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1
Ma, Pulong. "Hierarchical Additive Spatial and Spatio-Temporal Process Models for Massive Datasets". University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535635193581096.
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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.
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Sengupta, Aritra. "Empirical Hierarchical Modeling and Predictive Inference for Big, Spatial, Discrete, and Continuous Data". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1350660056.
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Katzfuss, Matthias. "Hierarchical Spatial and Spatio-Temporal Modeling of Massive Datasets, with Application to Global Mapping of CO2". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1308316063.
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Shi, Hongxiang. "Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802515691938.
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Li, Linhua. "A GIS-based Bayesian approach for analyzing spatial-temporal patterns of traffic crashes". [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1766.
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Pfarrhofer, Michael y Philipp Piribauer. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models". Elsevier, 2019. http://epub.wu.ac.at/6839/1/1805.10822.pdf.
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Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for spatial autoregressive models suffers from severe drawbacks both in terms of computational time and possible extensions to more flexible econometric frameworks. To alleviate these problems, this paper presents two global-local shrinkage priors in the context of high-dimensional matrix exponential spatial specifications. A simulation study is conducted to evaluate the performance of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. Moreover, we use pan-European regional economic growth data to illustrate the performance of the proposed shrinkage priors.
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Ross, Beth E. "Assessing Changes in the Abundance of the Continental Population of Scaup Using a Hierarchical Spatio-Temporal Model". DigitalCommons@USU, 2012. http://digitalcommons.usu.edu/etd/1147.
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In ecological studies, the goal is often to describe and gain further insight into ecological processes underlying the data collected during observational studies. Because of the nature of observational data, it can often be difficult to separate the variation in the data from the underlying process or `state dynamics.' In order to better address this issue, it is becoming increasingly common for researchers to use hierarchical models. Hierarchical spatial, temporal, and spatio-temporal models allow for the simultaneous modeling of both first and second order processes, thus accounting for underlying autocorrelation in the system while still providing insight into overall spatial and temporal pattern. In this particular study, I use two species of interest, the lesser and greater scaup (Aythya affnis and Aythya marila), as an example of how hierarchical models can be utilized in wildlife management studies. Scaup are the most abundant and widespread diving duck in North America, and are important game species. Since 1978, the continental population of scaup has declined to levels that are 16% below the 1955-2010 average and 34% below the North American Waterfowl Management Plan goal. The greatest decline in abundance of scaup appears to be occurring in the western boreal forest, where populations may have depressed rates of reproductive success, survival, or both. In order to better understand the causes of the decline, and better understand the biology of scaup in general, a level of high importance has been placed on retrospective analyses that determine the spatial and temporal changes in population abundance. In order to implement Bayesian hierarchical models, I used a method called Integrated Nested Laplace Approximation (INLA) to approximate the posterior marginal distribution of the parameters of interest, rather than the more common Markov Chain Monte Carlo (MCMC) approach. Based on preliminary analysis, the data appeared to be overdispersed, containing a disproportionately high number of zeros along with a high variance relative to the mean. Thus, I considered two potential data models, the negative binomial and the zero-inflated negative binomial. Of these models, the zero-inflated negative binomial had the lowest DIC, thus inference was based on this model. Results from this model indicated that a large proportion of the strata were not decreasing (i.e., the estimated slope of the parameter was not significantly different from zero). However, there were important exceptions with strata in the northwest boreal forest and southern prairie parkland habitats. Several strata in the boreal forest habitat had negative slope estimates, indicating a decrease in breeding pairs, while some of the strata in the prairie parkland habitat had positive slope estimates, indicating an increase in this region. Additionally, from looking at plots of individual strata, it seems that the strata experiencing increases in breeding pairs are experiencing dramatic increases. Overall, my results support previous work indicating a decline in population abundance in the northern boreal forest of Canada, and additionally indicate that the population of scaup has increased rapidly in the prairie pothole region since 1957. Yet, by accounting for spatial and temporal autocorrelation in the data, it appears that declines in abundance are not as widespread as previously reported.
