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model</title> <meta name="description" content="Search results for: spatial model"> <meta name="keywords" content="spatial model"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="spatial model"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 18568</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: spatial model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18568</span> Estimation of Missing Values in Aggregate Level Spatial Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amitha%20Puranik">Amitha Puranik</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20Binu"> V. S. Binu</a>, <a href="https://publications.waset.org/abstracts/search?q=Seena%20Biju"> Seena Biju</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20regression" title="spatial regression">spatial regression</a>, <a href="https://publications.waset.org/abstracts/search?q=missing%20data%20estimation" title=" missing data estimation"> missing data estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20autocorrelation" title=" spatial autocorrelation"> spatial autocorrelation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20analysis" title=" simulation analysis"> simulation analysis</a> </p> <a href="https://publications.waset.org/abstracts/58411/estimation-of-missing-values-in-aggregate-level-spatial-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58411.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18567</span> Spatial Econometric Approaches for Count Data: An Overview and New Directions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paula%20Sim%C3%B5es">Paula Simões</a>, <a href="https://publications.waset.org/abstracts/search?q=Isabel%20Nat%C3%A1rio"> Isabel Natário</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis" title="spatial data analysis">spatial data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20econometrics" title=" spatial econometrics"> spatial econometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20hierarchical%20models" title=" Bayesian hierarchical models"> Bayesian hierarchical models</a>, <a href="https://publications.waset.org/abstracts/search?q=count%20data" title=" count data"> count data</a> </p> <a href="https://publications.waset.org/abstracts/35788/spatial-econometric-approaches-for-count-data-an-overview-and-new-directions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35788.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">593</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18566</span> The Use of Geographically Weighted Regression for Deforestation Analysis: Case Study in Brazilian Cerrado</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20Paula%20Camelo">Ana Paula Camelo</a>, <a href="https://publications.waset.org/abstracts/search?q=Keila%20Sanches"> Keila Sanches</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deforestation" title="deforestation">deforestation</a>, <a href="https://publications.waset.org/abstracts/search?q=geographically%20weighted%20regression" title=" geographically weighted regression"> geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a> </p> <a href="https://publications.waset.org/abstracts/85043/the-use-of-geographically-weighted-regression-for-deforestation-analysis-case-study-in-brazilian-cerrado" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85043.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18565</span> Enhancing Spatial Interpolation: A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yakin%20Hajlaoui">Yakin Hajlaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Labib"> Richard Labib</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Fran%C3%A7ois%20Plante"> Jean-François Plante</a>, <a href="https://publications.waset.org/abstracts/search?q=Michel%20Gamache"> Michel Gamache</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20neural%20networks" title=" multi-layer neural networks"> multi-layer neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20interpolation" title=" spatial interpolation"> spatial interpolation</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20distance%20weighting" title=" inverse distance weighting"> inverse distance weighting</a> </p> <a href="https://publications.waset.org/abstracts/185810/enhancing-spatial-interpolation-a-multi-layer-inverse-distance-weighting-model-for-complex-regression-and-classification-tasks-in-spatial-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185810.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">52</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18564</span> A Comprehensive Procedure of Spatial Panel Modelling with R, A Study of Agricultural Productivity Growth of the 38 East Java’s Regencies/Municipalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahma%20Fitriani">Rahma Fitriani</a>, <a href="https://publications.waset.org/abstracts/search?q=Zerlita%20Fahdha%20Pusdiktasari"> Zerlita Fahdha Pusdiktasari</a>, <a href="https://publications.waset.org/abstracts/search?q=Herman%20Cahyo%20Diartho"> Herman Cahyo Diartho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial panel model is commonly used to specify more complicated behavior of economic agent distributed in space at an individual-spatial unit level. There are several spatial panel models which can be adapted based on certain assumptions. A package called splm in R has several functions, ranging from the estimation procedure, specification tests, and model selection tests. In the absence of prior assumptions, a comprehensive procedure which utilizes the available functions in splm must be formed, which is the objective of this study. In this way, the best specification and model can be fitted based on data. The implementation of the procedure works well. It specifies SARAR-FE as the best model for agricultural productivity growth of the 38 East Java’s Regencies/Municipalities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20panel" title="spatial panel">spatial panel</a>, <a href="https://publications.waset.org/abstracts/search?q=specification" title=" specification"> specification</a>, <a href="https://publications.waset.org/abstracts/search?q=splm" title=" splm"> splm</a>, <a href="https://publications.waset.org/abstracts/search?q=agricultural%20productivity%20growth" title=" agricultural productivity growth"> agricultural productivity growth</a> </p> <a href="https://publications.waset.org/abstracts/143954/a-comprehensive-procedure-of-spatial-panel-modelling-with-r-a-study-of-agricultural-productivity-growth-of-the-38-east-javas-regenciesmunicipalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143954.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18563</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18562</span> Development of a Spatial Data for Renal Registry in Nigeria Health Sector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adekunle%20Kolawole%20Ojo">Adekunle Kolawole Ojo</a>, <a href="https://publications.waset.org/abstracts/search?q=Idowu%20Peter%20Adebayo"> Idowu Peter Adebayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Egwuche%20Sylvester%20O."