A toolbox for hyperspectral image analysis

This paper introduces a new toolbox for hyperspectral imagery, developed under the MATLAB environment. This toolbox provides easy access to different supervised and unsupervised classification methods. This new application is also versatile and fully dynamic since the user can embody their own methods, that can be reused and shared. This toolbox, while extends the potentiality of MATLAB environment, it also provides a user-friendly platform to assess the results of different methodologies. In this paper it is also presented, under the new application, a study of several different supervised and unsupervised classification methods on real hyperspectral data.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Spectral and dynamical invariants in a complete clustered network

The main result of this work is a new criterion for the formation of good clusters in a graph. This criterion uses a new dynamical invariant, the performance of a clustering, that characterizes the quality of the formation of clusters. We prove that the growth of the dynamical invariant, the network topological entropy, has the effect of worsening the quality of a clustering, in a process of cluster formation by the successive removal of edges. Several examples of clustering on the same network are presented to compare the behavior of other parameters such as network topological entropy, conductance, coefficient of clustering and performance of a clustering with the number of edges in a process of clustering by successive removal.

Reducing the number of membership functions in linguistic variables

Dissertation presented at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia in fulfilment of the requirements for the Masters degree in Mathematics and Applications, specialization in Actuarial Sciences, Statistics and Operations Research

O doente com patologia múltipla em medicina geral e familiar: comorbilidade de 4 doenças crónicas

