Compared to desktop geographic information systems(GIS), current Web GIS are still rather poor in terms of interface and functionality. Much of this discrepancy is due to the limitations of current web standard(HTML4)...
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Compared to desktop geographic information systems(GIS), current Web GIS are still rather poor in terms of interface and functionality. Much of this discrepancy is due to the limitations of current web standard(HTML4). HTML5 is being developed as the new web standard to replace HTML4. It provides plugin-free graphic supports and enhanced functionality, which make it very promising for the future development of Web GIS. This article introduces three case studies to investigate whether HTML5 can work effectively in developing Web GIS. This is necessary as HTML5 is not designed primarily for geospatial domains. Results of the case studies show that HTML5 can help to develop cross-browser and highly interactive Web GIS with true vector graphic capabilities and enhanced functionality(e.g., spatial analysis).
The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We d...
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The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatialdata suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatialdatasets.
Due to the rapid proliferation of mobile devices and advanced tracking sensors, there is a significant increase in data production daily. In response, we have created tramoTDA, a Python library that uses Topological D...
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Due to the rapid proliferation of mobile devices and advanced tracking sensors, there is a significant increase in data production daily. In response, we have created tramoTDA, a Python library that uses Topological data Analysis (TDA) to enable intuitive and visually-oriented classification of trajectory data. This tool offers a unique approach by focusing on the data's topological properties, which enables the identification of subtle and critical patterns often missed by conventional methods. tramoTDA combines scientific rigor with userfriendly design, making it suitable for both technical and non-technical users in diverse applications such as urban planning and maritime navigation. Through its innovative use of TDA, tramoTDA not only enhances data interpretation but also facilitates new research opportunities in complex system analysis, positioning it as a pivotal resource in data science and analytics.
The use of open source software, which has been constantly evolving since the mid-2000s, has affected every research discipline. Disciplines using geographic information systems (GIS) and remote sensing (RS) data have...
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The use of open source software, which has been constantly evolving since the mid-2000s, has affected every research discipline. Disciplines using geographic information systems (GIS) and remote sensing (RS) data have been heavily affected owing to this evolution of technology. Researchers working on these data sets have begun to use open source software intensively. The analysis and visualization of spatialdata with the help of open source software has caused the emergence of new different features, which are cost effective and editable by other users. In this study, eight sample points have been used for the analysis of water quality in the Mamasin dam in the 2209/A group project of "Assessment and Modeling with GIS and RS data of the Land Use Effects on Water Quality of Mamasin Dam" supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under its program to support graduate students. While visualizing spatial features of the points, QGIS Desktop 2.18.0 and Studio programs with open source code have been used. The RStudio program is an open source software that allows the use of the functions of the R programming language. This study is an ideal application for spatial analysis studies with the R programming language. The sample points used in the study were analyzed in the laboratories of Department of Environmental Engineering, Aksaray University. spatial properties of the analyzed data were examined by coding in the Studio program that is free open source software. In the analysis process, first, the libraries, Leaflet(), ***(), rgdal(), sp(), raster(), and magrittr(), which are used in the study, have been uploaded. With the help of these libraries, the locations of the sample points are transferred to the OpenStreetMap using latitudes and longitudes of the geographic coordinate system as base map. The pH, conductivity, PO4-P, PO4, dissolved oxygen, and temperature information of each sample points are assigned to the va
The visualization and analysis of massive, mull-source, mull-dimensional, heterogeneous, dynamic, and complex spatialdata is playing an increasingly important role in 3D scene and geological modeling. There are great...
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The visualization and analysis of massive, mull-source, mull-dimensional, heterogeneous, dynamic, and complex spatialdata is playing an increasingly important role in 3D scene and geological modeling. There are great differences in various research fields regarding data acquisition techniques, morphological descriptions, and application purposes of spatial objects. Therefore, based on an existing three-dimensional visualization technology and analysis program of spatialdata, an integrated processing method for the effective fusion and analysis of mull-source complex spatialdata is proposed. This method combines a massive point cloud model with a digital elevation model, in addition to combining the complex stratum with fault modeling technology. The structural grid and geological grid are effectively combined to realize the unified modeling and visualization of natural and artificial objects. This method was then applied to a 3D modeling scene of deeply buried mines and a geological construction model for open pit mines. The comprehensive and effective utilization of multi-source geological data is realized by using this modeling method. Finally, the challenges of spatial data visualization from the aspects of the difficulties in spatialdata acquisition, the complexities of the spatial structure, the constraint relations of geological bodies, and the complexity of the 3D modeling process are discussed in this study. Three specific suggestions are proposed. This study enhances the application efficiency of multi-source spatialdata modeling technologies already in practice and provides reference for future applications and developments.