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Rice, Ketra Lachell. "A Multi-Method Analysis of the Role of Spatial Factors in Policy Analysis and Health Disparities Research". The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365613669.
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Acar, Alper. "Optimal Urban Planning and Housing Prices : a Spatial Analysis". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCG008.
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La thèse étudie l'impact de l'aménagement urbain optimal surla diffusion des prix des logements dans un marché immobilier local. À travers une analyse du champ de la localisation optimale et de l'économétrie spatiale, cette étude vise à considérer comment les propriétés des graphes et les modèles de localisation optimale peuvent contribuer à mieux comprendre et à évaluer les impacts des effets de multiplicateur spatial dans l'économie. Pour ce faire, la recherche s'appuie sur une méthodologie combinant la création d'outils d'aide à la décision et l'étude des prix immobiliers par un modèle économétrique spatiale hiérarchique. Les résultats démontrent que la prise en compte des relations spatiales optimales permet une étude plus précise des impacts de l'aménagement urbain sur la diffusion des prix. A contrario, la considération de relations spatiales “classiques" sur ou sous-estime les impactsThis dissertation studies the effect of optimal urban planning on housing prices diffusion in local real-estate markets. The study uses facility location theory and spatial econometrics to investigate how graph properties and optimal location models can contribute to a better understanding and evaluation of the impact of spatial multiplier effects in the economy. To this end, the research is based on a methodology that combines the creation of decision-support tools and the study of real estate prices using hierarchical spatial econometric models. The results states that using optimal spatial relationships enables a more precise analysis of the impacts of urban planning on the diffusion of prices. Conversely, the consideration of “classical” spatial relationships either underestimates or overestimates the spatial impacts
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Liu, Jia. "Heterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modeling". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78722.
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Online quality assurance is crucial for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining stable process operations and establishing efficacious online quality assurance schemes.
To address these challenges, four different advanced manufacturing processes, namely, fused filament fabrication (FFF), binder jetting, chemical mechanical planarization (CMP), and the slicing process in wafer production, are investigated in this dissertation for applications of online quality assurance, with utilization of various sensors, such as thermocouples, infrared temperature sensors, accelerometers, etc. The overarching goal of this dissertation is to develop innovative integrated methodologies tailored for these individual manufacturing processes but addressing their common challenges to achieve satisfying performance in online quality assurance based on heterogeneous sensor data. Specifically, three new methodologies are created and validated using actual sensor data, namely,
(1) Real-time process monitoring methods using Dirichlet process (DP) mixture model for timely detection of process changes and identification of different process states for FFF and CMP. The proposed methodology is capable of tackling non-Gaussian data from heterogeneous sensors in these advanced manufacturing processes for successful online quality assurance.
(2) Spatial Dirichlet process (SDP) for modeling complex multimodal wafer thickness profiles and exploring their clustering effects. The SDP-based statistical control scheme can effectively detect out-of-control wafers and achieve wafer thickness quality assurance for the slicing process with high accuracy.
(3) Augmented spatiotemporal log Gaussian Cox process (AST-LGCP) quantifying the spatiotemporal evolution of porosity in binder jetting parts, capable of predicting high-risk areas on consecutive layers. This work fills the long-standing research gap of lacking rigorous layer-wise porosity quantification for parts made by additive manufacturing (AM), and provides the basis for facilitating corrective actions for product quality improvements in a prognostic way.
These developed methodologies surmount some common challenges of advanced manufacturing which paralyze traditional methods in online quality assurance, and embody key components for implementing effective online quality assurance with various sensor data. There is a promising potential to extend them to other manufacturing processes in the future.Ph. D.
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Alglave, Baptiste. "Inférer la distribution spatio-temporelle des espèces d’intérêt halieutique et identifier leurs habitats essentiels : modéliser l’échantillonnage préférentiel et le changement de support pour intégrer des sources de données hétérogènes". Electronic Thesis or Diss., Rennes, Agrocampus Ouest, 2022. http://www.theses.fr/2022NSARH117.
