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Journal articles on the topic 'Geospatial data Environment'
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Charvat, Karel, Otakar Cerba, Dmitrij Kozuch, and Marek Splichal. "Geospatial Data Based Environment in INSPIRE4Youth." Procedia Computer Science 104 (2017): 183–89. http://dx.doi.org/10.1016/j.procs.2017.01.101.
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PAUL, MANOJ, and S. K. GHOSH. "A SERVICE-ORIENTED APPROACH FOR INTEGRATING HETEROGENEOUS SPATIAL DATA SOURCES REALIZATION OF A VIRTUAL GEO-DATA REPOSITORY." International Journal of Cooperative Information Systems 17, no. 01 (March 2008): 111–53. http://dx.doi.org/10.1142/s0218843008001774.
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Searching and accessing geospatial information in the open and distributed environments of geospatial information systems poses several challenges due to the heterogeneity in geospatial data. Geospatial data is highly heterogeneous — both at the syntactic and semantic level. The requirement for an integration architecture for seamless access of geospatial data has been raised over the past decades. The paper proposes a service-based model for geospatial integration where each geospatial data provider is interfaced on the web as services. The interface for these services has been described with Open Geospatial Consortium (OGC) specified service standards. Catalog service provides service descriptions for the services to be discovered. The semantic of each service description is captured in the form of ontology. The similarity assessment method of request service with candidate services proposed in this paper is aimed at resolving the heterogeneity in semantics of locational terms of service descriptions. In a way, we have proposed an architecture for enterprise geographic information system (E-GIS), which is an organization-wide approach to GIS integration, operation, and management. A query processing mechanism for accessing geospatial information in the service-based distributed environment has also been discussed with the help of a case study.
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Ronzhin, S., G. Bosch, E. Folmer, and R. Lemmens. "LINKDALE: A LIGHTWEIGHT LEARNING ENVIRONMENT FOR (GEOSPATIAL) LINKED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 187–90. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-187-2018.
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<p><strong>Abstract.</strong> Modern software tools for managing Linked Data are often designed for skilled users. Therefore, they cannot be used for education purposes because they require substantial a priori knowledge about the Resource Description Framework and the SPARQL query language. LinkDaLe is a single page application designed to teach students the concept of Linked Data and work with linked data at the same time. In the paper we showcase the interface and functionality of LinkDaLe by triplifying data on Geo4All member organizations. The application was built and evaluated within The Business Process Integration Lab, a master programme course in 2016 and 2017 years. Positive feedback from both students and teachers proved the relevance of the proposed design consideration. LinkDaLe showed usability working with domain specific data e.g. geospatial and logistic data.</p>
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Lee, Ahyun, Yoon-Seop Chang, and Insung Jang. "Planetary-Scale Geospatial Open Platform Based on the Unity3D Environment." Sensors 20, no. 20 (October 21, 2020): 5967. http://dx.doi.org/10.3390/s20205967.
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Digital twin technology based on building a virtual digital city similar to a real one enables the simulation of urban phenomena or the design of a city. A geospatial platform is an essential supporting component of digital twin cities. In this study, we propose a planetary-scale geospatial open platform that can be used easily in the most widely used game engine environment. The proposed platform can visualize large-capacity geospatial data in real time because it organizes and manages various types of data based on quadtree tiles. The proposed rendering tile decision method provides constant geospatial data visualization according to the camera controls of the user. The platform implemented is based on Unity3D, and therefore, one can use it easily by importing the proposed asset library. The proposed geospatial platform is available on the Asset Store. We believe that the proposed platform can meet the needs of various three-dimensional (3-D) geospatial applications.
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Olasz, A., D. Kristóf, B. Nguyen Thai, M. Belényesi, and R. Giachetta. "PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W2 (July 5, 2017): 137–38. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w2-137-2017.
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The Earth observation (EO) missions of the space agencies and space industry (ESA, NASA, national and commercial companies) are evolving as never before. These missions aim to develop and launch next-generation series of satellites and sensors and often provide huge amounts of data, even free of charge, to enable novel monitoring services. The wide geospatial sector is targeted to handle new challenges to store, process and visualize these geospatial data, reaching the level of Big Data by their volume, variety, velocity, along with the need of multi-source spatio-temporal geospatial data processing. Handling and analysis of remote sensing data has always been a cumbersome task due to the ever-increasing size and frequency of collected information. This paper presents the achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of geospatial big data in the context of flood and waterlogging detection.
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Heldens, Wieke, Cornelia Burmeister, Farah Kanani-Sühring, Björn Maronga, Dirk Pavlik, Matthias Sühring, Julian Zeidler, and Thomas Esch. "Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing." Geoscientific Model Development 13, no. 11 (November 27, 2020): 5833–73. http://dx.doi.org/10.5194/gmd-13-5833-2020.
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Abstract. The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized format. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This article shows how input data sets for three German cities were derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data.
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Zheng, Feng, Han Rong Lu, Yan An, and Le Jiang Guo. "The Research and Implementation of Geospatial Data Management Based on ArcGIS Service." Advanced Materials Research 605-607 (December 2012): 2379–82. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2379.
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The main objective of this paper is to design and develop a basis geospatial data management system. It offers database building, conversion, topology check, query statistics, data extraction, cartographic output, analysis and decision-making for massive geospatial data on a range of basic geospatial data management function. The main research includes the design of basis geospatial database, data management system and the key technologies in system development. Geographic information from 2D to 3D is the result of technical progress and demand-driven, 3D modeling and 3D visualization environment-based spatial analysis and query is the future development direction of the geospatial data management system.
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Karthi, S., and S. Prabu. "Secure geospatial data storage using SpatialHadoop framework in cloud environment." International Journal of Internet Technology and Secured Transactions 8, no. 4 (2018): 516. http://dx.doi.org/10.1504/ijitst.2018.095926.
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Karthi, S., and S. Prabu. "Secure geospatial data storage using SpatialHadoop framework in cloud environment." International Journal of Internet Technology and Secured Transactions 8, no. 4 (2018): 516. http://dx.doi.org/10.1504/ijitst.2018.10017026.
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Zhai, X., L. Jiang, and P. Yue. "Web-Based Geospatial Resource Sharing Through GeoPW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 131–35. http://dx.doi.org/10.5194/isprsarchives-xl-6-131-2014.
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As Web-related technologies have matured in recent years, an increasing amount of geospatial resources (e.g. geospatial services, workflows, and geospatial data) are available in the distributed Web environment. Consequently, effective and efficient sharing and management of geospatial resources on the Web are necessary for better utilizing these resources for education and scientific research. This matches the vision of Geoprocessing Web, which emphasizes the sharing and access of geoprocessing utilities from the perspectives of communication, collaboration, and participation. Previous work on GeoPW has provided a large number of geoprocessing services over the Web. In this paper, GeoPW goes further to offer a Web platform for sharing geospatial resources. The paper presents the design, implementation, and functions of the platform, which offers a user-friendly environment for publication, discovery, and communication of geospatial data, services, and workflows.
