Crime hotspots are areas on a map that have high crime intensity. 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 (Ratcliffe, 2004)  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.” (Eck, Chainey, Cameron, and Wilson, 2005: p. 10)  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 (Brantingham and Brantingham, 1999). 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 (Spatial and Temporal Analysis of Crime) 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 (Block, 1995). 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 (Clark and Evans, 1954). This article uses material from the Wikipedia article "Crime_hotspots", which is released under the Creative Commons Attribution-Share-Alike License 3.0.
Nearest neighbor distances, also known as the nearest neighbor index (NNI), 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. Clark and Evans (1954). 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 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” (Clark and Evans, 1954: p. 446). The methodology has been adapted into CrimeStat, a computer program built to analyze crime data. This program uses nearest neighbor index (NNI) to test for clustering to determine if there is a “hotspot” of crime. CrimeStat uses Clark and Evans theory and assumes that the distribution of crime used to perform global statistics have a random distribution (Eck, Chainey, Cameron, and Wilson, 2005). 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 (Ratcliffe, 2004) The following will explain in full detail the steps to calculate NNI according to Eck, Chainey, Cameron, and Wilson (2005). 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 amount of crime incidents on the map. According to Eck, Chainey, Cameron, and Wilson, (2005) 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. Eck, Chainey, Cameron, and Wilson (2005) 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 (Ratcliffe, 2004). This article uses material from the Wikipedia article "Crime_hotspots", which is released under the Creative Commons Attribution-Share-Alike License 3.0.