In the field of spatial statistics, it is often desirable to generate a smooth surface for a region over which only noisy observations of the surface are available at some locations, or even across time. Kriging and kernel estimations are two of the most popular methods. However, these two methods become problematic when the domain is not regular, such as when it is rectangular or convex. Bivariate B-splines developed by mathematicians provide a useful nonparametric tool in bivariate surface modeling. They inherit several appealing properties of univariate B-splines and are applicable in various modeling problems. More importantly, bivariate B-splines have advantages over kriging and kernel estimation when dealing with complicated domains. The purpose of this dissertation is to develop a nonparametric surface fitting method by using bivariate B-splines that can handle complex spatial domains. The dissertation consists of four parts. The first part of this dissertation explains the challenges of smoothing over complicated domains and reviews existing methods. The second part introduces bivariate B-splines and explains its properties and implementation techniques. The third and fourth parts discuss application of the bivariate B-splines in two nonparametric spatial surface fitting problems. In particular, the third part develops a penalized B-splines method to reconstruct a smooth surface from noisy observations. A numerical algorithm is derived, implemented, and applied to simulated and real data. The fourth part develops a reduced rank mixed-effects model for functional principal components analysis of sparsely observed spatial data. A numerical algorithm is used to implement the method and tested on simulated and real data.

of the Ph.D. Thesis Netrdová, P.: Current trends in quantitative analysis of geographical data: potentialities and limitations of spatial analysis The thesis is a contribution to the discussion about the potentialities of the quantitative approach in geography. It follows the current trends in quantitative analysis of geographical data, specifically spatial analysis, particularly from the perspective of changes in the concept and character of applied methods and their possible contribution in geographical research. Due to the research focus of the author, the entire work is focused primarily on the issue of using quantitative methods in terms of social geography. Attention is focused particularly on statistically spatial analyses, which are the most widely used techniques in social geography, with a wide range of possible applications. One of the goals of this work is to bring the current development in quantitative geography closer to the Czech academic community, and thus contribute to the increased awareness of the potentialities of the application of quantitative methods and spatial analyses in geographical research. Methodological problems in the analysis of spatial data, theoretical changes in the concept of quantitative analysis and also newly emerging quantitative methods have not so far...

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.

Spatial Data Analysis (SDA) is relatively narrower and constitutes one of the areas of Spatial Analysis. Geographic Information System (GIS) offers a potentially valuable platform for supporting SDA techniques. Integration of SDA with GIS helps SDA to benefit from the data input, storage, retrieval, data manipulation and display capabilities of GIS. Also, GIS can benefit from SDA techniques in which the integration of these techniques can increase the analysis capabilities of GIS. This integration serves for disseminating and facilitating improved understanding of spatial phenomena. How SDA techniques should be integrated with GIS arise the coupling problem. The complete integration of SDA techniques in GIS can be applied without the support of GIS vendor when the free/libre and open source software (FLOSS) development methodology is properly followed. This approach causes to interpret the coupling problem in a new way. This thesis aims to develop a fully coupled SDA with GIS in FLOSS environment. A fully coupled SDA in free GIS software as FLOSS system is developed by writing nearly 13,000 line Python code in 2.5 years. Usage of this system has reached to nearly 1600 unique visitors, 3000 visits and 8600 page views in two years. As the current status of development in GIS is considered, it is unlikely in commercial market to have full coupled SDA techniques in GIS software. However, it is expected to have more SDA developments in proprietary GIS software in the near future as there is an increasing trend for requesting more sophisticated SDA tools.

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The main objective of this study is to determine main activity locations and correlation between different artifact types in an archaeological site with geographical information systems (GIS) and spatial data analyses. Knidian amphora workshops in Dat&ccedil / a peninsula are studied in order to apply GIS and spatial statistical techniques. GIS capabilities are coupled with some spatial statistical software and spatial data analysis steps are followed. Both point and area datasets are examined for the effective analysis of the same set of spatial phenomena. Visualizing the artifact distribution with the help of GIS tools enables proposing hypotheses about the study area. In exploration part of the study, those assumptions are tested and developed with the help of explorative methods and GIS. The results are discussed and assessed in terms of archaeological framework. Finally the results are compared with the archeo-geophysical anomalies and excavation results.

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

Historically, coyotes were associated with the western United States. During their expansion eastward, coyotes have become more tolerant of humans and have been able to live in varying degrees of urbanization. One main question ecologists around the country are asking is how coyotes are surviving in anthropogenic environments. To aid in answering this question, I have compared coyote land use preference generally and specifically during coyote breeding season, winter and summer, human tourist seasons, and day and night. I also compared coyote land cover preference for deciduous and evergreen cover types during natural seasons. I found that, in general, there was a high variation of preference between and within land use categories. More broadly however, they prefer natural areas over non-natural areas. They used natural and non-natural land use types equally in winter and summer, and during tourist and off-tourist seasons with increased variation in preference during seasons with higher human activity. They had a higher preference for non-natural land use types at night. There is no difference in coyote preference for deciduous or evergreen cover types during the seasons.

As the sea rises in response to global climate changes, small islands will lose a significant portion of their land through ensuing erosion processes. The particular vulnerability of small island systems led me to choose Martha’s Vineyard (MV), a 248 km2 paraglacial island, 8 km off the south shore of Cape Cod, Massachusetts, as a model system with which to analyze the interrelated problems of sea level rise (SLR) and coastal erosion. Historical data documented ongoing SLR (~3mm/yr) in the vicinity of MV. Three study sites differing in geomorphological and climatological properties, on the island’s south (SS), northwest (NW), and northeastern (NE) coasts, were selected for further study. Mathematical models and spatial data analysis, as well as data on shoreline erosion from almost 1500 transects, were employed to evaluate the roles of geology, surficial geology, wetlands, land use, soils, percent of sand, slope, erodible land, wind, waves, and compass direction in the erosion processes at each site. These analyses indicated that: 1) the three sites manifested different rates of erosion and accretion, from a loss of approximately 0.1 m/yr at the NE and NW sites to over 1.7 m/yr at the SS site; 2) the NE and NW sites fit the ratio predicted by Bruun for the rate of erosion vs. SLR, but the SS site exceeded that ratio more than fivefold; 3) the shoreline erosion patterns for all three sites are dominated by short-range effects, not long-range stable effects; 4) geological components play key roles in erosion on MV, a possibility consistent with the island’s paraglacial nature; and 5) the south side of MV is the segment of the coastline that is particularly vulnerable to significant erosion over the next 100 years. These conclusions were not evident from simple statistical analyses. Rather, the recognition that multiple factors besides sea level positions contribute to the progressive change in coastal landscapes only emerged from more complex analyses, including fractal dimension analysis, multivariate statistics, and spatial data analysis. This suggests that analyses of coastal erosion that are limited to only one or two variables may not fully unravel the underlying processes.