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https://doi.org/10.37043/JURA.2019.11.2.6
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Nonparametric correlogram to identify the geographic distance of spatial dependence on land prices
[Zeitschriftenartikel]
Abstract The spatial autocorrelation measurement of land prices uses a covariance
function to describe the spatial dependence and it can be identified as a geographic
distance on the correlogram. The geographic distance of spatial dependence can state that
land prices are interdependent to each other and ... mehr
The spatial autocorrelation measurement of land prices uses a covariance
function to describe the spatial dependence and it can be identified as a geographic
distance on the correlogram. The geographic distance of spatial dependence can state that
land prices are interdependent to each other and scattered in the research area. Therefore,
the purpose of this research is to define the geographic distance of spatial dependence on
land prices using a nonparametric correlogram. A nonparametric approach to covariance
functions using the composition of Bessel and Gaussian-type functions are adopted
because they correspond to the positive definite characteristics. The cubic spline
interpolation is used to refine the curve fitting, while the intersection between the
nonparametric correlogram value C(h) against the horizontal axis is determined using the
Jenkins Traub algorithm. The results showed that the nonparametric correlogram identified
a geographic distance of land prices smaller than the correlogram used so far. A small
distance means that the land price in a location is greatly affected by the neighbors
compared to a larger distance.... weniger
Klassifikation
Raumplanung und Regionalforschung
Freie Schlagwörter
geographical distance; land prices; nonparametric correlogram; spatial dependence
Sprache Dokument
Englisch
Publikationsjahr
2019
Seitenangabe
S. 203-218
Zeitschriftentitel
Journal of Urban and Regional Analysis, 11 (2019) 2
ISSN
2067-4082
Status
Veröffentlichungsversion; begutachtet (peer reviewed)