We are aware of the extent to which police misconduct takes place in the city of Chicago, however very little is done to curb it. We believe that there is much harm done by ignoring police misconduct that leads to establishing a bad system for the years to come. Over 93% of the civilian and officer’s complaints are declared unsustained throughout the history of the Chicago Police Department, but why? We aim to uncover the variables, the relationships and the entities in play that let the officers get away with the allegations. This study aims to highlight the biases and weaknesses that afflict the police system in the city of Chicago.
We use various data science technologies and report the analysis in each Checkpoint report.
Database: CPDB
Checkpoint 1: Relational Analytics
Write SQL queries to answer questions over the CPDB database.
Checkpoint 2: Data Exploration
Use Tableau to answer the visualisation questions
Checkpoint 3: Interactive visualization
Use D3.js to build interactive visualizations on our project theme in order to explore the data for relevant patterns and to assist us in developing an intuition about what the data says about our project questions.
Checkpoint 4: Graph Analytics
Use Spark GraphX to implement graph or network modeling questions.
Checkpoint 5: Natural Language Processing
Use NLP techniques to analyze the role of complaint report narratives and freeform text in learning about our theme.