Academic literature on the topic 'Spatial Durbin model'
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Journal articles on the topic "Spatial Durbin model"
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Latipah, Nurul. "ANALISIS PERBANDINGAN MODEL REGRESI LINIER BERGANDA, SPATIAL DURBIN ERROR MODEL (SDEM), DAN SPATIAL LAG X (SLX) DALAM PERMODELAN DATA INDEKS PEMBANGUNAN MANUSIA (IPM) DI PROVINSI KALIMANTAN SELATAN." RAGAM: Journal of Statistics & Its Application 3, no. 1 (2024): 1. http://dx.doi.org/10.20527/ragam.v3i1.11622.
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This research aims to determine the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag In this research there are three independent variables, namely poverty severity (2022), population density (2022) and pure participation rate (2019), while the dependent variable is the human development index (2022). This research data is secondary in nature, namely obtained from the website of the South Kalimantan Central Statistics Agency. Based on the results and discussion, it is concluded that the best model from the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag x (SLX) in modeling human development index (HDI) data in South Kalimantan province is the spatial durbin error model (SDEM). This is because the spatial durbin error model (SDEM) has the smallest AIC value compared to the multiple linear regression model, and spatial lag x (SLX).
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Mukrom, Maghfiroh Hadadiah, Hasbi Yasin, and Arief Rachman Hakim. "PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL." Jurnal Gaussian 10, no. 1 (2021): 44–54. http://dx.doi.org/10.14710/j.gauss.v10i1.30935.
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Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.
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Lacombe, Donald J., and James P. LeSage. "Using Bayesian posterior model probabilities to identify omitted variables in spatial regression models." Papers in Regional Science 94, no. 2 (2013): 365–83. http://dx.doi.org/10.1111/pirs.12070.
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AbstractLeSage and Pace (2009) consider the impact of omitted variables in the face of spatial dependence in the disturbance process of a linear regression relationship and show that this can lead to a spatial Durbin model. Monte Carlo experiments and Bayesian model comparison methods are used to distinguish between spatial error and Durbin model specifications that arise with varying levels of correlation between included and omitted variables. The Monte Carlo results suggest use of the common factor relationship developed in Burridge (1981) as a way to test for the presence of omitted variables bias influencing specific explanatory variables.
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Chaturvedi, Anoop, Shalabh, and Sandeep Mishra. "Generalized Bayes Estimator for Spatial Durbin Model." Journal of Quantitative Economics 19, S1 (2021): 267–85. http://dx.doi.org/10.1007/s40953-021-00271-x.
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Bekti. "MAXIMUM LIKELIHOOD ESTIMATION FOR SPATIAL DURBIN MODEL." Journal of Mathematics and Statistics 9, no. 3 (2013): 169–74. http://dx.doi.org/10.3844/jmssp.2013.169.174.
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Xiao Li. "The Construction of Economic Education Model Based on the Spatial Durbin Model." Journal of Electrical Systems 20, no. 6s (2024): 619–29. http://dx.doi.org/10.52783/jes.2714.
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This research attempts to address the complexities and the nuances that are a part of the economic education by proposing an innovative model that is grounded in the Spatial Durbin Model (SDM). The spatial interdependencies which are crucial for the understanding of the regional variations in the economic implications and the efficacy of the educational policies are sometimes neglected by the conventional main frames of the economic education. The integration of the SDM, which is a sophisticated spatial econometric tool, aims to offer a thorough and detailed understanding of the intricate relationships between the education and the economic development in context of the diverse geographics. The model that is proposed in this research study extends beyond the traditional linear regressions as it accounts for the spatial autocorrelation and the spatial lag effects which thereby captures the spatial spillover and the diffusion of the educational investments and policies. With the help of the empirical analysis which moves forward by the utilization of the spatial econometric techniques such as the spatial lag and the spatial error models, strives to explore the spatial dynamics of the economic education while identifying the spatial patterns, the dependencies involved and the disparities that come up in the attainment of the educational and the economic performance. By incorporation of the spatial dimensions into the economic education model, the policymakers and the educators are able to get their hands on the in depth insights. This is into the spatially differentiated impacts of the educational interventions which tends to enable a more targeted and a more effective design and implementation of the policy. Furthermore, the proposed model also aims to facilitate the identification of the spatially targeted strategies in order to address the regional disparities with respect to the educational outcomes and the economic growth. Hence, it contributes to a more inclusive and a sustainable development trajectory. Through the interdisciplinary effort of bridging the economics, education and the spatial analysis, this research extends a valuable main frame for the policymakers, educators and the researchers which helps in attaining better understanding and helps to address the multifaceted challenges as well as the opportunities in the economic education and the regional development. Ultimately, this has enhanced the understanding of the spatial dimensions of the economic education which leads to the evidence based policies and interventions which are aimed at nurturing the equitable and resilient economic growth. In this paper, we utilize the spatial Durbin model as a framework to investigate the role that advanced degrees play in driving technological innovation in various parts of the world. Spatial self-correlation and instability in the geographic distribution of post-graduates in China were shown by data from the regional panel in China between 2004 and 2018. Advanced degrees facilitated the advancement of cutting-edge technologies.
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Fauzi, Fatkhurokhman Fauzi, Gabriella Hilary Wenur, and Rochdi Wasono. "SPATIAL DURBIN MODEL OF UNEMPLOYMENT RATE IN CENTRAL JAVA." Parameter: Journal of Statistics 3, no. 1 (2023): 7–18. http://dx.doi.org/10.22487/27765660.2023.v3.i1.16423.
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Unemployment is a labor problem that is often faced by developing countries like Indonesia. The number of unemployed in Indonesia has fluctuated from year to year, including in Central Java Province. One of the efforts made to overcome this problem is to know the factors that influence unemployment. The region effect greatly affects the open unemployment rate. Modeling involving area effects is very precise, one of which is the Spatial Durbin Model (SDM). In this study, modeling of the open unemployment rate was carried out using a spatial approach in each district/city in Central Java. The models used in this study are Ordinary Last Square (OLS), Spatial Auto Regressive (SAR), Spatial Error Models (SEM), Spatial Durbin Model (SDM), Spatial Error Durbin Model (SDEM). The five methods were evaluated using the Akaike Information Criteria (AIC). The spatial weighting used in this study is Queen Contiguity. Based on the smallest AIC value (115.42), the best method in this study is HR. Meanwhile, the significant factors are the percentage of labor force participation rate (X1), the number of poor people (X4), the lag of economic growth, the lag of poverty, and the lag of the district/city minimum wage
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Alvitiani, Siska, Hasbi Yasin, and Mochammad Abdul Mukid. "PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL." Jurnal Gaussian 8, no. 2 (2019): 220–32. http://dx.doi.org/10.14710/j.gauss.v8i2.26667.
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Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model
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Muradi, Hengki. "SPATIAL DURBIN MODEL UNTUK PERAMALAN INFLASI DI PULAU JAWA." Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam 2, no. 2 (2019): 149. http://dx.doi.org/10.32493/jsmu.v2i2.3322.
