WE ARE RECRUITING! Looking for a postdoctoral researcher in big data analytics in the area of human mobility and social interactions

Are you interested in studying human mobility and social interactions that take place in the cross-border context, and doing advanced spatial and content analysis using millions of social media posts? If so, consider applying for a postdoc position at the Digital Geography Lab (DGL) to work with Academy Research Fellow Olle Järv from November 2021 (or sooner/later as agreed)!

We are looking for an enthusiastic, innovative, and open-minded team player with strong technical knowledge and skills to join our interdisciplinary DGL research group and work in the Academy of Finland-funded project BORDERSPACE – Tracing Interactions and Mobilities Beyond State Borders: Towards New Transnational Spaces.

The possible research topic can be among the following four lines of research:

  • quantifying human mobility and activity spaces across country borders, based on social media data (Twitter), including developing concepts and methods of the field;
  • examining people’s perceptions and embodied experiences regarding transnational lifestyle, and their feelings of belonging using quantitative content analysis of millions of social media posts;
  • identifying functional cross-border regions based on human mobility and interactions, including developing concepts and methods of the field;
  • investigating the influence of external factors such as the global COVID-19 pandemic on cross-border mobilities, people’s perceptions and embodied experiences regarding transnational lifestyle, and their feelings of belonging to different societies.

The successful applicant is expected to have: 1) strong fluency in programming and automation; 2) experience in working with large-scale quantitative datasets (e.g., mobile phone & social media data) and machine-learning; 3) experience in advanced spatial analytics and/or (social media) textual content analysis; 4) an excellent command of oral and written English as demonstrated by published studies in peer-reviewed journals and active participation in research community; and 5) high motivation to pursue an academic career. Prior theoretical knowledge on one or several of the project’s four research lines (see above) is an asset.

Read more about the position announcement and apply HERE. The deadline is September 14th 2021.

For more information about the position, or any questions about the BORDERSPACE project and potential fit, please contact Olle Järv, olle.jarv(at)helsinki.fi.

Warm reflections from the course GEOG-326 on accessibility and human mobility research

Time flies – the course “GEOG-326 Quantitative methods for sustainable land use planning I: Accessibility & mobility analyses“, given by the researchers of the Digital Geography Lab, ended already before Christmas 2020. Yet it’s worthwhile to reflect on it!

It is heart-warming to go through the positive feedback from students regarding the course structure and balance between theory and practice, and constructive suggestions for improving the course.

The course aimed at linking the accessibility and mobility of people to sustainability, well-being and social (in)equality perspectives, exploring the potential of big data analysis approach, and studying the ways of implementing these in planning. We also focused on the impact of global crises on human mobility on the example of COVID-19.

Overall, all 35 students did a great job and received high grades, but most importantly, it was rewarding to see students getting motivated and inspired, and developing their skills and ideas during the course.

The final output of the course was an independent group work that was presented in the form of an academic poster. Me, Elias and Tuuli found the final poster presentation session excellent! Thus, we are delighted to share the posters here 🙂

Check out and get inspired!


GROUP 1: The change in mobility flows during the first COVID-19 restrictions: A case study for Helsinki (and the sub-region)

 Alisa Redding, Sanja-Riia Collin, Venla Salomaa


GROUP 2: Bike-sharing System Support for Public Transportation – studying the temporal aspects of shared bicycle trips in 2019

Emil Ehnström, Olivia Halme, Iivari Laaksonen & Tao Jiaxin


GROUP 3: Accessibility and spatial coverage of the bike-sharing systems in the Helsinki Metropolitan Area

Håvard Aagesen, Hanna Hirvonen, Miika Kastarinen, Jussi Torkko


GROUP 4: Bike sharing in Finland: A comparison of HSL and Föli city bikes ridership from 2018 2020

Emily Dovydaitis, Kia Kautonen, Matti Moisala, Juho Noro


GROUP 5: Impact of COVID-19 on dockless mobility in Austin, Texas

Benedikt Kohl, Tatu Leppämäki, Anna Levlin & Mathew Page


GROUP 7: Comparison of bike-sharing data in Helsinki and Espoo from 2019 and 2020 in relation to Covid-19

Matti Hästbacka, Jouko Lappalainen, Klara Lappalainen, Sarah Seidel


GROUP 8: Effects of COVID-19 on city bike usage

Emma Piela, Eero Perola, Antti Miettinen & Isaline Coquard


GROUP 9: Automating travel time matrix data preprocessing

Hanna Haurinen, Saku Saarimaa, Ekku Keurulainen, Jeanne Leroux


GROUP 10: Green Accessibility Index’ in dominant travel areas zonified by transportation mode in the Finnish Capital Region

Bryan Vallejo, Lauri Ovaska, Elli-Nora Kaarto & Miro Mujunen

Etsimme tutkijatohtoria tai tohtorikoulutettavaa!