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Mazroob Semnani, N., P. V. Kuper, M. Breunig, and M. Al-Doori. "TOWARDS AN INTELLIGENT PLATFORM FOR BIG 3D GEOSPATIAL DATA MANAGEMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 133–40. http://dx.doi.org/10.5194/isprs-annals-iv-4-133-2018.
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<p><strong>Abstract.</strong> The use of intelligent technologies within 3D geospatial data analysis and management will decidedly open the door towards efficiency, cost transparency, and on-time schedules in planning processes. Furthermore, the mission of smart cities as a future option of urban development can lead to an environment that provides high-quality life along stable structures. However, neither geospatial information systems nor building information modelling systems seem to be well prepared for this new development. After a review of current approaches and a discussion of their limitations we present our approach on the way to an intelligent platform for the management and analysis of big 3D geospatial data focusing on infrastructure projects such as metro or railway tracks planning. three challenges are presented focusing on the management of big geospatial data with existing geo-database management systems, the integration of heterogeneous data, and the 3D visualization for database query formulation and query results. The approach for the development of a platform for big geospatial data analysis is discussed. Finally, we give an outlook on our future research supporting intelligent 3D city applications in the United Arab Emirates.</p>
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Veenendaal, B. "USING THE GEOSPATIAL WEB TO DELIVER AND TEACH GISCIENCE EDUCATION PROGRAMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6/W1 (May 27, 2015): 17–21. http://dx.doi.org/10.5194/isprsarchives-xl-6-w1-17-2015.
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Geographic information science (GIScience) education has undergone enormous changes over the past years. One major factor influencing this change is the role of the geospatial web in GIScience. In addition to the use of the web for enabling and enhancing GIScience education, it is also used as the infrastructure for communicating and collaborating among geospatial data and users. The web becomes both the means and the content for a geospatial education program. However, the web does not replace the traditional face-to-face environment, but rather is a means to enhance it, expand it and enable an authentic and real world learning environment. This paper outlines the use of the web in both the delivery and content of the GIScience program at Curtin University. The teaching of the geospatial web, web and cloud based mapping, and geospatial web services are key components of the program, and the use of the web and online learning are important to deliver this program. Some examples of authentic and real world learning environments are provided including joint learning activities with partner universities.
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Huang, Weiming, Syed Amir Raza, Oleg Mirzov, and Lars Harrie. "Assessment and Benchmarking of Spatially Enabled RDF Stores for the Next Generation of Spatial Data Infrastructure." ISPRS International Journal of Geo-Information 8, no. 7 (July 19, 2019): 310. http://dx.doi.org/10.3390/ijgi8070310.
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Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). However, current SDIs are facing challenges in terms of data integration and semantic heterogeneity because of their partially siloed data organization. In this context, linked data provides a promising means to unravel these challenges, and it is seen as one of the key factors moving SDIs toward the next generation. In this study, we investigate the technical environment of the support for geospatial linked data by assessing and benchmarking some popular and well-known spatially enabled RDF stores (RDF4J, GeoSPARQL-Jena, Virtuoso, Stardog, and GraphDB), with a focus on GeoSPARQL compliance and query performance. The tests were performed in two different scenarios. In the first scenario, geospatial data forms a part of a large-scale data infrastructure and is integrated with other types of data. In this scenario, we used ICOS Carbon Portal’s metadata—a real-world Earth Science linked data infrastructure. In the second scenario, we benchmarked the RDF stores in a dedicated SDI environment that contains purely geospatial data, and we used geospatial datasets with both crowd-sourced and authoritative data (the same test data used in a previous benchmark study, the Geographica benchmark). The assessment and benchmarking results demonstrate that the GeoSPARQL compliance of the RDF stores has encouragingly advanced in the last several years. The query performances are generally acceptable, and spatial indexing is imperative when handling a large number of geospatial objects. Nevertheless, query correctness remains a challenge for cross-database interoperability. In conclusion, the results indicate that the spatial capacity of the RDF stores has become increasingly mature, which could benefit the development of future SDIs.
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Chang, Lu Qun, Jian Hua Li, Li Zhong Tian, Peng Lin, Ying Wu, and Wei Zheng Zhong. "DCGF in Ji’Nan: Construction and Issues." Advanced Materials Research 950 (June 2014): 333–38. http://dx.doi.org/10.4028/www.scientific.net/amr.950.333.
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The aim of this paper is to give a review of Digital China Geospatial Framework (DCGF) construction in Ji’nan which is named Digital Ji’nan Geospatial Framework (DJGF). As a national promotting project of DCGF, DJGF has seven components: Municipal Geospatial Information Resource Catalog (MGIRC), Geospatial Framework Datasets (GFD), Geographical Information Database Management System (GIDMS), Public Service Platform (PSP), Security system , Runtime Environment and Pilot Projects. The key methods used in DJGF mainly include Extraction-Transformation-Loading (ETL) technology and service extension. Along with the construction of DJGF, the integrated surveying business system has been built, automatial processing technologies also have been applied to improve the data production efficiency. Moreover, " sub-building, co-sharing, one data-one resource" mechanism has been Put forward and promote the benign circulation of datas. Now, as a fundamental platform for geospatial data exchanging and service supplying, DJGF have been widely used in urban planning, land resources, environment, climate, etc.
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ita, Kav, and M. Anji Reddy. "Geospatial Database Creation for Town Planning Using Satellite Data under GIS Environment." International Journal of Civil Engineering 4, no. 6 (June 25, 2017): 99–103. http://dx.doi.org/10.14445/23488352/ijce-v4i6p116.
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Janečka, Karel. "Transformation of 3D geospatial data into CityGML – a case of Prague." Reports on Geodesy and Geoinformatics 107, no. 1 (May 17, 2019): 41–48. http://dx.doi.org/10.2478/rgg-2019-0005.
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AbstractThe 3D geoinformation is becoming important for cities and their policies. The cities are therefore exploring the possibilities of 3D virtual city models for more efficient decision making. To maximize the economic benefit of such data, the cities can provide their 3D geospatial data for further usage, and so, new applications can be created. The paper defines a way how the freely available 3D geospatial data of Prague can be transformed from the proprietary data format into the open data model. The 3D geospatial data about the buildings, bridges and digital terrain model were transformed from the 3D shapefile into the CityGML. This is an application independent information model and exchange format. This will allow for the wider use of the 3D city model by different groups of users. The generated CityGML files were further imported into the spatial database with appropriate database CityGML-based scheme. It enables more efficient management and querying of CityGML data. To enable the wider audience to explore the 3D city model, the visualization in the web environment was also explored. The paper also presents the way how the attributes from the external data sources can be connected to the 3D objects in the web environment.
