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

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
Tinghua AI
Section GIS Technology,
Department of Geodesy
Delft University of Technology, the Netherlands
Wenping JIANG
Department of Geographic Information Science
Faculty of Resource and Environment Science,
Wuhan University, P. R. China
Keyworks: map generalization, building aggregation, polygon cluster, Delaunay Triangulation
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.
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
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