My mapping Journey Using ArcGIS for Crime Analysis during My Bachelor’s Degree
Introduction:
During my undergraduate studies (213-2018), I had an opportunity to delve into a fascinating project that combined my interests in geography, data analysis, and public safety. My crime analysis project using ArcGIS allowed me to explore how spatial data can be leveraged to understand and address crime patterns within a city. This project not only enriched my academic experience but also provided insights into how GIS can be a powerful tool for law enforcement and urban planners.
Background and Context:
The focus of my project was on Matara, Sri Lanka, a town area with diverse neighborhoods and varying crime rates. Crime analysis is critical for developing targeted interventions and improving public safety, especially in urban environments. However, traditional crime reports often fail to capture the spatial dynamics of criminal activities. My project aimed to fill this gap by applying GIS techniques to visualize and analyze crime data spatially, providing a clearer picture of where and when crimes were most likely to occur.
Methodology:
One of the unique challenges of my project was, no location data in the available crime reports. Because at that time our police stations did not collect location data. To overcome this, I conducted fieldwork to collect GPS points for the reported incidents. This involved visiting various locations mentioned in the crime reports and accurately recording their coordinates. This on-the-ground data collection was essential in creating an accurate spatial dataset for the analysis.
After gathering the location data, I used ArcGIS to geocode the crime incidents and create a spatial dataset. I then employed several spatial analysis tools, including Hot Spot Analysis (Getis-Ord Gi*), to identify statistically significant clusters of high crime activity.
In addition to hot spot analysis, I also used kernel density estimation to visualize the density of crime occurrences across the city and tracking analysis for identify the temporal patterns. This approach provided a smooth surface map that highlighted areas and peak times with higher concentrations of criminal activity, allowing for a more nuanced understanding of crime patterns.
Analysis and Findings:
The analysis revealed distinct spatial and temporal patterns in crime distribution, with certain neighborhoods consistently showing higher crime rates. The hot spot analysis indicated that Matara was particularly prone to incidents of robbery, suggesting the need for targeted law enforcement strategies in these areas.
The kernel density maps further supported these findings, highlighting not only the high-crime areas but also the corridors connecting them. This suggested that certain streets or routes might be facilitating criminal activities, which could be an important consideration for urban planners and law enforcement agencies.
Temporal analysis shows hourly, weekly monthly, yearly, and day patterns of crime incidents which could be important to public safety in this area.
Challenges and Learnings:
Collecting accurate location data was one of the most challenging aspects of this project. Fieldwork required meticulous planning and attention to detail, ensuring that the GPS points collected were accurate and reliable. This experience taught me the importance of primary data collection and how crucial it is for spatial analysis when secondary data is lacking.
Additionally, dealing with the complexity of spatial analysis tools required a solid understanding of both the software and the underlying spatial concepts. Through this project, I gained a better appreciation for the importance of data quality and the potential of GIS to reveal patterns that might not be immediately apparent through traditional analysis methods. The project also enhanced my skills in ArcGIS, particularly in spatial and temporal analysis and data visualization.
Conclusion:
This project underscored the value of GIS in crime analysis, demonstrating how spatial and temporal data can provide actionable insights for improving public safety. The findings from my analysis could inform targeted policing strategies, community outreach programs, and urban planning efforts aimed at reducing crime in high-risk areas.
Looking forward, I am excited to explore more applications of GIS in public policy and urban planning. I also hope to continue contributing to the field by sharing my work and learning from the experiences of others in the GIS community.
Call to Action:
I encourage other GIS enthusiasts to explore the use of spatial analysis in different fields, including public safety. Whether you’re a student, professional, or hobbyist, the potential applications of GIS are vast and impactful. Feel free to reach out if you have any questions or if you’d like to share your own experiences with crime analysis or similar projects.
