Forecasting burglary risk in small areas via network analysis of city streets

Maria Mahfoud, Sandjai Bhulai, R.D. van der Mei, Dimitry Erkin, E.R. Dugundji

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Predicting residential burglary can benefit from understanding human movement patterns within an urban area. Typically, these movements occur along street networks. To take the characteristics of such networks into account, one can use two measures in the analysis: betweenness and closeness. The former measures the popularity of a particular street segment, while the latter measures the average shortest path length from one node to every other node in the network. In this paper, we study the influence of the city street network on residential burglary by including these measures in our analysis. We show that the measures of the street network help in predicting residential burglary exposing that there is a relationship between conceptions in urban design and crime.
Original languageEnglish
Title of host publication7th International Conference on Data Analytics
EditorsSandjai Bhulai, Dimitris Kardaras, Ivana Semanjski
Place of PublicationAthens, Greece
Number of pages6
ISBN (Print)9781612086811
Publication statusPublished - 18 Nov 2018
EventIARIA DATA ANALYTICS 2018: The Seventh International Conference on Data Analytics - Athens, Greece
Duration: 18 Nov 201822 Nov 2018
Conference number: 7th



Bibliographical note


  • predictive analytics
  • forecasting
  • street network
  • betweenness centrality
  • closeness centrality
  • residential burglary

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