🎊 Hooray, another new co-authored #OpenAccess article out exploring how #migration intentions in #Europe are changing during the digital and green transitions.
1. Twin transition can mitigate or boost existing regional divides depending on how regions position themselves
2. Quality of life has become more important factor for migration
🥇 I am thankful to my fantastic co-authors Anastasia Panori, Elli Papastergiou, @miladmzdh and Olle Järv from our #MobiTwin project
It is such a pleasure to see the culmination of several months of stakeholder liaison work with the successful completion of today's regional policy lab workshop at the South-Savo Regional Council headquarters. We had a nice representation from regional stakeholders across private, public, academic and civil society sectors, and as a result we have two good initial policy drafts!
#Mikkeli #Savo #Karjala #Pohjanmaa #Lappi #MobiTwin #RegionalStudies #Geography
How attractive is your region?
Is it an economic power house? A digital pioneer? A green trailblazer? Or something else entirely?
Find out yourself with the new dashboard from our #MobiTwin project!
#30DayMapChallenge #Geography #Research #Europe #Tiede #Tutkimus
🚨🚨UPDATED DATA🚨🚨
I just added another year of #Erasmus student #mobility flow data to the dataset we published this year. This adds the mobility of over 280 000 students from 2023 to the data, bringing the grand total to over 2.5 million students covered by the data from 2014 until 2023.
💾 The data: https://doi.org/10.5281/zenodo.16737523
📜 The original article describing the data: https://doi.org/10.1038/s41597-025-04789-0
#OpenScience #OpenData #OriginDestination #OD #EU #EuropeanUnion #Education #Regional #MobiTwin
✨ NEW TOOL FOR #MOBILITY DATA #DATAVIZ ✈️🚚🚗🏃
Do your visualisations of origin-destination matrices look like a mess of criss-crossing hairs? Maybe you could benefit from our #EdgeBundling tool by me, Oula Inkeröinen, @miladmzdh and Olle Järv!
🛠️ The tool: https://doi.org/10.5281/zenodo.14532547
This tool is an output from the #MobiTwin project funded by the #EuropeanUnion through the #HorizonEU programme.
Edge-bundling tool for regional mobility flow data This repository hosts the scripts to perform edge-path bundling (Wallinger et al. 2022) for flow data. It's primary use case is to support visualization of complex mobility data, and has been used to bundle human mobility flows across NUTS regions in Europe. The tool's inputs are two CSV files, one for point feature data and associated coordinates, and another for flows (edges) to be bundled. After bundling, the tool outputs a GeoPackage file. The script expects the data to be in WGS84 coordinate reference system. The scripts in this repo are repurposed versions of the original scripts written by Peterka (2023). The updates to the original code aim to make the code more usable for analytical purposes. This tool is an additional output of the Mobi-Twin research project. Requirements The scripts within the repo require Python 3.10 or newer version with the following packages: pandas geopandas tqdm shapely On top of these Python requirements, the script expects the input CSV data (centroids and edges) to have a certain structure. Data structure for input files Centroid file | ID_COLUMN | X | Y | | ---- | :----- | :---------- | | Unique identifier for centroid (e.g., NUTS code) | X coordinate (WGS84) of the centroid | Y coordinate (WGS84) of the centroid | N.B.: The ID_COLUMN in the above is an example name, use the column name you have in your data. Edge file | ORIGIN | DESTINATION | OD_ID | COUNT | | ---- | :----- | :---------- | :---------- | | ID code of origin | ID code of destination | ID made of origin and destination codes joined by an underscore (_) | Integer/floating point number of flow strength | N.B.: The ID codes of origins and destinations have to match the IDs of your centroid file. Usage Clone this repository, and run the tool by typing in the following command: python bundle_edges.py -c /path/to/centroids.csv -id ID_COLUMN -ew /path/to/edges.csv -o /path/to/output.gpkg If you want to adjust some parameters of the bundling, such as weights or bundling threshold use the flags -ew for edge weights (default is 2), and -t for bundling threshold (default is 2). The edge weights dictate how powerful the "gravity" of long edges are. The bundling threshold sets the distance limit for how many times longer the bundled edges can be compared to straight line distances, flows that are longer than the threshold are not bundled but remain as straight line geometries in the output. Please note, the script expects the coordinates to be in WGS84 (EPSG:4326) Test files We have provided two test CSV files that demonstrate the data structure of the required CSV files. These files can be found under the example_data directory. References Wallinger, M., Archambault, D., Auber, D., Nöllenburg, M., & Peltonen, J. (2022). Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach. IEEE Transactions on Visualization and Computer Graphics, 28(1), 313–323. https://doi.org/10.1109/TVCG.2021.3114795 Peterka, O. (2024). Xpeterk1/edge-path-bundling. https://github.com/xpeterk1/edge-path-bundling (Original work published 2023). Related links Mobi-Twin project official webpage Digital Geography Lab webpage
🚨🌍 NEW ARTICLE 🌍🚨
We geocoded the #mobility of over 2 million #Erasmus students across #Europe from 2014 to 2022 with @miladmzdh Oula Inkeröinen & Olle Järv. The data descriptor article is published in #ScientificData, and is an output from the #MobiTwin project.
