Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users

Arai, Ayumi; Fan, Zipei; Matekenya, Dunstan; Shibasaki, Ryosuke

doi:10.3390/ijgi5060085

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…Previous studies demonstrate that mobile phone users are unevenly distributed in gender and geography [19,20] and population component [49]. This type of bias also exists in social media data [21,22].…”

Section: Representative Issues Of Big Datamentioning

confidence: 98%

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

Fang

Zhang

et al. 2017

IJGI

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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.

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Section: Representative Issues Of Big Datamentioning

confidence: 98%

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

Fang

Zhang

et al. 2017

IJGI

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“…In the case of mobility studies, for example, Batran et al [7] note that: "While traditional survey methods provide a snapshot of the traffic situation in a typical weekday, mobile phone data can capture weekday and weekend travel patterns, as well as seasonal variation of a large sample of the population at a low cost and wide geographical scale". The disadvantages of such datasets lie in the fact that they suffer from spatial and temporal sparseness [8,9], from a lack of-or an unknown degree of-representativeness [10,11], and from issues regarding anonymity [4].…”

Section: The Single-city Focus Of Urban Sensingmentioning

confidence: 99%

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

Cottineau

Vanhoof

2019

IJGI

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Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, and how these movements crystallise into the structural patterns observed by censuses. In this article we explore the convergence between mobile phone data and more traditional socioeconomic data from the national census in French cities. We extract mobile phone indicators from six months worth of Call Detail Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or the entropy of visited cell towers, and measures of economic organisation based on census data, such as the level of deprivation, inequality and segregation. Our findings show that some mobile phone indicators relate significantly with different socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several cases, changing the city delineation rule can change the significance and even the sign of the correlation. In general, cities delineated in a restricted way (central cores only) exhibit traces of human activity which are less related to their socioeconomic organisation than cities delineated as metropolitan areas and dispersed urban regions.

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“…Newer technologies, such as mobile phone call detail records 33 , geotagged social media posts 34 , Google location history 35 , and other mobile phone based locational services, may only have situational utility or limited representation of populations. These data tend to underrepresent rural populations 36 , specific demographics within a population 37 , or low income and under-resourced individuals 38 . Additionally, the use of newer technologies to track human movements, such as the previously mentioned mobile phone derived data streams, are restricted to dates that align with the widespread adoption of those technologies, which is a very recent or ongoing process in many areas of global health interest.…”

Section: Technical Validationmentioning

confidence: 99%

Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria

Bharti

Tatem

2018

Sci Data

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Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 2000–2005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.

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Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users

Cited by 20 publications

References 30 publications

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

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

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

Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria

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