Open space provides a wide range of ecosystem services to communities. In growing communities, open space offers relief from congestion and other negative externalities associated with rapid development. To make effective policy and planning decisions pertaining to open space preservation, it is important to estimate monetary values of its benefits. In addition, assessing public opinions regarding open space provides information on demand and how residents value open space. This study estimated the monetary value of open space in Mississippi and Alabama Gulf Coast communities. The study also collected information on coastal residents’ attitudes towards open space, working waterfronts, and their willingness to support waterfront open space preservation monetarily. Two methodological approaches were employed to estimate the monetary value of waterfront open space: contingent valuation (CVM) and hedonic price (HPM) methods. Data were collected using a mail survey, the Multiple Listing Service (MLS), and publicly available data sources such as the U.S. Census. Data were analyzed using an interval regression, ordinary least squares, and geographically weighted regression (GWR) models. Mail survey results indicated that the majority of residents valued open space and were willing to pay from $80.52 to $162.14 per household as estimated by four different interval-censored econometric models. Respondent’s membership in groups promoting conservation goals, income, age, and residence duration were major factors associated with their willingness to pay. Results from the HPM indicated proximities to waterfronts, with the exception of bayous, were positively related to home prices, suggesting open space produced positive economic benefits. Findings from the HPM analysis using publicly available data were consistent and comparable with the results from the HPM that used MLS data. This similarity of results indicates the use of publicly available data is feasible in HPM analysis, which is important for broad applications of the method during city planning. In addition, GWR estimates provided site specific monetary values of waterfront open space benefits, which will be helpful for policymakers and city planners in developing site-specific conservation and preservation strategies. Findings can help formulate future decisions related to alternative development scenarios of coastal areas and conservation efforts to preserve open space.

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The spatial distribution of future population and employment has become a focus of recent academic enquiry and planning policy concerns. This is largely driven by the rapid urban expansion in major Australian cities and the need to plan ahead for new housing growth and demand for urban infrastructure and services. At a national level forecasts for population and employment are produced by the government and research institutions; however there is a further need to break these forecasts down to a disaggregate geographic scale for growth management within regions. Appropriate planning for the urban growth needs forecasts for fine-grained spatial units. This thesis has developed methodologies to predict the future settlement of the population, employment and urban form by applying a spatial disaggregation approach. The methodology uses the existing regional forecasts reported at regional geographic units and applies a novel spatially-based technique to step-down the regional forecasts to smaller geographical units. South East Queensland (SEQ) is the experimental context for the methodologies developed in the thesis, being one of the fastest-growing metropolitan regions in Australia. The research examines whether spatial disaggregation methodologies that can be used to enhance the forecasts for urban planning purposes and to derive a deeper understanding of the urban spatial structure under growth conditions. The first part of this thesis develops a method by which the SEQ population forecasts can be spatially disaggregated. This is related to a classical problem in geographical analysis called to modifiable area unit problem, where spatial data disaggregation may give inaccurate results due to spatial heterogeneity in the explanatory variables. Several statistical regression and dasymetric techniques are evaluated to spatially disaggregate population forecasts over the study area and to assess their relative accuracies. An important contribution arising from this research is that: i) it extends the dasymetric method beyond its current simple form to techniques that incorporate more complex density assumptions to disaggregate the data and, ii) it selects a method based on balancing the costs and errors of the disaggregation for a study area. The outputs of the method are spatially disaggregated population forecasts across the smaller areas that can be directly used for urban form analysis and are also directly available for subsequent employment disaggregation. The second part in this thesis develops a method to spatially disaggregate the employment forecasts and examine their impact on the urban form. A new method for spatially disaggregating the employment data is evaluated; it analyses the trend and spatial pattern of historic regional employment patterns based on employment determinants (for example, the local population and the proximity of an area to a shopping centre). The method we apply, namely geographically weighted regression (GWR), accounts for spatial effects of data autocorrelation and heterogeneity. Autocorrelation is where certain variables for employment determinants are related in space, and hence violate traditional statistical independence assumptions, and heterogeneity is where the associations between variables change across space. The method uses a locally-fitted relationship to estimate employment in the smaller geography whilst being constrained by the regional forecast. Results show that, by accounting for spatial heterogeneity in the local dependency of employment, the GWR method generates superior estimates over a global regression model. The spatially disaggregate projections developed in this thesis can be used to better understand questions on urban form. From a planning perspective, the results of spatial disaggregation indicate that the future growth of the population for SEQ is likely to maintain a spatially-dispersed growth pattern, whilst the employment is likely to follow a more polycentric distribution focused around the new activity centres. Overall, the thesis demonstrates that the spatial disaggregation method can be applied to supplement the regional forecasts to seek a deeper understanding of the future urban growth patterns. The development, application and validation of the spatial disaggregation methods will enhance the planner’s toolbox whilst responding to the data issues to inform urban planning and future development in a region.

