Relevant bibliographies by topics / Geospatial data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Geospatial data'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Geospatial data"

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Heipke, Christian. "Crowdsourcing geospatial data." ISPRS Journal of Photogrammetry and Remote Sensing 65, no. 6 (November 2010): 550–57. http://dx.doi.org/10.1016/j.isprsjprs.2010.06.005.

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Venkatapuram, Sreeharsha S., and Avinash Gogineni. "Leveraging Open Geospatial Data for Public Visualization." International Journal of Research Publication and Reviews 5, no. 1 (January 24, 2024): 4340–49. http://dx.doi.org/10.55248/gengpi.5.0124.0328.

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He, Lianlian, and Ruixiang Liu. "Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach." ISPRS International Journal of Geo-Information 13, no. 5 (April 28, 2024): 143. http://dx.doi.org/10.3390/ijgi13050143.

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The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.
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Koh, Keumseok, Ayaz Hyder, Yogita Karale, and Maged N. Kamel Boulos. "Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health." Remote Sensing 14, no. 13 (June 23, 2022): 2996. http://dx.doi.org/10.3390/rs14132996.

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Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms ‘big’ geospatial data and geospatial ‘big data’ have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies.
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Kay, Sissiel E. "Challenges in sharing of geospatial data by data custodians in South Africa." Proceedings of the ICA 1 (May 16, 2018): 1–6. http://dx.doi.org/10.5194/ica-proc-1-60-2018.

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As most development planning and rendering of public services happens at a place or in a space, geospatial data is required. This geospatial data is best managed through a spatial data infrastructure, which has as a key objective to share geospatial data. The collection and maintenance of geospatial data is expensive and time consuming and so the principle of “collect once &amp;ndash; use many times” should apply. It is best to obtain the geospatial data from the authoritative source &amp;ndash; the appointed data custodian. In South Africa the South African Spatial Data Infrastructure (SASDI) is the means to achieve the requirement for geospatial data sharing. This requires geospatial data sharing to take place between the data custodian and the user. All data custodians are expected to comply with the Spatial Data Infrastructure Act (SDI Act) in terms of geo-spatial data sharing. Currently data custodians are experiencing challenges with regard to the sharing of geospatial data.<br> This research is based on the current ten data themes selected by the Committee for Spatial Information and the organisations identified as the data custodians for these ten data themes. The objectives are to determine whether the identified data custodians comply with the SDI Act with respect to geospatial data sharing, and if not what are the reasons for this. Through an international comparative assessment it then determines if the compliance with the SDI Act is not too onerous on the data custodians.<br> The research concludes that there are challenges with geospatial data sharing in South Africa and that the data custodians only partially comply with the SDI Act in terms of geospatial data sharing. However, it is shown that the South African legislation is not too onerous on the data custodians.
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Mooney, P., and M. Minghini. "GEOSPATIAL DATA EXCHANGE USING BINARY DATA SERIALIZATION APPROACHES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 307–13. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-307-2022.

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Abstract. In this paper we investigate the benefits of binary data serialization as a means of storing and sharing large amounts of geospatial data in an interoperable way. De-facto text-based exchange encodings typically exposed by modern Application Programming Interfaces (APIs), including eXtensible Markup Language (XML) and JavaScript Object Notation (JSON), are generally inefficient for an increasingly higher number of applications due to their inflated volumes of data, low speed and the high computational cost for parsing and processing. In this work we consider comparisons of JSON/Geospatial JSON (GeoJSON) and two popular binary data encodings (Protocol Buffers and Apache Avro) for storing and sharing geospatial data. Using a number of experiments, we illustrate the advantages and disadvantages of both approaches for common workflows that make use of geospatial data encodings such as GeoPackage and GeoJSON. The paper contributes a number of practical recommendations around the potential for binary data serialization for interoperable (geospatial) data storage and sharing in the future.
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Albakri, Maythm. "Development of Spatial Data Infrastructure based on Free Data Integration." Journal of Engineering 21, no. 10 (October 1, 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 effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of how the integration of free geospatial data can be beneficial within domains such as Spatial Data Infrastructures. This was carried out by suggesting a common methodology that uses road networks information such as lengths, centeroids, start and end points, number of nodes and directions to integrate free and open source geospatial datasets. The methodology has been proposed for a particular case study: the use of geospatial data from OpenStreetMap and Google Earth datasets as examples of free data sources. The results revealed possible matching between the roads of OpenStreetMap and Google Earth datasets to serve the development of Spatial Data Infrastructures.
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Plante, Katherine, and Marc Gervais. "Geospatial Data Quality Guarantee." GEOMATICA 69, no. 1 (March 2015): 29–48. http://dx.doi.org/10.5623/cig2015-102.

