Special Issue "Map Generalization"

Special Issue Editors

Prof. Dr. Barry Kronenfeld
E-Mail Website
Guest Editor
Department of Geology and Geography, Eastern Illinois University, Charleston, IL 61920 USA
Interests: cartography; visualization; spatial statistics; line simplification; spatial interaction; cartograms
Prof. Dr. Barbara P. Buttenfield
E-Mail Website
Guest Editor
Department of Geography, University of Colorado, Boulder CO 80309-0260 USA
Interests: geographic information science; spatial data modeling; generalization; multi-scale databases
Mr. Lawrence V. Stanislawski
E-Mail Website
Guest Editor
Research Cartographer, United States Geological Survey, Center of Excellence for Geospatial Information Science, 1400 Independence Road, Rolla, MO 65401, USA
Tel. 573 308-3914
Interests: geographic information systems; computational methods; geomorphic analysis; hydrography and hydrologic analysis; machine learning

Special Issue Information

Dear Colleagues,

The practice of cartographic generalization has advanced beyond display and legibility to include strategies that support analysis and feature recognition, that exploit spatial and semantic contexts, and that preserve relationships between and among features. The need to generalize the geospatial data is ubiquitous, and supports advanced modelling and analysis, in addition to map production. Generalization methods are in regular use in national mapping agencies that produce and steward very large data sets and data archives, private commercial organizations managing multi-scale data portals, geo-browsers that permit a range of zoom levels, and volunteer-driven and open source mapping hubs that provide services for downloading base maps and thematic data layers. Although standards for data production have been developed and are in wide use, formal methods to evaluate the impacts of generalization on higher level geometric, topologic, and semantic properties are still not widely available. Furthermore, presently, most organizations are not able to distribute data with feature level linkages that can span spatial resolutions, and many do not provide basic or advanced generalization services to users. All of these application domains can be refined and advanced by the development of new algorithms, processing methods, and evaluation protocols to improve support for mapping, modeling, and reasoning across multiple scales.

The purpose of this Special Issue is to highlight the emerging research in generalization and multiscale representation to support spatial modelling, analysis, or intelligent data distribution, in addition to static display. We invite papers for inclusion in the Special Issue relating to any of the following topics:

  • Tailored and adaptive generalization that takes geographic context into account
  • Generalization techniques that preserve high-level geometry characteristics such as feature density, sinuosity, complexity, and angularity
  • Advanced generalization methods involving feature identification, pattern recognition or extraction, machine learning, and artificial intelligence
  • Preserving topology within and between data layers
  • Vertical and horizontal data integration during generalization
  • Uncertainty and error propagation in generalization
  • Tools to evaluate, assess, or validate generalization techniques and protocols

Timeline

Authors are encouraged to contact the editor(s) by 31 October 2019, with their proposed topics or titles. Full papers (up to 8000 words) are due by 31 January 2020.

Prof. Dr. Barry Kronenfeld
Prof. Dr. Barbara P. Buttenfield
Mr. Lawrence V. Stanislawski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cartography
  • generalization
  • multiscale representation
  • adaptive algorithms
  • feature identification
  • pattern recognition
  • machine learning
  • spatial data modelling

Published Papers (2 papers)

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Research

Open AccessArticle
The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes
ISPRS Int. J. Geo-Inf. 2020, 9(1), 37; https://doi.org/10.3390/ijgi9010037 - 10 Jan 2020
Abstract
Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. [...] Read more.
Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement). Full article
(This article belongs to the Special Issue Map Generalization)
Open AccessArticle
A New Algorithms of Stroke Generation Considering Geometric and Structural Properties of Road Network
ISPRS Int. J. Geo-Inf. 2019, 8(7), 304; https://doi.org/10.3390/ijgi8070304 - 16 Jul 2019
Abstract
Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks’ geometric and structural properties. To remedy [...] Read more.
Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks’ geometric and structural properties. To remedy this, this paper proposes an algorithm for generating strokes that takes into account these additional geometric and structural road network properties and that reduces the impact of stroke generation on road network quality. To this end, we introduce a model of feature-based information entropy and then utilize this model to calculate road networks’ information volume and both the elemental and neighborhood level. To make our experimental results more objective, we use the Douglas-Peucker algorithm to simplify the information change curve and to obtain the optimal angle threshold range for generating strokes for different road network structures. Finally, we apply this model to three different road networks, and the optimal threshold ranges are 54°–63° (Chicago), 61°–63° (Moscow), 45°–48° (Monaco). And taking Monaco as an example, this paper conducts stroke selection experiments. The results demonstrate that our proposed algorithm has better connectivity and wider coverage than those based on a common angle threshold (60°). Full article
(This article belongs to the Special Issue Map Generalization)
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