What we will do today?

• explore data

• discuss insights, idea and limitations

• potentially co-author a workshop-report short paper (in less than 2 weeks, strict deadline) with some visualisations and insights

Survey-based human mobility

European Labour Force Surveys

Survey-based human mobility

German Mobility Survey

Census-based human mobility

UK Census

Census-based human mobility

UK Census

Register-based human mobility

Netherlands Register Data

IOT GPS Data

The Volkswagen leak

Mobile Apps + Mobile Network GPS Data

App usage by Orange customers in France

Mobile phone data

Flowminder

Mobile phone data for humanitarian and development efforts in low- and middle-income countries

Flowminder Software

FlowKit - privacy preserving mobile phone data aggregation

Spanish Open Mobility Big Data

~ 5 years of daily hourly flows

Spanish Open Mobility Big Data

3500+ zones across Spain and beyond

Spanish Open Mobility Big Data

Spanish Open Mobility Big Data

Spanish Open Mobility Big Data

Spanish Open Mobility Big Data

Academic research

Spanish Open Mobility Big Data

Effects of human mobility on dispersal of disease-transmitting mosquitoes

Multi-MNO Project

Multi-MNO Project

Mobility data aggregation using unified methodology

Multi-MNO Project

How it works

Multi-MNO Software

https://github.com/eurostat/multimno

Multi-MNO Outputs

What to expect?

spanishoddata

access Spanish Open Mobility Big Data from R

spanishoddata - access open human mobility data

A package with many companions

Detailed documentation

spanishoddata use cases

Split work and non-work trips

spanishoddata use cases

Split into different trip types

spanishoddata use cases

Split into different trip types

Get the data

Download one by one?

Get the data

Write your own XML parser?

Get the data

::::

Get the data

library(spanishoddata)
spod_set_data_dir("data")

od_data <- spod_get(
  type = "origin-destination",
  zones = "districts",
  dates = c(
    start = "2022-03-01",
    end = "2022-03-07"
  )
)

Get the data

library(spanishoddata)
spod_set_data_dir("data")

od_data <- spod_get(
  type = "origin-destination",
  zones = "districts",
  dates = c(
    start = "2022-01-01",
    end = "2022-01-04"
  )
)

library(dplyr)
glimpse(od_data)

Rows: ??
Columns: 20          
Database: DuckDB v1.2.1 [root@Darwin 24.4.0:R 4.5.0/:memory:]
$ date                        <date> 2022-01-04, 2022-01-04, 2…
$ hour                        <int> 0, 0, 0, 1, 1, 3, 4, 4, 5,…
$ id_origin                   <fct> 01001, 01001, 01001, 01001…
$ id_destination              <fct> 01009_AM, 01009_AM, 01009_…
$ distance                    <fct> 2-10, 2-10, 2-10, 2-10, 2-…
$ activity_origin             <fct> home, frequent_activity, w…
$ activity_destination        <fct> frequent_activity, home, h…
$ study_possible_origin       <lgl> FALSE, FALSE, FALSE, FALSE…
$ study_possible_destination  <lgl> FALSE, FALSE, FALSE, FALSE…
$ residence_province_ine_code <fct> 01, 01, 01, 01, 01, 01, 01…
$ residence_province_name     <fct> "Araba/Álava", "Araba/Álav…
$ income                      <fct> 10-15, >15, >15, >15, >15,…
$ age                         <fct> NA, NA, NA, NA, NA, NA, NA…
$ sex                         <fct> NA, NA, NA, NA, NA, NA, NA…
$ n_trips                     <dbl> 4.894, 1.779, 1.094, 1.094…
$ trips_total_length_km       <dbl> 27.966, 5.997, 4.081, 4.16…
$ year                        <int> 2022, 2022, 2022, 2022, 20…
$ month                       <int> 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ day                         <int> 4, 4, 4, 4, 4, 4, 4, 4, 4,…

DuckDB in Action

Imagine a typical laptop

DuckDB in Action

Filter and summary

library(dplyr)

od_data |>
  filter(
    year == 2022,
    month %in% c(2, 3, 4)
    ) |>
  summarise(
    n_trips = mean(n_trips)
  ) |>
  collect()

DuckDB in Action

Summary on full data set

library(dplyr)

od_data |>
  summarise(
    n_trips = mean(n_trips)
  ) |>
  collect()

DuckDB in Action

Summary over multiple groups on full data set

library(dplyr)

od_data |>
  group_by(
    year,
    month,
    day,
    id_origin,
    id_destination
  )
  summarise(
    n_trips = mean(n_trips)
  ) |>
  collect()

DuckDB in Action

Summary over multiple groups on full data set

library(dplyr)

od_data |>
  group_by(
    year,
    month,
    day,
    id_origin,
    id_destination
  )
  summarise(
    n_trips = mean(n_trips)
  ) |>
  collect()

Flowmaps with {flowmapper} and {flowmapblue}

{flowmapblue} (Boyandin 2024) for interactive flowmaps

{flowmapper} (Mast 2024) for static flowmaps and producing spatial data

Tutorial website

https://www.ekotov.pro/agit-2025-spanishoddata/