Integrated Multimedia City Data (iMCD): A composite survey and sensing approach to understanding urban living and mobility

•The Integrated Multimedia City Data (iMCD) is a data platform involving detailed survey, and Internet data.•The platform allows research into urban living and knowledge discovery regarding smart, future and learning cities.•One example analyzes the relationship between travel distance and measured financial literacy and numeracy.•Another example uses social media data to understand spatio-temporal aspects of urban activity patterns.•Deep learning applications of personal image data yields an isolation index that can be linked to socio-demographics. We describe the Integrated Multimedia City Data (iMCD), a data platform involving detailed person-level self-reported and sensed information, with additional Internet, remote sensing, crowdsourced and environmental data sources that measure the wider social, economic and physical context of the participant. Selected aspects of the platform, which covers the Glasgow, UK, city-region, are available to other researchers, and allows knowledge discovery on critical urban living themes, for example in transportation, lifelong learning, sustainable behavior, social cohesion, ways of being in a digital age, and other topics. It further allows research into the technological and methodological aspects of emerging forms of urban data. Key highlights of the platform include a multi-topic household and person-level survey; travel and activity diaries; a privacy and personal device sensitivity survey; a rich set of GPS trajectory data; accelerometer, light intensity and other personal environment sensor data from wearable devices; an image data collection at approximately 5-second resolution of participants’ daily lives; multiple forms of text-based and multimedia Internet data; high resolution satellite and LiDAR data; and data from transportation, weather and air quality sensors. We demonstrate the power of the platform in understanding personal behavior and urban patterns by means of three examples: an examination of the links between mobility and literacy/learning using the household survey, a social media analysis of urban activity patterns, and finally, the degree of physical isolation levels using deep learning algorithms on image data. The analysis highlights the importance of purposefully designed multi-construct and multi-instrument data collection approaches that are driven by theoretical frameworks underpinning complex urban challenges, and the need to link to policy frameworks (e.g., Smart Cities, Future Cities