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Automated map generalization is a necessary technique for the construction of multi-scale vector map databases that are crucial components in spatial data infrastructure of cities, provinces, and countries. Nevertheless, this is still a dream because many algorithms for map feature generalization are not parameter-free and therefore need human's interference. One of the major reasons is that map generalization is a process of spatial similarity transformation in multi-scale map spaces; however, no theory can be found to support such kind of transformation. This thesis focuses on the theory of spatial similarity relations in multi-scale map spaces, aiming at proposing the approaches and models that can be used to automate some relevant algorithms in map generalization. After a systematic review of existing achievements including the definitions and features of similarity in various communities, a classification system of spatial similarity relations, and the calculation models of similarity relations in the communities of psychology, computer science, music, and geography, as well as a number of raster-based approaches for calculating similarity degrees between images, the thesis achieves the following innovative contributions. First, the fundamental issues of spatial similarity relations are explored, i.e. (1) a classification system is proposed that classifies the objects processed by map generalization algorithms into ten categories; (2) the Set Theory-based definitions of similarity, spatial similarity, and spatial similarity relation in multi-scale map spaces are given; (3) mathematical language-based descriptions of the features of spatial similarity relations in multi-scale map spaces are addressed; (4) the factors that affect human's judgments of spatial similarity relations are proposed, and their weights are also obtained by psychological experiments; and (5) a classification system for spatial similarity relations in multi-scale map spaces is proposed. Second, the models that can calculate spatial similarity degrees for the ten types of objects in multi-scale map spaces are proposed, and their validity is tested by psychological experiments. If a map (or an individual object, or an object group) and its generalized counterpart are given, the models can be used to calculate the spatial similarity degrees between them. Third, the proposed models are used to solve problems in map generalization: (1) ten formulae are constructed that can calculate spatial similarity degrees by map scale changes in map generalization; (2) an approach based on spatial similarity degree is proposed that can determine when to terminate a map generalization system or an algorithm when it is executed to generalize objects on maps, which may fully automate some relevant algorithms and therefore improve the efficiency of map generalization; and (3) an approach is proposed to calculate the distance tolerance of the Douglas-Peucker Algorithm so that the Douglas-Peucker Algorithm may become fully automatic. Nevertheless, the theory and the approaches proposed in this study possess two limitations and needs further exploration. *More experiments should be done to improve the accuracy and adaptability of the proposed models and formulae. The new experiments should select more typical maps and map objects as samples, and find more subjects with different cultural backgrounds. *Whether it is feasible to integrate the ten models/formulae for calculating spatial similarity degrees into an identical model/formula needs further investigation. In addition, it is important to find out the other algorithms, like the Douglas-Peucker Algorithm, that are not parameter-free and closely related to spatial similarity relation, and explore the approaches to calculating the parameters used in these algorithms with the help of the models and formulae proposed in this thesis.
How does one determine how similar two maps are? This book aims at the theory of spatial similarity relations and its application in automated map generalization, including the definitions, classification and features of spatial similarity relations. Included also are calculation models of spatial similarity relations between arbitrary individual objects and between arbitrary object groups, and the application of the theory in the automation of the algorithms and procedures in map generalization.
This book presents a selection of manuscripts submitted to the 2017 International Cartographic Conference held in Washington, DC at the beginning of July and made available at the conference. These manuscripts have been selected by the Scientific Program Committee and represent the wide-range of research that is done in the discipline. It also forms an important international collection representing research from at least 30-40 countries.
This book focuses on the generalization of map features, providing descriptions and classifying groups of map objects into six categories: point clusters, groups of contours, road networks, river networks, continuous areal features and discrete areal features. Discussing the methods and algorithms in map generalization in equal measure, it also describes the approaches for describing map features. The book is a valuable reference for graduates and researchers who are interested in cartography and geographic information science/systems, especially those in automated map generalization and spatial databases construction.
