Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy

Abstract

In this paper, we propose a correction of the Mobility Entropy indicator (ME) used to describe the diversity of individual movement patterns as can be captured by data from mobile phones. We argue that a correction is necessary because standard calculations of ME show a structural dependency on the geographical density of observation points, rendering results biased and comparisons between regions incorrect. As a solution, we propose the Corrected Mobility Entropy (CME). We apply our solution to a French mobile phone dataset with ∼18.5 million users. Results show CME to be less correlated to cell-tower density (r =-0.17 instead of-0.59 for ME). As a spatial pattern of mobility diversity, we find CME values to be higher in suburban regions compared to their related urban centers, while both decrease considerably with lowering urban center sizes. Based on regression models, we find mobility diversity to relate to factors like income and employment. Additionally, using CME reveals the role of car use in relation to land use, which was not recognized when using ME values. Our solution enables a better description of individual mobility at a large scale, which has applications in official statistics, urban planning and policy, and mobility research.

Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

“…In the case of mobile phone data, creating individual indicators is possible at a nation-wide scale (as datasets are mostly provided by national operators) but it is not a straightforward task. For example, differences in the spatial resolution of observations make it hard to create comparable indicators for individual mobility [15], and it is known that home detection methods, which enable the spatial allocation and aggregation of individual users, still face severe challenges when it comes to validation and error estimation [9,16].…”

“…Frias-martinez et al [20] investigate the relations between several mobile phone indicators (call, movement and purchase behaviour) and multiple census variables on education, demographics and purchase power in a Latin American country. With the exception of Vanhoof et al [15], who study relations between mobile phone and census indicators for different urban areas in France, one clear shortcoming of these studies is that their analyses are fixed on the nation-level only, leaving a missed opportunity to explore the empirical relations between human mobility, social interactions, and the socioeconomic organisation of cities.…”

“…The dataset itself consists of Call Detailed Records (CDR), that collect information on the time, deployed cell tower, initializing user, receiving user and duration/length of each call or text made by about 18 millions Orange subscribers. The French CDR dataset has been extensively studied before [9,[15][16][17][30][31][32] and is one of the largest datasets that guarantee access to individual user data over such a long time period.…”

“…The entropy of visited cell towers is a reflection of the diversity of users' movement pattern. It is defined as the Shannon entropy of the pattern of visited cell towers [15,17]:…”

Differentiating Population Spatial Behavior Using Representative Features of Geospatial Mobility (ReFGeM)

“…Alternatively, there are some incidental activities, such as a barbeque at a park, which occur spontaneously and outside of routine behaviors. The predictability or regularity of spatial behavior can differentiate individuals or populations [64].…”