Short description: Urban neighbourhoods are inherently spatial and oftentimes defined through adminstrative districts. These neighbourhoods also represent urban places that are increasingly important units of focus in urban planning processes. Neighbourhoods are multi-dimensional in their nature, defined by physical, personal, political, organisational, socially enacted, and symbolic properties.This thesis project aims to develop a new methodology for defining urban neighbourhoods through uncovering dynamic phenomena of pattern formation. Therefore, the analysis of social media posts has the potential of defining neighbourhoods through social interaction and interests, going beyond previous administrative definitions. This will lead to a new understanding of urban structures, potentially supporting urban planning through serving the needs of homogeneous communities with similar interests.
Literature:
Anselin, L. and Williams, S. (2016) Digital Neighborhoods. Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 9(4), pp. 305-328.
Resch, B., Usländer, F. and Havas, C. (2018) Combining Machine-learning Topic Models and Spatiotemporal Analysis of Social Media Data for Disaster Footprint and Damage Assessment. Cartography and Geographic Information Science, 45(4), pp. 362-376.
Bernardo, F., & Palma-Oliveira, J. M. (2016) Urban Neighbourhoods and Intergroup Relations: The Importance of Place Identity. Journal of Environmental Psychology, 45, pp. 239-251.
Start date: ASAP
Prerequisites/qualifications: experience with geospatial analysis, data visualisation, and urban research
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