Data Gathering We have collected datasets, provided by you, that include geographic, demographic, temperature, crime records, socio-economic indicators, and law enforcement data.

Data Integration The collected data has been cleaned, structured, and joined to form a comprehensive dataset for analysis.

Data Analysis We have started analyzing the data using Mathematica to identify crime patterns, correlations between socio-economic factors, and seasonal crime trends. During our analysis, we realized that some counties, temperature, and per capita income data are missing. We are currently searching for additional datasets to fill these gaps.

Issue is how location,temperature,per capita income correlate with frequency or type of police cases.

1. Identifying regions with similar patterns of police cases based on socio-economic and environmental factors.

2. Examining the impact of these factors on cases over time, including seasonal crime trends influenced by temperature.

3. Analyzing the correlation between age groups and crime rates to determine the most affected demographics.

4. Investigating the relationship between per capita income, counties and crime rates.

5. Identifying police stations or officers handling the highest number of cases.