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.
Highlights
The crime rate is a common statistic that is often used to summarise the quantity and extent of criminal events
Choosing an appropriate denominator is non trivial and studies are often restricted to using residential population data that do not adequately describe the true ambient population
It will use messages generated on mobile devices and posted to the Twitter social media service
Summary
The crime rate is a common statistic that is often used to summarise the quantity and extent of criminal events. Choosing an appropriate denominator is non trivial and studies are often restricted to using residential population data that do not adequately describe the true ambient population. This can lead to the calculation of misleadingly high or low crime rates. In an attempt to alleviate some of these drawbacks, this research will utilise novel ‘crowd-sourced’ data to measure the ambient population. It will use messages generated on mobile devices (such as smart phones) and posted to the Twitter social media service. That is not to say that residential population is an unsuitable denominator for all crime types – there might be little difference between ‘traditional’ denominators and other measures for residential burglary and car theft (Cohen et al 1985) – but its applicability to crimes that do not rely on the number of residents in a neighbourhood is highly questionable
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