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La cartographie de la répartition des espèces d’intérêts halieutiques et l'identification de leurs zones fonctionnelles est cruciale pour assurer le renouvellement des espèces et pour l’aménagement de l’espace marin. Pour autant, la localisation des habitats essentiels des poissons, et plus particulièrement des frayères, reste incertaine pour de nombreuses espèces exploitées.Les données de référence pour cartographier la distribution des espèces exploitées et identifier leurs frayères sont issues de campagnes scientifiques qui bénéficient d'un protocole d'échantillonnage standardisé. Ces campagnes ont généralement lieu une ou deux fois par an, elles prélèvent un nombre limité d'échantillons et elles peuvent ne pas correspondre à la période de reproduction des espèces étudiées. Elles sont donc limitées pour identifier les frayères des espèces d’intérêt halieutique.Par ailleurs, les déclarations de capture des pêcheurs (logbook) fournissent des informations sur l'ensemble de l’année avec une densité d'échantillonnage supérieure à celle des données scientifiques.En les combinant aux données de géolocalisation des navires disponibles par le système de surveillance des navires de pêche (VMS), les données de déclarations peuvent permettre de compléter l’information apportée par les données de campagne.Dans cette thèse, nous avons développé un modèle statistique qui permet de combiner les données commerciales et scientifiques pour inférer la distribution des espèces d’intérêt halieutique à une résolution spatio-temporelle fine. Le modèle permet de prendre en compte le comportement de ciblage des pêcheurs (échantillonnage préférentiel) et d’intégrer les données de déclarations qui sont définies à une résolution spatiale grossière pour inférer la distribution des espèces à une résolution fine (changement de support). Les cartes de la distribution des espèces permettent d’identifier les zones d'agrégation pendant la saison de reproduction. Nous décrivons également les applications potentielles du cadre de modélisation pour l’aménagement de l'espace marin et les extensions qui pourraient être ajoutées à la version actuelle du modèleMapping fish distribution and identifying fish essential habitats grounds is key to ensure species renewal and manage the marine space. Information on the location of fish essential habitats and specifically of fish spawning grounds is still lacking for many harvested species.The reference data to map fish distribution and identify spawning grounds are scientific survey data. These data benefit from a standardized sampling protocol. However, due to their costs, they also generally suffer from a low sampling density in space and time. In particular, they generally occur once or twice a year and they may mismatch fish reproduction.Commercial declarations combined with Vessel Monitoring System data could prove highly valuable to complement the information brought by scientific survey data as fishermen landings provide information on the full year with a much denser sampling density. In this PhD, we developed an integrated statistical framework that allows to combine commercial and scientific data sources to infer fish distribution in space and time. Our approach accounts for fishermen targeting behavior towards areas of higher biomass (preferential sampling) and allows to infer fine scale species distribution based on spatially aggregated declarations data (change of support). We demonstrate the ability of the framework to produce monthly maps of fish distribution and to identify aggregation areas during reproduction season. We also outline the potential applications of the framework for Marine Spatial Planning and discuss several extensions that could be added to the actual model
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Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology". Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.
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Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
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Flask, Thomas V. "An Application of Multi-Level Bayesian Negative Binomial Models with Mixed Effects on Motorcycle Crashes in Ohio". University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1333046055.
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Wang, Shuang. "Novel approaches for patterning hierarchical hydrogels". Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121231/1/Shuang_Wang_Thesis.pdf.
