 Hi, I'm Jim Clute, I'm a data scientist from Chicago, Illinois, and my lightning talk is mapping 311 requests by community area in Chicago using shiny leaflet and the tidy verse. Chicago is divided in the 77 distinct neighborhoods called community areas. Community areas differ from wards or electoral districts in that their boundaries are static and don't change over time. The image on the right shows the boundaries for all 77 community areas. Students of Chicago can submit requests for non-emergency services like street repairs, free maintenance, or business complaints via the city's 311 program. 311 service request data are freely available in the Chicago data portal, which is a repository of public data sets from the city of Chicago. For my project, I want to know, is there variability in 311 utilization across Chicago community areas, and what does it look like, both spatially and temporarily? To begin investigating these questions, I built an interactive dashboard using public data in R, and I'll talk about the process for building that dashboard today. To get my data, I requested a free API key and used the Arsacrata library to query the data portal for new service requests every morning by running a window scheduled task on my desktop at home. Most service requests are tagged with their community area, enabling easy aggregation using tools from the tidy verse like deep plier and tidy R. In addition to 311 data, I used population information from the community areas in order to calculate service request rates. To make my community area map, I followed a tutorial from the University of Chicago for building interactive maps in R using leaflet. I followed another University of Chicago tutorial for working with shapefiles in R using SF, which enabled me to draw the community area boundaries on my leaflet map. Shapefiles for community areas are all available in the data portal. I created the user interface for my project in Shiny, along with a few additional packages and extensions. I used Plotly and GG Plot 2 for interactive time series visualizations. I used DT for interactive data tables, and I used Shiny BS and HTML widgets for additional dashboard functionality. To share the dashboard, it's live at jimtheflash.shinyapps.io slash chi 311 dash. The data should update daily, but again, this runs on a Windows desktop in my home office. The source code lives at github.com slash jimtheflash slash chi 311 dash. This is a screenshot of the visualizations tab of my dashboard. This is the landing page. You can search by specific service request here. If you don't add any specific request, it just gives you everything excluding general information and airport noise complaint requests. You can specify your date range here, and then the time series plot here on the right side has each point as a day and the total count of requests based on whatever you've selected, and it's in Plotly, so it would be interactive if this was actually the dashboard. Then on the explore data tab, you can search by specific neighborhoods or community areas and sort by service request types, total requests. So here I search by my community area, Edgewater, and I can see that abandoned vehicle complaints are the most frequent request in this date range. So to wrap up, public data is an awesome way to learn new things. This project helped me learn new tools, learned about my city, and gave me some really interesting things to talk about when interviewing for jobs. So the city of Chicago does a great job making useful data and tools available for the public. My project is really only possible because of this. And finally, Chicago is not the only municipality with tools for sharing public data. This tool could be easily adapted to other locations who might rely on similar infrastructure for tracking their service requests. Awesome. So thank you very much for your time, and enjoy the rest of the conference. Cheers.