Please use this identifier to cite or link to this item:
|Title:||Conflating survey data into sociocultural indicator maps|
|Authors:||Ehlschlaeger, Charles R.|
Burkhalter, Jeffrey A.
Myers, Natalie R. D.
Baxter, Carey L.
Hiett, Matthew D.
Hartman, Ellen R.
Tweddale, Scott A. (Scott Allen)
Westervelt, James D.
Lozar, Robert C.
Drigo, Marina V.
Brown, David A.
Cities and towns
Geographic information systems
|Publisher:||Construction Engineering Research Laboratory (U.S.)|
Engineer Research and Development Center (U.S.)
|Series/Report no.:||Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/CERL TR-18-32|
|Abstract:||This report 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 used to combine any number of survey responses to stochastically weight survey cases and to simulate survey-case locations in a study area. Through these methods, known errors from each input source can be retained. By keeping the individual survey case 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 using data and output maps for Chittagong Division, 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 for each input source via the automatically generated summary statistics created at the application’s geographic scale.|
|Gov't Doc #:||ERDC/CERL TR-18-32|
|Rights:||Approved for Public Release; Distribution is Unlimited|
|Size:||44 pages / 3.02 Mb|
|Types of Materials:||PDF/A|
|Appears in Collections:||Technical Report|
Files in This Item:
|ERDC-CERL TR-18-32.pdf||3.09 MB||Adobe PDF|