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Crowdsourcing Data in Mining Spatial Urban Activities - Dr Elisabette Silva

This project focuses on the crowdsourcing data harvesting and data- mining of the multi-dimensional mechanisms of urban segregation combining the geo-coding of information with the rich attributes of this type of data. This project will conduct pilots at Cambridge in the UK and then compare it with prior study of Ningbo in China from an international perspective.

The case of multi-dimensional analysis of Urban Segregation in Cambridge and Ningbo

“Crowdsourcing data” has been generated massively with the development of the information and communications technology (ICT) from large and diverse groups of people or internet users (Brabham, 2013) (Kitchin, 2014). These new non-traditional (i.e. census data) datasets also have been introduced as a data source in the recent urban analysis.

As one type of Big Data, crowdsourcing data is also characterised by the same 3 key attributes: volume, variety, and velocity, or the three V’s (Elgendy & Elragal, 2014),. One of the most significant characteristics of crowdsourcing data is that it is a deliberate blend of bottom-up crowd-derived processes and inputs, combined with top-down goals set and initiated by an organization(Brabham, 2013). Crowdsourcing data cannot only provide geo-coded geographic information for spatial analysis but can also contain urban human behaviour characteristics that enrich the quality of the positional data that we acquire (i.e. trajectory from continuous GPS record, emotions from social media content, the perception from geo-tagged photos, etc).

This project focuses on the crowdsourcing data harvesting and data- mining of the multi-dimensional mechanisms of urban segregation combining the geo-coding of information with the rich attributes of this type of data. This project will conduct pilots at Cambridge in the UK and then compare it with prior study of Ningbo in China from an international perspective.

Firstly, based on an understanding of the spatial fragmentation of urban districts, specific urban matrices are selected to present the spatial features of Cambridge. Then, due to the relation between spatial fragmentation and segregation, we conduct analysis in order to measure the economic and social segregation of urban districts using spatial fragmentation measures defined in the former stage. Next, user-generated content (UGC) images data are collected to characterise the built environment in different parts of Cambridge to assist in finding the link between social segregation and the built environment. Thereafter, in a second stage, we expect to validate the above ‘big data’ approach with data collected by smartphone detection and ‘eyes on the street’ type of questionnaires (soft data collection) – this phase in the study of urban segregation will answer the common criticism that crowdsourcing doesn’t capture important groups of society because these groups don’t own or use the devices producing such data (this is particularly important in low income and jobless groups of society). Lastly, as a third step, there is a comparison between two historical cities, Cambridge in UK and Ningbo in China, which allows us to summary the features of urban segregation and extract the general principles.

Researchers:


Department of Land Economy, University of Cambridge


Department of Land Economy, University of Cambridge

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