Abstract
The collection and storage of mass amounts of data have made crime fighting more efficient and effective. However, a common problem encountered by law enforcement is insufficient resources. Coupled with a lack of proper information on criminal activities because of data flaws can contribute to the improper use of the resources. Deficient data can cause limitations in discovering useful patterns. In developing countries, a common data issue can arise when the scope of public maps for streets is inadequate, consequently those data become geographically worthless. The deficiency of scope of public maps causes many post addresses to be rendered ungeocodable. However, this problem can be addressed by associating dimensions within the crime records to discover which crimes are geographically similar and obtain a nearby coordinate which will allow the event to be used in clustering. To improve association between events, fuzzy clustering applied to the raw data first can reduce variety among the data. In this thesis, the relationship between the spatial and temporal components, and crime dimensions are associated to place the ungeocodable crime events on the map and aid crime clustering.