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Inflasi merupakan indikator makro ekonomi yang penting bagi sebuah negara. Inflasi yang rendah dan terkendali merupakan harapan bagi semua bangsa di dunia. Penelitian ini bertujuan untuk memodelkan inflasi di Pulau Jawa dengan mengguankan pendekatan model spasial durbin. Data yang digunakan adalah data sekunder BPS selama periode 2015-2018. Peubah yang digunakan untuk menduga inflasi adalah tingkat kemiskinan dan UMK. Pada penelitian ini diperoleh nilai , yang berarti model Spasial Durbin yang dibangun mampu menjelaskan variasi pada Inflasi sebanyak 64,92%, pada inflasi terdapat pengaruh spasial autoregresive. Kemudian, ada pengaruh signifikan peubah tingkat kemiskinan dan UMK terhadap inflasi, namun secara spasial keduanya tidak berpengaruh signifikan.
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Karim, Abdul. "Regional Economic Growth: A Spatial Durbin Model Approach." Jurnal Matematika MANTIK 7, no. 2 (2021): 147–54. http://dx.doi.org/10.15642/mantik.2021.7.2.147-154.
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The purpose of this study is to determine the effect of spatial dependence on Gross Regional Domestic Product (GRDP) in Central Java Province. Spatial Durbin Model (SDM) is a regression model consisting of a spatial data structure which is the development of the Spatial Autoregressive Model (SAR). There is an additional spatial effect on the component of the independent variable that is not included in the SAR model or commonly referred to as an indirect effect on the independent variable. This indicates that SDM has advantages compared to SAR because there are spatial effects on the dependent and independent variables, the spatial weighted matrix used in this study is row-normalized binary contiguity. The data used in this study is sourced from the Central Java Statistics Agency (BPS) in 2019 for 35 districts and cities, which GRDP as the dependent variable, labor, human resources, and road infrastructure as independent variables. Based on the results of the analysis, the AIC value shows that SDM is significantly better than the ordinary least square (OLS) and SAR models. SDM results show that human resources have a positive sign and a direct effect of 88.5 percent and an indirect effect of 13.1 percent. In addition, the labor variable has an indirect effect on GRDP of 22.2 percent.
Dissertations / Theses on the topic "Spatial Durbin model"
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Fischer, Manfred M., and Philipp Piribauer. "Model uncertainty in matrix exponential spatial growth regression models." WU Vienna University of Economics and Business, 2013. http://epub.wu.ac.at/4013/1/wp158.pdf.
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This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural solution, supported by formal probabilistic reasoning, is the use of Bayesian model averaging which assigns probabilities on the model space and deals with model uncertainty by mixing over models, using the posterior model probabilities as weights. This paper proposes to adopt Bayesian information criterion model weights since they have computational advantages over fully Bayesian model weights. The approach is illustrated for both identifying model covariates and unveiling spatial structures present in pan-European growth data. (authors' abstract)<br>Series: Department of Economics Working Paper Series
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Fischer, Manfred M. "A spatial Mankiw-Romer-Weil model: Theory and evidence." Springer Verlag, 2011. http://dx.doi.org/10.1007/s00168-010-0384-6.
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This paper presents a theoretical growth model that extends the
Mankiw-Romer-Weil [MRW] model by accounting for technological
interdependence among regional economies. Interdependence is assumed to work
through spatial externalities caused by disembodied knowledge diffusion. The
transition from theory to econometrics leads to a reduced-form empirical spatial
Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system of 198 regions across 22 European countries over the period from 1995 to 2004 is used to empirically test the model. Testing is performed by assessing the importance of cross-region technological interdependence, and measuring direct and indirect (spillover) effects of the MRW
determinants on regional output. (author's abstract)
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Fischer, Manfred M. "A spatial Mankiw-Romer-Weil model: Theory and evidence." WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/3959/1/SSRN%2Did1364060.pdf.
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This paper presents a theoretical growth model that extends the
Mankiw-Romer-Weil [MRW] model by accounting for technological
interdependence among regional economies. Interdependence is assumed to work
through spatial externalities caused by disembodied knowledge diffusion. The
transition from theory to econometrics leads to a reduced-form empirical spatial
Durbin model specification that explains the variation in regional levels of per
worker output at steady state. A system of 198 regions across 22 European
countries over the period from 1995 to 2004 is used to empirically test the model.
Testing is performed by assessing the importance of cross-region technological
interdependence, and measuring direct and indirect (spillover) effects of the MRW
determinants on regional output. (author's abstract)
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Zhang, Bingling. "Development of a social weights matrix to consider friendship influences on air travel." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52312.
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People often make social-related trips to perform activities with their friends. An individual's group of friends can be characterized by his or her social network. While traditional social network data collection is time-consuming and dependent on memory recollection, new online social networking sites may address these shortcomings. This research focuses on the use of tie-strength, the strength of an individual's relationships in his or her social network, to characterize friendships and how this influences an individual's air travel behavior. Four candidate weighting schemes were developed using data collected from a web-based survey which included demographic information, an air travel diary, and friendship information retrieved from Facebook.com. The candidate weight matrices were then tested in a spatial Durbin count model (social model). The results of this study are threefold. First, candidate weighting schemes which consider mutual friendship (i.e. the number of mutual friends two people have in common) exclusively produced higher log-likelihoods than weighting schemes which also consider whether individuals are direct friends (i.e. whether the two individuals are friends themselves). Second, the results of the social model were compared with those of a non-social model. These results suggest that there exist major flaws in using a non-social model to represent variables which may be socially dependent and correlated. Finally, results suggest that individuals tend to have friends who, on average, make more trips than they do. With a growing number of people using online social networks, exploring and understanding friendship influences on travel behavior will help the transportation industry better recognize future travel needs.
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Fischer, Manfred M., Nico Pintar, and Benedikt Sargant. "Austrian Outbound Foreign Direct Investment in Europe: A spatial econometric study." The Romanian Regional Science Association, 2016. http://epub.wu.ac.at/5118/1/V1011.MMFischer.pdf.
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This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009-2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable.
The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the mark
et potential variable indicating that spatial spillovers do not matter. (authors' abstract)
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Fischer, Manfred M., Monika Bartkowska, Aleksandra Riedl, Sascha Sardadvar, and Andrea Kunnert. "The impact of human capital on regional labor productivity in Europe." WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/3963/1/SSRN%2Did1304654.pdf.
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This paper employs a spatial Durbin model for analyzing the impact of human
capital on regional productivity using for 198 NUTS-2 European regions for the
sample period from 1995 to 2004. The study provides evidence for the existence
of spatial externalities and interactions of the sort as emphasized by new growth
theory. To interpret results meaningfully, we calculate summary measures that
account for the simultaneous feedback nature of the underlying model. By
sampling from the parameter distribution we present measures of dispersion,
revealing that it is relative regional advantages in human capital that matter most
for productivity growth. (authors' abstract)
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Jin, Fei. "Essays in Spatial Econometrics: Estimation, Specification Test and the Bootstrap." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365612737.
