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analysis</title> <meta name="description" content="Search results for: spatial data analysis"> <meta name="keywords" content="spatial data analysis"> <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 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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="spatial data analysis"> <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> 42884</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: spatial data analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">42884</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">42883</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">42882</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">42881</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">42880</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">42879</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">42878</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">42877</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">42876</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">42875</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">42874</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">42873</span> Using Emerging Hot Spot Analysis to Analyze Overall Effectiveness of Policing Policy and Strategy in Chicago</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tyler%20Gill">Tyler Gill</a>, <a href="https://publications.waset.org/abstracts/search?q=Sophia%20Daniels"> Sophia Daniels</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper examines how accessing the spatial-temporal constrains of data will help inform policymakers and law enforcement officials. The authors utilize Chicago crime data from 2006-2016 to demonstrate how the Emerging Hot Spot Tool is an ideal hot spot clustering approach to analyze crime data. Traditional approaches include density maps or creating a spatial weights matrix to include the spatial-temporal constrains. This new approach utilizes a space-time implementation of the Getis-Ord Gi* statistic to visualize the data more quickly to make better decisions. The research will help complement socio-cultural research to find key patterns to help frame future policies and evaluate the implementation of prior strategies. Through this analysis, homicide trends and patterns are found more effectively and recommendations for use by non-traditional users of GIS are offered for real life implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crime%20mapping" title="crime mapping">crime mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20hot%20spot%20analysis" title=" emerging hot spot analysis"> emerging hot spot analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Getis-Ord%20Gi%2A" title=" Getis-Ord Gi*"> Getis-Ord Gi*</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial-temporal%20analysis" title=" spatial-temporal analysis"> spatial-temporal analysis</a> </p> <a href="https://publications.waset.org/abstracts/71653/using-emerging-hot-spot-analysis-to-analyze-overall-effectiveness-of-policing-policy-and-strategy-in-chicago" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71653.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">244</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">42872</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">42871</span> Impact of Map Generalization in Spatial Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Li">Lin Li</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20G.%20R.%20N.%20I.%20Pussella"> P. G. R. N. I. Pussella</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When representing spatial data and their attributes on different types of maps, the scale plays a key role in the process of map generalization. The process is consisted with two main operators such as selection and omission. Once some data were selected, they would undergo of several geometrical changing processes such as elimination, simplification, smoothing, exaggeration, displacement, aggregation and size reduction. As a result of these operations at different levels of data, the geometry of the spatial features such as length, sinuosity, orientation, perimeter and area would be altered. This would be worst in the case of preparation of small scale maps, since the cartographer has not enough space to represent all the features on the map. What the GIS users do is when they wanted to analyze a set of spatial data; they retrieve a data set and does the analysis part without considering very important characteristics such as the scale, the purpose of the map and the degree of generalization. Further, the GIS users use and compare different maps with different degrees of generalization. Sometimes, GIS users are going beyond the scale of the source map using zoom in facility and violate the basic cartographic rule 'it is not suitable to create a larger scale map using a smaller scale map'. In the study, the effect of map generalization for GIS analysis would be discussed as the main objective. It was used three digital maps with different scales such as 1:10000, 1:50000 and 1:250000 which were prepared by the Survey Department of Sri Lanka, the National Mapping Agency of Sri Lanka. It was used common features which were on above three maps and an overlay analysis was done by repeating the data with different combinations. Road data, River data and Land use data sets were used for the study. A simple model, to find the best place for a wild life park, was used to identify the effects. The results show remarkable effects on different degrees of generalization processes. It can see that different locations with different geometries were received as the outputs from this analysis. The study suggests that there should be reasonable methods to overcome this effect. It can be recommended that, as a solution, it would be very reasonable to take all the data sets into a common scale and do the analysis part. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalization" title="generalization">generalization</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=scales" title=" scales"> scales</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/43626/impact-of-map-generalization-in-spatial-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43626.