Appendix A — Appendix 1: Further recommended resources

This annotated bibliography provides verified references for the material covered in each chapter of this manual, together with additional resources for further reading. References marked with a chapter number in the main text are listed here in full. All URLs and DOIs were verified prior to publication. Practitioners are encouraged to check for updated editions before citing.

A.1 Chapter 1: Planning a Mobile Phone Data Initiative

UN Statistics Division (2019). Handbook on the Use of Mobile Phone Data for Official Statistics. United Nations. Available at: https://unstats.un.org/bigdata/task-teams/mobile-phone/MPD%20Handbook%2020191004.pdf → The foundational UN reference for MPD practitioners. Covers all key aspects of MPD planning, data pipeline design, quality assurance, and governance. Recommended as the primary companion text to this manual.

Blondel, V.D., Decuyper, A., & Krings, G. (2015). A survey of results on mobile phone datasets analysis. EPJ Data Science, 4(10). DOI: 10.1140/epjds/s13688-015-0046-0. Available at: https://link.springer.com/article/10.1140/epjds/s13688-015-0046-0 → A widely cited academic survey of CDR data analysis, covering social networks, mobility, urban planning, and development applications. Useful background reading for Sections 1.2.1 and 5.2.

Flowminder Foundation (2023). Flowminder standards in producing mobility and population estimates from call detail records in low- and middle-income countries. Flowminder Foundation. Available at: https://www.flowminder.org/resources/publications-reports/flowminder-standards-in-producing-mobility-and-population-estimates-from-call-details-records-in-low-and-middle-income-countries → Practical methodological standards from Flowminder covering CDR ingestion, cleaning, home/work detection, aggregation, bias adjustment, and quality assurance. Relevant to Chapters 1, 4, and 5.

GSMA (2016). Mobile Privacy Principles. GSMA. Available at: https://www.gsma.com/solutions-and-impact/connectivity-for-good/public-policy/gsma_resources/mobile-privacy-principles/ → The industry benchmark for privacy principles in mobile data use.

Additional resources: General introduction to Mobile Phone Data — UN CEBD Task Team website: https://unstats.un.org/bigdata/task-teams/mobile-phone/ Maturity Assessment Framework tool: https://worldbank.github.io/GDF-MPD/docs/project-resources/maturity_assessment_framework.html Theory of Change guidance for MPD initiatives: https://worldbank.github.io/GDF-MPD/docs/project-resources/theory-of-change.html

A.2 Chapter 2: Policy Applications

UN-CEBD Task Team on Mobile Phone Data (n.d.). Methodological Guide on the Use of Mobile Phone Data: Dynamic Population Mapping. United Nations Statistics Division. Available at: https://unstats.un.org/wiki/spaces/MPDDPM/overview → Authoritative UN methodology for population mapping applications. See Section 2.3.1.

UN-CEBD Task Team on Mobile Phone Data (n.d.). Methodological Guide on the Use of Mobile Phone Data: Displacement and Disaster Statistics. United Nations Statistics Division. Available at: https://unstats.un.org/wiki/spaces/MPDDS/overview → Core reference for displacement tracking methodology. See Section 2.3.2.

UN-CEBD Task Team on Mobile Phone Data (n.d.). Methodological Guide on the Use of Mobile Phone Data: Tourism Statistics. United Nations Statistics Division. Available at: https://unstats.un.org/wiki/display/MPDTS → Full methodological guide covering concepts, methods, and case studies for tourism. See Section 2.3.4.

UN-CEBD Task Team on Mobile Phone Data (n.d.). Methodological Guide on the Use of Mobile Phone Data: Migration Statistics. United Nations Statistics Division. Available at: https://unstats.un.org/wiki/spaces/MPDMS/overview → Relevant to Section 2.3 and any references to migration applications.

