Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data

Abstract

In the past decade, large scale mobile phone data have become available for the study of human movement patterns. These data hold an immense promise for understanding human behavior on a vast scale, and with a precision and accuracy never before possible with censuses, surveys or other existing data collection techniques. There is already a significant body of literature that has made key inroads into understanding human mobility using this exciting new data source, and there have been several different measures of mobility used. However, existing mobile phone based mobility measures are inconsistent, inaccurate, and confounded with social characteristics of local context. New measures would best be developed immediately as they will influence future studies of mobility using mobile phone data. In this article, we do exactly this. We discuss problems with existing mobile phone based measures of mobility and describe new methods for measuring mobility that address these concerns. Our measures of mobility, which incorporate both mobile phone records and detailed GIS data, are designed to address the spatial nature of human mobility, to remain independent of social characteristics of context, and to be comparable across geographic regions and time. We also contribute a discussion of the variety of uses for these new measures in developing a better understanding of how human mobility influences micro-level human behaviors and well-being, and macro-level social organization and change.

Promises and Pitfalls of Using Digital Traces for Demographic Research

“…The ability to represent time as “continuous, rather than bundled” (Ruppert et al 2013:36) opens a wealth of new opportunities to researchers, such as examining real-time trends in daily activities (Golder and Macy 2014), mobility (Williams et al 2015), attitudes (O’Connor et al 2010), health behaviors (Heaivilin et al 2011), and migration (Zagheni and Weber 2012; Zagheni et al 2014). Researchers may also examine these behaviors before, during, and after crisis events, such as natural disasters (Reeder et al 2014; Sutton et al 2014) or terrorist attacks (Starbird et al 2014).…”

A Data Mining Approach to Assess Privacy Risk in Human Mobility Data

“…Dividing Locs by the number of available locations in the considered territory, we obtain Locs r atio , which indicates the fraction of territory exploited by an individual in her mobility behavior. The maximum distance D max traveled by an individual is defined as the length of the longest trip of the individual during the period of observation (Williams et al 2015), while D tr ip max is defined as the ratio between D max and the maximum possible distance between the locations in the area of observation. The sum of all the trip lengths traveled by the individual during the period of observation is defined as D sum (Williams et al 2015).…”

“…The maximum distance D max traveled by an individual is defined as the length of the longest trip of the individual during the period of observation (Williams et al 2015), while D tr ip max is defined as the ratio between D max and the maximum possible distance between the locations in the area of observation. The sum of all the trip lengths traveled by the individual during the period of observation is defined as D sum (Williams et al 2015). It can be also averaged over the days in the period of observation, thus obtaining D sum .…”

The Potential of Volunteered Geographic Information (VGI) in Future Transport Systems

“…These devices equipped with an array of sensors and data capture equipment were also able to locate people and their information (Mohan, Padmanabhan, & Ramjee, 2008;Reddy et al, 2010;Williams, Thomas, Dunbar, Eagle, & Dobra, 2015;Yuan, Raubal, & Liu 2012). Since then much of the technology in terms of smartphone technology such as WiFI, Bluetooth, camera, GPS receiver, accelerometers, digital compass and microphone all able to collect information on the go has not only increased rapidly but also become cheaper and more pervasive amongst the population (Haklay, 2013).…”

“…Research has started in this field with some interesting results leading to new breakthroughs for transport planning (Iqbal, Choudhury, Wang, & Gonzalez, 2014) and transport behaviour research. Yuan et al (2012) and Williams et al (2015) looked at the potential of mobile phone usage records and how it correlates with travel behavior, and mobile phone use as a measure for human mobility. Both studies identify valuable new insights into travel behavior and the challenges posed by the current technologies in fully utilizing the data generated from these technologies.…”