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Dasymetric Mapping and Areal Interpolation


Research conducted by Jeremy Mennis and Torrin Hultgren


A dasymetric map seeks to display statistical surface data by exhaustively partitioning space into zones where the zone boundaries reflect the underlying statistical surface variation.  The process of dasymetric mapping is the transformation of data from a set of arbitrary source zones to a dasymetric map via the overlay of the source zones with an ancillary data set.  In practice, dasymetric mapping is often considered a particular type of areal interpolation technique where source zone data are excluded from certain classes in a categorical ancillary data set.  Dasymetric mapping is applicable to a wide variety of tasks where the user seeks to refine spatially aggregated data, for example in estimating local population characteristics in areas where only coarser, regional resolution census data are available.


This research addresses the design, implementation, validation, and application of a new ‘intelligent’ dasymetric mapping  (IDM) technique that supports a variety of methods for characterizing the relationship between the ancillary data and underlying statistical surface.  The technique is referred to as intelligent because an analyst may establish this relationship subjectively using their own domain knowledge, extract this relationship from the data using a novel empirical sampling technique, or combine the subjective and empirically-based methods. 



Details of the dasymetric mapping script


Case Study: Mapping Population in Delaware County, Pennsylvania




Download the IDM Script (Python 2.1 for ArcGIS 9.1/9.2)