Exploring US police incidents to understand who's involved and the role of body cameras. Join us in uncovering insights about policing practices and accountability.
Source: Our dataset is directly from The Washington Post, ensuring credibility.
Attributes: Each incident is detailed by victim, date, manner of death, weapon, age, gender, race, city, and state.
Relevance: These details are crucial for uncovering patterns related to demographics and body camera influence.
Demographics: We have age, gender, and race information, laying the foundation for understanding individuals involved.
Geographical Context: City and state details allow exploration of potential regional variations.
Mental Illness: An indicator provides insights into the mental health aspect of individuals.
Threat and Fleeing Details: Information about threat levels and fleeing adds context to each situation.
Body Camera Indicator: Crucially, we note whether body cameras were in use during each incident. We'll analyze the distribution of incidents with and without body cameras, exploring correlations.
Our data cleaning process ensures accuracy and reliability. We establish acronyms to streamline information and enhance clarity throughout the analysis.
We delve into the demographics, examining age, gender, and race distributions to gain comprehensive insights into the individuals involved in police incidents.
An in-depth look at incidents, exploring factors such as threat levels, mental health indicators, and fleeing details to paint a detailed picture of each situation.
Analysis of body camera usage across incidents to evaluate its impact on accountability and transparency in policing practices.
Examining temporal patterns to identify any correlations or trends in police incidents over time.
Summarizing key findings and insights derived from the analysis, shedding light on policing practices and accountability in the USA.
pip install pandas
pip install seaborn
pip install matplotlib
pip install folium
pip install -U scikit-learn