This book provides up-to-date, in-depth and accessible information on the concept of scale, and focuses on its applications in geography, Earth science, environmental science, and other fields in which the environment plays a significant role. Although the book presents methods and applications as a response to practical challenges, it is primarily concept-centered: it identifies a set of distinct, yet related notions of “scale”, analyzing and elucidating their evolving meanings in a systematic way. Concepts are defined with a focus on their practical operational applicability, and the introduction of methods is supported by concrete examples. The book links theoretical insights to illustrating applications, involving a broad range of themes, from maps, fractals, and chaos theory to fine art and literature. It approaches the subject in a spatial, temporal, and spatio-temporal context, including a wide diversity of spatial features from Earth and other planets, as well as time series and space-time patterns. This monograph is expected to be useful especially because in practice the various scale-focused concepts are not neatly separated and immiscible. It is therefore helpful for scholars in physical and human geography, Earth and environmental sciences, and other fields, to benefit from a clear conceptual framework that distinguishes and illuminates the various scale-related concepts and their interconnections. Selected chapters can also support a deeper understanding of the concept of scale for graduate and undergraduate students in geography, the natural sciences, and the humanities. Information on recommended additional literature and comments about specific sources offer a guide to further reading on the topics addressed in the book.
This book provides a cross-section of cutting-edge research areas being pursued by researchers in spatial data handling and geographic information science (GIS). It presents selected papers on the advancement of spatial data handling and GIS in digital cartography, geospatial data integration, geospatial database and data infrastructures, geospatial data modeling, GIS for sustainable development, the interoperability of heterogeneous spatial data systems, location-based services, spatial knowledge discovery and data mining, spatial decision support systems, spatial data structures and algorithms, spatial statistics, spatial data quality and uncertainty, the visualization of spatial data, and web and wireless applications in GIS.
The Routledge Handbook of Geospatial Technologies and Society provides a relevant and comprehensive reference point for research and practice in this dynamic field. It offers detailed explanations of geospatial technologies and provides critical reviews and appraisals of their application in society within international and multi-disciplinary contexts as agents of change. The ability of geospatial data to transform knowledge in contemporary and future societies forms an important theme running throughout the entire volume. Contributors reflect on the changing role of geospatial technologies in society and highlight new applications that represent transformative directions in society and point towards new horizons. Furthermore, they encourage dialogue across disciplines to bring new theoretical perspectives on geospatial technologies, from neurology to heritage studies. The international contributions from leading scholars and influential practitioners that constitute the Handbook provide a wealth of critical examples of these technologies as agents of change in societies around the globe. The book will appeal to advanced undergraduates and practitioners interested or engaged in their application worldwide.
This important work is an attempt to synthesize two areas that need to be treated in tandem. The book brings together the fields of robot spatial mapping and cognitive spatial mapping, which share some common core problems. One would expect some cross-fertilization of research between the two areas to have occurred, yet this has begun only recently. There are now signs that some synthesis is happening, so this work is a timely one for students and engineers in robotics.
The present monograph intends to establish a solid link among three fields: fuzzy set theory, information retrieval, and cluster analysis. Fuzzy set theory supplies new concepts and methods for the other two fields, and provides a common frame work within which they can be reorganized. Four principal groups of readers are assumed: researchers or students who are interested in (a) application of fuzzy sets, (b) theory of information retrieval or bibliographic databases, (c) hierarchical clustering, and (d) application of methods in systems science. Readers in group (a) may notice that the fuzzy set theory used here is very simple, since only finite sets are dealt with. This simplification enables the max min algebra to deal with fuzzy relations and matrices as equivalent entities. Fuzzy graphs are also used for describing theoretical properties of fuzzy relations. This assumption of finite sets is sufficient for applying fuzzy sets to information retrieval and cluster analysis. This means that little theory, beyond the basic theory of fuzzy sets, is required. Although readers in group (b) with little background in the theory of fuzzy sets may have difficulty with a few sections, they will also find enough in this monograph to support an intuitive grasp of this new concept of fuzzy information retrieval. Chapter 4 provides fuzzy retrieval without the use of mathematical symbols. Also, fuzzy graphs will serve as an aid to the intuitive understanding of fuzzy relations.