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Abdul Rasam, Abdul Rauf, Noresah Mohd Shariff, and JilorisF Dony. "The Invention of Geospatial Decision Support System for Malaysian Tuberculosis Surveillance Data Management." Environment-Behaviour Proceedings Journal 5, SI3 (December 28, 2020): 269–74. http://dx.doi.org/10.21834/ebpj.v5isi3.2564.
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The World Health Organization (WHO) recognizes the capabilities of a health information tool for disease preparedness and emergency responses. In Malaysia, the Ministry of Health (MOH) has been using MyTB system to support the national tuberculosis (TB) control program through data decision-making management. However, this present system does not seem to be considering geospatial element which is one of the important factors affecting TB control. Integrating the MyTB system with geospatial functions would enhance the explicit cognitive and behavior analyses of TB by proposing a MyGeoTBIS© to assist the local health authorities in exploring TB dynamics and multi-level infection control. Keywords: Geospatial, GIS, MyGeoTBIS, MyTB, tuberculosis disease eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bsby e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI3.2564
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Ye, Jiu Yan, Bin Chen, Zun Lin Ke, Jian Chen, and Yu Fang. "The Application of Big Geospatial Data in Smart Agriculture." Advanced Materials Research 955-959 (June 2014): 3835–39. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3835.
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With the fast development of data acquisition and web transmission, the age of big geospatial data(BGD) has come. BGD will greatly change peoples’ life-style of and our society’s work mode. In agriculture field, there will also be a lot of changes brought by BGD in future. In this paper, we had a study on the processing of BGD and its application in agriculture. Firstly, we introduced BGD including its definition, value and application areas. Secondly, we summarized the research progress and key technologies of BGD processing in the cloud computing environment. Lastly, we talked about the application of BGD in agriculture industry and looked forward to its development trend.
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Liu, Junnan, Haiyan Liu, Xiaohui Chen, Xuan Guo, Qingbo Zhao, Jia Li, Lei Kang, and Jianxiang Liu. "A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph." Sustainability 13, no. 4 (February 12, 2021): 2005. http://dx.doi.org/10.3390/su13042005.
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Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieval speed can be improved. Firstly, implicit semantic knowledge is studied and applied to construct a knowledge graph, integrating semantics in multi-source heterogeneous geospatial data. Then, the query expansion rules and the mappings between knowledge and database are designed to construct retrieval statements and obtain related spatial entities. Finally, the effectiveness and efficiency are verified through comparative analysis and practices. The experiment indicates that the method could automatically construct database retrieval statements and retrieve more relevant data. Additionally, users could reduce the dependence on data storage mode and database Structured Query Language syntax. This paper would facilitate the sharing and outreach of geospatial knowledge for various spatial studies.
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Jing, Changfeng, Yanli Zhu, Jiayun Fu, and Meng Dong. "A Lightweight Collaborative GIS Data Editing Approach to Support Urban Planning." Sustainability 11, no. 16 (August 16, 2019): 4437. http://dx.doi.org/10.3390/su11164437.
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Collaborative geospatial data editing is different from other collaborative editing systems, such as textual editing, owing to its geospatial nature. This paper presents a version-based lightweight collaborative geospatial editing method for urban planning. This method extracts editing data and generates a version for collaborative editing, which reduces the data size and thus allows for a high feedback speed. A replication mechanism is engaged to replicate a version for the client to freely edit, which ensures constraint-free editing in collaboration. Based on this method, realizing the fact that heterogeneous geospatial data and non-professional users are involved, a lightweight architecture, integrating web services, and component technologies, was proposed. This architecture provides a unified data access interface and powerful editing ability and ensures a high feedback speed and constraint-free editing. The result of the application of the proposed approach in a practical project demonstrates the usability of collaborative geospatial editing in urban planning. While this approach has been designed for urban planning, it can be modified for use in other domains.
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Lubishtani, Milot, Bashkim Idrizi, Subija Izeiroski, and Fitore Bajrami Lubishtani. "Infrastructural Organization of Geospatial Data in The Global Level: A Case Study of Albanian Global Mapping Dataset." Geosfera Indonesia 5, no. 1 (April 24, 2020): 106. http://dx.doi.org/10.19184/geosi.v5i1.16901.
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Today, the development of economic and financial situation concerning the protection of environment and natural resources in a wider scope depends on the use of geospatial data. One of the main aims of the infrastructural organization of geospatial data is to provide users to be capable of acquiring complete, exact and updated dataset at the right time. This is necessary for providing an ideal environment, where all stakeholders can work collaboratively in an effective way, in order to solve environmental issues and to achieve their targets. Global Mapping (GM), a project established by United Nations, is one of the crucial contributions to the development of Global Spatial Data Infrastructure (GSDI). This case study on Albanian GM dataset was aimed at performing analyses of infrastructural organization of geospatial data in global-intercontinental level. Data standardization of GM as contributor of GSDI was analyzed through developed Albanian GM dataset. The main components taken into consideration for performing research analyses were data and metadata, technology, institutional framework, policies, interoperability, network services, search opportunities, and data sharing within GSDI. The main findings of this study are the necessity of infrastructural organization of geospatial data in the global level, known as GSDI, by including official geospatial datasets developed by the national mapping organizations of countries all over the world, in order to be used for environmental monitoring and protection, as well as for early warning management in international level. Finally, based on the research results, four conclusions for GSDI are offered, in order to be considered as guideline for further development of unified and globally homogeneous infrastructure of spatial data set.
Keywords: GSDI; GM; spatial data infrastructure; Albania.
Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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Zhang, H., J. Jiang, W. Huang, and L. Yang. "DESIGN AND IMPLEMENTATION OF CROWDSOURCING BASED CHINA'S NATIONAL PUBLIC GEOSPATIAL INFORMATION COLLECTION SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1621–24. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1621-2019.