> Egwuche Sylvester O.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chronic Kidney Disease (CKD) is a significant cause of morbidity and mortality across developed and developing nations and is associated with increased risk. There are no existing electronic means of capturing and monitoring CKD in Nigeria. The work is aimed at developing a spatial data model that can be used to implement renal registries required for tracking and monitoring the spatial distribution of renal diseases by public health officers and patients. In this study, we have developed a spatial data model for a functional renal registry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=renal%20registry" title="renal registry">renal registry</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20informatics" title=" health informatics"> health informatics</a>, <a href="https://publications.waset.org/abstracts/search?q=chronic%20kidney%20disease" title=" chronic kidney disease"> chronic kidney disease</a>, <a href="https://publications.waset.org/abstracts/search?q=interface" title=" interface"> interface</a> </p> <a href="https://publications.waset.org/abstracts/150377/development-of-a-spatial-data-for-renal-registry-in-nigeria-health-sector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">212</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18561</span> Bayesian Flexibility Modelling of the Conditional Autoregressive Prior in a Disease Mapping Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davies%20Obaromi">Davies Obaromi</a>, <a href="https://publications.waset.org/abstracts/search?q=Qin%20Yongsong"> Qin Yongsong</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Ndege"> James Ndege</a>, <a href="https://publications.waset.org/abstracts/search?q=Azeez%20Adeboye"> Azeez Adeboye</a>, <a href="https://publications.waset.org/abstracts/search?q=Akinwumi%20Odeyemi"> Akinwumi Odeyemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Besag2" title="Besag2">Besag2</a>, <a href="https://publications.waset.org/abstracts/search?q=CAR%20models" title=" CAR models"> CAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20mapping" title=" disease mapping"> disease mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20models" title=" spatial models"> spatial models</a> </p> <a href="https://publications.waset.org/abstracts/77683/bayesian-flexibility-modelling-of-the-conditional-autoregressive-prior-in-a-disease-mapping-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18560</span> A Study on Spatial Morphological Cognitive Features of Lidukou Village Based on Space Syntax</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Man%20Guo">Man Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenyong%20Tan"> Wenyong Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> By combining spatial syntax with data obtained from field visits, this paper interprets the internal relationship between spatial morphology and spatial cognition in Lidukou Village. By comparing the obtained data, it is recognized that the spatial integration degree of Lidukou Village is positively correlated with the spatial cognitive intention of local villagers. The part with a higher spatial cognitive degree within the village is distributed along the axis mainly composed of Shuxiang Road. And the accessibility of historical relics is weak, and there is no systematic relationship between them. Aiming at the morphological problem of Lidukou Village, optimization strategies have been proposed from multiple perspectives, such as optimizing spatial mechanisms and shaping spatial nodes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traditional%20villages" title="traditional villages">traditional villages</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20syntax" title=" spatial syntax"> spatial syntax</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20integration%20degree" title=" spatial integration degree"> spatial integration degree</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20problem" title=" morphological problem"> morphological problem</a> </p> <a href="https://publications.waset.org/abstracts/184637/a-study-on-spatial-morphological-cognitive-features-of-lidukou-village-based-on-space-syntax" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">52</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18559</span> Fast Bayesian Inference of Multivariate Block-Nearest Neighbor Gaussian Process (NNGP) Models for Large Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gonzales">Carlos Gonzales</a>, <a href="https://publications.waset.org/abstracts/search?q=Zaida%20Quiroz"> Zaida Quiroz</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcos%20Prates"> Marcos Prates</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several spatial variables collected at the same location that share a common spatial distribution can be modeled simultaneously through a multivariate geostatistical model that takes into account the correlation between these variables and the spatial autocorrelation. The main goal of this model is to perform spatial prediction of these variables in the region of study. Here we focus on a geostatistical multivariate formulation that relies on sharing common spatial random effect terms. In particular, the first response variable can be modeled by a mean that incorporates a shared random spatial effect, while the other response variables depend on this shared spatial term, in addition to specific random spatial effects. Each spatial random effect is defined through a Gaussian process with a valid covariance function, but in order to improve the computational efficiency when the data are large, each Gaussian process is approximated to a Gaussian random Markov field (GRMF), specifically to the block nearest neighbor Gaussian process (Block-NNGP). This approach involves dividing the spatial domain into several dependent blocks under certain constraints, where the cross blocks allow capturing the spatial dependence on a large scale, while each individual block captures the spatial dependence on a smaller scale. The multivariate geostatistical model belongs to the class of Latent Gaussian Models; thus, to achieve fast Bayesian inference, it is used the integrated nested Laplace approximation (INLA) method. The good performance of the proposed model is shown through simulations and applications for massive data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Block-NNGP" title="Block-NNGP">Block-NNGP</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process" title=" gaussian process"> gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=GRMF" title=" GRMF"> GRMF</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20models." title=" multivariate models."