RESUMO: A tese de doutoramento visa demonstrar duas proposições: a comorbilidade de 4 situações de doença prevalentes, hipertensão arterial (HTA), diabetes (DM), doença cardíaca isquémica (DCI) e asma é um assunto importante em Medicina Geral e Familiar e o seu estudo tem diversas implicações na forma como os cuidados de saúde são prestados, na sua organização e no ensino-aprendizagem da disciplina. O documento encontra-se dividido em 4 partes: 1) justificação do interesse do tema e finalidades da dissertação; 2) revisão sistemática de literatura publicada entre 1992 e 2002; 3) apresentação de dois trabalhos de investigação, descritivos e exploratórios que se debruçam sobre a mesma população de estudo, o primeiro intitulado “Comorbilidade de quatro doenças crónicas e sua relação com factores sócio demográficos” e o segundo, “Diferenças entre doentes, por médico e por sub-região, na comorbilidade de 4 doenças crónicas”; 4) conclusões e implicações dos resultados dos estudos na gestão da prática clínica, nos serviços, no ensino da disciplina da MGF e no desenvolvimento posterior de uma linha de investigação nesta área. O primeiro estudo tem como objectivos: descrever a prevalência da comorbilidade entre as 4 doenças-índice; verificar se existe relação entre o tempo da primeira doença e o tempo decorrido até ao aparecimento da 2ª e da 3ª doença, nas 4 doenças; determinar a comorbilidade associada às 4 doenças; identificar eventuais agrupamentos de doenças e verificar se existe relação entre comorbilidade e factores sociais e demográficos. O segundo estudo pretende verificar se existem diferenças na comorbilidade a nível local, por médico, e por Sub-Região de Saúde. O trabalho empírico é descritivo e exploratório. A população é constituída pelos doentes, com pelo menos uma das 4 doenças crónicas índice, das listas de utentes de 12 Médicos de Família a trabalharem em Centros de Saúde urbanos, suburbanos e rurais dos distritos de Lisboa e Beja. Os dados foram colhidos durante um ano através dos registos médicos. As variáveis sócio demográficas estudadas são: sexo, idade, etnia/raça, escolaridade, situação profissional, estado civil, tipo de família, funcionalidade familiar, condições de habitação. A comorbilidade é definida pela presença de duas ou mais doenças e estudada pelo número de doenças coexistentes. O tempo de duração da doença é definido como o número de anos decorridos entre o ano de diagnóstico e 2003. Os problemas de saúde crónicos são classificados pela ICPC2. Nas comparações efectuadas aplicaram-se os testes de Mann-Whitney e de Friedman, de homogeneidade e de análise de resíduos. A Análise Classificatória Hierárquica foi utilizada para determinar o agrupamento de doenças e a Análise de Regressão Categórica e Análise de Correspondências na relação entre as características sócio demográficas e a comorbilidade. Identificaram-se 3998 doentes. A idade média é de 64,3 anos (DP=15,70). Há uma correlação positiva significativa (r =0,350 r=0) entre “anos com a primeira doença”e “idade dos doentes” em todos os indivíduos (homens r=0,129 mulheres r=0,231). A comorbilidade entre as quatro doenças crónicas índice está presente em 1/3 da população. As associações mais prevalentes são HTA+DM (14,3%) e HTA+DCI (6,25%). Existe correlação positiva, expressiva, entre a duração da primeira doença, quando esta é a HTA ou a DM, e o intervalo de tempo até ao aparecimento da 2ª e da 3ª doenças. Identificaram-se 18 655 problemas crónicos de saúde que se traduziram em 244 códigos da ICPC2. O número médio de problemas foi de 5,94 (DP=3,04). A idade, a actividade profissional, a funcionalidade familiar e a escolaridade foram as variáveis que mais contribuíram para diferenciar os indivíduos quanto à comorbilidade. Foram encontradas diferenças significativas entre médicos(c2=1165,368 r=0) e entre os agrupamentos de doentes por Sub-Região de Saúde (c2= 157,108 r=0) no respeitante à comorbilidade. Na partição por Lisboa o número médio de problemas é de 6,45 e em Beja de 5,35. Deste trabalho ressaltam várias consequências para os profissionais, para os serviços, para o ensino e para a procura de mais saber nesta área. Os médicos, numa gestão eficiente de cuidados são chamados a desempenhar um papel de gestores da complexidade e de coordenadores assim como a trabalhar num modelo organizativo apoiado numa colaboração em equipa. Por sua vez os serviços de saúde têm que desenvolver medidas de avaliação de cuidados que integrem a comorbilidade como medida de risco. O contexto social da cronicidade e da comorbilidade deverá ser incluído como área de ensino. A concluir analisa-se o impacto do estudo nos colaboradores e o possível desenvolvimento da investigação nesta área.----------------------------------------ABSTRACT: The PhD Thesis has two propositions, co-morbidity of four chronic conditions (hypertension, asthma, diabetes, cardiac ischaemic disease) is a prevalent and complex issue and its study has several implications in the way care is provided and organised as well as in the learning and teaching of the discipline of General Practice. In the first part of the document arguments of different nature are given in order to sustain the dissertation aims; the second part describes a systematic study of literature review from 1992 to 2002; the third presents two research studies "Comorbidity of four chronic diseases and its relation with socio demographic factors” and “Differences between patients among GPs at local and regional level”; implications of study results for practice management, teaching and research are presented in the last part. The prevalence of the four chronic diseases co-morbidity, the relation of the first disease duration with the time of diagnose of the next index condition, the burden of co-morbidity in the four chronic diseases, the clustering of those diseases, the relation between demographic and social characteristics and co-morbidity, are the objectives of the first study. The second intends to verify differences in comorbidity between patients at local and regional level of practice. Research studies were descriptive and exploratory. The population under study were patients enlisted in 12 GPs working in urban and rural health centres, in Lisbon and Beja districts, with at least one of the four mentioned diseases. Data were collected through medical records during one year (2003) and 3998 patients were identified. The social demographic variables were: sex, age, ethnicity/race, education, profession, marriage status, family status, family functionality, home living conditions. Co-morbidity is defined by the presence of two or more diseases, and studied by the number of co-existing diseases. The time duration of the disease is defined by the number of years between the diagnostic year and 2003. The chronic disease problems are classified in accord with ICPC2. The characterization of population is descriptive. The effected comparisons applied the Mann-Whitney, Friedman, homogeneity and analysis of residuals tests. The Classificatory Hierarchy Analysis was utilized to determine the grouping of diseases and the Regression Categorization and Correspondences Analysis was used to study the