Space-time clusters of crime in Stockholm, Sweden
2011 (English)In: Procedia Environmental Sciences, Enshede, The Netherlands: Elsevier, 2011, 1-8 p.Conference paper (Refereed)
The aim of the study is to detect geographical clustering of offences over time using Kulldorff’s scan test (SaTScan version 9.01; Kulldorff 2010) and police recorded data over Stockholm city, the capital of Sweden. This technique has a rigorous inference theory for identifying statistically significant clusters. The space–time scan statistics are used in a single retrospective analysis using data from 1st January 2006 to 31st December 2009. Four years dataset is collapsed into ‘one year’. All space and time dimensions of the data are kept (by day and location) except ‘year’. Clusters over the hours of the day, weekday and weekend and by seasons were tested. Total population but also day time and night time populations were used as reference. Findings show clear distinct patterns of concentration for violence (assault and threat) and property crimes (theft, robbery and burglary) over time and space. Whilst property crimes tend to happen more often in the afternoons in the centre and regional commercial centers in the southern and western parts of Stockholm, violence takes place more often in the night, and is heavily concentrated in large parts of the city centre. Weekends are more targeted than weekdays for both offences. Regardless day of the week, the main urban core contains the most likely cluster that extends also to commercial and socially disorganized areas in the west and south Stockholm. Whilst property crime does not show significant differences over the seasons, violence does (winter and summer). The most likely clusters tend to be fairly constant in space over time.
Place, publisher, year, edition, pages
Enshede, The Netherlands: Elsevier, 2011. 1-8 p.
spatial concentration; Kulldorff’s scan test; Poisson discrete; temporal variation"
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-74747OAI: oai:DiVA.org:kth-74747DiVA: diva2:489962
SPATIAL STATISTICS 2011 – Mapping Global Change
QC 201202062012-02-062012-02-032012-02-06Bibliographically approved