A multi‐criteria decision‐making approach for geometric matching of areal objects

Kim, Jiyoung; Yu, Kiyun; Bang, Yoonsik

doi:10.1111/tgis.12307

Cited by 6 publications

(5 citation statements)

References 55 publications

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“…Determining the weight of criteria by relying on limited expert information and experience can be challenging, especially when dealing with complex and multiple criteria (Kim, Yu, & Bang, 2018). In this research, for the calculation of

w_{k}

, we used the method of “criteria importance through inter‐criteria correlations” (CRITIC).…”

Section: Methodsmentioning

confidence: 99%

“…In this research, for the calculation of

w_{k}

, we used the method of “criteria importance through inter‐criteria correlations” (CRITIC). In this method, two concepts of the variability of each factor and incompatibility between factors are used to determine the weight of factors (Kim et al., 2018). The standard deviation of a factor indicates its variability; if

σ_{i}

is equal to the standard deviation of the factor

x_{i} (i = 1, \dots, n s)

, then a larger

σ_{i}

means more changes to the

x_{i}

criterion.…”

Section: Methodsmentioning

confidence: 99%

“…The standard deviation of a factor indicates its variability; if

σ_{i}

is equal to the standard deviation of the factor

x_{i} (i = 1, \dots, n s)

, then a larger

σ_{i}

means more changes to the

x_{i}

criterion. The incompatibility between the criteria is determined using the following equations (Kim et al., 2018):

R_{i} = {falsefalse}_{\sum}^{j = 1} ()(, 1 ‐ r_{ij})

r_{ij} = \frac{\sum_{i = 1}^{ns} ()(, x_{i} ‐ \overset{x_{i}}{true}) ()(, x_{j} ‐ \overset{x_{j}}{true})}{\sqrt{\sum_{i = 1}^{italicns} {()(, x_{i} ‐ \overset{x_{i}}{true})}^{2} \sum_{i = 1}^{italicns} {()(, x_{j} ‐ \overset{x_{j}}{true})}^{2}}}

…”

Section: Methodsmentioning

confidence: 99%

“…According to the CRITIC method, the volume of information on criterion

x_{i}

is given by

V_{i} = σ_{i} R_{i}

. The larger the volume of information on a criterion, the more important the criterion, so the weight of

w_{x_{i}}

for factor

x_{i}

is calculated as follows (Kim et al., 2018):

w_{x_{i}} = \frac{V_{i}}{\sum_{i = 1}^{ns} V_{i}}

…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Bijandi

Karimi

Bansouleh

et al. 2021

Transactions in GIS

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In conventional reallocation, farmers' preferences are used to determine the location of their new parcels in predetermined blocks. The most common conflict that can arise during this process is that demand may be high for some blocks. The manner of resolving such disputes, which deal directly with landowners' rights, can affect the success or failure of land consolidation projects. In this study, a novel model for land reallocation is proposed which is based on the principle that the initial situation of the landowner's parcels before land consolidation will be comparable with the new situation that includes all of his/her rights. For this, a spatial similarity‐based approach was proposed, taking into account geometric, ownership, physical, and locational criteria. Then, the agricultural land reallocation model (LR‐MOPSO) was developed using the multi‐objective particle swarm algorithm. Three objectives were defined: (1) simultaneous consideration of farmers' priority and spatial similarity criteria; (2) pooling of farmers' fragmented parcels; and (3) optimal placement of parcels within the block. The LR‐MOPSO model was applied to an Iranian case study and results were compared with a conventional approach. With this model decision‐makers in land consolidation projects will be able to redistribute parcels in a more transparent way, while dealing with landowners' rights.

show abstract

w_{k}

, we used the method of “criteria importance through inter‐criteria correlations” (CRITIC).…”

Section: Methodsmentioning

confidence: 99%

“…In this research, for the calculation of

w_{k}

σ_{i}

is equal to the standard deviation of the factor

x_{i} (i = 1, \dots, n s)

, then a larger

σ_{i}

means more changes to the

x_{i}

criterion.…”

Section: Methodsmentioning

confidence: 99%

“…The standard deviation of a factor indicates its variability; if

σ_{i}

is equal to the standard deviation of the factor

x_{i} (i = 1, \dots, n s)

, then a larger

σ_{i}

means more changes to the

x_{i}

criterion. The incompatibility between the criteria is determined using the following equations (Kim et al., 2018):

R_{i} = {falsefalse}_{\sum}^{j = 1} ()(, 1 ‐ r_{ij})

r_{ij} = \frac{\sum_{i = 1}^{ns} ()(, x_{i} ‐ \overset{x_{i}}{true}) ()(, x_{j} ‐ \overset{x_{j}}{true})}{\sqrt{\sum_{i = 1}^{italicns} {()(, x_{i} ‐ \overset{x_{i}}{true})}^{2} \sum_{i = 1}^{italicns} {()(, x_{j} ‐ \overset{x_{j}}{true})}^{2}}}

…”

Section: Methodsmentioning

confidence: 99%

“…According to the CRITIC method, the volume of information on criterion

x_{i}

is given by

V_{i} = σ_{i} R_{i}

. The larger the volume of information on a criterion, the more important the criterion, so the weight of

w_{x_{i}}

for factor

x_{i}

is calculated as follows (Kim et al., 2018):

w_{x_{i}} = \frac{V_{i}}{\sum_{i = 1}^{ns} V_{i}}

…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Bijandi

Karimi

Bansouleh

et al. 2021

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…Matching and analyzing geometric similarities play important roles in the field of GIS, and these tasks have garnered considerable research attention [1][2][3]. Geometric similarities can also be applied in map quality evaluation [4,5], spatial transmission [6,7], map updating [8,9], cartographic generalization [10][11][12], and others.…”

Section: Introductionmentioning

confidence: 99%

Geometric Similarity Measurement Method for Micro Scene Generalization

et al. 2022

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Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene’s geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little.

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A novel approach for multi-stakeholder agricultural land reallocation using agent-based modeling: A case study in Iran

Bijandi

Karimi

Knaap

et al. 2021

Landscape and Urban Planning

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A multi‐criteria decision‐making approach for geometric matching of areal objects

Cited by 6 publications

References 55 publications

Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Geometric Similarity Measurement Method for Micro Scene Generalization

A novel approach for multi-stakeholder agricultural land reallocation using agent-based modeling: A case study in Iran

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