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Journal articles on the topic 'Spatial data infrastructures'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

Wiemann, Stefan, and Lars Bernard. "Spatial data fusion in Spatial Data Infrastructures using Linked Data." International Journal of Geographical Information Science 30, no. 4 (2015): 613–36. http://dx.doi.org/10.1080/13658816.2015.1084420.

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Rajabifard, Abbas, Francisco Escobar, and Ian P. Williamson. "Hierarchical Spatial Reasoning Applied to Spatial Data Infrastructures." Cartography 29, no. 2 (2000): 41–50. http://dx.doi.org/10.1080/00690805.2000.9714107.

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3

Keßler, Carsten, Albert Remke, Henryk Hodam, Fabian Przybylak, and Andreas Rienow. "OER4SDI: Open Educational Resources for Spatial Data Infrastructures." AGILE: GIScience Series 5 (May 30, 2024): 1–10. http://dx.doi.org/10.5194/agile-giss-5-9-2024.

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Abstract. Education on spatial data infrastructures is an important building block of study programs focusing on spatial data. The focus is on equipping students with essential skills for future contributions to spatial data infrastructure development and application. Despite a wide range of available teaching materials, their reuse is hindered by a lack of harmonization and integration into learning modules. This paper reports on the activities in the "Open Educational Resources for Spatial Data Infrastructures" (OER4SDI) project, which addresses this gap by developing easily reusable teachin

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Janowicz, Krzysztof, Sven Schade, Arne Bröring, Carsten Keßler, Patrick Maué, and Christoph Stasch. "Semantic Enablement for Spatial Data Infrastructures." Transactions in GIS 14, no. 2 (2010): 111–29. http://dx.doi.org/10.1111/j.1467-9671.2010.01186.x.

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5

van Oort, P. A. J., G. W. Hazeu, H. Kramer, A. K. Bregt, and F. I. Rip. "Social networks in spatial data infrastructures." GeoJournal 75, no. 1 (2009): 105–18. http://dx.doi.org/10.1007/s10708-009-9294-5.

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Karim, Abdul, Akhmad Faturohman, Suhartono Suhartono, Dedy Dwi Prastyo, and Budi Manfaat. "Regression Models for Spatial Data: An Example from Gross Domestic Regional Bruto in Province Central Java." Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan 18, no. 2 (2017): 213. http://dx.doi.org/10.23917/jep.v18i2.4660.

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The important role of a region's transportation infrastructure strongly affects the economic growth of the region and tends to affect the surrounding areas. The effect is called spillover effect. The aim of the research was to recognize the direct effect and spillover effect (indirect) of transportation infrastructure on the economic growth in Central Java. To identify the spillover effects, it is necessary to recognize the different characteristics of each region which have the implications on the various transportation infrastructures at each region in Central Java. Therefore, the spatial mo

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Alvarez León, Luis Felipe. "The political economy of spatial data infrastructures." International Journal of Cartography 4, no. 2 (2017): 151–69. http://dx.doi.org/10.1080/23729333.2017.1371475.

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8

Béjar, R., M. Á. Latre, J. Nogueras‐Iso, P. R. Muro‐Medrano, and F. J. Zarazaga‐Soria. "An architectural style for spatial data infrastructures." International Journal of Geographical Information Science 23, no. 3 (2009): 271–94. http://dx.doi.org/10.1080/13658810801905282.

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9

Grus, L., J. Crompvoets, and A. K. Bregt. "Spatial data infrastructures as complex adaptive systems." International Journal of Geographical Information Science 24, no. 3 (2010): 439–63. http://dx.doi.org/10.1080/13658810802687319.

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Lutz, M., J. Sprado, E. Klien, C. Schubert, and I. Christ. "Overcoming semantic heterogeneity in spatial data infrastructures." Computers & Geosciences 35, no. 4 (2009): 739–52. http://dx.doi.org/10.1016/j.cageo.2007.09.017.

