Fig 1.
Detecting the neighboring region among building cluster using Delaunay triangulation.
Fig 2.
Formal definitions of the functions related to neighborhood analysis.
Fig 3.
The representation of spatial objects in three data models.
Fig 4.
The illustrations of region expanding and compressing.
r denotes the region, b the boundary, bin the inside neighbor, and bout the outside neighbor.
Fig 5.
The three types of triangle and the skeleton line connection for each one.
Fig 6.
The extraction of the skeleton from the triangle region.
Fig 7.
The local distance representation of Wi1Wi2 for three types of triangles.
Fig 8.
A comparison between the MAT-based skeleton (left) and Delaunay-triangulation-network-based (DTN-based) skeleton (right) using the same complex polygon data.
Fig 9.
(Left) performing skeletonizing outside street blocks results in the medial street line, (Right) performing skeletonizing on building clusters within a street block.
Fig 10.
The illustration of the building aggregation using FTDM and operators Expand(r), Skeleton(r).
Fig 11.
An illustration of the conflict skeletons, conflict OPs (Object Polygon) visualized as red lines, and the arrows represent the building movement direction.
Fig 12.
The progressive generalization of a building cluster based on the FTDM model.
Fig 13.
A comparison of building aggregation between the proposed method (B) and ArcGIS method (C).
Table 1.
The quantitative comparison between our proposed method and that of ArcGIS.
Fig 14.
The network of a connective conflict building object.