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COSTANTINO, Salvatore. "A Spatial Origin-Destination Analysis of International Tourism Demand. The Case of Italian Provinces." Doctoral thesis, Università degli Studi di Palermo, 2021. http://hdl.handle.net/10447/499047.
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Greening, Anthony N. "Social disorganisation theory and violent crime: A spatial-econometric analysis of Chicago and Sydney." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2528.
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The spatialisation of violent crime is explored in two large case studies, Chicago and Sydney, using spatial econometric methods and macro-sociological variables derived from Social Disorganisation Theory.
Social Disorganisation Theory (SDT) is introduced in terms of its formulation in response to highly specific conditions arising in Chicago, as well as its adoption of methodological and theoretical developments from existing traditions. This specificity belies its breadth of application and enduring presence in criminology. With “Social Disorganisation Theory” hosting a wealth of highly nuanced academic dialogue conducted under its banner, current incarnations of SDT appear as branches on an evolutionary tree. This research addresses the theoretical roots of that tree, from which two primary benefits are derived. The first is that the resulting focus on macro-structural variables permits large-scale urban studies to be conducted with existing datasets. The second is that this effectively isolates the spatial analysis from specific theoretical developments and generalises the results. The difference from the classical formulation of SDT is that an “augmented” set of five variables is used as independent variables: disadvantage, population heterogeneity, residential mobility, family disruption and urbanisation. Criminal violence forms the dependent variable.
All variables are observed to exhibit spatial autocorrelation. In response, Spatial Durbin Models are selected for each case study. This selection is supported by diagnostics, with some qualification noted. Initial results suggest a basis for further exploration. In Sydney, this leads to a fully mediated model with family disruption as the mediator. In the case of Chicago, the strong landscape of segregation leads to a model which accommodates for the resulting structural instability. This introduces a model which provides separate treatment to highly homogeneous areas.
Results indicate mixed support for SDT. With the exception of heterogeneity and - to a lesser extent - urbanisation, variables broadly align with expectations derived from SDT in the initial Sydney and Chicago studies. However, these observations are muted by other outcomes. Firstly, the spatial complexity portrayed in the results is not formally conveyed by SDT. Using the argument that methodology both enables and constrains theory development, this is to be expected. Social Disorganisation Theory is credited with founding the ecological tradition in criminology. However, concepts of criminogenic place which have evolved from it are typically intertwined with co-location models of space. This is regarded as a limitation in which spatial autocorrelation is treated as a nuisance, rather than being theoretically embraced. That gap is highlighted by spatially rich results. The second threat to SDT in results from the Sydney case study is that a more parsimonious model is derived from family disruption alone. Furthermore, when disruption is employed as a mediator, full mediation is observed. The final response to SDT is that it does not accommodate structural instability as indicated in Chicago.
The results of this exploratory study offer insights into the spatial richness of violence in urban areas from an ecological perspective. This complexity poses a challenge to SDT. The thesis closes by discussing this challenge and includes an outline of proposed future work.
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LeSage, James P., and Manfred M. Fischer. "The impact of knowledge capital on regional total factor productivity." WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/3953/1/SSRN%2Did1088301.pdf.
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This paper explores the contribution of knowledge capital to total factor productivity
differences among regions within a regression framework. The dependent variable is total factor
productivity, defined as output (in terms of gross value added) per unit of labour and physical
capital combined, while the explanatory variable is a patent stock measure of regional
knowledge endowments. We provide an econometric derivation of the relationship, which in the
presence of unobservable knowledge capital leads to a spatial regression model relationship. This
model form is extended to account for technological dependence between regions, which allows
us to quantify disembodied knowledge spillover impacts arising from both spatial and
technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from
1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects
of knowledge capital on regional total factor productivity, and to assess the relative importance
of knowledge spillovers from spatial versus technological proximity. (authors' abstract)
Books on the topic "Spatial Durbin model"
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Mazo, Aleksandr, and Konstantin Potashev. The superelements. Modeling of oil fields development. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1043236.
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This monograph presents the basics of super-element modeling method of two-phase fluid flows occurring during the development of oil reservoir. The simulation is performed in two stages to reduce the spatial and temporal scales of the studied processes. In the first stage of modeling of development of oil deposits built long-term (for decades) the model of the global dynamics of the flooding on the super-element computational grid with a step equal to the average distance between wells (200-500 m). Local filtration flow, caused by the action of geological and technical methods of stimulation, are modeled in the second stage using a special mathematical models using computational grids with high resolution detail for the space of from 0.1 to 10 m and time — from 102 to 105 C.
 The results of application of the presented models to the solution of practical tasks of development of oil reservoir. Special attention is paid to the issue of value transfer in filtration-capacitive properties of the reservoir, with a detailed grid of the geological model on the larger grid reservoir models.
 Designed for professionals in the field of mathematical and numerical modeling of fluid flows occurring during the development of oil fields and using traditional commercial software packages, as well as developing their own software. May be of interest to undergraduate and graduate students studying in areas such as "Mechanics and mathematical modeling", "Applied mathematics", "Oil and gas".
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Simons, Oliver. Carl Schmitt’s Spatial Rhetoric. Edited by Jens Meierhenrich and Oliver Simons. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199916931.013.42.
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By the end of the 1930s space (Raum) had become a common catchword in the writings of Carl Schmitt. This chapter argues that space was not merely a theme during this phase of his career, but was linked to a rhetorical strategy and mode of argumentation. Focusing on Land and Sea (1942) and “Nomos” of the Earth (1950), the first two sections show how Schmitt developed two contrasting modes of argumentation inextricably intertwined with his theory of space and the poetics of his writing. In the final section Agamben’s comments on Schmitt’s “topology” and the collaborative work A Thousand Plateaus by Deleuze and Guattari serve as case studies for recent reconfigurations of Schmitt’s spatial thought. The analysis of their appropriations of Schmitt points to major differences between his original perspective on space and these contemporary theories. Schmitt’s spatial theory is deeply rooted in the epistemology of the early twentieth century.
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Lopes, Hedibert, and Nicholas Polson. Analysis of economic data with multiscale spatio-temporal models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.12.
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This article discusses the use of Bayesian multiscale spatio-temporal models for the analysis of economic data. It demonstrates the utility of a general modelling approach for multiscale analysis of spatio-temporal processes with areal data observations in an economic study of agricultural production in the Brazilian state of Espìrito Santo during the period 1990–2005. The article first describes multiscale factorizations for spatial processes before presenting an exploratory multiscale data analysis and explaining the motivation for multiscale spatio-temporal models. It then examines the temporal evolution of the underlying latent multiscale coefficients and goes on to introduce a Bayesian analysis based on the multiscale decomposition of the likelihood function along with Markov chain Monte Carlo (MCMC) methods. The results from agricultural production analysis show that the spatio-temporal framework can effectively analyse massive economics data sets.
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Buchler, Justin. Incremental Polarization. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190865580.001.0001.
