Projects

You should email me a couple sentences about the project you choose and your team by Saturday, Sep 19th. You change projects/teams at any time; just drop me an email if/when you do. This page has some ideas, but feel free to come up with something else (perhaps by browsing the data here: http://data-cityofmadison.opendata.arcgis.com/).

Fall of 2020 Projects

• Water Main Breakages - Wen Ye, Gautam Agarwal, Bryan Jin [PDF]
• Dream Bus: Improving Teenagers’ Accessibility of Service via Mobile Library - Zishan Bai and Dingyi Zhou [PDF, SLIDES]
• Single Family Tax Assessments in Madison - Ben Kizaric, Aarushi Gupta, and Desmond Fung [HTML]
• Virtual Meetings in Madison - Tianyuan Zheng and Mo Xiao [PDF]
• An Analysis of the City of Madison’s Online Meeting Data - Shri Shruthi Shridhar and Terry Ghanty [PDF, SLIDES]
• Reducing Traffic Collisions on South Gammon Road - Turner Entenmann [PDF]
• Madison Metro Accessibility Analysis - Andy Lin, Jeremy Michael, Ray Hsieh, and Chang Xu [Markdown]
• Analysis of The Effects of COVID-19 on Bike Traffic in Madison, Wisconsin - Haoming Chen and Ian Cheng [Markdown]

Fall of 2019 Projects

• Consolidating Metro Bus Routes: Maximizing the Impact of the BRT while Minimizing Costs - Megan Tabbutt [PDF]
• Reducing Bus Route Redundancy - Jin Woo Lee, John Kitaoka, Hunter Olson [PDF, PPT]
• Vote More - Annie Zheng, Sally Hu, Yuzheng Zhang [PDF, PPT]
• Voter Registration and Turnout - Kristie Bodak, Emma Brostrom, Gunnar Schmitz [PDF, PPT, notebook]
• Eliminating Traffic Deaths in Our City of Madison - Detao Yu, Jing Huang, Boyu Shen [PDF, PPT]
• Vison Zero Madison - Tongkai Yang, Siyuan Ji, Clarence Kuo [PDF, PPT, report]
• State St. Traffic - Songyang Cheng, Mianzhi Huang, Nelson Linscott, Shou Kurosu [PDF, PPT]
• Optimizing State St. Light Patterns - Hanyu Cai [PDF]
• State St. Pedestrian Traffic - Peter Matthews, Aidan Deaven [PDF]
• Police Turnover Rate - Michael Faulkner [PDF]

Spring+Summer of 2019 Projects

• The Big Picture - Jake Kelly and Yuankun Huang [PDF, PPT]
• The City is on Fire - Anna Arpaci-Dusseau [PDF]
• Relationship Between Spending and Weather in Madison - Swaraj Rao and Whitney Long [PDF]
• Going to Work - Jay Jin Woo Lee [PDF, PPT]
• Housing in Madison - Annie Zheng [PDF, PPT]
• Budget Simulation - Xi He, Sally Hu, Christopher Harte, Kyle Wiessinger [PDF, PPT]
• Interagency Relationships in the City of Madison - Langston Nashold, Ellen Wieland, and Zan Zhang [PDF]
• Spend Planning: How Are We Doing? - April Zhong [PDF, PPT]
• Weapons Violations in Madison - Leon Tan [animations+code]
• Crashes in the City of Madison - Zhixin Wang, Yijing Gong, Haowen Hu, Alex Radicia [PDF, git]

Fall of 2020 Project Ideas

The following ideas were planned by our collaborators at the City of Madison. These have some big advantages: (1) we know we have data, (2) we have contacts to ask about the data, and (3) there is definitely an interested audience.

• City Service Usage Levels [PDF]
• Board, Committee, and Commission Meetings [PDF]

Other rough ideas:

• We know demographics from the census, which breaks the world into census tracts. But there might be other geographic boundaries we care about, like city wards or zip code areas. These do not overlap neatly with census tracts. Can we interpolate to estimate who lives inside various geographic boundaries other than census tracts?
• Are property assessments fair? Property taxes work like this: an assessor estimates how much a house or other property is worth, then the owner pays a percentage of that each year. Of course, when a property sells, you know if the estimate was too high or low. Some early work suggests "black families pay 13 percent more in property taxes each year than a white family would in the same situation" due to innaccurate assessments. Can we determine to what degree assessments in Madison are fair with respect to the race of property owners?
• Previous students (see below) have explored improvements to the Madison Metro system. Can we build a simulator to explore "what if?" questions? The idea is that you could feed in possible changes (e.g., eliminate a bus stop) and get metrics describing how positive/negative that change would be.