The morphing for continuous generalization of linear features based on Dynamic Time Warping distance
Shape morphing has been used to generate arbitrary scale maps in the field of continuous generalization in recent years. The morphing approach consists of two main steps: shape characteristic matching and trajectory interpolation. Most of the shape characteristic matching methods are difficult to co...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2197516 |
| _version_ | 1827814563021586432 |
|---|---|
| author | Jingzhong Li Kainan Mao |
| author_facet | Jingzhong Li Kainan Mao |
| author_sort | Jingzhong Li |
| collection | DOAJ |
| description | Shape morphing has been used to generate arbitrary scale maps in the field of continuous generalization in recent years. The morphing approach consists of two main steps: shape characteristic matching and trajectory interpolation. Most of the shape characteristic matching methods are difficult to consider the local and global characteristics of spatial data at the same time, which will result in distortion of interpolation results. This paper proposes a Dynamic Time Warping (DTW) distance-based morphing method for continuous generalization of linear features. In this method, the DTW distance is employed as shape similarity distance to find the optimal correspondence by minimizing the total cost between each pair of vertices, which considers both the local and global structure of two linear features. Firstly, we build a matrix to record the distance between vertices in two linear features at two different scales. Then we use the DTW algorithm to find the optimal warping path to establish the corresponding relationship between the vertices of two linear features. Finally, the linear interpolation method is used to dynamically generate the geometric shape at any intermediate scale. Experiment results demonstrate that the proposed method can generate continuous and smooth geometric shapes with a gradual transformation effect, and can be used for the continuous generalization of linear features. The generalization results are consistent with map representation rules and human cognition. |
| first_indexed | 2024-03-11T23:46:52Z |
| format | Article |
| id | doaj.art-37289c35a6e843bfaaac18979564de27 |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:46:52Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-37289c35a6e843bfaaac18979564de272023-09-19T09:13:18ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2023.21975162197516The morphing for continuous generalization of linear features based on Dynamic Time Warping distanceJingzhong Li0Kainan Mao1School of Resource and Environment Sciences, Wuhan UniversitySchool of Resource and Environment Sciences, Wuhan UniversityShape morphing has been used to generate arbitrary scale maps in the field of continuous generalization in recent years. The morphing approach consists of two main steps: shape characteristic matching and trajectory interpolation. Most of the shape characteristic matching methods are difficult to consider the local and global characteristics of spatial data at the same time, which will result in distortion of interpolation results. This paper proposes a Dynamic Time Warping (DTW) distance-based morphing method for continuous generalization of linear features. In this method, the DTW distance is employed as shape similarity distance to find the optimal correspondence by minimizing the total cost between each pair of vertices, which considers both the local and global structure of two linear features. Firstly, we build a matrix to record the distance between vertices in two linear features at two different scales. Then we use the DTW algorithm to find the optimal warping path to establish the corresponding relationship between the vertices of two linear features. Finally, the linear interpolation method is used to dynamically generate the geometric shape at any intermediate scale. Experiment results demonstrate that the proposed method can generate continuous and smooth geometric shapes with a gradual transformation effect, and can be used for the continuous generalization of linear features. The generalization results are consistent with map representation rules and human cognition.http://dx.doi.org/10.1080/10106049.2023.2197516map generalizationmorphingdynamic time warpingscale transformationcontinuous generalization |
| spellingShingle | Jingzhong Li Kainan Mao The morphing for continuous generalization of linear features based on Dynamic Time Warping distance Geocarto International map generalization morphing dynamic time warping scale transformation continuous generalization |
| title | The morphing for continuous generalization of linear features based on Dynamic Time Warping distance |
| title_full | The morphing for continuous generalization of linear features based on Dynamic Time Warping distance |
| title_fullStr | The morphing for continuous generalization of linear features based on Dynamic Time Warping distance |
| title_full_unstemmed | The morphing for continuous generalization of linear features based on Dynamic Time Warping distance |
| title_short | The morphing for continuous generalization of linear features based on Dynamic Time Warping distance |
| title_sort | morphing for continuous generalization of linear features based on dynamic time warping distance |
| topic | map generalization morphing dynamic time warping scale transformation continuous generalization |
| url | http://dx.doi.org/10.1080/10106049.2023.2197516 |
| work_keys_str_mv | AT jingzhongli themorphingforcontinuousgeneralizationoflinearfeaturesbasedondynamictimewarpingdistance AT kainanmao themorphingforcontinuousgeneralizationoflinearfeaturesbasedondynamictimewarpingdistance AT jingzhongli morphingforcontinuousgeneralizationoflinearfeaturesbasedondynamictimewarpingdistance AT kainanmao morphingforcontinuousgeneralizationoflinearfeaturesbasedondynamictimewarpingdistance |