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">328</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">42870</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">42869</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">42868</span> Spatial Scale of Clustering of Residential Burglary and Its Dependence on Temporal Scale</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20A.%20Alazawi">Mohammed A. Alazawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shiguo%20Jiang"> Shiguo Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Steven%20F.%20Messner"> Steven F. Messner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research has long focused on two main spatial aspects of crime: spatial patterns and spatial processes. When analyzing these patterns and processes, a key issue has been to determine the proper spatial scale. In addition, it is important to consider the possibility that these patterns and processes might differ appreciably for different temporal scales and might vary across geographic units of analysis. We examine the spatial-temporal dependence of residential burglary. This dependence is tested at varying geographical scales and temporal aggregations. The analyses are based on recorded incidents of crime in Columbus, Ohio during the 1994-2002 period. We implement point pattern analysis on the crime points using Ripley’s K function. The results indicate that spatial point patterns of residential burglary reveal spatial scales of clustering relatively larger than the average size of census tracts of the study area. Also, spatial scale is independent of temporal scale. The results of our analyses concerning the geographic scale of spatial patterns and processes can inform the development of effective policies for crime control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inhomogeneous%20K%20function" title="inhomogeneous K function">inhomogeneous K function</a>, <a href="https://publications.waset.org/abstracts/search?q=residential%20burglary" title=" residential burglary"> residential burglary</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20point%20pattern" title=" spatial point pattern"> spatial point pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20scale" title=" spatial scale"> spatial scale</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20scale" title=" temporal scale"> temporal scale</a> </p> <a href="https://publications.waset.org/abstracts/92371/spatial-scale-of-clustering-of-residential-burglary-and-its-dependence-on-temporal-scale" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92371.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">344</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">42867</span> A Spatial Point Pattern Analysis to Recognize Fail Bit Patterns in Semiconductor Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youngji%20Yoo">Youngji Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung%20Hwan%20Park"> Seung Hwan Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Daewoong%20An"> Daewoong An</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Shick%20Kim"> Sung-Shick Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun-Geol%20Baek"> Jun-Geol Baek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semiconductor" title="semiconductor">semiconductor</a>, <a href="https://publications.waset.org/abstracts/search?q=wafer%20bin%20map" title=" wafer bin map"> wafer bin map</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20point%20patterns" title=" spatial point patterns"> spatial point patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=contour%20map" title=" contour map"> contour map</a> </p> <a href="https://publications.waset.org/abstracts/5010/a-spatial-point-pattern-analysis-to-recognize-fail-bit-patterns-in-semiconductor-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5010.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">383</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">42866</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">42865</span> Spatial Variability of Brahmaputra River Flow Characteristics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hemant%20Kumar">Hemant Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brahmaputra River is known according to the Hindu mythology the son of the Lord Brahma. According to this name, the river Brahmaputra creates mass destruction during the monsoon season in Assam, India. It is a state situated in North-East part of India. This is one of the essential states out of the seven countries of eastern India, where almost all entire Brahmaputra flow carried out. The other states carry their tributaries. In the present case study, the spatial analysis performed in this specific case the number of MODIS data are acquired. In the method of detecting the change, the spray content was found during heavy rainfall and in the flooded monsoon season. By this method, particularly the analysis over the Brahmaputra outflow determines the flooded season. The charged particle-associated in aerosol content genuinely verifies the heavy water content below the ground surface, which is validated by trend analysis through rainfall spectrum data. This is confirmed by in-situ sampled view data from a different position of Brahmaputra River. Further, a Hyperion Hyperspectral 30 m resolution data were used to scan the sediment deposits, which is also confirmed by in-situ sampled view data from a different position. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerosol" title="aerosol">aerosol</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title=" change detection"> change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=trend%20analysis" title=" trend analysis"> trend analysis</a> </p> <a href="https://publications.waset.org/abstracts/128007/spatial-variability-of-brahmaputra-river-flow-characteristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128007.