With the large number of spatial queries for spatial data objects changing with time in many applications, e.g., the location based services and geographic information systems, spatio-temporal databases have been developed to manipulate them in spatial or temporal databases. We focus on queries for stationary and moving objects in the spatial database in the present. However, there is no total ordering for the large volume and complicated objects which may change their geometries with time. A spatial access method based on the spatial index structure attempts to preserve the spatial proximity as much as possible. Then, the number of disk access which takes the response time is reduced during the query processing. Therefore, in this dissertation, based on the NA-tree, first, we propose the NA-tree join method over the stationary objects. Our NA-tree join simply uses the correlation table to directly obtain candidate leaf nodes based on two NA-trees which have non-empty overlaps. Moreover, our NA-tree join accesses objects once from those candidate leaf nodes and returns pairs of objects which have non-empty overlaps. Second, we propose the NABP method for the continuous range queries over the moving objects. Our NABP method uses the bit-patterns of regions in the NA-tree to check the relation between the range queries and moving objects. Our NABP method searches only one path in the NA-tree for the range query, instead of more than one path in the R*-tree-based method which has the overlapping problem. When the number of range queries increases with time, our NABP method incrementally updates the affected range queries by bit-patterns checking, instead of rebuilding the index like the cell-based method. From the experimental results, we have shown that our NABP method needs less time than the cell-based method for range queries update and less time than the R*-tree-based method for moving objects update. Based on the Hilbert curve with the good clustering property, we propose the ANHC method to answer the all-nearest-neighbors query by our ONHC method. Our ONHC method is used to answer the one-nearest-neighbor query over the stationary objects. We generate direction sequences to store the orientations of the query block in the Hilbert curve of different orders. By using quaternary numbers and direction sequences of the query block, we obtain the relative locations of the neighboring blocks and compute their quaternary numbers. Then, we directly access the neighboring blocks by their sequence numbers which is the transformation of the quaternary numbers from base four to ten. The nearest neighbor can be obtained by distance comparisons in these blocks. From the experimental results, we have shown that our ONHC and ANHC methods need less time than CCSF method for the one-nearest-neighbor query and the method based on R*-trees for the all-nearest-neighbors query, respectively.

The purpose of the present study is to investigate the spatial strategies of adults and the difficulties they experience while solving spatial visualization problems. To achieve this purpose, a case study is conducted and the case of this study is the group of five adults studying secondary or elementary mathematics education in a public university in Ankara. Spatial Ability Test (SAT) and task based interviews are utilized to determine the participants&rsquo / spatial abilities / and to interpret their strategies, and their difficulties in solving spatial visualization problems. The present study reveals that, the participants&rsquo / spatial strategies are mainly categorized as: holistic, analytic and intermediate strategies. Moreover, substrategies are defined / for holistic strategies / mental rotation and mental manipulation strategies / for analytic strategies, key feature and counting strategies / and for intermediate strategies, partial rotation, partial manipulation and pattern-based strategies. Additionally, for each sub-strategy different ways of using that strategy are defined. As an example when using mental manipulation strategy, participants use two different ways / imagining the folding and imagining the sequence. It is also concluded that when the strategies are selected, characteristics of the problems are important. This study shows that the difficulties of the participants in solving spatial visualization problems can be mainly categorized as: limited flexibility and inadequate proficiency. The results of this study provides detailed descriptions of strategies and difficulties of adults in solving spatial visualization problems to be used in designing tools for assessment or development of spatial visualization ability.

Participatory sensing (PS) systems are a new emerging sensing paradigm based on the participation of cellular users in a cooperative way. Due to the spatio-temporal granularity that a PS system can provide, it is now possible to detect and analyze events that occur at different scales, at a low cost. While PS systems present interesting characteristics, they also create new problems. Since the measuring devices are cheaper and they are in the hands of the users, PS systems face several design challenges related to the poor accuracy and high failure rate of the sensors, the possibility of malicious users tampering the data, the violation of the privacy of the users as well as methods to encourage the participation of the users, and the effective visualization of the data. This dissertation presents four main contributions in order to solve some of these challenges. This dissertation presents a framework to guide the design and implementation of PS applications considering all these aspects. The framework consists of five modules: sample size determination, data collection, data verification, data visualization, and density maps generation modules. The remaining contributions are mapped one-on-one to three of the modules of this framework: data verification, data visualization and density maps. Data verification, in the context of PS, consists of the process of detecting and removing spatial outliers to properly reconstruct the variables of interest. A new algorithm for spatial outliers detection and removal is proposed, implemented, and tested. This hybrid neighborhood-aware algorithm considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The experimental results show that the proposed algorithm performs as good as the best estimator while reducing the execution time considerably. The problem of data visualization in the context of PS application is also of special interest. The characteristics of a typical PS application imply the generation of multivariate time-space series with many gaps in time and space. Considering this, a new method is presented based on the kriging technique along with Principal Component Analysis and Independent Component Analysis. Additionally, a new technique to interpolate data in time and space is proposed, which is more appropriate for PS systems. The results indicate that the accuracy of the estimates improves with the amount of data, i.e., one variable, multiple variables, and space and time data. Also, the results clearly show the advantage of a PS system compared with a traditional measuring system in terms of the precision and spatial resolution of the information provided to the users. One key challenge in PS systems is that of the determination of the locations and number of users where to obtain samples from so that the variables of interest can be accurately represented with a low number of participants. To address this challenge, the use of density maps is proposed, a technique that is based on the current estimations of the variable. The density maps are then utilized by the incentive mechanism in order to encourage the participation of those users indicated in the map. The experimental results show how the density maps greatly improve the quality of the estimations while maintaining a stable and low total number of users in the system. P-Sense, a PS system to monitor pollution levels, has been implemented and tested, and is used as a validation example for all the contributions presented here. P-Sense integrates gas and environmental sensors with a cell phone, in order to monitor air quality levels.

This thesis examines the role of function in representations of space by robots - that is, dealing directly and explicitly with those aspects of space and objects in space that serve some purpose for the robot. It is suggested that taking function into account helps increase the generality and robustness of solutions in an unpredictable and complex world, and the suggestion is affirmed by several instantiations of functionally conceived spatial models. These include perceptual models for the "on" and "in" relations based on support and containment; context-sensitive segmentation of 2-D maps into regions distinguished by functional criteria; and, learned predictive models of the causal relationships between objects in physics simulation. Practical application of these models is also demonstrated in the context of object search on a mobile robotic platform. / QC 20111125

[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.

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

abstract: As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description. A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models. Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior. / Dissertation/Thesis / Doctoral Dissertation Geography 2017

abstract: Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation. In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution. In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine. A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

In the present paper, we examine the effect of socioeconomic characteristics on the life expectancy of men and women in the Stockholm metropolitan area. Detailed individual data allows for a novel approach where observations can be displayed in high resolution. As is often the case with geographical data, the variables display high spatial autocorrelations, which imply that observations in proximity are more, or less, similar than what could be expected under the assumption of independent and identically distributed observations. Presence of spatial autocorrelation makes conventional regression models nonfunctional, and a model that accounts for this is therefore specified. In addition, a distance-band which reflects the distance and association between observations is determined. Lagrange Multiplier tests, AIC, log-likelihood, and the Schwarz criterion suggest that a spatial error model with a 300-meter distance band is appropriate for the data at hand. The findings suggest that: (1) Belonging to a minority group has the strongest effect on life expectancies and (2) the effect is negative for both genders, although the negative impact is stronger for males. Tests for spatial autocorrelation on the residuals suggest that the adopted spatial error model captures nearly all spatial autocorrelation in the data, compared to alternative models.

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