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spatial big data</title> <meta name="description" content="Search results for: real-time spatial big data"> <meta name="keywords" content="real-time spatial big data"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="real-time spatial big data"> <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> 26467</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: real-time spatial big data</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26467</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">26466</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">26465</span> Algorithms used in Spatial Data Mining GIS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Bairami%20Rad">Vahid Bairami Rad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20base" title="spatial data base">spatial data base</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20database" title=" knowledge discovery database"> knowledge discovery database</a>, <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=spatial%20relationship" title=" spatial relationship"> spatial relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20data%20mining" title=" predictive data mining"> predictive data mining</a> </p> <a href="https://publications.waset.org/abstracts/29004/algorithms-used-in-spatial-data-mining-gis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29004.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">26464</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">26463</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">26462</span> Spatial Data Mining by Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sihem%20Oujdi">Sihem Oujdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafida%20Belbachir"> Hafida Belbachir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=C4.5%20algorithm" title="C4.5 algorithm">C4.5 algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=S-CART" title=" S-CART"> S-CART</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20mining" title=" spatial data mining"> spatial data mining</a> </p> <a href="https://publications.waset.org/abstracts/11935/spatial-data-mining-by-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11935.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">612</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">26461</span> Recommender System Based on Mining Graph Databases for Data-Intensive Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Gamal">Mostafa Gamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20K.%20Mohamed"> Hoda K. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Islam%20El-Maddah"> Islam El-Maddah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Hamdi"> Ali Hamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20databases" title="graph databases">graph databases</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20systems" title=" recommendation systems"> recommendation systems</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20metrics" title=" similarity metrics"> similarity metrics</a> </p> <a href="https://publications.waset.org/abstracts/163018/recommender-system-based-on-mining-graph-databases-for-data-intensive-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163018.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">104</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">26460</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">43</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">26459</span> 150 KVA Multifunction Laboratory Test Unit Based on Power-Frequency Converter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bartosz%20Kedra">Bartosz Kedra</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Malkowski"> Robert Malkowski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper provides description and presentation of laboratory test unit built basing on 150 kVA power frequency converter and Simulink RealTime platform. Assumptions, based on criteria which load and generator types may be simulated using discussed device, are presented, as well as control algorithm structure. As laboratory setup contains transformer with thyristor controlled tap changer, a wider scope of setup capabilities is presented. Information about used communication interface, data maintenance, and storage solution as well as used Simulink real-time features is presented. List and description of all measurements are provided. Potential of laboratory setup modifications is evaluated. For purposes of Rapid Control Prototyping, a dedicated environment was used Simulink RealTime. Therefore, load model Functional Unit Controller is based on a PC computer with I/O cards and Simulink RealTime software. Simulink RealTime was used to create real-time applications directly from Simulink models. In the next step, applications were loaded on a target computer connected to physical devices that provided opportunity to perform Hardware in the Loop (HIL) tests, as well as the mentioned Rapid Control Prototyping process. With Simulink RealTime, Simulink models were extended with I/O cards driver blocks that made automatic generation of real-time applications and performing interactive or automated runs on a dedicated target computer equipped with a real-time kernel, multicore CPU, and I/O cards possible. Results of performed laboratory tests are presented. Different load configurations are described and experimental results are presented. This includes simulation of under frequency load shedding, frequency and voltage dependent characteristics of groups of load units, time characteristics of group of different load units in a chosen area and arbitrary active and reactive power regulation basing on defined schedule. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title="MATLAB">MATLAB</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20converter" title=" power converter"> power converter</a>, <a href="https://publications.waset.org/abstracts/search?q=Simulink%20Real-Time" title=" Simulink Real-Time"> Simulink Real-Time</a>, <a href="https://publications.waset.org/abstracts/search?q=thyristor-controlled%20tap%20changer" title=" thyristor-controlled tap changer"> thyristor-controlled tap changer</a> </p> <a href="https://publications.waset.org/abstracts/50924/150-kva-multifunction-laboratory-test-unit-based-on-power-frequency-converter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50924.