Whereas no prior contribution has focused on the case of a medium-sized city in a developing country, such as Mexico, to explore how urban structure and its expansion has affected the spatial distribution of employment, three distinct, but related papers were developed, which combine urban economics literature and spatial sciences techniques to fill this gap and provide new evidence. The first paper, entitled "Spatial Distribution of Employment in Hermosillo, 1999 and 2004" identifies where employment subcenters are. Testing the presence of spatial effects, it concludes that an incipient process of employment suburbanization has taken place; however, the city still exhibits a monocentric structure. As a complement, a second paper, "Employment Density in Hermosillo, 1999-2004: A Spatial Econometric Approach of Local Parameters" tests if the Central Business District (CBD), despite suburbanization, maintains the traditional attributes of attracting activities and influencing the organization of employment around it. The CBD is still attractive, but its influence varies across space and economic sector, conclusions that were masked by global estimations. Thirdly, a study was essential to uncover how important is the urban structure and the suburbanization of jobs in explaining the dispersion resulting of households and workplaces (commuting). The paper entitled "Commuting in a Developing City: The Case of Ciudad Obregon, Mexico" examines this issue. To take advantage of the commuting information available, the study area was switched. In general, the results are consistent with those suggested by urban economics; moreover, the inclusion of workplace characteristics was a novelty to model commuting behavior and proves that space matters. Additionally, new evidence was provided to the field of spatial science through the applications of techniques able to expose the spatial effects associated with the distribution of employment, more specifically, the Exploratory Spatial Data Analysis(ESDA), Geographically Weighted Regression (GWR) with spatial effects, as well as the generalized multilevel hierarchical linear model (GMHL) were used. The new findings produced for this dissertation provide a more comprehensive understanding of urban dynamics and could help to improve the planning process. It is hoped that this dissertation will contribute to that development as well as stimulate further research.

No description available.

abstract: Regional differences of inventive activity and economic growth are important in economic geography. These differences are generally explained by the theory of localized knowledge spillovers, which argues that geographical proximity among economic actors fosters invention and innovation. However, knowledge production involves an increasing number of actors connecting to non-local partners. The space of knowledge flows is not tightly bounded in a given territory, but functions as a network-based system where knowledge flows circulate around alignments of actors in different and distant places. The purpose of this dissertation is to understand the dynamics of network aspects of knowledge flows in American biotechnology. The first research task assesses both spatial and network-based dependencies of biotechnology co-invention across 150 large U.S. metropolitan areas over four decades (1979, 1989, 1999, and 2009). An integrated methodology including both spatial and social network analyses are explicitly applied and compared. Results show that the network-based proximity better defines the U.S. biotechnology co-invention urban system in recent years. Co-patenting relationships of major biotechnology centers has demonstrated national and regional association since the 1990s. Associations retain features of spatial proximity especially in some Midwestern and Northeastern cities, but these are no longer the strongest features affecting co-inventive links. The second research task examines how biotechnology knowledge flows circulate over space by focusing on the structural properties of intermetropolitan co-invention networks. All analyses in this task are conducted using social network analysis. Evidence shows that the architecture of the U.S. co-invention networks reveals a trend toward more organized structures and less fragmentation over the four years of analysis. Metropolitan areas are increasingly interconnected into a large web of networked environment. Knowledge flows are less likely to be controlled by a small number of intermediaries. San Francisco, New York, Boston, and San Diego monopolize the central positions of the intermetropolitan co-invention network as major American biotechnology concentrations. The overall network-based system comes close to a relational core/periphery structure where core metropolitan areas are strongly connected to one another and to some peripheral areas. Peripheral metropolitan areas are loosely connected or even disconnected with each other. This dissertation provides empirical evidence to support the argument that technological collaboration reveals a network-based system associated with different or even distant geographical places, which is somewhat different from the conventional theory of localized knowledge spillovers that once dominated understanding of the role of geography in technological advance. / Dissertation/Thesis / Ph.D. Geography 2011

abstract: Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering. / Dissertation/Thesis / Doctoral Dissertation Geography 2017

No description available.

