The Computability of the Distribution of the Nearest Neighbour Distance on a Network

This paper shows four statistical methods that examine the distribution of points on a network (such as the distribution of retail stores along streets). The first statistical method is an extension of the nearest-neighbor distance method (the Clark-Evans statistic) defined on a plane to the method defined on a network. The second statistical method examines the efect of categorical attribute values of links (say, types of streets) on the distribution of activity points on a network. The third statistical method examines the effect of infrastructural elements (such as railway stations) on the distribution of activity points on a network. The fourth statistical method examines the compound effect of multiple kinds of infrastructural elements (say, railway stations and big parks) on the distribution of activity points on a network. These methods are discussed with empirical examples.

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“…Through the above procedure, we can compute F ( t ) . Explicit formulas and computational methods are given by Yomono (1993).…”

Section: Let God(tls) 9o(tls) Gd(tls) 9si(tls) and Gb(tls)mentioning

confidence: 99%

“…Fortunately, however, the progress in GIS and computational geometry enables this computation in practice. In fact, we carried out empirical studies using the proposed methods with GIS softwares in Nishinomiya and found many interesting facts about retail store locations (Yomono 1993 andKitamura 1994).…”

Section: Tablementioning

confidence: 99%