Urban racial segregation has been a problem to many U.S. cities. Many researchers have interested on the urban segregation issues since long time ago. To understand and plan a better community, urban planners needs to know how to measure the segregation and interpret the results. However, over the past several decades, many scientists have proposed many types of urban segregation measures. Although a few of them are commonly used nowadays, this doesn?t mean the other measures are not appropriate. Disregarding the fact that some of the measures are mostly used or easily calculated this paper attempts to gather many of the proposed and the most discussed measures for comparison. The results of the comparison were categorized in one group measure, two group measure, and multi group measure. They are also divided in to the five dimensions of segregation such as the evenness, exposure, concentration, clustering, and centralization. Two U.S. metropolitan cities that are different in racial proportion, Houston, TX and Philadelphia, PA, were selected for the comparison. All the selected measures are evaluated in several criteria such as the scale, level of measures, data required, level of complexity, and tendencies of using different census data.

This thesis introduces a new lacunarity analysis add-in for ArcGIS.

A displasia fibrosa é uma alteração de desenvolvimento caracterizada pela substituição do osso normal por tecido conjuntivo denso e trabéculas ósseas imaturas, geralmente encontrada em adolescentes e adultos jovens. Uma alteração genética que envolve a proteína Gs-alfa parece ser a base do processo. A exata incidência e prevalência são difíceis de estabelecer, mas as lesões representam cerca de 5% a 7% dos tumores ósseos benignos. Nos ossos craniofaciais tem predileção pela maxila, podendo causar deformidade grave e assimetria, afetando igualmente ambos os sexos. Radiograficamente, pode apresentar diferentes padrões de imagem dependendo do grau de mineralização e maturação da lesão. .A avaliação da displasia fibrosa nas radiografias da região craniofacial pode ser difícil por causa das aparências variáveis e das estruturas que se sobrepõem, de modo que a tomografia computadorizada é um recurso relevante para o seu correto diagnóstico e planejamento de tratamento. O objetivo deste estudo foi caracterizar a displasia fibrosa através da análise da lacunaridade, um método multiescala para descrever padrões de dispersão espacial. Foram avaliados 10 pacientes (6 homens e 4 mulheres) comprometendo a maxila em sua grande maioria. Para a análise da lacunaridade, empregou-se cortes tomográficos axiais e coronais e, posteriormente, selecionou-se as regiões de interesse das áreas displásicas e do osso normal contralateral por meio do software MATLAB®. Após testes e análises estatísticas, concluiu-se que os cortes coronais, com ampliação de 3x do seu tamanho original, mostraram superioridade em relação aos axiais e, que a lacunaridade foi menor nas áreas da região displásica em relação ao osso normal, ou seja, a primeira apresentou uma maior homogeneidade de textura que a segunda. Mediante isso, pela técnica da validação cruzada leave-one-out é possível separar os grupos com uma alta acurácia (94,75%) concluindo-se que a lacunaridade é um método de análise de imagens contributivo na caracterização da displasia fibrosa. / Fibrous dysplasia is an alteration of development characterized by replacing normal bone for dense connective tissue and immature trabecular bones, typically found in teenagers and young adults. Genetic modification which involves alpha-Gs protein appears to be the basis of the process. The exact incidence and prevalence are difficult to be established, but injuries represent about 5% to 7% of benign bone tumors. On the craniofacial bones, the tumors have a predilection for the maxilla and often can cause severe deformity and asymmetry affecting both sexes equally. Radiographically, it may have different patterns depending on the image degree of mineralization and maturation of the lesion. The evaluation of radiographs of fibrous dysplasia in the craniofacial region can be difficult because of the different appearances and structures that overlaps, however, CT is an important resource for proper diagnosis and treatment planning. The aim of this study was to characterize the fibrous dysplasia by analyzing the lacunarity which is a multiscale method to describe patterns of spatial dispersion. We evaluated 10 patients (6 males and 4 females) and the maxillary was the most affected area. To the lacunarity analysis, we used an axial and coronal view and then were selected the regions of interest in the areas of dysplastic and contralateral normal bone by means of MATLAB® software. After tests and statistical analysis can be conclued that the coronal magnification 3x its original size showed superiority compared to thrust, and that the lacunarity was lower in the areas of dysplastic region in relation to normal bone, namely the first presented a more uniform texture than the second. Through this, the technique of cross-validation \"leave-one-out\" is possible to separate the groups with a high accuracy (94.75%) concluding that the lacunarity is a method of image analysis to characterize the contributory fibrous dysplasia.