A new model combining building block displacement and building block area reduction for resolving spatial conflicts
(2021) In Transactions in GIS 25. p.1366-1395- Abstract
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... (More)
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.
(Less)
- author
- Pilehforooshha, Parastoo ; Karimi, Mohammad LU and Mansourian, Ali LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Geographic Information System (GIS), Generalisation, Operational research, Geospatial Artificial Intelligence (GeoAI)
- in
- Transactions in GIS
- volume
- 25
- pages
- 30 pages
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85101908399
- ISSN
- 1361-1682
- DOI
- 10.1111/tgis.12731
- language
- English
- LU publication?
- yes
- id
- 5c3972f0-00b0-42dd-9977-86f1666a1c2d
- date added to LUP
- 2021-03-22 08:59:15
- date last changed
- 2023-10-08 23:10:42
@article{5c3972f0-00b0-42dd-9977-86f1666a1c2d,
abstract = {{<p>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.</p>}},
author = {{Pilehforooshha, Parastoo and Karimi, Mohammad and Mansourian, Ali}},
issn = {{1361-1682}},
keywords = {{Geographic Information System (GIS); Generalisation; Operational research; Geospatial Artificial Intelligence (GeoAI)}},
language = {{eng}},
pages = {{1366--1395}},
publisher = {{Wiley-Blackwell}},
series = {{Transactions in GIS}},
title = {{A new model combining building block displacement and building block area reduction for resolving spatial conflicts}},
url = {{http://dx.doi.org/10.1111/tgis.12731}},
doi = {{10.1111/tgis.12731}},
volume = {{25}},
year = {{2021}},
}