Leveraging Mobile Network Big Data for Developmental Policy
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Lead institution(s)
Summary
Many developing countries lack the capacity and resources to collect and analyze data for evidence-based policy-making. Is big data, which involves large and complex data sets, an opportunity to meet this challenge?Read more
Many developing countries lack the capacity and resources to collect and analyze data for evidence-based policy-making. Is big data, which involves large and complex data sets, an opportunity to meet this challenge? Or will it become difficult for developing countries to adopt for solving problems? This project will explore big data's potential to inform development policies in urban planning, infectious diseases, and socio-economic monitoring in Sri Lanka and Bangladesh.
Big data and research evidence
Some argue that big data and big data users offer advantages to generate evidence. The availability of information and communication technologies (ICTs) can help reduce efforts to collect large data sets. Mobile phone data, which is one form of big data, has greater reach. It can offer new insights into solving social and economic problems.
Transportation, diseases, socio-economic monitoring
The Sri Lankan think tank, Learning Initiatives on Reforms for Network Economies Asia (LIRNEasia), has been exploring the possibility of using big data to inform public policy since 2012. Supported by IDRC, this research focused on transportation planning in urban centres in Colombo to better integrate different parts of cities and suburbs.
Building on this pioneering research, LIRNEasia proposes to expand the scope to other development domains. More specifically, the project will explore the use of big data to inform policies in three domains: urban transportation, infectious diseases, and socio-economic monitoring.
The project provides an opportunity for LIRNEasia to delve further into linking big data from mobile phone operators to important development domains. In the area of urban transportation, mobile user data could have a significant impact on improving transportation systems in two major cities in South Asia: Colombo and Dhaka. The project may allow both cities to become more efficient in dealing with traffic congestion, pollution, and waste management.
Researchers will also explore big data to map the spread of emerging diseases in Sri Lanka. The goal: to improve public health policy.
Finally, they will develop methods for mapping poverty through mobile phone transactions such as airtime reloads. This exploratory work will give researchers a better understanding of the patterns of individual economic activities to inform government policy.
Research outputs
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Recent advancements in computer vision and machine learning techniques have made traffic monitoring systems highly effective in well structured traffic conditions such as highways. But these systems struggle in handling complex and irregular conditions that exist in developing countries, due to lack of infrastructure and regulation. This research breaks down the problem into different sub-tasks such as vehicle detection, vehicle tracking, and vehicle recognition, then combines each process into one pipeline that can be used for traffic monitoring. Implementing the final pipeline involves improving and aggregating existing techniques. Results demonstrate the potential of these techniques for automated traffic monitoring.
Understanding the strength and boundaries of human connections can help identify communities amongst a population, and is valuable knowledge for modeling disease spread, information flow, and mobility patterns. Administrative boundaries, formed by history and geography, do not necessarily reflect the actual communities or social interaction patterns within a region. In this study we employ community detection algorithms to a mobile Call Detail Records (CDR) network in Sri Lanka in order to compare natural communities existing in the interaction network against administrative regions of Sri Lanka. Additionally we explore how these communities segment into a further level of sub-communities.
The forecasting models developed in this work can be utilized to effect better resource mobilisation for combatting dengue. For understanding human mobility in disease propagation, Mobile Network Big Data (MNBD) is a low cost data exhaust that provides rich insight into human mobility patterns, including better spatial and temporal granularity. Research focuses on the development of a human mobility model, using MNBD that can accurately depict aggregate human population movements in Sri Lanka, and from this determine which machine learning technique provides the best disease forecasting model.
The study constructs a usable predictive model for any given Medical Officer of Health (MOH) division, which is the smallest medical administrative district in Sri Lanka, by taking human mobility into account. It includes the importation of dengue into immunologically ’naive’ regions. Derived mobility values for each region of the country are weighted using reported past dengue cases. The study introduces a generalizable methodology to fuse big data sources with traditional data sources, using machine learning techniques. Mobile Network Big Data (MNBD) consists of data categories such as Call Detail Records (CDR), Internet access usage records, and airtime recharge records.
The research addresses how big data can provide evidence to better inform public policy and allow for greater use of evidence in the policy making process. In addition to more detailed research in the area of transportation and urban planning (commuting patterns), this research articulates and answers questions in other domains such as health (modeling the spread of diseases) and official statistics (mapping poverty for instance). Guidelines were translated into legal language so that mobile operators can responsibly share data. Traditional survey methods that provide enough detail to accurately assess conditions are costly and can rarely reach a representative portion of the population, especially in poorer areas.