The Clearinghouse Concept - A Model for Geospatial Data Centralization and Dissemination in a Disaster

The disaster clearinghouse concept originates with the earthquake community as an effort to coordinate research and data collection activities. Though prior earthquake clearinghouses are small in comparison to what was needed in response to Hurricane Katrina, these seminal structures are germane to the establishment of our current model. On 3 September 2005, five days after Katrina wrought cataclysmic destruction along the Gulf Coast, FEMA and Louisiana State University personnel met to establish the LSU GIS Clearinghouse Cooperative (LGCC),a resource for centralization and dissemination of geospatial information related to Hurricane Katrina. Since its inception, the LGCC has developed into a working model for organization, dissemination, archiving and research regarding geospatial information in a disaster. This article outlines the formation of the LGCC, issues of data organization, and methods of data dissemination and archiving with an eye towards implementing the clearinghouse model as a standard resource for addressing geospatial data needs in disaster research and management

Spatial Search Strategies for Open Government Data: A Systematic Comparison

The increasing availability of open government datasets on the Web calls for ways to enable their efficient access and searching. There is however an overall lack of understanding regarding spatial search strategies which would perform best in this context. To address this gap, this work has assessed the impact of different spatial search strategies on performance and user relevance judgment. We harvested machine-readable spatial datasets and their metadata from three English-based open government data portals, performed metadata enhancement, developed a prototype and performed both a theoretical and user-based evaluation. The results highlight that (i) switching between area of overlap and Hausdorff distance for spatial similarity computation does not have any substantial impact on performance; and (ii) the use of Hausdorff distance induces slightly better user relevance ratings than the use of area of overlap. The data collected and the insights gleaned may serve as a baseline against which future work can compare.Comment: Paper accepted to GIR'19: 13th Workshop on Geographic Information Retrieval (Lyon, France

The role of geographic knowledge in sub-city level geolocation algorithms

Geolocation of microblog messages has been largely investigated in the lit- erature. Many solutions have been proposed that achieve good results at the city-level. Existing approaches are mainly data-driven (i.e., they rely on a training phase). However, the development of algorithms for geolocation at sub-city level is still an open problem also due to the absence of good training datasets. In this thesis, we investigate the role that external geographic know- ledge can play in geolocation approaches. We show how di)erent geographical data sources can be combined with a semantic layer to achieve reasonably accurate sub-city level geolocation. Moreover, we propose a knowledge-based method, called Sherloc, to accurately geolocate messages at sub-city level, by exploiting the presence in the message of toponyms possibly referring to the speci*c places in the target geographical area. Sherloc exploits the semantics associated with toponyms contained in gazetteers and embeds them into a metric space that captures the semantic distance among them. This allows toponyms to be represented as points and indexed by a spatial access method, allowing us to identify the semantically closest terms to a microblog message, that also form a cluster with respect to their spatial locations. In contrast to state-of-the-art methods, Sherloc requires no prior training, it is not limited to geolocating on a *xed spatial grid and it experimentally demonstrated its ability to infer the location at sub-city level with higher accuracy

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

Making Sense of Document Collections with Map-Based Visualizations

As map-based visualizations of documents become more ubiquitous, there is a greater need for them to support intellectual and creative high-level cognitive activities with collections of non-cartographic materials -- documents. This dissertation concerns the conceptualization of map-based visualizations as tools for sensemaking and collection understanding. As such, map-based visualizations would help people use georeferenced documents to develop understanding, gain insight, discover knowledge, and construct meaning. This dissertation explores the role of graphical representations (such as maps, Kohonen maps, pie charts, and other) and interactions with them for developing map-based visualizations capable of facilitating sensemaking activities such as collection understanding. While graphical representations make document collections more perceptually and cognitively accessible, interactions allow users to adapt representations to users’ contextual needs. By interacting with representations of documents or collections and being able to construct representations of their own, people are better able to make sense of information, comprehend complex structures, and integrate new information into their existing mental models. In sum, representations and interactions may reduce cognitive load and consequently expedite the overall time necessary for completion of sensemaking activities, which typically take much time to accomplish. The dissertation proceeds in three phases. The first phase develops a conceptual framework for translating ontological properties of collections to representations and for supporting visual tasks by means of graphical representations. The second phase concerns the cognitive benefits of interaction. It conceptualizes how interactions can help people during complex sensemaking activities. Although the interactions are explained on the example of a prototype built with Google Maps, they are independent iv of Google Maps and can be applicable to various other technologies. The third phase evaluates the utility, analytical capabilities and usability of the additional representations when users interact with a visualization prototype – VIsual COLlection EXplorer. The findings suggest that additional representations can enhance understanding of map-based visualizations of library collections: specifically, they can allow users to see trends, gaps, and patterns in ontological properties of collections

