Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning
This work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments
Abstract
This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012–2020 and cluster them into 22,872 unique crashes during this period. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.
This work is part of the smarTTrans: Road Safety in Kenya project
Citation
Milusheva S, Marty R, Bedoya G, Williams S, Resor E, Legovini A (2021) Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning. PLoS ONE 16(2): e0244317. https://doi.org/10.1371/journal.pone.0244317