Multi-local living can be defined by individuals or families having access to more than one residence in their everyday lives. It is a complex social phenomenon causing weekly and seasonal changes in population numbers as people move between regions. This means that the phenomenon is tightly connected to human mobility. In prior research, multi-locality has been mainly studied using official statistics that fail to capture the dynamic nature of people’s mobilities and dwelling. To address this in my thesis, I utilized spatially and temporally accurate big data sources − mobile phone and electricity consumption data − to capture people’s presence and mobility. More accurate information about multi-local living can be useful for local businesses and regional planning in rural areas.
How was the research done?
In my thesis, multi-local living was studied in Finland and in the county of South Savo, which has the highest proportion of second homes/free-time residences in the country. The study was done by analyzing spatiotemporal changes in people’s presence (mobile phone data from Telia Crowd Insights) and by examining how the changes relate to the number of second homes (official statistics) in different areas with correlation analyses. In addition to monthly comparisons, analyses were conducted separately for workdays and weekends to assess how people’s multi-local practices differ between weekdays. The study period of the thesis was from November 2018 to August 2019.
Mobile phone data also contains information about people’s origins (previous night location). This allowed to assess the proportions of origin counties of people visiting South Savo. Moreover, mobile phone data was used to assess the results of second home occupancy in South Savo gained from electricity consumption data which had been previously calculated in the MOPA research project.
It was our honour and pleasure to attend the 8th Mobile Tartu conference organized by the Mobility Lab of the University of Tartu, Estonia. The event was once again scientifically fruitful and socially rewarding — exactly the way how the founder of the conference, the late professor Rein Ahas had envisioned it!
The members of the Digital Geography Lab were well represented in organising PhD workshops, presenting latest research from various projects, chairing sessions and moderating a panel discussion.
Olle Järv and Oleksandr Karasov organized a PhD workshop on “Social media sources as a tool to monitor cross-border mobility”, and Christoph Fink and Tuuli Toivonen together with our former group member Age Poom organized a PhD workshop on “Data and tools for environmental exposure assessment during urban mobility”.
One of the goals of the BORDERSPACE project at the Digital Geography Lab is to examine whether and how social media data such as geo-located Twitter data can reveal cross-border mobility of people and provide new insights for understanding border regions. We demonstrate the feasibility of using Twitter data in two different recently published studies – the first study from the Greater Region of Luxembourg and the second study from the Nordic countries.
Study #1: “Revealing mobilities of people to understand cross-border regions: insights from Luxembourg using social media data”
Authors: Olle Järv, Håvard W. Aagesen, Tuomas Väisänen& Samuli Massinen
Conceptually, our approach was to make big data small and meaningful by: 1) using a bottom-up concept of activity space (e.g. Järv et al., 2014); 2) using mobility as a tool to capture individual activity spaces; and 3) contextualizing mobility from the border perspective.
We developed the OptiSS tool to optimize geodetic spatial joining for assigning geographical attributes to social media data in the BORDERSPACE project at the Digital Geography Lab. The tool has a user-friendly local app, yet its Python script can be easily used in any workflow.
Why we developed the tool?
In the BORDERSPACE project, we need to assign hierarchical spatial attributes (municipality, region, country) to each geo-located tweet. Mostly, geo-located tweets obtained from Twitter’s API already have geographical information such as an administrative unit and a country, in addition to exact coordinates. Yet, not all tweets have such information and, most importantly, some tweets are not located on land – some are just off the coast or somewhere at sea (Figure 1). However, geodetic spatial joining requires computational resources and is time consuming, especially when we have 100+ million geo-located tweets to handle. Thus, we created the OptiSS tool to make computation more efficient. The tool works for any social media data that have at least geographical coordinates.
Olle Järv from the Digital Geography Lab attended as an expert panellist in the Nordic-Baltic Migration Conference “Cross-border Mobility in the Nordic-Baltic Region” organized by the Nordic Council of Ministers’ Office in Tallinn, Estonia on September 18, 2020. Olle participated in the second panel ”New Challenges in Cross-Border Mobility, Nordic-Baltic Region” together with Uffe Palludan (Palludan Fremtidsforskning), Jonas Wendel (Nordic Council of Ministers’), Rolle Alho (Uni Helsinki), and Saara Pellander as a moderator (Migration Institute of Finland). In the panel, Olle briefly introduced his BORDERSPACE research project on cross-border mobility and transnational people, and how these research topics benefit from novel data sources such as social media and mobile phone data.