Caceres, N., Wideberg, J.P., & Benitez, F.G. (2007). Deriving origin–destination data from a mobile phone network. IET Intelligent Transport Systems, 1(1), 15–26. DOI: 10.1049/iet-its:20060020. Available at: https://digital-library.theiet.org/doi/10.1049/iet-its%3A20060020 → The foundational engineering paper demonstrating MPD-based origin–destination matrix derivation. See Section 2.3.5.

Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076. DOI: 10.1126/science.aac4420. Available at: https://www.science.org/doi/10.1126/science.aac4420 → The foundational Science paper on using CDR data to predict wealth distribution (Rwanda). See Section 2.4 and 2.4.1.

Aiken, E., Bedoya, G., Blumenstock, J., & Coville, A. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603, 864–870. DOI: 10.1038/s41586-022-04484-9. Available at: https://www.nature.com/articles/s41586-022-04484-9 → Peer-reviewed Nature paper documenting the Togo Novissi cash transfer targeting initiative. See Section 2.4.2.

Aiken, E. et al. (2022). Togo Novissi programme — World Bank Results Brief. World Bank. Available at: https://www.worldbank.org/en/results/2021/04/13/prioritizing-the-poorest-and-most-vulnerable-in-west-africa-togo-s-novissi-platform-for-social-protection-uses-machine-l → An accessible institutional reference on the Togo social protection programme for readers wanting a non-technical overview.

Additional resources: Dynamic Population Mapping (Netherlands): https://www.cbs.nl/en-gb/background/2025/14/using-cell-phones-to-compute-dynamic-population-densities-safely Disaster management and displacement case studies: https://www.sciencedirect.com/science/article/abs/pii/S0198971522000217 Migration statistics from MPD: https://www.migrationdataportal.org/resource/exploring-use-mobile-phone-data-national-migration-statistics Transport case studies (Latin America): https://www.nommon.es/case-studies/monitoring-travel-demand-bogota-buenos-aires-world-bank-transit-insights/ Tourism statistics — Estonia: https://positium.com/blog/estonia-leads-the-production-of-tourism-statistics-using-mobile-positioning-data Poverty mapping with MPD: https://www.flowminder.org/resources/publications-reports/mapping-poverty-using-mobile-phone-and-satellite-data

A.3 Chapter 3: Arranging Partnerships and Data Access

Positium (2025). A Roadmap to Accessing Mobile Network Data for Statistics. Produced for the Global Partnership for Sustainable Development Data. Available at: https://www.data4sdgs.org/roadmap-accessing-mobile-network-data-statistics → An excellent step-by-step practical guide on accessing MNO data for statistical purposes. Recommended companion reading for Sections 3.1 and 3.8.

GSMA (2019). Big Data for Social Good: Mobile Network Operator Data Sharing. GSMA. Available at: https://www.gsma.com/solutions-and-impact/connectivity-for-good/external-affairs/wp-content/uploads/2019/09/Big-Data-AI-Ethics_web.pdf → GSMA guidance on data sharing models for social good. Useful background for Chapter 3 discussions on MNO incentives and data-sharing frameworks.

Additional resources: Accessing Mobile Network Operator data — webinar: https://www.data4sdgs.org/accessing-mobile-data-national-strategies-and-challenges MoU templates for MPD partnerships: https://worldbank.github.io/GDF-MPD/docs/project-resources/mou_templates.html

A.4 Chapter 4: Data Processing and Data Pipelines

UN Statistics Division (2019). Handbook on the Use of Mobile Phone Data for Official Statistics, Chapter 2: Data Sources, Attributes and General Data Extraction Process. United Nations. Available at: https://unstats.un.org/bigdata/task-teams/mobile-phone/MPD%20Handbook%2020191004.pdf → Chapter 2 of the UN Handbook is the primary reference for MPD data sources, pipeline structure, and extraction processes covered in this chapter.