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This book provides a unified spatial model of legislative elections, parties, and roll call voting to address three primary questions: why do legislators adopt extreme positions, how do they win given their extremism, and what role do parties play in promoting polarization? The book links spatial models of elections to spatial models of roll call voting in the legislature, and suggests that the key to understanding polarization is to reverse the order of conventional models and place the legislative session before the election because legislators adopt positions in the policy space, extreme or otherwise, through the incremental process of casting roll call votes. Linking a spatial model of an election to a model of roll call voting, the book derives the following. When a legislative caucus is ideologically homogeneous, electorally diverse, and policy motivated, it will empower party leaders to solve the collective action problem of sincere voting by counterbalancing members’ electoral pressure to vote as centrists. The result is that the caucus achieves policy goals at the cost of some electoral security, but agenda paradoxes minimize the electoral damage done, so most incumbents win re-election anyway at only slightly diminished margins. This model explains the development of polarization in the House of Representatives throughout the post–World War II period, and key votes on legislation such as the Affordable Care Act. Moreover, even the unusual politics within the Republican Party during the divided government period from 2011 through 2016 follow naturally from extensions of the model.
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Fernández, Pedro Trapero. The Application of GIS Technologies in the Roman Period. Bloomsbury Publishing Plc, 2025. http://dx.doi.org/10.5040/9781350433731.
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Using a selection of archaeological cases studies from the Roman period in the Mediterranean region, Pedro Trapero Fernández shows how GIS technologies can be employed in the creation of spatial models to reproduce historical realities.An increasing number of researchers use this digital humanities tool as a means to model both territory and landscape. This book compiles different spatial models under a unified methodology described in separated chapters, such as mobility and visibility models, and discusses their limitations and potential for implementation in archaeological contexts. The result is a detailed analysis of each method, which consequently results in an accessible manual for understanding GIS technologies. Designed for students and scholars with varying degrees of training in GIS, who intend to carry out spatial analysis and historical models, the approach of this book establishes a reference framework to work with GIS technologies in other historical periods. Trapero concludes by discussing the future of GIS and spatial analysis, and how it can be studied and used as a methodological approach in archaeological practice and research. GIS in history and archaeology encompasses various applications, ranging from the creation of thematic maps to the management of geo-referenced databases and the development of spatial models that replicate historical realities. While the potential of spatial models is significant for advancing research in multiple areas, there is currently a lack of comparative and unified methodologies in this field. A model, regardless of the scientific discipline, serves as a simplified representation of reality, enabling the understanding of underlying criteria and the simulation of different outcomes. Creating a historical model requires more than just technical proficiency in GIS tools; it necessitates an understanding of the mindset prevalent during the period under investigation. In this book, we have compiled a comprehensive collection of successful spatial models, discussing their limitations and potential for application in other domains. Each method is thoroughly analysed and critically assessed, presented in a way that allows fellow researchers to replicate the models. To facilitate this, we focus on the Roman period, illustrating with several specific examples within the same thought and culture. Additionally, we examine the future prospects of these applications and propose collaborative initiatives to enhance these tools further. Throughout the book, we include an indispensable visual component, encompassing figures, graphs, and tables that exemplify and simplify technical aspects. Furthermore, we provide a critical review of the latest literature, ensuring the content remains up-to-date and informed.
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Robolin, Stéphane. Introduction. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252039478.003.0001.
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This introductory chapter provides an overview of the book's main themes. This book explores the literary relationships between black South Africans and African Americans during the years of South African apartheid (formally, 1948–1994). It offers a literary history informed by spatial and cultural theory. On the one hand, it advances a mode of cultural analysis that foregrounds the geographic in black lives and cultural imaginaries and, in doing so, models a way of reading black South African and African American writing attuned to the relevance of race, space, and place. On the other hand, this study interprets the two literary traditions in relation to one another. Bringing attention to underaccounted-for cultural traffic that has shaped both traditions in the latter half of the twentieth century, it develops a literary history based on defining moments of cross-cultural engagement.
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Wang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.
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The strongest Indian summer monsoon (ISM) on the planet features prolonged clustered spells of wet and dry conditions often lasting for two to three weeks, known as active and break monsoons. The active and break monsoons are attributed to a quasi-periodic intraseasonal oscillation (ISO), which is an extremely important form of the ISM variability bridging weather and climate variation. The ISO over India is part of the ISO in global tropics. The latter is one of the most important meteorological phenomena discovered during the 20th century (Madden & Julian, 1971, 1972). The extreme dry and wet events are regulated by the boreal summer ISO (BSISO). The BSISO over Indian monsoon region consists of northward propagating 30–60 day and westward propagating 10–20 day modes. The “clustering” of synoptic activity was separately modulated by both the 30–60 day and 10–20 day BSISO modes in approximately equal amounts. The clustering is particularly strong when the enhancement effect from both modes acts in concert. The northward propagation of BSISO is primarily originated from the easterly vertical shear (increasing easterly winds with height) of the monsoon flows, which by interacting with the BSISO convective system can generate boundary layer convergence to the north of the convective system that promotes its northward movement. The BSISO-ocean interaction through wind-evaporation feedback and cloud-radiation feedback can also contribute to the northward propagation of BSISO from the equator. The 10–20 day oscillation is primarily produced by convectively coupled Rossby waves modified by the monsoon mean flows. Using coupled general circulation models (GCMs) for ISO prediction is an important advance in subseasonal forecasts. The major modes of ISO over Indian monsoon region are potentially predictable up to 40–45 days as estimated by multiple GCM ensemble hindcast experiments. The current dynamical models’ prediction skills for the large initial amplitude cases are approximately 20–25 days, but the prediction of developing BSISO disturbance is much more difficult than the prediction of the mature BSISO disturbances. This article provides a synthesis of our current knowledge on the observed spatial and temporal structure of the ISO over India and the important physical processes through which the BSISO regulates the ISM active-break cycles and severe weather events. Our present capability and shortcomings in simulating and predicting the monsoon ISO and outstanding issues are also discussed.
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Michele, Luminati. Costruire, trasformare, controllare. Edizioni Casagrande, 2022. http://dx.doi.org/10.35263/casagrande-951-1.
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The essays collected in this volume draw from the history of law and institutions, history of art and urban planning, geography and cartography, economic and social history, and propose a reflection on center-periphery relations, the hybridization between cultures, and the adaptation of “external” models in the definition, construction and control of urban, rural and state space. The subject of this interdisciplinary investigation is mainly the city of Milan, the Lombard region and the area of Canton Ticino between the end of the eighteenth century and the first decades of the nineteenth century: a period marked by important political-legal transitions, during which on the one hand the efforts of state authorities were aimed at implementing new legislative references for spatial and territorial organization, and on the other hand phenomena of resistance and opposition to this new legislation emerged.
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Holmes, Jonathan, and Philipp Hoelzmann. The Late Pleistocene-Holocene African Humid Period as Evident in Lakes. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.531.
