: Get Spanish Origin-Destination Data

spanishoddata is an R package that provides functions for downloading and formatting Spanish open mobility data released by the Spanish government (Ministerio de Transportes y Movilidad Sostenible MITMS 2024).

It supports the two versions of the Spanish mobility data. The first version (2020 to 2021), covering the period of the COVID-19 pandemic, contains tables detailing trip numbers and distances, broken down by origin, destination, activity, residence province, time interval, distance interval, and date. It also provides tables of individual counts by location and trip frequency. The second version (2022 onwards) improves spatial resolution, adds trips to and from Portugal and France, and introduces new fields for study-related activities and sociodemographic factors (income, age, and sex) in the origin-destination tables, along with additional tables showing individual counts by overnight stay location, residence, and date. See the package website and vignettes for v1 and v2 data for more details.

spanishoddata is designed to save time by providing the data in analysis-ready formats. Automating the process of downloading, cleaning, and importing the data can also reduce the risk of errors in the laborious process of data preparation. It also reduces computational resources by using computationally efficient packages behind the scenes. To effectively work with multiple data files, it’s recommended you set up a data directory where the package can search for the data and download only the files that are not already present.

Examples of available data

Figure 1: Example of the data available through the package: daily flows in Barcelona on 7 April 2021

To create static maps like that see our vignette here.

![](https://ropenspain.github.io/spanishoddata/media/spain-folding-flows.gif) Figure 2: Example of the data available through the package: interactive daily flows in Spain

![](https://ropenspain.github.io/spanishoddata/media/barcelona-time.gif) Figure 3: Example of the data available through the package: interactive daily flows in Barcelona with time filter

To create interactive maps see our vignette here.

Install the package

Install from CRAN:

r install.packages("spanishoddata")

Alternative installation and developemnt

You can also install the latest development version of the package from rOpenSpain R universe: ``` r install.packages("spanishoddata", repos = c("https://ropenspain.r-universe.dev", "https://cloud.r-project.org")) ``` Alternative way to install the development version from GitHub: ``` r if (!require("remotes")) install.packages("remotes") remotes::install_github("rOpenSpain/spanishoddata", force = TRUE, dependencies = TRUE) ``` **For Developers** To load the package locally, clone it and navigate to the root of the package in the terminal, e.g. with the following: ``` bash gh repo clone rOpenSpain/spanishoddata code spanishoddata # with rstudio: rstudio spanishoddata/spanishoddata.Rproj ``` Then run the following command from the R console: ``` r devtools::load_all() ```

Load it as follows:

r library(spanishoddata)

Set the data directory

Choose where {spanishoddata} should download (and convert) the data by setting the data directory following command:

r spod_set_data_dir(data_dir = "~/spanish_od_data")

The function above will also ensure that the directory is created and that you have sufficient permissions to write to it.

Setting data directory for advanced users

You can also set the data directory with an environment variable: ``` r Sys.setenv(SPANISH_OD_DATA_DIR = "~/spanish_od_data") ``` The package will create this directory if it does not exist on the first run of any function that downloads the data. To permanently set the directory for all projects, you can specify the data directory globally by setting the `SPANISH_OD_DATA_DIR` environment variable, e.g. with the following command: ``` r usethis::edit_r_environ() # Then set the data directory globally, by typing this line in the file: ``` SPANISH_OD_DATA_DIR = "~/spanish_od_data" You can also set the data directory locally, just for the current project. Set the ‘envar’ in the working directory by editing `.Renviron` file in the root of the project: ``` r file.edit(".Renviron") ```

Overall approach to accessing the data

If you only need flows data aggregated by day at municipal level, you can use the spod_quick_get_od() function. This will download the data directly from the web API and let you analyse it in-memory. More on this in the Quickly get daily data vignette.

If you only want to analyse the data for a few days, you can use the spod_get() function. It will download the raw data in CSV format and let you analyse it in-memory. This is what we cover in the steps on this page.

