Our families have all had trouble in the past finding the right home in the right area. We believe that there needs to be a web app that provides a user with the most important statistics to find the ideal location to settle down. These statistics need to take into account the financial stability of a location, the safety and reliability of the neighborhood, and also how it compares to the rest of the nation. Moreover, while this information does exist out there, it is held within large data tables and buried in places that make it difficult to search and explore easily for most people. Our goal in beginning this project was to simplify this process and provide useful data visualization tools to view information as well as to design algorithms that calculate a simple credibility index for locations instead of showing our users an overwhelming amount of complicated data.

What it does

This web app summarizes the most important data and provides it in a clean and simple manner to the user.

How we built it

We used the Google Maps Embed API to display the map and update it using the user input. We used HTML, JavaScript, and CSS to build the website framework itself. Algolia was used to search our datasets for the proper queries based on the user input. Some of our datasets came from Fannie Mae while others, like the FBI: UCR API, were found separately. R was used to evaluate the data itself and analyze it to provide useful statistical values (for example, the financial rating).

Challenges we ran into

The biggest issue was finding a way to update the map in a useful way. Many ideas were scrapped in the data visualization aspect, as the possible options were either expensive or not possible for our framework.

Accomplishments that we're proud of

The way we intuitively used Algolia to run our queries allowed our searches to provide meaningful data even when the user did not provide a suitable input. The clean look of the website was another bright spot for us.

What we learned

We learned how to incorporate multiple APIs and a data search engine into a web app.

What's next for ZipJudge

The future of zipJudge involves adding in more parameters to the algorithm used to evaluate zipcodes. Moreover, the ability to view models by individual categories is an area of interest. Currently, our data model and backend supports the ability to query and search by zipcodes. An additional goal is being able to add in support to query several unique parameters and be able to retrieve results based on that. These advanced filters and search parameters will better help users find the information they are looking for. We also seek to not just provide a financial solution to the problem of finding a stable neighborhood, but we also want to make our information easily accessible to people with a multitude of other purposes including research and academia. We also feel that with more information, we can be a one-stop-shop for the majority of details regarding a neighborhood and support in endeavors to equalize the difference between places with a great degree of variance in credibility ratings. Another area that we are looking to explore in adding interactive mapping software to visualize data in a more intuitive and comforting manner.

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