Mapas pedológicos são importantes fontes de informação necessárias à agricultura, mas praticamente inexistentes em escalas adequadas para o Brasil, e seu levantamento pelo método convencional para a demanda brasileira é inviável. Como alternativa ao problema, mapeamento pedológico digital apresenta-se como uma área do conhecimento que envolve as relações das informações de campo, laboratório e pontuais de solos com métodos quantitativos via imagens de satélite e atributos do relevo para inferir atributos e classes. A literatura destaca, portanto, a importância do estudo da posição espacial de pontos amostrais na estimativa de atributos do solo a partir dos valores espectrais de imagens de satélite, aliado a isso, faz-se importante o cruzamento dos atributos do solo estimados e espacializados para chegar a classes de solo. Face ao exposto, o objetiva-se o desenvolvimento de uma técnica via imagem de satélite, dados espectrais e atributos do relevo, integrados por lógica booleana, para determinar mapas pedológicos. O trabalho foi realizado no município de Rio das Pedras, SP e entornos, numa área total de 47.882 ha. Onde, realizou-se processamento de imagens de satélites multitemporais, para obtenção da informação espectral da superfície de solo exposto. Esta informação foi correlacionada com espectro de laboratório de pontos amostrais em subsuperfície (profundidade 80-100 cm) e estimou-se os espectros simulando bandas de satélite para locais desconhecidos. Elaborou-se uma chave de classificação de solos por cruzamento de mapas de atributos via lógica booleana, onde definiu os seguintes atributos a serem mapeados: argila, V% e matéria orgânica (M.O) na profundidade 0-20 cm e argila, CTC, V%, m%, Al, ferro total, matiz, valor e croma na profundidade 80-100 cm. As estimativas de espectros em subsuperfície e dos atributos dos solos nas duas profundidades foram realizadas pela técnica multivariada regressão geograficamente ponderada (GWR), que teve seu desempenho preditivo avaliado pela comparação com desempenho preditivo da técnica de regressão linear múltipla (MRL). Os resultados mostraram correlação entre os espectros das duas profundidades, com R2 de validação acima 0.6. Argila (0-20 e 80-100 cm), matiz, valor e croma foram os atributos do solo que obtiveram as melhores estimativas com R2 acima 0.6. A técnica multivariada GWR obteve-se desempenho superior ao MRL. O mapa pedológico digital comparado aos mapas de solos detalhados de levantamentos convencionais obteve índice kappa de 34.65% e acurácia global de 54,46%. Tal resultado representa um nível regular de classificação. Por outro lado, deve se considerar que se trata de uma região de alta complexidade geológica e compreendendo heterogeneidade de solos. A técnica desenvolvida mostra-se com potencial de evolução no mapeamento digital de solos à medida que forem evoluindo as estimativas de atributos de solos e ajustes nos critérios da chave de classificação. / Soil maps are important sources of information necessary for agriculture, but practically absent in appropriate scales for Brazil, and its mapping by the conventional method for the brazilian demand is impracticable. How an alternative to the problem, digital pedological mapping appears as an area of knowledge that involves the relationship of field information, laboratory and point of soils with quantitative methods by satellite images and relief attributes to predict attributes and classes. The literature highlights therefore the importance of studying the spatial position of sampling points in the estimation of soil attributes from spectral values of satellite images, combined to this, is an important the crossing of the estimated and spatialized soil attributes to get the soil classes. In view of exposed, the objective is the development of a technique satellite image, spectral data and attributes of relief, integrated by boolean logic to determine soil maps. The work was carried out in Rio das Pedras county, SP, and surroundings, in a total area of 47,882 ha. Which was held processing multitemporal satellite images, to obtain spectral information of exposed soil surface. This information was correlated with laboratory spectra of sample points in the subsurface (depth 80-100 cm) and was estimated spectra simulating satellite bands to unknown locations. Produced is a soil classification key for cross attribute maps by boolean logic, which defines the following attributes to be mapped: clay, cation saturation and organic matter (OM) in the 0-20 cm depth and clay, CEC, cation saturation, aluminiu saturation, Al, total iron, hue, value and chroma in depth 80-100 cm. The estimates spectra subsurface and soil attributes in two depths were performed by multivariate technique geographically weighted regression (GWR), which had its predictive performance is evaluated by comparison with predictive performance of multiple linear regression (MRL). The results showed a correlation between the spectra of the two depths, with validation R2 above 0.6. Clay (0-20 and 80-100 cm), hue, value and chroma were the soil attributes obtained the best estimates R2 above 0.6. The GWR multivariate technique yielded better performance than MRL. The digital soil map compared to the detailed soil maps of conventional surveys obtained kappa index of 34.65% and overall accuracy of 54.46%. This result is a regular level of classification. On the other hand, it must be considered that it is a highly complex geological region and comprising heterogeneity of soils. The technique developed shows with potential developments in digital soil mapping as they evolve estimates of soil attributes and adjustments to the classification key criteria.