relation of socio-demographic and co-morbidity. The median age of the population under study is 64,3 (SD= 15,70). There is a significant positive correlation (r =0,350 r=0)between “years with the first disease” and “patient age” for all individuals (men r=0,129 women r=0,231). Co-morbidity of the four index diseases is present in 1/3 of the studied population. The most prevalent associations for the four diseases are HTA+DM (14,03%) and HTA+IHD (6,25%). Expressive positive correlation between the duration of the first disease and the second and the third index disease interval is found. For the 3988 patients, 18 655 chronic health problems, translated in 244 ICPC2 codes, were identified. The mean number of problems is 5,94 (SD=3,04). Age, professional activity, family functionality and education level are the socio demographic characteristics that most contribute to differentiate individuals concerning the overall co-morbidity. Significant differences in co-morbidity between GP patients at local (c2=1165,368 r=0) and regional level (c2= 157,108 r=0) are found. This study has several consequences for professionals, for services, for the teaching and learning of General Practice and for the pursuit of knowledge in this area. New competences and performances have to be implemented. General Practitioners, assuming a role of co-ordination, have to perform the role of complexity managers in patient's care, working in practices supported by a strong team in collaboration with other specialists. In order to assess provided care, services have to develop tools where co-morbidity is included as a risk measure. The social context of comorbidity and chronicity has to be included in the curricula of General Practice learning and teaching areas. The dissertation ends describing the added value to participant's performance for their participation in the research and an agenda for further research, in this area, based on a community of practice.--------RÉSUMÉ:Cette thèse de doctorat prétend démontrer deux postulats : le premier, que la comorbidité de quatre maladies fréquentes, hypertension artérielle (HTA), diabète (DM), maladie cardiaque ischémique (DCI) et asthme, est un thème important en Médecine Générale et Familiale et que son étude a plusieurs implications au niveau de l'approche pour dispenser les soins, de leur organisation et de l'enseignement/apprentissage de la discipline. Le document comprend quatre parties distinctes : 1) justification de l'intérêt du sujet et objectifs de la dissertation ; 2) étude systématique de publications éditées entre 1992 et 2002 ; 3) présentation de deux travaux de recherche, descriptifs et exploratoires, un premier intitulée « Comorbidité de quatre maladies chroniques et leur relation avec des facteurs sociodémographiques » et un deuxième « Différences entre malades, selon le médecin et la sous région, dans la comorbilité de quatre maladies chroniques» ; 4) conclusions et conséquences des résultats des études dans la gestion de la pratique clinique, dans les services, dans l'enseignement de la discipline de MGF et dans le développement postérieur de la recherche dans ce domaine. Les objectifs de la première étude sont les suivants : décrire la prévalence de la comorbidité entre les quatre maladies chroniques, vérifier s'il existe une relation entre temps de durée de la première maladie et l'espace de temps jusqu'à le diagnostic de la 2ème ou 3ème maladie; déterminer la comorbidité entre les 4 maladies ; identifier d'éventuelles groupements de maladies et vérifier s'il existe une relation entre comorbidité et facteurs sociodémographiques. La deuxième étude prétend vérifier s'il existe des différences de comorbidité entre médecins et par groupement régional. Le travail empirique est descriptif et exploratoire. La population est composée des malades ayant au moins une des quatre maladies chroniques parmi les listes de malades de douze Médecins de Famille qui travaillent dans des Centres de Santé urbains, suburbains et ruraux (Districts de Lisbonne et Beja). Les données ont été extraites pendant l'année 2003 des registres des médecins. Les variables sociodémographiques étudiées sont : le sexe, l'âge, l'ethnie/race, la scolarité, la situation professionnelle, l'état civil, le type de famille, sa fonctionnalité, les conditions de logement. La comorbidité est définie lorsqu'il existe deux ou plusieurs maladies et est étudiée d'après le nombre de maladies coexistantes. La durée de la maladie est établie en comptant le nombre d'années écoulées entre le diagnostique et 2003. Les problèmes de santé chroniques sont classés par l'ICPC 2. Pour les comparaisons les tests de Mann-Whitney et Friedman, de homogénéité et analyse de résidues ont été appliqués. L'Analyse de Classification Hiérarchique a été utilisée pour procéder au regroupement des maladies et l'Analyse de Régression Catégorique et l'Analyse de Correspondances pour étudier la relation entre les caractéristiques sociodémographiques et la comorbilité. Les principaux résultats sont les suivants : les 3998 malades identifiés ont 64,3 ans d'âge moyen (DP=15,70). Il existe une corrélation positive significative (r =0,350 r=0) entre « les années avec la première maladie » et « l'âge des malades », chez tous les individus (hommes r=0,129 femmes r=0,231). La comorbidité entre les quatre maladies chroniques est une réalité chez 1/3 des patients. Les associations les plus fréquentes sont HTA+DM (14%) et HTA+DCI (6,25%). Il existe une corrélation positive significative entre la durée de la première maladie, HTA ou DM, et l'écart jusqu'à l'apparition de la deuxième et de la troisième maladie. Chez les malades, 18.655 problèmes chroniques de santé ont été identifiés et traduits en 244 codes de l'ICPC2. La moyenne des problèmes a été de 5,94 (DP=3,04). L'âge, l'activité professionnelle, la fonctionnalité familiale et la scolarité sont les variables qui ont le plus contribué à différencier les individus face à la comorbilité. Des différences notoires ont été trouvées entre médecins (c2=1165,368 r=0) et entre les groupements régionaux (c2=157,108 r=0) en ce qui concerne la comorbidité. Dans le groupe de patients de Lisbonne, le chiffre moyen de problèmes est de 6,45 et à Beja il est de 5,35. Cette étude met en évidence plusieurs conséquences pour les professionnels, les services, l'enseignement et l'élargissement du savoir dans ce domaine. Les médecins, soucieux de gérer efficacement les soins sont appelés à jouer un rôle de gestionnaires de la complexité et de coordinateurs, de même qu'à travailler dans un modèle d'organisation soutenus par un travail d'équipe. D'autre part, les services de santé doivent eux aussi développer des mesures d'évaluation des soins qui intègrent la comorbidité comme mesure de risque. Le contexte social de la chronicité et de la comorbidité devra être inclus comme domaines à étudier. La fin de cette thèse décrit l'impact de cette étude sur les collaborateurs et le développement futur de la recherche dans ce domaine.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Demand Response Programs Definition Using Demand Price Elasticity to Define Consumers Aggregation for an Improved Remuneration Structure