https://doi.org/10.1038/s41597-025-04789-0
#GIScience #Geospatial #GIS #MobiTwin #OpenData #OpenScience
Are you looking for some regional #mobility data from Europe on #NUTS 2 level?
The #MobiTwin project has published the description of the project data, and is looking for interested collaborators to study #RegionalEconomics #Migration #Commuting #SeasonalWork and #CrossBorder mobility.
The data contains mobility data for seven types of mobility across Europe, and will become fully open-access after the project, but it can be shared with collaborators now already 😉
Complete Mobi-Twin Dataset In a nutshell The Complete Mobi-Twin dataset is created in the Mobi-Twin (Twin transition and changing patterns of spatial mobility: a regional approach) project funded by the European Union’s Horizon Europe Research and Innovation Programme (Grant Agreement no. 101094402). This dataset combines existing European-level survey and register datasets with publicly available open and big data sources to produce a data product containing information on mobility flows and regional characteristics from Europe. The dataset is at NUTS 2 (Nomenclature of territorial units for statistics) regional level and covers data from 2005 to 2023. The dataset is an outcome of Mobi-Twin research project. The complete dataset includes five interlinked sections: 1) Regional characteristics, 2) Mobility data, 3) Microsimulation data for five pilot regions, 4) The Mobi-Twin Survey data, 5) The NUTS 2 spatial layers. The regional characteristics dataset provides essential information on the NUTS 2 regions in Europe for understanding and redefining regional attractiveness in the twin transition. This data consists of seven themes ranging from variables describing digitalization and environmental characteristics of the regions to socio-economic, demographic, and typological information. The mobility dataset provides information on mobility flows of the three identified mobility forms – long-term, short-term and circular mobility. The long-term mobility form contains permanent migration and long-term student mobility. The short-term mobility form contains short-term student mobility and seasonal work mobility. The circular mobility form contains long distance commuting, cross-border commuting and multilocal living. The mobility data is extracted from Labour Force Survey data, Twitter data, and Eramus+ mobility data. Microsimulation dataset provides essential socio-economic and demographic input data for agent-based modelling in the five case study regions of the Mobi-Twin project. The Mobi-Twin Survey data is a final and clean dataset from the survey conducted during the project, including additional geographical and mobility type profiling variables derived from the initial survey questions. The NUTS 2 spatial layer dataset includes all official versions of the NUTS 2 territorial division. This documentation describes in detail the creation and structure of the dataset. The complete dataset can be updated with newer or corrected data during the project. The complete Mobi-Twin dataset will be made openly available after the project ends. General Description The Mobi-Twin dataset is a curated collection of five datasets: regional characteristics, mobility flow data, the Mobi-Twin survey data, input data for agent-based modelling in the five pilot regions of the project, and spatial layers for official versions of NUTS 2 division over time. The datasets are interconnected to each other based on the unique NUTS 2 identifier code (ID) and mappable via the spatial layer of NUTS 2 regions in Europe (Figure 1). These data have been collected by the Mobi-Twin partners from various sources and are presented in tabular and spatial formats. The dataset is the main output from data collection performed in the beginning of the project and will provide the input data for analyses done later in the project. Figure 1. The relation between the five sections of the complete Mobi-Twin dataset. Author: Tuomas Väisänen. Full-sized figure HERE. The mobility data section covers seven types of mobility, each of which belongs to one of the three main mobility forms – long-term, short-term, and circular mobility (Section 2). The long-term mobility form covers permanent migration and long-term student mobility types. This mobility form refers to mobility where the individual is staying in the destination region for longer than 12 months. The short-term mobility form covers short-term student mobility and seasonal work mobility types. This mobility form refers to mobility where the individual is staying in the region for a duration between three and 11 months. Finally, the circular mobility form covers mobility where the mobility between origin and destination region is habitual, frequent, and implies a return trip to the origin region. This form contains the following mobility types: long-distance commuting, cross-border commuting, and multilocal living. The regional characteristics data section of the complete Mobi-Twin dataset provides information on the regions in Europe from 