Geo-Located Tweets. Enhancing Mobility Maps and Capturing Cross-Border Movement

Abstract

Capturing human movement patterns across political borders is difficult and this difficulty highlights the need to investigate alternative data streams. With the advent of smart phones and the ability to attach accurate coordinates to Twitter messages, users leave a geographic digital footprint of their movement when posting tweets. In this study we analyzed 10 months of geo-located tweets for Kenya and were able to capture movement of people at different temporal (daily to periodic) and spatial (local, national to international) scales. We were also able to capture both long and short distances travelled, highlighting regional connections and cross-border movement between Kenya and the surrounding countries. The findings from this study has broad implications for studying movement patterns and mapping inter/intra-region movement dynamics.

From neuroscience to computer science: a topical approach on Twitter

“…As Twitter has been a valuable tool to track and to identify patterns of mobility and activity [5,13,32,38], this section examines and captures all locations where our collected tweets were posted.…”

TravelDiff: Visual comparison analytics for massive movement patterns derived from Twitter

“…By contrast, our method is rulebased and allows for an interactive trade-off between precision and recall. With a more regional and application-specific focus, Blanford et al [13] analyze cross-border movement in Africa derived from Twitter and build graph models to aggregate the movements. Flow Sampler [22] also adapts a graph-based methods and infers movement pathways from point-based geo-located microblog data to explore movement flow.…”

“…Recently, a number of research projects have started to interpret movements reconstructed from microblogging platforms [13,18,19,20,25,38,42,45] (see Section 2). While these approaches already reconstruct and classify trajectories, we still face many open research questions and challenges: Are the reconstructed trajectories reflecting real world patterns, comparable to explicit movement data for urban analysis?…”

Visualizing the Data City

“…Por ejemplo, ha sido posible capturar el movimiento de la movilidad en Kenia a diferentes escalas espaciales y temporales gracias a que cada persona deja una huella digital única con cada tweet que envían (Blanford et al, 2015). En Leeds, se han recogido tweets de la ciudad inglesa durante un año.…”