If you need longer periods (several months or years), you should use the spod_convert() and spod_connect() functions, which will convert the data into special format which is much faster for analysis, for this see the Download and convert OD datasets vignette. spod_get_zones() will give you spatial data with zones that can be matched with the origin-destination flows from the functions above using zones ’id’s. Please see a simple example below, and also consult the vignettes with detailed data description and instructions in the package vignettes with spod_codebook(ver = 1) and spod_codebook(ver = 2), or simply visit the package website at https://ropenspain.github.io/spanishoddata/. The Figure 4 presents the overall approach to accessing the data in the spanishoddata package.

Figure 4: The overview of package functions to get the data

Showcase

To run the code in this README we will use the following setup:

r library(tidyverse) theme_set(theme_minimal()) sf::sf_use_s2(FALSE)

Get metadata for the datasets as follows (we are using version 2 data covering years 2022 and onwards):

r metadata <- spod_available_data(ver = 2) # for version 2 of the data metadata

# A tibble: 9,442 × 6
   target_url           pub_ts              file_extension data_ym data_ymd  
   <chr>                <dttm>              <chr>          <date>  <date>    
 1 https://movilidad-o… 2024-07-30 10:54:08 gz             NA      2022-10-23
 2 https://movilidad-o… 2024-07-30 10:51:07 gz             NA      2022-10-22
 3 https://movilidad-o… 2024-07-30 10:47:52 gz             NA      2022-10-20
 4 https://movilidad-o… 2024-07-30 10:14:55 gz             NA      2022-10-18
 5 https://movilidad-o… 2024-07-30 10:11:58 gz             NA      2022-10-17
 6 https://movilidad-o… 2024-07-30 10:09:03 gz             NA      2022-10-12
 7 https://movilidad-o… 2024-07-30 10:05:57 gz             NA      2022-10-07
 8 https://movilidad-o… 2024-07-30 10:02:12 gz             NA      2022-08-07
 9 https://movilidad-o… 2024-07-30 09:58:34 gz             NA      2022-08-06
10 https://movilidad-o… 2024-07-30 09:54:30 gz             NA      2022-08-05
# ℹ 9,432 more rows
# ℹ 1 more variable: local_path <chr>

Zones

Zones can be downloaded as follows:

r distritos <- spod_get_zones("distritos", ver = 2) distritos_wgs84 <- distritos |> sf::st_simplify(dTolerance = 200) |> sf::st_transform(4326) plot(sf::st_geometry(distritos_wgs84), lwd = 0.2)

OD data

r od_db <- spod_get( type = "origin-destination", zones = "districts", dates = c(start = "2024-03-01", end = "2024-03-07") ) class(od_db)

[1] "tbl_duckdb_connection" "tbl_dbi"               "tbl_sql"              
[4] "tbl_lazy"              "tbl"

r colnames(od_db)

 [1] "full_date"                   "hour"                  
 [3] "id_origin"                   "id_destination"             
 [5] "distance"                    "activity_origin"            
 [7] "activity_destination"        "study_possible_origin"      
 [9] "study_possible_destination"  "residence_province_ine_code"
[11] "residence_province"          "income"                     
[13] "age"                         "sex"                        
[15] "n_trips"                     "trips_total_length_km"      
[17] "year"                        "month"                      
[19] "day"

The result is an R database interface object (tbl_dbi) that can be used with dplyr functions and SQL queries ‘lazily’, meaning that the data is not loaded into memory until it is needed. Let’s do an aggregation to find the total number trips per hour over the 7 days:

r n_per_hour <- od_db |> group_by(date, hour) |> summarise(n = n(), Trips = sum(n_trips)) |> collect() |> mutate(Time = lubridate::ymd_h(paste0(date, hour, sep = " "))) |> mutate(Day = lubridate::wday(Time, label = TRUE)) n_per_hour |> ggplot(aes(x = Time, y = Trips)) + geom_line(aes(colour = Day)) + labs(title = "Number of trips per hour over 7 days")

The figure above summarises 925,874,012 trips over the 7 days associated with 135,866,524 records.

`spanishoddata` advantage over accessing the data yourself

As we demonstrated above, you can perform very quick analysis using just a few lines of code.

