Can social media data be used to predict spatiotemporal human population change?

Authors: Alan Smith*, University of Plymouth
Topics: Geographic Information Science and Systems, Population Geography, Spatial Analysis & Modeling
Keywords: GIS, Population, Big Data, Spatiotemporal
Session Type: Paper
Day: 4/3/2019
Start / End Time: 12:40 PM / 2:20 PM
Room: Marriott Ballroom Salon 1, Marriott, Lobby Level
Presentation File: No File Uploaded

This paper will examine how Twitter data can be used to estimate magnitudinal change in spatiotemporal gridded populations. The density of tweets over time and space has been combined with known gridded population census data. This paper seeks to evaluate the potential for social media data to reliably inform the magnitude in population uplift. Populations are not static in space or time and vary considerably by time of data between residential and workplace location. The research method presented utilises the Twitter developer’s API and explores the potential for improved reliability of geo-located twitter user information over small areas where there may have been previously insufficient data. Examples from across the United Kingdom will be used to demonstrate the potential for social media and other emerging datasets that directly have the ability to enhance our understanding of human population dynamics. It is proposed that when these datasets are combined with established, traditional population data products (e.g. censuses) that their ability to inform change on an ever increasing spatiotemporal scale can be demonstrated.

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