Entre as técnicas de análise exploratória de dados espaciais encontram-se os indicadores de associação espacial, que mensuram o grau de dependência espacial dos dados analisados e são aplicáveis apenas a dados quantitativos. Outro procedimento disponível é a geoestatística, a qual se baseia no variograma, descrevendo quantitativa e qualitativamente a estrutura espacial de determinada variável. Neste trabalho, utilizam-se conceitos do variograma para desenvolver um indicador de associação espacial global (SIVAR-G) e um indicador de associação espacial local (SIVAR-L). São utilizados dois bancos de dados: dados binários de escolha modal de uma Cidade Fictícia e dados de média de viagens por modo automóvel por domicílio para um recorte da região central da cidade de São Paulo (Pesquisa de Mobilidade 2012). Em ambos os casos, o indicador global, para diferentes vizinhanças, foi calculado com base em valores padronizados, provenientes do variograma experimental e teórico. Em seguida, aplicou-se um teste de hipótese baseado em pseudo-significância para avaliar a significância do indicador proposto previamente. Por fim, os resultados do indicador proposto foram comparados ao índice de Moran, calculado com os mesmos parâmetros. Para o indicador local, foi elaborado um procedimento similar, porém os cálculos foram feitos pontualmente. Cada observação do banco de dados teve um variograma experimental calculado e um variograma teórico modelado para uma análise omniderecional. Um teste de hipótese similar ao do indicador global foi desenvolvido e aplicado. Assim obtiveram-se indicadores de associação espacial local ponto a ponto. Conclui-se que o indicador SIVAR-G possui desempenho satisfatório na estimação de associação espacial para dados contínuos e binários, mostrando-se sensível a anisotropia dos dados. O indicador SIVAR-L é capaz de identificar \"bolsões\" de associação espacial. É aplicável a dados contínuos e binários. Os indicadores propostos permitem a modelagem de variogramas teóricos globais e locais, fornecendo uma maior riqueza de detalhes da estrutura espacial dos dados. Os indicadores SIVAR baseiam-se na dissimilaridade espacial, enquanto o índice de Moran e LISA baseiam-se na similaridade espacial. / Among the exploratory spatial data analysis tools, there are the indicators of spatial association, which measure the degree of spatial dependence of the analyzed data and can be applied to quantitative data. Other procedure available is the geoestatistics, which is based on the variogram, describing quantitatively and qualitatively the spatial structure of a variable. The aim of this thesis is to use the concept of the variogram to develop a global indicator of spatial association (SIVAR-G) and a local indicator of spatial association (SIVAR-L). Two data bases were used: binary data of travel mode choice of a hypothetical city and mean of automobile trips by household to a region of São Paulo\'s center (Mobility Survey, 2012). In both cases, the global indicator, for different neighborhoods, was calculated based on standardized values, derived from the experimental and theoretical variogram. Then, a pseudo-significance test was applied to evaluate the significance of the previously proposed indicator. The results of the proposed indicator were compared to Moran\'s I, calculated with the same parameters. For the local indicator, it was made a similar procedure, however the calculation was punctual. For each observation of the database, it was calculated experimental variogram and adjusted a theoretical variogram for a omnidirectional analysis. A hypothesis test similar to the one applied in the global indicator was developed and applied. Therefore, it was obtained local indicators point by point. It was concluded that the indicator SIVAR-G has a satisfactory performance for spatial association of binary and continuous data, with sensibility for anisotropy cases. The SIVAR-L indicator is able to identify spatial association pockets and outliers. The local indicator is suitable for continuous and binary data. The developed indicators allow the modeling of theoretical global and local variograms, providing more details of the spatial structure of the data. The SIVAR indicators are based on spatial dissimilarity, while the Moran and LISA index are based on spatial similarity.

