<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sritart, Hiranya</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kanbara, Sakiko</style></author><author><style face="normal" font="default" size="100%">Miyagawa, Shoko</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Geographic Information System (GIS) and Data Visualization</style></title><secondary-title><style face="normal" font="default" size="100%">Disaster Nursing, Primary Health Care and Communication in Uncertainty</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-030-98297-3_26</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">297–307</style></pages><isbn><style face="normal" font="default" size="100%">978-3-030-98297-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This chapter is to understand the functionality of GIS focusing on disaster nursing operations, comprising (1) data acquisition, (2) data analysis, (3) data visualization, and (4) data management and sharing. The reader will understand the conceptual basics of GIS, which is useful for designing GIS-based information management systems and applications. By reading and understanding this chapter, the readers will start GIS applications in finding their projects and activities.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peungnumsai, Apantri</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Witayangkurn, Apichon</style></author><author><style face="normal" font="default" size="100%">Kim, Sohee Minsun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Grid-Based Spatial Analysis for Detecting Supply–Demand Gaps of Public Transports: A Case Study of the Bangkok Metropolitan Region</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainability</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2071-1050/12/24/10382</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">10382</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Public transport service has been promoted to reduce the problems of traffic congestion and environmental impacts due to car dependency. Several public transportation modes are available in Bangkok Metropolitan Region (BMR) such as buses, heavy rails, vans, boats, taxis, and trains while in some areas have fewer modes of public transport available. The disparity of public transport service negatively impacts social equity. This study aims to identify the gaps between public transport supply and demand and to demonstrate introduced indicators to assess the public transport performance incorporating transport capacity and equilibrium access aspects. Supply index was used to evaluate the level of service, and the demand index was applied to estimate travel needs. Furthermore, the Lorenz curves and the Gini coefficients were used to measure the equity of public transport. The results highlight that more than half of the BMR population is living in low-supply high-demand areas for public transportation. Moreover, the equitable access analysis has identified that the high-income population has better access to public transport than the low-income population. The results suggest that public transport gaps and equity indicate the inclusiveness of public transportation, as well as to the areas where to improve the public transport service. Thus, the methodology used in this study can be applied to another city or region similar to BMR.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Shao, Xiaowei</style></author><author><style face="normal" font="default" size="100%">Iwao, Koki</style></author><author><style face="normal" font="default" size="100%">Shibasaki, Ryosuke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global Urban Area Mapping in High Resolution using ASTER Satellite Images</style></title><secondary-title><style face="normal" font="default" size="100%">International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ASTER</style></keyword><keyword><style  face="normal" font="default" size="100%">land cover classification</style></keyword><keyword><style  face="normal" font="default" size="100%">urban area mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pub-location><style face="normal" font="default" size="100%">Kyoto</style></pub-location><volume><style face="normal" font="default" size="100%">XXXVIII</style></volume><pages><style face="normal" font="default" size="100%">847–852</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present development of automated algorithm for mapping global urban area in high resolution using ASTER satellite images and coarse-resolution urban area maps. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it against 340 scenes of ASTER satellite images. LLGC trimmed up 500-m-resolution clusters of urban area into 15-m-resoluton clusters. However accuracy assessment on LLGC result showed 75% user’s accuracy, 41% producer’s accuracy, 94% overall accuracy and 0.50 kappa coefficient, indicating LLGC had considerable misclassifications due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC result with existing urban area maps. Accuracy assessment on result of the integration showed 74% user’s accuracy, 43% producer’s accuracy, 94% overall accuracy and 0.51 kappa coefficient, indicating that the results were more accurate than LLGC result and existing urban area maps. We concluded our method would improve global urban area map not only in terms of spatial resolution, but also in that of accuracy.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Shao, Xiaowei</style></author><author><style face="normal" font="default" size="100%">Iwao, Koki</style></author><author><style face="normal" font="default" size="100%">Shibasaki, Ryosuke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global Urban Area Mapping using Global ASTER Satellite Images</style></title><secondary-title><style face="normal" font="default" size="100%">31th Asian Conference on Remote Sensing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ASTER</style></keyword><keyword><style  face="normal" font="default" size="100%">cover classification</style></keyword><keyword><style  face="normal" font="default" size="100%">high resolution</style></keyword><keyword><style  face="normal" font="default" size="100%">land</style></keyword><keyword><style  face="normal" font="default" size="100%">LLGC</style></keyword><keyword><style  face="normal" font="default" size="100%">logistic regression</style></keyword><keyword><style  face="normal" font="default" size="100%">urban area mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pub-location><style face="normal" font="default" size="100%">Hanoi</style></pub-location><pages><style face="normal" font="default" size="100%">TS07–2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present development of global urban area map using ASTER satellite images, which has much higher resolution than that of existing global urban area maps. To complete the map for world’s cities, we developed automated algorithm for mapping urban area in high resolution. The algorithm consists of two steps: classifying pixels of ASTER satellite images into urban or non-urban by Learning with Global and Local Consistency (LLGC) technique; and integration with existing urban area maps using logistic regression. We implemented the algorithm and demonstrated it on 775 scenes of ASTER satellite images. LLGC classified pixels of ASTER satellite images into urban or non-urban in 15-m resolution, though it had considerable amount of misclassification due to similarity in surface reflectance among non-vegetative land cover. To complement the misclassifications, we integrated LLGC results with existing urban area maps using logistic regression. The misclassifications were corrected well, especially in dry zone. We also developed ground truth database using global gazetteer of world’s cities so that we conduct comprehensive accuracy assessment on the urban area map. We visually interpreted land cover of urban or non-urban on 3734 points coordinates derived from global gazetteer, and combined them with 4211 data points of Degree Confluence Project into a database, which had 2185 data points of urban and 5559 of non-urban. Accuracy assessment using the database indicates that our map is more accurate than existing urban area maps. Finally, we applied the method on broad coverage of ASTER satellite images rather than single one scene. The result showed quite well classification as a whole, indicating possibility of developing global urban area map in high-resolution; however considerable problems due to availability of cloud-free satellite image is still remained.</style></abstract></record></records></xml>