Geostatistical Smoothing of Areal Data: Mapping Employment Density with Factorial Kriging. 面状数据的地统计滤波：采用因子克里格方法制作就业密度图

Abstract

This article summarizes area‐to‐point (ATP) factorial kriging that allows the smoothing of aggregate, areal data into a continuous spatial surface. Unlike some other smoothing methods, ATP factorial kriging does not suppose that all of the data within an area are located at a centroid or other arbitrary point. Also, unlike some other smoothing methods, factorial kriging allows the user to utilize an autocovariance function to control the smoothness of the output. This is beneficial because the covariance function is a physically meaningful statement of spatial relationship, which is not the case when other spatial kernel functions are used for smoothing. Given a known covariance function, factorial kriging gives the smooth surface that is best in terms of minimizing the expected mean squared prediction error. I present an application of the factorial kriging methodology for visualizing the structure of employment density in the Denver metropolitan area.

Geostatistical disaggregation of polygon maps of average crop yields by area-to-point kriging

“…The theory of ATP kriging is well established, and its potentials have been shown in many application areas, for instance in soil science (Schirrmann et al, 2012;Brus et al, 2014), spatial socio-economic studies (Nagle, 2010), disease mapping (Lin et al, 2014) and environmental health studies (VoPham et al, 2016). We are not aware of papers explaining how this statistical technique can be used for spatial disaggregation of polygon maps of average crop yields.…”

Implementation of Adaptive Localization for Enhancing Ensemble-Based History Matching in Hydrocarbon Reservoir Management

“…technique is performed with the geomodelling software (RMS) to solve abrupt changes of the tapering maps in the nearest gridblocks where the tapering coefficients change from being equal to the CDF probability (when the CDF probability of being drained is equal or larger than 50%) to zero (when the CDF probability of being drained is less than 50%), (Nagle, 2010;von Harten et al, 2021).…”

“…Non_adapt_80 case: In the same way than the non_adapt_50 case, the non_adapt_80 case applies a cut off to the CDF probability drainage maps computed with Workflow 3 for each producer. The tapering maps in the non_adapt_80 case only include the gridblocks that have a probability of being case uses smoothing technique to solve abrupt changes of the tapering maps in the nearest gridblocks where the tapering coefficients change from being equal to the CDF probability (when the CDF probability of being drained is equal or larger than 80%) to zero (when the CDF probability of being drained is less than 80%), (Nagle, 2010;von Harten et al, 2021).…”

Policentrismo y estructuración del espacio: una revisión crítica desde la perspectiva de los programas de investigación

“…Uno de los inconvenientes de estos índices es su interpretación, puesto que patrones de autocorrelación positivos o negativos podrían ser indicativos tanto de monocentrismo como de policentrismo. Por su parte, el desarrollo de las técnicas de geoestadística en la segunda mitad del siglo XX permite utilizar indicadores globales basados en técnicas como el nearest neighbor index, K-statistics, Rypley K, etc., aunque su aplicación ha sido aún anecdótica (Nagle, 2010).…”