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Sam Langton is a PhD candidate at Manchester Metropolitan Crime and Well-Being Big Data Centre. His research focuses on the geographic distribution of offender residences using longitudinal data from a large metropolitan area in England. Sam is supervised by Jon Bannister (Sociology), Gary Pollock (Sociology) and Liangxiu Han (Computing). He obtained a BSc in Social Policy and Government from the London School of Economics in 2012. After working in accounting for two years, he moved to the Netherlands to complete a two-year MSc in Sociology and Social Research at Utrecht University. Whilst there, he worked as an intern at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) in Amsterdam, where he completed his master’s dissertation with Utrecht University on residential burglary target selection. Sam is particularly interested in the impact of spatial scale, longitudinal methods (growth trajectories, clustering) and data visualisation using open software (R and QGIS).
SH. Langton, R. Solymosi (2021). Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations. Environment and Planning B: Urban Analytics and City Science. 48(2), pp.348-357.
D. Buil-Gil, A. Moretti, S. Langton (2020). The integrity of crime statistics: Assessing the impact of police data bias on crime mapping.
S. Langton, R. Solymosi (2019). Cartograms, hexograms and regular grids: a commentary on minimising misrepresentation in spatial data visualisations.
SH. Langton, W. Steenbeek (2017). Residential burglary target selection: An analysis at the property-level using Google Street View. Applied Geography. 86, pp.292-299.