Assessing the socio-demographic representativeness of mobile phone application data
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Assessing the socio-demographic representativeness of mobile phone application data
Authors
Keywords
-
Journal
APPLIED GEOGRAPHY
Volume 158, Issue -, Pages 102997
Publisher
Elsevier BV
Online
2023-07-13
DOI
10.1016/j.apgeog.2023.102997
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Mobile phone location data for disasters: A review from natural hazards and epidemics
- (2022) Takahiro Yabe et al. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
- Evaluating geographic and social inequity of urban parks in Shanghai through mobile phone-derived human activities
- (2022) Xiyuan Ren et al. URBAN FORESTRY & URBAN GREENING
- Analysis of the Activity and Travel Patterns of the Elderly Using Mobile Phone-Based Hourly Locational Trajectory Data: Case Study of Gangnam, Korea
- (2021) Kwang-Sub Lee et al. Sustainability
- Mapping urban greenspace use from mobile phone GPS data
- (2021) Meghann Mears et al. PLoS One
- Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
- (2021) Luca Pappalardo et al. EPJ Data Science
- Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic
- (2021) Xiao Huang et al. Annals of the American Association of Geographers
- The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity
- (2021) Álvaro Bernabeu-Bautista et al. ISPRS International Journal of Geo-Information
- Understanding post-disaster population recovery patterns
- (2020) Takahiro Yabe et al. Journal of the Royal Society Interface
- Where the wild things are! Do urban green spaces with greater avian biodiversity promote more positive emotions in humans?
- (2020) Ross W. F. Cameron et al. URBAN ECOSYSTEMS
- Using social media to estimate visitor provenance and patterns of recreation in Germany's national parks
- (2020) Michael Sinclair et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Using Mobile Phone Data for Emergency Management: a Systematic Literature Review
- (2020) Yanxin Wang et al. INFORMATION SYSTEMS FRONTIERS
- The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology
- (2020) Kyra H. Grantz et al. Nature Communications
- Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US
- (2020) Song Gao et al. JAMA Network Open
- Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example
- (2019) Feilong Wang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Accessibility to urban parks for elderly residents: Perspectives from mobile phone data
- (2019) Sihui Guo et al. LANDSCAPE AND URBAN PLANNING
- On data processing required to derive mobility patterns from passively-generated mobile phone data
- (2018) Feilong Wang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Understanding the bias of call detail records in human mobility research
- (2016) Ziliang Zhao et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Analyzing Cell Phone Location Data for Urban Travel
- (2015) Serdar Çolak et al. TRANSPORTATION RESEARCH RECORD
- Quantifying socio-economic indicators in developing countries from mobile phone communication data: applications to Côte d’Ivoire
- (2015) Huina Mao et al. EPJ Data Science
- Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data
- (2012) Santi Phithakkitnukoon et al. PLoS One
- Understanding individual mobility patterns from urban sensing data: A mobile phone trace example
- (2012) Francesco Calabrese et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started