The thesis introduces several methods of real estate price modelling suitable either for prediction of the housing prices or for exploring the relationships between the price and its determinants. We compared the conventional linear regression approach to the tree-based methods of machine learning. The comparison analysis on the dataset of 28 019 apartments in Prague suggests that regression trees (especially the Random forest) yield a higher accuracy in the price prediction. Another objective was to examine the effects of location attributes (especially its accessibility and environmental quality) on the prices of nearby apartments. To address the spatial interactions in the geographical data, we employed three spatially conscious models to achieve more reliable results. The local analysis performed with the geographically weighted regression confirmed the presence of spatial heterogeneity and described the price effects relative to the location. In some areas, an increase of 100 meters in distance from the nearest metro station and the nearest park are associated with a decrease in the apartment prices by 644 CZK/m2 and 916 CZK/m2 , respectively. These findings are especially important for the apartments near the stations of the new metro line, which is currently in construction.

The strong emphasis placed on improving equality and well-being for all in the Sustainable Development Goals underscores the importance of tackling persistent within-country disparities in maternal mortality and poor health outcomes. Addressing maternal healthcare access barriers is, thus, crucial, particularly in low-resource settings. Numerous studies investigating determinants of maternal healthcare service use in Ethiopia exist but are limited by their focus on individual and household factors, and by methodological weaknesses. A nuanced understanding of the role of socioeconomic and geographic context in influencing access to care is needed to respond effectively. Maternity waiting homes (MWHs) are a potential strategy to address geographical barriers that delay women’s access to obstetric care. However, in addition to concerns about service quality, there is limited evidence on their effectiveness and on what models meet women’s needs. My research goals were, therefore, to contribute to the understanding of what contextual factors influence maternal healthcare service use in general; and to determine whether or not upgraded MWHs operating in an enabling environment could improve delivery care use in rural Ethiopia. My primary data sources were household surveys conducted as part of a cluster-randomized controlled trial evaluating MWHs and local leader training in Jimma Zone, Ethiopia. Random effects multivariable logistic regression analysis of survey data brought to light the social and financial resources that facilitate MWH use, highlighting the need for complementary interventions to make access more equitable. Spatial analyses identified subnational variation in service use at a finer scale than routinely reported and unmasked local variation in the relevance and magnitude of associations between individual-, interpersonal-, and health system factors and maternal healthcare use. These findings have implications for relying upon homogenous national responses to improve equality in access to care and health outcomes. Finally, analysis of trial data found a non-significant effect of interventions on delivery care use likely due to implementation issues and extraneous factors. The need to generate strong evidence of effectiveness of MWHs in improving maternal healthcare service use using sustainable and equitable MWH models using methods appropriate for complex intervention evaluation remains.

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The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.

