Prática de ensino supervisionadas do 1º e 2º ciclo do ensino básico: um estudo sobre as estratégias de cálculo mental utilizadas por alunos do 2º ciclo do ensino básico

Relatório Final apresentado à Escola Superior de Educação de Lisboa para obtenção do grau de mestre em Ensino do 1º e do 2º Ciclo de Ensino Básico

Disparities in cervical and breast cancer mortality in Brazil

OBJECTIVE To analyze cervical and breast cancer mortality in Brazil according to socioeconomic and welfare indicators. METHODS Data on breast and cervical cancer mortality covering a 30-year period (1980-2010) were analyzed. The data were obtained from the National Mortality Database, population data from the Brazilian Institute of Geography and Statistics database, and socioeconomic and welfare information from the Institute of Applied Economic Research. Moving averages were calculated, disaggregated by capital city and municipality. The annual percent change in mortality rates was estimated by segmented linear regression using the joinpoint method. Pearson’s correlation coefficients were conducted between average mortality rate at the end of the three-year period and selected indicators in the state capital and each Brazilian state. RESULTS There was a decline in cervical cancer mortality rates throughout the period studied, except in municipalities outside of the capitals in the North and Northeast. There was a decrease in breast cancer mortality in the capitals from the end of the 1990s onwards. Favorable socioeconomic indicators were inversely correlated with cervical cancer mortality. A strong direct correlation was found with favorable indicators and an inverse correlation with fertility rate and breast cancer mortality in inner cities. CONCLUSIONS There is an ongoing dynamic process of increased risk of cervical and breast cancer and attenuation of mortality because of increased, albeit unequal, access to and provision of screening, diagnosis and treatment. 

Spatial study of homicide rates in the state of Bahia, Brazil, 1996-2010

OBJECTIVE To analyze the spatial distribution of homicide mortality in the state of Bahia, Northeastern Brazil. METHODS Ecological study of the 15 to 39-year old male population in the state of Bahia in the period 1996-2010. Data from the Mortality Information System, relating to homicide (X85-Y09) and population estimates from the Brazilian Institute of Geography and Statistics were used. The existence of spatial correlation, the presence of clusters and critical areas of the event studied were analyzed using Moran’s I Global and Local indices. RESULTS A non-random spatial pattern was observed in the distribution of rates, as was the presence of three clusters, the first in the north health district, the second in the eastern region, and the third cluster included townships in the south and the far south of Bahia. CONCLUSIONS The homicide mortality in the three different critical areas requires further studies that consider the socioeconomic, cultural and environmental characteristics in order to guide specific preventive and interventionist practices.

Trends in healthy life expectancy among older Brazilian women between 1998 and 2008

OBJECTIVE To analyze conditional and unconditional healthy life expectancy among older Brazilian women.METHODS This cross-sectional study used the intercensal technique to estimate, in the absence of longitudinal data, healthy life expectancy that is conditional and unconditional on the individual’s current health status. The data used were obtained from the Pesquisa Nacional por Amostra de Domicílios (National Household Sample Survey) of 1998, 2003, and 2008. This sample comprised 11,171; 13,694; and 16,259 women aged 65 years or more, respectively. Complete mortality tables from the Brazilian Institute of Geography and Statistics for the years 2001 and 2006 were also used. The definition of health status was based on the difficulty in performing activities of daily living.RESULTS The remaining lifetime was strongly dependent on the current health status of the older women. Between 1998 and 2003, the amount of time lived with disability for healthy women at age 65 was 9.8%. This percentage increased to 66.2% when the women already presented some disability at age 65. Temporal analysis showed that the active life expectancy of the women at age 65 increased between 1998-2003 (19.3 years) and 2003-2008 (19.4 years). However, life years gained have been mainly focused on the unhealthy state.CONCLUSIONS Analysis of conditional and unconditional life expectancy indicated that live years gained are a result of the decline of mortality in unhealthy states. This pattern suggests that there has been no reduction in morbidity among older women in Brazil between 1998 and 2008.

A Geografia e o Direito: a informação geográfica ao serviço da justiça

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Ensino da geografia e formação acrescida em sistemas de informação geográfica

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Formação de alunos histórica e geograficamente competentes no ensino básico - contributo das imagens e dos mapas mentais

Relatório de investigação apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino de 1º e 2º ciclo do Ensino Básico

Soil erosion after forest fires: evaluation of mitigation measures applied to drainage channels in the northwest of Portugal

The development and implementation of measures which promote the reduction of the impacts of forest fires on soils is imperative and should be part of any strategy for forest and soil preservation and recovery, especially considering the actual scenario of continuous growth in the number of fires and burnt area. Consequently, with the dendrocaustologic reality that has characterized the Portuguese mainland in recent decades, a research project promoted by the Center for the Study of Geography and Spatial Planning (CEGOT) was implemented with the objective of applying several erosion mitigation measures in a burned area of the Peneda-Geres National Park in NW Portugal. This paper therefore seeks to present the measures applied in the study area within the project Soil Protec, relating to triggered channel processes and the results of preliminary observations concerning the evaluation of the effectiveness of erosion mitigation measures implemented, as well as their cost/benefit ratio.

A Batalha de Aljubarrota – Uma explicação geográfica

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Potencial didáctico dos sistemas de informação geográfica no ensino da Geografia

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

CARTO-SOM - Cartogram creation using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Exploratory geospatial data analysis using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Identificação e representação automática de percursos de autocarros

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. 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. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Os sistemas de informação geográfica no ensino da Geografia: aplicação a uma turma do 3º ciclo do Ensino Básico

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.