> multivariate models.</a> </p> <a href="https://publications.waset.org/abstracts/170871/fast-bayesian-inference-of-multivariate-block-nearest-neighbor-gaussian-process-nngp-models-for-large-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170871.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18558</span> A Spatial Information Network Traffic Prediction Method Based on Hybrid Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jingling%20Li">Jingling Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Zhang"> Yi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Liang"> Wei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Cui"> Tao Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Li"> Jun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20information%20network" title="spatial information network">spatial information network</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title=" traffic prediction"> traffic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20decomposition" title=" wavelet decomposition"> wavelet decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20model" title=" time series model"> time series model</a> </p> <a href="https://publications.waset.org/abstracts/106062/a-spatial-information-network-traffic-prediction-method-based-on-hybrid-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106062.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18557</span> Urban Energy Demand Modelling: Spatial Analysis Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hung-Chu%20Chen">Hung-Chu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Han%20Qi"> Han Qi</a>, <a href="https://publications.waset.org/abstracts/search?q=Bauke%20de%20Vries"> Bauke de Vries</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20demand%20model" title="energy demand model">energy demand model</a>, <a href="https://publications.waset.org/abstracts/search?q=geographically%20weighted%20regression" title=" geographically weighted regression"> geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20built-up%20index" title=" normalized difference built-up index"> normalized difference built-up index</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index" title=" normalized difference vegetation index"> normalized difference vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20statistics" title=" spatial statistics"> spatial statistics</a> </p> <a href="https://publications.waset.org/abstracts/101697/urban-energy-demand-modelling-spatial-analysis-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101697.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18556</span> A Review of Spatial Analysis as a Geographic Information Management Tool</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chidiebere%20C.%20Agoha">Chidiebere C. Agoha</a>, <a href="https://publications.waset.org/abstracts/search?q=Armstong%20C.%20Awuzie"> Armstong C. Awuzie</a>, <a href="https://publications.waset.org/abstracts/search?q=Chukwuebuka%20N.%20Onwubuariri"> Chukwuebuka N. Onwubuariri</a>, <a href="https://publications.waset.org/abstracts/search?q=Joy%20O.%20Njoku"> Joy O. Njoku</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial analysis is a field of study that utilizes geographic or spatial information to understand and analyze patterns, relationships, and trends in data. It is characterized by the use of geographic or spatial information, which allows for the analysis of data in the context of its location and surroundings. It is different from non-spatial or aspatial techniques, which do not consider the geographic context and may not provide as complete of an understanding of the data. Spatial analysis is applied in a variety of fields, which includes urban planning, environmental science, geosciences, epidemiology, marketing, to gain insights and make decisions about complex spatial problems. This review paper explores definitions of spatial analysis from various sources, including examples of its application and different analysis techniques such as Buffer analysis, interpolation, and Kernel density analysis (multi-distance spatial cluster analysis). It also contrasts spatial analysis with non-spatial analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aspatial%20technique" title="aspatial technique">aspatial technique</a>, <a href="https://publications.waset.org/abstracts/search?q=buffer%20analysis" title=" buffer analysis"> buffer analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title=" epidemiology"> epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=interpolation" title=" interpolation"> interpolation</a> </p> <a href="https://publications.waset.org/abstracts/171698/a-review-of-spatial-analysis-as-a-geographic-information-management-tool" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171698.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18555</span> A Deep Learning Based Integrated Model For Spatial Flood Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinayaka%20Gude%20Divya%20Sampath">Vinayaka Gude Divya Sampath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20management" title=" disaster management"> disaster management</a>, <a href="https://publications.waset.org/abstracts/search?q=flood%20prediction" title=" flood prediction"> flood prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20flooding" title=" urban flooding"> urban flooding</a> </p> <a href="https://publications.waset.org/abstracts/129566/a-deep-learning-based-integrated-model-for-spatial-flood-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18554</span> The Grand Unified Theory of Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tory%20Erickson">Tory Erickson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The "Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow" (BST-SCWPDF) Model introduces a framework aimed at unifying general relativity (GR) and quantum mechanics (QM). By proposing a concept of bidirectional spacetime, this model suggests that time can flow in more than one direction, thus offering a perspective on temporal dynamics. Integrated with spatial covariance and wave-particle duality in spacetime flow, the BST-SCWPDF Model resolves long-standing discrepancies between GR and QM. This unified theory has profound implications for quantum gravity, potentially offering insights into quantum entanglement, the collapse of the wave function, and the fabric of spacetime itself. The Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow" (BST-SCWPDF) Model offers researchers a framework for a better understanding of theoretical physics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=astrophysics" title="astrophysics">astrophysics</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20mechanics" title=" quantum mechanics"> quantum mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20relativity" title=" general relativity"> general relativity</a>, <a href="https://publications.waset.org/abstracts/search?q=unification%20theory" title=" unification theory"> unification theory</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20physics" title=" theoretical physics"> theoretical physics</a> </p> <a href="https://publications.waset.org/abstracts/183765/the-grand-unified-theory-of-bidirectional-spacetime-with-spatial-covariance-and-wave-particle-duality-in-spacetime-flow-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183765.