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不動產價格的影響因素可按影響範圍區分為三大類，分別為影響整體不動產市場的「總體環境因素」，對一定範圍內不動產產生價格影響的「區域環境因素」，及對於單一不動產價格有所影響的「房屋個體因素」。其中，區域環境因素為影響個別不動產價格之首要因素，不動產之價格會受到所屬區域之政治、經濟、自然、社會等因素影響，「公共建設因素」為重要之區域環境之一，包含公共設施水準及其配置狀態。影響個別不動產價格之次要因素為「房屋個體因素」，可再次細分為三大影響因素如下：房屋本身所具有的特徵因素，即建築物之內部結構；房屋的建築方式，住宅類型等與全棟房屋有關的因素；與房屋鄰近地區環境有關的因素。而集合式與獨立式住宅因分屬不同房屋類型，即上述房屋價格形成因素中「房屋之建築方式」。實際交易上，獨立式住宅多半以「整棟建物」作為交易計算單位，對於坐落之基地權利持分通常為全部，而集合式住宅係以「樓層」、「戶」作為交易之計算單位，所有之基地持分與其他住戶共同持有，基於上述差異，過去研究多將建築方式視為影響房屋價格的條件之一，並據此分類次市場，因此較少有研究同時探討二者在空間分布上所具有的區位差異，及購屋者對於環境的偏好是否有所不同。且過去文獻多半以使用傳統迴歸模型為主要分析方法。但傳統迴歸分析所使用最小平方法迴歸模型，經常會產生殘差項存在有空間自相關的問題，及空間本身所存在之空間異質性偏誤，即空間不穩定性。因此 本文以台中市都會區內之住家使用房屋為樣本，依特徵價格理論將獨立式住宅與集合式住宅視為差異化商品，其內外特徵納入變數，使用GeoDa軟體進行空間自相關分析，並使用ArcGIS軟體中的地理加權迴歸模組（GWR）進行迴歸分析，藉以探討不同類型房屋所偏好之外部特徵，瞭解不同空間環境對房屋價格之影響及台中市都會區空間發展型態，並驗證其於規劃建設產生的空間不穩定性。 研究結果顯示，台中市建立之重大市政建設及土地開發計畫會影響集合式住宅與獨立式住宅之地價熱點分布，其共同之房價熱點均座落於高地價市地重劃區及重大市政建設分布位置，而獨立式住宅之房價熱點，進一步分布於與高地價市重劃區鄰近之市地重劃區；在購屋者對周圍設施偏好方面，集合式住宅購屋者對於國中小學、大學、重大市政建設、市場、公園均有顯著偏好，惟獨立式住宅購屋者對於大學、重大市政建設、公園有顯著偏好，對於國中小學、市場有不偏好情形，顯示不同類型住宅對於公共設施之偏好不完全相同；集合式住宅與獨立式住宅之房屋特徵屬性呈現空間不穩定性，分析結果顯示，上述二種住宅類型，對於本研究所有公共設施距離特徵屬性均呈現空間不穩定、非均質性的結果，顯示不同類型住宅均會與彼此具有相依性，並形成各區域間的異質性。 / Locational characteristics are the determinants of house prices. While former research have examined the effects of proximity to resources and facilities have on residential property values, and the change of the importance as located regions or submarkets vary, the effects of different types of houses are rarely compared due to their dissimilarity in ways of building and ownership. Do house price effects of the same facility alter when properties are situated in different submarkets? Further, the issues of spatial non-stationarity are usually overlooked by previous studies. By using transaction data of two common types of residential houses in Taichung City, we found house price hot spots of both detached houses and condos in regions with major constructions and development plans. Apart from the mutual hot spots found in high land price redevelopment zones, we also discovery hot spots of detached houses in areas in proximity to these redevelopment zones. As for desirable facilities for home buyers, neighborhood schools, universities, major constructions, local markets and parks were found to have an notable price impact on condos, whereas only universities, major constructions and parks in vicinity of in detached houses can we found significant price effects, suggesting the differences in the preference of consumers in distinct regions. Also, spatial dependence and heterogeneity are verified in both types of houses, making the entire market area spatial non-stationary.

The competitiveness of public transport on example of the Pardubice Region Abstract This diploma thesis is devoted to the competitiveness of public transport in the municipalities of the Pardubice Region. Competitiveness is understood mainly in terms of the price difference between travel time and cost of public and individual car transport, and also in terms of the real demand in the municipalities, thus the share of commuting by public transport. Other parameters of mode choice, that is understood as the main indicator of competitiveness, is the number of public transport lines and automobilization. The main objective of this work is to determine how public transportation depends on the other transport characteristics of municipalities. To select significant variables, multiple linear regression analysis was used. After that, geographically weighted regression was applied in order to explain the share of commuting from municipalities. Most data originate in public databases (The Register of vehicles of Department of Transport, population census, digital geographic databases ArcČR and CEDA) and web portals (OREDO, IDOS), part of the data was obtained within questionnaire survey in selected municipalities. An expected negative relationship between the degree of automobilization and the number of public...