2005 until 2023. These characteristics have been further sectioned into seven themes, that capture different characteristics of these regions. These themes include social fabric, living conditions, economy and labour market, access and connectivity, digitalization, landscape and environment, and finally regional typologies. Each theme includes several variables describing each region throughout the years in the context of the theme. For instance, the social fabric files include variables describing gender balance, population at risk of poverty, income levels and median age of population. The information in the regional characteristics section of the dataset provides essential background information for understanding the mobilities through differences between the receiving and sending regions. To exemplify in the context of the twin transition, student mobility might be better explained by a large difference in the penetration rates of affordable high-speed broadband and mobile internet connections between the regions than climate differences. The microsimulation data section provides a basic regional information on demographics and employment in the five pilot regions for agent-based modelling (Section 4). using the NUTS on its third level (NUTS 3). This data is used to model mobility patterns within the five focus NUTS 2 regions of the project: Spain: Castilla-La Mancha (ES42) The Netherlands: Groningen (NL11) Italy: Lombardy (ITC4) Greece: Central Macedonia (EL52) Finland: Northern and Eastern Finland (FI1D). This information consists of tabular data on age/sex structure, marital status, education levels, employment status, and household sizes per NUTS 3 regions that make up the above-mentioned NUTS 2 regions. This data covers the years 2001, 2011, and 2021 where data is available, and some of the years in between depending on data availability per country. Any annual gaps in the data will be filled with interpolation in WP3. The Mobi-Twin survey data section provides survey data on the populations in the five pilot countries regarding their mobility patterns relating to the twin transition (Section 5). The survey data was collected from the Netherlands, Spain, Italy, Greece, and Finland, but also on a more general level from all over Europe. It contains questions on past, current, and future regions of residence, but also on demographics, employment, and attitudes towards the twin transition. The survey data has been processed by the partners to extract mobilities between NUTS 2 regions, to classify respondents as digital nomads, return migrants and retirement migrants, and to provide weights for the respondents. The NUTS 2 spatial layer dataset ensures the interoperability of the four Mobi-Twin datasets with each other (Figure 1), and potential outside sources of the data (Section 6). Here, each data record of the dataset is associated with a NUTS unique code. Regional NUTS 2 regional codes enable enriching mobility flow information with regional characteristics of the origin and destination regions, which is essential for modelling the effects of the twin transition on inter-regional mobilities. Spatial NUTS divisions are available on three levels for statistical analyses of different scopes. Mobi-Twin project focuses on the NUTS 2 level, as that is the level where regional policies are applied, and the availability of the data is better compared to the NUTS 3 or LAU (local area unit) levels. Interested to collaborate and use the dataset? Feel free to reach out to Olle Järv ([email protected]). How to cite the Complete Mobi-Twin Dataset? Väisänen, T., Malekzadeh, M., Havusela, M., Inkeröinen, O. & Järv, O. (2024) The Complete Mobi-Twin Dataset. DOI: 10.5281/zenodo.14228376
Alright, I will share my map for Day 2 of #30DayMapChallenge again. It depicts regional student #mobility across Europe a few years ago. As the data contains 200 000+ flows, straight line geometries make a fuzzy mess, but through aggregation and a technique called #EdgeBundling one can reduce visual clutter in the image.
The data and methods will be published openly in the near future. The work is a part of the #MobiTwin #EUHorizon project.
#30DayMapChallenge sure is a nice way to discover the #geospatial community on here!
https://mastodon.online/@digigeolab/113412538814187617
@digigeolab - Our contribution to the #Lines day of #30DayMapChallenge comes from @waeiski with special thanks to Oula Inkeröinen. The map shows the value of aggregation and edge-bundling for complex mobility data, such as regional student mobility across Europe. The data and methods will be published openly in the near future as a part of the #MobiTwin project.
Attached: 1 image Our contribution to the #Lines day of #30DayMapChallenge comes from @[email protected] with special thanks to Oula Inkeröinen. The map shows the value of aggregation and edge-bundling for complex mobility data, such as regional student mobility across Europe. The data and methods will be published openly in the near future as a part of the #MobiTwin project.
Tuomas Väisänen 📼🧟♂️
Ruth Mottram