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From the end of the last glacial stage until the mid-Holocene, large areas of arid and semi-arid North Africa were much wetter than present, during the interval that is known as the African Humid Period (AHP). During this time, large areas were characterized by a marked increase in precipitation, an expansion of lakes, river systems, and wetlands, and the spread of grassland, shrub land, and woodland vegetation into areas that are currently much drier. Simulations with climate models indicate that the AHP was the result of orbitally forced increase in northern hemisphere summer insolation, which caused the intensification and northward expansion of the boreal summer monsoon. However, feedbacks from ocean circulation, land-surface cover, and greenhouse gases were probably also important.Lake basins and their sediment archives have provided important information about climate during the AHP, including the overall increases in precipitation and in rates, trajectories, and spatial variations in change at the beginning and the end of the interval. The general pattern is one of apparently synchronous onset of the AHP at the start of the Bølling-Allerød interstadial around 14,700 years ago, although wet conditions were interrupted by aridity during the Younger Dryas stadial. Wetter conditions returned at the start of the Holocene around 11,700 years ago covering much of North Africa and extended into parts of the southern hemisphere, including southeastern Equatorial Africa. During this time, the expansion of lakes and of grassland or shrub land vegetation over the area that is now the Sahara desert, was especially marked. Increasing aridity through the mid-Holocene, associated with a reduction in northern hemisphere summer insolation, brought about the end of the AHP by around 5000–4000 years before present. The degree to which this end was abrupt or gradual and geographically synchronous or time transgressive, remains open to debate. Taken as a whole, the lake sediment records do not support rapid and synchronous declines in precipitation and vegetation across the whole of North Africa, as some model experiments and other palaeoclimate archives have suggested. Lake sediments from basins that desiccated during the mid-Holocene may have been deflated, thus providing a misleading picture of rapid change. Moreover, different proxies of climate or environment may respond in contrasting ways to the same changes in climate. Despite this, there is evidence of rapid (within a few hundred years) termination to the AHP in some regions, with clear signs of a time-transgressive response both north to south and east to west, pointing to complex controls over the mid-Holocene drying of North Africa.
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Claussen, Martin, Anne Dallmeyer, and Jürgen Bader. Theory and Modeling of the African Humid Period and the Green Sahara. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.532.
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There is ample evidence from palaeobotanic and palaeoclimatic reconstructions that during early and mid-Holocene between some 11,700 years (in some regions, a few thousand years earlier) and some 4200 years ago, subtropical North Africa was much more humid and greener than today. This African Humid Period (AHP) was triggered by changes in the orbital forcing, with the climatic precession as the dominant pacemaker. Climate system modeling in the 1990s revealed that orbital forcing alone cannot explain the large changes in the North African summer monsoon and subsequent ecosystem changes in the Sahara. Feedbacks between atmosphere, land surface, and ocean were shown to strongly amplify monsoon and vegetation changes. Forcing and feedbacks have caused changes far larger in amplitude and extent than experienced today in the Sahara and Sahel. Most, if not all, climate system models, however, tend to underestimate the amplitude of past African monsoon changes and the extent of the land-surface changes in the Sahara. Hence, it seems plausible that some feedback processes are not properly described, or are even missing, in the climate system models.Perhaps even more challenging than explaining the existence of the AHP and the Green Sahara is the interpretation of data that reveal an abrupt termination of the last AHP. Based on climate system modeling and theoretical considerations in the late 1990s, it was proposed that the AHP could have ended, and the Sahara could have expanded, within just a few centuries—that is, much faster than orbital forcing. In 2000, paleo records of terrestrial dust deposition off Mauritania seemingly corroborated the prediction of an abrupt termination. However, with the uncovering of more paleo data, considerable controversy has arisen over the geological evidence of abrupt climate and ecosystem changes. Some records clearly show abrupt changes in some climate and terrestrial parameters, while others do not. Also, climate system modeling provides an ambiguous picture.The prediction of abrupt climate and ecosystem changes at the end of the AHP is hampered by limitations implicit in the climate system. Because of the ubiquitous climate variability, it is extremely unlikely that individual paleo records and model simulations completely match. They could do so in a statistical sense, that is, if the statistics of a large ensemble of paleo data and of model simulations converge. Likewise, the interpretation regarding the strength of terrestrial feedback from individual records is elusive. Plant diversity, rarely captured in climate system models, can obliterate any abrupt shift between green and desert state. Hence, the strength of climate—vegetation feedback is probably not a universal property of a certain region but depends on the vegetation composition, which can change with time. Because of spatial heterogeneity of the African landscape and the African monsoon circulation, abrupt changes can occur in several, but not all, regions at different times during the transition from the humid mid-Holocene climate to the present-day more arid climate. Abrupt changes in one region can be induced by abrupt changes in other regions, a process sometimes referred to as “induced tipping.” The African monsoon system seems to be prone to fast and potentially abrupt changes, which to understand and to predict remains one of the grand challenges in African climate science.
Book chapters on the topic "Spatial Durbin model"
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Huang, Tingting, Gilbert Saporta, and Huiwen Wang. "A Spatial Durbin Model for Compositional Data." In Advances in Contemporary Statistics and Econometrics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73249-3_24.
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Nguyen, Phuong Anh, Tat Hien Phan, and Michel Simioni. "Productivity Convergence in Vietnamese Manufacturing Industry: Evidence Using a Spatial Durbin Model." In Causal Inference in Econometrics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27284-9_39.
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Song, Zhiguang. "The Spatial Intercorrelation of Global Carbon Dioxide Emissions: An Empirical Verification of China’s Impacts by Spatial Durbin Panel Data Model." In Towards Implementation of Sustainability Concepts in Developing Countries. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74349-9_24.
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Hu, Yu, and Chaofan Zheng. "Environmental Regulation, Land Use Effiiciency, and Industrial Structure Upgrading: Test Analysis Based on Spatial Durbin Model and Threshold Effect." In New Frontiers in Regional Science: Asian Perspectives. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4901-0_11.
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Jiang, Yanjie. "Establishment of the Evaluation Index System for the Economic and Environmental Coordination Evaluation Based on the Spatial Durbin Econometric Model." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3250-4_194.
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Chen, Yushan. "Research on the Impact of Openness to the World on Economic Growth: An Application of the Spatial Durbin Model Using Chinese Data." In Applied Economics and Policy Studies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3236-7_58.
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Scimone, Riccardo, Alessandra Menafoglio, Laura M. Sangalli, and Piercesare Secchi. "The Death Process in Italy Before and During the Covid-19 Pandemic: A Functional Compositional Approach." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_36.
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AbstractIn this talk, based on [1], we propose a spatio-temporal analysis of daily death counts in Italy, collected by ISTAT (Italian Statistical Institute), in Italian provinces and municipalities. While in [1] the focus was on the elderly class (70+ years old), we here focus on the middle class (50–69 years old), carrying out analogous analyses and comparative observations. We analyse historical provincial data starting from 2011 up to 2020, year in which the impacts of the Covid-19 pandemic on the overall death process are assessed and analysed. The cornerstone of our analysis pipeline is a novel functional compositional representation for the death counts during each calendar year: specifically, we work with mortality densities over the calendar year, embedding them in the Bayes space B2 of probability density functions. This Hilbert space embedding allows for the formulation of functional linear models, which are used to split each yearly realization of the mortality density process in a predictable and an unpredictable component, based on the mortality in previous years. The unpredictable components of the mortality density are then spatially analysed in the framework of Object Oriented Spatial Statistics. Via spatial downscaling of the results obtained at the provincial level, we obtain smooth predictions at the fine scale of Italian municipalities; this also enable us to perform anomaly detection, identifying municipalities which behave unusually with respect to the surroundings.