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Geospatial data has become ubiquitous in our society and abundantly used by public institutions fulfilling their mandates as well as citizen managing their day-to-day affairs. But the dissemination of geospatial data raises certain issues surrounding the nature of the contract involved along with the quality guarantees that may be applicable. Should this data be treated as a tangible or intangible asset? Would the standard guarantees defined by our legislation apply if it were considered intangible? What about the specific characteristics of geospatial data? How simple would it be to guarantee its quality? This article presents an overview of geospatial data quality guarantees under Quebec law. We will first address the intrinsic characteristics of geospatial data, the concepts of quality guarantees and precision, along with implied and conventional guarantees. Next, we will investigate the potential effects of various contract categories on the scope, if not the very existence, of quality guarantees. The results of the analysis hold that a number of quality guarantee variations are possible and that some legal uncertainties remain, which further complicates the dissemination of geospatial data for any organization that seeks to do so.
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Lage, Kathryn. "Cataloging Digital Geospatial Data." Journal of Map & Geography Libraries 3, no. 1 (April 23, 2007): 39–55. http://dx.doi.org/10.1300/j230v03n01_04.

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Sun, Kai, Yunqiang Zhu, Peng Pan, Zhiwei Hou, Dongxu Wang, Weirong Li, and Jia Song. "Geospatial data ontology: the semantic foundation of geospatial data integration and sharing." Big Earth Data 3, no. 3 (July 3, 2019): 269–96. http://dx.doi.org/10.1080/20964471.2019.1661662.

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Dissertations / Theses on the topic "Geospatial data"

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Xiao, Jun. "WWW access to geospatial data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0033/MQ65529.pdf.

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Lee, Donald C. "Geospatial data sharing in Saudi Arabia." University of Southern Queensland, Faculty of Engineering and Surveying, 2003. http://eprints.usq.edu.au/archive/00001458/.

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This research started with a realization that two organizations in Saudi Arabia were spending large amounts of money, millions of dollars in fact, in acquiring separate sets of geospatial data that had identical basemap components. Both the organizations would be using the data for similar engineering purposes, yet both would be independently outsourcing the data gathering. In all probability, resources are being wasted through two organizations each developing and operating stand-alone geographic information systems and then populating the databases with geospatial data obtained separately. Surely with some cooperation, a shared database could be established, with a diffusion of economic benefits to both organizations. Preliminary discussions with representatives from both the organizations revealed high levels of enthusiasm for the principle of sharing geospatial data, but the discussions also revealed even higher levels of scepticism that such a scheme could be implemented. This dichotomy of views prompted an investigation into the issues, benefits and the barriers involved in data sharing, the relative weight of these issues, and a quest for a workable model. Sharing geospatial data between levels of government, between governmental and private institutions, and within institutions themselves has been attempted on large and small scales in a variety of countries, with varying degrees of accomplishment. Lessons can be learned from these attempts at data sharing, confirming that success is not purely a function of financial and technical benefits, but is also influenced by institutional and cultural aspects. This research is aimed at defining why there is little geospatial data sharing between authorities in Saudi Arabia, and then presenting a workable model as a pilot arrangement. This should take into account issues raised in reference material; issues evidenced through experience in the implementation of systems that were configured as independent structures; issues of culture; and issues apparent in a range of existing data sharing arrangements. The doubts expressed by engineering managers towards using a geospatial database that is shared between institutions in Saudi Arabia have been borne out by the complexity of interrelationships which this research has revealed. Nevertheless, by concentrating on a two party entry level, a model has been presented which shows promise for the implementation of such a scheme. The model was derived empirically and checked against a case study of various other similar ventures, with a consideration of their applicability in the environment of Saudi Arabia. This model follows closely the generic structure of the Singapore Land Hub. The scalability of the model should allow it to be extended to other, multi-lateral data sharing arrangements. An alternative solution could be developed based on a Spatial Data Infrastructure model and this is suggested for ongoing investigation. Major unresolved questions relate to cultural issues, whose depth and intricacy have the potential to influence the realization of successful geospatial data sharing in the Kingdom of Saudi Arabia.
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Berndtsson, Carl. "Open Geospatial Data for Energy Planning." Thesis, KTH, Energisystemanalys, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186392.