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18553</span> Enhanced Analysis of Spatial Morphological Cognitive Traits in Lidukou Village through the Application of Space Syntax</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Man%20Guo">Man Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper delves into the intricate interplay between spatial morphology and spatial cognition in Lidukou Village, utilizing a combined approach of spatial syntax and field data. Through a comparative analysis of the gathered data, it emerges that the spatial integration level of Lidukou Village exhibits a direct positive correlation with the spatial cognitive preferences of its inhabitants. Specifically, the areas within the village that exhibit a higher degree of spatial cognition are predominantly distributed along the axis primarily defined by Shuxiang Road. However, the accessibility to historical relics remains limited, lacking a coherent systemic relationship. To address the morphological challenges faced by Lidukou Village, this study proposes optimization strategies that encompass diverse perspectives, including the refinement of spatial mechanisms and the shaping of strategic spatial nodes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traditional%20villages" title="traditional villages">traditional villages</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20syntax" title=" spatial syntax"> spatial syntax</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20integration%20degree" title=" spatial integration degree"> spatial integration degree</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20problem" title=" morphological problem"> morphological problem</a> </p> <a href="https://publications.waset.org/abstracts/186493/enhanced-analysis-of-spatial-morphological-cognitive-traits-in-lidukou-village-through-the-application-of-space-syntax" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186493.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">42</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18552</span> Spatial Differentiation Patterns and Influencing Mechanism of Urban Greening in China: Based on Data of 289 Cities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fangzheng%20Li">Fangzheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiong%20Li"> Xiong Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Significant differences in urban greening have occurred in Chinese cities, which accompanied with China's rapid urbanization. However, few studies focused on the spatial differentiation of urban greening in China with large amounts of data. The spatial differentiation pattern, spatial correlation characteristics and the distribution shape of urban green space ratio, urban green coverage rate and public green area per capita were calculated and analyzed, using Global and Local Moran's I using data from 289 cities in 2014. We employed Spatial Lag Model and Spatial Error Model to assess the impacts of urbanization process on urban greening of China. Then we used Geographically Weighted Regression to estimate the spatial variations of the impacts. The results showed: 1. a significant spatial dependence and heterogeneity existed in urban greening values, and the differentiation patterns were featured by the administrative grade and the spatial agglomeration simultaneously; 2. it revealed that urbanization has a negative correlation with urban greening in Chinese cities. Among the indices, the the proportion of secondary industry, urbanization rate, population and the scale of urban land use has significant negative correlation with the urban greening of China. Automobile density and per capita Gross Domestic Product has no significant impact. The results of GWR modeling showed that the relationship between urbanization and urban greening was not constant in space. Further, the local parameter estimates suggested significant spatial variation in the impacts of various urbanization factors on urban greening. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=China%E2%80%99s%20urbanization" title="China’s urbanization">China’s urbanization</a>, <a href="https://publications.waset.org/abstracts/search?q=geographically%20weighted%20regression" title=" geographically weighted regression"> geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20differentiation%20pattern" title=" spatial differentiation pattern"> spatial differentiation pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20greening" title=" urban greening"> urban greening</a> </p> <a href="https://publications.waset.org/abstracts/67935/spatial-differentiation-patterns-and-influencing-mechanism-of-urban-greening-in-china-based-on-data-of-289-cities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67935.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">460</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18551</span> A Spatial Approach to Model Mortality Rates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yin-Yee%20Leong">Yin-Yee Leong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jack%20C.%20Yue"> Jack C. Yue</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsin-Chung%20Wang"> Hsin-Chung Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mortality%20improvement" title="mortality improvement">mortality improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=Lee%E2%80%93Carter%20model" title=" Lee–Carter model"> Lee–Carter model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20statistics" title=" spatial statistics"> spatial statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20detection" title=" cluster detection"> cluster detection</a> </p> <a href="https://publications.waset.org/abstracts/93746/a-spatial-approach-to-model-mortality-rates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93746.