Search improvement within the geospatial web in the context of spatial data infrastructures

El trabajo desarrollado en esta tesis doctoral demuestra que es posible mejorar la búsqueda en el contexto de las Infraestructuras de Datos Espaciales mediante la aplicación de técnicas y buenas prácticas de otras comunidades científicas, especialmente de las comunidades de la Web y de la Web Semántica (por ejemplo, Linked Data). El uso de las descripciones semánticas y las aproximaciones basadas en el contenido publicado por la comunidad geoespacial pueden ayudar en la búsqueda de información sobre los fenómenos geográficos, y en la búsqueda de recursos geoespaciales en general. El trabajo comienza con un análisis de una aproximación para mejorar la búsqueda de las entidades geoespaciales desde la perspectiva de geocodificación tradicional. La arquitectura de geocodificación compuesta propuesta en este trabajo asegura una mejora de los resultados de geocodificación gracias a la utilización de diferentes proveedores de información geográfica. En este enfoque, el uso de patrones estructurales de diseño y ontologías en esta aproximación permite una arquitectura avanzada en términos de extensibilidad, flexibilidad y adaptabilidad. Además, una arquitectura basada en la selección de servicio de geocodificación permite el desarrollo de una metodología de la georreferenciación de diversos tipos de información geográfica (por ejemplo, direcciones o puntos de interés). A continuación, se presentan dos aplicaciones representativas que requieren una caracterización semántica adicional de los recursos geoespaciales. El enfoque propuesto en este trabajo utiliza contenidos basados en heurísticas para el muestreo de un conjunto de recursos geopesaciales. La primera parte se dedica a la idea de la abstracción de un fenómeno geográfico de su definición espacial. La investigación muestra que las buenas prácticas de la Web Semántica se puede reutilizar en el ámbito de una Infraestructura de Datos Espaciales para describir los servicios geoespaciales estandarizados por Open Geospatial Consortium por medio de geoidentificadores (es decir, por medio de las entidades de una ontología geográfica). La segunda parte de este capítulo desglosa la aquitectura y componentes de un servicio de geoprocesamiento para la identificación automática de ortoimágenes ofrecidas a través de un servicio estándar de publicación de mapas (es decir, los servicios que siguen la especificación OGC Web Map Service). Como resultado de este trabajo se ha propuesto un método para la identificación de los mapas ofrecidos por un Web Map Service que son ortoimágenes. A continuación, el trabajo se dedica al análisis de cuestiones relacionadas con la creación de los metadatos de recursos de la Web en el contexto del dominio geográfico. Este trabajo propone una arquitectura para la generación automática de conocimiento geográfico de los recursos Web. Ha sido necesario desarrollar un método para la estimación de la cobertura geográfica de las páginas Web. Las heurísticas propuestas están basadas en el contenido publicado por os proveedores de información geográfica. El prototipo desarrollado es capaz de generar metadatos. El modelo generado contiene el conjunto mínimo recomendado de elementos requeridos por un catálogo que sigue especificación OGC Catalogue Service for the Web, el estandar recomendado por deiferentes Infraestructuras de Datos Espaciales (por ejemplo, the Infrastructure for Spatial Information in the European Community (INSPIRE)). Además, este estudio determina algunas características de la Web Geoespacial actual. En primer lugar, ofrece algunas características del mercado de los proveedores de los recursos Web de la información geográfica. Este estudio revela algunas prácticas de la comunidad geoespacial en la producción de metadatos de las páginas Web, en particular, la falta de metadatos geográficos. Todo lo anterior es la base del estudio de la cuestión del apoyo a los usuarios no expertos en la búsqueda de recursos de la Web Geoespacial. El motor de búsqueda dedicado a la Web Geoespacial propuesto en este trabajo es capaz de usar como base un motor de búsqueda existente. Por otro lado, da soporte a la búsqueda exploratoria de los recursos geoespaciales descubiertos en la Web. El experimento sobre la precisión y la recuperación ha demostrado que el prototipo desarrollado en este trabajo es al menos tan bueno como el motor de búsqueda remoto. Un estudio dedicado a la utilidad del sistema indica que incluso los no expertos pueden realizar una tarea de búsqueda con resultados satisfactorios