It is usually a challenging task to integrate and analyze huge amounts of heterogeneous data in large-medical research projects. Often meaningful new insights can be achieved by visualizing medical data on geographica...
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It is usually a challenging task to integrate and analyze huge amounts of heterogeneous data in large-medical research projects. Often meaningful new insights can be achieved by visualizing medical data on geographical maps. For instance in epidemiological studies, data is often explored on a spatial dimension. LIFE is a large epidemiological study, managed by the LIFE Research Center for Civilization Diseases at Leipzig University. The study investigates the health-related states of the local population, e.g. by looking at the role of lifestyle factors on major civilization diseases. To allow for an effective data exploration, the development of sophisticated data analysis and spatialvisualization techniques is necessary. Here, we present the interactive web application LIFE spatial data visualization System(LIFE-SDVS) that adds a geographical facet to the data integration and analysis workflow of the LIFE research project.
The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We d...
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ISBN:
(纸本)9781538631850
The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatialdata suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatialdatasets.
Teleconnections refer to links between regions that are distant to each other, but nevertheless exhibit some relation. The study of such teleconnections is a well-known task in climate research. Climate simulation sha...
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Teleconnections refer to links between regions that are distant to each other, but nevertheless exhibit some relation. The study of such teleconnections is a well-known task in climate research. Climate simulation shall model known teleconnections. Detecting teleconnections in climate simulations is a crucial aspect in judging the quality of the simulation output. It is common practice to run scripts to execute a sequence of analysis steps on the climate simulations to search for teleconnections. Such a scripting approach is not flexible and targeted towards one specific goal. It is desirable to have one tool that allows for a flexible analysis of all teleconnection patterns with a dataset. We present such a tool, where the extracted information is provided in an intuitive visual form to users, who then can interactively explore the data. We developed an analysis workflow that is modeled around four views showing different facets of the data with coordinated interaction. We present a teleconnection study with simulation ensembles and reanalysis data obtained by data assimilation to observe how well the teleconnectivity patterns match and to demonstrate the effectiveness of our tool.
In computational science and computer graphics, there is a strong requirement to represent and visualize information in the real domain, and many visualizationdata structures and algorithms have been proposed to achi...
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In computational science and computer graphics, there is a strong requirement to represent and visualize information in the real domain, and many visualizationdata structures and algorithms have been proposed to achieve this aim. Unfortunately, the dataflow model that is often selected to address this issue in visualization systems is not flexible enough to visualize newly invented data structures and algorithms because this scheme can accept only specific data structures. To address this problem, we propose a new visualization tool, RViz, which is independent of the input information data structures. Since there is no requirement for additional efforts to manage the flow networks and the interface to abstracted information is simple in RViz, any scientific information visualization algorithms are easier to implement than the dataflow model. In this paper, we provide case studies in which we have successfully implemented new data structures and related algorithms using RViz, including geometry synthesis, distance field representation, and implicit surface reconstruction. Through these cases, we show how RViz helps users visualize and understand any hidden insights in input information.
We briefly introduce R2G2, an R CRAN package to visualize spatially explicit biological data within the Google Earth interface. Our package combines a collection of basic graph-editing features, including automated pl...
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We briefly introduce R2G2, an R CRAN package to visualize spatially explicit biological data within the Google Earth interface. Our package combines a collection of basic graph-editing features, including automated placement of dots, segments, polygons, images (including graphs produced with R), along with several complex three-dimensional (3D) representations such as phylogenies, histograms and pie charts. We briefly present some example data sets and show the immediate benefits in communication gained from using the Google Earth interface to visually explore biological results. The package is distributed with detailed help pages providing examples and annotated source scripts with the hope that users will have an easy time using and further developing this package. R2G2 is distributed on http://***/web/packages.
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