Visualization of 40 Years of Tropical Cyclone Positions and Their Rainfall

Correos de investigadores: [email protected] || [email protected] || [email protected] || [email protected] article focuses on a visualization of tropical cyclone track data occurring over a 40- year period (1970–2010) and their relationship with (extremely) heavy rainfall reported by 88 Central American weather stations. The purpose of the visualization is to associate the paths of tropical cyclones in oceanic areas with heavy rainfall inland. Thus, the potential for producing a set of rainfall patterns might somehow help in predicting where different impacts like flooding might occur when tropical cyclones develop in specific oceanic regions. The visualization will serve as a key tool for CIGEFI scientists to apply in their work to determine critical positions of the tropical cyclones associated with extremely heavy rainfall events at daily timescales.Universidad de Costa Rica/[805-B9-454]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-610]/UCR/Costa RicaUniversidad de Costa Rica/[EC-497]/UCR/Costa RicaUniversidad de Costa Rica/[805-A4-906]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-074]/UCR/Costa RicaUniversidad de Costa Rica/[805-A1-715]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de FísicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencias del Mar y Limnología (CIMAR

Computing watertight volumetric models from boundary representations to ensure consistent topological operations

To simulate environmental processes, noise, flooding in cities as well as the behaviour of buildings and infrastructure, ‘watertight’ volumetric models are a measuring prerequisite. They ensure topologically consistent 3D models and allow the definition of proper topological operations. However, in many existing city or other geo-information models, topologically unchecked boundary representations are used to store spatial entities. In order to obtain consistent topological models, including their ‘fillings’, in this paper, a triangulation combined with overlay and path-finding methods is presented by climbing up the dimension, beginning with the wireframe model. The algorithms developed for this task are presented, whereby using the philosophy of graph databases and the Property Graph Model. Examples to illustrate the algorithms are given, and experiments are performed on a data-set from Erfurt, Thuringia (Germany), providing complex geometries of buildings. The heavy influence of double precision arithmetic on the results, in particular the positional and angular precision, is discussed in the end

