Mapping neighborhood scale survey responses with uncertainty metrics

This paper presents a methodology of mapping population-centric social, infrastructural, and environmental metrics at neighborhood scale. This methodology extends traditional survey analysis methods to create cartographic products useful in agent-based modeling and geographic information analysis. It utilizes and synthesizes survey microdata, sub-upazila attributes, land use information, and ground truth locations of attributes to create neighborhood scale multi-attribute maps. Monte Carlo methods are employed to combine any number of survey responses to stochastically weight survey cases and to simulate survey cases\u27 locations in a study area. Through such Monte Carlo methods, known errors from each of the input sources can be retained. By keeping individual survey cases as the atomic unit of data representation, this methodology ensures that important covariates are retained and that ecological inference fallacy is eliminated. These techniques are demonstrated with a case study from the Chittagong Division in Bangladesh. The results provide a population-centric understanding of many social, infrastructural, and environmental metrics desired in humanitarian aid and disaster relief planning and operations wherever long term familiarity is lacking. Of critical importance is that the resulting products have easy to use explicit representation of the errors and uncertainties of each of the input sources via the automatically generated summary statistics created at the application\u27s geographic scale

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Mapping neighborhood scale survey responses with uncertainty metrics

This paper presents a methodology of mapping population-centric social, infrastructural, and environmental metrics at neighborhood scale. This methodology extends traditional survey analysis methods to create cartographic products useful in agent-based modeling and geographic information analysis. It utilizes and synthesizes survey microdata, sub-upazila attributes, land use information, and ground truth locations of attributes to create neighborhood scale multi-attribute maps. Monte Carlo methods are employed to combine any number of survey responses to stochastically weight survey cases and to simulate survey cases\u27 locations in a study area. Through such Monte Carlo methods, known errors from each of the input sources can be retained. By keeping individual survey cases as the atomic unit of data representation, this methodology ensures that important covariates are retained and that ecological inference fallacy is eliminated. These techniques are demonstrated with a case study from the Chittagong Division in Bangladesh. The results provide a population-centric understanding of many social, infrastructural, and environmental metrics desired in humanitarian aid and disaster relief planning and operations wherever long term familiarity is lacking. Of critical importance is that the resulting products have easy to use explicit representation of the errors and uncertainties of each of the input sources via the automatically generated summary statistics created at the application\u27s geographic scale