The unavailability of crash-related data has been a long lasting challenge in Brazil. In addition to the poor implementation and follow-up of road safety strategies, this drawback has hampered the development of studies that could contribute to national goals toward road safety. In contrast, developed countries have built their effective strategies on solid data basis, therefore, investing a considerable time and money in obtaining and creating pertinent information. In this research, we aim to assess the potential impacts of supplementary data on spatial model performance and the suitability of different spatial modeling approaches on crash prediction. The intention is to notify the authorities in Brazil and other developing countries, about the importance of having appropriate data. In this thesis, we set two specific objectives: (I) to investigate the spatial model prediction accuracy at unsampled subzones; (II) to evaluate the performance of spatial data analysis approaches on crash prediction. Firstly, we carry out a benchmarking based on Geographically Weighted Regression (GWR) models developed for Flanders, Belgium, and São Paulo, Brazil. Models are developed for two modes of transport: active (i.e. pedestrians and cyclists) and motorized transport (i.e. motorized vehicles occupants). Subsequently, we apply the repeated holdout method on the Flemish models, introducing two GWR validation approaches, named GWR holdout1 and GWR holdout2. While the former is based on the local coefficient estimates derived from the neighboring subzones and measures of the explanatory variables for the validation subzones, the latter uses the casualty estimates of the neighboring subzones directly to estimate outcomes for the missing subzones. Lastly, we compare the performance of GWR models with Mean Imputation (MEI), K-Nearest Neighbor (KNN) and Kriging with External Drift (KED). Findings showed that by adding the supplementary data, reductions of 20% and 25% for motorized transport, and 25% and 35% for active transport resulted in corrected Akaike Information Criterion (AICc) and Mean Squared Prediction Errors (MSPE), respectively. From a practical perspective, the results could help us identify hotspots and prioritize data collection strategies besides identify, implement and enforce appropriate countermeasures. Concerning the spatial approaches, GWR holdout2 out performed all other techniques and proved that GWR is an appropriate spatial technique for both prediction and impact analyses. Especially in countries where data availability has been an issue, this validation framework allows casualties or crash frequencies to be estimated while effectively capturing the spatial variation of the data. / A indisponibilidade de variáveis explicativas de acidentes de trânsito tem sido um desafio duradouro no Brasil. Além da má implementação e acompanhamento de estratégias de segurança viária, esse inconveniente tem dificultado o desenvolvimento de estudos que poderiam contribuir com as metas nacionais de segurança no trânsito. Em contraste, países desenvolvidos tem construído suas estratégias efetivas com base em dados sólidos, e portanto, investindo tempo e dinheiro consideráveis na obtenção e criação de informações pertinentes. O objetivo dessa pesquisa é avaliar os possíveis impactos de dados suplementares sobre o desempenho de modelos espaciais, e a adequação de diferentes abordagens de modelagem espacial na previsão de acidentes. A intenção é notificar as autoridades brasileiras e de outros países em desenvolvimento sobre a importância de dados adequados. Nesta tese, foram definidos dois objetivos específicos: (I) investigar a acurácia do modelo espacial em subzonas sem amostragem; (II) avaliar o desempenho de técnicas de análise espacial de dados na previsão de acidentes. Primeiramente, foi realizado um estudo comparativo, baseado em modelos desenvolvidos para Flandres (Bélgica) e São Paulo (Brasil), através do método de Regressão Geograficamente Ponderada (RGP). Os modelos foram desenvolvidos para dois modos de transporte: ativos (pedestres e ciclistas) e motorizados (ocupantes de veículos motorizados). Subsequentemente, foi aplicado o método de holdout repetido nos modelos Flamengos, introduzindo duas abordagens de validação para GWR, denominados RGP holdout1 e RGP holdout2. Enquanto o primeiro é baseado nas estimativas de coeficientes locais derivados das subzonas vizinhas e medidas das variáveis explicativas para as subzonas de validação, o último usa as estimativas de acidentes das subzonas vizinhas, diretamente, para estimar os resultados para as subzonas ausentes. Por fim, foi comparado o desempenho de modelos RGP e outras abordagens, tais como Imputação pela Média de dados faltantes (IM), K-vizinhos mais próximos (KNN) e Krigagem com Deriva Externa (KDE). Os resultados mostraram que, adicionando os dados suplementares, reduções de 20% e 25% para o transporte motorizado, e 25% e 35% para o transporte ativo, foram resultantes em termos de Critério de Informação de Akaike corrigido (AICc) e Erro Quadrático Médio da Predição (EQMP), respectivamente. Do ponto de vista prático, os resultados poderiam ajudar a identificar hotspots e priorizar estratégias de coleta de dados, além de identificar, implementar e aplicar contramedidas adequadas. No que diz respeito às abordagens espaciais, RGP holdout2 teve melhor desempenho em relação a todas as outras técnicas e, provou que a RGP é uma técnica espacial apropriada para ambas as análises de previsão e impactos. Especialmente em países onde a disponibilidade de dados tem sido um problema, essa estrutura de validação permite que as acidentes sejam estimados enquanto, capturando efetivamente a variação espacial dos dados.