The Linkage of Data Describing Overlapping Geographical Units

Markoff, John; Shapiro, Gilbert

doi:10.1080/00182494.1973.10112670

Cited by 64 publications

(27 citation statements)

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“…The units for which data exist are often called source units, and those for which data are desired are called target units (Markoff and Shapiro 1973). The process of superimposing source and target units has become known in the geographic literature as the polygon overlay problem.…”

Section: Map Overlay Operations and The Change Of Support Problemmentioning

confidence: 99%

“…Two primary forms of areal interpolation have been used: polygon overlay methods and smoothing methods. In polygon overlay methods, the weights are equal to the proportion of the target unit comprised by each source unit (Markoff and Shapiro 1973;Goodchild and Lam 1980). This is often referred to as proportional allocation or areal weighting.…”

Section: Pixel Aggregation and Areal Weightingmentioning

confidence: 99%

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Combining Incompatible Spatial Data

Gotway¹,

Young

2002

Journal of the American Statistical Association

614

434

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Global positioning systems (GPSs) and geographical information systems (G ISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates fast and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-ofthe-art statistical solutions to this complex and important problem.

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Section: Map Overlay Operations and The Change Of Support Problemmentioning

confidence: 99%

Section: Pixel Aggregation and Areal Weightingmentioning

confidence: 99%

Combining Incompatible Spatial Data

Gotway¹,

Young

2002

Journal of the American Statistical Association

614

434

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“…Thus the analyst has to estimate the population within the areas from the data aggregated across census tracts. This type of data estimation, that is, data transfer from the source zones to the target zone, is called areal interpolation (Lam 1983, Goodchild et al 1993, and there have been proposed a lot of areal interpolation methods in the literature (Wright 1936, Markoff and Shapiro 1973, Tobler 1979, Goodchild and Lam 1980, Lam 1983, Flowerdew 1988, Flowerdew and Green 1991, Goodchild et al 1993, Fisher and Langford 1995, Burrough and McDonnell 1998.…”

Section: Introductionmentioning

confidence: 99%

“…We focus on the areal weighting interpolation method (Markoff and Shapiro 1973) because it has been widely used in GIS. Other areal interpolation methods are left for future research.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of count data transferred through the areal weighting interpolation method

Sadahiro¹

2000

International Journal of Geographical Information Science

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This paper analyzes the accuracy of count data transferred from a zonal system to an incompatible zone through the areal weighting interpolation method. To treat a variety of situations in a theoretical framework, stochastic models representing areal weighting interpolation are developed. The relationship between the accuracy of estimates and the size of source zones is analytically investigated by use of a proposed model. The results strongly suggested that smaller zones give better estimates on a wide range of zonal systems. The effect of the shape of source zones and the target zone on estimation accuracy is also numerically examined, and it was found to be significant as well as the effect of the size of source zones.2

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“…21. The original county-level data were allocated to the new fractioned counties using the areal weighting procedure first described by Markoff and Shapiro (1973). Although this procedure is known to be less accurate than more sophisticated alternatives, it is adequate for the purposes of this paper.…”

Section: The Modifiable Areal Unit Problemmentioning

confidence: 99%

Some Provisional Techniques for Quantifying the Degree of Field Effect in Social Data

Martin

Slez

Borkenhagen

2016

Socius: Sociological Research for a Dynamic World

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The authors present some relatively simple measures for the degree of organization of a social field, illustrating them with the intuitively accessible cases of fields that are organized by geographic space, and demonstrate that the measures are able to indicate when fields become more organized and that they can be applied to nongeographic data. Finally, the general approach highlights the danger of ignoring complexities of the functional form of spatial interdependence for social data.

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The Linkage of Data Describing Overlapping Geographical Units

Cited by 64 publications

References 0 publications

Combining Incompatible Spatial Data

Combining Incompatible Spatial Data

Accuracy of count data transferred through the areal weighting interpolation method

Some Provisional Techniques for Quantifying the Degree of Field Effect in Social Data

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