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Who Is Left Behind and Where? Spatial Inequalities in Healthcare Access Among Women in Bangladesh
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Temporal Interpolation of Spatially Dynamic Object
GeoInformatica, 2000Summary: During the past decade there has been increasing research into the temporal component of geographic information systems (GIS). Most of the research has focused on the treatment of discrete changes in spatial objects, for example in cadastral parcels.
Wei Zhang, Gary J. Hunter
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Mechanisms of Interpolation in Human Spatial Vision.
American Journal of Ophthalmology, 1982Recently, interest has revived in the classical problems of vernier and stereoscopic acuity. The precision with which observers can align two bars in a vernier task is as high as 2 sec arc, which is much finer than the grain of the retinal receptor mosaic, where the cones are separated by 20–30 sec arc even at their most densely packed.
M J, Morgan, R J, Watt
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2009
Spatial interpolation is a core component of data processing and analysis in geoinformatics. The purpose of this chapter is to discuss the concept and techniques of spatial interpolation. It begins with an overview of the concept and brief history of spatial interpolation.
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Spatial interpolation is a core component of data processing and analysis in geoinformatics. The purpose of this chapter is to discuss the concept and techniques of spatial interpolation. It begins with an overview of the concept and brief history of spatial interpolation.
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Practical Spatial Statisics for Areal Interpolation [PDF]
Environment and Planning B: Planning and Design, 2012 Differences in spatial units among spatial data often complicate analyses. Spatial unit conversion, called areal interpolation, is often applied to address this problem. Of the many proposed areal interpolation methods, few consider spatial autocorrelation, which is the general property of spatial data.
Daisuke Murakami, Morito Tsutsumi
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Spatial Interpolation and Disaggregation of Multipliers
Geographical Analysis, 2004Most non‐survey methods of estimating single‐region multipliers have been shown to produce a systematic upward bias, unless a considerable amount of “superior” data is added. Here it is argued that this conclusion does not need to apply to the case of a non‐survey, spatial disaggregation of multipliers.
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Analysis of interpolated arrays with spatial smoothing
Signal Processing, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Reddy, K Maheswara, Reddy, VU
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