Proposal for a network on big data for development : proposed network function and structure

Annex VII for IDL-56905The network’s objective is to fund policy relevant research in and on big data for development in the Global South, and develop capacity amongst researchers from the Global South

Big data and SDGs : the state of play in Sri Lanka and India

Annex II for IDL-56905Encompassing the economic, environmental and social dimensions of development, the United Nations 17 Sustainable Development Goals (SDGs) present ambitious targets. Sri Lanka’s Ministry of Sustainable Development and Wildlife is formulating a Sustainable Development Act as well as conducting a gap analysis revealing an indicator gap of around 65% for SDGs. Lack of reliable big data on availability of food staples is a threat to food security and sustainable agriculture in India. Although issues such as these are recognized, technical scholarship fails to address the rights framework within which big data operates. Goal-based reviews are presented for both countries

Workshop report on shaping a research and policy agenda on Big Data for Development in the Global South

Annex VI for IDL-56905LIRNEasia in partnership with the Centre for Internet and Society (CIS) convened a two-day workshop to discuss a ‘research and policy agenda on big data for sustainable development in the Global South.’ The workshop was held on 8th and 9th October, 2016 on the sidelines of the International Open Data Conference 2016 (IODC 2016)

Improving disease outbreak forecasting models for efficient targeting of public health resources

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

Mapping big data solutions for the sustainable development goals : draft

Annex I for IDL-56905This report aims to capture the applications of big data sources to measure sustainable development goals and targets by reviewing relevant literature and reports. It outlines current concerns with uses of big data (privacy, marginalization, competition, etc.) and provides a discussion of the interplay of these issues. Developing economies in particular have much lower levels of ‘datafication’ than developed economies, which means some of the most interesting and relevant data exists amongst the private sector. The state of the art in innovative development-focused applications of new data sources is still very much in its embryonic stages

Annex 17 : deep semantic segmentation for built-up area extraction and mapping from satellite imagery

Research focuses on generating more usable built-up area maps, as traditional methods (such as surveys and census) are infrequent and costly. The work proposes a modified Fully Convolutional Network (FCN) architecture that will improve semantic segmentation operation on satellite imagery for built-up area extraction and urban mapping. This method could bridge the gap between existing extraction techniques and actual land cover/built-up area maps used by practitioners. Applications are potentially to socio-economic classification and urban planning, where building density functions as a proxy measure for socio-economic level, and building distribution for urban area estimates and growth, respectively

Understanding communities using mobile network big data CPRsouth 2015

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

Using mobile network big data for land use classification CPRsouth 2015

The traditional way of generating insights on land use involve surveys and censuses, which are both infrequent as well as costly. This paper explores the potential of leveraging massive amounts of human mobile phone data to understand the spatiotemporal activity of mass populations, and by extension, provide a useful proxy for activity-based classification of land use. Understanding and monitoring land use characteristics is critical for urban planning. The study demonstrates possibilities for use of mobile network big data, and how it can be leveraged to infer three distinct land use characteristics: commercial/ economic, residential, and mixed-use

Annex 19 : predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka

National census information is time-consuming and expensive to collect. This research helps determine whether mobile phone data can provide a reliable, cheap proxy for census data within Sri Lanka, especially where post-conflict regions need more frequent data collection. Study findings suggest that socio-economic levels (SEL) can affect call detail records (CDR) data in a post-conflict, Sri Lankan setting. Analysis demonstrates the potential for telecom data to predict census features. The results correspond to assumptions about the population under study, which includes a high percentage of vulnerable, highly mobile groups displaced due to conflict