Crime hotspots
Crime hotspots are areas that have high crime intensity. These are usually visualized using a map. They are developed for researchers and analysts to examine geographic areas in relation to crime. Researchers and theorists examine the occurrence of hotspots in certain areas and why they happen, and analysts examine the techniques used to perform the research. Developing maps that contain hotspots are becoming a critical and influential tool for policing; they help develop knowledge and understanding of different areas in a city and possibly why crime occurs there.
Crime theories can be a useful guide for researchers and analyst, in regard to analyzing crime hotspots. There are many theories of crime that explain why crime occurs in certain places and why crime does not in others. Place theories look at crime at specific places, which can also be viewed as "points on a map." Another crime theory used in regard to crime hotspots is neighborhood theories. These theories view crime at a larger level, and in a larger viewing area. When viewing these types of areas, statistical information is typically used to determine hotspots. A widely used theory to explain crime is crime pattern theory. Crime pattern theory explains that crime is not random. Crime hotspots can help aid in determining spatial-temporal patterns. This theory allows making generalized statements about area hotspots, and hotspot areas can be predicted using crime pattern theory. When creating hotspots, theories that can help explain their occurrence should be evaluated to determine underlying causes.
Crime hotspots can be created using many different methods. Depending on what type of analysis needed, different methods should be employed. Two different methods to create hotspots are STAC and nearest neighbor. Samuel Bates created STAC in the early 1990s. He created a tool that was designed to create a hotspot that contained a high area density of crime in a form of circle on a map. Clark and Evans examined spatial arrangements of points, creating the foundation of nearest neighbor. Clark and Evans created this method to study populations of plants and animals, but the method later was adapted to study crime patterns.
Key concepts and critical developments
Nearest neighbor distances
Nearest neighbor distances, also known as the nearest neighbor index, was an area of interest of two botanists in the early 1950s, Philip Clark and Francis Evans. The two botanists began designing a formula to distinguish patterns of plants and animals and their distributions in their environment. proposed a formula that would measure the spacing between plants and animals in a population that have a random distribution. If it was randomly distributed, a mean distance to the nearest neighbor could be developed. They defined a random distribution as "a set of points on a given area that have the same chance of occurring in any sub-area as any other point".The methodology has been adapted into CrimeStat, a computer program built to analyze crime data. This program uses nearest neighbor index to test for clustering to determine if there is a "hotspot" of crime. CrimeStat uses Clark's and Evans' theory and assumes that the distribution of crime used to perform global statistics have a random distribution. NNI compares observed distances between each point on a map and its nearest neighbor, or in other terms between each crime incident. The distances are then computed to create an average distance to determine if a crime pattern is randomly dispersed.
The following will explain in full detail the steps to calculate NNI according to. First, crime incidents are geocoded on a map, and then the distance between one crime incident and its neighbor is calculated. Following that all the distances are added up and divided by the number of crime incidents on the map. According to this value is called the observed average nearest neighbor distance. Then a map of random incidents needs to be made covering the same area being analyzed. The same process of calculations needs to be made to make the average random nearest neighbor distance. These two numbers then create a ratio that compares the observed incidents to the random incidents that is called the nearest neighbor index.
further explain that if the results generated are less than 1.0 the crime incident data are considered clustered. If the results are equal to 1.0, the crime incident data are randomly distributed on the map. Finally a nearest neighbor index that is greater than 1.0, the data set shows a significant uniform crime pattern in then data set. Using the nearest neighbor index tests for complete randomness in a set of data points. This is useful for analysts because it is a technique that can measure changes of density over periods of time.
Spatial and temporal analysis of crime ellipses
The development of spatial and temporal analysis of crime ellipses, or STAC ellipses, started off as a program to determine a "hot circle" of crime incidents on crime maps. Samuel Bates created a formula that used a grid, rectangular or triangular, to create boundaries around an area. A radius then would be defined, and a circle would be created around a pinpoint of each crime incident. Following this, another grid is created that creates circles that are half of the original radius defined. This grid is then combined with the first grid to create a circle that contains the highest number of incidents, creating the "hot circle". This method created the foundation of what is now used to create hot spot ellipses.Bate's original formula did not answer if the "hot circle" represented an area that clearly had a higher density of crime incidents or not. The formula had other problems as some "hot circles" would overlap and share same crime incidents. The "hot circles" also sometimes became elongated creating ovals. These problems led to the creation of the hot spot ellipses.
Ellipses are created now to show different levels of dispersion of crime incidents. They are always used in analysis to examine if there are any directional trends in the data set. First a user sets the size of the ellipses, typically for a crime data set on a map, miles are used. Following this, the user defines the standard deviation amount they want to use; this determines the number of data points wanted to be included in the ellipse. Typically one or two standard deviations are used; one standard deviation includes sixty-eight percent of the data, and two includes ninety-five percent of the data.
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STAC ellipses have become an essential tool for analysts because of its efficiency and quickness. Studies typically use STAC ellipses to compare different data sets. Usually, areas of crime over periods of time are examined using the ellipses. Ellipses are called first-order statistics because they give analyst a starting point in examining a data set, while looking at the global statistics. Ellipses create a firm boundary for the data set that does not necessarily follow streets or neighborhood outlines. Therefore, when examining these ellipses, more statistical analyses should be used on top of the ellipses.
Empirical support
Study 1: A microspatial analysis of robbery
A study that uses nearest neighbor index, and STAC Ellipses was completed for the City of Roanoke, Virginia. The study focuses on data reported to police on robberies that occurred between January 1, 2004, and December 31, 2007, with a total of 904 robberies reported. The purpose of this study was to determine if there were localized areas of robberies using hotspot analysis. The project first began by geo-coding all data onto a pinpoint map. The records of all robbery data came from the cities records and management system. After receiving satisfying results from geocoding the data, the data was then tested for global and spatial clustering. To test for spatial randomness, NNI was employed. For each year, 2004–2007, NNI was calculated and compared to a set of random points. Each year presents a NNI value of less than one. A value less than one, according to signifies that the clustering in the data set is consistent in its distribution.. concluded that the data set has significant global spatial clustering that applies to the entire study population.Following the testing of random clustering, using NNI hotspot analysis, was employed in the study. The study examined hotspot using many different spatial analysis techniques. The study used nearest neighbor hierarchal clustering and other kernel density estimation. The following will look at the analysis of STAC ellipses in further details for the purpose of this section. Ellipses were developed for each year and then were further examined using different techniques. To create the ellipses, parameter settings were made based on the distance a person can travel on foot in approximately five minutes before looking for another form of transportation. A search radius of a quarter-mile was set for the data. Ellipses were made for the total number of robbery incidents, 904. Fifteen offenses per ellipse were used. Offenses were dropped to seven incidents per ellipse for a single year, and for two-year increments 7, 10, and 15 incidents were evaluated.
With all the different techniques employed in this study, it was concluded that STAC ellipses had the greatest reliability rate. It was determined that ellipses tend to be less accurate than other methods utilized; but, by far were more consistent. concluded in this study that all methods utilized converge around the same areas of the city. This indicated there is random spatial clustering and agreement between the different methods employed. Using the hotspot analysis, different areas in the city were identified as "problem areas." There were areas that were determined to be crime generators and others attractors. recommend that for areas of attractors increase in guardianship, and better place management should be the area of focus. Areas that contain crime generators would require more strategic approaches by police to make an impact.