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A new model combining building block displacement and building block area reduction for resolving spatial conflicts
Author(s) -
Pilehforooshha Parastoo,
Karimi Mohammad,
Mansourian Ali
Publication year - 2021
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12731
Subject(s) - block (permutation group theory) , displacement (psychology) , correctness , reduction (mathematics) , computer science , generalization , model building , scale (ratio) , transformation (genetics) , block model , algorithm , mathematics , geography , geometry , engineering , cartography , psychology , psychotherapist , mathematical analysis , biochemistry , chemistry , physics , mining engineering , quantum mechanics , gene
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.