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Location Insights

Awarded the Most Innovative Hack at the Mckinsey&Company 2016 Data Challenge

Identifying High Income Areas of London Location Insights - Identifying High Income Areas of London

Inspiration

Deciding where to open up a new business can be a time-consuming and difficult endeavour. Many factors can influence the success of a business in a certain location, including the region's local demographics. Location Insights aims to help inform business owners on the demographics of regions within a city and assist them in making a decision on the location of their business.

What it does

Using publicly available datasets from censuses and other sources, Location Insights constructs an interactive heat-map of a city that can be customized according to a business's desired target market demographic. This allows business owners to quickly and easily identify the most promising regions to open their business.

How we built it

The data processing and normalization was done using a combination of python and MATLAB. The result was a JSON file containing all of the normalized values for each region, with each value corresponding to a certain demographic group. To display the specific regions on the map, publicly available GeoJSON data was used and displayed using the Google Maps API.

Challenges we ran into

Finding the necessary data- in particular, finding a matching set of geographical (GeoJSON) and demographic (Census) data was very challenging. Oftentimes a complete set of demographic data was found but no corresponding GeoJSON data for each of the regions in the demogrpahic dataset. Data processing was also a challenge as we had to decide on the best way to normalize and structure the data in order for it to be usable in the front-end.

What's next

Possibly making a larger set of cities available, as well as a greater range of demographics and other useful values for business starters (e.g. commercial renting prices in the area)

Demo Pictures

Filter By Ethnicity - Indian Filtering By Ethnicity - Indian

Filtering By Parameters: (19-25) (Females) (High Income) Filtering By Parameters: (19-25) (Females) (High Income)

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Mckinsey Data Challenge 2016

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