This study conducted a logistic regression to determine the relationship of factors associated with burglary to determine the variables necessary to predict criminal activity. Predictors utilized in the study; included time of day, day of week, connectors, barriers, and repeat victimization. These predictors were all incorporated to develop a model that would best predict burglary activity as it relates from a determine epicenter of activity. The predictors selected have all be shown, through research to be significant, characteristics of activity as they relate to burglary but have not been incorporated together to develop a significant model. The model compared the strength of the predictors as they relate to the distance they occur from the identified epicenter of a density plot. Data was collected from a statistically significant area associated with burglary activity. Additional information was extrapolated from criminal activity records provided by the local law enforcement. The predictors and model were statistically evaluated to determine the significance of the predictors and the ability of the model to accurately predict burglary activity within the sampled area. Analysis showed the model was significant, however the comparison in area size in association with the predictors showed to be insignificant. Multiple comparisons of area sizes were observed only to discover that greater comparison in area yielded less significant results. Further research would benefit by observing smaller clusters of activity within a one kilometer area utilizing the same predictors.
","abstract_html":"<p>This study conducted a logistic regression to determine the relationship of factors associated with burglary to determine the variables necessary to predict criminal activity. Predictors utilized in the study; included time of day, day of week, connectors, barriers, and repeat victimization. These predictors were all incorporated to develop a model that would best predict burglary activity as it relates from a determine epicenter of activity. The predictors selected have all be shown, through research to be significant, characteristics of activity as they relate to burglary but have not been incorporated together to develop a significant model. The model compared the strength of the predictors as they relate to the distance they occur from the identified epicenter of a density plot. Data was collected from a statistically significant area associated with burglary activity. Additional information was extrapolated from criminal activity records provided by the local law enforcement. The predictors and model were statistically evaluated to determine the significance of the predictors and the ability of the model to accurately predict burglary activity within the sampled area. Analysis showed the model was significant, however the comparison in area size in association with the predictors showed to be insignificant. Multiple comparisons of area sizes were observed only to discover that greater comparison in area yielded less significant results. Further research would benefit by observing smaller clusters of activity within a one kilometer area utilizing the same predictors.</p>","abstract_has_math":false,"creators":["Antolos, Daniel"],"institution":null,"degree_name":"Master of Science in Human Factors & Systems","degree_level":"Thesis - Open Access","degree_discipline":"Human Factors and Systems","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-10-01T07:00:00Z","date_published":"2011-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:45Z","subjects":["burglary","crime","logistic regression","Criminology and Criminal Justice"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/15","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Antolos, Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Human Factors and Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Human Factors & Systems"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["burglary","crime","logistic regression","Criminology and Criminal Justice"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/15"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study conducted a logistic regression to determine the relationship of factors associated with burglary to determine the variables necessary to predict criminal activity. Predictors utilized in the study; included time of day, day of week, connectors, barriers, and repeat victimization. These predictors were all incorporated to develop a model that would best predict burglary activity as it relates from a determine epicenter of activity. The predictors selected have all be shown, through research to be significant, characteristics of activity as they relate to burglary but have not been incorporated together to develop a significant model. The model compared the strength of the predictors as they relate to the distance they occur from the identified epicenter of a density plot. Data was collected from a statistically significant area associated with burglary activity. Additional information was extrapolated from criminal activity records provided by the local law enforcement. The predictors and model were statistically evaluated to determine the significance of the predictors and the ability of the model to accurately predict burglary activity within the sampled area. Analysis showed the model was significant, however the comparison in area size in association with the predictors showed to be insignificant. Multiple comparisons of area sizes were observed only to discover that greater comparison in area yielded less significant results. Further research would benefit by observing smaller clusters of activity within a one kilometer area utilizing the same predictors.
"]},{"key":"dc:title","label":"Title","values":["Investigating Factors Associated with Burglary Crime Analysis using Logistic Regression Modeling"]}]}],"canonical_facts":{"dc:creator":["Antolos, Daniel"],"dc:description.abstract":["This study conducted a logistic regression to determine the relationship of factors associated with burglary to determine the variables necessary to predict criminal activity. Predictors utilized in the study; included time of day, day of week, connectors, barriers, and repeat victimization. These predictors were all incorporated to develop a model that would best predict burglary activity as it relates from a determine epicenter of activity. The predictors selected have all be shown, through research to be significant, characteristics of activity as they relate to burglary but have not been incorporated together to develop a significant model. The model compared the strength of the predictors as they relate to the distance they occur from the identified epicenter of a density plot. Data was collected from a statistically significant area associated with burglary activity. Additional information was extrapolated from criminal activity records provided by the local law enforcement. The predictors and model were statistically evaluated to determine the significance of the predictors and the ability of the model to accurately predict burglary activity within the sampled area. Analysis showed the model was significant, however the comparison in area size in association with the predictors showed to be insignificant. Multiple comparisons of area sizes were observed only to discover that greater comparison in area yielded less significant results. Further research would benefit by observing smaller clusters of activity within a one kilometer area utilizing the same predictors.
"],"dc:identifier":["https://commons.erau.edu/edt/15"],"dc:subject":["burglary","crime","logistic regression","Criminology and Criminal Justice"],"dc:title":["Investigating Factors Associated with Burglary Crime Analysis using Logistic Regression Modeling"],"thesis:degree_discipline":["Human Factors and Systems"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Human Factors & Systems"]},"updated_at":"2026-07-27T19:25:45Z"}