Recording-2025-03-11T13:32:03.533Z

Introduction to Crime Analysis Project

• Focus on analyzing crime at neighborhood levels using various datasets.

• Importance of including 311 call counts in polygon layers: Provides a richer context to understand community issues that correlate with crime patterns.

Choosing Variables

• Purpose of Variables: Explore correlations to test hypotheses like broken windows theory.

• Choice in Variables: Students can select which variables align with their chosen crimes, allowing for personalized analysis.

• Suggested approach: Choose one property crime and one violent crime for a balanced analysis.

Developing Hypotheses

• Crafting a Hypothesis:

  • Example: "The crime of robbery is clustered in neighborhoods with a higher population of homeless individuals."

  • Must formulate hypotheses for each selected crime and associated variable.

• Analysis of Hypothesis: No hypotheses are wrong; they can be tested for validity during projects.

Mapping and Analysis Steps

• Bivariate Maps: Create two separate maps to visualize the relationship between crimes and variables.

  • Consider high-count neighborhoods identified in previous maps for further analysis.

• Point Maps: Develop maps that show specifics about neighborhoods with high crime rates.

  • Choose neighborhoods wisely based on previous analytical findings for effective study.

Project Components

• Reporting: Capture your analysis results and findings in a report.

  • Access directions, datasets, and relevant materials contained in course modules on Brightspace.

• Crime Prevention Proposals: For each selected crime, propose specific prevention or reduction strategies.

  • Avoid generic solutions like increasing police presence; instead, aim for community-focused preventative measures.

Considerations for Crime Selection

• Understand differences between crime types:

  • Robbery vs. Burglary: A critical distinction that impacts response strategies and preventive measures.

• Select crimes that generate interest, particularly for students pursuing Criminal Justice.

Data Sources

• Use neighborhood polygons and demographic data effectively to understand community needs.

• Neighborhood characteristics should inform your analysis (e.g., population density, socio-economic factors).

Project Timeline

• Deadline: Project is due on March 30, providing ample time (19 days) for completion.

• Emphasis on independence in learning; encourage students to engage deeply with the software.

Summary of the Process

• Complete prior mapping exercises before starting the project.

• Photos of maps must be included with the project report for grading.

• Maintain a clear focus on data-driven analysis and utilize prior knowledge from previous lectures to enhance the current project.