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<p><strong>Abstract.</strong> As the basis of public geospatial information service, geospatial information data with wide coverage, strong timeliness, rich content and high positioning accuracy is the key infrastructure of geospatial infrastructure for building smart cities and digital China. How to collect and update massive geospatial information resources fast and efficiently has always been the bottleneck for the rapid development of surveying and mapping geospatial information science and technology and industries, and the construction of China's national spatial data infrastructure. At present, the mainstream of geospatial data collecting and updating in China is that the government and enterprise employ professional engineers to for this work, but the problems of professional and complex data processing process, data management level by level, closed and off-line data update mode lead to a relatively slow update rate and poor data actuality, which seriously restricted the applications of geospatial information service in the fields of government decision-making, planning and construction, resource and environment monitoring, and emergency response. The contributions to the geospatial information data from the public community has also been ignored. This paper addresses the issues of low efficiency on data collection and update occurring in China's national geospatial information service platform, proposing a solution for public geospatial data collection and update based on crowdsourcing. The key technologies of data collection, storage, reviews and publication are studied, and the basic technical process of online data update for the government and the public users is designed. In order to verify the effectiveness and practicality of the mode discussed above, a prototype system is developed by expanding China's national geospatial information service platform, which fulfilled data online collection and reviewing, such as POIs, roads, and residential areas. The system can provide new methods and reference ideas for the collection and update of spatial data for the national geospatial information public service platform. It is a useful supplement to the current spatial data collection and update, and has important significances.</p>
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Corley, Brittany, Shannon Bartelt-Hunt, Eleanor Rogan, Donald Coulter, John Sparks, Lorena Baccaglini, Madeline Howell, Sidra Liaquat, Rex Commack, and Alan S. Kolok. "Using Watershed Boundaries to Map Adverse Health Outcomes: Examples From Nebraska, USA." Environmental Health Insights 12 (January 1, 2018): 117863021775190. http://dx.doi.org/10.1177/1178630217751906.
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In 2009, a paper was published suggesting that watersheds provide a geospatial platform for establishing linkages between aquatic contaminants, the health of the environment, and human health. This article is a follow-up to that original article. From an environmental perspective, watersheds segregate landscapes into geospatial units that may be relevant to human health outcomes. From an epidemiologic perspective, the watershed concept places anthropogenic health data into a geospatial framework that has environmental relevance. Research discussed in this article includes information gathered from the literature, as well as recent data collected and analyzed by this research group. It is our contention that the use of watersheds to stratify geospatial information may be both environmentally and epidemiologically valuable.
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Olanrewaju, Olajumoke Esther, and Kayode Adewale Adepoju. "Geospatial Assessment of Cholera in a Rapidly Urbanizing Environment." Journal of Environmental and Public Health 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/6847376.
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This study mapped out and investigated the spatial relationship between cholera incidences and environmental risk factors in the study area. The study area was stratified into eight zones. Water samples from each zone were collected and analyzed to determine the colony forming units. GIS layers including housing density, digitized roads, rivers, buildings, and cholera incidence data from hospital archives were also collected and analyzed using ArcGIS 10.1. It was observed that there was an association between the ERFs (p<0.001). Similarly, 18 out of the 44 waste dump sites, seven out of 18 markets, and two out of 36 abattoirs were found near the historical cholera cases. Similarly, 4 (21.1%) locations were traced to be predominantly close to rivers and waste dump site. All the historical cholera cases were found adjoining to roads and buildings. Highest CFU count was found in the wells and streams of areas with a cluster of all the environmental risk factors and high housing density. This study revealed that waste dump sites and market had the highest predisposing attribute while the least was abattoir. The uniqueness of the study lies in the combination of mapping and microbial analyses to identify and assess the pattern of cholera risk and also to provide clear information for development of strategies for environmental supervision.
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Dudar, T., O. Titarenko, A. Nekos, O. Vysotska, and A. Porvan. "Geospatial Modeling of Radon-Prone Areas." Nuclear and Radiation Safety, no. 3(87) (September 15, 2020): 28–37. http://dx.doi.org/10.32918/nrs.2020.3(87).04.
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Methods for identification of potentially radon-prone areas using geospatial analysis in ArcGIS 10.6 software environment and mathematical modeling in SPSS 19.0 on the example of high background radiation area have been developed. High level of natural radioactivity associated with uranium content in environment objects and natural uranium occurrences, and also the spatial density of faults (reliable and unreliable) and lineaments were taken into account as well as the distance from uranium mine located nearby.
The method of linear discriminant functions was used to make a math model for determining the level of radon hazard. To do this, data on all locations were divided into training and test samples. Determination of predictors of the mathematical model was performed using Fisher's criterion by their sequential inclusion in discriminant equations. Among the considered 13 factors of radon hazard, seven of them turned out to be informative. For them, canonical coefficients were calculated using the least squares method for first- and second-order polynomials. Based on the values of discriminant functions, a territorial map was constructed to assign the new location to a certain level of radon hazard.
The maps obtained present the correlation of the radon-prone areas with the zones of high spatial density of faults and lineaments, and confirmed by the data of direct indoor radon measurements. In a limited number of measurements, the methods might get a good help in prioritization for round-the-country radon survey. As far as the model for identification of potentially radon-prone areas is mainly based on geological studies, the further research is supposed to be directed to its approbation for a different geological environment of the Ukrainian shield.
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Frederick, Donna Ellen. "Data, Open Science and libraries – The Data Deluge Column." Library Hi Tech News 33, no. 8 (October 3, 2016): 11–16. http://dx.doi.org/10.1108/lhtn-09-2016-0040.
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Purpose For those immersed in the environment of academic and research libraries, the word “data” seems to be everywhere. One hears about linked data, big data, open data, proprietary data, research data, metadata, geospatial data, data repositories, etc. Design/methodology/approach Some libraries even have data librarians and data services departments. Findings The author of this column wonders if she were to collect all of the library and information science literature published in the past three years and plug it into a word cloud app, which of the two, i.e. “data” or “books”, would be displayed in a larger font. Originality/value The author suspects that the chances are more than good that “data” would come out on top.
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Giachetta, Roberto. "A framework for processing large scale geospatial and remote sensing data in MapReduce environment." Computers & Graphics 49 (June 2015): 37–46. http://dx.doi.org/10.1016/j.cag.2015.03.003.
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Rubinyi, Steven, Brian Blankespoor, and Jim W. Hall. "The utility of built environment geospatial data for high-resolution dasymetric global population modeling." Computers, Environment and Urban Systems 86 (March 2021): 101594. http://dx.doi.org/10.1016/j.compenvurbsys.2021.101594.
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Manikanta, K., and K. S. Rajan. "LSIVIEWER 2.0 – A CLIENT-ORIENTED ONLINE VISUALIZATION TOOL FOR GEOSPATIAL VECTOR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 107–13. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-107-2017.
