Scrape every zoning variance request from the city of Buffalo's public meetings website and create an interactive visual allowing readers to explore the data themselves, as it is data that hasn't been captured before.

• Linking - Clicking on any of the neighborhoods in the choropleth map filters the other map and the bar chart to just cover that area. This allows readers to see a breakdown for each neighborhood and to look at each request individually if they so wish.
• Filtering – I created five categories and placed each request in the appropriate one. Readers can choose a category from the drop-down menu on the top right or flip through them with the arrow buttons, filtering the other three maps.
• Details on Demand – Hovering over a neighborhood on the choropleth map gives a breakdown of how the zoning board ruled on variance requests from that area. Hovering over a dot on the other map shows details for each request including the address it took place at, what it was for, and when a ruling was issued.

Python – Used to scrape, clean, and sort the data.

Requests – Used along with BeautifulSoup to scrape data from Buffalo's public meetings website.

BeautifulSoup – Indexes HTML to allow for easy extraction.

Pandas – Used to clean, filter, sort, and export data.

Numpy – Used to filter datetimes.

FuzzyMatcher – Used to match addresses in the public meetings dataset with their counterparts in the city's property assessment database, which includes coordinates for each address, thus allowing them to be mapped.

Tableau – Used to generate a dashboard featuring a choropleth map showing the frequency of variance requests by neighborhood, a map showing every variance request in the city and how the zoning board ruled on it, a bar chart showing the frequency of each ruling, and a dropdown menu allowing filtering by category.

OpenRefine – Used to consolidate zoning variances into arbitrary categories to clean up the visualization.

• Tableau – Tableau is poorly optimized and doesn't translate well to mobile (or to small browser windows) and can only be customized up to a point. I chose it because I wasn't sure what form the final visualization would take or even if I could embed a visualization at all, but if I knew this was the product I would create from the beginning I would make it with D3.

Using aggregated data from AirDNA, create an interactive choropleth map to show where in the city Airbnb listings cluster.

• History - A slider linked to the map allows readers to see data from different years.
• Details on Demand – Hovering over an area on the map reveals the zipcode and average monthly listings for that area.

Python – Used to clean and sort the data.

Pandas – Used to clean, filter, sort, and export data.

ObserableHQ (JavaScript) – Used to generate an interactive choropleth map showing where Airbnb listings are located, using a

Leaflet.js – Used to generate the map, captions, legend, and labels.

D3.js Used to generate colors and the year slider.

OpenRefine – Used to consolidate zoning variances into arbitrary categories to clean up the visualization.