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
 
Description Spatial networks, such as those representing commuting or migration, contain a plethora of information that can and should be utilised to better understand the movement of people or goods. Using all this information necessitates updating the existing methods or creating new methods to analyse the behavioural patterns. Moreover, these networks are usually on the scale of Big Data and thus automatically finding meaningful patterns would allow for a better understanding of the data and the processes.
By using geographical information that is contained within the movement network, we were able to identify subsets (mathematical communities) within the network. These communities are then used to investigate different movement types and patterns that are happening in the real world. Applying the method to the home-to-work census data of entire Scotland allows us to distinguish two different types of commuting; people who commute to a different town and people who work within the same place where they live. This is currently work in progress, but we are getting promising results.
In terms of training in the specialist skills that I received so far, I took courses that allowed me to expand my knowledge of complex network theory, spatial modelling and statistical analysis. I have also received training in soft skills such as R and Python for qualitative analysis, presentation and writing skills, networking skills, time management skills and work organisation.
Exploitation Route Making network analysis tools location-aware opens possibilities in both academic and non-academic sector. This tool will open new ways to analyse space and movement in GIScience. It will also create a new way of looking at transportation and human mobility and will allow a different way for the researcher to use explore data and interpret results.
The non-academic sector could benefit from the analytics and automated part of the method. It leads to a better understanding of movement processes happening within the area and can be used to improve traffic infrastructure (public transport, cars, bicycle routes) and in urban planning (school and hospital locations). Publishing the method as Free and Open Source Software will open the possibility for the industry to analyse their collected network and identify areas and routes used by the subset of the travellers and target it with their product as necessary.
Sectors Communities and Social Services/Policy,Environment,Transport