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Geospatial data visualization systems have been predominantly through applications that are installed and run in a desktop environment. Over the last decade, with the advent of web technologies and its adoption by Geospatial community, the server-client model for data handling, data rendering and visualization respectively has been the most prevalent approach in Web-GIS. While the client devices have become functionally more powerful over the recent years, the above model has largely ignored it and is still in a mode of serverdominant computing paradigm. In this paper, an attempt has been made to develop and demonstrate LSIViewer &ndash; a simple, easy-to-use and robust online geospatial data visualisation system for the user’s own data that harness the client’s capabilities for data rendering and user-interactive styling, with a reduced load on the server. The developed system can support multiple geospatial vector formats and can be integrated with other web-based systems like WMS, WFS, etc. The technology stack used to build this system is Node.js on the server side and HTML5 Canvas and JavaScript on the client side. Various tests run on a range of vector datasets, upto 35&thinsp;MB, showed that the time taken to render the vector data using LSIViewer is comparable to a desktop GIS application, QGIS, over an identical system.
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Siao Him Fa, J., T. W. Reed, C. Tan, G. West, D. A. McMeekin, S. Moncrieff, and S. Cox. "SEARCH AND ORCHESTRATION OF DATA AND PROCESSES IN A FEDERATED ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W7 (June 30, 2015): 35–39. http://dx.doi.org/10.5194/isprsarchives-xl-4-w7-35-2015.
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This paper describes on-going research on streamlining the access and use of spatial data and processes in Australia. Spatial data in Australia is available on-line at many levels of government from local authorities, state and territories (jurisdictions), and nationally from the Commonwealth and other sources. Much of this data is available via Open Geospatial Consortium and World Wide Web Consortium standard web services. This abstract discusses three related research topics that have been identified by a wide range of stakeholders through a comprehensive consultation process. These are search and discovery, federation and orchestration of data and processes. The commonality across the three research topics is that they all require Semantic Web and Artificial Intelligence methods and embrace the various standards, and if needed, propose modifications to such standards.
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Spur, Maxim, Vincent Tourre, Erwan David, Guillaume Moreau, and Patrick Le Callet. "Exploring Multiple and Coordinated Views for Multilayered Geospatial Data in Virtual Reality." Information 11, no. 9 (August 31, 2020): 425. http://dx.doi.org/10.3390/info11090425.
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Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, as compared to traditional environments with monitors or printouts. The controllers for these devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we make use of these advantages to implement a novel multiple and coordinated view (MCV) system in the form of a vertical stack, showing tilted layers of geospatial data. In a formal study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data, we compared it against more conventional systems similarly implemented in VR: a simpler grid of layers, and one map that allows for switching between layers. Performance and oculometric analyses showed a slight advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal multiplexing in blitting. Subgrouping the participants based on their preferences, characteristics, and behavior allowed a more nuanced analysis, allowing us to establish links between e.g., saccadic information, experience with video games, and preferred system. In conclusion, we found that none of the three systems are optimal and a choice of different MCV systems should be provided in order to optimally engage users.
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Lao, L. J., and B. J. Harder. "GATEWAY: A GEOSPATIAL ANALYTICS SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 283–88. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-283-2019.
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Abstract. This paper presents an overview of the Gateway web platform, a proprietary geospatial analytics system developed by Cobena Business Analytics and Strategy, Inc.1 The application is intended to serve as a user-friendly and easily-accessible tool for spatial data analysis and visualization geared toward non-technical specialists. Gateway’s core functionalities hinge on mapping and data visualization (choropleths and points) alongside traditional scoring methods and built-in machine learning algorithms for area prioritization and site selection. Gateway provides an interactive, cloud-based environment that abstracts and simplifies common location-based analyses. A core strength of the platform is also its heavy localization to the Philippine context through a curated database of market information — with future plans to create local counterparts across SEA — which reduces the need for extensive external market data collection and reconciliation. The paper gives a brief review of the system design and key features of the platform. It also highlights some key applications across industries such as real estate, consumer goods, and retail in informing expansion and distribution strategies, prioritizing resource allocation, and analyzing historical performance against market factors.
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Omidipoor, Morteza, Ara Toomanian, Najmeh Neysani Samany, and Ali Mansourian. "Knowledge Discovery Web Service for Spatial Data Infrastructures." ISPRS International Journal of Geo-Information 10, no. 1 (December 31, 2020): 12. http://dx.doi.org/10.3390/ijgi10010012.
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The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.
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Gong, J., H. Wu, W. Jiang, W. Guo, X. Zhai, and P. Yue. "Geospatial Service Platform for Education and Research." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 31–36. http://dx.doi.org/10.5194/isprsarchives-xl-6-31-2014.
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We propose to advance the scientific understanding through applications of geospatial service platforms, which can help students and researchers investigate various scientific problems in a Web-based environment with online tools and services. The platform also offers capabilities for sharing data, algorithm, and problem-solving knowledge. To fulfil this goal, the paper introduces a new course, named "Geospatial Service Platform for Education and Research", to be held in the ISPRS summer school in May 2014 at Wuhan University, China. The course will share cutting-edge achievements of a geospatial service platform with students from different countries, and train them with online tools from the platform for geospatial data processing and scientific research. The content of the course includes the basic concepts of geospatial Web services, service-oriented architecture, geoprocessing modelling and chaining, and problem-solving using geospatial services. In particular, the course will offer a geospatial service platform for handson practice. There will be three kinds of exercises in the course: geoprocessing algorithm sharing through service development, geoprocessing modelling through service chaining, and online geospatial analysis using geospatial services. Students can choose one of them, depending on their interests and background. Existing geoprocessing services from OpenRS and GeoPW will be introduced. The summer course offers two service chaining tools, GeoChaining and GeoJModelBuilder, as instances to explain specifically the method for building service chains in view of different demands. After this course, students can learn how to use online service platforms for geospatial resource sharing and problem-solving.
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Tripathi, A. K., S. Agrawal, and R. D. Gupta. "WPS ENABLED SDI: AN OPEN SOURCE APPROACH TO PROVIDE GEOPROCESSING IN WEB ENVIRONMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5/W2 (December 5, 2019): 119–26. http://dx.doi.org/10.5194/isprs-annals-iv-5-w2-119-2019.
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Abstract. Sharing and management of geospatial data among different communities and users is a challenge which is suitably addressed by Spatial Data Infrastructure (SDI). SDI helps people in the discovery, editing, processing and visualization of spatial data. The user can download the data from SDI and process it using the local resources. However, large volume and heterogeneity of data make this processing difficult at the client end. This problem can be resolved by orchestrating the Web Processing Service (WPS) with SDI. WPS is a service interface through which geoprocessing can be done over the internet. In this paper, a WPS enabled SDI framework with OGC compliant services is conceptualized and developed. It is based on the three tier client server architecture. OGC services are provided through GeoServer. WPS extension of GeoServer is used to perform geospatial data processing and analysis. The developed framework is utilized to create a public health SDI prototype using Open Source Software (OSS). The integration of WPS with SDI demonstrates how the various data analysis operations of WPS can be performed over the web on distributed data sources provided by SDI.
