1998 Fiscal Year Final Research Report Summary
VISUALIZATION OF POINT DISTRIBOTION IN URBAN AND REGIONAL GIS
| Project/Area Number |
09680417
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | THE UNIVERSITY OF TOKYO |
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Principal Investigator |
SADAHIRO Yukio Center for Spatial Information Scleuce, University of Tokyo, Associate Professor, 空間情報科学研究センター, 助教授 (10240722)
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| Project Period (FY) |
1997 – 1998
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| Keywords | Spatial information / visualization / GIS / urban and regional analysis |
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| Research Abstract |
In this research we first analyzed a model representing spatial clusters perceived in the distribution of point objects. Three spatial factors, that is, proximity, clustering intensity, arid density change, were considered to cause cluster perception. To measure these factors, two spatial notions based on the density of point objects were proposed : local density and relative local density. These densities were defined as the functions of a location in maps, and indicate how points are distributed around the location. Cluster perception was represented as a probabilistic function of the measures derived from the relative local density, and its likelihood was shown for model estimation. An experiment of cluster perception indicates that the model was significantly valid, and some empirical findings related to the heterogeneity of map readers in cluster perception were shown.
We then analyzed the cognition of spatial variance in the distribution of point objects. Spatial variance is one of the fundamental spatial concepts communicated by GIS, and has been widely used in its relevant fields such as geography, biology, and epidemiology. Communication of this concept, however, requires correct understanding of its cognition and proper choice of visualizing method. We hence employed two experiments to analyze the cognition of spatial variance in the distribution of point objects and built a model of the cognition of spatial variance. The obtained model was proved to be valid for representing the cognition of spatial variance.
We finally implemented the above models into a commercial GIS as a Macro-language program. The prediction models worked successfully, and the system turned out to be useful for visualizing point distributions in GIS in a practical manner.
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