Hotspots - Netflix
Hotspots, fronted by award-winning Sky News war-zone journalists Stuart Ramsay and Alex Crawford, will illustrate the dangers, complexities and emotions of reporting from troubled spots around the world.
Runtime: 60 minutes
Hotspots - Crime hotspots - Netflix
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).
Hotspots - Study 1: A Microspatial analysis of robbery - Netflix
A study that uses nearest neighbor index (NNI), 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 (Patten, Mckenlden-Coner & Cox, 2009). 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 (Patten, Mckenlden-Coner & Cox, 2009). 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 (Patten, Mckenlden-Coner & Cox, 2009). A value less than one, according to Eck, Chainey, Cameron, and Wilson (2005), signifies that the clustering in the data set is consistent in its distribution. Patten, Mckenlden-Coner & Cox (2009) 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 (NNH) and other kernel density estimation (KDE). 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 (Patten, Mckenlden-Coner & Cox, 2009). Ellipses were made for the total amount of robbery incidents, 904. Fifteen offenses per ellipse were used. Offenses were dropped to 7 incidents per ellipse for a single year, and for two year increments 7,10, and 15 incidents were evaluated (Patten, Mckenlden-Coner & Cox, 2005). 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. Patten, Mckenlden-Coner & Cox (2009) 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. Patten, Mckenlden-Coner & Cox (2009) 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 (Patten, Mckenlden-Coner & Cox, 2009, p. 27).
Hotspots - References - Netflix