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Geographic information systems (GIS) are increasingly being used in energy planning and by private sector practitioners. Through qualitative interviews with 49 leading practitioners in the public and private sector, this thesis establishes the data of most importance, current open access data sources for energy access along with the information currently lacking from open data sources. The interviews revealed grid infrastructure, population density, renewable power potential and energy expenditure to be the most sought after data for both practitioners’ groups. However, it was evident that the private sector had a stronger focus on land, water resource and climate data determining the renewable power potential for a specific area of interest, while the public sector focused on socioeconomic indicators and energy expenditure. A following data aggregation and analysis of the most desired datasets showed that a majority of the needed datasets were available with the exception of energy expenditure. A least-cost option electrification model developed by KTH-dESA has proven to be a powerful tool in assessing the cost of nationwide electrification. This thesis compares the average least-cost option electrification cost for each region in Tanzania with a projected average income. The comparison showed that the average household cost for least-cost option electrification as a share of projected household income varies between regions. The average share per household in the western regions of Tanzania were significantly higher compared to households in the central and eastern regions. The comparison was combined with the geographical location of donor-supported energy development projects showing that majority of the projects were located in the central parts of Tanzania and not targeting the most vulnerable households in regions furthest away from the national grid. In order to successfully introduce electricity nationwide in Tanzania, more support needs to be provided to the poorest regions.  Open data aggregation and coordination are the key to expand the support from GIS for energy access. Even though multiple data sources have been identified, they are scattered and leads to data being collected again. Coordinated efforts aimed to provide means to share aggregated updated and freely accessible data can help reduce high transaction costs, helping to alleviate energy poverty.
Geografiska informationssystem (GIS) används i allt större utsträckning inom energiplanering och av privata aktörer. Genom kvalitativa intervjuer med 49 ledande aktörer i offentlig och privat sektor redogör denna rapport för de viktigaste dataseten för aktörer, befintliga källor för öppen data och vilka informationsluckor som finns i dessa källor. Intervjuerna visade att dataseten gällande energiinfrastruktur, befolkningstäthet, potential för förnybar energi och energiutgifter var viktigast för både offentlig och privat sektor. Privat sektor hade ett större fokus på land, vatten och klimatdata, som alla är viktiga för att avgöra ett områdes potential för förnybar energi. Offentlig sektor hade ett större intresse av socioekonomiska faktorer och energiutgifter. En dataaggregation och analys visade att de mest eftertraktade dataseten fanns öppet tillgängliga med undantag för energiutgifter. En modell för energialternativ till lägsta kostnad utvecklad av KTH-dESA har visat sig vara ett kraftfullt verktyg för att kostnadsbedöma en landsomfattande elektrifiering. I en fallstudie för Tanzania jämför denna rapport den genomsnittliga kostnaden för hushåll för en implementering av en sådan elektrifiering med en beräknad genomsnittlig hushållsinkomst. Jämförelsen visade att kostnaden för hushållen som andel av total hushållsinkomst varierar kraftigt mellan regioner. Den genomsnittliga andelen av hushållsinkomsten som skulle läggas på elektricitet i de västra regionerna av Tanzania var betydligt högre jämfört med de centrala och östra regionerna. Jämförelsen kombinerade även detta resultat med den geografiska positionen hos biståndsstödda energiprojekt. vilken visade att majoriteten av dessa projekt fanns i de centrala delarna av landet och inte i de mest utsatta regionerna som präglas av låg genomsnittlig inkomst och långa avstånd till det nationella kraftnätet. För att framgångsrikt kunna genomföra en landsomfattande elektrifiering behöver mer stöd ges till dessa regioner. Aggregation av öppen data och koordinering är nyckeln till att framgångsrikt utveckla GIS som stöd vid framtida energiprojekt som syftar till att ge fler tillgång till elektricitet. Trots att flertalet datakällor kunde identifieras är dessa spridda vilket leder till att data behöver samlas in gång på gång. Koordinerade insatser för att öka möjligheten till att dela redan insamlad öppen och uppdaterad data kan bidra till att minska transaktionskostnader och därmed minska energifattigdomen
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Swain, Bradley Andrew. "Path understanding using geospatial natural language." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000182.