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">323</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">26458</span> Data Mining Spatial: Unsupervised Classification of Geographic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chahrazed%20Zouaoui">Chahrazed Zouaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mining" title="mining">mining</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=geo-clustering" title=" geo-clustering"> geo-clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=neighborhood" title=" neighborhood"> neighborhood</a> </p> <a href="https://publications.waset.org/abstracts/31341/data-mining-spatial-unsupervised-classification-of-geographic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31341.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">375</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">26457</span> Performance of the Abbott RealTime High Risk HPV Assay with SurePath Liquid Based Cytology Specimens from Women with Low Grade Cytological Abnormalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexandra%20Sargent">Alexandra Sargent</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Ferris"> Sarah Ferris</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Theofanous"> Ioannis Theofanous</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Abbott RealTime High Risk HPV test (RealTime HPV) is one of five assays clinically validated and approved by the English NHS Cervical Screening Programme (CSP) for HPV triage of low grade dyskaryosis and test-of-cure of treated Cervical Intraepithelial Neoplasia. The assay is a highly automated multiplex real-time PCR test for detecting 14 high risk (hr) HPV types, with simultaneous differentiation of HPV 16 and HPV 18 versus non-HPV 16/18 hrHPV. An endogenous internal control ensures sample cellularity, controls extraction efficiency and PCR inhibition. The original cervical specimen collected in SurePath (SP) liquid-based cytology (LBC) medium (BD Diagnostics) and the SP post-gradient cell pellets (SPG) after cytological processing are both CE marked for testing with the RealTime HPV test. During the 2011 NHSCSP validation of new tests only the original aliquot of SP LBC medium was investigated. Residual sample volume left after cytology slide preparation is low and may not always have sufficient volume for repeat HPV testing or for testing of other biomarkers that may be implemented in testing algorithms in the future. The SPG samples, however, have sufficient volumes to carry out additional testing and necessary laboratory validation procedures. This study investigates the correlation of RealTime HPV results of cervical specimens collected in SP LBC medium from women with low grade cytological abnormalities observed with matched pairs of original SP LBC medium and SP post-gradient cell pellets (SPG) after cytology processing. Matched pairs of SP and SPG samples from 750 women with borderline (N = 392) and mild (N = 351) cytology were available for this study. Both specimen types were processed and parallel tested for the presence of hrHPV with RealTime HPV according to the manufacturer´s instructions. HrHPV detection rates and concordance between test results from matched SP and SPGCP pairs were calculated. A total of 743 matched pairs with valid test results on both sample types were available for analysis. An overall-agreement of hrHPV test results of 97.5% (k: 0.95) was found with matched SP/SPG pairs and slightly lower concordance (96.9%; k: 0.94) was observed on 392 pairs from women with borderline cytology compared to 351 pairs from women with mild cytology (98.0%; k: 0.95). Partial typing results were highly concordant in matched SP/SPG pairs for HPV 16 (99.1%), HPV 18 (99.7%) and non-HPV16/18 hrHPV (97.0%), respectively. 19 matched pairs were found with discrepant results: 9 from women with borderline cytology and 4 from women with mild cytology were negative on SPG and positive on SP; 3 from women with borderline cytology and 3 from women with mild cytology were negative on SP and positive on SPG. Excellent correlation of hrHPV DNA test results was found between matched pairs of SP original fluid and post-gradient cell pellets from women with low grade cytological abnormalities tested with the Abbott RealTime High-Risk HPV assay, demonstrating robust performance of the test with both specimen types and reassuring the utility of the assay for cytology triage with both specimen types. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abbott%20realtime%20test" title="Abbott realtime test">Abbott realtime test</a>, <a href="https://publications.waset.org/abstracts/search?q=HPV" title=" HPV"> HPV</a>, <a href="https://publications.waset.org/abstracts/search?q=SurePath%20liquid%20based%20cytology" title=" SurePath liquid based cytology"> SurePath liquid based cytology</a>, <a href="https://publications.waset.org/abstracts/search?q=surepath%20post-gradient%20cell%20pellet" title=" surepath post-gradient cell pellet"> surepath post-gradient cell pellet</a> </p> <a href="https://publications.waset.org/abstracts/61325/performance-of-the-abbott-realtime-high-risk-hpv-assay-with-surepath-liquid-based-cytology-specimens-from-women-with-low-grade-cytological-abnormalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61325.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">258</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">26456</span> Integrating of Multi-Criteria Decision Making and Spatial Data Warehouse in Geographic Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zohra%20Mekranfar">Zohra Mekranfar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Saidi"> Ahmed Saidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdellah%20Mebrek"> Abdellah Mebrek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=MCDM" title=" MCDM"> MCDM</a>, <a href="https://publications.waset.org/abstracts/search?q=SOLAP" title=" SOLAP"> SOLAP</a> </p> <a href="https://publications.waset.org/abstracts/131660/integrating-of-multi-criteria-decision-making-and-spatial-data-warehouse-in-geographic-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131660.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">177</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">26455</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">26454</span> Simulation Data Summarization Based on Spatial Histograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhao">Jing Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshiharu%20Ishikawa"> Yoshiharu Ishikawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuan%20Xiao"> Chuan Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kento%20Sugiura"> Kento Sugiura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simulation%20data" title="simulation data">simulation data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20summarization" title=" data summarization"> data summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20histograms" title=" spatial histograms"> spatial histograms</a>, <a href="https://publications.waset.org/abstracts/search?q=exploration" title=" exploration"> exploration</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/98571/simulation-data-summarization-based-on-spatial-histograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98571.