Browsing by Author "Lokanathan, Sriganesh"

Now showing 1 - 17 of 17
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    Annex 15 : analyzing Facebook data to understand regional connectivity
    (2018-03) Wijeratne, Yudhanjaya; Samarajiva, Rohan; Lokanathan, Sriganesh; Surendra, Aparna; Fernando, Lasantha
    Social network connectivity between nations potentially serves as a way of exploring networks of international communities, human movement and economic activity. This connectivity often appears to transcend governmental and political barriers (such as between India and Pakistan, for example). Initial exploratory analysis reveals that social network data does strongly correlate to trade and migration between countries, but not to the distance between countries. Some prospective challenges are also outlined for this study.
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    Annex 17 : deep semantic segmentation for built-up area extraction and mapping from satellite imagery
    (2018-03) Athuraliya, C. D.; Ramasinghe, Sameera; Lokanathan, Sriganesh
    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.
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    Annex 19 : predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka
    (2018) Surendra, Aparna; Lokanathan, Sriganesh; Fernando, Lasantha; Perera-Gomez, Thavisha
    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.
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    Annex 19 : predictive model for the dengue incidences in Sri Lanka using mobile network big data
    (2018) Dharmawardana, K.G.S.; Lokuge, K. S.; Dassanayake, P. S. B.; Sirisena, M. L.; Fernando, Lasantha; Perera, Amal Shehan; Lokanathan, Sriganesh
    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.
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    Big data and SDGs : the state of play in Sri Lanka and India
    (2017) Perera-Gomez, Thavisha; Lokanathan, Sriganesh
    Encompassing 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.
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    Improving disease outbreak forecasting models for efficient targeting of public health resources
    (2018-03) Fernando, Lasantha; Perera, Amal; Lokanathan, Sriganesh
    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.
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    Mapping big data for development and the global goals : final technical report
    (2017) Lokanathan, Sriganesh
    The opportunity of big data for development (BD4D) applied to sustainable development goals (SDGs) lies in leveraging new/non-traditional data sources and techniques. The popularity of BD4D underscores the importance of multi-disciplinary engagement in order to address developmental issues using data science while being mindful of privacy and legal issues. As such, this research conducts a preliminary assessment of the BD4D landscape with a focus on the global south. The associated mapping exercise documents activities related to big data in terms of actors, organizations, regions, domain trends and research gaps.
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    Mapping big data solutions for the sustainable development goals : draft
    (2017-03) Lokanathan, Sriganesh; Perera-Gomez, Thavisha; Zuhyle, Shazna
    This 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.
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    Preliminary methodology for comparisons of mobile tariffs, version 2.1
    (LIRNEasia, Colombo, LK, 2006) Lokanathan, Sriganesh; Iqbal, Tahani
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    Proposal for a network on big data for development : proposed network function and structure
    (2016) Lokanathan, Sriganesh
    The 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.
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    Research, capacity-building, advocacy and dissemination by LIRNEasia : advancing evidence-based policymaking and regulation in the emerging Asia-Pacific to ensure greater participation in ICTs (Phase II); final technical report
    (LIRNEasia, Colombo, LK, 2010) Samarajiva, Rohan; Galpaya, Helani; Gamage, Sujata; Lokanathan, Sriganesh; Wattegama, Chanuka
    Much of LIRNEasia’s research in this cycle is based on the idea that mobile phones will be the primary device through which the “bottom of the pyramid” (BOP) in emerging markets engages with the Internet, and tasks associated with the Internet such as information retrieval, payments and remote computing. In addition to this research and the CPRsouth programme, the project included capacity building among National regulatory agency (NRA) national statistical organization (NSO) staff and non-governmental, non-private sector actors who can influence policy reform processes. The purpose of this project was advancement of evidence-based policy making and regulation through an integrated program of research, capacity building and advocacy. Detailed project Appendices are included.
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    Systematic mapping of big data for development stakeholders with a focus on the ‘Global South’ : final report
    (2017) Hickok, Elonnai; Sinha, Amber; Rakesh, Vanya; Lokanathan, Sriganesh; Perera-Gomez, Thavisha
    Drawn from a larger database, the final mapping consists of 215 actors worldwide found to be most relevant or potentially relevant to big data and development (BD4D) at this time. The entries are coded based on region, organization type, actor, domain and gender. A broad view of activities related to BD4D are captured along with original sources and search terms. Though most of the domains developed in the research reflect sustainable development goals (SDGs), SDG is included as a separate domain, to include work that looks at uses of big data solely in light of the United Nations 17 SDGs.
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    Understanding communities using mobile network big data CPRsouth 2015
    (LIRNEasia, Colombo, LK, 2015-07) Madhawa, Kaushalya; Lokanathan, Sriganesh; Samarajiva, Rohan; Maldeniya, Danaja
    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.
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    Using mobile network big data for land use classification CPRsouth 2015
    (LIRNEasia, Colombo, LK, 2015-07) Madhawa, Kaushalya; Lokanathan, Sriganesh; Maldeniya, Danaja; Samarajiva, Rohan
    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.
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    Where did you come from? : where did you go?; robust policy relevant evidence from mobile network big data
    (LIRNEasia, Colombo, LK, 2015-03) Maldeniya, Danaja; Kumarage, Amal; Lokanathan, Sriganesh; Kreindler, Gabriel; Madhawa, Kaushalya
    The paper discusses how output from mobility analysis based on mobile network big data (MNBD) can be aligned with the different stages of traditional forecasting frameworks familiar to transport planners and policy makers. Levels of accuracy and detail are estimated, so that mobility insights-based MNBD can be delivered. Recently developed approaches for estimating mobility are compared, and results are validated against data from traditional methods. The limitations of MNBD are presented, and alternatives are proposed to address these limitations in future work. The research aims to extend state of the art data mining to support and transform efficiencies in transportation planning.
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    Workshop report on shaping a research and policy agenda on Big Data for Development in the Global South
    (2016-10) Lokanathan, Sriganesh; Perera-Gomez, Thavisha
    LIRNEasia 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).