Month: May 2017
The purpose of Fitterer et al‘s paper, “Predictive crime mapping”, was to spatially predict commercial and residential break and entries (B&Es) in Vancouver, Canada. This paper aims to present a pilot project to have an automated model implemented within a mobile GIS that provides continually updated predictive maps to assist patrol units in self-deployment decisions. The data used for the study consisted of commercial and residential B&E data (provided by the Vancouver Police Department), population data from the 2011 Census and LandScan (ambient population), road network and land use data, graffiti data as a proxy for ‘lawlessness’, and data on average property values and house types were also used.
Fitterer et al’s methods suggested that an exploratory analysis was done in examining the space-time patterns of Break and Enters between 2001-2012, and a density map of BNEs was used to examine spatial patterns and hot spots. Furthermore, Ratcliffe’s near-repeat calculator was used to measure the spatial and temporal distance between each residential and commercial crime event that occurred (Fitterer et al, 2014). Based on observations made in assessing B&Es within 500m, 850m and 1000m from the original event and from one to 30 days since the event, two predictive models were developed for B&E crimes. Model 1 (Residential and Commercial) was based on integrating crime data with additional data using a generalized linear logistic regression estimation, while Model 2 was based only on observed crime data. Model 2 also assessed variable importance using the Knox ratio statistic, and assessed variable correlations to ensure models were statistically robust (Fitterer et al, 2014). Model accuracy was also assessed by visually comparing between observed and predicted spatial patterns. For Model 1, eleven data sets were found to be statistically significant predictors for residential BNEs, and 14 for commercial B&Es. These included B&Es within 850m from the event within the last 24 hours, 48 hours and seventh day, proportion of historical crime by time and day, road density, property value and type, count of B&E crimes in each cell, ambient and census population, and graffiti rate (Fitterer et al, 2014).
The results of the exploratory analysis found patterns in the characteristics of BNEs, including a progressive decrease in annual trends in the frequency of B&Es for both residential and commercial property types. On an hourly basis, residential offences decreased between 1:00 – 6:00, when residents were most likely to be at home, and peaked at 8:00, 12:00, and 18:00. Commercial B&Es occurred most often during 3:00, 4:00, 5:00, 17:00, and 18:00, while offences decreased during daylight hours (6:00-16:00). Spatially, the authors found consistency in patterns between 2001 and 2012, with Downtown parts of Vancouver having the majority of residential B&Es, with sections of the Westend and area around the Downtown core (Strathcona, Kitsilano, Fairview, Mount Pleasant, Oakridge, and Marpole) suffering from high numbers of property crime. Moreover, spatial consistency was found in commercial B&Es, with clusters around the Downtown neighbourhoods and surrounding regions of Fairview and Mount Pleasant, and local hot spots in the southern Arbutus ridge and Marpole areas. Overall, the results indicated that within short spurts of time and nearby vicinity of a break and enter, there is a high possibility of another break and enter occurring as well.
The limitations of the model are indicated by Fitterer et al., which includes the relatively rare occurrence of B&Es, spatial autocorrelation, and the difficulty to predict crimes at fine spatial resolutions due to the importance of scale between 850 m to 500 m (Fitterer et al, 2014). The methods portion of the paper also lacked any thorough explanation, specifically about the minimization of model choice bias through the use of considerations like AICc and Bandwidth parameter, or any other kinds of model accuracy evaluation. Due to this, I decided to give this paper an 8/10.
Article: Predictive Crime Mapping by J. Fitterer, T.A. Nelson & F. Nathoo
http://www-tandfonline-com.ezproxy.library.ubc.ca/doi/full/10.1080/15614263.2014.972618?needAccess=true