Building Displacement Based on the Topological Structure

Sun, Yageng; Qingsheng, Guo; Liu, Yuangang; Lv, Xiuqin; Yang, Ning

doi:10.1179/1743277414y.0000000089

Cited by 8 publications

(10 citation statements)

References 10 publications

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“…Additionally, some scholars (Harrie & Sarjakoski, 2002; Sester, 2000) presented displacement using least‐squares adjustment. Some studies applied displacement based on the beams model (Bader, Barrault, & Weibel, 2005; Liu et al., 2014) and the snake algorithm (Sun, Guo, Liu, Lv, et al., 2016). However, as stated by Sun, Guo, Liu, Lv, et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, as stated by Sun, Guo, Liu, Lv, et al. (2016), in these approaches, parameters are difficult to determine and the use of an intelligent method is required to identify geometric parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, in congested areas, the displacement alone cannot resolve all the spatial conflicts. So, a combination of the displacement with other generalization operators, such as elimination and aggregation, has been suggested in several studies (Ai & Zhang, 2007; Ai et al., 2015; Bader et al., 2005; Huang et al., 2017; Liu et al., 2014; Ruas, 1998; Sun, Guo, Liu, Lv, et al., 2016; Sun, Guo, Liu, Ma, et al., 2016; Wei, He, et al., 2018; Wilson et al., 2003).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The generalization of building blocks (we use the term “buildings” in the remainder of the article for the sake of simplicity) has attracted much research attention over recent decades (e.g., Ai, Zhang, Zhou, & Yang, 2015; Basaraner & Selcuk, 2008; Deng, Tang, Liu, & Wu, 2018; Pilehforooshha & Karimi, 2019). In building generalization, applying pre‐processing steps and geometric transformation can lead to spatial conflicts of objects, so that they overlap or the distance between them (between buildings or between buildings and other map objects such as roads) becomes shorter than the minimum identifiable separation distance (Ai et al., 2015; Liu, Guo, Sun, & Ma, 2014; Mackaness, 1994; Sun, Guo, Liu, Lv, & Yang, 2016; Wei, He, Wang, Wang, & Guo, 2018). In this situation, objects may compete to occupy the limited space on the map.…”

Section: Introductionmentioning

confidence: 99%

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A new model combining building block displacement and building block area reduction for resolving spatial conflicts

Pilehforooshha

Karimi

Mansourian

2021

Transactions in GIS

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Applying pre‐processing and geometric transformation, for the generalization of building blocks, can lead to spatial conflicts which are mainly resolved by displacement. However, the conflicts may not be resolved, due to the insufficient space for displacement or the existence of other objects that prevent displacement. This article proposes a novel model combining building block displacement and building block area reduction for resolving spatial conflicts. In this model, first the immune genetic algorithm with improved objective function, with the goal of minimizing the total conflicting area, is used for displacement. Second, a building block area reduction model is applied to reduce the area of the unresolved conflicting building blocks. For this, a building segment partitioning model is developed for partitioning the boundaries of conflicting building blocks. Then, the conflicting segments are locally simplified for more adaptation to the initial generalized ones. We generalized two datasets from scale 1:25 to 1:50k and then used the datasets with scale 1:50k to assess the proposed model. The results demonstrate that the proposed model has improved the correctness and completeness by 3.62 and 4.24% for dataset 1 and 5.67 and 7.92% for dataset 2, respectively, compared to one of the recent models for resolving conflicts.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new model combining building block displacement and building block area reduction for resolving spatial conflicts

Pilehforooshha

Karimi

Mansourian

2021

Transactions in GIS

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“…To satisfy the cartographic constraints of building displacement, various algorithms have been proposed over the years. Those algorithms can be divided into two categories: sequential algorithms (Ai et al, 2015; Basaraner, 2011; Fei, 2004; Lichtner, 1979; Mackaness, 1994; Peng et al, 1995; Pilehforooshha et al, 2021; Ruas, 1998; Wei, Guo, et al, 2018) and optimization algorithms (Bader, 2001; Bader et al, 2005; Bobrich, 2001; Burghardt & Meier, 1997; Harrie, 1999, 2001, 2003; Højholt, 2000; Huang et al, 2017; Li et al, 2020; Liu et al, 2014; Lonergan & Jones, 2001; Mackaness & Purves, 2001; Pilehforooshha et al, 2021; Sahbaz & Basaraner, 2021; Sester, 2005; Sun, Guo, Liu, Lv, et al, 2016; Sun, Guo, Liu, Ma, et al, 2016; Wang, Guo, Wei, et al, 2017; Ware et al, 2002, 2003; Ware & Jones, 1998; Wilson et al, 2003) (see details in Sections 2.1 and 2.2). In sequential algorithms, buildings are handled based on a trial and error strategy.…”

Section: Introductionmentioning

confidence: 99%

A spatial contextual immune genetic algorithm to building cartographic displacement

Liu

Zhang

et al. 2023

Transactions in GIS

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In map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations.Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings.Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested.The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns.

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An integrated framework for linear pattern extraction in the building group generalization process

Pilehforooshha

Karimi

2018

Geocarto International

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Building Displacement Based on the Topological Structure

Cited by 8 publications

References 10 publications

A new model combining building block displacement and building block area reduction for resolving spatial conflicts

A new model combining building block displacement and building block area reduction for resolving spatial conflicts

A spatial contextual immune genetic algorithm to building cartographic displacement

An integrated framework for linear pattern extraction in the building group generalization process

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