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Barik, Rabindra K. "CloudGanga." International Journal of Agricultural and Environmental Information Systems 8, no. 4 (October 2017): 54–71. http://dx.doi.org/10.4018/ijaeis.2017100104.
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The present research paper proposes and develops a Cloud computing based Spatial Data Infrastructure (SDI) Model named as CloudGanga for sharing, analysis and processing of geospatial data particularly in River Ganga Basin management in India. The main purpose of the CloudGanga is to integrate all the geospatial information such as dam location, well location, irrigation project, hydro power project, canal network and central Water Commission gauge stations locations related to River Ganga. CloudGanga can help the decision maker/ planner or common users to get enough information for their further research and studies. The open source software (Quantum GIS) has been used for the development of geospatial database. QGIS Plugin has been linked with Quantum GIS for invoking cloud computing environment. It has also discussed about the various overlay analysis in CloudGanga environment. In the present research, machine learning approaches are also used in a R tool for well locations which are associated with the basin of River Ganga.
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Bhattacharya, D., and M. Painho. "DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 33–39. http://dx.doi.org/10.5194/isprsannals-iii-4-33-2016.
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Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. It is about research to harness the sensed environment by utilizing domain specific sensor data to create a generalized sensor webframework. The challenges being semantic enablement for Spatial Data Infrastructures, and connecting the interfaces of SDI with interfaces of Sensor Web. The proposed research plan is to Identify sensor data sources, Setup an open source SDI, Match the APIs and functions between Sensor Web and SDI, and Case studies like hazard applications, urban applications etc. We take up co-operative development of SDI best practices to enable a new realm of a location enabled and semantically enriched World Wide Web - the "Geospatial Web" or "Geosemantic Web" by setting up one to one correspondence between WMS, WFS, WCS, Metadata and 'Sensor Observation Service' (SOS); 'Sensor Planning Service' (SPS); 'Sensor Alert Service' (SAS); a service that facilitates asynchronous message interchange between users and services, and between two OGC-SWE services, called the 'Web Notification Service' (WNS). Hence in conclusion, it is of importance to geospatial studies to integrate SDI with Sensor Web. The integration can be done through merging the common OGC interfaces of SDI and Sensor Web. Multi-usability studies to validate integration has to be undertaken as future research.
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Bhattacharya, D., and M. Painho. "DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 33–39. http://dx.doi.org/10.5194/isprs-annals-iii-4-33-2016.
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Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. It is about research to harness the sensed environment by utilizing domain specific sensor data to create a generalized sensor webframework. The challenges being semantic enablement for Spatial Data Infrastructures, and connecting the interfaces of SDI with interfaces of Sensor Web. The proposed research plan is to Identify sensor data sources, Setup an open source SDI, Match the APIs and functions between Sensor Web and SDI, and Case studies like hazard applications, urban applications etc. We take up co-operative development of SDI best practices to enable a new realm of a location enabled and semantically enriched World Wide Web - the "Geospatial Web" or "Geosemantic Web" by setting up one to one correspondence between WMS, WFS, WCS, Metadata and 'Sensor Observation Service' (SOS); 'Sensor Planning Service' (SPS); 'Sensor Alert Service' (SAS); a service that facilitates asynchronous message interchange between users and services, and between two OGC-SWE services, called the 'Web Notification Service' (WNS). Hence in conclusion, it is of importance to geospatial studies to integrate SDI with Sensor Web. The integration can be done through merging the common OGC interfaces of SDI and Sensor Web. Multi-usability studies to validate integration has to be undertaken as future research.
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Tan, X., L. Di, M. Deng, A. Chen, Z. Sun, C. Huang, Y. Shao, and X. Ye. "AGENT- AND CLOUD-SUPPORTED GEOSPATIAL SERVICE AGGREGATION FOR FLOOD RESPONSE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 13–18. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-13-2015.
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Flooding caused serious losses in China in the past two decades; therefore, responding to and mitigating the impact of flooding is a task of critical importance. The traditional flood response process is usually very time-consuming and labor-intensive. The Service-Oriented Architecture （SOA）-based flood response is a method with low efficiency due to the large volume of geospatial data transfer, and this method cannot meet the real-time requirement of a rapid response to flooding. This paper presents an Agent- and Cloud-supported geospatial service aggregation to obtain a more efficient geospatial service system for the response to flooding. The architecture of this method is designed and deployed on the Cloud environment, and the flooding response prototype system is built on the Amazon AWS Cloud to demonstrate that the proposed method can avoid transferring large volumes of geospatial data or Big Spatial Data. Consequently, this method is able to achieve better performance than that of the SOA-based method.
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Camara, G. S., S. P. Camboim, and J. V. M. Bravo. "USING JUPYTER NOTEBOOKS FOR VIEWING AND ANALYSING GEOSPATIAL DATA: TWO EXAMPLES FOR EMOTIONAL MAPS AND EDUCATION DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W2-2021 (August 19, 2021): 17–24. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w2-2021-17-2021.
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Abstract. This article presents two applications developed using Jupyter Notebook in the Google Colab, combining several Python libraries that enable an interactive environment to query, manipulate, analyse, and visualise spatial data. The first application is from an educational context within the MAPFOR project, aiming to elaborate an interactive map of the spatial distributions of teachers with higher education degrees or pedagogical complementation per vacancies in higher education courses. The Jupyter solutions were applied in MAPFOR to better communicate within the research team, mainly in the development area. The second application is a framework to analyse and visualise collaborative emotional mapping data in urban mobility, where the emotions were collected and represented through emojis. The computational notebook was applied in this emotional mapping to enable the interaction of users, without a SQL background, with spatial data stored in a database through widgets to analyse and visualise emotional spatial data. We developed these different contexts in a Jupyter Notebook to practice the FAIR principles, promote the Open Science movement, and Open Geospatial Resources. Finally, we aim to demonstrate the potential of using a mix of open geospatial technologies for generating solutions that disseminate geographic information.
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Voland, Patrick, and Hartmut Asche. "Processing and Visualizing Floating Car Data for Human-Centered Traffic and Environment Applications." International Journal of Agricultural and Environmental Information Systems 8, no. 2 (April 2017): 32–49. http://dx.doi.org/10.4018/ijaeis.2017040103.
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In the era of the Internet of Things and Big Data modern cars have become mobile electronic systems or computers on wheels. Car sensors record a multitude of car and traffic related data as well as environmental parameters outside the vehicle. The data recorded are spatio-temporal by nature (floating car data) and can thus be classified as geodata. Their geospatial potential is, however, not fully exploited so far. In this paper, we present an approach to collect, process and visualize floating car data for traffic- and environment-related applications. It is demonstrated that cartographic visualization, in particular, is as effective means to make the enormous stocks of machine-recorded data available to human perception, exploration and analysis.