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Synthetic hydrogels featuring tunable biological functionalities and hierarchical structures are of compelling interest as scaffolds for tissue engineering applications. With the expectation of regulating cell fate within the soft materials, many efforts have been placed on creating niches that can mimic the complexity of the native extracellular matrix. In this study, a sacrificial moulding process was used to produce porous hydrogels, while two patterning approaches were developed to site-specifically immobilize molecules inside the hydrogels, resembling natural extracellular matrix networks in terms of geometrical interconnectivity and cell-guidance functionalization. The simple approaches allow reproducible control over the size and architecture of the channels, as well as the spatial distribution and concentration of the patterning molecules, enabling controlled study of cell-substrate behaviour.
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Zhuang, Lili. "Bayesian Dynamical Modeling of Count Data". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315949027.
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Brynjarsdóttir, Jenný. "Dimension Reduced Modeling of Spatio-Temporal Processes with Applications to Statistical Downscaling". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312935520.
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Baker, Jannah F. "Bayesian spatiotemporal modelling of chronic disease outcomes". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104455/1/Jannah_Baker_Thesis.pdf.
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This thesis contributes to Bayesian spatial and spatiotemporal methodology by investigating techniques for spatial imputation and joint disease modelling, and identifies high-risk individual profiles and geographic areas for type II diabetes mellitus (DMII) outcomes. DMII and related chronic conditions including hypertension, coronary arterial disease, congestive heart failure and chronic obstructive pulmonary disease are examples of ambulatory care sensitive conditions for which hospitalisation for complications is potentially avoidable with quality primary care. Bayesian spatial and spatiotemporal studies are useful for identifying small areas that would benefit from additional services to detect and manage these conditions early, thus avoiding costly sequelae.
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Dou, Yiping. "Dynamic Bayesian models for modelling environmental space-time fields". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/634.
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This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential
problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine-
scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and
multivariate responses. The result generalizes a number of current approaches in this field.
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Low, Choy Samantha Jane. "Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour". Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37098/12/Samantha%20Low%20Choy%20Thesis.pdf.
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This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions.
The essence of the thesis can be described as follows.
Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this.
Two main approaches to modelling and inference are investigated in this thesis.
The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors.
The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics.
Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method.
Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7.
Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time.
Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.
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Iddrisu, Abdul-Karim. "Bayesian hierarchical spatial and spatio-temporal modeling and mapping of tuberculosis in Kenya". Thesis, 2013. http://hdl.handle.net/10413/10279.
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Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes and space-time epidemic processes [Waller et al., 1997, Hosseini et al., 2006]. The use of frequentist methods in Biostatistics and epidemiology are common and are therefore extensively utilized in answering varied research questions. In this thesis, we proposed the Hierarchical Bayesian approach to study the spatial and the spatio-temporal pattern of tuberculosis in Kenya [Knorr-Held et al., 1998, Knorr-Held, 1999, L opez-Qu lez and Munoz, 2009, Waller et al., 1997, Julian Besag, 1991]. Space and time interaction of risk (ψ[ij]) is an important factor considered in this thesis. The Markov Chain Monte Carlo (MCMC) method via WinBUGS
and R packages were used for simulations [Ntzoufras, 2011, Congdon, 2010, David et al., 1995, Gimenez et al., 2009, Brian, 2003], and the Deviance Information Criterion (DIC), proposed by [Spiegelhalter et al., 2002], used for models comparison and selection. Variation in TB risk is
observed among Kenya counties and clustering among counties with high TB relative risk (RR). HIV prevalence is identified as the dominant determinant of TB. We found clustering and heterogeneity of risk among high rate counties and the overall TB risk is slightly decreasing from
2002-2009. Interaction of TB relative risk in space and time is found to be increasing among rural counties that share boundaries with urban counties with high TB risk. This is as a result of the ability of models to borrow strength from neighbouring counties, such that near by counties have similar risk. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial and spatio-temporal methodology for the statistical analysis of risk from TB in Kenya.Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.
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Pittman, Tyler. "The Association Between Neighbourhood Stressors and Asthma Prevalence of School Children in Winnipeg". Master's thesis, 2011. http://hdl.handle.net/10048/1917.