CHUB: um modelo cartográfico para a visualização e análise do corpo humano

Tese de Doutoramento em Tecnologias e Sistemas de Informação - Área do Conhecimento Engenharia de Programação e dos Sistemas InformáticosA visualização é a representação visual realística ou abstracta de um conjunto de dados que são gerados por modelos computacionais ou resultantes de medições físicas realizadas no mundo real. É fundamental para auxiliar as pessoas a compreenderem dados e processos complexos e pode ser classificada consoante os seus objectivos (nomeadamente a visualização científica e de informação). A correcta modelação e caracterização dos dados são partes fundamentais para a escolha de técnicas visuais eficazes e a produção de uma visualização válida. O grande desafio é exactamente o de identificar como a análise dos resultados pode e deve ser mostrada ao potencial utilizador de uma forma simultaneamente sucinta, coerente e útil. O conceito de modelação cartográfica ou álgebra de mapas foi desenvolvido por Dana Tomlin em 1983 com o Map Analysis Package1 [Sendra2000]. Um modelo cartográfico pode ser visualizado como uma colecção de mapas registados numa base cartográfica comum, em que cada mapa é uma variável sujeita a operações matemáticas tradicionais. A modelação é um processo que decorre de operações primitivas de pontos, vizinhança e regiões sobre diferentes mapas, numa lógica sequencial para interpretar e resolver problemas espaciais. Neste contexto, a sequência de operações é similar à solução algébrica de um conjunto de equações. A criação de ferramentas informáticas para a análise e visualização de dados relacionados com o corpo humano é uma área em forte expansão e de especial interesse. Apesar destas ferramentas serem muito úteis, sofrem bastante da limitação imposta pela arquitectura dos modelos utilizados para o seu desenvolvimento e consequente implementação. Isto ocorre porque estes modelos adoptam os mesmos princípios e ponderações que são aplicados a dados de natureza não humana ou biológica e tratando-os de forma independente e atómica. Por outro lado, a utilização de técnicas visuais pouco intuitivas no sentido de denotar a interdependência espacial inerente a este tipo de informação é outra limitação a salientar neste tipo de ferramentas. Os dados relacionados com o corpo humano apresentam uma forte componente espacial. Para que seja possível uma análise e investigação correctas é necessário ter isso sempre em consideração. Um bom exemplo desta situação é o diagnóstico médico. A combinação de informação oriunda de diferentes partes do corpo humano é normalmente necessária para que um médico possa diagnosticar a doença de um paciente. O acto de diagnosticar pode ser traduzido por um conjunto de operações de álgebra de mapas executadas sobre os dados relacionados com o corpo humano do paciente. Qualquer modelo que pretenda servir de base para o desenvolvimento e implementação de ferramentas informáticas orientadas para a medicina, e em especial, para a análise e visualização de dados relacionado com o corpo humano, deve incorporar os princípios fundamentais da modelação cartográfica. Desta maneira, é possível que os dados possam ser devidamente modelados e consequentemente extrapolada mais informação útil. Por outro lado, a utilização da visualização como instrumento de comunicação de resultados, com a inclusão de metáforas visuais cartográficas é outra mais-valia a ter em conta. O modelo CHUB (Cartographic Human Body), que é apresentado neste trabalho, pretende colmatar essa falha identificada no tratamento e visualização de dados relacionados com o corpo humano. Utiliza a modelação cartográfica como alicerce fundamental para a análise dos dados e a visualização científica e de informação como meio para a comunicação de resultados. Para ser possível a sua avaliação e validação foram considerados dois estudos de caso: diagnóstico da artrose no joelho e a análise de sessões de hidrocinesioterapia. Para estes dois estudos de caso foi implementado um protótipo que instancia o modelo CHUB nestes casos particulares, permitindo a sua utilização, avaliação e validação em dois domínios específicos. Os resultados obtidos após a utilização e avaliação do protótipo permitiram validar com sucesso o modelo CHUB proposto nesta tese de doutoramento.Visualization is the realistic or abstract visual representation of a dataset that is generated by computer models or resulting from physical measurements of the real world. Visualization is fundamental to help people understand data and complexes processes and can be categorized according its goals (scientific or information). The correct data model and characterization are essential to the right choice of the visualization techniques and the production of useful visualizations. The great challenge lies in how to determine that the results are showed to the final users at the same time in a coherent, useful and simple way. The cartographic model concept was developed by Dana Tomlin in 1983 with the Map Analysis Package2 [Sendra2000]. A cartographic model can be seen as a collection of maps that are registered in a cartographic database, where each map is a “variable” that can be mathematically operated. These operations may involve primitives such as points or areas of different maps, for example, in a sequential order to interpret and solve spatial problems. In this context, the sequence of operations is similar to the algebraic solution of a group of equations. The creation of automatic tools for human’s body data analysis and visualization is a field in expansion and of great interest. However these tools are very valuable, they suffer from a common limitation that is imposed by their basis architectural model. In general, they rarely represent in a suitable way biological, morphological and/or biomedical data spatial interdependency. These models treat data in an almost total focused and independent way. The human body systems and organs work as a complex machine, where each part depends strongly on the others. This dependency might be stronger or weaker to the system or organ importance on the overall patient condition. The doctor diagnoses an illness by comparing and analyzing information not only directly related to the mostly affected organ, but also to the body as a whole. In fact the doctor performs a subtle spatial analysis, and therefore, executes a typical algebraic map operation in his/her mind, when diagnosing a patient. An illness might arouse different symptoms and physiological changes in systems/organs that are not directly related to the spatial location of it. CHUB is a model that was developed taking into consideration the main principles of cartographic modelling. It structures data according to different layers of information. Each layer is associated to a specific organ and/or system, and might contain geometric data or attributes that are “human-referenced”. CHUB has not been developed as a dynamic model. It is considered that dynamic issues related to human’s body data, such as body movement, blood flow or heartbeat (besides others) will be accomplished by other models that should be used as a specialized extension to CHUB. In order to validate CHUB two cases of study were considered – osteoarthritis knee diagnosis and hydrokinetic therapy sessions analysis, proposed two strategies for its validation and a prototype implemented. This prototype allowed its utilization, evaluation and validation in two different domains. The results achieved after its utilization and test lead to a complete CHUB validation

Une approche pour supporter l'analyse qualitative des suites d'actions dans un environnement géographique virtuel et dynamique : l'analyse " What-if " comme exemple

