Dasymetric Modeling and Uncertainty

• Published in: Annals of the Association of American Geographers 2014, Vol.104 (1), p.80-95
• Main Authors: Nagle, Nicholas N., Buttenfield, Barbara P., Leyk, Stefan, Spielman, Seth
• Format: Article
• Language: English
• Subjects: Bgi / Prodig; Census tracts; Censuses; Classification; dasymetric modeling; Entropy; entropía máxima; estimación de área pequeña; Estimation; Geography; Households; Housing units; Human geography; Interpolation; Land cover; maximum entropy; Methodology; Methods, Models, and GIS; modelación dasimétrica; population; Population estimates; small area estimation; Spatial analysis; Spatial models; Spatial resolution; Statistical variance; Survey data; Uncertainty; Urban geography; Urban population and society; 分区密度模型; 小区域估算; 最大熵
• ISSN: 0004-5608; 2469-4452; 1467-8306; 2469-4460
• DOI: 10.1080/00045608.2013.843439

Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology-the penalized maximum entropy dasymetric model (P-MEDM)-is presented that enables these sources of uncertainty to be represented and modeled. The P-MEDM propagates uncertainty through the model and yields fine-resolution population estimates with associated measures of uncertainty. This methodology contains a number of other benefits of theoretical and practical interest. In dasymetric modeling, researchers often struggle with identifying a relationship between population and ancillary data layers. The P-MEDM model simplifies this step by unifying how ancillary data are included. The P-MEDM also allows a rich array of data to be included, with disparate spatial resolutions, attribute resolutions, and uncertainties. Although the P-MEDM does not necessarily produce more precise estimates than do existing approaches, it does help to unify how data enter the dasymetric model, it increases the types of data that can be used, and it allows geographers to characterize the quality of their dasymetric estimates. We present an application of the P-MEDM that includes household-level survey data combined with higher spatial resolution data such as from census tracts, block groups, and land cover classifications.