Crime Analysis in New York City 2023

• Project layers into the same coordinate system.

• Isolate and retain only the data pertaining to the year 2023.

• Re-assign records with inappropriate or missing precinct numbers or borough information.

• Relate crime data with the boroughs and police precincts layers.
• Subsequently displaying all crime records associated with that specific area.

• Quantify the aggregate criminal records by police precincts, borough, level of offense, and type of offenses.
• Create statistics tables for further analysis.

• Join the statistics tables with borough and police precincts' attribute tables.

• Introduced new fields for storing statistic summary results.
• Assign values to the new fields referred to the joined table fields.

• Create Time line plot, Pie chart, and Bar chart for visualization.

• Label the borough name or precincts number to corresponding areas.
• Create boundaries for boroughs and precincts.
• Symbolize each crime record as a unique value, shown as a bot.
• Use heat map as the primary symbology showing the crime density.

• Set the visibility range for crime record layer, avoiding distraction from overall analysis standpoint

• Create a sophisticated Crime Density map, facilitating a deeper understanding of spatial crime distribution

• Select records of specific time range, for observing changes across different periods

• No clear seasonal trend is apparent.
• The marked decrease at the end of September.

• Influence by non seasonal factors.
• There is either a real decline in crime reports for September or incomplete data collection for that month.

Our analysis is specifically focused on the data from 2023, which is currently only available up to September. This may caused the following issues:

• Analysis only based on the 9-months data, missing the fourth quarter trends and impacting the full-year crime understanding.
• Resource and policy decisions based on partial data may not align with actual annual crime trends.

• Implement real-time crime data integration in ArcGIS Pro for dynamic mapping and timely response.
• Apply advanced spatial analysis in ArcGIS Pro to forecast crime with hotspot and predictive models.
• Create a community platform for crime reporting to feed into ArcGIS Pro for richer analysis.
• Enrich the analysis with deeper demographic data for nuanced insights into crime trends.

Our analysis of New York City's crime data for 2023, using ArcGIS Pro, has identified critical patterns and relationships between crime density, population density, and income levels.

By merging NYPD complaint data with demographic information, our project has revealed nuanced insights into the spatial distribution of crime, contributing to a more comprehensive understanding of urban crime dynamics that are essential for developing targeted public safety strategies and resource allocation. The methodology adopted, inspired by established crime mapping projects, highlights the significant role of GIS in deciphering the intricate fabric of urban crime. It is important to acknowledge the time frame of our dataset, which is limited to September 2023, as it may not provide a complete picture of the annual crime trends. Future work should aim to incorporate extended data and further socio-economic variables to enhance the accuracy and depth of our urban crime analysis.