The study of geo-tagged tweet data has been increasing. In such a situation, the authors considered the method of noise reduction for improving the precision in the situation visualization by using geo-tagged tweets. Specifically, it is a study from the combination method of the auto-generation of noise reduction filter by using Natural Language Processing (NLP), and the noise reduction by the multiple people in the same time zone and near distance. In the NLP method, precision level at the about 53% was observed from the test by using the morpheme-3gram. And nominal significant difference at the 0.5% was observed in comparison with non-filter method. In the near distance method, precision level at the about 80% was observed, and nominal significant difference at the 0.5% were observed in comparison with NLP method. In the combination method of NLP and near distance, precision level at the about 84% was observed, and nominal significant difference at the 0.5% was observed in comparison with near distance method. Furthermore, in the verification by type of rainfall, it was revealed that the combination method can extract with higher accuracy than the NLP method or the near distance method from the extraction result with high accuracy exceeding 95% in the rainy situation. As a result, under the conditions in urban areas with many tweets, the results from this study on combination method indicated the certain effect.

This paper is based on a study of 52 general waste incineration in Hokkaido. By considering ― from economical and geographical perspectives ― the degree to which each facility promotes the fiscal efficiency of its surrounding local government, the study recommends that the number of facilities in Hokkaido be reduced to 33 by the year 2025, principally by merging them with larger scale facilities in and around urbanized and populated areas. Factors central to this recommendation include the gradual decline of the Hokkaido population, new construction costs and waste transit costs. The study also recommends ongoing evaluation of remaining facilities post-2025, given that Hokkaido’s population decline is likely to cause a reduction in the amount of waste in the region.

Geomorphological Land Classification Maps of Flood-stricken Areas (GLCM) are thematic maps that estimate flood conditions by reading geomorphic elements. Due to their being paper maps that are not published, it is difficult to get hold and utilize GLCM, although they are very useful in order to know and understand flood-disaster risks in local areas. In light of this, we established an online digital GLCM archive. The characteristics of the archive are: 1) GIS data release, and 2) arranged usage rules for re-use. First, we created raster GIS data from paper maps. Second, we obtained permission for re-use from publishers and the Geospatial Information Authority of Japan (GSI). Third, we arranged usage rules for re-use based upon ID. Fourth, we prepared map image APIs (Application Programming Interface), such as Web Map Service (WMS), XYZ tile and Tile Map Service (TMS). Fifth and finally, we opened the digital archive in March 2016.

In this paper, we reviewed the notes on using Web map image provided by Web map service, from the viewpoint of copyright act. The copyright act aims to contribute to creation of culture by protecting the rights of authors and others, and promoting fair exploitation of cultural products. Therefore, everyone can use copyrighted materials to the extent of the copyright limitation based on copyright act. The Web map image, including maps, aerial photo and satellite image, are one of copyrighted materials, so it can be used within the limits of copyright. However, the available range of Web map image under the copyright act is not wide. In addition, it is pointed out that the copyright act has not been able to follow the progress of digitalization of copyrighted materials. It is expected to revise the copyright act corresponding to digitalization of copyrighted work.