Using Open Source Data to Map Crime – Seasonal Analysis; A Case Study of Burglary in Cleveland Middlesbrough
This is a repository submitted in fulfillment of the degree of MSc Crime Intelligence & Data Analytics by Chigozie Obianefo. The datasets and Jupyter notebook used in the project is also included in this repository. The full report is also included in this repository.
The study is based on the use of open source data to map out crime, with a focus on burglary in Cleveland, Middlesbrough. Her Majesty's Inspectorate of Constabulary and Fire & Rescue Services (HMICERS) rated Cleveland Police as the first force in England and Wales to be rated inadequate across all areas of performance in the Police effectiveness, efficiency and legitimacy 2018/2019 (PEEL) report of 2019. In order to verify the veracity of this report, the study used the statistical tools of Orange, Pandas, and Tableau to address the spatial dimension of Burglars.
The research through the study of environmental criminology observed the hotspot zone crime attractors are public facilities like shopping malls, high schools, taverns, convenience stores, apartment buildings and public housing projects. Different types of facilities increase or decrease crime in their immediate environment. Defensible space features of the built environment, demographics and, to some extent, the temporary environment are the major variables affecting the rates of crimes.
Conclusively, the research recommended PANDA, SARA and SPATIAL crime models which are all models used to support problem-solving policing. The main focus of the models is the need to reduce crime in urban and suburban communities.
The study was conducted within the spatial environment of Cleveland under the operational jurisdiction of the Cleveland Police. The Cleveland Police area encompassess councils of Hartlepool, Redcar and Cleveland, Middlesbrough and Stockton on Tees. Data for the study was obtained from crime datasets within January to December 2019 from https://data.police.uk/data/ in vis-à-vis with the Cleveland Police experience.The Crime includes; Anti-social behavior, Criminal damage and arson, Shoplifting, Violence and sexual offences, Burglary, Public order, other theft, other crime, Burglary, Bicycle theft, Vehicle crime, theft from the person, Drugs, Robbery, Possession of weapons.
In order to achieve the Crime type of Burglary for the study, the research work employed the tool "Find and Select" from the Microsoft Excel worksheet. In addition, the researcher used the "Find and Select" tool to expunge other Crime types that are not within the scope of the project. The research work was based on the tools of Orange, Pandas, and Tableau to address the spatial dimension of Burglary, percentage of Burglary across the four seasons in the UK and response of the public sector towards Burglary respectively for the year 2019.
This final section concludes with some suggestions for the crime in study to be curbed. PANDA, SPATIAL, SARA are all crime models that are all recommended, although they're similar crime models but overlapping themes. The main focus of the models is the need to reduce crime in urban and suburban communities.