To highlight the benefits of the package, here is how you would do this manually:

download the xml file with the download links
parse this xml to extract the download links
write a script to download the files and locate them on disk in a logical manner
figure out the data structure of the downloaded files, read the codebook
translate the data (columns and values) into English, if you are not familiar with Spanish
write a script to load the data into the database or figure out a way to claculate summaries on multiple files
and much more…

We did all of that for you and present you with a few simple functions that get you straight to the data in one line of code, and you are ready to run any analysis on it.

Desire lines

We’ll use the same input data to pick-out the most important flows in Spain, with a focus on longer trips for visualisation:

# A tibble: 96,404 × 3
   id_origin id_destination    Trips
   <fct>     <fct>             <dbl>
 1 2807908   2807908        2441404.
 2 0801910   0801910        2112188.
 3 0801902   0801902        2013618.
 4 2807916   2807916        1821504.
 5 2807911   2807911        1785981.
 6 04902     04902          1690606.
 7 2807913   2807913        1504484.
 8 2807910   2807910        1299586.
 9 0704004   0704004        1287122.
10 28106     28106          1286058.
# ℹ 96,394 more rows

The results show that the largest flows are intra-zonal. Let’s keep only the inter-zonal flows:

r od_national_interzonal <- od_national_aggregated |> filter(id_origin != id_destination)

We can convert these to geographic data with the {od} package (Lovelace and Morgan 2024):

r od_national_sf <- od::od_to_sf( od_national_interzonal, z = distritos_wgs84 ) distritos_wgs84 |> ggplot() + geom_sf(fill = "grey") + geom_sf(data = spData::world, fill = NA, colour = "black") + geom_sf(aes(linewidth = Trips), colour = "blue", data = od_national_sf) + coord_sf(xlim = c(-10, 5), ylim = c(35, 45)) + theme_void() + scale_linewidth_continuous(range = c(0.2, 3))

Let’s focus on trips in and around a particular area (Salamanca):

r salamanca_zones <- zonebuilder::zb_zone("Salamanca") distritos_salamanca <- distritos_wgs84[salamanca_zones, ] plot(distritos_salamanca)

We will use this information to subset the rows, to capture all movement within the study area:

r ids_salamanca <- distritos_salamanca$id od_salamanca <- od_national_sf |> filter(id_origin %in% ids_salamanca) |> filter(id_destination %in% ids_salamanca) |> arrange(Trips)

Let’s plot the results:

r od_salamanca_sf <- od::od_to_sf( od_salamanca, z = distritos_salamanca ) ggplot() + geom_sf(fill = "grey", data = distritos_salamanca) + geom_sf(aes(colour = Trips), size = 1, data = od_salamanca_sf) + scale_colour_viridis_c() + theme_void()

Further information

For more information on the package, see:

The pkgdown site
- Functions reference
- v1 data (2020-2021) codebook
- v2 data (2022 onwards) codebook (work in progress)
- Download and convert data
- The OD disaggregation vignette showcases flows disaggregation
- Making static flowmaps vignette shows how to create flowmaps using the data acquired with {spanishoddata}
- Making interactive flowmaps shows how to create an interactive flowmap using the data acquired with {spanishoddata}
- Quickly getting daily aggregated 2022+ data at municipality level

Citation

To cite the spanishoddata R package use:

Kotov E, Lovelace R, Vidal-Tortosa E (2024). spanishoddata. doi:10.32614/CRAN.package.spanishoddata https://doi.org/10.32614/CRAN.package.spanishoddata, https://github.com/rOpenSpain/spanishoddata.

To cite the official website of the mobility study use:

Ministerio de Transportes y Movilidad Sostenible (MITMS) (2024). “Estudio de la movilidad con Big Data (Study of mobility with Big Data).” https://www.transportes.gob.es/ministerio/proyectos-singulares/estudio-de-movilidad-con-big-data.

To cite the methodology for 2022 and onwards data use:

Ministerio de Transportes y Movilidad Sostenible (MITMS) (2024). 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). https://www.transportes.gob.es/recursos_mfom/paginabasica/recursos/a3_informe_metodologico_estudio_movilidad_mitms_v8.pdf.

