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Our dashboard
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POIs near Königsplatz
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A regular inner city route fed into PolicyDriver
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An accessibility map for Munich
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Air quality data in munich as seen by a measuring station near Arcisstraße
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An overview of public WIFIs provided by the city
Enjoy a working prototype of PolicyDriver by opening the PolicyDriver web app in Google Chrome.
Inspiration
Smart cities require smart policies. With increasing population density, a wide variety of different modes of transportation and environmental concerns, a major challenge in city planning is shaping the future of transportation. In order to make smart decisions in this area, policy makers need to have a good overview of traffic safety, air quality and accessibility of their city. By leveraging open data and intuitive visualization, we provide this overview.
What it does
PolicyDriver offers an interactive dashboard for the policy makers of Munich. Our dashboard includes different map overlays that provide insights into the the cities traffic safety, accessibility and much more. Additionally, policy makers can look into the details of traffic safety along certain routes or at places of interest using our "Routes and Places" layer. We highlight historical accidents along the routes and compute a safety score for the route. This can for example be very helpful when assessing whether children have safe ways to school. For our "Accessibility Layer" we compute how well different areas of the city are connected to the heart of the city and visualize it using an intuitive heat map. We also provide layers that allow to check details about accident statistics, air quality in the city and WiFi coverage.
How we built it
We built the dashboard using Angular in combination with Ionic. For the map and its overlays we leveraged the Google Maps API. Any data pre-processing was done using Python. For the design we kept Material Design principles in mind and aimed to give as much space for the most important part of our application: the enhanced maps. We obtained data from multiple different sources: a) We used open data sets such as a data set of labeled accidents in Germany. b) We crawled data from websites (e.g., for the map of open WiFi hotspots in the city). c) We generated data using the Google Maps API (e.g., distance metrics for our accessibility score).
Challenges we ran into
Initially, we struggled to find single datasets for Munich that are rich enough to solve transportation problems. We solved this by aggregating multiple datasets and by focusing on visualization to enable a deeper understanding of our city. Another challenge was integrating Google Maps with Angular but after switching from the a third party library for Angular to the vanilla JavaScript API it was smooth sailing.
Accomplishments that we're proud of
We are especially proud of the number of different layers we included in our dashboard via leveraging the Google Maps API and different open data sets. It was also interesting for us to compute the heat map for the accessibility of the city we live in.
What we learned
A major learning was how to use the Google Maps API beyond simply displaying a map by enhancing it with different overlays. Furthermore, we learned a lot about working together as a team on a challenging task in the short time frame of a Hackathon.
What's next for PolicyDriver
Our dashboard is easily extensible with additional layers and statistics by adding more data. We implemented PolicyDriver as a case study for Munich specifically but the same concepts can be applied to other cities. Additionally, it would be interesting to integrate more live data and data about the utilization of different places.
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