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">176</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">26453</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">26452</span> Research on the Development and Space Optimization of Rental-Type Public Housing in Hangzhou</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xuran%20Zhang">Xuran Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Huiru%20Chen"> Huiru Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, China has made great efforts to cultivate and develop the housing rental market, especially the rental-type public housing, which has been paid attention to by all sectors of the society. This paper takes Hangzhou rental-type public housing as the research object, and divides it into three development stages according to the different supply modes of rental-type public housing. Through data collection and field research, the paper summarizes the spatial characteristics of rental-type public housing from the five perspectives of spatial planning, spatial layout, spatial integration, spatial organization and spatial configuration. On this basis, the paper proposes the optimization of the spatial layout. The study concludes that the spatial layout of rental-type public housing should be coordinated with the development of urban planning. When planning and constructing, it is necessary to select more mixed construction modes, to be properly centralized, and to improve the surrounding transportation service facilities.  It is hoped that the recommendations in this paper will provide a reference for the further development of rental-type public housing in Hangzhou. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hangzhou" title="Hangzhou">Hangzhou</a>, <a href="https://publications.waset.org/abstracts/search?q=rental-type%20public%20housing" title=" rental-type public housing"> rental-type public housing</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20distribution" title=" spatial distribution"> spatial distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20optimization" title=" spatial optimization"> spatial optimization</a> </p> <a href="https://publications.waset.org/abstracts/93082/research-on-the-development-and-space-optimization-of-rental-type-public-housing-in-hangzhou" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93082.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">323</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">26451</span> Nonparametric Quantile Regression for Multivariate Spatial Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Arnaud%20Kanga">S. H. Arnaud Kanga</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Hili"> O. Hili</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dabo-Niang"> S. Dabo-Niang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20quantile" title="conditional quantile">conditional quantile</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel" title=" kernel"> kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric" title=" nonparametric"> nonparametric</a>, <a href="https://publications.waset.org/abstracts/search?q=stationary" title=" stationary"> stationary</a> </p> <a href="https://publications.waset.org/abstracts/109937/nonparametric-quantile-regression-for-multivariate-spatial-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109937.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">154</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">26450</span> Analysis of Spatial and Temporal Data Using Remote Sensing Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kapil%20Pandey">Kapil Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Goyal"> Vishnu Goyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GIS" title="GIS">GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=landuse%2Flandcover" title=" landuse/landcover"> landuse/landcover</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20and%20temporal%20data" title=" spatial and temporal data"> spatial and temporal data</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/40918/analysis-of-spatial-and-temporal-data-using-remote-sensing-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40918.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">433</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">26449</span> Spatial Integrity of Seismic Data for Oil and Gas Exploration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afiq%20Juazer%20Rizal">Afiq Juazer Rizal</a>, <a href="https://publications.waset.org/abstracts/search?q=Siti%20Zaleha%20Misnan"> Siti Zaleha Misnan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zairi%20M.%20Yusof"> M. Zairi M. Yusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Seismic data is the fundamental tool utilized by exploration companies to determine potential hydrocarbon. However, the importance of seismic trace data will be undermined unless the geo-spatial component of the data is understood. Deriving a proposed well to be drilled from data that has positional ambiguity will jeopardize business decision and millions of dollars’ investment that every oil and gas company would like to avoid. Spatial integrity QC workflow has been introduced in PETRONAS to ensure positional errors within the seismic data are recognized throughout the exploration’s lifecycle from acquisition, processing, and seismic interpretation. This includes, amongst other tests, quantifying that the data is referenced to the appropriate coordinate reference system, survey configuration validation, and geometry loading verification. The direct outcome of the workflow implementation helps improve reliability and integrity of sub-surface geological model produced by geoscientist and provide important input to potential hazard assessment where positional accuracy is crucial. This workflow’s development initiative is part of a bigger geospatial integrity management effort, whereby nearly eighty percent of the oil and gas data are location-dependent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oil%20and%20gas%20exploration" title="oil and gas exploration">oil and gas exploration</a>, <a href="https://publications.waset.org/abstracts/search?q=PETRONAS" title=" PETRONAS"> PETRONAS</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20data" title=" seismic data"> seismic data</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20integrity%20QC%20workflow" title=" spatial integrity QC workflow"> spatial integrity QC workflow</a> </p> <a href="https://publications.waset.org/abstracts/80296/spatial-integrity-of-seismic-data-for-oil-and-gas-exploration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80296.