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-01T19:09:05Z No. of bitstreams: 1 Leandro Ricardo Rodrigues de Lucena.pdf: 4745359 bytes, checksum: 74da71d24d73158d808a456fd5365dc9 (MD5) / Made available in DSpace on 2016-08-01T19:09:05Z (GMT). No. of bitstreams: 1 Leandro Ricardo Rodrigues de Lucena.pdf: 4745359 bytes, checksum: 74da71d24d73158d808a456fd5365dc9 (MD5) Previous issue date: 2015-08-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The phenomena studied in agricultural and environmental sciences are characterized by their natural evolution and are also affected by human activity. They are comprised of a large number of components with non linear interactions on some scales, which produce emergent properties on other scales. These are characteristics of complex systems and can be studied using various concepts developed over the last decades such as fractals, multifractals, self-organized criticality and entropy. In this paper we analyzed the temporal dynamics of climate variables rainfall, temperature and stream flow, and spatial distribution of vegetation fires represented by hot pixels detected by satellites. We used the method of lacunarity that serves to describe the distribution of gaps in a data set. Using lacunarity values for different time scales it was possible to describe the rainfall regime (fragmentation of rainy periods) and classify two precipitation patterns in the state of Sergipe, one formed by stations approaching the Sergipe coast, and other formed by stations that are located in semiarid region. The lacunarity analysis was successful for evaluation of impact of human activity on natural regime of river flow, in the case of rivers Atibaia and Jaguari, caused by the construction of Cantareira system reservoirs. After the construction of the reservoirs lacunarity values of stream flow decreased, indicating a reduction of heterogeneity of flow dynamics due to regulation of water flow. The results of lacunarity analysis of time series of temperature and precipitation in the state of Pernambuco showed that this method can be used to identify the geographic regions with characteristic temporal behavior of climatic variables. The lacunarity analysis for two-dimensional data was applied to spatial distribution of hot pixels detected in Amazonia during the period 2000-2013. In the years with high number of hot pixels the lacunarity values were lower, indicating a more homogeneous spatial distribution compared with periods with low number of hot pixels. The lacunarity method has also shown to be effective in identifying the dry and rainy seasons, by capturing the heterogeneity of spatial distribution of hot pixels. These results may be useful in planning the use of water resources, planning of agricultural activities, developing strategies to reduce the impact of extreme weather events (drought, flood) and natural disasters such as forest fires. / Os fenômenos estudados em ciências agrárias e ambientais além da própria evolução são fortemente influenciados pela atividade humana e caracterizam-se pelo grande número de componentes interagindo de forma não linear em uma escala e produzindo as propriedades emergentes em outras escalas. Com essas características os fenômenos agroambientais considerados sistemas complexos e estudados utilizando vários conceitos desenvolvidos durante as últimas décadas entre os quais: fractais, multifractais, criticalidade auto-organizada e entropias. Neste trabalho analisou-se a dinâmica temporal das variáveis climáticas: precipitação, temperatura e vazão do rio e a distribuição espacial de focos de queimadas. Foi utilizado o método de lacunaridade, que serve para avaliar a distribuição de lacunas em um conjunto de dados. Utilizando os valores de lacunaridade para diferentes escalas temporais foi possível descrever o regime da chuva (fragmentação dos períodos chuvosos) e classificar dois padrões de precipitação no estado de Sergipe, um padrão formado pelas estações que se aproximam da costa sergipana e outro das estações que se localizam nas regiões do semiárido e agreste. A análise de lacunaridade mostrou-se útil para avaliação do impacto da atividade humana no regime natural da vazão do rio, no caso dos rios Atibaia e Jaguari a construção dos reservatórios do sistema Cantareira. Depois da construção dos reservatórios os valores da lacunaridade diminuíram em relação ao período antes da construção dos reservatórios indicando a diminuição de heterogeneidade da dinâmica da vazão devido à regulação do fluxo hídrico. Os resultados da análise de lacunaridade de séries temporais de temperatura e de precipitação do estado de Pernambuco demostraram que este método pode ser usado para identificar as regiões geográficas com comportamento temporal característico das variáveis climáticas. A análise de lacunaridade para dados bidimensionais foi aplicada na distribuição espacial de focos de queimadas detectadas na Amazônia legal durante o período 2000-2013. Nos anos com maior número de queimadas os valores de lacunaridade indicaram uma distribuição espacial mais homogênea comparando com períodos com menor número das queimadas. O método de lacunaridade (avaliando a heterogeneidade da distribuição espacial de queimadas) também mostrou se eficaz em identificação das estações seca e chuvosa. Estes resultados podem ser úteis em planejamento do uso de recursos hídricos, em planejamento das atividades agrícolas, desenvolvimento de estratégias para diminuir as consequências de fenômenos climáticos extremos (seca, cheia) e desastres naturais como incêndios florestais.

