Exploring the effect of location and environment on human mobility flows with Big Data Analytics

Lead Research Organisation: University of St Andrews
Department Name: Geography and Sustainable Development

Abstract

Contemporary flow data are used to investigate a number of social science phenomena: examples include flows inferred from mobile phone data, migration flows, commuting flows, or taxi flows between pick-up and set-down points. This PhD thesis will develop new bespoke advanced quantitative methods for flow data to better understand patterns in flows and their relationship with geography and environment.
Flow data are mathematically represented as geographic networks and are due to their size and complicated structure a typical example of big data. In geography, there is a lack of appropriate methods for their analysis. While some disciplines, such as physics, have recently developed flow methods, these methods are not suitable for flow networks which are geographically constrained, as the methods are not scalable to large geographic networks, nor do they consider the effects of geographic location. Further, in physics there is no consideration for environmental conditions which affect human mobility, such as the effect of weather on commuting behaviour, as reflected in commuters' flows.
In this project we will develop new analytical methods for geographic flows which will 1) inherently incorporate geography into flow analysis (location-aware methods) and 2) integrate flow data with environmental data (context-aware methods). We will evaluate our new location-aware methods on migration flows from UK censuses in 1981, 1991, 2001 and 2011. The context-aware methods will be used to integrate human mobility data from the iMCD project at the Urban Big Data Centre with open environmental and contextual data, such as data from the Open Data Glasgow portal, meteorological data and remotely sensed satellite data. All methods will be implemented as FOSS software to achieve maximum impact. The ultimate goal is to provide new rigorous and extensively tested methods for flow data and make them accessible to any social scientist interested in flows and related phenomena.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/R500938/1 01/10/2017 31/03/2021
1948471 Studentship ES/R500938/1 27/10/2017 31/01/2021 Sebastijan Sekulic