Our team set out to identify the factors that contribute to the success of existing tech cities in the United States with the long-term goal of finding the next emerging tech hub. In tackling this project, we analyzed the following well-known tech hubs:

• San Francisco, California

• Chicago, Illonois

• New York City, New York

• Austin, Texas

• Atlanta Georgia

We selected the following factors to analyze:

• Tech-related employment (Bureau of Labor Statistics)

• Wages (Bureau of Labor Statistics)

• Real estate value (Zillow)

• Crime (Government OpenData)

Our objective was to find usable data from the data sources listed above and make readable in a JSON format to work with our JavaScript visualization libraries. Our approach starts with gathering CSV or JSON files.

The data files gathered from BLS and Crime data was cleaned in an appropriate structure to create the end visualizations. However, the real estate data required cleaning before use. In making manipulations, we imported a CSV and geojson file into a pandas dataframe. The CSV contained the real estate value of the city of interest drilled down by neighborhoods. We used a geojson file to create choropleth map of the same neighborhood names. This required to merge the data frames on the neighborhood names.

From here, all the data was loaded in MongoDB Atlas - a NoSQL cloud database. With the data in MongoDB Atlas, the data now can be directly access quickly from our Flask application and get converted to a json object to be read by Javascript.

The geojson for each neighborhood was merged with a data frame. This data was used to plot a choropleth overlay on Leaflet.

Json with crime data were obtained from open data portals from four of the five tech hub cities. After parsing through the data to find the coordinates, we used L.marker to create the crime markers.

• Clusters

• Markers

• Drop down

• Toggle Later

The jsonified data was read by JavaScript and unpacked into the javascript libraries.

• Drop down by occupation

• Clickable filters by wage percentiles

• Double-click into city for occupation volume data

• Crime was expected to occur more in areas of poverty, but appeared even across all real estate pricing values

• In areas with the highest real estate prices, the top criminal activities were

  • Dangerous drugs

  • Petit Larceny

  • Offenses related to theft

• Of the 5 tech hubs, NYC holds the highest employment in tech at 38%. Followed by:

  • San Francisco (20%)

  • Chicago (18%)

  • Atlanta (16%)

  • Austin (8%)

• Largest employed occupation in tech are Software Developers and Software Quality Assurance Analysts

• Lowest employed occupation in tech are Mathematicians and Actuaries