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11

Albakri, Maythm. "Development of Spatial Data Infrastructure based on Free Data Integration." Journal of Engineering 21, no. 10 (2015): 133–49. http://dx.doi.org/10.31026/j.eng.2015.10.09.

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In recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved ef

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Risimati, Brightnes, Trynos Gumbo, and James Chakwizira. "Spatial Integration of Non-Motorized Transport and Urban Public Transport Infrastructure: A Case of Johannesburg." Sustainability 13, no. 20 (2021): 11461. http://dx.doi.org/10.3390/su132011461.

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Sustainability of transport infrastructure integration begins with involving an all-inclusive transportation chain instead of only focusing on one part of the journey. This is achieved by facilitating spatial integration between diverse transport modalities to allow for a multiplicity of travel opportunities. This paper unpacks the extent of the spatial integration of non-motorized transport and urban public transport infrastructure within the city of Johannesburg in South Africa. Cycling activity datasets derived from Strava Metro and the spatial data of urban public transport infrastructures

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13

Rieke, Matthes, Lorenzo Bigagli, Stefan Herle, et al. "Geospatial IoT—The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures." ISPRS International Journal of Geo-Information 7, no. 10 (2018): 385. http://dx.doi.org/10.3390/ijgi7100385.

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The nature of contemporary spatial data infrastructures lies in the provision of geospatial information in an on-demand fashion. Although recent applications identified the need to react to real-time information in a time-critical way, research efforts in the field of geospatial Internet of Things in particular have identified substantial gaps in this context, ranging from a lack of standardisation for event-based architectures to the meaningful handling of real-time information as “events”. This manuscript presents work in the field of event-driven architectures as part of spatial data infras

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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 (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.

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15

Dale, Peter, and A. L. Allan. "Developing Spatial Data Infrastructures - from concept to reality." Survey Review 37, no. 292 (2004): 498–99. http://dx.doi.org/10.1179/sre.2004.37.292.498.

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16

Scholten, M., R. Klamma, and C. Kiehle. "Evaluating Performance in Spatial Data Infrastructures for Geoprocessing." IEEE Internet Computing 10, no. 5 (2006): 34–41. http://dx.doi.org/10.1109/mic.2006.97.

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Hjelmager, Jan, Harold Moellering, Antony Cooper, et al. "An initial formal model for spatial data infrastructures." International Journal of Geographical Information Science 22, no. 11-12 (2008): 1295–309. http://dx.doi.org/10.1080/13658810801909623.

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18

Bregt, Arnold K. "Building European spatial data infrastructures, by Ian Masser." International Journal of Geographical Information Science 25, no. 11 (2011): 1905–6. http://dx.doi.org/10.1080/13658816.2011.557075.

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19

Birnie, Richard V. "Developing spatial data infrastructures: from concept to reality." Land Use Policy 21, no. 1 (2004): 97–98. http://dx.doi.org/10.1016/j.landusepol.2003.09.002.

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20

Bishop, Ian D., Francisco J. Escobar, Sadasivam Karuppannan, et al. "Spatial data infrastructures for cities in developing countries." Cities 17, no. 2 (2000): 85–96. http://dx.doi.org/10.1016/s0264-2751(00)00004-4.

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21

de Andrade, Fabio Gomes, Cláudio de Souza Baptista, and Clodoveu Augusto Davis. "Improving geographic information retrieval in spatial data infrastructures." GeoInformatica 18, no. 4 (2014): 793–818. http://dx.doi.org/10.1007/s10707-014-0202-x.

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22

Georgiadou, Yola, Lars Bernard, and Sundeep Sahay. "Implementation of spatial data infrastructures in transitional economies." Information Technology for Development 12, no. 4 (2006): 247–53. http://dx.doi.org/10.1002/itdj.20046.

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Georgiadou, Yola, Lars Bernard, and Sundeep Sahay. "Implementation of spatial data infrastructures in transitional economies." Information Technology for Development 13, no. 1 (2007): 1–6. http://dx.doi.org/10.1002/itdj.20054.

