XSpacious uses technology to engage citizens with the environment around them. It's all about exploring (statistical) space, and demonstrates a new presentation tool in the style of an augmented reality mobile app. This fuses and displays environmental statistics and relationships in ways which make them easy to understand. Crucially, users can contribute data within the same app. This allows users an engaging experience whilst acting as a crowd-sourced network of environmental sensors.

This project is solving the Smart Cities, Smart Climate challenge.


Our Team

In the Leicester UK hub we sat down to brainstorm ideas for the Smart Cities app, as eight people who had never worked together before, and most of us had never even met until Space Apps Challenge 2013.

Why XSpacious?

We were particularly inspired by those phrases in the challenge which said “Explore innovative applications for... sensors to benefit science and society”, and “The goal is to encourage...other cities and to make creative use of the data for urban environment research”. It became clear that, for us, this challenge is all about using technology to engage citizens with the environment around them. That’s why we’ve called our idea XSpacious. It’s all about eXploring your (statistical) space.

Design Ideas

We decided to create an original presentation tool to show environmental data, and realised we could use locative information, in the style of an augmented reality app, to present information about the surrounding area to an individual within that space on their own mobile device. Then within the same app, we could provide the user with tools to contribute information. The idea is to crowd-source the sensor network requested by this challenge, thus making the users feel involved at the same time as gathering new urban data sets for scientists. We realised that some of the most useful feedback could come from combining data, so for example a citizen could see whether local climate, air quality, or noise variations within a city might have impact on health issues found in nearby areas. And from there we knew we’d also need some larger scale map-based visualisation to understand wider areas and more complex statistics.

Data Sources

We needed to source sample environmental data sets to present via the app. We contacted the Exeter group who were working on this challenge, and they told us they were able to upload the Birmingham data to a web server with public API, so that it could be accessible by our app. We also created 15 fictitious sensor locations around Leicester which we used to collocate land surface temperature (LST) data from the MODIS instruments aboard NASA's Aqua and Aura platforms in order to create a temperature time series for the city. We used this in conjunction with model air quality data from ECMWF and some synthetic air quality indices. In conjunction with the environmental data we took government social economic statistics. All were put onto a common grid for use by the app and prototype visualizations. We also made a web page to crowd-source data during the weekend, and some simulated data sets we could try straight away.


We sketched out a diagram of how the different bits of our app would fit together. We chose the language and tools to use and how to interface. Playing to the strengths of the team, we used the R and Java languages to take available data sources and convert them to a form which could be served to our app, and algorithms to parameterise this by location and compass heading. We prototyped the map-based visualisation using the Processing environment. To target the widest range of devices we decided to use the open standards of HTML5 and Javascript to create the app, and a web server to deliver it and receive crowd-sourced inputs. We definitely wanted to try everything out using our own local space in Leicester, UK.

The Result

In 48 hours we built XSpacious - a fully working proof-of-concept for exploring and contributing to the local statistical space. The video below shows the app running on a mobile device, with statistical data sent to and from the Java-based web server. Data values are visualised using colour intensity and text, and we see them update according to current location and compass heading. A pictorial icon is shown on a particular heading, as an example of how particular features in the data could be highlighted. We also show that data values can be uploaded via the same app, to contribute to the data set. Code for the working server and app is available via the main source URL. Different statistics can be served to the mobile device depending on which method and input data is chosen from the SmartCities Java submodule to run within the Java web server. The wider area map visualisations have been prototyped within Processing, and will run as JavaScript, meaning that they are suitable for incorporation into the final HTML5 mobile app.

We hope this inspires others using apps to explore the environment in their cities.

Next Steps

The next steps would be to make the web server publicly accessible and begin some trials. This would also involve consulting citizens on which data sources and comparisons might be most useful for them to see, and which information they would find most engaging to be able to upload themselves and visualise. The results of such consultation would also affect the way in which the map-based visualisation can be incorporated alongside the augmented-reality-style display. Our team has already scheduled some meetings to discuss how we might take this forward.

Project Information

License: Creative Commons BY 3.0
Source Code/Project URL: https://github.com/XSpacious/SpaceApps2013


Web request for crowd-sourcing - http://leicestersmartcities.blogspot.co.uk/
Live demo in Leicester UK hub on Sunday 21st April 2013 - http://www.youtube.com/watch?v=3EoU_LsfT4k&list=PLk9LSE1tc0LU1o6pEltXo4C4ECQzJoFFn
Q&A in Leicester UK hub on Sunday 21st April 2013 - http://www.youtube.com/watch?v=64TmFLN2Ut0&list=PLk9LSE1tc0LU1o6pEltXo4C4ECQzJoFFn