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18550</span> Developing Integrated Model for Building Design and Evacuation Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hao-Hsi%20Tseng">Hao-Hsi Tseng</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsin-Yun%20Lee"> Hsin-Yun Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the process of building design, the designers have to complete the spatial design and consider the evacuation performance at the same time. It is usually difficult to combine the two planning processes and it results in the gap between spatial design and evacuation performance. Then the designers cannot complete an integrated optimal design solution. In addition, the evacuation routing models proposed by previous researchers is different from the practical evacuation decisions in the real field. On the other hand, more and more building design projects are executed by Building Information Modeling (BIM) in which the design content is formed by the object-oriented framework. Thus, the integration of BIM and evacuation simulation can make a significant contribution for designers. Therefore, this research plan will establish a model that integrates spatial design and evacuation planning. The proposed model will provide the support for the spatial design modifications and optimize the evacuation planning. The designers can complete the integrated design solution in BIM. Besides, this research plan improves the evacuation routing method to make the simulation results more practical. The proposed model will be applied in a building design project for evaluation and validation when it will provide the near-optimal design suggestion. By applying the proposed model, the integration and efficiency of the design process are improved and the evacuation plan is more useful. The quality of building spatial design will be better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20information%20modeling" title="building information modeling">building information modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=evacuation" title=" evacuation"> evacuation</a>, <a href="https://publications.waset.org/abstracts/search?q=design" title=" design"> design</a>, <a href="https://publications.waset.org/abstracts/search?q=floor%20plan" title=" floor plan"> floor plan</a> </p> <a href="https://publications.waset.org/abstracts/64492/developing-integrated-model-for-building-design-and-evacuation-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64492.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">456</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18549</span> Spatially Downscaling Land Surface Temperature with a Non-Linear Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Liu">Kai Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remote sensing-derived land surface temperature (LST) can provide an indication of the temporal and spatial patterns of surface evapotranspiration (ET). However, the spatial resolution achieved by existing commonly satellite products is ~1 km, which remains too coarse for ET estimations. This paper proposed a model that can disaggregate coarse resolution MODIS LST at 1 km scale to fine spatial resolutions at the scale of 250 m. Our approach attempted to weaken the impacts of soil moisture and growing statues on LST variations. The proposed model spatially disaggregates the coarse thermal data by using a non-linear model involving Bowen ratio, normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). This LST disaggregation model was tested on two heterogeneous landscapes in central Iowa, USA and Heihe River, China, during the growing seasons. Statistical results demonstrated that our model achieved better than the two classical methods (DisTrad and TsHARP). Furthermore, using the surface energy balance model, it was observed that the estimated ETs using the disaggregated LST from our model were more accurate than those using the disaggregated LST from DisTrad and TsHARP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bowen%20ration" title="Bowen ration">Bowen ration</a>, <a href="https://publications.waset.org/abstracts/search?q=downscaling" title=" downscaling"> downscaling</a>, <a href="https://publications.waset.org/abstracts/search?q=evapotranspiration" title=" evapotranspiration"> evapotranspiration</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20surface%20temperature" title=" land surface temperature"> land surface temperature</a> </p> <a href="https://publications.waset.org/abstracts/69946/spatially-downscaling-land-surface-temperature-with-a-non-linear-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69946.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18548</span> Engineering Topology of Ecological Model for Orientation Impact of Sustainability Urban Environments: The Spatial-Economic Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moustafa%20Osman%20Mohammed">Moustafa Osman Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The modeling of a spatial-economic database is crucial in recitation economic network structure to social development. Sustainability within the spatial-economic model gives attention to green businesses to comply with Earth’s Systems. The natural exchange patterns of ecosystems have consistent and periodic cycles to preserve energy and materials flow in systems ecology. When network topology influences formal and informal communication to function in systems ecology, ecosystems are postulated to valence the basic level of spatial sustainable outcome (i.e., project compatibility success). These referred instrumentalities impact various aspects of the second level of spatial sustainable outcomes (i.e., participant social security satisfaction). The sustainability outcomes are modeling composite structure based on a network analysis model to calculate the prosperity of panel databases for efficiency value, from 2005 to 2025. The database is modeling spatial structure to represent state-of-the-art value-orientation impact and corresponding complexity of sustainability issues (e.g., build a consistent database necessary to approach spatial structure; construct the spatial-economic-ecological model; develop a set of sustainability indicators associated with the model; allow quantification of social, economic and environmental impact; use the value-orientation as a set of important sustainability policy measures), and demonstrate spatial structure reliability. The structure of spatial-ecological model is established for management schemes from the perspective pollutants of multiple sources through the input–output criteria. These criteria evaluate the spillover effect to conduct Monte Carlo simulations and sensitivity analysis in a unique spatial structure. The balance within “equilibrium patterns,” such as collective biosphere features, has a composite index of many distributed feedback flows. The following have a dynamic structure related to physical and chemical properties for gradual prolong to incremental patterns. While these spatial structures argue from ecological modeling of resource savings, static loads are not decisive from an artistic/architectural perspective. The model attempts to unify analytic and analogical spatial structure for the development of urban environments in a relational database setting, using optimization software to integrate spatial structure where the process is based on the engineering topology of systems ecology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ecological%20modeling" title="ecological modeling">ecological modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20structure" title=" spatial structure"> spatial structure</a>, <a href="https://publications.waset.org/abstracts/search?q=orientation%20impact" title=" orientation impact"> orientation impact</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20index" title=" composite index"> composite index</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20ecology" title=" industrial ecology"> industrial ecology</a> </p> <a href="https://publications.waset.org/abstracts/182581/engineering-topology-of-ecological-model-for-orientation-impact-of-sustainability-urban-environments-the-spatial-economic-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182581.