An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps

Chehreghan, Alireza; Abbaspour, Rahim Ali

doi:10.1080/10106049.2016.1155659

Cited by 28 publications

(19 citation statements)

References 26 publications

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“…To address the first issue (determining the type of similarity measures), Chehreghan [18] studied methods for evaluating the spatial similarity in aspects such as distance, angle, area, and shape. Distance is the most widely used geometric measure for evaluating the matching degree of two spatial targets [19,20].…”

Section: Related Workmentioning

confidence: 99%

A Study on a Matching Algorithm for Urban Underground Pipelines

Wang

Qingsheng

et al. 2019

IJGI

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Urban underground pipelines are known as "urban blood vessels". To detect changes in integrated pipelines and professional pipelines, the matching of same-name spatial objects is critical. Existing algorithms used for vector network matching were analyzed to develop an improved matching algorithm that can adapt to underground pipeline networks. Our algorithm improves the holistic matching of pipeline strokes, and also a partial matching algorithm is provided. In this study, appropriate geometric measures were selected to calculate the geometric similarity between pipeline strokes in their holistic matching. Existing methods for evaluating similarities in spatial scene structures in partial underground pipeline networks were improved. A method of partial matching of strokes was additionally investigated, and it compensates for the deficiencies of holistic stroke matching. Experiments showed that the matching performance was good, and the operation efficiency was high. geometric connection rules [5,12]. This principle was combined with the characteristics of road and water network spatial data to propose the stroke concept [12,13]. From a local indicator, two road segments can be connected on the condition that their deflection angle is less than a specified threshold. The deflection angle, which ranges from 0 • to 180 • represents the degree of deviation formed by two linked road segments [14], as shown in Figure 1. Figure 1a shows the pipeline spatial data organized in the traditional "node-segment" manner, and Figure 1b shows the data processed by stroke connection. There are four strokes: stroke1, stroke2, stroke3, and stroke4. "Good continuation" usually refers to an approximately level angle (180 • ) between segments. The geometric morphology of a stroke is characterized by a smooth and continuous distribution in space, which maintains the integrity of urban underground pipelines. Therefore, the stroke can be used as the basic unit for matching and analyzing urban underground pipelines.

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Section: Related Workmentioning

confidence: 99%

A Study on a Matching Algorithm for Urban Underground Pipelines

Wang

Qingsheng

et al. 2019

IJGI

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“…Next, and to provide another comparison with some additional common geometrical parameters, Table 12 summarizes two geometrical properties derived for these trajectories, that is, shape and complexity. The upper and lower triangle of presented matrix in Table 12 present a one-to-one geometric comparison of these trajectories according to their shape and complexity similarities as introduced in [64]. Shape evaluates the length and angle of turning at each node using specified function as signature function while complexity is derived using the average distance of each node from the connecting line between start and end nodes.…”

Section: Spatial and Temporal Entropymentioning

confidence: 99%

A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity

Milaghardan

Abbaspour

Claramunt

2018

Entropy

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The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people's movements at the city scale is one of the most promising research avenues still to explore. Extracting trajectory patterns and outliers in urban environments is a direction still requiring exploration for many management and planning tasks. The research developed in this paper introduces a spatio-temporal framework, so-called STE-SD (Spatio-Temporal Entropy for Similarity Detection), based on the initial concept of entropy as introduced by Shannon in his seminal theory of information and as recently extended to the spatial and temporal dimensions. Our approach considers several complementary trajectory descriptors whose distribution in space and time are quantitatively evaluated. The trajectory primitives considered include curvatures, stop-points, self-intersections and velocities. These primitives are identified and then qualified using the notion of entropy as applied to the spatial and temporal dimensions. The whole approach is experimented and applied to urban trajectories derived from the Geolife dataset, a reference data benchmark available in the city of Beijing.

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“…Although the definition of shape is still an open issue, and the relationship between shape similarity and scale is still unclear [15], it is believed that 'different levels of perception will consider different shapes of a line' [16]. Therefore, it is of great significance to find an effective method of modelling the shapes of coastlines under different perception levels to evaluate the shape similarity in coastline generalization.…”

Section: Introductionmentioning

confidence: 99%

Shape Similarity Assessment Method for Coastline Generalization

Zhai

2018

IJGI

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Although shape similarity is one fundamental element in coastline generalization quality, its related research is still inadequate. Consistent with the hierarchical pattern of shape recognition, the Dual-side Bend Forest Shape Representation Model is presented by reorganizing the coastline into bilateral bend forests, which are made of continuous root-bends based on Constrained Delaunay Triangulation and Convex Hull. Subsequently, the shape contribution ratio of each level in the model is expressed by its area distribution in the model. Then, the shape similarity assessment is conducted on the model in a top–down layer by layer pattern. Contrast experiments are conducted among the presented method and the Length Ratio, Hausdorff Distance and Turning Function, showing the improvements of the presented method over the others, including (1) the hierarchical shape representation model can distinguish shape features of different layers on dual-side effectively, which is consistent with shape recognition, (2) its usability and stability among coastlines and scales, and (3) it is sensitive to changes in main shape features caused by coastline generalization.

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An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps

Cited by 28 publications

References 26 publications

A Study on a Matching Algorithm for Urban Underground Pipelines

A Study on a Matching Algorithm for Urban Underground Pipelines

A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity

Shape Similarity Assessment Method for Coastline Generalization

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