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24

Merodio Gómez, Paloma, Macarena Pérez García, Gabriela García Seco, Andrea Ramírez Santiago, and Catalina Tapia Johnson. "The Americas’ Spatial Data Infrastructure." ISPRS International Journal of Geo-Information 8, no. 10 (2019): 432. http://dx.doi.org/10.3390/ijgi8100432.

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During the last decade, the production of geospatial information has increased considerably; however, managing and sharing this information has become increasingly difficult for the organizations that produce it, because it comes from different data sources and has a wide variety of users. In this sense, to have a better use of geospatial information, several countries have developed national spatial data infrastructures (SDIs) to improve access, visualization, and integration of their data and in turn, have the need to cooperate with other countries to develop regional SDIs, which allow bette

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Velkova, Julia, and Jean-Christophe Plantin. "Data centers and the infrastructural temporalities of digital media: An introduction." New Media & Society 25, no. 2 (2023): 273–86. http://dx.doi.org/10.1177/14614448221149945.

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While data centres are predominantly studied via their spatial and territorial dimensions, we investigate this critical part of the contemporary Internet infrastructures via its temporalities and their multiple mediations. With this introduction and the articles of this special issue, we collectively complement existing scholarship on critical data studies and media infrastructure by investigating the role that data infrastructure plays in shaping the temporalities of data. Focusing on data centres, the contributors analyze the vast infrastructural assemblage that supports such temporalities.

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Rossa, Maciej, and Mariusz Rogulski. "The Applicability Of SWE In Polish Spatial Data Infrastructures - The Example Of The SensorML Language." Foundations of Computing and Decision Sciences 40, no. 3 (2015): 187–201. http://dx.doi.org/10.1515/fcds-2015-0012.

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Abstract Mobile and stationary sensors currently used to measure various environmental parameters, functioning independently or as part of monitoring networks and measurement stations, provide vast amounts of data on the state and quality of the environment on the Earth. If the data is to be used effectively, they must be exchanged and shared among IT systems. Systems which offer services of searching, exchange, sharing, visualisation and analysis of dispersed and varied data resources on the widely understood environment are, for example, spatial data infrastructures. The article presents an

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27

Seip, Christian, and Ralf Bill. "A Framework for the Evaluation of Marine Spatial Data Infrastructures – Accompanied by International Case-Studies." GeoScience Engineering 62, no. 2 (2016): 27–43. http://dx.doi.org/10.1515/gse-2016-0015.

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Abstract Germany is currently developing a marine data infrastructure addressing different topics such as coastal engineering, hydrography and surveying, protection of the marine environment, maritime conservation, regional planning, and coastal research. This undertaking is embedded in a series of regulations and developments at many administrative levels, from which specifications and courses of action are derived. To set up a conceptual framework for the marine data infrastructure of Germany (MDI-DE), scientists at the Chair for Geodesy and Geoinformatics at the Rostock University are build

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28

Boucquey, Noëlle, Kevin St Martin, Luke Fairbanks, Lisa M. Campbell, and Sarah Wise. "Ocean data portals: Performing a new infrastructure for ocean governance." Environment and Planning D: Society and Space 37, no. 3 (2019): 484–503. http://dx.doi.org/10.1177/0263775818822829.

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We are currently in what might be termed a “third phase” of ocean enclosures around the world. This phase has involved an unprecedented intensity of map-making that supports an emerging regime of ocean governance where resources are geocoded, multiple and disparate marine uses are weighed against each other, spatial tradeoffs are made, and exclusive rights to spaces and resources are established. The discourse and practice of marine spatial planning inform the contours of this emerging regime. This paper examines the infrastructure of marine spatial planning via two ocean data portals recently

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Harvey, F., and D. Tulloch. "Local‐government data sharing: Evaluating the foundations of spatial data infrastructures." International Journal of Geographical Information Science 20, no. 7 (2006): 743–68. http://dx.doi.org/10.1080/13658810600661607.

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30

Corti, P., and B. Lewis. "MAKING TEMPORAL SEARCH MORE CENTRAL IN SPATIAL DATA INFRASTRUCTURES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 93–95. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-93-2017.