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18547</span> Multi-scale Spatial and Unified Temporal Feature-fusion Network for Multivariate Time Series Anomaly Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Yang">Hang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jichao%20Li"> Jichao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kewei%20Yang"> Kewei Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyang%20Lei"> Tianyang Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20system" title=" industrial system"> industrial system</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a> </p> <a href="https://publications.waset.org/abstracts/193205/multi-scale-spatial-and-unified-temporal-feature-fusion-network-for-multivariate-time-series-anomaly-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193205.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18546</span> Spatio-Temporal Analysis and Mapping of Malaria in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krisada%20Lekdee">Krisada Lekdee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunee%20Sammatat"> Sunee Sammatat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nittaya%20Boonsit"> Nittaya Boonsit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a GLMM with spatial and temporal effects for malaria data in Thailand. A Bayesian method is used for parameter estimation via Gibbs sampling MCMC. A conditional autoregressive (CAR) model is assumed to present the spatial effects. The temporal correlation is presented through the covariance matrix of the random effects. The malaria quarterly data have been extracted from the Bureau of Epidemiology, Ministry of Public Health of Thailand. The factors considered are rainfall and temperature. The result shows that rainfall and temperature are positively related to the malaria morbidity rate. The posterior means of the estimated morbidity rates are used to construct the malaria maps. The top 5 highest morbidity rates (per 100,000 population) are in Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4, 97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82). According to the DIC criterion, the proposed model has a better performance than the GLMM with spatial effects but without temporal terms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20mixed%20model%20%28GLMM%29" title=" generalized linear mixed model (GLMM)"> generalized linear mixed model (GLMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria" title=" malaria"> malaria</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20effects" title=" spatial effects"> spatial effects</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20correlation" title=" temporal correlation"> temporal correlation</a> </p> <a href="https://publications.waset.org/abstracts/10300/spatio-temporal-analysis-and-mapping-of-malaria-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10300.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18545</span> Forecasting Regional Data Using Spatial Vars</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taisiia%20Gorshkova">Taisiia Gorshkova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the 1980s, spatial correlation models have been used more often to model regional indicators. An increasingly popular method for studying regional indicators is modeling taking into account spatial relationships between objects that are part of the same economic zone. In 2000s the new class of model – spatial vector autoregressions was developed. The main difference between standard and spatial vector autoregressions is that in the spatial VAR (SpVAR), the values of indicators at time t may depend on the values of explanatory variables at the same time t in neighboring regions and on the values of explanatory variables at time t-k in neighboring regions. Thus, VAR is a special case of SpVAR in the absence of spatial lags, and the spatial panel data model is a special case of spatial VAR in the absence of time lags. Two specifications of SpVAR were applied to Russian regional data for 2000-2017. The values of GRP and regional CPI are used as endogenous variables. The lags of GRP, CPI and the unemployment rate were used as explanatory variables. For comparison purposes, the standard VAR without spatial correlation was used as “naïve” model. In the first specification of SpVAR the unemployment rate and the values of depending variables, GRP and CPI, in neighboring regions at the same moment of time t were included in equations for GRP and CPI respectively. To account for the values of indicators in neighboring regions, the adjacency weight matrix is used, in which regions with a common sea or land border are assigned a value of 1, and the rest - 0. In the second specification the values of depending variables in neighboring regions at the moment of time t were replaced by these values in the previous time moment t-1. According to the results obtained, when inflation and GRP of neighbors are added into the model both inflation and GRP are significantly affected by their previous values, and inflation is also positively affected by an increase in unemployment in the previous period and negatively affected by an increase in GRP in the previous period, which corresponds to economic theory. GRP is not affected by either the inflation lag or the unemployment lag. When the model takes into account lagged values of GRP and inflation in neighboring regions, the results of inflation modeling are practically unchanged: all indicators except the unemployment lag are significant at a 5% significance level. For GRP, in turn, GRP lags in neighboring regions also become significant at a 5% significance level. For both spatial and “naïve” VARs the RMSE were calculated. The minimum RMSE are obtained via SpVAR with lagged explanatory variables. Thus, according to the results of the study, it can be concluded that SpVARs can accurately model both the actual values of macro indicators (particularly CPI and GRP) and the general situation in the regions <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=regional%20data" title=" regional data"> regional data</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20econometrics" title=" spatial econometrics"> spatial econometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20autoregression" title=" vector autoregression"> vector autoregression</a> </p> <a href="https://publications.waset.org/abstracts/122115/forecasting-regional-data-using-spatial-vars" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18544</span> Survey of Methods for Solutions of Spatial Covariance Structures and Their Limitations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Thomas%20Eghwerido">Joseph Thomas Eghwerido</a>, <a href="https://publications.waset.org/abstracts/search?q=Julian%20I.