CitySense is supported by the National Science Foundation and Microsoft Corporation.
The visualization accesses the City Sense sensor database, containing all records since the inception of the project. Currently, the types of data recorded by each sensor varies and new types of data will be recorded in the future. Therefor, it was requested that the visualization be capable of handling new types of sensor data without future code changes. This restriction limited the way in which the data could be displayed. For example, icons use is limited, because an icon for one type of data, say rain, does not necessarily translate easily to wind direction data. The only other requirement was that the visualization be web-enabled, preferrably using python.
There are four main sections in this visualization. The most dominant is the google map displaying the location of each sensor. The sensors are currently placed in three groups across Boston. Therefore, some additional navigation was providded in addition to the default google maps zooming and dragging functionality. These radio buttons provide quick centering of the map over the different groups of sensors.
A key is provided showing some minimal information about each sensor. Additionally, the user may click on a sensor icon to see detailed information, including the last recording for each sensor. Please note that different sensors provide different information.
The search section allows the user to enter parameters for the available sensor data types. The measurement field is dynamically created based on the sensor data types table. This allows the site Administrator to add a new sensor data types to the sensor data type table, thus providing the ability to search on the new sensor data types without requiring any code changes. All fields are required in the search form. This is necessary due to the large amount of data in the database. Also, since sensor data is not available for the future, future dates in the calendar drop down have been disabled.
Once the search criteria are entered, any matching sensors' icons change to yellow. Additionally, a google chart is displayed showing the sensor data fulfilling the requested criteria. Sensor data is color coordinated with the sensor name at the bottom of the chart.
This visualization provides the ability for users to see the current information recorded by each sensor and look up specific information based on sensor data type, which can lead to some interesting questions for further research. For example, searching the max wind speed for a given day displays sensors that are not all located within close proximity, leading one to question why the max wind speed can vary so widely among sensors within close proximity. This, in turn, could lead one to future research on how civil engineering could reduce the total energy costs for entire communities located in cold northern climates.
I hope to continue improving this visualization before it is deployed on the citySense.net homepage. Areas for improvement include adding the ability to search multiple data types and better integration of the google chart and the google map.
Finally, I would like to thank Professor Welsh and Ian Rose for their assistance in bringing me up to speed on the project and providing access to the city sense sensor data.