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Karayazi, Sevim Sezi, Gamze Dane, and Bauke de Vries. "Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam." ISPRS International Journal of Geo-Information 10, no. 4 (March 25, 2021): 198. http://dx.doi.org/10.3390/ijgi10040198.
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Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam.
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Trainor, Timothy. "The World at a Crossroads – The Need for Cartography and Cartographers: How cartographers can contribute as data integrators in finding solutions to global problems." Abstracts of the ICA 2 (October 8, 2020): 1. http://dx.doi.org/10.5194/ica-abs-2-3-2020.
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Abstract. The world is at a crossroads. The current global pandemic has had an impact on every person, their national and global economies, and their ability to preserve a sustainable environment. To understand the health impacts of such an intrusive consequence requires understanding of the pandemic’s cause, its spread among populations, preventive measures to contain its spread and plans for protecting people from future outbreaks. Each one of these factors requires specific types of data and each data point requires location to make it meaningful. This is complicated because of the extent of the challenges. It is further complicated by the lack of timely data, the required location precision, and concerns over policy issues like privacy. Maps, mapping, and cartographers are needed as part of the formula for finding solutions on each of these factors.The International Cartographic Association (ICA) is positioned to help in providing solutions. The structure of the ICA, through its various programs, is sufficiently flexible to respond to these critical issues in practical ways that yield useful results. As an example, new ICA Working Groups were recently established to focus on sustainability and bolstering national mapping and national geospatial organizations while a longer-term commitment centers on a revised research agenda to meet current and future cartographic and GIScience needs. These examples are in addition to other components of the ICA program including the ICA Commissions, conferences, and publications.As the pandemic is global, local to global solutions are needed. The ICA has been actively engaged with the United Nations through the UN Committee of Experts on Global Geospatial Information Management. Three UN initiatives, two of which are geospatial frameworks, call out to the cartographic community for their help. The first is the Sustainable Development Goals that began in 2015 with a 2030 target to focus on a better understanding of national populations, their economies and their environments. Measuring and monitoring progress requires data, processes, systems, leadership, and commitment to be effective. The second effort is the Integrated Geospatial Information Framework, the next rendition of National Spatial Data Infrastructures (NSDI), that focuses on nine Strategic Pathways that are needed to create and maintain a sustainable geospatial program. The third initiative responds to the statistical and geospatial communities working together to create the Global Statistical Geospatial Framework which focuses on the integration of these two linked data types.Cartographers can contribute in two areas. The first calls on their knowledge and expertise in working with different data types. For example, geospatially referenced statistical data oftentimes benefits from basic generalization principles such as combination, simplification, exaggeration and displacement. The absence of a small-area global geography sometimes inhibits the usefulness of statistical information which has become painfully evident during this pandemic. The second area that calls out to cartographers is to use their skills in making as many useful maps as possible. These maps need to show current local conditions, illuminate deficiencies, tell a story, and/or inform strategies and plans for addressing the many challenges we face between the pandemic and the underlying conditions of people, their communities, and circumstances oftentimes outside of their control.The presentation shows examples of some of these points and the ICA’s participation thus far. Opportunities for involvement are left to the imaginations of each of us. What is needed now is for cartographers and the cartographic community to act in ways that help in solving current challenges, informing some of the root causes and systemic problems that need attention while outlining approaches for a path forward in our ever increasingly complex world.
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Cheng, Quanying, Yunqiang Zhu, Hongyun Zeng, Jia Song, Shu Wang, Jinqu Zhang, Lang Qian, and Yanmin Qi. "A Method for Identifying Geospatial Data Sharing Websites by Combining Multi-Source Semantic Information and Machine Learning." Applied Sciences 11, no. 18 (September 18, 2021): 8705. http://dx.doi.org/10.3390/app11188705.
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Geospatial data sharing is an inevitable requirement for scientific and technological innovation and economic and social development decisions in the era of big data. With the development of modern information technology, especially Web 2.0, a large number of geospatial data sharing websites (GDSW) have been developed on the Internet. GDSW is a point of access to geospatial data, which is able to provide a geospatial data inventory. How to precisely identify these data websites is the foundation and prerequisite of sharing and utilizing web geospatial data and is also the main challenge of data sharing at this stage. GDSW identification can be regarded as a binary website classification problem, which can be solved by the current popular machine learning method. However, the websites obtained from the Internet contain a large number of blogs, companies, institutions, etc. If GDSW is directly used as the sample data of machine learning, it will greatly affect the classification precision. For this reason, this paper proposes a method to precisely identify GDSW by combining multi-source semantic information and machine learning. Firstly, based on the keyword set, we used the Baidu search engine to find the websites that may be related to geospatial data in the open web environment. Then, we used the multi-source semantic information of geospatial data content, morphology, sources, and shared websites to filter out a large number of websites that contained geospatial keywords but were not related to geospatial data in the search results through the calculation of comprehensive similarity. Finally, the filtered geospatial data websites were used as the sample data of machine learning, and the GDSWs were identified and evaluated. In this paper, training sets are extracted from the original search data and the data filtered by multi-source semantics, the two datasets are trained by machine learning classification algorithms (KNN, LR, RF, and SVM), and the same test datasets are predicted. The results show that: (1) compared with the four classification algorithms, the classification precision of RF and SVM on the original data is higher than that of the other two algorithms. (2) Taking the data filtered by multi-source semantic information as the sample data for machine learning, the precision of all classification algorithms has been greatly improved. The SVM algorithm has the highest precision among the four classification algorithms. (3) In order to verify the robustness of this method, different initial sample data mentioned above are selected for classification using the same method. The results show that, among the four classification algorithms, the classification precision of SVM is still the highest, which shows that the proposed method is robust and scalable. Therefore, taking the data filtered by multi-source semantic information as the sample data to train through machine learning can effectively improve the classification precision of GDSW, and comparing the four classification algorithms, SVM has the best classification effect. In addition, this method has good robustness, which is of great significance to promote and facilitate the sharing and utilization of open geospatial data.
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Li, R., X. Wang, and X. Shi. "A replacement strategy for a distributed caching system based on the spatiotemporal access pattern of geospatial data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4 (April 23, 2014): 133–37. http://dx.doi.org/10.5194/isprsarchives-xl-4-133-2014.