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">222</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">26448</span> Analyzing the Relationship between the Spatial Characteristics of Cultural Structure, Activities, and the Tourism Demand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deniz%20Karag%C3%B6z">Deniz Karagöz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is attempt to comprehend the relationship between the spatial characteristics of cultural structure, activities and the tourism demand in Turkey. The analysis divided into four parts. The first part consisted of a cultural structure and cultural activity (CSCA) index provided by principal component analysis. The analysis determined four distinct dimensions, namely, cultural activity/structure, accessing culture, consumption, and cultural management. The exploratory spatial data analysis employed to determine the spatial models of cultural structure and cultural activities in 81 provinces in Turkey. Global Moran I indices is used to ascertain the cultural activities and the structural clusters. Finally, the relationship between the cultural activities/cultural structure and tourism demand was analyzed. The raw/original data of the study official databases. The data on the cultural structure and activities gathered from the Turkish Statistical Institute and the data related to the tourism demand was provided by the Republic of Turkey Ministry of Culture and Tourism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cultural%20activities" title="cultural activities">cultural activities</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20structure" title=" cultural structure"> cultural structure</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20characteristics" title=" spatial characteristics"> spatial characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism%20demand" title=" tourism demand"> tourism demand</a>, <a href="https://publications.waset.org/abstracts/search?q=Turkey" title=" Turkey"> Turkey</a> </p> <a href="https://publications.waset.org/abstracts/48404/analyzing-the-relationship-between-the-spatial-characteristics-of-cultural-structure-activities-and-the-tourism-demand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48404.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">560</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">26447</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">26446</span> Real-Time Data Stream Partitioning over a Sliding Window in Real-Time Spatial Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sana%20Hamdi">Sana Hamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Emna%20Bouazizi"> Emna Bouazizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Faiz"> Sami Faiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time%20spatial%20big%20data" title="real-time spatial big data">real-time spatial big data</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20service" title=" quality of service"> quality of service</a>, <a href="https://publications.waset.org/abstracts/search?q=vertical%20partitioning" title=" vertical partitioning"> vertical partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=horizontal%20partitioning" title=" horizontal partitioning"> horizontal partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20algorithm" title=" matching algorithm"> matching algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hamming%20distance" title=" hamming distance"> hamming distance</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20query" title=" stream query"> stream query</a> </p> <a href="https://publications.waset.org/abstracts/93904/real-time-data-stream-partitioning-over-a-sliding-window-in-real-time-spatial-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93904.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">157</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">26445</span> Spatially Random Sampling for Retail Food Risk Factors Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guilan%20Huang">Guilan Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In 2013 and 2014, the U.S. Food and Drug Administration (FDA) collected data from selected fast food restaurants and full service restaurants for tracking changes in the occurrence of foodborne illness risk factors. This paper discussed how we customized spatial random sampling method by considering financial position and availability of FDA resources, and how we enriched restaurants data with location. Location information of restaurants provides opportunity for quantitatively determining random sampling within non-government units (e.g.: 240 kilometers around each data-collector). Spatial analysis also could optimize data-collectors’ work plans and resource allocation. Spatial analytic and processing platform helped us handling the spatial random sampling challenges. Our method fits in FDA’s ability to pinpoint features of foodservice establishments, and reduced both time and expense on data collection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geospatial%20technology" title="geospatial technology">geospatial technology</a>, <a href="https://publications.waset.org/abstracts/search?q=restaurant" title=" restaurant"> restaurant</a>, <a href="https://publications.waset.org/abstracts/search?q=retail%20food%20risk%20factor%20study" title=" retail food risk factor study"> retail food risk factor study</a>, <a href="https://publications.waset.org/abstracts/search?q=spatially%20random%20sampling" title=" spatially random sampling"> spatially random sampling</a> </p> <a href="https://publications.waset.org/abstracts/48950/spatially-random-sampling-for-retail-food-risk-factors-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48950.