A displasia fibrosa é uma alteração de desenvolvimento caracterizada pela substituição do osso normal por tecido conjuntivo denso e trabéculas ósseas imaturas, geralmente encontrada em adolescentes e adultos jovens. Uma alteração genética que envolve a proteína Gs-alfa parece ser a base do processo. A exata incidência e prevalência são difíceis de estabelecer, mas as lesões representam cerca de 5% a 7% dos tumores ósseos benignos. Nos ossos craniofaciais tem predileção pela maxila, podendo causar deformidade grave e assimetria, afetando igualmente ambos os sexos. Radiograficamente, pode apresentar diferentes padrões de imagem dependendo do grau de mineralização e maturação da lesão. .A avaliação da displasia fibrosa nas radiografias da região craniofacial pode ser difícil por causa das aparências variáveis e das estruturas que se sobrepõem, de modo que a tomografia computadorizada é um recurso relevante para o seu correto diagnóstico e planejamento de tratamento. O objetivo deste estudo foi caracterizar a displasia fibrosa através da análise da lacunaridade, um método multiescala para descrever padrões de dispersão espacial. Foram avaliados 10 pacientes (6 homens e 4 mulheres) comprometendo a maxila em sua grande maioria. Para a análise da lacunaridade, empregou-se cortes tomográficos axiais e coronais e, posteriormente, selecionou-se as regiões de interesse das áreas displásicas e do osso normal contralateral por meio do software MATLAB®. Após testes e análises estatísticas, concluiu-se que os cortes coronais, com ampliação de 3x do seu tamanho original, mostraram superioridade em relação aos axiais e, que a lacunaridade foi menor nas áreas da região displásica em relação ao osso normal, ou seja, a primeira apresentou uma maior homogeneidade de textura que a segunda. Mediante isso, pela técnica da validação cruzada leave-one-out é possível separar os grupos com uma alta acurácia (94,75%) concluindo-se que a lacunaridade é um método de análise de imagens contributivo na caracterização da displasia fibrosa. / Fibrous dysplasia is an alteration of development characterized by replacing normal bone for dense connective tissue and immature trabecular bones, typically found in teenagers and young adults. Genetic modification which involves alpha-Gs protein appears to be the basis of the process. The exact incidence and prevalence are difficult to be established, but injuries represent about 5% to 7% of benign bone tumors. On the craniofacial bones, the tumors have a predilection for the maxilla and often can cause severe deformity and asymmetry affecting both sexes equally. Radiographically, it may have different patterns depending on the image degree of mineralization and maturation of the lesion. The evaluation of radiographs of fibrous dysplasia in the craniofacial region can be difficult because of the different appearances and structures that overlaps, however, CT is an important resource for proper diagnosis and treatment planning. The aim of this study was to characterize the fibrous dysplasia by analyzing the lacunarity which is a multiscale method to describe patterns of spatial dispersion. We evaluated 10 patients (6 males and 4 females) and the maxillary was the most affected area. To the lacunarity analysis, we used an axial and coronal view and then were selected the regions of interest in the areas of dysplastic and contralateral normal bone by means of MATLAB® software. After tests and statistical analysis can be conclued that the coronal magnification 3x its original size showed superiority compared to thrust, and that the lacunarity was lower in the areas of dysplastic region in relation to normal bone, namely the first presented a more uniform texture than the second. Through this, the technique of cross-validation \"leave-one-out\" is possible to separate the groups with a high accuracy (94.75%) concluding that the lacunarity is a method of image analysis to characterize the contributory fibrous dysplasia.

It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation. It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation.