%20Mbegbu"> Julian I. Mbegbu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modelling environment processes, we apply multidisciplinary knowledge to explain, explore and predict the Earth's response to natural human-induced environmental changes. Thus, the analysis of spatial-time ecological and environmental studies, the spatial parameters of interest are always heterogeneous. This often negates the assumption of stationarity. Hence, the dispersion of the transportation of atmospheric pollutants, landscape or topographic effect, weather patterns depends on a good estimate of spatial covariance. The generalized linear mixed model, although linear in the expected value parameters, its likelihood varies nonlinearly as a function of the covariance parameters. As a consequence, computing estimates for a linear mixed model requires the iterative solution of a system of simultaneous nonlinear equations. In other to predict the variables at unsampled locations, we need to know the estimate of the present sampled variables. The geostatistical methods for solving this spatial problem assume covariance stationarity (locally defined covariance) and uniform in space; which is not apparently valid because spatial processes often exhibit nonstationary covariance. Hence, they have globally defined covariance. We shall consider different existing methods of solutions of spatial covariance of a space-time processes at unsampled locations. This stationary covariance changes with locations for multiple time set with some asymptotic properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parametric" title="parametric">parametric</a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationary" title=" nonstationary"> nonstationary</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel" title=" Kernel"> Kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriging" title=" Kriging"> Kriging</a> </p> <a href="https://publications.waset.org/abstracts/59194/survey-of-methods-for-solutions-of-spatial-covariance-structures-and-their-limitations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59194.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">255</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18543</span> Temporal and Spatial Distribution Prediction of Patinopecten yessoensis Larvae in Northern China Yellow Sea </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=RuiJin%20Zhang">RuiJin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=HengJiang%20Cai"> HengJiang Cai</a>, <a href="https://publications.waset.org/abstracts/search?q=JinSong%20Gui"> JinSong Gui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It takes Patinopecten yessoensis larvae more than 20 days from spawning to settlement. Due to the natural environmental factors such as current, Patinopecten yessoensis larvae are transported to a distance more than hundreds of kilometers, leading to a high instability of their spatial and temporal distribution and great difficulties in the natural spat collection. Therefore predicting the distribution is of great significance to improve the operating efficiency of the collecting. Hydrodynamic model of Northern China Yellow Sea was established and the motions equations of physical oceanography and verified by the tidal harmonic constants and the measured data velocities of Dalian Bay. According to the passivity drift characteristics of the larvae, combined with the hydrodynamic model and the particle tracking model, the spatial and temporal distribution prediction model was established and the spatial and temporal distribution of the larvae under the influence of flow and wind were simulated. It can be concluded from the model results: ocean currents have greatest impacts on the passive drift path and diffusion of Patinopecten yessoensis larvae; the impact of wind is also important, which changed the direction and speed of the drift. Patinopecten yessoensis larvae were generated in the sea along Zhangzi Island and Guanglu-Dachangshan Island, but after two months, with the impact of wind and currents, the larvae appeared in the west of Dalian and the southern of Lvshun, and even in Bohai Bay. The model results are consistent with the relevant literature on qualitative analysis, and this conclusion explains where the larvae come from in the perspective of numerical simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title="numerical simulation">numerical simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=Patinopecten%20yessoensis%20%20larvae" title=" Patinopecten yessoensis larvae"> Patinopecten yessoensis larvae</a>, <a href="https://publications.waset.org/abstracts/search?q=predicting%20model" title=" predicting model"> predicting model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20and%20temporal%20distribution" title=" spatial and temporal distribution"> spatial and temporal distribution</a> </p> <a href="https://publications.waset.org/abstracts/47330/temporal-and-spatial-distribution-prediction-of-patinopecten-yessoensis-larvae-in-northern-china-yellow-sea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18542</span> The Influence of 3D Printing Course on Middle School Students' Spatial Thinking Ability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wang%20Xingjuan">Wang Xingjuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Qian%20Dongming"> Qian Dongming</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a common thinking ability, spatial thinking ability plays an increasingly important role in the information age. The key to cultivating students' spatial thinking ability is to cultivate students' ability to process and transform graphics. The 3D printing course enables students to constantly touch the rotation and movement of objects during the modeling process and to understand spatial graphics from different views. To this end, this article combines the classic PSVT: R test to explore the impact of 3D printing courses on the spatial thinking ability of middle school students. The results of the study found that: (1) Through the study of the 3D printing course, the students' spatial ability test scores have been significantly improved, which indirectly reflects the improvement of the spatial thinking ability level. (2) The student's spatial thinking ability test