11 research outputs found
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
Exploring multi-granular documentation strategies for the representation, discovery and use of geographic information
This thesis explores how digital representations of geography and Geographic
Information (GI) may be described, and how these descriptions facilitate the use of
the resources they depict. More specifically, it critically examines existing geospatial
documentation practices and aims to identify opportunities for refinement therein,
whether when used to signpost those data assets documented, for managing and
maintaining information assets, or to assist in resource interpretation and
discrimination. Documentation of GI can therefore facilitate its utilisation; it can be
reasonably expected that by refining documentation practices, GI hold the potential
for being better exploited. The underpinning theme connecting the individual papers
of the thesis is one of multi-granular documentation. GI may be recorded at varying
degrees of granularity, and yet traditional documentation efforts have predominantly
focussed on a solitary level (that of the geospatial data layer). Developing
documentation practices to account for other granularities permits the description of
GI at different levels of detail and can further assist in realising its potential through
better discovery, interpretation and use. One of the aims of the current work is to
establish the merit of such multi-granular practices. Over the course of four research
papers and a short research article, proprietary as well as open source software
approaches are accordingly presented and provide proof-of-concept and conceptual
solutions that aim to enhance GI utilisation through improved documentation
practices. Presented in the context of an existing body of research, the proposed
approaches focus on the technological infrastructure supporting data discovery, the
automation of documentation processes and the implications of describing geospatial
information resources of varying granularity. Each paper successively contributes to the notion that geospatial resources are potentially better exploited when
documentation practices account for the multi-granular aspects of GI, and the
varying ways in which such documentation may be used. In establishing the merit of
multi-granular documentation, it is nevertheless recognised in the current work that
instituting a comprehensive documentation strategy at several granularities may be
unrealistic for some geospatial applications. Pragmatically, the level of effort
required would be excessive, making universal adoption impractical. Considering
however the ever-expanding volumes of geospatial data gathered and the demand for
ways of managing and maintaining the usefulness of potentially unwieldy
repositories, improved documentation practices are required. A system of
hierarchical documentation, of self-documenting information, would provide for
information discovery and retrieval from such expanding resource pools at multiple
granularities, improve the accessibility of GI and ultimately, its utilisation
Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms
Automatic Geospatial Data Conflation Using Semantic Web Technologies
Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes
A Knowledge-based Approach for Creating Detailed Landscape Representations by Fusing GIS Data Collections with Associated Uncertainty
Geographic Information Systems (GIS) data for a region is of different types and collected from different sources, such as aerial digitized color imagery, elevation data consisting of terrain height at different points in that region, and feature data consisting of geometric information and properties about entities above/below the ground in that region. Merging GIS data and understanding the real world information present explicitly or implicitly in that data is a challenging task. This is often done manually by domain experts because of their superior capability to efficiently recognize patterns, combine, reason, and relate information. When a detailed digital representation of the region is to be created, domain experts are required to make best-guess decisions about each object. For example, a human would create representations of entities by collectively looking at the data layers, noting even elements that are not visible, like a covered overpass or underwater tunnel of a certain width and length. Such detailed representations are needed for use by processes like visualization or 3D modeling in applications used by military, simulation, earth sciences and gaming communities. Many of these applications are increasingly using digitally synthesized visuals and require detailed digital 3D representations to be generated quickly after acquiring the necessary initial data.
Our main thesis, and a significant research contribution of this work, is that this task of creating detailed representations can be automated to a very large extent using a methodology which first fuses all Geographic Information System (GIS) data sources available into knowledge base (KB) assertions (instances) representing real world objects using a subprocess called GIS2KB. Then using reasoning, implicit information is inferred to define detailed 3D entity representations using a geometry definition engine called KB2Scene. Semantic Web is used as the semantic inferencing system and is extended with a data extraction framework. This framework enables the extraction of implicit property information using data and image analysis techniques. The data extraction framework supports extraction of spatial relationship values and attribution of uncertainties to inferred details. Uncertainty is recorded per property and used under Zadeh fuzzy semantics to compute a resulting uncertainty for inferred assertional axioms. This is achieved by another major contribution of our research, a unique extension of the KB ABox Realization service using KB explanation services.
Previous semantics based research in this domain has concentrated more on improving represented details through the addition of artifacts like lights, signage, crosswalks, etc. Previous attempts regarding uncertainty in assertions use a modified reasoner expressivity and calculus. Our work differs in that separating formal knowledge from data processing allows fusion of different heterogeneous data sources which share the same context. Imprecision is modeled through uncertainty on assertions without defining a new expressivity as long as KB explanation services are available for the used expressivity. We also believe that in our use case, this simplifies uncertainty calculations. The uncertainties are then available for user-decision at output. We show that the process of creating 3D visuals from GIS data sources can be more automated, modular, verifiable, and the knowledge base instances available for other applications to use as part of a common knowledge base. We define our methodâs components, discuss advantages and limitations, and show sample results for the transportation domain
Application of Geographic Information Systems
The importance of Geographic Information Systems (GIS) can hardly be overemphasized in todayâs academic and professional arena. More professionals and academics have been using GIS than ever â urban & regional planners, civil engineers, geographers, spatial economists, sociologists, environmental scientists, criminal justice professionals, political scientists, and alike. As such, it is extremely important to understand the theories and applications of GIS in our teaching, professional work, and research. âThe Application of Geographic Information Systemsâ presents research findings that explain GISâs applications in different subfields of social sciences. With several case studies conducted in different parts of the world, the book blends together the theories of GIS and their practical implementations in different conditions. It deals with GISâs application in the broad spectrum of geospatial analysis and modeling, water resources analysis, land use analysis, infrastructure network analysis like transportation and water distribution network, and such. The book is expected to be a useful source of knowledge to the users of GIS who envision its applications in their teaching and research. This easy-to-understand book is surely not the end in itself but a little contribution to toward our understanding of the rich and wonderful subject of GIS