Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

Abstract

Abstract:The advent of big data has aided understanding of the driving forces of human mobility, which is beneficial for many fields, such as mobility prediction, urban planning, and traffic management. However, the data sources used in many studies, such as mobile phone location and geo-tagged social media data, are sparsely sampled in the temporal scale. An individual's records can be distributed over a few hours a day, or a week, or over just a few hours a month. Thus, the representativeness of sparse mobile phone location data in characterizing human mobility requires analysis before using data to derive human mobility patterns. This paper investigates this important issue through an approach that uses subscriber mobile phone location data collected by a major carrier in Shenzhen, China. A dataset of over 5 million mobile phone subscribers that covers 24 h a day is used as a benchmark to test the representativeness of mobile phone location data on human mobility indicators, such as total travel distance, movement entropy, and radius of gyration. This study divides this dataset by hour, using 2-to 23-h segments to evaluate the representativeness due to the availability of mobile phone location data. The results show that different numbers of hourly segments affect estimations of human mobility indicators and can cause overestimations or underestimations from the individual perspective. On average, the total travel distance and movement entropy tend to be underestimated. The underestimation coefficient results for estimation of total travel distance are approximately linear, declining as the number of time segments increases, and the underestimation coefficient results for estimating movement entropy decline logarithmically as the time segments increase, whereas the radius of gyration tends to be more ambiguous due to the loss of isolated locations. This paper suggests that researchers should carefully interpret results derived from this type of sparse data in the era of big data.

On data processing required to derive mobility patterns from passively-generated mobile phone data

“…This correlation also suggests that for those studies relying only on active users, their results are likely affected by this issue (Alexander et al, 2015b; Wang et al, 2010). Lastly, because of the temporal sparsity and spatial uncertainty naturally associated with mobile phone data, there is a need for new thinking and new interpretations on the scale and meanings of representativeness (Lu et al, 2017; Zhao et al, 2016). In other words, a day of trajectory from mobile phone data may not be comparable to a trajectory derived from a household travel survey; rather, to be comparable to the latter, it may take trajectories of multiple days.…”

Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut Using the Cellular Device Network

“…Analyses have been performed using cell phone infrastructure based on Call Detail Records (CDRs), which are generated by applications or phone calls on cellular devices [Becker et al, 2013;Lu et al, 2017;Thuillier et al, 2018;Marques-Neto et al, 2018]. These records are generated primarily for billing purposes and include information including lo-4 cation (triangulated via towers), data usage, etc.…”

Exploring the Associations Between Urban Form and Neighborhood Vibrancy: A Case Study of Chengdu, China

“…The collection of human activity data in existing research is commonly achieved through surveys or questionnaires [16]. However, these methods are time-consuming and laborious, and the representativeness of data is also questionable [17][18][19].Secondly, questions still remain regarding how to quantify urban form measurements. The nonphysical environmental factors of urban space (such as social order, living standards, psychological emotions and other factors) also affect urban vitality [20].…”