The process of buying a house and finding the right neighborhood has always been difficult and we wanted to create an application that would allow potential buyers to easily access a multitude of characteristics in order to assess the neighborhood's match with their preferences.

What it does

Our web-based application provides 5 main features that we believe will be immensely helpful to our target audience. The first is Google Map/Street View representation of the neighborhood they are looking at. Based on the inputted address, this part of the page will automatically pull up a map or street view (depending on the viewer's preference) so the user can see how the neighborhood and its surrounding look. The second feature is a side panel that displays information about POIs (places of interest) within the radius of the neighborhood. This will allow users to get a better understanding of the services nearby including food, entertainment, schools, and businesses. The third feature is a bottom panel that display an immense amount of community information that would otherwise be hard to find in one place. This includes information ranging from general demographics to category-specific crime risks and pollution rates. Perhaps our most unique feature is the implementation of Snapchat maps. When brainstorming we realized that one of the most important parts of the community are the people. The Snap Maps allow users to view public stories in the neighborhood so they can better gauge the members and culture of the community. Our fifth feature is a comparison option that allows the users to compare multiple neighborhoods in various aspects so that they can more easily understands the differences, strengths, and weaknesses between any set of neighborhoods.

How we built it

To collect data we used the Attomdata API, which provides a callable dataset for various demographic information on communities and surrounding areas. We integrated this data into a website that we created with HTML5, CSS3, JavaScript, and Node.JS. On the backend of the website, we created a simple node.js server on a publicly available endpoint. On the frontend, we integrated many of our features including Google Cloud Maps API, Google Cloud Places API, Google Cloud Geocoding API, Google Charts API, Snap-Kit, and ajax requests to the Attomdata API. To provide a seamless design, we incorporated MaterializeCSS and used Handlebars.

Challenges we ran into

Overlaying streetview Properly implementing Attomdata API Implementing Microstrategy

Accomplishments that we're proud of

Clean User Interface Use of the Snap-Kit API Live-time data collection

What we learned

How To Use Google Cloud, Snap-Kit, and Attomdata, AJAX Requests, Google Charts

What's next for NeighborhoodIQ

There are many avenues that we could take for NeighborhoodIQ. One avenue we considered was implementing an overlay on streetview and integrating it with VR. This would allow people to virtually walk through neighboorhoods and see information on each community. Another path that his project could take is implementing Google Cloud Vision API's for Images and Videos on the public stories that are available on Snapchat by taking advantange of the Snap-Kit API. Over time, we could also interactively train a machine learning model to learn preferences of users based on their input to our site.

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