<p><strong>Abstract.</strong> Social vulnerability is an important aspect in determining the level of disaster risk in a region. Social vulnerability index (SoVI) is influenced by several supporting factors, such as age, gender, health, education, etc. When different sets of parameters are considered, the SoVI analyzed results are likely to be also different from one to another. In this paper, we will discuss the quantitative assessments of SoVI based on two different models. The first model, proposed by Frigerio et al. (2016), is used to analyze the spatial diversity of social vulnerability due to seismic hazards in Italy. The second model is based on the regulations of the head of the National Disaster Management Agency (BNPB) No. 2 of 2012. GIS is used to present and compare the results of the two selected models. In additive impact factor on the SoVI is also done. The result is that there are regions that belong to the same class on both models such as Pemalang, there are regions that enter in different classes on both models such as Cilacap. The result also shows the model of Frigerio et al. (2016) is more representative than the BNPB model (2012) by additionally considering the education and unemployment factors in determining the SoVI, while the BNPB model (2012) only includes internal factors such as age, gender. By considering education and unemployment factors, we get more detailed conditions about society from social vulnerability.</p>

Progress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS images that precisely meets user application needs. Some spatial measuring methods specific to the recommendation of RS images have been proposed and could be used to score and sort RS images according to users’ requests. Our previous study introduced two measuring methods, namely, available space (AS) and image extension (IE), which have similar results but complementary effects for spatially ranking recommended images. The AS indicator could cover the inadequacies of the IE indicator in some cases and vice versa. The current study combines these two indicators using principal component analysis and produced a new indicator called INDEX, which we used in the RS image spatial recommendation. The ranking results were measured using a normalized discounted cumulative gain (NDCG) and several other statistic criteria. The results indicate that users are more satisfied with the recommendations of the INDEX indicator than those of AS, IE and Hausdorff distance for single RS image type selections which is the most common scenario for RS image applications. When dealing with hybrid RS image types, the INDEX indicator performs very closely to the dominant IE indicator, yet maintaining the characteristics of the AS indicator.

Successful data sharing is a critical challenge in the development of National Spatial Data Infrastructure, but the heterogeneity of geospatial data often impedes the correct use of distributed geospatial data. For example, although map interfaces have been widely used in GIS software, misunderstandings of the geospatial data illustrated in map interfaces may still produce unpredictable and unnoticeable risks. It is necessary for users to have a thorough understanding about the acquired data before they can make correct decisions. Based on the concept of valid extent and illustrated extent, this article has proposed a new model that incorporates visual aids of data completeness information in the GIS-based map interface by integrating metadata, Geography Markup Language, and cartographic knowledge. Compared to current GIS map interfaces, the introduced open format of distributed data and knowledge-based visual aids enables users to correctly interpret the illustrated results of the map interface in an interoperable and visualized way, such that users are always aware of the status of data completeness regarding the illustrated content of the map interfaces. Despite the fact that the discussions of this article are restricted to the issue of data completeness, the results have clearly indicated that such a visualized mechanism for data quality information should be included as a necessary component in the future OpenGIS distributed environment.

<p><strong>Abstract.</strong> In recent years, the demands of 3D cyber-city have been steadily growing. With strong links to the citizens’ lives, building information is considered as the most important component in the 3D urban model. To further facilitate the best usage of 3D data, the development of 3D SDI requires creative thinking to meet different application needs. While many current applications are restricted to visualization only, we argue the 3D building data in 3D SDI must at least consider the issues of feature modelling, identification, semantics, level of details, cross-domain linking and services. This paper intends to assess the use of the semantic-enriched 3D building data in the applications of disaster management. Based on CityGML, we first create 3D building data based on a hierarchy of building-storey-household representation. Identifier systems are respectively developed for each level of features for the purpose of identifying individual features and linking to other sources of data, e.g., the household registration information. By reviewing and comparing the outcomes of the past research of 3D flood simulation, we demonstrate the improved 3D building data additionally enables the direct impact analysis at the chosen level of features, as well as visually present enriched analyzed outcomes for decision making, e.g., the number of trapped people in specific floor. As the merits of the SDI is to share reliable information, encourage multiple-purpose applications and avoid duplicated spending, we thereby conclude the necessity to further examine the level of details and multiple representation of the serviced 3D building data for cost-effective and cross-domain application development.</p>