results are influenced by the parent's occupation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20printing" title="3D printing">3D printing</a>, <a href="https://publications.waset.org/abstracts/search?q=middle%20school%20students" title=" middle school students"> middle school students</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20thinking%20ability" title=" spatial thinking ability"> spatial thinking ability</a>, <a href="https://publications.waset.org/abstracts/search?q=influence" title=" influence"> influence</a> </p> <a href="https://publications.waset.org/abstracts/109150/the-influence-of-3d-printing-course-on-middle-school-students-spatial-thinking-ability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18541</span> Research on Air pollution Spatiotemporal Forecast Model Based on LSTM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=JingWei%20Yu">JingWei Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Yang%20Yu"> Hong Yang Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSTM" title="LSTM">LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=PM2.5" title=" PM2.5"> PM2.5</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20prediction" title=" spatio-temporal prediction"> spatio-temporal prediction</a> </p> <a href="https://publications.waset.org/abstracts/147644/research-on-air-pollution-spatiotemporal-forecast-model-based-on-lstm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18540</span> Study on the Spatial Evolution Characteristics of Urban Agglomeration Integration in China: The Case of Chengdu-Chongqing Urban Agglomeration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guoqin%20Ge">Guoqin Ge</a>, <a href="https://publications.waset.org/abstracts/search?q=Minhui%20Huang"> Minhui Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yazhou%20Zhou"> Yazhou Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The growth of the Chengdu-Chongqing urban agglomeration has been designated as a national strategy in China. Analyzing its spatial evolution characteristics is crucial for devising relevant development strategies. This paper enhances the gravitational model by using temporal distance as a factor. It applies this improved model to assess the economic interconnection and concentration level of each geographical unit within the Chengdu-Chongqing urban agglomeration between 2011 and 2019. On this basis, this paper examines the spatial correlation characteristics of economic agglomeration intensity and urban-rural development equalization by employing spatial autocorrelation analysis. The study findings indicate that the spatial integration in the Chengdu-Chongqing urban agglomeration is currently in the "point-axis" development stage. The spatial organization structure is becoming more flattened, and there is a stronger economic connection between the core of the urban agglomeration and the peripheral areas. The integration of the Chengdu-Chongqing urban agglomeration is currently hindered by conflicting interests and institutional heterogeneity between Chengdu and Chongqing. Additionally, the connections between the relatively secondary spatial units are largely loose and weak. The strength and scale of economic ties and the level of urban-rural equilibrium among spatial units within the Chengdu-Chongqing urban agglomeration have increased, but regional imbalances have continued to widen, and such positive and negative changes have been characterized by the spatial and temporal synergistic evolution of the "core-periphery". Ultimately, this paper presents planning ideas for the future integration development of the Chengdu-Chongqing urban agglomeration, drawing from the findings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=integration" title="integration">integration</a>, <a href="https://publications.waset.org/abstracts/search?q=planning%20strategy" title=" planning strategy"> planning strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20organization" title=" space organization"> space organization</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20evolution" title=" space evolution"> space evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20agglomeration" title=" urban agglomeration"> urban agglomeration</a> </p> <a href="https://publications.waset.org/abstracts/179163/study-on-the-spatial-evolution-characteristics-of-urban-agglomeration-integration-in-china-the-case-of-chengdu-chongqing-urban-agglomeration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">49</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18539</span> An Aesthetic Spatial Turn - AI and Aesthetics in the Physical, Psychological, and Symbolic Spaces of Brand Advertising</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu%20Chen">Yu Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In line with existing philosophical approaches, this research proposes a conceptual model with an innovative spatial vision and aesthetic principles for Artificial Intelligence (AI) application in brand advertising. The model first identifies the major constituencies in contemporary advertising on three spatial levels—physical, psychological, and symbolic. The model further incorporates the relationships among AI, aesthetics, branding, and advertising and their interactions with the major actors in all spaces. It illustrates that AI may follow the aesthetic principles-- beauty, elegance, and simplicity-- to reinforce brand identity and consistency in advertising, to collaborate with stakeholders, and to satisfy different advertising objectives on each level. It proposes that, with aesthetic guidelines, AI may assist consumers to emerge into the physical, psychological, and symbolic advertising spaces and helps transcend the tangible advertising messages to meaningful brand symbols. Conceptually, the research illustrates that even though consumers’ engagement with brand mostly begins with physical advertising and later moves to psychological-symbolic, AI-assisted advertising should start with the understanding of brand symbolic-psychological and consumer aesthetic preferences before the physical design to better resonate. Limits of AI and future AI functions in advertising are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI" title="AI">AI</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial" title=" spatial"> spatial</a>, <a href="https://publications.waset.org/abstracts/search?q=aesthetic" title=" aesthetic"> aesthetic</a>, <a href="https://publications.waset.org/abstracts/search?q=brand%20advertising" title=" brand advertising"> brand advertising</a> </p> <a href="https://publications.waset.org/abstracts/164356/an-aesthetic-spatial-turn-ai-and-aesthetics-in-the-physical-psychological-and-symbolic-spaces-of-brand-advertising" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164356.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> 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