This master thesis deals with the analysis of housing prices in Prague. The main goal is to identify and explain the factors which have an influence on the prices of flats at the macro and micro level. Two spatial statistic methods, namely multiple linear regressions and geographically weighted regressions (GWR), are used in the first part of the thesis, which deals with the prices in Prague in general. The influence on the values of flats in Prague basic settlement units caused by several factors such as the distance from the Old Town Square, age of dwellings, the presence of migrants or air pollution was investigated using these two methods. The price map of the association of real estate agencies, the Czech Statistical Office and the Prague Institute of Planning and Development provided the data used in the presented research. Price profiles from the centre of Prague to the suburbs in various directions were also created and analyzed. Factors with an influence on housing prices at the micro level in a case study of the Prague cadastral territory of Modřany are described in the second part of the thesis. The analysis of new developer projects and older flats in panel houses investigates the influence on the housing prices caused by factors such as noise, physical condition of apartments and the quality of...

abstract: Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR. / Dissertation/Thesis / Doctoral Dissertation Geography 2020

One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.

West Nile virus (WNV) is an emerging infectious disease that has widespread implications for public health practitioners across the world. Within a few years of its arrival in the United States the virus had spread across the North American continent. This research focuses on the development of a spatially explicit GIS-based predictive epidemiological model based on suitable environmental factors. We examined eleven commonly mapped environmental factors using both ordinary least squares regression (OLS) and geographically weighted regression (GWR). The GWR model was utilized to ascertain the impact of environmental factors on WNV risk patterns without the confounding effects of spatial non-stationarity that exist between place and health. It identifies the important underlying environmental factors related to suitable mosquito habitat conditions to make meaningful and spatially explicit predictions. Our model represents a multi-criteria decision analysis approach to create disease risk maps under data sparse situations. The best fitting model with an adjusted R2 of 0.71 revealed a strong association between WNV infection risk and a subset of environmental risk factors including road density, stream density, and land surface temperature. This research also postulates that understanding the underlying place characteristics and population composition for the occurrence of WNV infection is important for mitigating future outbreaks. While many spatial and aspatial models have attempted to predict the risk of WNV transmission, efforts to link these factors within a GIS framework are limited. One of the major challenges for such integration is the high dimensionality and large volumes typically associated with such models and data. This research uses a spatially explicit, multivariate geovisualization framework to integrate an environmental model of mosquito habitat with human risk factors derived from socio-economic and demographic variables. Our results show that such an integrated approach facilitates the exploratory analysis of complex data and supports reasoning about the underlying spatial processes that result in differential risks for WNV. This research provides different tools and techniques for predicting the WNV epidemic and provides more insights into targeting specific areas for controlling WNV outbreaks.

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.

This thesis deals with the modelling of applying techniques of ordinary least squares method and geographically weighted regression for all administrative divisions of the Czech Republic using ArcGIS software. In general this thesis introduces a GWR method which partially solves the problems associated with the analysis of spatial data. The research compares benefits of using geographically weighted regression with spatial data compared with linear regression in thesis conclusion.