Entre as técnicas de análise exploratória de dados espaciais encontram-se os indicadores de associação espacial, que mensuram o grau de dependência espacial dos dados analisados e são aplicáveis apenas a dados quantitativos. Outro procedimento disponível é a geoestatística, a qual se baseia no variograma, descrevendo quantitativa e qualitativamente a estrutura espacial de determinada variável. Neste trabalho, utilizam-se conceitos do variograma para desenvolver um indicador de associação espacial global (SIVAR-G) e um indicador de associação espacial local (SIVAR-L). São utilizados dois bancos de dados: dados binários de escolha modal de uma Cidade Fictícia e dados de média de viagens por modo automóvel por domicílio para um recorte da região central da cidade de São Paulo (Pesquisa de Mobilidade 2012). Em ambos os casos, o indicador global, para diferentes vizinhanças, foi calculado com base em valores padronizados, provenientes do variograma experimental e teórico. Em seguida, aplicou-se um teste de hipótese baseado em pseudo-significância para avaliar a significância do indicador proposto previamente. Por fim, os resultados do indicador proposto foram comparados ao índice de Moran, calculado com os mesmos parâmetros. Para o indicador local, foi elaborado um procedimento similar, porém os cálculos foram feitos pontualmente. Cada observação do banco de dados teve um variograma experimental calculado e um variograma teórico modelado para uma análise omniderecional. Um teste de hipótese similar ao do indicador global foi desenvolvido e aplicado. Assim obtiveram-se indicadores de associação espacial local ponto a ponto. Conclui-se que o indicador SIVAR-G possui desempenho satisfatório na estimação de associação espacial para dados contínuos e binários, mostrando-se sensível a anisotropia dos dados. O indicador SIVAR-L é capaz de identificar \"bolsões\" de associação espacial. É aplicável a dados contínuos e binários. Os indicadores propostos permitem a modelagem de variogramas teóricos globais e locais, fornecendo uma maior riqueza de detalhes da estrutura espacial dos dados. Os indicadores SIVAR baseiam-se na dissimilaridade espacial, enquanto o índice de Moran e LISA baseiam-se na similaridade espacial. / Among the exploratory spatial data analysis tools, there are the indicators of spatial association, which measure the degree of spatial dependence of the analyzed data and can be applied to quantitative data. Other procedure available is the geoestatistics, which is based on the variogram, describing quantitatively and qualitatively the spatial structure of a variable. The aim of this thesis is to use the concept of the variogram to develop a global indicator of spatial association (SIVAR-G) and a local indicator of spatial association (SIVAR-L). Two data bases were used: binary data of travel mode choice of a hypothetical city and mean of automobile trips by household to a region of São Paulo\'s center (Mobility Survey, 2012). In both cases, the global indicator, for different neighborhoods, was calculated based on standardized values, derived from the experimental and theoretical variogram. Then, a pseudo-significance test was applied to evaluate the significance of the previously proposed indicator. The results of the proposed indicator were compared to Moran\'s I, calculated with the same parameters. For the local indicator, it was made a similar procedure, however the calculation was punctual. For each observation of the database, it was calculated experimental variogram and adjusted a theoretical variogram for a omnidirectional analysis. A hypothesis test similar to the one applied in the global indicator was developed and applied. Therefore, it was obtained local indicators point by point. It was concluded that the indicator SIVAR-G has a satisfactory performance for spatial association of binary and continuous data, with sensibility for anisotropy cases. The SIVAR-L indicator is able to identify spatial association pockets and outliers. The local indicator is suitable for continuous and binary data. The developed indicators allow the modeling of theoretical global and local variograms, providing more details of the spatial structure of the data. The SIVAR indicators are based on spatial dissimilarity, while the Moran and LISA index are based on spatial similarity.

