Web-based public participation GIS application : a case study on flood emergency management

Scientific summary The increasing prevalence of natural disasters is driving people to pay more and more attention to emergency management. Progress in catastrophe analysis capabilities based on Geographical Information System (GIS) may allow the needs of public participation to be considered. Synchronous data sharing between citizens and emergency workers could effectively promote the process of decision making. This thesis introduces an interactive web-based application which mainly deals with flood risk management in Kamloops in Canada. The application is built for citizens and emergency workers using three layers: (1) the client side is developed in HTML and JavaScript; (2) the web server layer, which connects the users and the database, is implemented in PHP; and (3) the database contains PostgreSQL, GeoServer and OSM. Except the city map, PostgreSQL stores the spatial information with the support of OpenGIS. Generally, the application meets the initial objectives. Citizens can access present shelter information and register their own requirements for shelter, while emergency workers have the power to manage all the shelters and warehouses based on the available flood information and figure out the supply allocation solution based on the response from the public. On the other hand, the application also provides useful routing functions for both citizens and emergency workers, such as searching the available shortest path to a shelter, and computing the optimized allocation routes between all the shelters and warehouses. This practical study proved that Public Participation GIS (PPGIS), combined with IT knowledge, can provide very useful tools for decision making when facing a flood risk.Popularized summary Nowadays, the growing prevalence of natural disasters is driving people to pay more and more attention to emergency management. Progress in catastrophe analysis capabilities based on Geographical Information System (GIS) may allow the needs of public participation to be considered. Synchronous data sharing between citizens and emergency workers could effectively promote the process of decision making. This thesis introduces an interactive web-based application which mainly deals with flood risk management in Kamloops in Canada. The application contains various data sources and adopts spatial database. Citizens can access present shelter information and register their own requirements for shelter, while emergency workers have the power to manage all the shelters and warehouses based on the available flood information and figure out the supply allocation solution based on the response from the public. On the other hand, the application also provides useful routing functions for both citizens and emergency workers, such as searching the available shortest path to a shelter, and computing the optimized allocation routes between all the shelters and warehouses. This practical study proved that Public Participation GIS (PPGIS), combined with IT knowledge, can provide very useful tools for decision making when facing a flood risk

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application

<p>Abstract</p> <p>Background</p> <p>Climate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multi-scale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.</p> <p>Results</p> <p>Based on recent spatial decision-support technologies, this paper presents a spatio-temporal web-based application that goes beyond GIS applications with regard to speed, ease of use, and interactive analysis capabilities. It supports the multi-scale exploration and analysis of integrated socio-economic, health and environmental geospatial data over several periods. This project was meant to validate the potential of recent technologies to contribute to a better understanding of the interactions between public health and climate change, and to facilitate future decision-making by public health agencies and municipalities in Canada and elsewhere. The project also aimed at integrating an initial collection of geo-referenced multi-scale indicators that were identified by Canadian specialists and end-users as relevant for the surveillance of the public health impacts of climate change. This system was developed in a multidisciplinary context involving researchers, policy makers and practitioners, using BI and web-mapping concepts (more particularly SOLAP technologies), while exploring new solutions for frequent automatic updating of data and for providing contextual warnings for users (to minimize the risk of data misinterpretation). According to the project participants, the final system succeeds in facilitating surveillance activities in a way not achievable with today's GIS. Regarding the experiments on frequent automatic updating and contextual user warnings, the results obtained indicate that these are meaningful and achievable goals but they still require research and development for their successful implementation in the context of surveillance and multiple organizations.</p> <p>Conclusion</p> <p>Surveillance of climate-related health vulnerabilities may be more efficiently supported using a combination of BI and GIS concepts, and more specifically, SOLAP technologies (in that it facilitates and accelerates multi-scale spatial and temporal analysis to a point where a user can maintain an uninterrupted train of thought by focussing on "what" she/he wants (not on "how" to get it) and always obtain instant answers, including to the most complex queries that take minutes or hours with OLTP systems (e.g., aggregated, temporal, comparative)). The developed system respects Newell's cognitive band of 10 seconds when performing knowledge discovery (exploring data, looking for hypotheses, validating models). The developed system provides new operators for easily and rapidly exploring multidimensional data at different levels of granularity, for different regions and epochs, and for visualizing the results in synchronized maps, tables and charts. It is naturally adapted to deal with multiscale indicators such as those used in the surveillance community, as confirmed by this project's end-users.</p

A conceptual framework and a risk management approach for interoperability between geospatial datacubes

