Crime And Demographic Analysis

University of Chicago sociologist started using crime mapping around 1900s. Among the pioneers were Sophensiba and Breckenridge and Edith Abbott of Progressive Movement, social workers who mapped delinquent children in Chicago over the period of 1899 to 1905. Their map used dot for every house is called point map that pinpoints and represent geographical locations (coordinates or addresses). (Chamard, 2006)

In United States, urban sociologists Robert Park, Clifford Shaw and Henry McKay were credited as the first people who sustained scholarly project of crime mapping occurred at the University of Chicago in 1930s. The project tried to explain the relationship of crime and geography. Chicago is best suited place to conduct their study because

Research findings of other researchers still made the explanations and geographic methods of analysis remained easy. These findings may have been influenced by previous researchers and their focus on different factors and lack of adequate technology. In 1960s, researchers started spatial analysis of crime with the aid of computers and other visual methods. (Groff & La Vigne, 2002).

Ahmadi’s 2003 concluded in his research:

“Crime mapping and spatial analysis are important tools for mapping, analysis, and visualization of crime data. Adapting spatial clustering (block) analysis to support crime analysis and decision making. Law enforcement has been using these methods to examine the associations between crime and environment features, to allocate resources for crime prevention in areas where they are most needed.” He also added that best crime analysis should create a good crime prevention policy and planning. Essentially good management would reduce crime rate when it follows new information

One advantage of using the hierarchical clustering technique is that it can identify small geographic areas that may have higher rates of crime activity. This technique also is useful for identifying the linkages in some small clusters going to high cluster areas. The limitations of hierarchical clustering include a certain arbitrariness based on what constitutes a meaningful cluster size. The size of the grouping area also depends on sample size. In conclusion crime distributions that has many incident locations (e.g., residential robbery cases) will show small group areas, while crime rates with few incident locations (e.g., homicides) will have larger grouped areas. (Eck,