Comparing Measures of the Concentration of Crime at Places and Times

Since place-based crime has been studied, scholars have employed a variety of ways to describe the concentration of crime at places. Most usefully, they sometimes provide a full distribution of crime across street segments, or among addresses, or other small geographic areas of interest. This is feasible if the researcher is showing the distribution of crime at places throughout one or two larger areas, such as a city. In such circumstances, a few tables or graphs will be sufficient. But once researchers started looking at spatial areas numbering in the hundreds and thousands, like street segments, then describing the internal distribution of crime within each becomes cumbersome. We need summary measures of crime concentration. The mean, median, and mode are not appropriate for this task: the first two because of the highly skewed nature of crime distributions, and while the mode is better, it does not provide enough information

Comparing Measures of the Concentration of Crime at Places and Times

Since place-based crime has been studied, scholars have employed a variety of ways to describe the concentration of crime at places. Most usefully, they sometimes provide a full distribution of crime across street segments, or among addresses, or other small geographic areas of interest. This is feasible if the researcher is showing the distribution of crime at places throughout one or two larger areas, such as a city. In such circumstances, a few tables or graphs will be sufficient. But once researchers started looking at spatial areas numbering in the hundreds and thousands, like street segments, then describing the internal distribution of crime within each becomes cumbersome. We need summary measures of crime concentration. The mean, median, and mode are not appropriate for this task: the first two because of the highly skewed nature of crime distributions, and while the mode is better, it does not provide enough information

Longitudinal Study of Crime Hot SpotsLongitudinal Study of Crime Hot Spots: Dynamics and Impact on Part 1 Violent Crime

Objectives: Design and estimate the impacts of a prevention program for part 1 violent crimes in micro-place crime hot spots. Methods: A longitudinal study of crime hot spots using 21 years of crime offense report data on part 1 violent crimes from Pittsburgh, Pennsylvania. Based on kernel density smoothing for a definition of micro-place crime hot spots, we replicate past work on the existence of “chronic” hot spots, but then with such hot spots accounted for introduce “temporary” hot spots. Results: Chronic hot spots are good targets for prevention. They are easily identified and they tend to persist. Temporary hot spots, however, predominantly last only one month. Thus the common practice of identifying hot spots using a short time window of crime data and assuming that the resulting hot spots will persist is ineffective for temporary hot spots. Instead it is necessary to forecast the emergence of temporary hot spots to prevent their crimes. Over time chronic hot spots, while still important, have accounted for less crime while temporary hot spots have grown, accounting for a larger share. Chronic hot spots are relatively easy targets for police whereas temporary hot spots require forecasting methods not commonly in use by police. Conclusions: The paper estimates approximately a 10 to 20 percent reduction in part 1 violent crimes in Pittsburgh if the hot spot enforcement program proposed in this paper were implemented

Flag and Boost Theories for Hot Spot Forecasting: An Application of NIJ’s Real-Time Crime Forecasting Algorithm using Colorado Springs Crime Data

By operationalizing two theoretical frameworks, we forecast crime hot spots in Colorado Springs. First, we use a population heterogeneity (flag) framework to find places where the hot spot forecasting is consistently successful over months. Second, we use a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm is implemented in Microsoft Excel®, making it simple to apply and completely transparent. Results shows high accuracy and high efficiency in hot spot forecasting, even if the data set and the type of crime we used in this study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non-serious crime for forecasting are different from each other. We also find that the spatial patterns of forecasted hot spots are different between calls for service and crime event. Future research should consider both flag and boost theories in hot spot forecasting

Comparing Measures of the Concentration of Crime at Places

The primary objectives of this research are (1) to introduce summary measures of concentration that are relatively new to our field; (2) compare four concentration measures to determine whether there are reasons to use one in favor of the others; and (3) apply the measures to a real-case data to further understand the concentration phenomenon. Using the crime data of Cincinnati, we compare four commonly used social science measures of concentration: Gini, Simpson, Shannon, and Decile indices. For some purposes, the measures are interchangeable, while for other purposes the measures may suggest different interpretations for the same set of data. This paper is the first quantitative comparison of multiple measures of crime place concentration. We describe the benefits and limitations of each index and the circumstances for which each is most useful. We also answer the question: is crime within street segments spread along the segment or is it concentrated at a few addresses, as most place-based crime studies have overlooked the interior variability of crime on street segments

Crime and Land use in Pittsburgh: A Micro-size Grid-cell Analysis of the Influence of Land-uses on Area Crime

Though substantial amount of research of routine activities/opportunity theory investigated the relationship between land use and crime, very few studies considered various types of land uses at the micro-scale of area. Using 2013 crime data geocoded on the 500-ft2 grid cells overlaid on Pittsburgh, results from multivariate regression models show that certain types of facility such as retail shops, schools and bus stops increase the number of crimes at grid cells. Further results show that, net of the socioeconomic factors, the number of crimes in a grid cell varies both by facility and crime type. However, potential guardianship and target suitability of the facility are not found to have significant influence on the number of crimes in grid cells. Attention to various types of land uses across the city is required to help effective allocation of social control resources against crime