An Automated Processing Method for Agglomeration Areas

Li, Chengming; Yin, Yong; Liu, Xiaoli; Wu, Pengda

doi:10.3390/ijgi7060204

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…It will not be comprehensive enough for parcel groups if we evaluate the results merely by geometric indications [50], since the parcels have abundant semantic information. Some methods, from the viewpoint of area aggregation, have been proposed based on mesh density, geometric and semantics of blocks [53][54][55]. To ensure suitable forms of aggregation results, Haunert [56] presented an area aggregation method by mixed-integer programming, which optimised the problem.…”

Section: Related Workmentioning

confidence: 99%

“…The first step of grouping is to analyse proximity relationships [50]. Constraint Delaunay triangulation (CDT) is often used for the extraction of skeletons from map patches, as it possesses several highly desirable traits, such as adjacency and regional character [48,51,55].…”

Section: Identification Of Adjacent Relationships Among Parcelsmentioning

confidence: 99%

“…The graph-based grouping method is the most common approach in the grouping of polygons [43,45,[48][49][50]55]. As the parcel proximity calculation method proposed in this paper is a binary operation and satisfies commutative law (i.e., the adjacency relationship is bi-directional), an adjacency matrix of an undirected graph can be established for storing the proximity between parcels.…”

Section: Description Of Urban Parcel Grouping (Upg) Algorithmmentioning

confidence: 99%

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Urban Parcel Grouping Method Based on Urban Form and Functional Connectivity Characterisation

Zhang

et al. 2019

IJGI

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The grouping of parcel data based on proximity is a pre-processing step of GIS and a key link of urban structure recognition for regional function discovery and urban planning. Currently, most literature abstracts parcels into points and clusters parcels based on their attribute similarity, which produces a large number of coarse granularity functional regions or discrete distribution of parcels that is inconsistent with human cognition. In this paper, we propose a novel parcel grouping method to optimise this issue, which considers both the urban morphology and the urban functional connectivity. Infiltration behaviours of urban components provide a basis for exploring the correlation between morphology mechanism and functional connectivity of urban areas. We measured the infiltration behaviours among adjacent parcels and concluded that the occurrence of infiltration behaviours often appears in the form of groups, which indicated the practical significance of parcel grouping. Our method employed two parcel morphology indicators: the similarity of the line segments and the compactness of the distribution. The line segment similarity was used to establish the adjacent relationship among parcels and the compactness was used to optimise the grouping result in obtain a satisfactory visual expression. In our study, constrained Delaunay triangulation, Hausdorff distance, and graph theory were employed to construct the proximity, delineate the parcel adjacency matrix, and implement the grouping of parcels. We applied this method for grouping urban parcel data of Beijing and verified the rationality of grouping results based on the quantified results of infiltration behaviours. Our method proved to take a good account of infiltration behaviours and satisfied human cognition, compared with a k-means++ method. We also presented a case using Xicheng District in Beijing to demonstrate the practicability of the method. The result showed that our method obtained fine-grained groups while ensuring functional regions-integrity.

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Section: Related Workmentioning

confidence: 99%

Section: Identification Of Adjacent Relationships Among Parcelsmentioning

confidence: 99%

Section: Description Of Urban Parcel Grouping (Upg) Algorithmmentioning

confidence: 99%

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Urban Parcel Grouping Method Based on Urban Form and Functional Connectivity Characterisation

Zhang

et al. 2019

IJGI

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“…(1) General Broken Contour Lines By using the Delaunay triangulation method [19] and Equation (8), the distance between contours that were reconstructed is statistically significant. The results are shown in Table 2.…”

Section: Superiority Analysismentioning

confidence: 99%

“…(1) General Broken Contour Lines 321 By using the Delaunay triangulation method [19] and Equation (8), the distance between 322 contours that were reconstructed is statistically significant. The results are shown in Table 2 To further illustrate the superiority of the method in this paper, the minimum point pair method 318 and the method presented in this paper were used to carry out an experiment on reconstructed 319 broken contour lines under the same conditions.…”

mentioning

confidence: 99%

A Reconstruction Method for Broken Contour Lines Based on Similar Contours

Liu

et al. 2018

IJGI

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The reconstruction of a broken contour line is a prerequisite for the automated processing of contour lines. When the situation with broken contour lines is severe or the terrain is more complex, incorrect and missing connections are most likely to occur using traditional methods. In this paper, a reconstruction method for broken contour lines based on similar and completely closed contours is proposed. First, node densification is conducted on broken contour lines to improve the identification accuracy of the reference line. Second, the discrete Fréchet distance is used to select a reference line and perform reconstruction. Finally, the actual data of Yunnan Province are utilized for verification. The results show that the method proposed in this paper can achieve better reconstruction of broken contour lines, especially for severe broken contour situations or complex terrains; the reconstruction accuracy is significantly improved over that of the traditional method, indicating good feasibility. Figure 1. Schematic diagram of the minimum point pair method for reconstruction.82 However, the reconstructed line created with this method is usually a straight line, and when 83 the contour line is complex, the reconstructed line is prone to cross dislocation. For this reason, some 84 scholars have put forward a method to add direction for auxiliary identification (based on the 85 extraction of the interbreakpoint Euclidean distance) and a direction angle [3]; the search window is 86 set to calculate the directions of all breakpoints in the window, which allows the appointed 87 breakpoint and alternative breakpoint in the neighborhood search window to extend in each 88 direction. If the extension lines intersect, the breakpoints are matched and connected; the effect is 89 shown in Figure 2. 90 91 Figure 2. Schematic diagram of the breakpoint extension intersection identification matching method 92 for reconstruction.93 Limitations in Existing Methods 94The method for the reconstruction of broken contour lines is easy to implement by using the 95 Euclidean distance with an auxiliary direction, making it suitable for the reconstruction of contour 96 lines with a better surface effect, smaller breakpoint distance, and flatter terrain, as shown in Figure 97 3 A. However, when the following cases occur due to only considering the individual characteristics 98 of a broken contour line for connection, it is difficult to accurately determine the original trend of the 99 broken contour line, making it prone to incorrect and missing connections. 100(1) The overall breaking of a contour line is severe, and only a small part of the break remains, as 101 shown in Figure 3 B; 102 (2) The topography of the broken contour line area is complex (e.g., ridges, valleys, and saddles), as 103 shown in Figure 3 C. 104According to the above two cases, some scholars have put forward the strategy of using 105 breakpoint classification and directional differences for the reconstruction of broken contour lines 106[10] to enhance the connection ...

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