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Thesis (M.S.)--University of West Florida, 2009.
Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 45 pages. Includes bibliographical references.
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Joshi, Kripa. "Combining Geospatial and Temporal Ontologies." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/JoshiK2007.pdf.

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Banks, Mitchakima D. "Maintaining Multimedia Data in a Geospatial Database." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17318.

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The maintenance and organization of data in any profession, government or commercial, is becoming increasingly more challenging. Adding components, whether those components are two- or three dimensional, further increases the complexity of databases. It is harder to determine which database software to choose to meet the needs of the organization. This thesis evaluates the performance of two databases as spatial functions are executed on columns containing spatial data using benchmark testing. Evaluating the performance of spatial databases makes it possible to identify performance issues with spatial queries. The process of conducting a performance evaluation of multiple databases, in this thesis, focuses on the measurement of each elapsed time within each database. The work already implemented in evaluating the performance of spatial databases did not explore a databases performance as it returned large and small result sets. The overhead of returning large or small result sets was not considered. Therefore, a custom test was developed to engage the aspects of prior work found beneficial. Using a database the researchers built with well over one million records, the elapsed time in adding records was measured. The elapsed time of the spatial functions queries was measured next. The results showed areas where each database excelled given multiple conditions. A different look at PostgreSQL and MySQL as spatial databases was offered. Given their results, as each database produced result sets from zero to 100,000, it was learned that the performance of each database could differ depending on the volume of information it is expected to return.
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Demšar, Urška. "Data mining of geospatial data: combining visual and automatic methods." Doctoral thesis, KTH, School of Architecture and the Built Environment (ABE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3892.

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Most of the largest databases currently available have a strong geospatial component and contain potentially useful information which might be of value. The discipline concerned with extracting this information and knowledge is data mining. Knowledge discovery is performed by applying automatic algorithms which recognise patterns in the data.

Classical data mining algorithms assume that data are independently generated and identically distributed. Geospatial data are multidimensional, spatially autocorrelated and heterogeneous. These properties make classical data mining algorithms inappropriate for geospatial data, as their basic assumptions cease to be valid. Extracting knowledge from geospatial data therefore requires special approaches. One way to do that is to use visual data mining, where the data is presented in visual form for a human to perform the pattern recognition. When visual mining is applied to geospatial data, it is part of the discipline called exploratory geovisualisation.

Both automatic and visual data mining have their respective advantages. Computers can treat large amounts of data much faster than humans, while humans are able to recognise objects and visually explore data much more effectively than computers. A combination of visual and automatic data mining draws together human cognitive skills and computer efficiency and permits faster and more efficient knowledge discovery.

This thesis investigates if a combination of visual and automatic data mining is useful for exploration of geospatial data. Three case studies illustrate three different combinations of methods. Hierarchical clustering is combined with visual data mining for exploration of geographical metadata in the first case study. The second case study presents an attempt to explore an environmental dataset by a combination of visual mining and a Self-Organising Map. Spatial pre-processing and visual data mining methods were used in the third case study for emergency response data.

Contemporary system design methods involve user participation at all stages. These methods originated in the field of Human-Computer Interaction, but have been adapted for the geovisualisation issues related to spatial problem solving. Attention to user-centred design was present in all three case studies, but the principles were fully followed only for the third case study, where a usability assessment was performed using a combination of a formal evaluation and exploratory usability.

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Demšar, Urška. "Data mining of geospatial data: combining visual and automatic methods /." Stockholm : Department of urban planning and environment, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3892.

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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
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Sherif, Mohamed Ahmed Mohamed. "Automating Geospatial RDF Dataset Integration and Enrichment." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-215708.