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Cache replacement strategy is the core for a distributed high-speed caching system, and effects the cache hit rate and utilization of a limited cache space directly. Many reports show that there are temporal and spatial local changes in access patterns of geospatial data, and there are popular hot spots which change over time. Therefore, the key issue for cache replacement strategy for geospatial data is to get a combination method which considers both temporal local changes and spatial local changes in access patterns, and balance the relationship between the changes. And the cache replacement strategy should fit the distribution and changes of hotspot. This paper proposes a cache replacement strategy based on access pattern which have access spatiotemporal localities. Firstly, the strategy builds a method to express the access frequency and the time interval for geospatial data access based on a least-recently-used replacement (LRU) algorithm and its data structure; secondly, considering both the spatial correlation between geospatial data access and the caching location for geospatial data, it builds access sequences based on a LRU stack, which reflect the spatiotemporal locality changes in access pattern. Finally, for achieving the aim of balancing the temporal locality and spatial locality changes in access patterns, the strategy chooses the replacement objects based on the length of access sequences and the cost of caching resource consumption. Experimental results reveal that the proposed cache replacement strategy is able to improve the cache hit rate while achieving a good response performance and higher system throughput. Therefore, it can be applied to handle the intensity of networked GISs data access requests in a cloud-based environment.
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Nesterenko, Svitlana, Grigoriy Shariy, Vira Shchepak, and Tetiana Odariuk. "FEATURES OF FUNCTIONING OF THE NATIONAL CADASTRE SYSTEM OF UKRAINE IN THE CONDITIONS OF REFORMING THE INDUSTRY." Urban development and spatial planning, no. 76 (March 1, 2021): 182–94. http://dx.doi.org/10.32347/2076-815x.2021.76.182-194.
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Identified problematic issues of land management in Ukraine, namely, the uncertainty of the organizational structure of those responsible (administrators and data holders) for the creation and updating of geospatial data and metadata; delaying registration of land plots; the need to improve inventory data and adapt to European inventories. The connection of cartographic data with land management and town planning documentation has been investigated. The necessity of updating the cartographic base, including orthophoto maps, has been proved. The ways of obtaining an actual cartographic basis are revealed. The efficiency of creating a single geoportal of cadastres and geospatial data of the national infrastructure of geospatial data, in particular layers of cartographic information, agro-industrial soil groups, utilities, restrictions on land use, the address directory of streets and houses, public buildings, investment, tourist, historical and architectural objects, has been investigated. The work of the State Geocadastre system, the effectiveness of land reforms, and ways of combating the corruption environment are characterized. It is noted that with the entry into force from January 1, 2021 of the Law of Ukraine «On the National Geospatial Data Infrastructure», with the start of the work of a single geoportal of cadastres and geospatial data, prerequisites have appeared for the creation of a digitized cartographic base, will be the basis for high-quality urban planning and land management documentation; to ensure the effective adoption of administrative decisions by state authorities and local authorities; to meet the needs of society in all types of geographic information; for integration into global and European geospatial data infrastructures.
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Olasz, A., B. Nguyen Thai, and D. Kristóf. "A NEW INITIATIVE FOR TILING, STITCHING AND PROCESSING GEOSPATIAL BIG DATA IN DISTRIBUTED COMPUTING ENVIRONMENTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 111–18. http://dx.doi.org/10.5194/isprs-annals-iii-4-111-2016.
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Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. <br><br> This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (<a href="https://github.com/posseidon/IQLib/"target="_blank">https://github.com/posseidon/IQLib/</a>) developed in the frame of the IQmulus EU FP7 research and development project (<a href="http://www.iqmulus.eu"target="_blank">http://www.iqmulus.eu</a>). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.
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Li, Mike, Elija Perrier, and Chang Xu. "Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 647–54. http://dx.doi.org/10.1609/aaai.v33i01.3301647.
Full textAbstract:
Geographic information systems’ (GIS) research is widely used within the social and physical sciences and plays a crucial role in the development and implementation by governments of economic, education, environment and transportation policy. While machine learning methods have been applied to GIS datasets, the uptake of powerful deep learning CNN methodologies has been limited in part due to challenges posed by the complex and often poorly structured nature of the data. In this paper, we demonstrate the utility of GCNNs for GIS analysis via a multi-graph hierarchical spatial-filter GCNN network model in the context of GIS systems to predict election outcomes using socio-economic features drawn from the 2016 Australian Census. We report a marked improvement in performance accuracy of Hierarchical GCNNs over benchmark generalised linear models and standard GCNNs, especially in semi-supervised tasks. These results indicate the widespread potential for GIS-GCNN research methods to enrich socio-economic GIS analysis, aiding the social sciences and policy development.
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Tischler, Michael A. "Accelerating Geospatial Modeling in ArcGIS with Graphical Processor Units." International Journal of Applied Geospatial Research 7, no. 4 (October 2016): 41–52. http://dx.doi.org/10.4018/ijagr.2016100104.
Full textAbstract:
Geospatial data can be enormous in size and tedious to process efficiently on standard computational workstations. Distributing the processing tasks through highly parallelized processing reduces the burden on the primary processor and processing times can drastically shorten as a result. ERSI's ArcGIS, while widely used in the military, does not natively support multi-core processing or utilization of graphic processor units (GPUs). However, the ArcPy Python library included in ArcGIS 10 provides geospatial developers with the means to process geospatial data in a flexible environment that can be linked with GPU application programming interfaces (APIs). This research extends a custom desktop geospatial model of spatial similarity for remote soil classification which takes advantage of both standard ArcPy/ArcGIS geoprocessing functions and custom GPU kernels, operating on an NVIDIA Tesla S2050 equipped with potential access to 1792 cores. The author will present their results which describe hardware and software configurations, processing efficiency gains, and lessons learned.
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Carbonell-Carrera, Carlos, Jose Luis Saorin, and Stephany Hess-Medler. "A Geospatial Thinking Multiyear Study." Sustainability 12, no. 11 (June 4, 2020): 4586. http://dx.doi.org/10.3390/su12114586.
Full textAbstract:
In the field of environmental sustainability and landscape management, geospatial thinking is necessary. A good level of geospatial thinking is related to academic success in engineering degrees. It is relevant, therefore, to detect the possible deficiencies that university students may have in tasks related to geospatial thinking. This research presents the results of a 2014‒2019 multiyear study with agricultural engineering students, in which seven geospatial tasks were analyzed. The statistical analysis shows that geospatial tasks related to slope, stream/water flow, visibility, and relief interpretation are the best at predicting the final course mark. The present research provides quantitative data on the efficiency that four technologies have to reinforce geospatial thinking focused on each task. Augmented Reality is an appropriate 3D technology for geospatial tasks related to route search, stream/water flow, and elevation points. SketchUp Make 2017 and Autodesk 123D Make showed their potential to solve tasks related to terrain slope and visibility analysis. Spatial Data Infrastructure has given the best results in geospatial tasks related to the photointerpretation of the relief and with topographic profiles of the terrain. Our findings will help teachers to select the most appropriate geospatial tasks to include in their courses.