Integration Of Geographic Data Research Articles

أثرُ التفسير الجُغْرافيّ للقرآن الكريم في التَّرجِيحِ بَيْن أَقوالِ المفسِّرِين في تَفْسير آياتِ غَزْوَتَي أُحُدٍ والأَحْزَابِ

This study examines the impact of geographical interpretation on the differing opinions among Quranic commentators, specifically in the context of the Battles of Uhud and Al-Ahzab. The Holy Quran, rich in multi-faceted meanings, often leads to varying interpretations among scholars, especially concerning verses with geographical implications. This research focuses on how geographical context influences the understanding and preference for specific interpretations over others in these two significant battles. By analyzing the geographical elements mentioned in the Quranic verses, the study aims to provide a more precise understanding of the locations and events described, thereby resolving discrepancies in interpretations. The research is unique in its approach to incorporating modern geographical tools and digital mapping technologies to enhance the study of Quranic exegesis. Through detailed examination of the relevant verses, the study identifies the role of geographical context in shaping the historical and theological narratives of the Battles of Uhud and Al-Ahzab. The findings suggest that a deeper geographical awareness can significantly contribute to resolving interpretative conflicts and advancing the scholarly understanding of these events. This work not only underscores the importance of geographical considerations in Quranic exegesis but also opens new avenues for research that integrates traditional Islamic scholarship with contemporary geographical analysis. The study concludes with recommendations for further research in the integration of geographical data into the interpretation of other Quranic verses with similar implications.

EventGeoScout: Fostering Citizen Empowerment and Augmenting Data Quality through Collaborative Geographic Information Governance and Optimization

In this manuscript, we present EventGeoScout, an innovative framework for collaborative geographic information management, tailored to meet the needs of the dynamically changing landscape of geographic data integration and quality enhancement. EventGeoScout enables the seamless fusion of open data from different sources and provides users with the tools to refine and improve data quality. A distinctive feature of our framework is its commitment to platform-agnostic data management, ensuring that processed datasets are accessible via standard Geographic Information System (GIS) tools, reducing the maintenance burden on organizations while ensuring the continued relevance of the data. Our approach goes beyond the boundaries of traditional data integration, enabling users to fully harness the power of geospatial information by simplifying the data creation process and providing a versatile solution to the complex challenges posed by layered geospatial data. To demonstrate the versatility and robustness of EventGeoScout as an optimization tool, we present a case study centered on the Uncapacitated Facility Location Problem (UFLP), where a genetic algorithm was used to achieve outstanding performance on both traditional computing platforms and smartphone devices. As a concrete case study, we applied our solution in the context of the Málaga City Marathon, using the latest data from the last edition of the marathon.

Geographic Information System Mapping Risk Factors Stunting Using Methods Geographically Weighted Regression

Technological developments in this era of globalization are very rapid. This requires humans to enter life together with information and technology. Stunting as a chronic nutritional problem in children, continues to be a global challenge. Geographic Information Systems (GIS) have proven to be effective tools in spatial analysis and distribution mapping stunting. In this context, method Geographically Weighted Regression (GWR) has been used to model the spatial relationship between factors that contribute to stunting. This research will produce a Geographic Information System using the method Geographically Weighted Regression. With this Geographic Information System, it can display location points and affected information stunting. Because of this system, the Padang Lawas Utara District Health Office does not need to store location data stunting in archive form again but digitally. This study underscores the importance of using GIS with the GWR method in mapping patient locations stunting. Through the integration of geographic data and spatial analysis, we can generate a better understanding of the influencing factors stunting at the local level, which in turn can support prevention and response efforts stunting which is more effective.

Design and Implementation of Earthquake Information Publishing System Based on Mobile Computing and Machine Learning Technology in GIS

This paper proposes a semi-automatic method of geographic information linking based on spatial relationships, entity names, entity categories and other features, combined with semantic and machine learning methods. First, we extracted geographic information from three geographic information sources: Open Street Map, Wikimapia, and Google places. The extracted geographic information is mainly for urban buildings in different regions. Secondly, we analyzed and extracted the characteristics of geographic information data to construct a geographic information ontology, and realized the integration of geographic data through the mapping of geographic information source data and geographic information ontology. Finally, the linking method of fusion classification algorithm support vector machine and K-nearest neighbor method are discussed separately. At the same time, it is compared with the linking method proposed by Samal et al. to comprehensively verify the accuracy of the method proposed in this paper from multiple angles, laying a good foundation for seismic information integration.