To cite the methodology for 2020-2021 data use:

Ministerio de Transportes, Movilidad y Agenda Urbana (MITMA) (2021). 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). https://cdn.mitma.gob.es/portal-web-drupal/covid-19/bigdata/mitma_-_estudio_movilidad_covid-19_informe_metodologico_v3.pdf.

See package website for more details: https://ropenspain.github.io/spanishoddata/

BibTeX:

@Manual{r-spanishoddata,
  title = {spanishoddata},
  author = {Egor Kotov and Robin Lovelace and Eugeni Vidal-Tortosa},
  year = {2024},
  url = {https://github.com/rOpenSpain/spanishoddata},
  doi = {10.32614/CRAN.package.spanishoddata},
}

@Misc{mitms_mobility_web,
  title = {Estudio de la movilidad con Big Data (Study of mobility with Big Data)},
  author = {{Ministerio de Transportes y Movilidad Sostenible (MITMS)}},
  year = {2024},
  url = {https://www.transportes.gob.es/ministerio/proyectos-singulares/estudio-de-movilidad-con-big-data},
}

@Manual{mitms_methodology_2022_v8,
  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)},
  author = {{Ministerio de Transportes y Movilidad Sostenible (MITMS)}},
  year = {2024},
  url = {https://www.transportes.gob.es/recursos_mfom/paginabasica/recursos/a3_informe_metodologico_estudio_movilidad_mitms_v8.pdf},
}

@Manual{mitma_methodology_2020_v3,
  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)},
  author = {{Ministerio de Transportes, Movilidad y Agenda Urbana (MITMA)}},
  year = {2021},
  url = {https://cdn.mitma.gob.es/portal-web-drupal/covid-19/bigdata/mitma_-_estudio_movilidad_covid-19_informe_metodologico_v3.pdf},
}

Working with Python?

Try the new work-in-progress package: https://github.com/pySpainMobility/pySpainMobility.

References

Lovelace, Robin, and Malcolm Morgan. 2024. “Od: Manipulate and Map Origin-Destination Data,” August. .

Ministerio de Transportes y Movilidad Sostenible MITMS. 2024. “Estudio de La Movilidad Con Big Data (Study of Mobility with Big Data).” .

Owner

Name: rOpenSpain
Login: rOpenSpain
Kind: organization
Email: hola@ropenspain.es
Location: Spain

Website: http://ropenspain.es
Repositories: 21
Profile: https://github.com/rOpenSpain

rOpenSci is our form, Spanish public data our matter

Citation (CITATION.cff)

