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Title: Big data : geodemographics and representation
Author: Lansley, Guy David
ISNI:       0000 0004 7230 8901
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Due to the harmonisation of data collection procedures with everyday activities, Big Data can be harnessed to produce geodemographic representations to supplement or even replace traditional sources of population data which suffer from low response rates or intermittent refreshes. Furthermore, the velocity and diversity of new forms of data also enable the creation entirely new forms of geodemographic insight. However, their miscellaneous data collection procedures are inconsistent, unregulated and are not robustly sampled like conventional social sciences data sources. Therefore, uncertainty is inherent when attempting to glean representative research on the population at large from Big Data. All data are of partial coverage; however, the provenance Big Data is poorly understood. Consequently, the use of said data has epistemologically shifted how geographers build representations of the population. In repurposing Big Data, researchers might encounter a variety of data types that are not readily suitable for quantitative analysis and may represent geodemographic phenomena indirectly. Furthermore, whilst there are considerable barriers acquiring data pertaining to people and their actions, it is also challenging to link Big Data. In light of this, this work explores the fundamental challenges of using geospatial Big Data to represent the population and their activities across space and time. These are demonstrated through original research on various big datasets, they include Consumer Registers (which comprise public versions of the Electoral Register and consumer data), Driver and Vehicle Licencing Agency (DVLA) car registration data, and geotagged Twitter posts. While this thesis is critical of Big Data, it remains optimistic of their potential value and demonstrates techniques through which uncertainty can be identified or mitigated to an extent. In the process it also exemplifies how new forms of data can produce geodemographic insight that was previously unobservable on a large scale.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available