Abstract

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, UNESCO Learning Cities agendas) that have the potential to translate data to impactful decision-making.

Highlights

  • There has been a great deal of recent interest on high-fidelity and timely measurements of the social, economic and functional characteristics of cities

  • The design of the Integrated Multimedia City Data (iMCD) was motivated by research questions in sustainable transportation, healthy cities, lifelong learning, and their interrelationships

  • The data system enables a comprehensive look at transportation and mobility behaviors; education and lifelong learning; sustainable resource consumption and behaviors; social cohesion, cultural values and political preferences; health and wellbeing; and behaviors around use of ICT and digitalisation of our daily lives

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Summary

Introduction

There has been a great deal of recent interest on high-fidelity and timely measurements of the social, economic and functional characteristics of cities. Whereas large numbers of cities around the world are extensively instrumented with CCTV cameras yielding video data for traffic management or crime prevention, social media sites such as Flickr, Instagram and even Google Street View which are populated with image user-generated content or street-based photos have opened up new research questions in the areas of event detection, information propagation, mobility and flow detection, and cultural aspects of cities (Chen & Roy, 2009; Hochman & Manovich, 2013; Sun, Fan, Bakillah, & Zipf, 2015; Yin, Soliman, Yin, & Wang, 2017). Recent developments with small portable cameras either integrated in smartphones or in other devices, or as stand-alone portable/wearable devices, generate a wealth of visual lifelogs consisting of unstructured image data on people’s daily lives, that allow detailed analytics of behaviors and movements (Bolanos, Dimiccoli, & Radeva, 2017; Doherty et al, 2011). Social media data has been used for traffic incident detection (D’Andrea, Ducange, Lazzerini, & Marcelloni, 2015), crisis management (Rand, Herrmann, Schein, & Vodopivec, 2015), activity patterns (Hasan & Ukkusuri, 2015), and measurement of labor market (Antenucci, Cafarella, Levenstein, Ré, & Shapiro, 2014)

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