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">42864</span> Hydrochemical Contamination Profiling and Spatial-Temporal Mapping with the Support of Multivariate and Cluster Statistical Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Barbosa">Sofia Barbosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariana%20Pinto"> Mariana Pinto</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Ant%C3%B3nio%20Almeida"> José António Almeida</a>, <a href="https://publications.waset.org/abstracts/search?q=Edgar%20Carvalho"> Edgar Carvalho</a>, <a href="https://publications.waset.org/abstracts/search?q=Catarina%20Diamantino"> Catarina Diamantino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioural profiles and to generate synthetic evolutionary hydrochemical maps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Contamination%20plume%20migration" title="Contamination plume migration">Contamination plume migration</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20of%20PCA%20scores" title=" K-means of PCA scores"> K-means of PCA scores</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20and%20mine%20water%20monitoring" title=" groundwater and mine water monitoring"> groundwater and mine water monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial-temporal%20hydrochemical%20trends" title=" spatial-temporal hydrochemical trends"> spatial-temporal hydrochemical trends</a> </p> <a href="https://publications.waset.org/abstracts/139590/hydrochemical-contamination-profiling-and-spatial-temporal-mapping-with-the-support-of-multivariate-and-cluster-statistical-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139590.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">234</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">42863</span> A Dynamic Spatial Panel Data Analysis on Renter-Occupied Multifamily Housing DC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jose%20Funes">Jose Funes</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeff%20Sauer"> Jeff Sauer</a>, <a href="https://publications.waset.org/abstracts/search?q=Laixiang%20Sun"> Laixiang Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research examines determinants of multifamily housing development and spillovers in the District of Columbia. A range of socioeconomic factors related to income distribution, productivity, and land use policies are thought to influence the development in contemporary U.S. multifamily housing markets. The analysis leverages data from the American Community Survey to construct panel datasets spanning from 2010 to 2019. Using spatial regression, we identify several socioeconomic measures and land use policies both positively and negatively associated with new housing supply. We contextualize housing estimates related to race in relation to uneven development in the contemporary D.C. housing supply. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neighborhood%20effect" title="neighborhood effect">neighborhood effect</a>, <a href="https://publications.waset.org/abstracts/search?q=sorting" title=" sorting"> sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20spillovers" title=" spatial spillovers"> spatial spillovers</a>, <a href="https://publications.waset.org/abstracts/search?q=multifamily%20housing" title=" multifamily housing"> multifamily housing</a> </p> <a href="https://publications.waset.org/abstracts/160620/a-dynamic-spatial-panel-data-analysis-on-renter-occupied-multifamily-housing-dc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160620.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">42862</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">100</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">42861</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">42860</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">42859</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">42858</span> Medical Image Augmentation Using Spatial Transformations for Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Trupti%20Chavan">Trupti Chavan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramachandra%20Guda"> Ramachandra Guda</a>, <a href="https://publications.waset.org/abstracts/search?q=Kameshwar%20Rao"> Kameshwar Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title="data augmentation">data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20SSD" title=" enhanced SSD"> enhanced SSD</a>, <a href="https://publications.waset.org/abstracts/search?q=knee%20detection%20and%20localization" title=" knee detection and localization"> knee detection and localization</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20analysis" title=" medical image analysis"> medical image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=openKnee" title=" openKnee"> openKnee</a>, <a href="https://publications.waset.org/abstracts/search?q=Spatial%20transformations" title=" Spatial transformations"> Spatial transformations</a> </p> <a href="https://publications.waset.org/abstracts/122628/medical-image-augmentation-using-spatial-transformations-for-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122628.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">42857</span> Spatial Differentiation of Elderly Care Facilities in Mountainous Cities: A Case Study of Chongqing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xuan%20Zhao">Xuan Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen%20Jiang"> Wen Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a web crawler was used to collect POI sample data from 38 districts and counties of Chongqing in 2022, and ArcGIS was combined to coordinate and projection conversion and realize data visualization. Nuclear density analysis and spatial correlation analysis were used to explore the spatial distribution characteristics of elderly care facilities in Chongqing, and K mean cluster analysis was carried out with GeoDa to study the spatial concentration degree of elderly care resources in 38 districts and counties. Finally, the driving force of spatial differentiation of elderly care facilities in various districts and counties of Chongqing is studied by using the method of geographic detector. The results show that: (1) in terms of spatial distribution structure, the distribution of elderly care facilities in Chongqing is unbalanced, showing a distribution pattern of ‘large dispersion and small agglomeration’ and the asymmetric pattern of ‘west dense and east sparse, north dense and south sparse’ is prominent. (2) In terms of the spatial matching between elderly