The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Chen, Jun

Bibliographic data

Monograph

Persistent identifier:: 856566209

Author:: Chen, Jun

Title:: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Sub title:: May 23 - 25, 2001, Bangkok, Thailand

Scope:: VI, 434 Seiten

Year of publication:: 2001

Place of publication:: Pathumthani, Thailand

Publisher of the original:: AIT

Identifier (digital):: 856566209

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: DISTRIBUTION ANALYSIS AND AUTOMATIC GENERALIZATION OF URBAN BUILDING CLUSTER. Tinghua AI

Document type:: Monograph

Structure type:: Chapter

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 DISTRIBUTION ANALYSIS AND AUTOMATIC GENERALIZATION OF URBAN BUILDING CLUSTER Tinghua AI Section GIS Technology, Department of Geodesy Delft University of Technology, the Netherlands a.tinghua@qeo.tudelft.ni Wenping JIANG Department of Geographic Information Science Faculty of Resource and Environment Science, Wuhan University, P. R. China jwpinq@public.wh.hb.cn Keyworks: map generalization, building aggregation, polygon cluster, Delaunay Triangulation Abstract This paper presents a model for building cluster distribution analysis based on Delaunay triangulation skeleton. Through selective organization, the skeleton connection within gap area between building polygons obtains a special geometrical construction which is similar to Voronoi diagram with properties of spatial partitioning equally. Each building polygon is surrounded by a partitioning polygon which can be regarded as growth region of building seed. Based on this model, several cluster structure variables can be computed for building distribution analysis, such as distribution density, topological neighbor, adjacent distance, adjacent direction. Considering the constraints of position accuracy, statistical area balance, orgothonal characteristics in building cluster generalization, the paper gives a progressive algorithm of building cluster aggregation, including conflict detection (where), object (who) displacement, and geometrical combination operation (how).The context relationship of cluster structure is reflected through weighted distance computation, vector combination and other strategies. The algorithm has been realized in an active generalization system and some experiment illustrations are provided in the paper. 1. INTRODUCTION Map generalization has to take into account spatial object properties in geometrical, semantic and topological aspects. The Objects with the same geometrical type, but different geographic meaning should be executed with different generalization model and algorithm. In recent years, the study of geo-oriented generalization is active, which aims at some special geographical categories (Poorten and Jones, 1999, Ruas 1998, Bader and Weibel 1999). The research on urban building abstract is an example. As a polygon object with human culture characteristics, the building has different properties in spatial distribution, shape structure, Gestalt nature compared with natural features such as soil parcel, vegetable, lake. Disjoint cluster distribution and orthogonal shape properties requires to be considered specially in building generalization. Building cluster generalization includes multiple level analysis and operation. Grouping is the first decision-making which is based on conflict detection, distribution pattern recognition, Gestalt nature cognition. The following displacement involves how far and what direction identification. Thirdly, the geometrical combination and simplification has to maintain orthogonal geometric nature. Three level processes require special model to derive such descriptions variables as distribution density, distribution pattern, adjacency distance, adjacency direction etc. Independent building simplification is active in this field and achieves some methods and algorithms. From the point of readable view, Regnauld and Edwardes (1999) discuss three operations for building simplification: detail removal, squaring, local enlargement. Lee(1999) presents some ideas on single building simplification focusing on shape maintenance. Based on divide-and-conquer idea, Guo and Ai(2000) give an algorithm to simplify building polygon through separating a building into multiple hierarchical organization of rectangle elements. For building cluster aggregation, Regnauld (1996) develops a method to classify building group applying MST model in graph theory. Ruas(1998) presents an algorithm of building displacement to resolve conflicts between building and street edge. These works are relative independent in the whole procedure of map generalization. This paper attempts to combine the first two issues of building generalization, concentrating on cluster structure displacement and aggregation and giving a model to support cluster analysis. In building cluster generalization, it is difficult to satisfy all constraints. Group combination which exactly maintains position accuracy ( no displacement) of each building may result in area increasing greatly, due to gap area between original buildings being included. The compromise strategy is to sacrifice each constraint partly, not respecting anyone condition completely. This strategy requires to handle three level operation interdependently. It also needs generalization model containing different functions to support both high level decision making and low level geometric operation, to answer such questions of where happens conflict, how to displace(direction and offset), how to aggregate. This paper will present one data model having this kind of properties to support building aggregation. Based on Delaunay triangulation skeleton, we will construct a geometrical construction which is similar to Voronoi diagram with properties of spatial partitioning equally. Each building polygon is surrounded by one partitioning polygon which can be regarded as the growth region of building seed. Based on this model, several cluster structure variables can be computed for building distribution analysis, such as distribution density, topological neighbor, adjacency distance, adjacency direction. This model makes use of the powerful function of Delaunay triangulation in spatial adjacency analysis. Remained contents is organized as following: The constraints in building polygon generalization is discussed in section 2. Section 3 gives the model of partitioning geometrical construction and some variable computation based on this model. A progressive algorithm of building cluster aggregation is presented in section 4 with experiment illustrations, and then some future improvements are discussed in conclusion, section 5. 2. CONSTRAINS OF BUILDING GENERALIZATION Based on geometric, topological and semantic analysis, the constraints of building generalization involves position accuracy maintenance, short space distance avoidance, the whole area balance maintenance, Gestalt keep in distribution structure, and square shape retain. The main purpose in building generalization is to remove spatial conflict and during the procedure to respect above constraints as much as possible. From the point of readable view, when distance between buildings is shorter than cognition tolerance, we may think the spatial conflict generating. To resolve conflict, the candidate operations include deletion, displacement, aggregation. Deleting some buildings needs to consider semantic importance and

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