Browsing by Author "Maldeniya, Danaja"
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ItemUnderstanding communities using mobile network big data CPRsouth 2015(LIRNEasia, Colombo, LK, 2015-07) Madhawa, Kaushalya; Lokanathan, Sriganesh; Samarajiva, Rohan; Maldeniya, DanajaUnderstanding 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. ItemUsing mobile network big data for land use classification CPRsouth 2015(LIRNEasia, Colombo, LK, 2015-07) Madhawa, Kaushalya; Lokanathan, Sriganesh; Maldeniya, Danaja; Samarajiva, RohanThe 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. ItemWhere 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, KaushalyaThe 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.