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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "spanishoddata" in publications use:'
type: software
license: MIT
title: 'spanishoddata: Get Spanish Origin-Destination Data'
version: 0.2.1
doi: 10.32614/CRAN.package.spanishoddata
identifiers:
- type: doi
  value: 10.32614/CRAN.package.spanishoddata
abstract: Gain seamless access to origin-destination (OD) data from the Spanish Ministry
  of Transport, hosted at <https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/opendata-movilidad>.
  This package simplifies the management of these large datasets by providing tools
  to download zone boundaries, handle associated origin-destination data, and process
  it efficiently with the 'duckdb' database interface. Local caching minimizes repeated
  downloads, streamlining workflows for researchers and analysts. Extensive documentation
  is available at <https://ropenspain.github.io/spanishoddata/index.html>, offering
  guides on creating static and dynamic mobility flow visualizations and transforming
  large datasets into analysis-ready formats.
authors:
- family-names: Kotov
  given-names: Egor
  email: kotov.egor@gmail.com
  orcid: https://orcid.org/0000-0001-6690-5345
- family-names: Lovelace
  given-names: Robin
  email: rob00x@gmail.com
  orcid: https://orcid.org/0000-0001-5679-6536
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  title: spanishoddata
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    email: kotov.egor@gmail.com
    orcid: https://orcid.org/0000-0001-6690-5345
  - family-names: Lovelace
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    email: rob00x@gmail.com
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  - family-names: Vidal-Tortosa
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contact:
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  orcid: https://orcid.org/0000-0001-6690-5345
keywords:
- cdr
- data
- data-package
- mobile-telephone-data
- mobility
- origin-destination
- rstats
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- type: generic
  title: Estudio de la movilidad con Big Data (Study of mobility with Big Data)
  authors:
  - name: Ministerio de Transportes y Movilidad Sostenible (MITMS)
  year: '2024'
  url: https://www.transportes.gob.es/ministerio/proyectos-singulares/estudio-de-movilidad-con-big-data
- type: manual
  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)
  authors:
  - name: Ministerio de Transportes y Movilidad Sostenible (MITMS)
  year: '2024'
  url: https://www.transportes.gob.es/recursos_mfom/paginabasica/recursos/a3_informe_metodologico_estudio_movilidad_mitms_v8.pdf
- type: manual
  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)
  authors:
  - name: Ministerio de Transportes, Movilidad y Agenda Urbana (MITMA)
  year: '2021'
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    orcid: https://orcid.org/0000-0002-1416-3412
  year: '2025'
  doi: 10.32614/CRAN.package.here
- type: software
  title: httr2
  abstract: 'httr2: Perform HTTP Requests and Process the Responses'
  notes: Imports
  url: https://httr2.r-lib.org
  repository: https://CRAN.R-project.org/package=httr2
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2025'
  doi: 10.32614/CRAN.package.httr2
- type: software
  title: jsonlite
  abstract: 'jsonlite: A Simple and Robust JSON Parser and Generator for R'
  notes: Imports
  url: https://jeroen.r-universe.dev/jsonlite
  repository: https://CRAN.R-project.org/package=jsonlite
  authors:
  - family-names: Ooms
    given-names: Jeroen
    email: jeroenooms@gmail.com
    orcid: https://orcid.org/0000-0002-4035-0289
  year: '2025'
  doi: 10.32614/CRAN.package.jsonlite
- type: software
  title: lifecycle
  abstract: 'lifecycle: Manage the Life Cycle of your Package Functions'
  notes: Imports
  url: https://lifecycle.r-lib.org/
  repository: https://CRAN.R-project.org/package=lifecycle
  authors:
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  year: '2025'
  doi: 10.32614/CRAN.package.lifecycle
- type: software
  title: lubridate
  abstract: 'lubridate: Make Dealing with Dates a Little Easier'
  notes: Imports
  url: https://lubridate.tidyverse.org
  repository: https://CRAN.R-project.org/package=lubridate
  authors:
  - family-names: Spinu
    given-names: Vitalie
    email: spinuvit@gmail.com
  - family-names: Grolemund
    given-names: Garrett
  - family-names: Wickham
    given-names: Hadley
  year: '2025'
  doi: 10.32614/CRAN.package.lubridate
- type: software
  title: memoise
  abstract: 'memoise: ''Memoisation'' of Functions'
  notes: Imports
  url: https://memoise.r-lib.org
  repository: https://CRAN.R-project.org/package=memoise
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@rstudio.com
