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What if an Entrepreneur (Say me) and wanted to set up a small working space in Pittsburgh for a startup? As its a small firm, employees would have to use outside resources for food and places to hang out during breaks. What would be a good location for me to set up my office? I try to find this out by looking at the following data.

Raw data-set used

Four Square data for Pittsburgh, listing places to shop and restaurants with ratings, check-ins. Foursquare also points out trends and this could be a tool as well. Twitter data about how people feel about a certain place or what are their 'moods' when they are at a certain place would also help determine a balanced choice.


Using the data about restaurants and shopping from Foursquare, hotspots would be created at locations around the city and a list could be generated of possible spots for the office space. Based on walkability to amenities, a preference list would be created for the top 6 picks (1st, 2nd, etc.). Twitter data can be used to verify and validate this information as twitter users are known to be direct and unbiased. This data, when combined with property cost data from earlier research could then be used to find a perfect balance between utility & costs and choose a suitable location.

Rationale for approach

It is very important to look at the availability of offices and their accessibility. Also, Setting up a new office would mean that the employees will have to rely on outdoor amenities for food and leisure and hence these were looked into. Other factors such as shopping and recreation were also considered as employees would prefer is they could use these once they are done with office and their proximity would definitely help.

Overlapping these maps gave a bigger picture combining all the considerations and hence added value to the analysis by the virtue of ‘A whole is greater than the sum of its parts’. A walk-able service area seemed to be a good tool to gauge the distances highlighted by the overlapping hotspots.

Data Set Assembled

An overlay map was prepared by overlapping each of the following layers on the map of Pittsburgh in the form of hotspots:

•Professional Spaces – Co-work spaces, office buildings, parking, event space, corporate amenities
•Rental housing – Apartment/Condo, assisted living
•Transportation – Bus Stops, Bus stations, Bikeshare
•Food – Places in Pittsburgh with at least a thousand check-ins (only popular places)
•Nightlife - Places in Pittsburgh with at least five hundred check-ins (only popular places)
•Shops and Services - Places in Pittsburgh with at least two hundred check-ins (only popular places)
•Recreation and Parks – Gym, Gardens, Parks, Fitness Centers
•Hotel & Extended Stay – Hotels, Hostels, Bed & Breakfast

Each of these datasets was filtered based on the above said contents and imported as a new csv into carto as a separate layer. Each of this layer was made transparent and then overlapped to create a combined hotspot map. This map was then used to draw service areas of 0.5 mile radius from the chosen spaces.

Aspect of Focus: Amenities within 0.5 mile (roughly 5 minute walk) radius

The next slide shows a sample screenshot of the data set used for the layer 'Food'. The following two slides show the different layers to be overlapped. The graphic is recreated in black and white to show contrast and highlight the hotspots.

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