Understanding Dynamic Spatial Behaviour using an App-based Survey

Authors: Jed Long*, University of Western Ontario
Topics: Temporal GIS, Transportation Geography, Spatial Analysis & Modeling
Keywords: GPS tracking, mobile application, experience sampling, self-employment, transportation, network analysis, GIS
Session Type: Paper
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded

The modern digital economy has brought about great changes in the requirements on the modern workforce – for many workers being present at a job site is no longer important. This shift has occurred rapidly and has been enhanced by technologies allowing people to connect from anywhere. Thus, understanding how new ways of working are shaping human mobility patterns in cities is highly relevant. As part of a larger project – The WorkAndHome project – we have developed a bespoke mobile survey application, combining GPS tracking technology and an experience sampling interface, to study where people are going, what they are doing, who they are with, and how they feel when they are there. The survey was administered to approximately 900 participants during Autumn 2018 in two cities in England (Leeds and Brighton and Hove) with contrasting levels of self-employment. Each participant used the mobile survey app for a minimum period of 7 days. We recruited participants systematically across strata to contrast between self-employed and/or freelance workers and those in traditional employment roles. We over-sampled females to ensure approximately equal representation. Preliminary analysis is used to show differences in spatial behaviour between different sample groups. More broadly, in this presentation I will highlight the opportunities and pitfalls for understanding human dynamics and behaviour using (now widely available) mobile applications. I will specifically demonstrate the potential of app-based tracking data, when combined with experience sampling, for answering targeted and innovative social science questions using modern computational and geographical analysis methods.

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