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Pinto, Felipe Tajá C., Krishna Milani, Leandro Guedes, et al. "Spatial Uncertainty in Pore Pressure Models at the Brazilian Continental Margin." In Springer Proceedings in Earth and Environmental Sciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_9.
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AbstractAccurate pore pressure models in wells are essential for ensuring the lowest cost and operational safety during exploration/development projects. This modeling requires the integration of several sources of information such as well data, formation pressure tests, geophysical logs, mud weight, geological models, seismic data, geothermal and sedimentation rate modeling. An empirical relationship between overpressure and compressional wave velocity is commonly applied to model the pore pressure. This deterministic approach does not allow uncertainty quantification and ignores other variables related to pore pressure. This paper presents a case study with real data to evaluate and quantify spatial pore pressure uncertainty. The exhaustive secondary variable came from the combination of seismic velocity and geothermal models. The methodology uses Sequential Gaussian Cosimulation with Intrinsic Collocated Cokriging. The results demonstrate the usefulness and applicability of the workflow proposed.
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Caffaro, Fabio, Lorenzo Bongiovanni, and Claudio Rossi. "Geo-temporal Crime Forecasting Using a Deep Learning Attention-Based Model." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_26.
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AbstractCrime prediction is a crucial problem in law enforcement, and the ability to forecast where and when crimes are likely to occur can help police departments allocate their resources effectively and prevent crimes. In this chapter, we propose a geo-temporal crime forecasting model based on a transformer architecture. We use a public dataset from the Boston Police Department and forecast crimes in each cell of a 1 km × 1 km grid. We use an encoder–decoder structure to capture the spatiotemporal patterns of the crimes. The encoder elaborates the crimes that occurred in each cell during the previous n days, and the decoder generates predictions of future crimes in each cell for the next m days. Our model considers both spatial and temporal correlations, which is challenging for traditional models. We evaluate the model on the Boston crime dataset and compare it with traditional solutions. Our experiments show that our model outperforms traditional models, achieving better accuracy in crime prediction. Overall, our proposed geo-temporal crime forecasting model is a promising approach for predicting crime in a given area.
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Armesto, Juan J., Steward T. A. Pickett, and Mark J. McDonnell. "Spatial Heterogeneity During Succession: A Cyclic Model of Invasion and Exclusion." In Ecological Studies. Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3062-5_13.
Full textConference papers on the topic "Spatial Durbin model"
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Li, Mingrui, Douglas A. Allan, San Dinh, et al. "NMPC for Mode-Switching Operation of Reversible Solid Oxide Cell Systems." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.103553.
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Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC system was developed in the open-source, equation-based IDAES modeling framework. Our IDAES dynamic simulation results show that NMPC can ramp the SOC system between hydrogen and power production targets within short mode switching times. Moreover, NMPC can comply with operating limits in the SOC system more effectively than PI, and only NMPC can directly enforce user-specified limits for mixed spatial temporal partial derivatives of temperature. This allows for management of the trade-off between operating efficiency and cell degradation, which is dependent on these temperature curvatures.
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Wang, Jiaqi, Jingyi Wang, Jingyi Hu, et al. "Baseline alpha wave predicts post-cue alpha during visual spatial attention with linear mixed model." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782793.
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Kessler, Sylvia. "Spatial Evaluation of the Corrosion Condition of Reinforced Concrete Structures." In CORROSION 2018. NACE International, 2018. https://doi.org/10.5006/c2018-11133.
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Abstract Reinforced concrete structures form the backbone of our aging infrastructure. Most of these existing structures have experienced damage or deterioration. As part of ongoing condition assessment it is necessary to identify the extent, nature, cause and prognosis of deterioration using a range of tools and methods, including prediction models. Deterioration of reinforced concrete structures is often attributed to reinforcement corrosion initiated due to application of deicing salt during wintertime or chlorides from marine environment. Thus, corrosion detection, e.g. half-cell potential measurement, is of major importance for condition assessment. The information gained during corrosion detection can be used for updating the probabilistic service life prediction at time of inspection. Major impact on the accuracy of the updated service life prediction besides the probabilistic model itself is the reliability of the used inspection methods. Since first results on the reliability of half-cell potential measurements are published this data can be used to update corrosion probability taking into account not only the temporal aspect but also the spatial variability of reinforcement corrosion. This paper presents a case study for updating the corrosion probability with half-cell potential measurement data.
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Kusrini, D. E., and Mukhtasor. "Spatial Durbin model analysis macroeconomic loss due to natural disasters." In SYMPOSIUM ON BIOMATHEMATICS (SYMOMATH 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4914437.
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Mustaqim, Setiawan, Suhartono, and Brodjol Sutijo Suprih Ulama. "Efficient estimation of simultaneous equations of spatial durbin panel data model." In THE 8TH ANNUAL BASIC SCIENCE INTERNATIONAL CONFERENCE: Coverage of Basic Sciences toward the World’s Sustainability Challanges. Author(s), 2018. http://dx.doi.org/10.1063/1.5062788.
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Atikah, Nur, Swasono Rahardjo, and Trianingsih Eni Lestari. "Parameter estimation of spatial durbin model (SDM) using method of moment." In 28TH RUSSIAN CONFERENCE ON MATHEMATICAL MODELLING IN NATURAL SCIENCES. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0000716.
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Dewi, Dyra Fitri Kesuma, Asep Saefuddin, and Utami Dyah Syafitri. "Spatial Durbin Model for Identifying the Factors Affecting Diarrhea in East Java." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290532.
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Shen, Xiaoqun, Xiaobo Luo, and Xingang Sang. "A geographically weighted Durbin model for spatial downscaling of land surface temperatures." In International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), edited by Chao Zuo. SPIE, 2023. http://dx.doi.org/10.1117/12.2682581.
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Zhang, Boya, Yifan He, Jingjing Li, Jin Liu, and YuJia Wei. "Research on Carbon Emission Benchmark Regression Analysis Based on Spatial Durbin Model." In Proceedings of the 3rd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2024, May 24–26, 2024, Jinan, China. EAI, 2024. http://dx.doi.org/10.4108/eai.24-5-2024.2350083.
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Ye, Jian, Wenhua Xu, and Jialin Zheng. "The Digital Economy, Spatial Effects and Green Technology Innovation: Analysis Based on The Spatial Durbin Model." In Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China. EAI, 2023. http://dx.doi.org/10.4108/eai.9-12-2022.2327624.
Full textReports on the topic "Spatial Durbin model"
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Morales, Leonardo Fabio, and Eleonora Dávalos. Diffusion of crime control benefits: Forced eradication and coca crops in Colombia. Banco de la República Colombia, 2022. http://dx.doi.org/10.32468/dtseru.314.