care resources and the elderly population, there is a weak coordination between the input of elderly care resources and the distribution of the elderly population at the county level in Chongqing. (3) The analysis of the results of the geographical detector shows that the single factor influence is mainly the number of elderly population, public financial revenue and district and county GDP. The high single factor influence is mainly caused by the elderly population, public financial income, and district and county GDP. The influence of each influence factor on the spatial distribution of elderly care facilities is not simply superimposed but has a nonlinear enhancement effect or double factor enhancement. It is necessary to strengthen the synergistic effect of two factors and promote the synergistic effect of multiple factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aging" title="aging">aging</a>, <a href="https://publications.waset.org/abstracts/search?q=elderly%20care%20facilities" title=" elderly care facilities"> elderly care facilities</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20differentiation" title=" spatial differentiation"> spatial differentiation</a>, <a href="https://publications.waset.org/abstracts/search?q=geographical%20detector" title=" geographical detector"> geographical detector</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%20force%20analysis" title=" driving force analysis"> driving force analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Mountain%20city" title=" Mountain city"> Mountain city</a> </p> <a href="https://publications.waset.org/abstracts/186486/spatial-differentiation-of-elderly-care-facilities-in-mountainous-cities-a-case-study-of-chongqing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186486.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">38</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">42856</span> Spatial and Temporal Analysis of Violent Crime in Washington, DC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pallavi%20Roe">Pallavi Roe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Violent crime is a significant public safety concern in urban areas across the United States, and Washington, DC, is no exception. This research discusses the prevalence and types of crime, particularly violent crime, in Washington, DC, along with the factors contributing to the high rate of violent crime in the city, including poverty, inequality, access to guns, and racial disparities. The organizations working towards ensuring safety in neighborhoods are also listed. The proposal to perform spatial and temporal analysis on violent crime and the use of guns in crime analysis is presented to identify patterns and trends to inform evidence-based interventions to reduce violent crime and improve public safety in Washington, DC. The stakeholders for crime analysis are also discussed, including law enforcement agencies, prosecutors, judges, policymakers, and the public. The anticipated result of the spatial and temporal analysis is to provide stakeholders with valuable information to make informed decisions about preventing and responding to violent crimes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crime%20analysis" title="crime analysis">crime analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20analysis" title=" temporal analysis"> temporal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=violent%20crime" title=" violent crime"> violent crime</a> </p> <a href="https://publications.waset.org/abstracts/167107/spatial-and-temporal-analysis-of-violent-crime-in-washington-dc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167107.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">320</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">42855</span> Mapping of Urban Green Spaces Towards a Balanced Planning in a Coastal Landscape</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rania%20Ajmi">Rania Ajmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Faiza%20Allouche%20Khebour"> Faiza Allouche Khebour</a>, <a href="https://publications.waset.org/abstracts/search?q=Aude%20Nuscia%20Taibi"> Aude Nuscia Taibi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sirine%20Essasi"> Sirine Essasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urban green spaces (UGS) as an important contributor can be a significant part of sustainable development. A spatial method was employed to assess and map the spatial distribution of UGS in five districts in Sousse, Tunisia. Ecological management of UGS is an essential factor for the sustainable development of the city; hence the municipality of Sousse has decided to support the districts according to different green spaces characters. And to implement this policy, (1) a new GIS web application was developed, (2) then the implementation of the various green spaces was carried out, (3) a spatial mapping of UGS using Quantum GIS was realized, and (4) finally a data processing and statistical analysis with RStudio programming language was executed. The intersection of the results of the spatial and statistical analyzes highlighted the presence of an imbalance in terms of the spatial UGS distribution in the study area. The discontinuity between the coast and the city's green spaces was not designed in a spirit of network and connection, hence the lack of a greenway that connects these spaces to the city. Finally, this GIS support will be used to assess and monitor green spaces in the city of Sousse by decision-makers and will contribute to improve the well-being of the local population. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributions" title="distributions">distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20space" title=" green space"> green space</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance" title=" imbalance"> imbalance</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/144279/mapping-of-urban-green-spaces-towards-a-balanced-planning-in-a-coastal-landscape" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144279.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">204</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis&page=5">5</a></li> <li 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