Briefly in English: We are sharing an announcement for a post-doc/PhD student position at Digital Geography Lab and Ruralia Institute. The position benefits from the knowledge of Finnish language and hence is published in Finnish only.

Ja sitten suomeksi:

Kiinnostaako monipaikkaisuus, aluekehitys ja ihmisten liikkuvuus? Haluaisitko tietää, miten erilaisten digitaalisten aineistojen avulla voi monipaikkaisuutta tarkastella tai miten COVID-19 pandemia on vaikuttanut kakkosasuntojen käyttöä Suomessa?

Ruralia-instituutti ja Digital Geography Lab yhteiistyössä hakee tutkijatohtoria/tohtorikoulutettavaa hankkeeseen ”Monipaikkaisen asumisen rytmit”.

Tehtävät käsittävät digitaalisten massa-aineistojen (mm. rakennusten sähkönkäytön, matkapuhelinverkon ja Twitter aineistoja) hallinnointia, prosessointia ja analysointia. Hakijalta edellytetään riittäviä geoinformatiikan taitoja analyysien tekoon, massa-aineistojen käsittelyyn vaativaa osaamista (Python, R, PostgreSQL) ja tilastollista osaamista. Suurteholaskennan kokemus katsotaan eduksi.

Tarkemmat hakuohjeet löytyvät täältä.

Hakuaika päättyy 22.3.2021, ole nopea!

Lisätietoja saa: akatemiatutkija Olle Järv olle.jarv(at)helsinki.fi ja professori Tuuli Toivonen, tuuli.toivonen(at)helsinki.fi

WE ARE RECRUITING! Doctoral student with an interest in Big Data analytics, human mobility & social interaction

Are you interested in geoinformatics, big data and social media analytics? Are you curious about the phenomena of human mobility, cross-border mobilities and social interactions of people and transnational people? If yes, check this open four-year doctoral student position at the Digital Geography Lab starting from March 2021 or as agreed with the selected applicant!

We are looking for an enthusiastic, innovative, and highly motivated doctoral student with strong technical knowledge and skills to join our interdisciplinary research group Digital Geography Lab and work in the Academy of Finland-funded project BORDERSPACE – Tracing Interactions and Mobilities Beyond State Borders: Towards New Transnational Spaces.

The doctoral project has three objectives. First, to develop methodologies for quantifying human mobility and activity spaces across country borders based on social media data (Twitter). Second, to develop quantitative methodologies for uncovering activity practices of social media users and their feeling of belonging based on the content of their social media posts. Third, to conduct critical research on dynamic cross-border mobility flows derived from big data, integration of transnational people through their cross-border mobilities and social interactions, and how these are influenced by external factors such as the COVID-19 pandemic.

The successful applicant is expected to have strong fluency in programming (Python or R), experience in advanced spatial analytics and/or social media content analysis, and has worked with big data sources such as mobile phone, smart card and social media data. Prior experience in publishing research in academic journals, participating in research community and having a network of international scholars is an asset.

Read more about the position announcement and apply HERE. The deadline is January 31st 2021.

For further information, please contact Academy Research Fellow Olle Järv, olle.jarv(at)helsinki.fi.

Reflections on the 8th Nordic-Baltic Migration Conference

The second panel ”New Challenges in Cross-Border Mobility, Nordic-Baltic Region” in the Nordic-Baltic Migration Conference in Tallinn, Estonia

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.

In conclusion, the panellists’ discussion highlighted several (new) challenges in the cross-border mobility and interaction in the Nordic-Baltic region:

  • Cross-border mobility and interactions as well as transnational people are taken for granted. In reality, we don’t know much about transnational people, their cross-border practices and how these influence the broader society.
  • We should treat transnational people more than just migrants or tourists. Their daily lives are not confined to only one country, but take place in several countries.
  • We need to acknowledge transnational people and consider them better in policymaking in both sides of the border. The COVID-19 crisis with the closed country borders in spring 2020 showed how poorly transnational people are considered in political decision-making.
  • Developing only infrastructure (e.g. Öresund Bridge between Denmark and Sweden) is not enough for promoting cross-border collaboration and transnational functional regions. We also need political will and collaboration to promote cross-border interaction, and to benefit from existing cross-border infrastructure.
  • One main challenge is the ability of envisioning and executing long-term plans, both from the infrastructure and policy perspective, for developing cross-border interaction and collaboration in the Nordic-Baltic region.

