This paper aimed to analyze effects and causative factors for revitalization of local communities which can be affected from regional cooperation policies and public investment. The structural equation model was applied to data based on statistics and preference of residents. The results suggest that the performance of the model estimated was not so high because of low correlation ratios between economic and social factors. However, the model could describe interrelationships of economic and social factors with statistically significant level, showing positive effects of public facilities on rural revitalization level. Rural revitalization was also affected by the residents' activities, and improved livability and satisfaction level as well as preservation of irrigation facilities.

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This paper performs prediction of the benefit area and the number of beneficiary in a familiar recreation site. The benefit area and the number of beneficiary are predicted by the visit rate function. The visit rate function is presumed using the data of visit and the geographic information around a reservoir. The data of visit is the result of asking neighboring residents about experience of visiting a reservoir. The questionnaire vote was distributed by mail.
The probit model is used for the visit rate function. Dependent variable is the existence of visit experience. Independent variables are the shortest route distance from a house to a reservoir, the visit prevention factors, and the maintenance attributes of a reservoir. The visit prevention factors are roughly divided into two. One is the number of the trunk roads crossed in the route to the goal. Another is a loading dose by the vertical drop on a visit course. The loading dose of a vertical drop computes two data, the portion of an uphill, and the portion of a downhill.
Calculation of the benefit area and the number of beneficiary uses the mesh data created around the site. The data of the distance of shortest route from a house to a reservoir and the visit prevention factors are registered into these mesh data. These data are computed by a geographic information system. A visit rate is computed for every mesh using the estimated visit rate function. The benefit area can be determined with a visit rate. The number of beneficiary totals the population of the mesh within the benefit area. The result of analysis is shown that the visit rate function is applicable to prediction of the benefit area and the number of beneficiary.
This paper also examines possibility of transfer of the visit rate function. The transferred visit rate function is presumed with the visit data which pooled two or more sites. The transferred visit rate function is applied to the site which is not used for presumption. Verification of possibility of transfer is performed by comparing the directly estimated beneficiary number and estimated beneficiary number with the transferred visit rate function. It is shown that the transfer of the visit rate function is possible by the result of analysis. However, prediction of a characteristic reservoir needs the improvement of the data and the model which are used for analysis.
Topic of this paper is about oasis project launched in Osaka.

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