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A temporally enabled Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users, and tools intended to provide an efficient and flexible way to use spatial information which includes the historical dimension. One of the key software components of an SDI is the catalogue service which is needed to discover, query, and manage the metadata. A search engine is a software system capable of supporting fast and reliable search, which may use any means necessary to get users to the resources they need quickly and efficiently. These techniques may include features such as full

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31

Béjar, Rubén, Miguel Á. Latre, Javier Nogueras-Iso, Pedro R. Muro-Medrano, and F. Javier Zarazaga-Soria. "An RM-ODP enterprise view for spatial data infrastructures." Computer Standards & Interfaces 34, no. 2 (2012): 263–72. http://dx.doi.org/10.1016/j.csi.2011.10.001.

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32

Craglia, Max. "Reviews: Developing Spatial Data Infrastructures: From Concept to Reality." Environment and Planning B: Planning and Design 33, no. 3 (2006): 477–78. http://dx.doi.org/10.1068/b3303rvw.

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33

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

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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

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35

Rehm, Imke-Sophie, John Friesen, Kevin Pouls, Christoph Busch, Hannes Taubenböck, and Peter F. Pelz. "A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data." Water 13, no. 16 (2021): 2299. http://dx.doi.org/10.3390/w13162299.

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Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the exa

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Zwirowicz-Rutkowska, Agnieszka. "On the use of IT investment assessment methods in the area of spatial data infrastructure." Geodesy and Cartography 65, no. 1 (2016): 111–22. http://dx.doi.org/10.1515/geocart-2016-0005.

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Abstract One of the important issues concerning development of spatial data infrastructures (SDIs) is the carrying out of economic and financial analysis. It is essential to determine expenses and also assess effects resulting from the development and use of infrastructures. Costs and benefits assessment could be associated with assessment of the infrastructure effectiveness and efficiency as well as the infrastructure value, understood as the infrastructure impact on economic aspects of an organisational performance, both of an organisation which realises an SDI project and all users of the i

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Boisvert, Eric, and Boyan Brodaric. "GroundWater Markup Language (GWML) – enabling groundwater data interoperability in spatial data infrastructures." Journal of Hydroinformatics 14, no. 1 (2011): 93–107. http://dx.doi.org/10.2166/hydro.2011.172.

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Increasing stress on global groundwater resources is leading to new approaches to the management and delivery of groundwater data. These approaches include the deployment of a Spatial Data Infrastructure (SDI) to enable online data interoperability amongst numerous and heterogeneous data sources. Often an important component of an SDI is a global domain schema, which serves as a central structure for the query and transport of data, but at present there does not exist a schema for groundwater data that is strongly compliant with SDI concepts, standards, and technologies. In this paper we prese

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Yan, Jingya, Siow Jaw, Kean Soon, Andreas Wieser, and Gerhard Schrotter. "Towards an Underground Utilities 3D Data Model for Land Administration." Remote Sensing 11, no. 17 (2019): 1957. http://dx.doi.org/10.3390/rs11171957.

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With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three-dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and b

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Yamashkin, Anatoliy A., Stanislav A. Yamashkin, and Milan M. Radovanovic. "Formation of geospatial information databases in the system of digital spatial data infrastructures." E3S Web of Conferences 208 (2020): 08007. http://dx.doi.org/10.1051/e3sconf/202020808007.

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The article discusses the key aspects of the development of spatial data infrastructures and models of spatio-temporal data based on the study of geosystems, as well as ensuring the updating of the geospatial storage of information based on Earth remote sensing data. The authors have shown that for the formation of the software and hardware infrastructure of a digital storage of spatial data, it is advisable to follow an organized technological process. At the same time, spatial data warehouses should provide system integration of data with spatial and temporal reference. The solution to the p

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Ponjavic, Mirza, and Almir Karabegovic. "Location Intelligence Systems and Data Integration for Airport Capacities Planning." Computers 8, no. 1 (2019): 13. http://dx.doi.org/10.3390/computers8010013.

