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Hiroyuki Usui Akihiro Teraki Kei-ichi Okunuki Toshiaki Satoh 《International journal of geographical information science》2020,34(11):2177-2203
ABSTRACT The aim of this article is to describe a convenient but robust method for defining neighbourhood relations among buildings based on ordinary Delaunay diagrams (ODDs) and area Delaunay diagrams (ADDs). ODDs and ADDs are defined as a set of edges connecting the generators of adjacent ordinary Voronoi cells (points representing centroids of building polygons) and a set of edges connecting two centroids of building polygons, which are the generators of adjacent area Voronoi cells, respectively. Although ADDs are more robust than ODDs, computation time of ODDs is shorter than that of ADDs (the order of their computation time complexity is O(nlogn)). If ODDs can approximate ADDs with a certain degree of accuracy, the former can be used as an alternative. Therefore, we computed the ratio of the number of ADD edges to that of ODD edges overlapping ADDs at building and regional scales. The results indicate that: (1) for approximately 60% of all buildings, ODDs can exactly overlap ADDs with extra ODD edges; (2) at a regional scale, ODDs can overlap approximately 90% of ADDs with 10% extra ODD edges; and (3) focusing on judging errors, although ADDs are more accurate than ODDs, the difference is only approximately 1%. 相似文献
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A Computational Method for Estimating the Demand of Retail Stores on a Street Network and its Implementation in GIS 总被引：9，自引：0，他引：9
This paper describes a computational method for estimating the demand of retail stores on a street network using GIS. First, the 'network Huff model' is formulated on a network with the shortest-path distance as an extension of the ordinary Huff model (which assumes a continuous plane with Euclidean distance). Second, using this model, a formula for estimating the demand is derived. This estimation formula is similar to that with the ordinary Huff model, but it has an advantage in that the formula exactly computes the demand on a network. Third, a practical method for computing the formula is developed. Finally, a method of implementing this computational method in a GIS environment is shown. 相似文献
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Kei-ichi Okunuki 《GeoJournal》2000,52(3):181-188
This paper discusses how GIS (Geographic Information System) can contribute to the research field of urban analysis. The purpose
of urban analysis is to explain the processes of spatial distributions in urban areas. For this purpose, urban analysts often
need to manipulate large amounts of spatial data. This is the reason why urban analysis cannot advance without GIS. Analysts
can visualize urban affairs using GIS and find the processes of spatial distributions. For example, in this paper, land use
distributions in Nagoya City, Japan are visually analyzed with ArcView GIS. This analysis demonstrates that GIS can contribute
to urban analysis. After this analysis, future work is discussed based on the reviews of recent studies on urban analysis
using GIS.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
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The SANET Toolbox: New Methods for Network Spatial Analysis 总被引：3，自引：0，他引：3
This paper describes new methods, called network spatial methods, for analyzing spatial phenomena that occur on a network or alongside a network (referred to as network spatial phenomena). First, the paper reviews network spatial phenomena discussed in the related literature. Second, the paper shows the uniform network transformation, which is used in the study of non‐uniform distributions on a network, such as the densities of traffic and population. Third, the paper outlines a class of network spatial methods, including nearest neighbor distance methods, K‐function methods, cell count methods, clumping methods, the Voronoi diagrams and spatial interpolation methods. Fourth, the paper shows three commonly used computational methods to facilitate network spatial analysis. Fifth, the paper describes the functions of a GIS‐based software package, called SANET, that perform network spatial methods. Sixth, the paper compares network spatial methods with the corresponding planar spatial methods by applying both methods to the same data set. This comparison clearly demonstrates how different conclusions can result. The conclusion summarizes the major findings. 相似文献
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