Shape Similarity Assessment Method for Coastline Generalization

Li, Zhaoxing; Zhai, Jingsheng; Wu, Fang

doi:10.3390/ijgi7070283

Cited by 10 publications

(8 citation statements)

References 48 publications

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“…Because of the differences between the types of linear and polygonal features, the method for evaluating their degree of similarity differs. As a result, various indices are utilized to measure the degree of similarity between them (Li et al, 2019, Chen et al, 2020. In this study, the intersection and minimum central distance indexes were employed to assess the degree of similarity of citizens' proposed areas (Figure 3).…”

Section: Spatial Indexesmentioning

confidence: 99%

Measuring the Spatial Similarities in Volunteered Geographic Information

Mahmoody-Vanolya

Jelokhani‐Niaraki²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Volunteered citizens have the potential to be used as social and distributed sensors, monitoring their surroundings and producing and sharing massive amounts of geographic data. The degree of spatial similarities in Volunteered Geographic Information (VGI) somehow indicates an index for the accuracy and precision of user-generated spatial data. In other words, the spatial similarities in VGI refer to how close a citizen-generated spatial feature is to the true (or accepted one) or how close the citizen-generated spatial features of the same geographic phenomenon are to each other. The present study aims at developing a Web-based GIS tool to collect VGI and extract the spatial similarity indexes. To this end, a case study involving the identification of optimal areas for restaurants in Babolsar, Mazandaran province was used. The degree of similarity between the areas (polygons) proposed by citizens was then investigated using spatial indicators of intersection and minimum central distance. The results show that with the increase in the frequency of citizen-generated polygons, the geometric dispersion of the polygons decreases, and the similarity of citizens’ polygons to establish a restaurant increases. With the increasing agreement, the amount of standard deviation in the area, perimeter, and minimum central distance of intersection areas reduces from 4645 to 15.4, 134.5 to 21.6, and 42.4 to 4.2, respectively.

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Section: Spatial Indexesmentioning

confidence: 99%

Measuring the Spatial Similarities in Volunteered Geographic Information

Mahmoody-Vanolya

Jelokhani‐Niaraki²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In addition, the algorithm should include the extraction of global and local features [27]. The former involves the use statistical methods to extract global features, such as area, perimeter, circumscribed rectangle, and Hausdorff distance, from geometric figures; the latter describes the local structure of the graph based on curvature [28], key points [29], concave-convex morphology [30], Hough variation [31], etc.…”

Section: Shape Similaritymentioning

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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“…According to the previous research, it can be known that compared with Euclidian distance (Peuquet 1992), Bottleneck distance (Efrat et al 2001), and Fréchet distance (Nayyeri et al 2015), Hausdorff distance is one of the most used distances for spatial objects in GIS, but it is sensitive to the shape of the objects, especially to the outliers. Besides, it does not satisfy the change law of similarity (Li et al, 2018). Therefore, Mean-Hausdorff distance (MHD) is selected as the distance similarity metric to obtain more stable and accurate result (Deng et al 2007).…”

Section: Selection Of Evaluation Index and Determination Its Weightsmentioning

confidence: 99%

“…The essence of shape similarity is to judge the coincidence degree between polylines. Therefore, Li (2018) 1)) could eliminate the interference of detail difference, which is more suitable for the comparison between multi-scale polylines of the same target.…”

Section: Selection Of Evaluation Index and Determination Its Weightsmentioning

confidence: 99%

Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

Wang

Shafeeque

Yan

et al. 2021

Preprint

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It is qualitatively evident that the greater the map scale change, the greater the optimal distance threshold of the Douglas-Peucker Algorithm, which is used in polyline simplification. However, no specific quantitative relationships between them are known by far, causing uncertainties in complete automation of the algorithm. To fill this gap, the current paper constructs quantitative relationships based on the spatial similarity theories of polylines. A quantitative spatial similarity relationship model was proposed and evaluated by setting two groups of control experiments and taking <C, T> as coordinates. In order to realize the automatic generalization of the polyline, we verified whether these quantitative relationships could be fitted using the same function with the same coefficients. The experiments revealed that the unary quadratic function is the best, whether the polylines were derived from different or the same geographical feature area(s). The results also show that using the same optimal distance threshold is unreasonable to simplify all polylines from different geographical feature areas. On the other hand, the same geographical feature area polylines could be simplified using the same optimal distance threshold. The uncertainties were assessed by evaluating the automated generalization results for position and geometric accuracy perspectives using polylines from the same geographic feature areas. It is demonstrated that in addition to maintaining the geographical features, the proposed model maintains the shape characteristics of polylines. Limiting the uncertainties would support the realization of completely automatic generalization of polylines and the construction of vector map geodatabases.

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Shape Similarity Assessment Method for Coastline Generalization

Cited by 10 publications

References 48 publications

Measuring the Spatial Similarities in Volunteered Geographic Information

Measuring the Spatial Similarities in Volunteered Geographic Information

Geometric Similarity Measurement Method for Micro Scene Generalization

Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

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