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">350</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">26444</span> Developing a Multiagent-Based Decision Support System for Realtime Multi-Risk Disaster Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Moser">D. Moser</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Pinto"> D. Pinto</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Cipriano"> A. Cipriano </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Disaster Management System (DMS) for countries with different disasters is very important. In the world different disasters like earthquakes, tsunamis, volcanic eruption, fire or other natural or man-made disasters occurs and have an effect on the population. It is also possible that two or more disasters arisen at the same time, this means to handle multi-risk situations. To handle such a situation a Decision Support System (DSS) based on multiagents is a suitable architecture. The most known DMSs deal with one (in the case of an earthquake-tsunami combination with two) disaster and often with one particular disaster. Nevertheless, a DSS helps for a better realtime response. Analyze the existing systems in the literature and expand them for multi-risk disasters to construct a well-organized system is the proposal of our work. The here shown work is an approach of a multi-risk system, which needs an architecture, and well-defined aims. In this moment our study is a kind of case study to analyze the way we have to follow to create our proposed system in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20system" title="decision support system">decision support system</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20management%20system" title=" disaster management system"> disaster management system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-risk" title=" multi-risk"> multi-risk</a>, <a href="https://publications.waset.org/abstracts/search?q=multiagent%20system" title=" multiagent system"> multiagent system</a> </p> <a href="https://publications.waset.org/abstracts/26119/developing-a-multiagent-based-decision-support-system-for-realtime-multi-risk-disaster-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26119.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">431</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">26443</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">26442</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">15</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">26441</span> Generating Real-Time Visual Summaries from Located Sensor-Based Data with Chorems </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Bouattou">Z. Bouattou</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Laurini"> R. Laurini</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Belbachir"> H. Belbachir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a new approach for the automatic generation of the visual summaries dealing with cartographic visualization methods and sensors real time data modeling. Hence, the concept of chorems seems an interesting candidate to visualize real time geographic database summaries. Chorems have been defined by Roger Brunet (1980) as schematized visual representations of territories. However, the time information is not yet handled in existing chorematic map approaches, issue has been discussed in this paper. Our approach is based on spatial analysis by interpolating the values recorded at the same time, by sensors available, so we have a number of distributed observations on study areas and used spatial interpolation methods to find the concentration fields, from these fields and by using some spatial data mining procedures on the fly, it is possible to extract important patterns as geographic rules. Then, those patterns are visualized as chorems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geovisualization" title="geovisualization">geovisualization</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analytics" title=" spatial analytics"> spatial analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time" title=" real-time"> real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20data%20streams" title=" geographic data streams"> geographic data streams</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=chorems" title=" chorems"> chorems</a> </p> <a href="https://publications.waset.org/abstracts/30697/generating-real-time-visual-summaries-from-located-sensor-based-data-with-chorems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30697.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">400</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">26440</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">147</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">26439</span> Mapping Poverty in the Philippines: Insights from Satellite Data and Spatial Econometrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Htet%20Khaing%20Lin">Htet Khaing Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the relationship between a diverse set of variables, encompassing both environmental and socio-economic factors, and poverty levels in the Philippines for the years 2012, 2015, and 2018. Employing Ordinary Least Squares (OLS), Spatial Lag Models (SLM), and Spatial Error Models (SEM), this study delves into the dynamics of key indicators, including daytime and nighttime land surface temperature, cropland surface, urban land surface, rainfall, population size, normalized difference water, vegetation, and drought indices. The findings reveal consistent patterns and unexpected correlations, highlighting the need for nuanced policies that address the multifaceted challenges arising from the interplay of environmental and socio-economic factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=poverty%20analysis" title="poverty analysis">poverty analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=OLS" title=" OLS"> OLS</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20lag%20models" title=" spatial lag models"> spatial lag models</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20error%20models" title=" spatial error models"> spatial error models</a>, <a href="https://publications.waset.org/abstracts/search?q=Philippines" title=" Philippines"> Philippines</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20earth%20engine" title=" google earth engine"> google earth engine</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20data" title=" satellite data"> satellite data</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20dynamics" title=" environmental dynamics"> environmental dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=socio-economic%20factors" title=" socio-economic factors"> socio-economic factors</a> </p> <a href="https://publications.waset.org/abstracts/179134/mapping-poverty-in-the-philippines-insights-from-satellite-data-and-spatial-econometrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179134.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">101</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">26438</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 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