Flowminder Foundation (2023). Flowminder standards in producing mobility and population estimates from call detail records in low- and middle-income countries. Flowminder Foundation. Available at: https://www.flowminder.org/resources/publications-reports/flowminder-standards-in-producing-mobility-and-population-estimates-from-call-details-records-in-low-and-middle-income-countries → Detailed methodological standards document covering CDR ingestion, cleaning, home/work detection, aggregation, bias adjustment, and quality assurance as described in this chapter.

Flowminder Foundation (2023). Correcting measurement biases in the detection of long and short stay locations in sparse Call Detail Records. Flowminder Foundation. Available at: https://www.flowminder.org/resources/publications-reports/correcting-measurement-biases-in-the-detection-of-long-and-short-stay-locations-in-sparse-call-detail-records-cdrs → Technical paper on bias correction relevant to Sections 4.7 and 5.7 (scaling and bias adjustment).

Blondel, V.D., Decuyper, A., & Krings, G. (2015). A survey of results on mobile phone datasets analysis. EPJ Data Science, 4(10). DOI: 10.1140/epjds/s13688-015-0046-0. Available at: https://link.springer.com/article/10.1140/epjds/s13688-015-0046-0 → Broad survey of CDR data analysis methods including pipeline approaches relevant to Chapter 4.

A.5 Chapter 5: Data Quality and Characteristics

Flowminder Foundation (2023). Flowminder standards in producing mobility and population estimates from call detail records in low- and middle-income countries. Flowminder Foundation. Available at: https://www.flowminder.org/resources/publications-reports/flowminder-standards-in-producing-mobility-and-population-estimates-from-call-details-records-in-low-and-middle-income-countries → Covers CDR strengths and limitations, spatial and temporal precision, and methodological standards for quality assurance, all directly relevant to Chapter 5.

Flowminder Foundation (2023). Correcting measurement biases in the detection of long and short stay locations in sparse Call Detail Records. Flowminder Foundation. Available at: https://www.flowminder.org/resources/publications-reports/correcting-measurement-biases-in-the-detection-of-long-and-short-stay-locations-in-sparse-call-detail-records-cdrs → Peer-reviewed technical paper on bias correction in CDR data. Directly relevant to Section 5.7 on adjusting for bias.

Blondel, V.D., Decuyper, A., & Krings, G. (2015). A survey of results on mobile phone datasets analysis. EPJ Data Science, 4(10). DOI: 10.1140/epjds/s13688-015-0046-0. Available at: https://link.springer.com/article/10.1140/epjds/s13688-015-0046-0 → See for further details relevant to the overview of CDR strengths and limitations in Section 5.2.

Wesolowski, A., Eagle, N., Tatem, A.J., Smith, D.L., Noor, A.M., Snow, R.W., & Buckee, C.O. (2013). The impact of biases in mobile phone ownership on estimates of human mobility. Journal of the Royal Society Interface, 10(81), 20120986. DOI: 10.1098/rsif.2012.0986. → The foundational paper on representativeness bias in CDR data, covering differential phone ownership by gender, age, and wealth. Essential citation for Section 5.6. Note: this paper is paywalled and must be accessed via institutional library subscription.

A.6 Chapter 6: Data Governance and Safeguards

Jansen, R. et al. (2021). Guiding principles to maintain public trust in the use of mobile operator data for policy purposes. Data & Policy, 3, E24. DOI: 10.1017/dap.2021.21. → The peer-reviewed paper behind the UN guiding principles described in Section 6.8.1. Provides the full evidence base and rationale for the five principles.

United Nations (2014). Fundamental Principles of Official Statistics. United Nations Statistics Division. Available at: https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx → The UN Fundamental Principles apply to data governance, quality assurance, and public trust in official statistics. See Sections 6.2, 6.8, and throughout Chapter 6 where governance obligations are discussed.

African Union (2018). African Union Convention on Cyber Security and Personal Data Protection (Malabo Convention). African Union. Available at: https://au.int/en/treaties/african-union-convention-cyber-security-and-personal-data-protection → The continental African framework for data protection.