De nos jours, nous observons un intérêt grandissant pour les bases de données géospatiales multidimensionnelles. Ces bases de données sont développées pour faciliter la prise de décisions stratégiques des organisations, et plus spécifiquement lorsqu’il s’agit de données de différentes époques et de différents niveaux de granularité. Cependant, les utilisateurs peuvent avoir besoin d’utiliser plusieurs bases de données géospatiales multidimensionnelles. Ces bases de données peuvent être sémantiquement hétérogènes et caractérisées par différent degrés de pertinence par rapport au contexte d’utilisation. Résoudre les problèmes sémantiques liés à l’hétérogénéité et à la différence de pertinence d’une manière transparente aux utilisateurs a été l’objectif principal de l’interopérabilité au cours des quinze dernières années. Dans ce contexte, différentes solutions ont été proposées pour traiter l’interopérabilité. Cependant, ces solutions ont adopté une approche non systématique. De plus, aucune solution pour résoudre des problèmes sémantiques spécifiques liés à l’interopérabilité entre les bases de données géospatiales multidimensionnelles n’a été trouvée. Dans cette thèse, nous supposons qu’il est possible de définir une approche qui traite ces problèmes sémantiques pour assurer l’interopérabilité entre les bases de données géospatiales multidimensionnelles. Ainsi, nous définissons tout d’abord l’interopérabilité entre ces bases de données. Ensuite, nous définissons et classifions les problèmes d’hétérogénéité sémantique qui peuvent se produire au cours d’une telle interopérabilité de différentes bases de données géospatiales multidimensionnelles. Afin de résoudre ces problèmes d’hétérogénéité sémantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication s’établit entre deux agents système représentant les bases de données géospatiales multidimensionnelles impliquées dans un processus d’interopérabilité. Cette communication vise à échanger de l’information sur le contenu de ces bases. Ensuite, dans l’intention d’aider les agents à prendre des décisions appropriées au cours du processus d’interopérabilité, nous évaluons un ensemble d’indicateurs de la qualité externe (fitness-for-use) des schémas et du contexte de production (ex., les métadonnées). Finalement, nous mettons en œuvre l’approche afin de montrer sa faisabilité.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organization’s strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility

Case study in the selection of warehouse location for WFP in Ethiopia

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Includes bibliographical references (leaves 96-99).Humanitarian logistic organizations struggle to make strategic and tactical decisions due to their lack of resources, the unpredictability of humanitarian events and the lack of readily available information; the existing tools that assist optimal decision making require large amounts of precise information. As a consequence of all these challenges, most of the work in humanitarian logistics concentrates on the operational level that can only offer short term benefits. Alternatively, optimal strategic decisions maximize the resources of humanitarian organizations making them more flexible and effective in the long term; this directly impacts the ability to help the millions of people in need. This thesis presents a model that assists the largest humanitarian organization in the world, The World Food Programme, to make optimal strategic decisions. The model uses the Analytic Hierarchy Process, a multiple attribute decision tool that provides structure to decisions where there is limited availability of quantitative information. This methodology uses a framework that determines and prioritizes multiple criteria by using qualitative data and it scores each alternative based on these criteria. The optimal alternative will be the one that has the highest weighted score. This model solves the challenges that The World Food Programme, as any other humanitarian organization face when making complex strategic decisions. The model, not only works with easily acquired information but, it is also flexible in order to consider the ever-changing dynamics in the humanitarian field.(cont.) The application of this model focuses on the optimization of warehouse locations for the World Food Programme in the Somali region of Ethiopia. However, this model can easily be scaled in order to be used in any other decision making process in the humanitarian field.by Gina Malaver [and] Colin Regnier.M.Eng.in Logistic