Check out the conference video HERE.

Read the summary about the second panel HERE (in English) and HERE (in Estonian) by the Nordic Council of Ministers’ Office in Estonia.

Digital Geography Lab is an interdisciplinary research team focusing on spatial Big Data analytics for fair and sustainable societies. We aim to understand spatial interactions between people, and between people and their environment, from local to global scales.

BORDERSPACE project led by Academy Research Fellow Olle Järv focuses on cross-border mobilities and interactions, transnational people, and functional transnational spaces. The novelty of the project stems from the use of novel big data sources to provide valuable insights for cross-border research and practice.

Can we use Twitter data to estimate population distribution in Finland?

One of the main data processing steps before making use of novel data sources (e.g. Twitter data) for better understanding social processes and phenomena is the detection of users’ origins – be it at country, municipality or neighborhood level. This allows us to know whose Tweets in some geographical area (say, in a certain city or a neighborhood) we investigate. The most basic way is to distinguish locals from non-locals when examining mobility and activity locations of people. A more advanced analysis would require knowledge about origin countries in tourism studies and origin neighborhoods in segregation studies, for example.

This data processing step is also a prerequisite for cross-border mobility research – we need to know origins of people in order to categorize and analyze movements across country borders extracted from geotagged Tweets. Hence, it is the priority for our cross-border project. See, for example, the recent recent cross-border mobility analysis in the case of the Greater Region of Luxembourg from the MSc thesis by Samuli Massinen.

For analyzing cross-border mobility between Finland and Estonia, we first collected publicly available geotagged Tweets from Twitter Streaming API. We also got additional data from prof. Matthew Zook and Ate Poorthuis who collect Twitter data via the DOLLY project at Uni Kentucky. For extracting individual mobility trajectories and analyzing movements between Finland and Estonia, we used the Twitter Search API to collect Tweet histories (up to 3200 latest Tweets) for each Twitter user who had geotagged a Tweet in Finland and Estonia at least once. Finally, we collected digital traces of roughly 80,000 Twitter users (public accounts) between 2012 and 2019. Using Samuli’s algorithm to detect the country of origin, we found that some 34,000 Twitter users live in Finland.

Now we were excited to know where the Finnish Twitter users in our sample come from and does their spatial distribution at municipality level make sense. So, we slightly modified Samuli’s algorithm to detect their origin municipality in Finland and mapped the result. To evaluate our results, we applied simple regression model to estimate population derived from Twitter data and compared it with the official residential statistics at municipality level (Figure 1).



Figure 1. The comparison of Finnish population distribution by municipality between the official residential register by Statistics Finland and population estimation based on Twitter data using quadratic regression model.

What can we see from the comparison?

First, the positive side. The population estimation from Twitter at municipality level has very high correlation with the official residential registration statistics – the correlation coefficient is 0.98! This is a great outcome and gives the confidence to continue using the data extracted from Twitter for national scale analyses. In particular, given the applied simplistic modelling, there is a huge potential to further develop estimation models. For example, one could now evaluate domestic tourism between different regions or examine the network of central cities with their catchment areas based on people’s mobility derived from Tweets.

Second, the challenge. The exceptionally high concentration of Twitter users in the capital city of Helsinki compared to other municipalities is a challenge for the regression model – basically, it is an outlier in the model. According to our approach, 33% of all origins are located in Helsinki, although the proportion of Finnish population living in Helsinki is only 12% according to the residential register. We were able to minimize the over representation of Helsinki to some extent by using the best fit quadratic regression, instead of simple linear regression model (R2 = 0.83). In both models we also weighted Twitter data with age and gender according to Twitter users’ profile, but this increased the model performance marginally.

The city of Helsinki as an outlier can be explained by two issues, at least: 1) certain (other than age and gender related) social group using Twitter is more represented in Helsinki than in other municipalities; 2) information extraction from Twitter data (data enrichment) has not yet taken into account the detection of work locations – one of the main anchor points in our daily lives. Luckily, these issues can be tackled in future by advancing the data enrichment. For the former, one could enrich data to provide additional background attributes to profile Twitter users. One could also weight Twitter data with the proportion of higher education and/or university students in a municipality. For the latter issue, one could apply the framework of the anchor point model by Ahas et al. (2010) to reveal both the home and work (school) locations to better pinpoint residential locations. Currently, we believe that our simplistic model has assigned many Twitter users’ residential locations to Helsinki who actually are commuting from the Helsinki wider metropolitan area to Helsinki for work.

