Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Abstract

Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver

“…Also, considering theft‐from‐vehicle, mischief, and other theft, there was a large variation between tweets and crime‐tweets coefficients. This is interesting because the presence of tweets can be used as a measure of the ambient population that is at‐risk for criminal victimization (Malleson and Andresen , ; Kounadi et al ), and is positively related to criminal activity; however, the presence of crime‐tweets was a much stronger predictor of changes in the volume of crime. It is worth noting that the distributions for tweets and residential population differ in the study area, thus both explanatory variables were used for crime regression models so that population at‐risk for crime could be represented by a calculated variable from these data.…”

Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

“…Two datasets were used to estimate population in the explanatory models. There is an emerging discussion in the literature about which population is better to use for the population at risk, because some crime types are influenced by a dynamic population [57,58]. With this in mind, residential population from GEOSTAT database (2011) and ambient population from LandScanTM (2008) were used.…”

Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations

“…In these cases, sharp discontinuities might be expected (for example, in popular riverside developments). However, pycnophylactic interpolation is an elegant method and has been adopted in many applications (Kounadi, Ristea, Leitner, & Langford, 2018;Monteiro, Martins, & Pires, 2018) as well as in hybrid approaches (Comber, Proctor, & Anthony, 2008).…”