Optimizing routing and tower spotting of electricity transmission lines: An integration of geographical data and engineering aspects into decision-making

In many parts of the world, electric sectors are already experiencing considerable rising in generation from renewable energy sources. Large amounts of new generation are expected in the near-term future, which will require additional transmission investments to properly integrate these resources into the existing electric power system. The transmission expansion planning has an important role in this environment in order to guarantee the security of the supply with the required levels of quality and price. Therefore, the implementation of new transmission lines (TL) must be fast and accurate in order to avoid delays to connect new power sources and potential supply and reliability problems. In this sense, Geographical Information Systems (GIS) can be a powerful tool that provides decision support techniques, which enables a transparent, sustainable, faster planning process for TLs in power systems. This paper presents a novel approach for the design of overhead TLs, considering geographical, engineering and cost aspects into the decision-making process. For this, routing and tower spotting optimization approaches are integrated into the proposed methodology, which is divided into three main steps: (i) Route Guideline Definition based on a raster-based least-cost path approach; (ii) Vertex Siting based on graph theory and the Dijkstra shortest path algorithm, applied in order to find the optimal vertex set along the route guideline; (iii) Tower Spotting based on Dynamic Programming, which is applied in order to find the optimal distribution of towers along the topographical profile of the route obtained in the previous step. The proposed methodology is focused on preliminary planning and decision-making for TL auctions, where the objective is to find design alternatives with the least cost. We show a case study using the proposed methodology for a real project of a 525kV TL that interconnects Machadinho and Campos Novos (located in the Santa Catarina state in Brazil). The outcomes show that the proposed approach is capable of representing the technical and geographical constraints of a TL design, providing results with lower costs when compared to the original TL design.

LinBi: Linking biodiversity and culture information

The LinBi project aims to enhance the discoverability of digitized objects from natural history collections hosted by institutes all over Europe. This enhancement is achieved by publishing new and enriched content to the Europeana collections platform. The use of simple vocabularies and machine-readable metadata encourages reuse and has the additional advantage of facilitating the clustering of interesting content for user groups beyond biodiversity and natural history researchers. Linking the collections of Europe together in an openly-available platform and sharing our common cultural and natural heritage with a broad audience will increase the public’s awareness of biodiversity collections. Furthermore, it will help us reach out to new user groups such as teachers, journalists and artists, who were previously unaware of, or distant from, our collections. Suitable content was selected and harmonized for interlinking with Europeana. Contributions include a large quantity of herbarium sheets, digitized glass plate negatives taken between 1880 and 1930 and a portrait collection dating from the late 19th and early 20th century. With the help of the DoeDat crowdsourcing platform, existing metadata were enriched and mobilized to allow for publication in the form of open linked data. The integration of geographical data and common names allows the Europeana platform to link scientific specimens with literature and fine art from different collections and to guide users to interesting and inspiring content via themed virtual exhibitions. One such theme is composed of content from the "Wild Roses of Crépin" collection, which will be enriched by pictures of living plants, herbarium specimens and illustrations old and modern. A second content cluster consists of an enriched and curated collection of botanical illustrations originating from a corpus of special and rare books ranging from the 15th to 19th centuries. Careful curation increases the potential for re-use and provides additional oppertunities for the general public to interact with this collection. The LinBi platform has the long-term ambition of forming a sustainable and open source solution integrated into the Europeana Core Service. This will further improve cooperation between institutes by building international infrastructure and networks, thus contributing to a more open cross-border society.

Decadal scale patterns of shoreline variability in Paulatuk, N.W.T, Canada

ABSTRACTDynamic changes in shoreline position as a result of natural processes and the effects of climate variability increase the susceptibility of Arctic communities that reside along the coastal zone. The application of new geospatial approaches is critical to providing updated measurements of shoreline change, necessary for sustainable coastal management strategies. This research is an integration of geographic data demarcating shoreline position and its analysis to detect change using an updated modeling application – Analyzing Moving Boundaries Using R (AMBUR). Rates-of-change were evaluated over three time periods: long-term (1984–2016); and two short-term eras (1995–2005; 2006–2016). The short-term periods were specifically chosen to assess the influence of changing sea-ice regimes, increased storm intensity, and elevated air and sea-surface temperatures. Results indicate a significant alongshore increase in the rates of erosion and the spatial extent of land loss across both segments of the study area over the short-term. Mean annual rates of change increased over the most recent period (2006–2016) along the eastern segment (−0.34 m/yr) of the study area, while the western shoreline retreated at a rate of −0.24 m/yr over the same interval. These are the highest erosion rates over any time period examined in this study. As air and ocean temperature increases continue to facilitate sea ice reduction and increased permafrost thawing, shoreline erosion may be exacerbated along the Paulatuk coastline in the coming years.

GeOnt: geo-ontologies integration tool

In Geographic Information Science, the concept of semantic interoperability has been proposed to simplify and enhance the sharing, reuse and integration of geographic data. Semantic interoperability can be achieved by integrating semantic components and necessarily involves the detection and resolution of semantic conflicts that may exist between these components. The use of ontologies, although it allows to partially solve semantic conflicts, does not completely solve heterogeneity issues. Heterogeneity issues can be solved by the integration of ontologies, an issue that we address in this paper through geo-ontologies. Geo-ontologies describe conceptual hierarchies and interrelations of terminologies in the domain of geospatial science. This paper proposes an approach for integrating geospatial ontologies based on three processes: matching, mapping and integration. The GeOnt tool has been developed to automatically integrate geo-ontologies. Experiments are conducted on real data and an evaluation of the GeOnt tool is also discussed.

