Tag: Twitter

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Two new studies on revealing cross-border mobility and border regions

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”

Published in European Planning Studies

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.

Figure 1. The conceptual framework of data collection and data enrichment using the activity space approach to reveal cross-border mobilities and its motives from an individual perspective.

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OptiSS 🧐 — A tool to optimize spatial joining of social media data

Authors: Bryan Vallejo, Olle Järv

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.

Figure 1. The OptiSS tool assigns geographical attributes like municipality or country efficiently to social media posts. This is useful particularly when posts are not only located on land, but also off the coast (highlighted in red circles). Continue reading “OptiSS 🧐 — A tool to optimize spatial joining of social media data”