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    Combining water point data and machine learning to predict the potential spatial distribution of specific type or group of drinking-water source/service

    by Weiyu Yu 1506318350000

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      Description

      Although SDG monitoring on drinking-water progress is often based on national level indicators, sub-nationally and geospatially disaggregated indicators may become increasingly important as they could effectively reveal inequalities in services between different geographic locations and population groups. As more recent concerns are raised about issues such as water quality, the functionality of facility, and continuity of service, it may also become increasingly important to disaggregate data by specific type of water service. Currently, disadvantaged drinking water services are not often reported at high levels of geospatial disaggregation. Fortunately, as more geospatial data sources become available with the transition from the MDGs to the SDGs, predicting the potential spatial distribution of specific type(s) of drinking-water source using machine learning method becomes possible. DHS modelled surfaces as one of such novel data could potentially be the important sources of predictive covariates for modelling the potential distribution of specific type(s) of drinking-water service.

      Co-authors to your solution

      Jim Wright, Nicola Wardrop

      Link to source code or original files

      https://geoterry.github.io/GEOWAT-SDGinsights/case1_maxent_code.R

      Please enter a link to your solution (working demo)

      https://geoterry.github.io/GEOWAT-SDGinsights/

      Submission status

      Link to Solution / Working Demo

      water,wash,sdg,mapping,maxent
      Total Reviews: 3
      Expert Review
      Reviewed by: All Users
      Score: 95.99999
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      Alignment with Challenge ObjectivesThe idea is aligned with the strategic direction of the challenge. 1 = Strongly Disagree ; 5 = Strongly Agree

      Score: 100.0 Weight: 100

      Sound data analysis & accuracy of results The idea provides sound data analysis & accuracy of results. 1 = Strongly Disagree ; 5 = Strongly Agree

      Score: 93.33333 Weight: 100

      OriginalityThe idea adds originality that the market will value and/or is clearly an improvement over our/the current approach. 1 = Strongly Disagree ; 5 = Strongly Agree

      Score: 93.33333 Weight: 100

      Clarity of InvestmentThe idea potentially outweighs the cost/risk of developing it further. 1 = Strongly Disagree ; 5 = Strongly Agree

      Score: 100.0 Weight: 100

      User FriendlinessThe idea is logical, useful, systematic, understandable, "do-able," not overly difficult or complex for the intended benefits. 1 = Strongly Disagree ; 5 = Strongly Agree

      Score: 93.33333 Weight: 100

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      Task Assigned to Due Date Status
      Spigit Prototype
      Expert Review Marcelo LaFleur 12/16/2017 Completed
      on 12/13/2017
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