  - family-names: Hester
    given-names: Jim
  - family-names: Chang
    given-names: Winston
    email: winston@rstudio.com
  - family-names: Müller
    given-names: Kirill
    email: krlmlr+r@mailbox.org
  - family-names: Cook
    given-names: Daniel
    email: danielecook@gmail.com
  year: '2025'
  doi: 10.32614/CRAN.package.memoise
- type: software
  title: openssl
  abstract: 'openssl: Toolkit for Encryption, Signatures and Certificates Based on
    OpenSSL'
  notes: Imports
  url: https://jeroen.r-universe.dev/openssl
  repository: https://CRAN.R-project.org/package=openssl
  authors:
  - family-names: Ooms
    given-names: Jeroen
    email: jeroenooms@gmail.com
    orcid: https://orcid.org/0000-0002-4035-0289
  year: '2025'
  doi: 10.32614/CRAN.package.openssl
- type: software
  title: parallelly
  abstract: 'parallelly: Enhancing the ''parallel'' Package'
  notes: Imports
  url: https://parallelly.futureverse.org
  repository: https://CRAN.R-project.org/package=parallelly
  authors:
  - family-names: Bengtsson
    given-names: Henrik
    email: henrikb@braju.com
    orcid: https://orcid.org/0000-0002-7579-5165
  year: '2025'
  doi: 10.32614/CRAN.package.parallelly
- type: software
  title: paws.storage
  abstract: 'paws.storage: ''Amazon Web Services'' Storage Services'
  notes: Imports
  url: https://paws-r.r-universe.dev/paws.storage
  repository: https://CRAN.R-project.org/package=paws.storage
  authors:
  - family-names: Kretch
    given-names: David
    email: david.kretch@gmail.com
  - family-names: Banker
    given-names: Adam
    email: adam.banker39@gmail.com
  year: '2025'
  doi: 10.32614/CRAN.package.paws.storage
  version: '>= 0.4.0'
- type: software
  title: purrr
  abstract: 'purrr: Functional Programming Tools'
  notes: Imports
  url: https://purrr.tidyverse.org/
  repository: https://CRAN.R-project.org/package=purrr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  year: '2025'
  doi: 10.32614/CRAN.package.purrr
- type: software
  title: readr
  abstract: 'readr: Read Rectangular Text Data'
  notes: Imports
  url: https://readr.tidyverse.org
  repository: https://CRAN.R-project.org/package=readr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  - family-names: Hester
    given-names: Jim
  - family-names: Bryan
    given-names: Jennifer
    email: jenny@posit.co
    orcid: https://orcid.org/0000-0002-6983-2759
  year: '2025'
  doi: 10.32614/CRAN.package.readr
- type: software
  title: rlang
  abstract: 'rlang: Functions for Base Types and Core R and ''Tidyverse'' Features'
  notes: Imports
  url: https://rlang.r-lib.org
  repository: https://CRAN.R-project.org/package=rlang
  authors:
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2025'
  doi: 10.32614/CRAN.package.rlang
- type: software
  title: sf
  abstract: 'sf: Simple Features for R'
  notes: Imports
  url: https://r-spatial.github.io/sf/
  repository: https://CRAN.R-project.org/package=sf
  authors:
  - family-names: Pebesma
    given-names: Edzer
    email: edzer.pebesma@uni-muenster.de
    orcid: https://orcid.org/0000-0001-8049-7069
  year: '2025'
  doi: 10.32614/CRAN.package.sf
- type: software
  title: stats
  abstract: 'R: A Language and Environment for Statistical Computing'
  notes: Imports
  authors:
  - name: R Core Team
  institution:
    name: R Foundation for Statistical Computing
    address: Vienna, Austria
  year: '2025'
- type: software
  title: stringr
  abstract: 'stringr: Simple, Consistent Wrappers for Common String Operations'
  notes: Imports
  url: https://stringr.tidyverse.org
  repository: https://CRAN.R-project.org/package=stringr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2025'
  doi: 10.32614/CRAN.package.stringr
- type: software
  title: tibble
  abstract: 'tibble: Simple Data Frames'
  notes: Imports
  url: https://tibble.tidyverse.org/
  repository: https://CRAN.R-project.org/package=tibble
  authors:
  - family-names: Müller
    given-names: Kirill
    email: kirill@cynkra.com
    orcid: https://orcid.org/0000-0002-1416-3412
  - family-names: Wickham
    given-names: Hadley
    email: hadley@rstudio.com
  year: '2025'
  doi: 10.32614/CRAN.package.tibble
- type: software
  title: xml2
  abstract: 'xml2: Parse XML'
  notes: Imports
  url: https://xml2.r-lib.org
  repository: https://CRAN.R-project.org/package=xml2
  authors:
  - family-names: Wickham
    given-names: Hadley
  - family-names: Hester
    given-names: Jim
  - family-names: Ooms
    given-names: Jeroen
    email: jeroenooms@gmail.com
  year: '2025'
  doi: 10.32614/CRAN.package.xml2
- type: software
  title: flowmapblue
  abstract: 'flowmapblue: Flow Map Rendering'
  notes: Suggests
  url: https://flowmapblue.github.io/flowmapblue.R/