In conclusion, this comparison gives us confidence that we can detect users’ origins from social media data, and that we can use it as one background attribute in our work-in-progress cross-border mobility analysis between Finland and Estonia. We also continue with the origin detection algorithm development. Stay tuned!




Geotagged Twitter Data to Reveal Cross-Border Mobility of People

Overview of Samuli Massinen’s MSc thesis:

How can Big Data sources, such as georeferenced social media, be used in cross-border research? What kind of cross-border mobility patterns can be detected geographically over time? How can daily cross-border movements be separated from other movements? These were the main questions I was trying to find answers for in my Master’s thesis “Modeling Cross-Border Mobility Using Geotagged Twitter in the Greater Region of Luxembourg”.

The Greater Region of Luxembourg is the largest cross-border labor market in the European Union with the greatest number of cross-border workers in the area. European integration, the Schengen Area, and socio-economical divergences between neighboring countries have been the main factors facilitating human cross-border movements in the region and thus the emergence and expansion of the borderland community. Despite the freedom of movement, country borders still exist as well as their socio-economic differences. We witness the growing trend of people migrating to the other side of the country border while still working in Luxembourg. This actuates daily cross-border mobilities, which are not well known, to date. Thus, there is a distinct need to understand cross-border mobility dynamics in the region, especially border crossings on a daily basis.

Thus far, cross-border mobility studies have mainly leaned on national registers, surveys, and census data. However, these datasets have mostly been too scarce in trying to understand the complexities of cross-border mobility in time and space. Many studies have only focused on aggregate-level movement patterns, and the viewpoint of individuals has been missing due to a lack of suitable data. One promising option to provide individual-level data to cross-border mobility research is the implementation of novel data sources, such as mobile phone positioning and social media data.

In my thesis, I employed a person-based approach using geotagged Twitter data to study cross-border mobility in the Greater Region of Luxembourg. The aim was to examine how to implement social media in cross-border mobility research and how to move beyond aggregate-level inspections. Being one of the first studies of its kind, I utilized a heuristic programmatic approach. To my knowledge, social media data sources have not been previously applied to distinguish different cross-border mobility types. Thus, I have published all scripts and algorithms developed in the study on Digital Geography Lab’s GitHub -pages.

Figure 1. Daily cross-border mover activity location density distribution in

The results show that social media can be implemented in cross-border mobility research, and social media data can provide a relatively good proxy for daily cross-border mobility of people on a regional level. Aggregate-level cross-border mobility patterns and activity location densities (Figure 1) correspond closely with previous studies, and outcomes from temporal frequency inspections indicate that it’s a promising approach for identifying and classifying border crossers  – Twitter users classified as daily cross-border movers are more mobile on working days whereas infrequent border crossers (potentially leisure and tourism-related) are mobile on weekends (Figure 2). Daily cross-border mobility patterns also provided new information about the spatial extent of the movements, indicating home-work commuting (Figure 3).

Figure 2. Weekday variation of cross-border trips to Luxembourg (both directions) by cross-border mover type within the Greater Region of Luxembourg in 2010–2018.

To obtain meaningful information from a Big Data source, several data processing steps are required, one crucial stage being the origin detection of social media users. I used a heuristic approach in home location detection which resulted in high accuracy – the “unique weeks” algorithm introduced in this study gave an accuracy of 88.6 % concerning the ground truth (i.e. Twitter user-given information about the individual’s home location).

Figure 3. Cross-border trips for identified daily cross-border movers in 2010–2018. Movements cover both ways between Luxembourg and neighboring areas. Note: each map has a different flow intensity scale.

Although the applied approach is promising in providing new knowledge about spatio-temporal patterns and trends, the results should be considered with slight caution. For example, this study did not consider population densities or Twitter use activity that might cause bias – both attributes vary spatio-temporally and effect where and when Twitter data is being recorded. Also, the sample size was rather limited in this study (~3200 Twitter users).

Hence, further research and method development are needed with larger sample sizes to draw more sound conclusions about cross-border mobility. Still, in my research, I was able to identify that the coverage of geotagged Twitter data is dependent on data acquisition processes and that Twitter Streaming API can provide valuable information for cross-border mobility research. In future studies, I recommend multi-level data acquisition processes to be applied jointly with a person-based approach combining both spatio-temporal and content analysis methodologies.

My research was part of the cross-border mobilities project at the Digital Geography Lab.


Text by: Samuli Massinen

My MSc thesis can be found here: E-Thesis

All my developed scripts and algorithms can be found here: DGL@GitHub