Generating vague neighbourhoods through data mining of passive web data

Brindley, Paul; Goulding, James; Wilson, Max L.

Abstract

Neighbourhoods have been described as \the building blocks of public services society". Their subjective nature, however, and the resulting difficulties in collecting data, means that in many countries there are no officially defined neighbourhoods either in terms of names or boundaries. This has implications not only for policy but also business and social decisions as a whole. With the absence of neighbourhood boundaries many studies resort to using standard administrative units as proxies. Such administrative geographies, however, often have a poor fit with those perceived by residents. Our approach detects these important social boundaries by automatically mining the Web en masse for passively declared neighbourhood data within postal addresses. Focusing on the United Kingdom (UK), this research demonstrates the feasibility of automated extraction of urban neighbourhood names and their subsequent mapping as vague entities. Importantly, and unlike previous work, our process does not require any neighbourhood names to be established a priori.

Citation

Brindley, P., Goulding, J., & Wilson, M. L. (in press). Generating vague neighbourhoods through data mining of passive web data. International Journal of Geographical Information Science, 32(3), https://doi.org/10.1080/13658816.2017.1400549

• Journal Article Type: Article
• Acceptance Date: Oct 31, 2017
• Online Publication Date: Nov 16, 2017
• Deposit Date: Oct 31, 2017
• Publicly Available Date: Nov 17, 2018
• Journal: International Journal of Geographical Information Science
• Print ISSN: 1365-8816
• Electronic ISSN: 1365-8824
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 3
• DOI: https://doi.org/10.1080/13658816.2017.1400549
• Keywords: Neighbourhoods, Vague Geographies, Geographic Information Retrieval, Geocomputation
• Public URL: https://nottingham-repository.worktribe.com/output/895123
• Publisher URL: http://www.tandfonline.com/doi/abs/10.1080/13658816.2017.1400549
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science on 16 Nov 2017 available online: http://www.tandfonline.com/10.1080/13658816.2017.1400549
• Contract Date: Oct 31, 2017