  repository: https://CRAN.R-project.org/package=flowmapblue
  authors:
  - family-names: Boyandin
    given-names: Ilya
    email: ilya@boyandin.me
    orcid: https://orcid.org/0000-0001-5585-7587
  year: '2025'
  doi: 10.32614/CRAN.package.flowmapblue
- type: software
  title: flowmapper
  abstract: 'flowmapper: Draw Flows (Migration, Goods, Money, Information) on ''ggplot2''
    Plots'
  notes: Suggests
  url: https://github.com/JohMast/flowmapper
  repository: https://CRAN.R-project.org/package=flowmapper
  authors:
  - family-names: Mast
    given-names: Johannes
    email: johannes.mast@dlr.de
    orcid: https://orcid.org/0000-0001-6595-5834
  year: '2025'
  doi: 10.32614/CRAN.package.flowmapper
  version: '>= 0.1.2'
- type: software
  title: furrr
  abstract: 'furrr: Apply Mapping Functions in Parallel using Futures'
  notes: Suggests
  url: https://furrr.futureverse.org/
  repository: https://CRAN.R-project.org/package=furrr
  authors:
  - family-names: Vaughan
    given-names: Davis
    email: davis@rstudio.com
  - family-names: Dancho
    given-names: Matt
    email: mdancho@business-science.io
  year: '2025'
  doi: 10.32614/CRAN.package.furrr
- type: software
  title: future
  abstract: 'future: Unified Parallel and Distributed Processing in R for Everyone'
  notes: Suggests
  url: https://future.futureverse.org
  repository: https://CRAN.R-project.org/package=future
  authors:
  - family-names: Bengtsson
    given-names: Henrik
    email: henrikb@braju.com
    orcid: https://orcid.org/0000-0002-7579-5165
  year: '2025'
  doi: 10.32614/CRAN.package.future
- type: software
  title: future.mirai
  abstract: 'future.mirai: A ''Future'' API for Parallel Processing using ''mirai'''
  notes: Suggests
  url: https://future.mirai.futureverse.org
  repository: https://CRAN.R-project.org/package=future.mirai
  authors:
  - family-names: Bengtsson
    given-names: Henrik
    email: henrikb@braju.com
    orcid: https://orcid.org/0000-0002-7579-5165
  year: '2025'
  doi: 10.32614/CRAN.package.future.mirai
- type: software
  title: mapSpain
  abstract: 'mapSpain: Administrative Boundaries of Spain'
  notes: Suggests
  url: https://ropenspain.github.io/mapSpain/
  repository: https://CRAN.R-project.org/package=mapSpain
  authors:
  - family-names: Hernangómez
    given-names: Diego
    email: diego.hernangomezherrero@gmail.com
    orcid: https://orcid.org/0000-0001-8457-4658
  year: '2025'
  doi: 10.32614/CRAN.package.mapSpain
- type: software
  title: quarto
  abstract: 'quarto: R Interface to ''Quarto'' Markdown Publishing System'
  notes: Suggests
  url: https://quarto-dev.github.io/quarto-r/
  repository: https://CRAN.R-project.org/package=quarto
  authors:
  - family-names: Allaire
    given-names: JJ
    email: jj@posit.co
    orcid: https://orcid.org/0000-0003-0174-9868
  - family-names: Dervieux
    given-names: Christophe
    email: cderv@posit.co
    orcid: https://orcid.org/0000-0003-4474-2498
  year: '2025'
  doi: 10.32614/CRAN.package.quarto
- type: software
  title: remotes
  abstract: 'remotes: R Package Installation from Remote Repositories, Including ''GitHub'''
  notes: Suggests
  url: https://remotes.r-lib.org
  repository: https://CRAN.R-project.org/package=remotes
  authors:
  - family-names: Csárdi
    given-names: Gábor
    email: csardi.gabor@gmail.com
  - family-names: Hester
    given-names: Jim
  - family-names: Wickham
    given-names: Hadley
  - family-names: Chang
    given-names: Winston
  - family-names: Morgan
    given-names: Martin
  - family-names: Tenenbaum
    given-names: Dan
  year: '2025'
  doi: 10.32614/CRAN.package.remotes
- type: software
  title: scales
  abstract: 'scales: Scale Functions for Visualization'
  notes: Suggests
  url: https://scales.r-lib.org
  repository: https://CRAN.R-project.org/package=scales
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  - family-names: Pedersen
    given-names: Thomas Lin
    email: thomas.pedersen@posit.co
    orcid: https://orcid.org/0000-0002-5147-4711
  - family-names: Seidel
    given-names: Dana
  year: '2025'
  doi: 10.32614/CRAN.package.scales
- type: software
  title: testthat
  abstract: 'testthat: Unit Testing for R'
  notes: Suggests
  url: https://testthat.r-lib.org
  repository: https://CRAN.R-project.org/package=testthat
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2025'
  doi: 10.32614/CRAN.package.testthat
  version: '>= 3.0.0'
- type: software
  title: tidyverse
  abstract: 'tidyverse: Easily Install and Load the ''Tidyverse'''
  notes: Suggests
  url: https://tidyverse.tidyverse.org
  repository: https://CRAN.R-project.org/package=tidyverse
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@rstudio.com
  year: '2025'
  doi: 10.32614/CRAN.package.tidyverse

