Using time-serial geographic information such as olden topographic maps and olden aerial photos, the authors try to detect land cover and vegetation change or transition on Tama Hill area, Tokyo. The authors produce land cover data on Early Meiji Period using Jinsoku-sokuzu published by the Sanbou-honbu (the Japanese Olden Army), on just after World War Two using aerial photos taken by the US Army, and on the end of Showa Period using Vegetation Maps published by the Ministry of Environment. There are big differences of positioning accuracy and classification category on each geographic information. Vegetation Maps have low positioning accuracy compared with other geographic information. Jinsoku-sokuzu has low positioning accuracy in valley area. However, it is possible to detect not only development but also reforestation or succession. The authors can estimate that this method is useful to detect land cover or vegetation change over 90% area of study area, and letter information about vegetation on Jinsoku-sokuzu has important information of excellent vegetation condition on early Meiji period.

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This paper shows the projection of population by age and sex with 500m mesh. We modify the cohort-change rate method in the following four points; interpolating secret data, calculating cohort-change rate using the data in the neighborhood of each mesh, revising the formula for computation of cohort-change rate and adjusting subtotal value. We projected 500m mesh-based population for 2005 using the Japanese 500m mesh-based population census data in 1995 and 2000 and examined accuracy of the projected data by comparing it with the observed population in 2005. As a result, the coefficient of correlation between the observed value and the estimated value is over 0.99. This shows that the result is capable of projecting population at an acceptable level.

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Although the significant progress in Free and Open Source Software for Geospatial (FOSS4G) development, the application of FOSS4G has not so progressed because of its lack of usability. To improve usability of FOSS4G, we developed a FOSS4G based Web-GIS package called Open Cafe System (OCS) . Users make use of OCS with web-browsers, without struggling with FOSS4G, because FOSS4G works on back-end in OCS. OCS has four types of architecture, namely, Ubiquitous, Hub, Local, and Separate that are classified based on the network infrastructure. Each type of OCS has a compensatory function for the lack of networks to use OCS without network in the natural environmental fields where networks are often unavailable. From application studies for various fields of natural environment, it was suggested that the functions of network compensation enable the users to make use of OCS without consciousness of network infrastructure.

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In traditional spatial statistics, the spatial dependence between two arbitrary sites on a plane is expressed in terms of the Euclidean distance between the two sites. However, in actual cities or regions, infrastructure networks such as railway networks cause spatial interaction and thus strongly affect the spatial dependence. We propose a new spatial prediction method, based on a primal spatial statistical method called Kriging, that explicitly considers such dependence. We first divide the entire plane into Voronoi regions and configure a network node in each region to create a hypothetical network. Then, in order to investigate the spatial interaction enforced by the network, we estimate the shortest-path distance by considering a transportation network and present the covariance function of that distance. Next, we combine the covariance functions based on both the shortest-path distance and the Euclidean distance. Finally, we assess the effectiveness of the proposed method by applying it to a case study of land price prediction.

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Population decrease is one of the most critical issues in urban planning in developed countries. This requires us the closure, integration and size reduction of public facilities for economic efficiency. This paper proposes a new decision support method of facility relocation in population decrease. A focus is on providing a knowledge basis for participants of collaborative planning to understand the properties of facility location problem that they are facing. Specifically, the relation- ship between the flexibility of facility location and regulative factors is analyzed. The properties of facility location problem are described by quantitative measures and visualized by tables, figures and maps. The method is applied to the school reduction planning in Inage Ward, Japan. It reveals the properties of the method as well as provides empirical findings.

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