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Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10,000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly verlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD . In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for point set measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end, we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing information detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a conference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches.
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Books on the topic "Geospatial data"

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Werner, Martin, and Yao-Yi Chiang, eds. Handbook of Big Geospatial Data. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-55462-0.

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Tapia-McClung, Rodrigo, Oscar Sánchez-Siordia, Karime González-Zuccolotto, and Hugo Carlos-Martínez, eds. Advances in Geospatial Data Science. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98096-2.

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Kraak, M. J. Cartography: Visualization of geospatial data. 3rd ed. Harlow: Prentice Hall, 2010.

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1942-, Ormeling Ferjan, ed. Cartography: Visualization of geospatial data. 3rd ed. Harlow: Prentice Hall, 2010.

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1942-, Ormeling Ferjan, ed. Cartography: Visualization of geospatial data. 2nd ed. Harlow, England: Prentice Hall, 2003.

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Kundu, Sandeep Narayan, ed. Geospatial Data Analytics and Urban Applications. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7649-9.

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Sarjakoski, Tapani, Maribel Yasmina Santos, and L. Tiina Sarjakoski, eds. Geospatial Data in a Changing World. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33783-8.

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Karimi, Hassan A., and Bobak Karimi, eds. Geospatial Data Science Techniques and Applications. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315228396.

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Schade, Sven. Ontology-driven translation of geospatial data. Heidelberg: AKA, 2010.

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Geospatial, Autodesk, and Autodesk Inc, eds. Best practices for managing geospatial data. 2nd ed. San Rafael, Calif: Autodesk, 2007.

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Book chapters on the topic "Geospatial data"

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Arnold, Taylor, and Lauren Tilton. "Geospatial Data." In Quantitative Methods in the Humanities and Social Sciences, 95–111. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20702-5_7.

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Urquhart, Neil. "GeoSpatial Data." In Natural Computing Series, 147–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98108-2_7.

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Bianconi, Francesco. "Geospatial Data." In Data and Process Visualisation for Graphic Communication, 141–55. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57051-3_10.

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Nikolaou, Charalampos. "Geospatial Ontologies." In Geospatial Data Science, 67–84. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581906.3581912.

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Wang, Lily. "Interactive Geospatial Visualization." In Data Science for Infectious Disease Data Analytics, 131–58. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003256328-6.

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Pantazi, Despina-Athanasia. "Linked Geospatial Data." In Geospatial Data Science, 85–98. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581906.3581913.

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Koubarakis, Manolis. "Geospatial Data Modeling." In Geospatial Data Science, 9–30. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581906.3581909.

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Shekhar, Shashi, and Hui Xiong. "Geospatial Data Alignment." In Encyclopedia of GIS, 385. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_506.

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Shekhar, Shashi, and Hui Xiong. "Geospatial Data Grid." In Encyclopedia of GIS, 385. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_507.

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Shekhar, Shashi, and Hui Xiong. "Geospatial Data Reconciliation." In Encyclopedia of GIS, 386. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_508.

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Conference papers on the topic "Geospatial data"

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Rhyne, Theresa Marie, Alan MacEachern, and Theresa-Marie Rhyne. "Visualizing geospatial data." In the conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1103900.1103931.

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Valachamy, Mageshwari, Shamsul Sahibuddin, Noor Azurati Ahmad, and Nur Azaliah Abu Bakar. "Geospatial Data Sharing." In ICSCA 2020: 2020 9th International Conference on Software and Computer Applications. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3384544.3384596.

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Sahay, Rajat, and Andreas E. Savakis. "On aligning SAM to remote sensing data." In Geospatial Informatics XIV, edited by Kannappan Palaniappan and Gunasekaran Seetharaman. SPIE, 2024. http://dx.doi.org/10.1117/12.3014163.

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Rahman, Zahidur, Leonid Roytman, Abdelhamid Kadik, Dilara Rosy, Pradipta Nandi, Shahedur Rahman, and Ataul Mahmud. "Public health precautions for preventing malaria using environmental data." In Geospatial Informatics XI, edited by Kannappan Palaniappan, Gunasekaran Seetharaman, and Joshua D. Harguess. SPIE, 2021. http://dx.doi.org/10.1117/12.2583919.

