Abstract
Police work can be a difficult task in the urban cities of developing nations, high expectations combined with a lack of resources are common occurrences. These stresses are further compounded by the sporadic nature of crime, causing officers to experience periods of intense work activity. As a result officers spend a very small amount of their available time to ensure flawless report creation. Errors in report data coupled with inconsistent representations make geocoding this data very difficult. These difficulties causes the majority of incident reports to remain ungeocodable, and by extension unusable for clustering. However, this problem is mitigated through the application of fuzzy set theory, relationships between incident reports can be formed. Relationships between geocodable data and ungeocodable data are used to generate an approximation of the ungeocodable incident’s location. In this thesis the relationships found in topographic features, temporal features and the modeling of police officer information are used to generate approximate location information for ungeocodable crime incidents. Which can then be used to enrich geocoded incidents in crime cluster generation.