Sensor Data and the City: Urban Visualisation and Aggregation of Well-Being Data

TL;DR

This paper presents a quantitative method using multi-model sensor data and visualization techniques to analyze urban well-being and emotional characteristics in city environments.

Contribution

It introduces a novel approach combining sensor data, self-reports, and spatial analysis to understand urban well-being at high granularity.

Findings

Sensor data reveals emotional patterns in urban spaces.

Spatial and temporal analysis identifies behavior clusters.

Visualization aids in interpreting collective urban well-being.

Abstract

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.