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Yuan, Jie, Peng Yue, and Jianya Gong. "Intelligent geospatial data retrieval based on the geospatial grid portal." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815762.

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Cumberbatch, Iman, and Stefan A. Robila. "An extended reality environment for urban area environmental data analysis." In Geospatial Informatics XIII, edited by Kannappan Palaniappan, Gunasekaran Seetharaman, and Joshua D. Harguess. SPIE, 2023. http://dx.doi.org/10.1117/12.2668407.

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Qian, Lei, and Vadas Gintautas. "Fusing sensor data with publicly available information (PAI) for autonomy applications." In Geospatial Informatics IX, edited by Kannappan Palaniappan, Gunasekaran Seetharaman, and Peter J. Doucette. SPIE, 2019. http://dx.doi.org/10.1117/12.2518933.

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Wang, F., and W. Reinhardt. "Spatial data quality concerns for field data collection in mobile GIS." In Geoinformatics 2006: Geospatial Information Science, edited by Jianya Gong and Jingxiong Zhang. SPIE, 2006. http://dx.doi.org/10.1117/12.712733.

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Sun, Yumei, Xianfang Xing, Bin Chen, Zhou Huang, and Yu Fang. "Distributed geospatial data integration based on OGC standards-compliant geospatial data grid services." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567912.

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Shangguan, Boyi, Peng Yue, Zhipeng Cao, and Bo Wang. "Towards a Geospatial Big Data Platform for Geospatial Information Services." In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019. http://dx.doi.org/10.1109/agro-geoinformatics.2019.8820437.

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Reports on the topic "Geospatial data"

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Snow, Meagan A. Preserving Geospatial Data. Digital Preservation Coalition, July 2023. http://dx.doi.org/10.7207/twr23-01.

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Dilks, Kelly, Jeffery Miller, Amit Patel, and Jim Cookas. Geospatial Data Computer-Based Training. Fort Belvoir, VA: Defense Technical Information Center, February 2000. http://dx.doi.org/10.21236/ada375198.

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Sears, G. Executive summary, geospatial data policy study. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/292107.

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Rice, Matthew T., Fabiana I. Paez, Aaron P. Mulhollen, Brandon M. Shore, and Douglas R. Caldwell. Crowdsourced Geospatial Data: A Report on the Emerging Phenomena of Crowdsourced and User-Generated Geospatial Data. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada576607.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
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McGarva, Guy, Steve Morris, and Greg Janée. DPC Technology Watch Report Preserving Geospatial Data. Digital Preservation Coalition, May 2009. http://dx.doi.org/10.7207/twr09-01.

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Wright, E. Federal environmental scan of geospatial building data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/314732.

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Mitterling, Thomas, Katharina Fenz, Arturo Martinez Jr., Joseph Bulan, Mildred Addawe, Ron Lester Durante, and Marymell Martillan. Compiling Granular Population Data Using Geospatial Information. Asian Development Bank, December 2021. http://dx.doi.org/10.22617/wps210519-2.

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Ruiz, Marilyn O. Geospatial Data Repository. Sharing Data Across the Organization and Beyond. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada392686.

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Johnson, Eric M., Robert Urquhart, and Maggie O'Neil. The Importance of Geospatial Data to Labor Market Information. RTI Press, June 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0017.1806.

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School-to-work transition data are an important component of labor market information systems (LMIS). Policy makers, researchers, and education providers benefit from knowing how long it takes work-seekers to find employment, how and where they search for employment, the quality of employment obtained, and how steady it is over time. In less-developed countries, these data are poorly collected, or not collected at all, a situation the International Labour Organization and other donors have attempted to change. However, LMIS reform efforts typically miss a critical part of the picture—the geospatial aspects of these transitions. Few LMIS systems fully consider or integrate geospatial school-to-work transition information, ignoring data critical to understanding and supporting successful and sustainable employment: employer locations; transportation infrastructure; commute time, distance, and cost; location of employment services; and other geographic barriers to employment. We provide recently collected geospatial school-to-work transition data from South Africa and Kenya to demonstrate the importance of these data and their implications for labor market and urban development policy.
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