Sensor Data and the City: Urban Visualisation and Aggregation of Well-Being Data
This work addresses the challenge for city councils to reduce urban stress by providing insights into people's behavior and well-being, though it appears incremental in applying existing visualization and analysis methods to new sensor data.
The researchers tackled the problem of understanding collective well-being in urban spaces by analyzing high-granularity multi-modal sensor data, including environmental and physiological sensors, along with self-reports and location tags, to quantify emotional characteristics of places through data visualization and spatial analysis.
The growth of mobile sensor technologies have made it possible for city councils to understand peoples' behaviour in urban spaces which could help to reduce stress around the city. We present a quantitative approach to convey a collective sense of urban places. The data was collected at a high level of granularity, navigating the space around a highly popular urban environment. We capture people's behaviour by leveraging continuous multi-model sensor data from environmental and physiological sensors. The data is also tagged with self-report, location coordinates as well as the duration in different environments. The approach leverages an exploratory data visualisation along with geometrical and spatial data analysis algorithms, allowing spatial and temporal comparisons of data clusters in relation to people's behaviour. Deriving and quantifying such meaning allows us to observe how mobile sensing unveils the emotional characteristics of places from such crowd-contributed content.