Nous proposons une approche basée sur la géosimulation multi-agent et un outil d’aide à la décision pour supporter l’analyse « What-if » durant la planification des suites d’actions (plans) dans un environnement géographique dynamique. Nous présentons les caractéristiques du raisonnement « What-if » en tant 1) que simulation mentale 2) suivant un processus en trois étapes et 3) basé sur du raisonnement causal qualitatif. Nous soulignons les limites de la cognition humaine pour appliquer ce raisonnement dans le cadre de la planification des suites d’actions dans un environnement géographique dynamique et nous identifions les motivations de notre recherche. Ensuite, nous présentons notre approche basée sur la géosimulation multi-agent et nous identifions ses caractéristiques. Nous traitons en particulier trois problématiques majeures. La première problématique concerne la modélisation des phénomènes géographiques dynamiques. Nous soulignons les limites des approches existantes et nous présentons notre modèle basé sur le concept de situation spatio-temporelle que nous représentons en utilisant le formalisme de graphes conceptuels. En particulier, nous présentons comment nous avons défini ce concept en nous basant sur les archétypes cognitifs du linguiste J-P. Desclés. La deuxième problématique concerne la transformation des résultats d’une géosimulation multi-agent en une représentation qualitative exprimée en termes de situations spatio-temporelles. Nous présentons les étapes de traitement de données nécessaires pour effectuer cette transformation. La troisième problématique concerne l’inférence des relations causales entre des situations spatio-temporelles. En nous basant sur divers travaux traitant du raisonnement causal et de ses caractéristiques, nous proposons une solution basée sur des contraintes causales spatio-temporelles et de causalité pour établir des relations de causation entre des situations spatio-temporelles. Finalement, nous présentons MAGS-COA, une preuve de concept que nous avons implémentée pour évaluer l’adéquation de notre approche comme support à la résolution de problèmes réels. Ainsi, les principales contributions de notre travail sont: 1- Une approche basée sur la géosimulation multi-agent pour supporter l’analyse « What-if » des suites d’actions dans des environnements géographiques virtuels. 2- L’application d’un modèle issu de recherches en linguistique à un problème d’intérêt pour la recherche en raisonnement spatial. 3- Un modèle qualitatif basé sur les archétypes cognitifs pour modéliser des situations dynamiques dans un environnement géographique virtuel. 4- MAGS-COA, une plateforme de simulation et d’analyse qualitative des situations spatio-temporelles. 5- Un algorithme pour l’identification des relations causales entre des situations spatio-temporelles.We propose an approach and a tool based on multi-agent geosimulation techniques in order to support courses of action’s (COAs) “What if” analysis in the context of dynamic geographical environments. We present the characteristics of “What if” thinking as a three-step mental simulation process based on qualitative causal reasoning. We stress humans’ cognition limits of such a process in dynamic geographical contexts and we introduce our research motivations. Then we present our multi-agent geosimulation-based approach and we identify its characteristics. We address next three main problems. The first problem concerns modeling of dynamic geographical phenomena. We stress the limits of existing models and we present our model which is based on the concept of spatio-temporal situations. Particularly, we explain how we define our spatio-temporal situations based on the concept of cognitive archetypes proposed by the linguist J-P. Desclés. The second problem consists in transforming the results of multi-agent geosimulations into a qualitative representation expressed in terms of spatio-temporal situations and represented using the conceptual graphs formalism. We present the different steps required for such a transformation. The third problem concerns causal reasoning about spatio-temporal situations. In order to address this problem, we were inspired by works of causal reasoning research community to identify the constraints that must hold to identify causal relationships between spatio-temporal situations. These constraints are 1) knowledge about causality, 2) temporal causal constraints and 3) spatial causal constraints. These constraints are used to infer causal relationships among the results of multi-agent geosimulations. Finally, we present MAGS-COA, a proof on concept that we implemented in order to evaluate the suitability of our approach as a support to real problem solving. The main contributions of this thesis are: 1- An approach based on multi-agent geosimulation to support COA’s “What if” analysis in the context of virtual geographic environments. 2- The application of a model proposed in the linguistic research community to a problem of interest to spatial reasoning research community. 3- A qualitative model based on cognitive archetypes to model spatio-temporal situations. 4- MAGS-COA, a platform of simulation and qualitative analysis of spatio-temporal situations. 5- An algorithm to identify causal relationships between spatio-temporal situations