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One explanation for the increasing number of hectares with coca cultivation is that eradication strategies displace coca crops but fail to completely clear affected areas. In the drug policy literature, that dynamic shifting is commonly known as the balloon effect. This study integrates georeferenced agricultural data through spatially explicit econometric models to test the hypothesis that forced eradication displace coca crops. Using annual data for 1,116 contiguous municipalities in Colombia between 2001 and 2015, we estimate a spatial Durbin model with municipal and time fixed effects. Our results suggest that, on average, aerial fumigation in a municipality diffuses the benefits of this crime control strategy to neighboring municipalities.
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Parkins and Leis. L51654 Spatial Densities of Stress-Corrosion Cracks in Line-Pipe Steels. Pipeline Research Council International, Inc. (PRCI), 1992. http://dx.doi.org/10.55274/r0010367.
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There was a need to define the spatial density of stress-corrosion crack arrays that develop in operating gas-transmission pipelines and in laboratory test specimens of line-pipe steel, to improve understanding of the factors that control the density and provide data to test models of pipeline cracking. Within the broad definition of crack density are included the locations, numbers, lengths, depths, and degree of linkage of cracks. An analysis has been conducted of location, numbers, lengths, depths, and degree of linkage of stress-corrosion crack colonies in samples from the field and from laboratory tests. This has provided data to test any model of the cracking of pipelines. Thus, it is shown that crack densities are of the order of 5 cracks/mm of distance normal to the crack length direction and that the depths and lengths of cracks are distributed according to log normal and Weibull functions. During the growth stage of cracks in a colony, their spatial distribution is not random, because of the interaction of their associated stress fields. That interaction also results in the coalescence of cracks, an extremely important part of the overall growth process, and that interaction is defined quantitatively to provide further information to which modeling must aspire.
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Kwon, Jaymin, Yushin Ahn, and Steve Cheung. Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2022): Data-Driven Exposure Analysis by Transportation Modes. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2220.
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Particulate matter (PM) pollution poses significant health risks, influenced by various meteorological factors and seasonal variations. This study investigates the impact of temperature and other meteorological variables on PM10 and PM2.5 levels in Fresno County, known for high air pollution. Multiple linear regression (MLR) and generalized additive models (GAMs) assess the significance of these relationships. Analyzing data from Fresno County, we examine PM10 and PM2.5 levels across "hot" (June to August) and "cool" (September to May) seasons. Findings indicate PM10, both MLR and GAM models identify statistically significant variables, excluding temperature and wind direction in each season. However, during the hot season, both temperature and wind direction become statistically significant predictors of PM10. These variables remain insignificant during the cool season. For PM2.5, the MLR model suggests that temperature, humidity, and wind direction are not significant throughout the entire season, while the GAM model finds only wind direction to be insignificant. The temperature is highly significant for hot and cool seasons under the MLR model, whereas humidity becomes insignificant under the GAM model. Model performance is evaluated using measures of fit, indicating that MLR outperforms GAM for PM10 during the entire and hot seasons, while GAM performs better during the cool season. For PM2.5, GAM outperforms MLR during the cool seasons, with no clear distinction in performance during the hot season. The regional air quality PM2.5 at Fresno and meteorological conditions were closely related to the concentration of on-road particulate matter. From the intercity monitoring of PM2.5 and BC, on-road concentrations were statistically significantly higher than those measured in-vehicle (p<.001). Therefore, in-vehicle particle concentrations were safe compared to the on-road concentrations. In most cases, PM2.5 on the highways was higher than PM2.5 on the local roadways. On-road transportation-related particles measured in the San Joaquin Valley were significantly higher than those measured in the Bay Area. The results from a daily dose of transportation-related PM2.5 estimation based on a 2-hour commute and an 8-hour trip demonstrated that children under 11 years of age are more vulnerable than adults. In-vehicle daily doses were significantly lower than the on-road daily doses. This study highlights the importance of considering seasonal variations and meteorological factors when modeling PM pollution. It underscores PM's sensitivity to temperature and wind direction in Fresno County's hot season, offering insights for effective pollution management from transportation and policy implementation to mitigate the adverse health effects.
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Ansari, S. M., E. M. Schetselaar, and J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.
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Unconstrained magnetotelluric inversion commonly produces insufficient inherent resolution to image ore-system fluid pathways that were structurally thinned during post-emplacement tectonic activity. To improve the resolution in these complex environments, we synthesized the 3-D magnetotelluric (MT) response for geologically realistic models using a finite-element-based forward-modelling tool with unstructured meshes and applied it to the Lalor volcanogenic massive-sulfide deposit in the Snow Lake mining camp, Manitoba. This new tool is based on mapping interpolated or simulated resistivity values from wireline logs onto unstructured tetrahedral meshes to reflect, with the help of 3-D models obtained from lithostratigraphic and lithofacies drillhole logs, the complexity of the host-rock geological structure. The resulting stochastic model provides a more realistic representation of the heterogeneous spatial distribution of the electric resistivity values around the massive, stringer, and disseminated sulfide ore zones. Both models were combined into one seamless tetrahedral mesh of the resistivity field. To capture the complex resistivity distribution in the geophysical forward model, a finite-element code was developed. Comparative analyses of the forward models with MT data acquired at the Earth's surface show a reasonable agreement that explains the regional variations associated with the host rock geological structure and detects the local anomalies associated with the MT response of the ore zones.
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Peralta, Airy, and Chris Ray. Lagomorph ladders: Assessing a multi-host community and potential for spillover of rabbit hemorrhagic disease at Great Sand Dunes National Park and Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2303667.
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Rabbit hemorrhagic disease virus type 2 (RHDV2) has caused dramatic declines in rabbits and hares on several continents, with cascading effects on local ecology. Recent mortalities have been reported for several rabbit and hare species in the United States, suggesting broad susceptibility of lagomorphs. If this susceptibility extends to the American pika (Ochotona princeps), the most cold-adapted lagomorph, it could compound climate-mediated threats to this species. Due to climate change, American pikas are predicted to experience significant upslope range retraction during this century. Using an analogy borrowed from wildfire scenarios, other lagomorph species occurring at lower and mid-elevations could act as ?ladder fuels? to wick RHDV2 into high-elevation pika populations. To address this concern, we investigated spatial patterns of habitat use by pikas and other lagomorphs in Great Sand Dunes National Park and Preserve (GRSA), which borders several counties that have reported RHDV2. In 2022, we surveyed 115 plots from a spatially balanced sample of pika habitats in the park, including 48 legacy plots from a pika survey conducted in 2010-2012. Pika detections at the plot level were paired with topographic and environmental indices to estimate minimum habitat occupancy and determine its covariates. Leporid (rabbit and hare) detections at these same plots were used to model presence using similar covariates and correcting for imperfect detection. Our best-supported models of pika and leporid presence were then used to estimate the probability of contact between these taxa within the park. Our mean estimate of pika habitat occupancy was at least 95% during 2022 in GRSA, slightly higher than in 2010-2012, and effects of elevation and precipitation on pika occupancy were as expected from the previous study. Leporid presence at these same plots was 48% after correcting for imperfect detection. The best model of leporid presence supported a negative effect of elevation, in agreement with other studies of these taxa. The best pika and leporid models also included a positive effect of incoming solar radiation. Finally, we used our best models of pika habitat occupancy and leporid presence within the park to map the potential for areas of contact and RHDV2 transmission between these taxa. Our results indicate some potential for contact within subalpine forests, Specifically in the northern half of the park near the lower reach of the Sand Creek Trail and in the far south just north of California Peak.