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This paper describes an approach introducing location intelligence using open-source software components as the solution for planning and construction of the airport infrastructure. As a case study, the spatial information system of the International Airport in Sarajevo is selected. Due to the frequent construction work on new terminals and the increase of existing airport capacities, as one of the measures for more efficient management of airport infrastructures, the development team has suggested to airport management to introduce location intelligence, meaning to upgrade the existing inform

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41

Boxall, James. "Spatial Data Infrastructures: Developments, Trends, and Perspectives from Converging Viewpoints." Cartography and Geographic Information Systems 25, no. 3 (1998): 129–31. http://dx.doi.org/10.1559/152304098782383070.

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42

Corti, Paolo, Athanasios Tom Kralidis, and Benjamin Lewis. "Enhancing discovery in spatial data infrastructures using a search engine." PeerJ Computer Science 4 (May 21, 2018): e152. http://dx.doi.org/10.7717/peerj-cs.152.

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A spatial data infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, whic

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Sjoukema, Jaap-Willem, Arnold Bregt, and Joep Crompvoets. "Evolving Spatial Data Infrastructures and the Role of Adaptive Governance." ISPRS International Journal of Geo-Information 6, no. 8 (2017): 254. http://dx.doi.org/10.3390/ijgi6080254.

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Vidal-Filho, Jarbas Nunes, Valéria Cesário Times, Jugurta Lisboa-Filho, and Chiara Renso. "Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures." ISPRS International Journal of Geo-Information 10, no. 12 (2021): 825. http://dx.doi.org/10.3390/ijgi10120825.

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The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such as data produced by social networks or collected by an electronic equipment with embedded sensors, causes the STMO to require services and standards for enabling data documentation and ensuring the quality of STMOs. Spatial Data Infrastructures (SDI), on the other hand, provide a shared interoperable

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45

Manso, Miguel-Ángel, Monica Wachowicz, and Miguel-Ángel Bernabé. "Towards an Integrated Model of Interoperability for Spatial Data Infrastructures." Transactions in GIS 13, no. 1 (2009): 43–67. http://dx.doi.org/10.1111/j.1467-9671.2009.01143.x.

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46

Squividant, H., R. Bera, P. Aurousseau, and C. Cudennec. "Online watershed boundary delineation: sharing models through Spatial Data Infrastructures." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 144–49. http://dx.doi.org/10.5194/piahs-368-144-2015.

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Abstract. The proposal in this paper is to make accessible the hydrology analysis tools that were developed by our research team in the past years through an interoperable Spatial Data Infrastructure. To this aim we chose to develop add-ons for the geOrchestra OGC-compliant platform. Such add-ons trigger algorithms and retrieve their output in real time through OGC standard WPS. We then introduce a watershed WPS add-on and its functioning modes. In so doing we exemplify the fact that the use of OGC standards make it straightforward (and transparent to the user operating a common web browser) t

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Mwange, Collins, Galcano Canny Mulaku, and David N. Siriba. "Reviewing the status of national spatial data infrastructures in Africa." Survey Review 50, no. 360 (2016): 191–200. http://dx.doi.org/10.1080/00396265.2016.1259720.

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48

Thellufsen, Christian, Abbas Rajabifard, Stig Enemark, and Ian Williamson. "Awareness as a foundation for developing effective spatial data infrastructures." Land Use Policy 26, no. 2 (2009): 254–61. http://dx.doi.org/10.1016/j.landusepol.2008.03.002.

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49

Winter, S., and I. Williamson. "Preface: Current issues in spatial data infrastructures and system design." Journal of Spatial Science 51, no. 1 (2006): 59–61. http://dx.doi.org/10.1080/14498596.2006.9635064.

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Ulutaş, Deniztan, Gülten Kara, and Çetin Cömert. "Semantic definition and matching for implementing national spatial data infrastructures." Journal of Spatial Science 61, no. 2 (2016): 441–59. http://dx.doi.org/10.1080/14498596.2016.1142397.

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