CodeMeta (codemeta.json)

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  "identifier": "spanishoddata",
  "description": "Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at <https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/opendata-movilidad>. This package simplifies the management of these large datasets by providing tools to download zone boundaries, handle associated origin-destination data, and process it efficiently with the 'duckdb' database interface. Local caching minimizes repeated downloads, streamlining workflows for researchers and analysts. Extensive documentation is available at <https://ropenspain.github.io/spanishoddata/index.html>, offering guides on creating static and dynamic mobility flow visualizations and transforming large datasets into analysis-ready formats.",
  "name": "spanishoddata: Get Spanish Origin-Destination Data",
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  "fileSize": "2123.967KB",
  "citation": [
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      "@type": "SoftwareSourceCode",
      "datePublished": "2024",
      "author": [
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}

GitHub Events

Total

Create event: 32
Release event: 4
Issues event: 74
Watch event: 16
Delete event: 35
Issue comment event: 163
Push event: 290
Pull request review comment event: 67
Pull request review event: 57
Pull request event: 67
Fork event: 3

Last Year

Create event: 32
Release event: 4
Issues event: 74
Watch event: 16
Delete event: 35
Issue comment event: 163
Push event: 290
Pull request review comment event: 67
Pull request review event: 57
Pull request event: 67
Fork event: 3

Committers

Last synced: 8 months ago

All Time

Total Commits: 537
Total Committers: 5
Avg Commits per committer: 107.4
Development Distribution Score (DDS): 0.169

Past Year

Commits: 537
Committers: 5
Avg Commits per committer: 107.4
Development Distribution Score (DDS): 0.169

Top Committers

Name	Email	Commits
Egor Kotov	k**r@g**m	446
robinlovelace	r**x@g**m	79
eugenividal	e**l@h**m	9
Raul Nanclares	1****s	2
Vaughn Blandy	b**c@o**u	1

Committer Domains (Top 20 + Academic)

oregonstate.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 86
Total pull requests: 92
Average time to close issues: 23 days
Average time to close pull requests: 2 days
Total issue authors: 3
Total pull request authors: 5
Average comments per issue: 2.23
Average comments per pull request: 2.87
Merged pull requests: 80
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 53
Pull requests: 67
Average time to close issues: 20 days
Average time to close pull requests: 2 days
Issue authors: 3
Pull request authors: 5
Average comments per issue: 1.92
Average comments per pull request: 2.16
Merged pull requests: 59
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

e-kotov (49)
Robinlovelace (10)
eugenividal (4)
llrs (1)

Pull Request Authors

e-kotov (84)
Robinlovelace (17)
eugenividal (10)
rnanclares (4)
VaughnbBCN (2)

Top Labels

Issue Labels

enhancement (10) bug (7) help wanted (6) ro-hackathon-2025 (5) good first issue (4) note (3) documentation (3) ideas (1)

Pull Request Labels

enhancement (2)

spanishoddata

Science Score: 77.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

spanishoddata: Get Spanish Origin-Destination Data

Examples of available data

Install the package

Set the data directory

Overall approach to accessing the data

Showcase

Zones

OD data

spanishoddata advantage over accessing the data yourself

Desire lines

Further information

Citation

Working with Python?

References

Owner

Citation (CITATION.cff)

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies

`spanishoddata` advantage over accessing the data yourself