Cette thèse défend l’intérêt d’appliquer une démarche d’analyse exploratoire de données spatiales pour examiner un phénomène complexe irréductible, dans un contexte limité en données : le paludisme-infection à Dakar. Chaque partie du système pathogène du paludisme est nécessaire mais non suffisante au fonctionnement du système. Il n’y a paludisme-infection que lorsque les trois composantes sont en contact : le parasite, le vecteur et l’hôte humain. La recherche des lieux où ces contacts peuvent s’opérer facilement est donc primordiale dans la lutte contre le paludisme et l’amélioration des programmes visant à la diminution voire l’élimination de la maladie. L’analyse exploratoire, encore très peu appliquée dans les pays dits du Sud, se définit ainsi comme une démarche de recherche mais aussi comme un moyen d’apporter des réponses aux besoins sanitaires. Elle pousse à l’observation, sous différents angles, des déterminants sociaux qui sont impliqués dans la réalisation du phénomène, tout comme à l’examen des interactions existantes entre eux. Nous avons récolté des informations quantitatives variées, en lien direct et indirect avec l’étude du paludisme. Interprétation d’images satellites, données censitaires, résultats d’enquêtes sociales et sanitaires ont été intégrées dans un système d’information géographique pour décrire la ville et ses habitants. Le croisement de ces sources a permis d’étudier les faces spatiales du risque épidémique palustre. Le recours à des analyses statistiques et géostatistiques, bivariées et multivariées, a permis de souligner que le risque d’infection des populations dépendait fortement d’une distance, que l’on a qualifié de sociale. / This thesis applies an Exploratory Spatial Data Analysis (ESDA) approach to study a complex phenomenon in a data scarce environment: malaria infection in Dakar. Each component of the malaria pathogenic system is necessary but not sufficient to result in an infection when acting in isolation. For malaria infection to occur, three components need to interact: the parasite, the vector, and the human host. The identification of areas where these three components can easily interact is therefore essential in the fight against malaria and the improvement of programs for the prevention and control or elimination of the disease. ESDA, still rarely applied in developing countries, is thus defined as a research approach but also as a way to provide answers to global health challenges. It leads to observation, from different angles, on the social and spatial determinants of malaria infection, as well as the examination of existing interactions between its three components. Several streams of quantitative information were collected, both directly and indirectly related to the study of malaria. More specifically, multi-temporal satellite imagery, census data, and results from social and health surveys have been integrated into a Geographic Information System (GIS) to describe the city and its inhabitants. Combining these datasets has enabled to study the spatial variability of the risk of malaria infection.

Foreign Direct Investment (FDI) in the United States, which predominately occurs in the manufacturing sector, remains critically important for a strong regional and local economy, due to the resulting increase in employment, wages, and tax revenue. Traditionally, local economic development strategies have focused on attracting external manufacturing plants or facilities as the primary route to economic growth, through the expansion of the tax base and/or an increase in employment. In comparison, Sustainable Local Economic Development (SLED) emphasizes the establishment of a minimum standard of living for all and an increase in this standard over time; a reduction in the steady growth in inequality among people; a reduction in spatial inequality; and the promotion and encouragement of sustainable resource use and production (Blakely & Leigh, 2010). These essential SLED principles motivate this study, which will seek to develop a better understanding of whether and how FDI contributes to SLED in terms of its spatial patterns and its impact on middle class earnings. By selecting Georgia as a case study area, this research specifically examines whether and how the location of manufacturing FDI has reduced (or increased) spatial inequality at the intra-state and intra-metropolitan levels. It also identifies whether and how manufacturing FDI has reduced (or increased) inequality among people, focusing on its impact on middle class earnings. This study finds a strong spatial concentration of manufacturing FDI employment in metropolitan areas, particularly in a large metropolitan area, at the intra-state spatial pattern analysis. The results of panel regression analysis suggest that presence of agglomeration economies in metropolitan areas has positively influenced the location of manufacturing FDI jobs. The study also finds a suburbanization pattern of manufacturing FDI employment in the intra-metropolitan spatial pattern analysis. This intra-metropolitan suburbanization of FDI in manufacturing jobs is associated with loss of urban industrial land in the central areas within a large metropolitan area. These uneven distribution patterns of manufacturing FDI jobs indicate increased spatial inequality at both intra-state and intra-metropolitan levels, but the implications of this finding are mixed. Using individual earnings data from the American Community Survey Public Use Microdata Sample files, this study also conducts a quantile regression to estimate the earnings distribution effects that a concentration of manufacturing FDI may have on different earnings groups. The findings both from place-of-work and place-of-residence earnings analysis suggest that manufacturing FDI generally has reduced inequality among people. The concentration of manufacturing FDI in a certain area show the largest distribution effects on area workers in the lower earnings group and residents in the middle earnings group.