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Neighbourhood stressors have an incubating effect for a variety of health-related disorders involving children. It is of interest is to determine if asthma prevalence is greater amongst school children at age 7-8 resident of chronic stress neighbourhoods in Winnipeg, after adjusting for family history of asthma and socioeconomic status. The urban component of children (1472 entire; 698 birth home) extracted from the Study of Asthma, Genes and the Environment (SAGE) Survey administered in 2002-2003 to a birth cohort from 1995 in Manitoba. Dichotomous parent report of child asthma from the SAGE Survey nested within birth cohort was geocoded by postal code, which allowed designation of neighbourhood in hierarchical linear modelling. Children living in census tracts assigned low SES scores by compositional stressors were found to have a decreased odds of parent report of asthma, while those inhabiting profiles with high contextual crime rates were at increased risk.
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Lee, JaeYoung. "Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification". Doctoral diss., 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6127.
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Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act – A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process.
Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation.
In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.Ph.D.
Doctorate
Civil, Environmental and Construction Engineering
Engineering and Computer Science
Civil Engineering
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MINGIONE, MARCO. "On the wide applicability of Bayesian hierarchical models". Doctoral thesis, 2022. http://hdl.handle.net/11573/1613592.
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This dissertation attempts to gather the main research topics I engaged during the past four years, in collaboration with several national and international researchers from ``La Sapienza'' and other universities. The primary focus is the application of Bayesian hierarchical models to phenomena in several domains such as economics, environmental health, and epidemiology. One common point is the attention to their fast implementation and results' interpretability. Typically, these two main goals are challenging to be simultaneously achieved in the Bayesian setting for two main reasons: on the one hand, the fast implementation of Bayesian machineries requires an oversimplification of the modeling structure, which does not necessarily reflect the complexity of the analyzed phenomenon; on the other hand, if the estimation of complex models is sought, parameters' interpretation may not be straightforward, especially when intricate dependence structures are present.
The reader must be aware that all the presented applications with related solutions stemmed from these premises.
The first chapter of this dissertation introduces the advantages of adopting the hierarchical paradigm for the model formulation from a conceptual perspective. Following this conceptual introduction, the second chapter delves more into the technical aspects of hierarchical model formulation and estimation. Far from being exhaustive, it provides all the essential ingredients for a thorough understanding of their theoretical foundations and optimal implementation. These first two chapters pave the road for the four original developments presented thereafter. In particular, the third chapter describes a new statistical protocol aiming at variable selection within a Beta regression model for the estimation of food losses percentages at the country-commodity level. The work has been carried out in collaboration with the Food and Agricultural Organization of the United Nations, which started in 2017 for my Master's thesis and led to the recent publication by Mingione et al. (2021a). The fourth chapter includes an extended version of the work developed during my Visiting Research period at the University of California, Los Angeles. It describes a modeling framework for the fast estimation of temporal Gaussian processes in the presence of high-frequency biometrical sampled data. Nowadays, such data are easily collected using new non-invasive wearable devices (e.g., accelerometers) and generate substantial interest in monitoring human activity. The work is currently under review and is available in Alaimo Di Loro et al. (2021a) as a pre-print. The fifth chapter presents two modeling proposals to estimate epidemiological incidence indicators, typically collected during an epidemic for surveillance purposes. The methodology was applied to the Italian publicly available data for the monitoring of the COVID-19 epidemic. Both proposals consider probability distributions coherent with the nature of the data, which are counts, and adopt a generalized logistic function for the parametrization of the mean term. However, the second proposal allows for a latent component accounting for dependence among geographical units. Notice that, in the first work by Alaimo Di Loro et al. (2021b), the inference is pursued under a likelihood-based framework. This work helps highlighting even more the advantages of using a Bayesian approach, as subsequently described by Mingione et al. (2021b).
The last chapter summarizes the main points of the dissertation, underlining the most relevant findings, the original contributions, and stressing out how Bayesian hierarchical models altogether yield a cohesive treatment of many issues.