Letourneau, StevenEll, NathanCheung, PeterMcCaskill, JordanEl-Hajj, Mohamad2020-10-162022-05-312022-05-312018S. Letourneau, N. Ell, P. Cheung, J. McCaskill and M. El-Hajj, "The effects of neighbourhood characteristics on crime incidence," 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 2018, pp. 720-726.doi: 10.1109/CCWC.2018.8301675https://hdl.handle.net/20.500.14078/1947Using data from the City of Edmonton, Canada Open Data Portal, an exploration process is undergone using data mining techniques to help detect unseen relationships between tangible spatial characteristics and non-tangible crime incidences. These findings will help law enforcement and city planners make empirically based decisions and avoid the misappropriation of public resources. Using frequent pattern analysis to examine neighbourhood attributes that occur alongside crime provides insight into why crime occurs. These techniques include clustering, classification algorithms, and association algorithms. Results of the analysis on neighbourhood spatial characteristics indicate that dwelling structure type and tree density relate to incidence of neighbourhood crime, while other neighbourhood spatial characteristics bear no relationship. Results also show that intangible neighbourhood characteristics indicate that the distribution of yearly household income and employment and school enrollment levels relate to incidence of neighbourhood crime. The distribution of yearly household income bears a relationship to crime type, specifically violent vs non-violent types.enAll Rights Reserveddata miningcrime incidencefrequent pattern analysisneighbourhood clusteringPresentationhttps://doi.org/10.1109/CCWC.2018.8301675