Tropical forests store more than half of the world’s forest carbon and are particularly threatened by deforestation and degradation processes, which together represent the second largest source of anthropogenic CO2 emissions. Consequently, tropical forests are the focus of international climate policies (i.e. Reducing emissions from deforestation and forest degradation, REDD) aiming at reducing forest-related CO2 emissions. The REDD initiative lies on our ability to map forest carbon stocks (i.e. spatial dynamics) and to detect deforestation and degradations (i.e. temporal dynamics) at large spatial scales (e.g. national, forested basin), with accuracy and precision. Remote-sensing is as a key tool for this purpose, but numerous sources of error along the carbon mapping chain makes meeting REDD criteria an outstanding challenge. In the present thesis, we assessed carbon (quantified through aboveground biomass, AGB) estimation error at the tree- and plot-level using a widely used pantropical AGB model, and at the landscape-level using a remote sensing method based on canopy texture features from very high resolution (VHR) optical data. Our objective was to better understand and reduce AGB estimation error at each level using information on large canopy tree structure, distribution and spatial organization. Although large trees disproportionally contributed to forest carbon stock, they are under-represented in destructive datasets and subject to an under-estimation bias with the pantropical AGB model. We destructively sampled 77 very large tropical trees and assembled a large (pantropical) dataset to study how variation in tree form (through crown sizes and crown mass ratio) contributed to this error pattern. We showed that the source of bias in the pantropical model was a systematic increase in the proportion of tree mass allocated to the crown in canopy trees. An alternative AGB model accounting for this phenomenon was proposed. We also propagated the AGB model bias at the plot-level and showed that the interaction between forest structure and model bias, although often overlooked, might in fact be substantial. We further analyzed the structural properties of crown branching networks in light of the assumptions and predictions of the Metabolic Theory of Ecology, which supports the power-form of the pantropical AGB model. Important deviations were observed, notably from Leonardo’s rule (i.e. the principle of area conservation), which, all else being equal, could support the higher proportion of mass in large tree crowns. A second part of the thesis dealt with the extrapolation of field-plot AGB via canopy texture features of VHR optical data. A major barrier for the development of a broad-scale forest carbon monitoring method based on canopy texture is that relationships between canopy texture and stand structure parameters (including AGB) vary among forest types and regions of the world. We investigated this discrepancy using a simulation approach: virtual canopy scenes were generated for 279 1-ha plots distributed on contrasted forest types across the tropics. We showed that complementing FOTO texture with additional descriptors of forest structure, notably on canopy openness (from a lacunarity analysis) and tree slenderness (from a bioclimatic proxy) allows developing a stable inversion frame for forest AGB at large scale. Although the approach we proposed requires further empirical validation, a first case study on a forests mosaic in the Congo basin gave promising results. Overall, this work increased our understanding of mechanisms behind AGB estimation errors at the tree-, plot- and landscape-level. It stresses the need to better account for variation patterns in tree structure (e.g. ontogenetic pattern of carbon allocation) and forest structural organization (across forest types, under different environmental conditions) to improve general AGB models, and in fine our ability to accurately map forest AGB at large scale.

A city consists of many elements such as humans, buildings, and roads. The complexity of cities is difficult to measure using Euclidean geometry. In this study, we use fractal geometry (scaling analysis) to measure the complexity of urban areas. We observe urban development from different perspectives using the bottom-up approach. In a bottom-up approach, we observe an urban region from a basic to higher level from our daily life perspective to an overall view. Furthermore, an urban environment is not constant, but it is complex; cities with greater complexity are more prosperous. There are many disciplines that analyze changes in the Earth’s surface, such as urban planning, detection of melting ice, and deforestation management. Moreover, these disciplines can take advantage of remote sensing for research. This study not only uses satellite imaging to analyze urban areas but also uses check-in and points of interest (POI) data. It uses straightforward means to observe an urban environment using the bottom-up approach and measure its complexity using fractal geometry.   Web 2.0, which has many volunteers who share their information on different platforms, was one of the most important tools in this study. We can easily obtain rough data from various platforms such as the Stanford Large Network Dataset Collection (SLNDC), the Earth Observation Group (EOG), and CloudMade. The check-in data in this thesis were downloaded from SLNDC, the POI data were obtained from CloudMade, and the nighttime lights imaging data were collected from EOG. In this study, we used these three types of data to derive natural cities representing city regions using a bottom-up approach. Natural cities were derived from open geographic data without human manipulation. After refining data, we used rough data to derive natural cities. This study used a triangulated irregular network to derive natural cities from check-in and POI data.   In this study, we focus on the four largest US natural cities regions: Chicago, New York, San Francisco, and Los Angeles. The result is that the New York City region is the most complex area in the United States. Box-counting fractal dimension, lacunarity, and ht-index (head/tail breaks index) can be used to explain this. Box-counting fractal dimension is used to represent the New York City region as the most prosperous of the four city regions. Lacunarity indicates the New York City region as the most compact area in the United States. Ht-index shows the New York City region having the highest hierarchy of the four city regions. This conforms to central place theory: higher-level cities have better service than lower-level cities. In addition, ht-index cannot represent hierarchy clearly when data distribution does not fit a long-tail distribution exactly. However, the ht-index is the only method that can analyze the complexity of natural cities without using images.