The positioning of the consumers in the power systems operation has been changed in the recent years, namely due to the implementation of competitive electricity markets. Demand response is an opportunity for the consumers’ participation in electricity markets. Smart grids can give an important support for the integration of demand response. The methodology proposed in the present paper aims to create an improved demand response program definition and remuneration scheme for aggregated resources. The consumers are aggregated in a certain number of clusters, each one corresponding to a distinct demand response program, according to the economic impact of the resulting remuneration tariff. The knowledge about the consumers is obtained from its demand price elasticity values. The illustrative case study included in the paper is based on a 218 consumers’ scenario.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

Remuneration Structure Definition for Distributed Generation Units and Demand Response Participants Aggregation

The implementation of competitive electricity markets has changed the consumers’ and distributed generation position power systems operation. The use of distributed generation and the participation in demand response programs, namely in smart grids, bring several advantages for consumers, aggregators, and system operators. The present paper proposes a remuneration structure for aggregated distributed generation and demand response resources. A virtual power player aggregates all the resources. The resources are aggregated in a certain number of clusters, each one corresponding to a distinct tariff group, according to the economic impact of the resulting remuneration tariff. The determined tariffs are intended to be used for several months. The aggregator can define the periodicity of the tariffs definition. The case study in this paper includes 218 consumers, and 66 distributed generation units.

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

American cutaneous leishmaniasis outbreak, Tartagal city, province of Salta, Argentina, 1993

An American cutaneous leishmaniasis outbreak, with cases clustering during 1993 in Tartagal city, Salta, was reported. The outbreak involved 102 individuals, 43.1% of them with multiple ulcers. Age (mean: 33 years old) and sex distribution of cases (74.5% males), as well as working activity (70 forest-related), support the hypothesis of classical forest transmission leishmaniasis, despite the fact that the place of permanent residence was in periurban Tartagal. Moreover, during July, sandflies were only collected from one of the 'deforestation areas'. Lutzomyia intermedia was the single species of the 491 phlebotomines captured, reinforcing the vector incrimination of this species. Most infections must have been acquired during the fall (April to June), a pattern consistent with previous sandfly population dynamics data. Based on the epidemiological and entomological results, it was advised not to do any vector-targeted periurban control measures during July. Further studies should be done to assess if the high rate of multiple lesions was due to parasite factors or to infective vector density factors.

High prevalence of GB Virus C/Hepatitis G Virus RNA among Brazilian blood donors

The aim of this study was to investigate the presence of the Hepatitis G Virus on a population of blood donors from São Paulo, Brazil and to evaluate its association to sociodemographic variables. Two RT-PCR systems targeting the putative 5'NCR and NS3 regions were employed and the former has shown a higher sensitivity. The observed prevalence of HGV-RNA on 545 blood donors was 9.7% (CI 95% 7.4;12.5). Statistical analysis depicted an association with race/ethnicity, black and mulatto donors being more frequently infected; and also with years of education, less educated donors presenting higher prevalences. No association was observed with other sociodemographic parameters as age, gender, place of birth and of residence. DNA sequencing of nine randomly chosen isolates demonstrated the presence of genotypes 1, 2 and 3 among our population but clustering of these Brazilian isolates was not detected upon phylogenetic analysis.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.