Attaining the Sustainable Development Goals depends on timely and cost effective decision-making.

For many applications, governments and development actors require access to data on population distributions, characteristics, and dynamics at high frequencies and at detailed geographic levels. 

This is particularly important in rapidly changing contexts and in sectors where decision making is directly dependent on understanding mobility, displacement and migration of populations. Data generated by network operators and new data types from satellites can, in combination with traditional data sources, allow radical improvements in development outcomes and in the effectiveness of humanitarian operations.

SDG Wheel Transparent WEB

Data Science

Nigeria Aerial view mobility

Scientific excellence and innovation

To improve support to humanitarian and development interventions in low- and middle-income countries (LMICs), Flowminder bases its methods on robust academic research, much of it developed and published by in-house researchers in high-impact peer reviewed academic journals.

Flowminder researchers were the first to develop, validate and operationalise the use of mobile operator data to monitor population displacement in a humanitarian emergency, and were the first to show that mobile operator data can be used to predict post disaster population movements based on pre-disaster mobile data.

Similarity Flowminder researchers pioneered the use of mobile operator data to respond to a large-scale infectious disease outbreak and show that mobile data can be used to predict the spatial spread of an infectious disease. Flowminder has published a number of advances in the use and integration of satellite and traditional data sources to improve our understanding and use of data on population distributions, characteristics and dynamics in LMICs.

Flowminder has operationalised these method advances to support public decision making through a large number of projects in LMIC countries. Find out more about our projects and impact:

Data applications

Data Analysis

Nigeria Aerial view mobility
  • Mobility analysis

    Improving countries’ understanding of human mobility

  • Site placement optimisation

    Informing facility placement for maximum coverage

  • GridSample

    Strengthening household surveys design and implementation in low- and middle-income countries

Mobility analysis: Improving countries’ understanding of human mobility 

Flowminder provides privacy-conscious data on where people are, where they are going, and the routes they take to get there. Data from mobile network operators can be analysed in near real-time and provide an overview of population movements within a country. Using this data in combination with survey data and geospatial data, we provide governments and other decision makers with timely mobility analyses at local, regional or national levels.

Nigeria Aerial view mobility

Analysing mobility with Call Detail Records

To analyse human mobility patterns in low- and middle-income countries, we primarily rely on Call Detail Records, also known as CDR data.

CDRs are owned and automatically generated by mobile network operators for billing purposes. They are produced each time a subscriber uses their phone for a billable event. It includes making or receiving a call or an SMS, or using mobile data. Every time a call, text, and mobile data is made, it automatically creates a record in the operator’s database. This record includes an anonymous subscriber ID, a timestamp, and the ID of the cell tower routing the event.

From a CDR dataset, we can tell the approximate whereabouts of de-identified subscribers, based on the tower’s location, associated with the time of the event that is included in the dataset. A CDR dataset therefore contains billions of data points from millions of users, covering large geographic areas over time.

CDR process and dataset - graph
Flow map - blue

Governance and privacy

Once aggregated into anonymous statistics, CDR data can be used to inform humanitarian and development decision-making. All of the outputs produced by Flowminder’s code and methods are aggregated data, meaning that they do not contain any information about individuals. Aggregated data characterise the overall behaviour of an entire group of subscribers. Aggregates are calculated by combining the data in a group into a single number that represents the entire group (for instance a population change in a geographic area).

Flowminder provides a fully EU GDPR compliant approach to data use. For mobile operator data we ensure that data stays with the mobile network operators to be processed within their own internal secure systems. Any outputs that may be shared with external parties for analysis purposes are fully anonymised and aggregated, guaranteeing that the privacy of all individuals is maintained.

Data outputs & applications

While we work to enable others to deliver our vision, we undertake analysis and provide insights to decision makers, applicable to a wide range of contexts, such as for disaster management, epidemiology and public health, urban planning, service access assessment and migration.

Our analyses can take the form of graphs, maps, statistics or reports, which can be used by governments, humanitarian and development practitioners or other scientists for evidence-based decision-making.

Data applications

Understanding demographic shifts and movements is an important factor in determining access to resources and implementing strategic interventions for the wellbeing of a country’s population.

FlowKit: an open source toolkit to facilitate analysis of CDR data

To facilitate the analysis of de-identified subscribers’ mobility patterns and network usage, we built FlowKit, a mobile data analytics toolkit. Our continuously-evolving open-source toolkit not only allows mobile network operators to securely control and monitor access to their aggregated data, but also provides humanitarian and development practitioners with analytical tools developed specifically for their needs, based on Flowminder’s years of experience and scientific research. Examples of analytical features include:

Data applications

  • Identification of meaningful locations for groups of anonymous subscribers, useful for dynamic population mapping and post- disaster displacement monitoring
  • Production of origin-destination matrices, to estimate mobility and commuter flows
  • Identification and unusual patterns of mobility post-disaster.
  • Estimation of a range of subscriber features, (such as nocturnal calls, network size  etc.) as input to estimate the distribution of poverty and other vulnerability indicators.
  • Extraction of network activity by location to monitor post-disaster network recovery.

Download Fact File (PDF)

Optimising placement of services

To maximise the use of public and private physical services, such as schools, health facilities and financial services, facilities need to be placed in a way that supports physical access to the largest possible share of the population.

Nigeria aerial motorway

Detailed population distributions, roads and river networks, population mobility patterns and a range of other geospatial data are now available to support decision makers. Decisions are also informed by a range of other considerations beyond data. Flowminder has developed data-driven methodologies to inform facility placement by providing estimates of the coverage provided by existing service sites and by guiding the selection of locations for new or expanded sites based on trade off between important priorities selected by policy makers  and stakeholders.

Flowminder has developed two related methodologies:

  • placement of services using static high-resolution gridded population estimates and
  • approaches additionally using population mobility data.

The work supports cost efficient use of  resources as well as enabling transparency and accountability of resource allocation decisions e.g. in the health, education and financial services sectors.

GridSample: Strengthening household surveys design and implementation in low- and middle-income countries

Household surveys are a major source of information for development indicators in low- and middle-income countries. They provide critical information on development indicators and characteristics of a population. 

GridSample cover

Current household survey design workflows, based on census data, present some challenges in ensuring that poor, vulnerable or mobile populations are not excluded from the survey.

To improve the accuracy and feasibility of household surveys in complex settings, Flowminder developed GridSample, a user-friendly tool to generate household survey sampling units using gridded population estimates, and improve the inclusion of vulnerable populations in household survey design and implementation.

The tool is free to use. Flowminder provides capacity building and supports implementation of gridded surveys on demand. 

Visit GridSample's website

Download Fact File (PDF)