A Data-Driven Approach for Detecting and Quantifying Modeling Biases in Geo- Ontologies by Using a Discrepancy Index

GIScience 2016 Short Paper Proceedings A Data-Driven Approach for Detecting and Quantifying Modeling Biases in Geo-Ontologies Using a Discrepancy Index Bo Yan, Krzysztof Janowicz, and Yingjie Hu STKO Lab, Department of Geography, University of California, Santa Barbara, USA {boyan,jano,yingjiehu}@geog.ucsb.edu Abstract Geo-ontologies play an important role in fostering the publication, retrieval, reuse, and integration of geographic data within and across domains. The status quo of geo-ontology engineering often follows a centralized top-down approach, namely a group of domain experts collaboratively formalizing key concepts and their relationships. On the one hand, such an approach makes use of the invaluable knowledge and experience of subject matter experts and captures their perception of the world. On the other hand, however, it can introduce biases and ontological commitments that do not well correspond to the data that will be semantically lifted using these ontologies. In this work, we propose a data-driven method to calculate a Discrepancy Index in order to identify and quantify the potential modeling biases in current geo-ontologies. In other words, instead of trying to measure quality, we determine how much the ontology di↵ers from what would be expected when looking at the data alone. Keywords: geo-ontology; ontology engineering; DBpedia; Linked Data; Discrepancy Index Introduction Due to the diverse and eclectic nature of geographic information, geographic data usually comes from di↵erent sources, in di↵erent formats, and are conceptualized from di↵erent perspectives. These hetero- geneities in terms of provenance and standards create a barrier for integrating data to perform more comprehensive analysis. Geo-ontologies provide a promising way to alleviate this long-standing issue by enabling a flexible integration of geographic information based on semantics, i.e., regardless of represen- tational choices and syntax. However, the common ways in which geo-ontologies are developed top-down by a team of knowledge engineers and domain experts carry the risk of generating biased or unsuitable geo-ontologies (Hu and Janowicz, 2016). To give a concrete example, in the current version of DBpedia’s ontology (DBpedia 2015-10), the class Canal is classified as a sibling class of River, and both are defined as subclasses of Stream. This seems to be a rational classification at first glance since canals are usually channels of water. However, Stream is a subclass of BodyOfWater and BodyOfWater is a subclass of NaturalPlace. Due to the transitivity of the rdfs:subClassOf relationship, canals become natural places. However, this seems like an odd modeling choice as canals are defined as “an artificial waterway constructed to allow the passage of boats or ships inland or to convey water for irrigation” according to the Oxford dictionary. Words such as “artificial” and “constructed” make canals man-made features rather than natural place. This example indicates that top-down geo-ontologies may su↵er from the issues such as modeling biases, oversights, and ontological commitments that do not well represent the real data needs. Scrutinizing the geo-ontologies and making revisions manually on a regular basis are common solutions to such problems. But such methods are usually labor-intensive and create a gap between the geo-ontology and its corresponding Linked Dataset. In this research, we introduce initial results on a Discrepancy Index that helps geo-ontology engineers by detecting and quantifying potential issues using a series of data mining steps. Proposed Method Our approach consists of two parallel threads. The first thread comes from Linked Datasets that are transformed from unstructured data, such as Wikipedia pages. This thread focuses on the bottom-up

Overcoming semantic heterogeneity in spatial data infrastructures

In current spatial data infrastructures (SDIs), it is still often difficult to effectively exchange or re-use geographic data sets. A main reason for this is semantic heterogeneity, which occurs at different levels: at the metadata, the schema and the data content level. It is the goal of the work presented in this paper to overcome the problems caused by semantic heterogeneity on all three levels. We present a method based on ontologies and logical reasoning, which enhances the discovery, retrieval, interpretation and integration of geographic data in SDIs. Its benefits and practical use are illustrated with examples from the domains of geology and hydrology.

Qualitative reasoning about consistency in geographic information

This paper explores the development and use of a qualitative reasoning system for describing consistency between different geographic data sets. Consistency is closely related to issues of uncertainty and interoperability in geographic information, and the paper assesses how automated reasoning about consistency can be used to support the integration of heterogeneous geographic data sets. The system developed is based on description logic. The decidability and tractability characteristics of description logic allow consistency checking to be deferred during data integration, so minimizing the information loss that usually accompanies any data integration task. Further, the user interface allows users to negotiate with the system in defining consistency. The results of the research suggest that further work could significantly increase the level of automation for many geographic data integration tasks.