Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions.
In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate a safety rank based on these reports. We first provide a survey of campus safety and security measures. We utilize our survey results to provide an automated data-gathering method for capturing standard campus safety data from Clery-act reports. We then utilize the collected information to classify existing campuses based on their safety conditions. Our research model is also capable to predict the safety rank of campuses based on their Clery-Act report by comparing it to existing Clery-Act reports of other campuses and reported rank on public resources.
Our research on this thesis uses a number of languages, tools, and technologies such as Python, shell scripts, text conversion, data mining, spreadsheets, and others. We provide a detailed description of our research work on this topic, explain our research methodology, and finally describe our findings and results. This research contributes to the automated campus safety data generation, classification, and ranking
