The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform

Allan Findlay - One of the best experts on this subject based on the ideXlab platform.

  • re thinking residential Mobility linking lives through time and space
    Progress in Human Geography, 2016
    Co-Authors: Rory Coulter, Allan Findlay
    Abstract:

    While Researchers are increasingly re-conceptualizing international migration, far less attention has been devoted to re-thinking short-distance residential Mobility and imMobility. In this paper we harness the life course approach to propose a new conceptual framework for residential Mobility Research. We contend that residential Mobility and imMobility should be re-conceptualized as relational practices that link lives through time and space while connecting people to structural conditions. Re-thinking and re-assessing residential Mobility by exploiting new developments in longitudinal analysis will allow geographers to understand, critique and address pressing societal challenges.

  • New Directions for Residential Mobility Research: Linking Lives Through Time and Space
    2013
    Co-Authors: Rory Coulter, Allan Findlay
    Abstract:

    While Researchers are increasingly reconceptualising international migration, less interest is being shown in rethinking the geographies of short-distance residential Mobility and imMobility. Short-distance moves are crucial for the structuration of everyday life, the operation of housing and labour markets and the (re)production of social inequalities. This paper argues that a deeper understanding of residential Mobility and imMobility can be gained by exploring developments in longitudinal analysis while seeking theoretical innovations derived from extending life course theories. Rethinking the geographies of residential Mobility around notions of 'linked lives' will allow us to understand, critique and address major contemporary challenges.

Ling Yin - One of the best experts on this subject based on the ideXlab platform.

  • Understanding the bias of call detail records in human Mobility Research
    International Journal of Geographical Information Science, 2016
    Co-Authors: Ziliang Zhao, Shih Lung Shaw, Feng Lu, Yang Xu, Jie Chen, Ling Yin
    Abstract:

    ABSTRACT: In recent years, call detail records (CDRs) have been widely used in human Mobility Research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human Mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used Mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human Mobility Research and we suggest that Researchers use caution to interpret results derived from CDR data.

  • Re-Identification Risk versus Data Utility for Aggregated Mobility Research Using Mobile Phone Location Data.
    PloS one, 2015
    Co-Authors: Ling Yin, Shih Lung Shaw, Qian Wang, Zhixiang Fang, Ye Tao, Wei Wang
    Abstract:

    Mobile phone location data is a newly emerging data source of great potential to support human Mobility Research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city’s mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated Mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -axb+c, (a, b, c>0; 0

  • re identification risk versus data utility for aggregated Mobility Research using mobile phone location data
    PLOS ONE, 2015
    Co-Authors: Ling Yin, Shih Lung Shaw, Qian Wang, Zhixiang Fang, Ye Tao, Wei Wang
    Abstract:

    Mobile phone location data is a newly emerging data source of great potential to support human Mobility Research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city’s mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated Mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -axb+c, (a, b, c>0; 0risks and a privacy-utility tradeoff benchmark for improving privacy protection when sharing detailed trajectory data.

Zhewei Liu - One of the best experts on this subject based on the ideXlab platform.

  • A Review of Human Mobility Research Based on Big Data and Its Implication for Smart City Development
    ISPRS International Journal of Geo-Information, 2020
    Co-Authors: Anqi Wang, Anshu Zhang, Edwin H.w. Chan, Wenzhong Shi, Xiaolin Zhou, Zhewei Liu
    Abstract:

    Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data Research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic Research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human Mobility Research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of Mobility, and (4) applying to city management. Then, the application of the Research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

Rory Coulter - One of the best experts on this subject based on the ideXlab platform.

  • re thinking residential Mobility linking lives through time and space
    Progress in Human Geography, 2016
    Co-Authors: Rory Coulter, Allan Findlay
    Abstract:

    While Researchers are increasingly re-conceptualizing international migration, far less attention has been devoted to re-thinking short-distance residential Mobility and imMobility. In this paper we harness the life course approach to propose a new conceptual framework for residential Mobility Research. We contend that residential Mobility and imMobility should be re-conceptualized as relational practices that link lives through time and space while connecting people to structural conditions. Re-thinking and re-assessing residential Mobility by exploiting new developments in longitudinal analysis will allow geographers to understand, critique and address pressing societal challenges.

  • New Directions for Residential Mobility Research: Linking Lives Through Time and Space
    2013
    Co-Authors: Rory Coulter, Allan Findlay
    Abstract:

    While Researchers are increasingly reconceptualising international migration, less interest is being shown in rethinking the geographies of short-distance residential Mobility and imMobility. Short-distance moves are crucial for the structuration of everyday life, the operation of housing and labour markets and the (re)production of social inequalities. This paper argues that a deeper understanding of residential Mobility and imMobility can be gained by exploring developments in longitudinal analysis while seeking theoretical innovations derived from extending life course theories. Rethinking the geographies of residential Mobility around notions of 'linked lives' will allow us to understand, critique and address major contemporary challenges.

Shih Lung Shaw - One of the best experts on this subject based on the ideXlab platform.

  • Understanding the bias of call detail records in human Mobility Research
    International Journal of Geographical Information Science, 2016
    Co-Authors: Ziliang Zhao, Shih Lung Shaw, Feng Lu, Yang Xu, Jie Chen, Ling Yin
    Abstract:

    ABSTRACT: In recent years, call detail records (CDRs) have been widely used in human Mobility Research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human Mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used Mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human Mobility Research and we suggest that Researchers use caution to interpret results derived from CDR data.

  • Re-Identification Risk versus Data Utility for Aggregated Mobility Research Using Mobile Phone Location Data.
    PloS one, 2015
    Co-Authors: Ling Yin, Shih Lung Shaw, Qian Wang, Zhixiang Fang, Ye Tao, Wei Wang
    Abstract:

    Mobile phone location data is a newly emerging data source of great potential to support human Mobility Research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city’s mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated Mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -axb+c, (a, b, c>0; 0

  • re identification risk versus data utility for aggregated Mobility Research using mobile phone location data
    PLOS ONE, 2015
    Co-Authors: Ling Yin, Shih Lung Shaw, Qian Wang, Zhixiang Fang, Ye Tao, Wei Wang
    Abstract:

    Mobile phone location data is a newly emerging data source of great potential to support human Mobility Research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city’s mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated Mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -axb+c, (a, b, c>0; 0risks and a privacy-utility tradeoff benchmark for improving privacy protection when sharing detailed trajectory data.