nÃo hà / A anÃlise de imagens à um importante campo da visÃo computacional cujo propÃsito à extrair informaÃÃes significativas de imagens. Entre os vÃrios atributos relevantes que podem ser analisados, a textura à um dos mais importantes por ser uma fonte rica de informaÃÃes. O objetivo desta tese à desenvolver novos mÃtodos de anÃlise de textura (nÃveis de cinza e coloridas) baseados em modelos gravitacionais simplicados e caminhos mais curtos em grafos, que propiciem vetores de caracterÃsticas mais discriminativos do que os mÃtodos jà estabelecidos pela literatura. A primeira abordagem converte uma imagem em um sistema gravitacional simplificado cujo processo de colapso à explorado por meio de descritores de dimensÃo fractal e lacunaridade. A segunda abordagem converte os pixels de uma imagem em vÃrtices de um grafo ponderado nÃo-orientado e explora os caminhos mais curtos entre pares de vÃrtices em diferentes escalas e orientaÃÃes. Adicionalmente, nesta tese à proposto o estudo dessas abordagens na anÃlise de imagens de folhas de plantas para facilitar o moroso processo de taxonomia vegetal (problema este especialmente relevante para os botÃnicos) e de imagens mÃdicas para identificaÃÃo/classificaÃÃo de patologias, auxiliando o diagnÃstico mÃdico. Os experimentos sÃo realizados nas bases de imagens: Brodatz, UIUC, VisTex, USPTex, Outex, texturas foliares, parÃnquima paliÃÃdico, pap-smear e de tecido mamÃrio. Os resultados mais significativos de classificaÃÃo sÃo obtidos das bases UIUC, USPTex e parÃnquima paliÃÃdico, com taxas de acertos de 55,00%, 96,57% e 91,56% (menores taxas) obtidas pelos mÃtodos propostos, respectivamente. Essas taxas de acertos sÃo quase sempre superiores aos resultados obtidos pelos mÃtodos usados para comparaÃÃo, demonstrando que os mÃtodos propostos abrem promissoras fontes de pesquisa para os estudos de anÃlise de texturas em nÃveis de cinza e coloridas. / Image analysis is an important field of computer vision whose role is to extract significant information from images. Among several relevant attributes, texture is one of the most important because it is a rich source of information. This thesis aims to develop novel texture analysis methods (for grayscale and color images) based on simplified gravitational systems and shortest paths in graphs which provide feature vectors more discriminative than the methods already established in literature. The first approach converts an image into a simplified gravitational system whose collapse process is explored by using fractal dimension and lacunarity descriptors. The second approach converts the pixels of an image into vertices of a non-oriented weighted graph and explores the shortest paths between pairs of vertices in different scales and orientations. Additionally, this thesis proposes to apply these approaches to plant leaf identification (a relevant problem for botanists), and medical image identification/classication, increasing the confidence of medical diagnosis. The experiments are performed on the following image databases: Brodatz,UIUC, VisTex, USPTex, Outex, leaf textures, palisade parenchyma, pap-smear and breast tissues. The most significant comparison results are obtained from UIUC, USPTex and palisade parenchyma, with success rates of 55,00%, 96,57% and 91,56% (lower success rates) obtained by the proposed methods, respectively. These success rates are almost always superior to the results obtained by the methods used for comparison. This demonstrates that the proposed methods open promising sources of research in grayscale and color texture analysis.

A classificação automática de gênero musical tem como finalidade o conforto de ouvintes de músicas auxiliando no gerenciamento das coleções de músicas digitais. Existem sistemas que se baseiam em cabeçalhos de metadados (tais como nome de artista, gênero cadastrado, etc.) e também os que extraem parâmetros dos arquivos de música para a realização da tarefa. Enquanto a maioria dos trabalhos do segundo tipo utilizam-se do conteúdo rítmico e tímbrico, este utiliza-se apenas de conceitos da teoria da informação e da geometria de fractais. Entropia, lacunaridade e dimensão do fractal são os parâmetros que treinam os classificadores. Os testes foram realizados com duas coleções criadas para este trabalho e os resultados foram proeminentes / The goal of automatic music genre classification is givingmusic listeners ease and confort when managing digital music databases. Some systems are based on tags of metadata (such as artist name, genre labeled, etc.), while others explore characteristics from the music files to complete the task. While the majority of works of the second type analyse rhytmic, timbric and pitch content, this one explores only information theoretic and fractal geometry concepts. Entropy, fractal dimension and lacunarity are the parameters adopted to train the classifiers. Tests were carried out on two databases assembled by the author. Results were prominent