Overview

In my semester-long Geographic Information Systems (GIS) course, I was tasked with selecting a topic for my final project. Inspired by previous students work, I wanted my project to be of something useful to work going on in my community.

The hardest part of approaching this problem was understanding what types of datasets were both available and impactful for analysis and informing action.

I led a co-design process with a local grassroots activist organization, Silver Spring Justice Coalition, and the MoCo Office of Legislative Oversight, to create an analysis that was both actionable and directly relevant to local efforts to reimagine policing in Montgomery County.

Connecting with the community

I knew that I wouldn’t have all the answers when it came to diving into this project. I decided to cold email a few different social justice organizations and Montgomery County government offices for ideas. Since I knew I was interested in policing, I contacted organizations already working in this space. I also looked in local news beats for information about potential data sources of interest and the landscape of policing data in the county.

In my email to potential partners in this project (middle), I introduced myself and the goals of this project. I also included an example of a GIS Story Map that I created for my midterm project (bottom). I was sure to come prepared with questions and take detailed notes during the Zoom calls I was able to secure. Ultimately, my primary supports in this work was the Silver Spring Justice Coalition and the Montgomery County Office of Legislative Oversight.

Collecting and cleaning data

This was truly the most time consuming part of the process. When I started the project, I went in with the goal to analyze three different datasets: traffic violations, crime, and police dispatches.

Cleaning involved trying to make sense of the data. There were so many attributes that were new to me and I needed to consult with my partners to understand what fields they were most interested in analyzing.

Throughout the course of the project, I regularly documented my process in a process log (right). This would allow me to routinely refer back to what I had done before and understand where I might have made a mistake or reference in future steps. I plan on sharing a cleaner version of this process log publicly to enable others to follow my workflow, should they decide to do this analysis on their own.

Running spatial analysis (and more cleaning)

This was the most important part of the process since it required me to take my cleaned dataset and identify the appropriate analysis tool.

In the beginning I ran a emerging hot spot analysis to identify hot and cold spots for crime and mental health dispatches. I also used the one-hot encoding tool to sum and group traffic violations into discrete stops. I orchestrated spatial joins to map incidents to specific census blocks and county subdivisions. Lastly, I employed kernel density to understand spots of concern.

Initial map designs and clarity

I quickly realized that although fancy analysis tools are great, if I wanted to make this analysis as actionable as possible, I needed to use tools that were straightforward and easy to understand. Getting user feedback from my partners was invaluable to redirecting my project goals.

Crafting a story

This Story Map would embed parts of my map and allow the public to intimately engage with this data. I included 3 case studies for policymakers to consider, identified specific policy recommendations, and outlined potential directions for future work related to the datasets I explored.