Abstract
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 language | English |
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Title of host publication | 7th International Conference on Data Analytics |
Subtitle of host publication | [Proceedings] |
Editors | Sandjai Bhulai, Dimitris Kardaras, Ivana Semanjski |
Place of Publication | Athens, Greece |
Publisher | IARIA |
Pages | 109-114 |
Number of pages | 6 |
ISBN (Print) | 9781612086811 |
Publication status | Published - 2018 |
Event | IARIA DATA ANALYTICS 2018: The Seventh International Conference on Data Analytics - Athens, Greece Duration: 18 Nov 2018 → 22 Nov 2018 Conference number: 7th |
Conference
Conference | IARIA DATA ANALYTICS 2018 |
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Country/Territory | Greece |
City | Athens |
Period | 18/11/18 → 22/11/18 |
Keywords
- predictive analytics
- forecasting
- street network
- betweenness centrality
- closeness centrality
- residential burglary