Spatio-temporal crime hotspots and the ambient population

Abstract

It is well known that, due to that inherent differences in their underlying causal mechanisms, different types of crime will have variable impacts on different groups of people. Furthermore, the locations of vulnerable groups of people are highly temporally dynamic. Hence an accurate estimate of the true population at risk in a given place and time is vital for reliable crime rate calculation and hotspot generation. However, the choice of denominator is fraught with difficulty because data describing popular movements, rather than simply residential location, are limited. This research will make use of new 'crowd-sourced' data in an attempt to create more accurate estimates of the population at risk for mobile crimes such as street robbery. Importantly, these data are both spatially and temporally referenced and can therefore be used to estimate crime rate significance in both space and time. Spatio-temporal cluster hunting techniques will be used to identify crime hotspots that are significant given the size of the ambient population in the area at the time.

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

“…Malleson & Andresen (2015a, 2015b, 2016) used Twitter messages to determine if crime hot spots are changing considering different populations (i.e. residential and ambient/mobile).…”

“…In the previous section, we discussed how population is the core input in spatial crime analysis. Up to now, when spatial analytical methods require population information, most studies use the residential population and few recent studies have used ambient population (Malleson & Andresen, 2015b, 2016). Residential population is inappropriate for crime types that involve mobile population (e.g.…”

Crime Risk Maps: A Multivariate Spatial Analysis of Crime Data

“…Moreover, we focused on the GMCAR model and considered only the spatial dependence in the multivariate analysis of the crime data. Several recent studies have considered both spatial and temporal dependence (Uittenbogaard and Ceccato ; Yu et al ; Malleson and Andresen ). Thus, future research could extend our work using a GMCAR model to consider both spatial and temporal dependence and to predict future crime hotspots.…”

Les médias sociaux comme prédicteurs de la criminalité urbaine

“…Certains auteurs ont affirmé que les messages pouvaient « renforcer l'explication de l'activité criminelle dans les zones urbaines » (Bendler, Brandt, Wagner et Neumann, 2014, p. 1, notre traduction) et ont souligné leur importance dans la prédiction des voies de fait, des vols et des troubles à la paix et des risques de victimisation violente (Malleson et Andresen, 2015a). Les messages sur Twitter sont disponibles en sources ouvertes, et chaque tweet, si les options de géolocalisation sont activées, est relié à des données spatiotemporelles extrêmement précises.…”