Abstract
ABSTRACTPopulation 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.
Highlights
Population is not randomly distributed in space, but it usually follows a clustering pattern
Most people are commuters with varying mobility patterns during work days. These patterns result in spatiotemporal variations of the population and, in spatiotemporal variations of crime occurrences
LandScan has an approximate resolution of 1 km and was modeled by the Oak Ridge National Laboratory (ORNL) using spatial data, imagery analysis, and a multivariable dasymetric modeling approach to disaggregate census counts within administrative boundaries (ORNL, 2016)
Summary
Population is not randomly distributed in space, but it usually follows a clustering pattern (i.e. has high concentrations in some locations and lower in others). Most people are commuters with varying mobility patterns during work days These patterns result in spatiotemporal variations of the population and, in spatiotemporal variations of crime occurrences. Other crime studies used the LandScan global population model to represent the ambient population (Andresen, 2011; Andresen & Jenion, 2008). Is that it estimates a yearly average population and there is no possibility for examining seasonal, weekly, and daily variations of the population Another approach is to use social media data as a proxy indicator for the ambient population in spatial crime analysis. The same authors in a more recent publication merged aggregated mobile telephone activity with census and social media data to create an ambient population “collective” dataset (Malleson & Andresen, 2016). A drawback of using social media to develop population at risk models is that they underestimate older people living or working in a study area because social media are mostly used by young people (Correa, Hinsley, & De Zuniga, 2010)
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