A Geostatistical Framework for Area-to-Point Spatial Interpolation

Kyriakidis, Phaedon C.

doi:10.1353/geo.2004.0009

Cited by 117 publications

(135 citation statements)

References 42 publications

(23 reference statements)

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“…Preliminary results show that such regional model predictions contribute significantly to the elucidation of spatiotemporal patterns of precipitation that cannot be discerned from the sparse network of rain gauge measurements in the region. Scale differences between such model predictions and rain gauge measurements can be addressed using a variant of block (co)kriging (Kyriakidis, 2004).…”

Section: Summary and Discussionmentioning

confidence: 99%

A spatial time series framework for simulating daily precipitation at regional scales

Kyriakidis

Miller

Kim

2004

Journal of Hydrology

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Section: Summary and Discussionmentioning

confidence: 99%

A spatial time series framework for simulating daily precipitation at regional scales

Kyriakidis

Miller

Kim

2004

Journal of Hydrology

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“…Flowerdew and Green (1994) adopted the expectation/ maximization (EM) algorithm (Dempster, Laird, & Rubin, 1977) for areal interpolation, which incorporates ancillary data directly in an optimization process that derives spatially refined population estimates. There also exist examples using geostatistical methods, such as area to point kriging (Kyriakidis, 2004) and co-kriging (e.g. Liu, Kyriakidis, & Goodchild, 2008;Wu & Murray, 2005).…”

Section: Dasymetric Modelingmentioning

confidence: 99%

Exploiting temporal information in parcel data to refine small area population estimates

Zoraghein

Leyk

Ruther

et al. 2016

Computers, Environment and Urban Systems

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Temporal analysis of small-area demographic data commonly relies on areal interpolation methods to create temporally consistent and compatible areal units. In this study, cadastral (parcel) data are used to identify residential land and to dasymetrically refine census tracts, with the goal of achieving more accurate small-area estimates. The built date recorded for residential parcel units is used to create residential land layers for two different time points used in the areal interpolation. Three different areal interpolation methods are employed with and without dasymetric refinement, including areal weighting (AW), target density weighting (TDW) and pycnophylactic modeling (PM). The methods interpolate tract-level population counts in Hennepin County, Minnesota, in 2000 into census tract boundaries from the year 2010. The mean absolute error, median absolute error, root mean square error and the 90th percentile of absolute error are calculated for each of the methods, and spatial variation in the interpolations are displayed in maps. Parcel-based refinements are also compared with refinements using the National Land Cover Dataset (NLCD). Results show that spatial refinement using residential parcels has the potential to improve the accuracy of areal interpolation for temporal analysis. Parcel-refined TDW out-performs the other tested methods, as well as the NLCD-refined TDW in this example. Parcel data identify residential land more reliably in rural areas. However, parcel units can have very large extents potentially biasing residential area delineation and population counts. Parcel-based refinement has the potential to further advance demographic change analysis over long time periods and large areas where the built date attribute is included in the dataset.

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“…Point interpolation and Tobler's pycnophylatic method are examples of this approach. Kyriakidis (2004) has formulated areal interpolation as a geostatistical area-to-point kriging problem and Yoo, Kyriakidis, and Tobler (2010) have shown how geostatistical methods are by their design pycnophylatic. Ensuring the volume-preserving property requires solving the problem with quadratic programming methods, however (Yoo, Kyriakidis, and Tobler 2010).…”

Section: Introductionmentioning

confidence: 99%

A Quantile Regression Approach to Areal Interpolation

Cromley

Hanink

Bentley

2012

Annals of the Association of American Geographers

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Areal interpolation has been developed to provide attribute estimates whenever data compilation or an analysis requires a change in the measurement support. Over time numerous approaches have been proposed to solve the problem of areal interpolation. Quantile regression is used in this study as the basis of the areal interpolator because it provides estimates conditioned on local parameters rather than global ones. An empirical case study is provided using a data set in northern New England. Land cover data, provided by the National Oceanic Atmospheric Administration, derived from remotely sensed images for 2001 captured by the LANDSAT Thematic Mapper at a resolution of 30 × 30 meters, are used for the ancillary variables for the regression model. The utility of quantile regression as an areal interpolation method is evaluated against simple averages, areal weighting, dasymetric interpolation, and ordinary least squares and spatial regression methods. For the empirical data set used in the study, results show that quantile regression was a better interpolator for the given data set but that binary dasymetric interpolation was a close second. These results were only for one data set and further evaluation is necessary before more general conclusions can be made. Key Words: areal interpolation, dasymetric mapping, linear programming, quantile regression.La interpolación areal ha sido desarrollada para proveer estimativos de atribución cuandoquiera que la compilación de datos o un análisis requiera un cambio en el apoyo de la medición. Con el tiempo se han propuesto numerosos enfoques para resolver el problema de la interpolación areal. La regresión de cuantiles se utiliza en este estudio como la base de la interpolación areal porque esta proporciona estimativos más condicionados en parámetros locales que en los globales. Se presenta un estudio empírico de caso en el que se utilizó un conjunto de datos referidos a la parte norte de Nueva Inglaterra. Se usaron datos de cobertura del suelo suministrados por la Administración Nacional Oceánica Atmosférica -que habían sido derivados en 2001 de imágenes de percepción remota captadas por el Mapeador Temático LANDSAT a una resolución de 30 × 30 en metros -para las variables auxiliares del modelo de regresión. La utilidad de la regresión de cuantiles como método de interpolación areal es evaluada frente a promedios simples, peso porárea, interpolación dasimétrica, mínimos cuadrados ordinarios y métodos de regresión espacial. Para el conjunto de datos empíricos usados en el estudio, los resultados muestran que la regresión de cuantiles fue un mejor interpolador para el conjunto de datos dado, aunque la interpolación binaria dasimétrica estuvo muy cerca en el segundo lugar. Estos resultados se refieren a un solo conjunto de datos, por lo que se hace necesaria una evaluación adicional antes de que se puedan formular conclusiones más generales. Palabras clave: interpolación areal, mapeo dasimétrico, programación linear, regresión de cuantiles.

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A Geostatistical Framework for Area-to-Point Spatial Interpolation

Cited by 117 publications

References 42 publications

A spatial time series framework for simulating daily precipitation at regional scales

A spatial time series framework for simulating daily precipitation at regional scales

Exploiting temporal information in parcel data to refine small area population estimates

A Quantile Regression Approach to Areal Interpolation

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