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Peralta, Airy, Chris Ray, Airy Peralta, and Chris Ray. Lagomorph ladders: Assessing a multi-host community and potential for spillover of rabbit hemorrhagic disease at Great Sand Dunes National Park and Preserve (revised). National Park Service, 2024. http://dx.doi.org/10.36967/2306370.
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Rabbit hemorrhagic disease virus type 2 (RHDV2) has caused dramatic declines in rabbits and hares on several continents, with cascading effects on local ecology. Recent mortalities have been reported for several rabbit and hare species in the United States, suggesting broad susceptibility of lagomorphs. If this susceptibility extends to the American pika (Ochotona princeps), the most cold-adapted lagomorph, it could compound climate-mediated threats to this species. Due to climate change, American pikas are predicted to experience significant upslope range retraction during this century. Using an analogy borrowed from wildfire scenarios, other lagomorph species occurring at lower and mid-elevations could act as ?ladder fuels? to wick RHDV2 into high-elevation pika populations. To address this concern, we investigated spatial patterns of habitat use by pikas and other lagomorphs in Great Sand Dunes National Park and Preserve (GRSA), which borders several counties that have reported RHDV2. In 2022, we surveyed 115 plots from a spatially balanced sample of pika habitats in the park, including 48 legacy plots from a pika survey conducted in 2010-2012. Pika detections at the plot level were paired with topographic and environmental indices to estimate minimum habitat occupancy and determine its covariates. Leporid (rabbit and hare) detections at these same plots were used to model presence using similar covariates and correcting for imperfect detection. Our best-supported models of pika and leporid presence were then used to estimate the probability of contact between these taxa within the park. Our mean estimate of pika habitat occupancy was at least 95% during 2022 in GRSA, slightly higher than in 2010-2012, and effects of elevation and precipitation on pika occupancy were as expected from the previous study. Leporid presence at these same plots was 48% after correcting for imperfect detection. The best model of leporid presence supported a negative effect of elevation, in agreement with other studies of these taxa. The best pika and leporid models also included a positive effect of incoming solar radiation. Finally, we used our best models of pika habitat occupancy and leporid presence within the park to map the potential for areas of contact and RHDV2 transmission between these taxa. Our results indicate some potential for contact within subalpine forests, Specifically in the northern half of the park near the lower reach of the Sand Creek Trail and in the far south just north of California Peak.
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Engel, Chandler, Jeremy Giovando, and Grant Halvorson. Analysis of Paxton Siphon frazil ice blockage event during January 2022. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47078.
Full textAbstract:
In early January 2022, the Paxton Siphon, owned and operated by the Nebraska Public Power District, filled with frazil ice creating a blockage that resulted in a rapid upstream stage rise for the Sutherland Canal. An event of this type has never happened in the over 80 years of operating the Paxton Siphon. An analysis of the available weather and canal data suggests a rapid air temperature change resulted in the water becoming supercooled, which combined with the moderately low flows in the canal resulted in an anomalous frazil ice formation event. To address this issue for future cold-weather events, a water-temperature model was developed using the Hydrologic Engineering Center’s River Analysis System and can be used to determine the spatial extents of the supercooling event using forecasted weather information. In addition, we developed a heat-exchange forecast tool that can be used operationally to screen for potential frazil ice formation periods with a 1-week outlook period.
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Mathew, Jijo K., Haydn Malackowski, Yerassyl Koshan, et al. Development of Latitude/Longitude (and Route/Milepost) Model for Positioning Traffic Management Cameras. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317720.
Full textAbstract:
Traffic Incident Management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring the safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile-marker based with cameras that operate in a Pan-Tilt-Zoom (PTZ) coordinate system relies on dispatchers having detailed knowledge of hundreds of cameras and perhaps some presets. During real-time incident dispatching, reducing the time it takes to identify the most relevant cameras and view the incident improves incident management dispatch times. This research developed a camera-to-mile marker mapping technique that automatically sets the camera view to a specified mile marker within the field-of-view of the camera. A new performance metric on verification time (TEYE) that captures the time it takes for TMC operators to have the first visual on roadside cameras is proposed for integration into the FHWA TIM event sequence. Performance metrics that summarize spatial camera coverage and image quality for use in both dispatch and long-term statewide planning for camera deployments were also developed. Using mobile mapping and LiDAR geospatial data to automate the mapping of mile markers to camera PTZ settings, and the integration of connected vehicle trajectory data to detect incidents and set the nearest camera view on the incident are both discussed for future studies.
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Ziegler, Nancy, Nicholas Webb, John Gillies, et al. Plant phenology drives seasonal changes in shear stress partitioning in a semi-arid rangeland. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47680.
Full textAbstract:
Accurate representation of surface roughness in predictive models of aeolian sediment transport and dust emission is required for model accuracy. While past studies have examined roughness effects on drag partitioning, the spatial and temporal variability of surface shear velocity and the shear stress ratio remain poorly described. Here, we use a four-month dataset of total shear velocity (u*) and soil surface shear velocity (us*) measurements to examine the spatiotemporal variability of the shear stress ratio (R) before, during, and after vegetation green-up at a honey mesquite (Prosopis glandulosa Torr.) shrub-invaded grassland in the Chihuahuan Desert, New Mexico, USA. Results show that vegetation green-up, the emergence of leaves, led to increased drag and surface aerodynamic sheltering and a reduction in us* and R magnitude and variability. We found that us* decreased from 20% to 5% of u* as the vegetation form drag and its sheltering effect increased. Similarly, the spatiotemporal variability of R was found to be linked directly to plant phenological phases. We conclude that drag partition schemes should incorporate seasonal vegetation change, via dynamic drag coefficients and/or R, to accurately predict the timing and magnitude of seasonal aeolian sediment fluxes.
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Bastawros, Ashraf. DTPH56-16H-CAP01 Mechanochemistry-Based Detection of Early Stage Corrosion Degradation of Pipeline Steels. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011990.
Full textAbstract:
The aim of the work is to provide measurable precursor signals associated with the initiation stage of near-surface damage and cracking, as depicted in Fig. 1.1. We have identified many salient features during the early stage of the SCC process (Stages 1, 2 on Fig. 1.1), including residual stress build-up, near-surface (within few microns) defect percolation, and changes of dislocation dynamics and measurable changes of the surface osmic resistance. We developed a model-based prediction of the onset and progression of SCC subsurface damage and assessed the electrochemical impedance spectroscopy (EIS) to measure the extent of surface damage. Such a framework would enable the development of appropriate field-deployable NDE technology with the needed spatial and temporal resolutions.