Infraestrutura para análise de tráfego e comportamento de condutores

Mestrado em Engenharia de Computadores e TelemáticaO trabalho realizado nesta dissertação pode ser visto como um sistema de apoio à decisão para tráfego. Foi motivado pelos projetos smart cities dos quais os transportes são uma área importante. Com a evolução das tecnologias nas viaturas é possível fazer uma recolha de cada vez mais informação sobre veículos num ambiente real, permitindo assim fazer uma análise mais detalhada sobre o tráfego e comportamento dos condutores. A pesquisa efetuada sobre trabalho relacionado nesta área revelou que muitas das análises efetuadas não tem em consideração o contexto sendo que alguns estudos apontavam integrar fatores influentes na condução como trabalho futuro. Nesta dissertação os conceitos do trabalho relacionado são integrados assim como fontes de dados heterogénias com informação sobre o contexto. Foi também feito um estudo sobre diferentes paradigmas de bases de dados, onde foram estudados os principais paradigmas NoSQL, os seus casos de uso e as sua principais implementações. Esta dissertação tem como objetivo propor o desenho e a implementação de uma infraestrutura para análise de tráfego e comportamento de condutores a partir de dados sobre trajetórias obtidos de viaturas em circulação. Para a prova de conceito, foram efetuados dois casos de estudo com dados extraidos de duas fontes distintas. Um conjunto de ferramentas de extração, transformação e carregamento de dados foi criado para alimentar os data marts desenvolvidos. Ferramentas de visualização foram usadas de modo a poder fazer uma análise visual através de gráficos para as medidas agregadas e software sistemas de informação geográficos para os detalhes espaciais. Esta infraestrutura foi desenhada de modo a poder ser adaptada para diferentes casos de uso da área, desde gestão de transportes públicos até seguros com base em comportamento. Os resultados obtidos permitem estudar o comportamento dos condutores de modo a obter conhecimento nesta área e possivelmente melhorar o tráfego ou a experiência de condução.The work in this dissertation can be seen as a traffic decision support system. It was motivated for the smart cities project which transportation are a major area. With the technology evolution on vehicles it is possible to gather even more information about vehicles in a real scenario, this allows to perform a more detailed analysis about traffic and drivers’ behavior. The research done about related work in this area showed that a lot of the analysis performed did not have into consideration the context, some of this studies even proposed to integrate factors that influence the driving experience in the future. In this dissertation the concepts of the related work are integrated as well as heterogeneous data sources with context information. It was also performed a study about different database paradigms, in which were studied the most relevant NoSQL paradigms, their use cases and most used implementations. This dissertation proposes the design and implementation of a framework for traffic data analysis and drivers’ behavior based on trajectory data gathered from moving vehicles. For the proof of concept, it was performed two different case studies with data extracted from two distinct datasets with vehicles trajectories. A set of tools was developed to extract, transform and load data to the data marts developed. Visualization tools were used in order to perform a visual analysis through charts for aggregate measures and GIS software for the geospatial details. This framework was designed to be adaptable for different application scenarios involving moving vehicles, from public transportation management to behavior based insurance. The achieved results allows the study of traffic and drivers’ behavior in order to obtain knowledge in this area and possibly improve traffic management or the driving experience

The application of data mining techniques to interrogate Western Australian water catchment data sets

Current environmental challenges such as increasing dry land salinity, waterlogging, eutrophication and high nutrient runoff in south western regions of Western Australia may have both cultural and environmental implications in the near future. Advances in computer science disciplines, more specifically, data mining techniques and geographic information services provide the means to be able to conduct longitudinal climate studies to predict changes in the Water catchment areas of Western Australia. The research proposes to utilise existing spatial data mining techniques in conjunction of modern open-source geospatial tools to interpret trends in Western Australian water catchment land use. This will be achieved through the development of a innovative data mining interrogation tool that measures and validates the effectiveness of data mining methods on a sample water catchment data set from the Peel Harvey region of WA. In doing so, the current and future statistical evaluation on potential dry land salinity trends can be eluded. The interrogation tool will incorporate different modern geospatial data mining techniques to discover meaningful and useful patterns specific to current agricultural problem domain of dry land salinity. Large GIS data sets of the water catchments on Peel-Harvey region have been collected by the state government Shared Land Information Platform in conjunction with the LandGate agency. The proposed tool will provide an interface for data analysis of water catchment data sets by benchmarking measures using the chosen data mining techniques, such as: classical statistical methods, cluster analysis and principal component analysis.The outcome of research will be to establish an innovative data mining instrument tool for interrogating salinity issues in water catchment in Western Australia, which provides a user friendly interface for use by government agencies, such as Department of Agriculture and Food of Western Australia researchers and other agricultural industry stakeholders

A LOCAL SPATIAL DATA INFRASTRUCTURE TO SUPPORT THE MERAPI VOLCANIC RISK MANAGEMENT: A CASE STUDY AT SLEMAN REGENCY, INDONESIA

This research aims to implement an application of a Local Spatial DataInfrastructure (SDI) for evacuation planning of Merapi Volcano disaster. Theprocesses, problems and information flows in evacuation planning were examined.Geo-collaboration Portal was customized in order to provide spatial resources fordecision makers. It is equipped with usable maps presentation and interaction toolsto support collaborative decisions. User group assessment was carried out toevaluate usability of the application. The evaluation results showed thatcollaborative portals on top of a local SDI can facilitate effective decision makingprocess and improve coordination among involved stakeholders in thecontext of disaster preparedness and mitigation. Several aspects need to beconsidered in order to achieve a functional local SDI e.g. availability and qualityof the spatial data, establishment of local regulations and standards, developmentof metadata, and strengthening capable human resources