“…Euclidian distances were calculated between the homes of the offenders and the midpoints of each of the 24,594 census blocks. Although some recent research in environmental criminology used street-network distance (or travel time) as a measures of distance (e.g., Xu and Griffiths 2016 ), Chicago has few natural or man-made barriers to mobility and street network distance is very strongly correlated with Euclidian distance.…”
Objectives:This article examines the hypothesis that in street robbery location choices, the importance of location attributes is conditional on the time of day and on the day of the week.Method:The hypothesis is assessed by estimating and comparing separate discrete location choice models for each two-hour time block of the day and for each day of the week. The spatial units of analysis are census blocks. Their relevant attributes include presence of various legal and illegal cash economies, presence of high schools, measures of accessibility, and distance from the offender’s home.Results:The hypothesis is strongly rejected because for almost all census block attributes, their importance hardly depends on time of day or day of week. Only the effect of high schools in census blocks follows expectations, as its effect is only demonstrated at the times and on the days that schools are open.Conclusions:The results suggest that street robbers’ location choices are not as strongly driven by spatial variations in immediate opportunities as has been suggested in previous studies. Rather, street robbers seem to perpetrate in the environs of cash economies and transit hubs most of the time irrespective of how many potential victims are around.
“…Euclidian distances were calculated between the homes of the offenders and the midpoints of each of the 24,594 census blocks. Although some recent research in environmental criminology used street-network distance (or travel time) as a measures of distance (e.g., Xu and Griffiths 2016 ), Chicago has few natural or man-made barriers to mobility and street network distance is very strongly correlated with Euclidian distance.…”
Objectives:This article examines the hypothesis that in street robbery location choices, the importance of location attributes is conditional on the time of day and on the day of the week.Method:The hypothesis is assessed by estimating and comparing separate discrete location choice models for each two-hour time block of the day and for each day of the week. The spatial units of analysis are census blocks. Their relevant attributes include presence of various legal and illegal cash economies, presence of high schools, measures of accessibility, and distance from the offender’s home.Results:The hypothesis is strongly rejected because for almost all census block attributes, their importance hardly depends on time of day or day of week. Only the effect of high schools in census blocks follows expectations, as its effect is only demonstrated at the times and on the days that schools are open.Conclusions:The results suggest that street robbers’ location choices are not as strongly driven by spatial variations in immediate opportunities as has been suggested in previous studies. Rather, street robbers seem to perpetrate in the environs of cash economies and transit hubs most of the time irrespective of how many potential victims are around.
“…1 a, however we only focus on spatial dependence with a distance less than 500 m as shown in Fig. 1 b, since the apparent spatial influence typically attenuated at a steep rate with distance 24 . Figure 1 Characterizing cross point pattern between OOEs and animal complains related 311 calls, 2013.…”
Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.
“…The plot on the left shows how the distance to the nearest train station has a steeply decreasing function, although does not reach zero until after 3,000 feet away. Previous work by Ratcliffe (2012) and Xu and Griffiths (2017) used change-point models to draw very similar 14 Because of the size of the dataset and number of variables, to calculate this we selected a stratified sample of 2,000 cases within 10 strata (so overall 20,000 cases). The strata were defined by the crime counts in the cells, with ties broken by the predicted number of crimes.…”
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Objectives: We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the 'black box' of Random Forests, considerably improving their interpretability. Methods: We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model's inner workings. Results: We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space. Conclusions: We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky.
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