MapVida is the World’s best neighborhood comparison tool. The platform evaluates millions of data points when studying neighborhoods, providing context, and making them familiar to its users
MapVida’s mission is to help users make sense of any neighborhood in seconds. This cutting-edge neighborhood mapping tool learns the characteristics in a neighborhood that are desirable to the users and identifies other neighborhoods that share those characteristics, allowing users to make informed decisions rather than operating in the dark. Unlike tools like walkability scores and school quality that provide insight in to only one aspect of a neighborhood, MapVida‘s tool incorporates millions of data points and feedback from users around the world to provide a comprehensive, meaningful, and detailed analytical comparison of neighborhoods in seconds — helping to get a clearer picture by putting it in the context of a neighborhood users already know.
The biggest challenge was to ensure smooth system performance. When processing millions of data points, the BI part had to provide results from complicated search queries. Visualization of search results on the small screen of the iPhone was also a challenging task from the design point of view. A lot of effort was put to application profiling, tweaking database model and designing the system to meet non-functional requirements, especially performance and scalability.
The goal of the project was to create a mobile application for individual users that could check the analytical abilities of the MapVida platform. The iOS app was interfaced with a BI backend and provided on-demand calculations based on the historical data gathered in the system. Another goal was to improve the performance of the BI platform by optimizing the underlying database model. Also, Espeo updated the user interface part of the BI solution offered to corporate customers by adding new ways of charts rendering and data visualization.
The solution that Espeo provided was an iOS app that presents charts and analytics of neighborhoods based on raw data stored on the application. The data was updated via backend through the MapVida API when the statistics changed. The app allowed users to compare and filter neighborhoods and search for similar places. Highcharts was used for drawing the charts and Google Maps for drawing the neighborhoods on the map. The backend part was done using a .NET stack, along with a MSSQL database. The data rendering part on the web application part was also done using the Highcharts library. Testing of the delivered solutions was also a part of the project.
Agile project management