Abstract
In this paper the districts of Dortmund, a big German city, are ranked concerning their level of risk to be involved in an offence. In order to measure this risk the offences reported by police press reports in the year 2011 (Presseportal, http://www.presseportal.de/polizeipresse/pm/4971/polizei-dortmund?start=0, 2011) were analyzed and weighted by their maximum penalty corresponding to the German criminal code. The resulting danger index was used to rank the districts. Moreover, the socio-demographic influences on the different offences are studied. The most probable influences appear to be traffic density (Sierau, Dortmunderinnen und Dortmunder unterwegs—Ergebnisse einer Befragung von Dortmunder Haushalten zu Mobilität und Mobilitätsverhalten, Ergebnisbericht, Dortmund-Agentur/Graphischer Betrieb Dortmund 09/2006, 2006) and the share of older people. Also, the inner city parts appear to be much more dangerous than the outskirts of the city of Dortmund. However, can these results be trusted? Following the press office of Dortmund’s police, offences might not be uniformly reported by the districts to the office and small offences like pick-pocketing are never reported in police press reports. Therefore, this case could also be an example how an unsystematic press policy may cause an unintended bias in the public perception and media awareness.
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R Development Core Team. (2011). A language and environment for statistical computing. Wien, Österreich: R Foundation for Statistical Computing. URL http://www.R-project.org/. ISBN 3-900051-07-0.
Sierau, U. (2006). Dortmunderinnen und Dortmunder unterwegs - Ergebnisse einer Befragung von Dortmunder Haushalten zu Mobilität und Mobilitätsverhalten, Ergebnisbericht. Dortmund-Agentur/Graphischer Betrieb Dortmund 09/2006.
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© 2014 Springer International Publishing Switzerland
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Beige, T., Terhorst, T., Weihs, C., Wormer, H. (2014). The Most Dangerous Districts of Dortmund. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_2
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DOI: https://doi.org/10.1007/978-3-319-01595-8_2
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