The spatial accessibility of urban parks is an important indicator of the livability level of cities. In this paper, we propose a comprehensive multimodal two-step floating catchment area (CM2SFCA) method which integrates supply capacity, the selection probability of individuals, and variable catchment sizes into the traditional multimodel 2SFCA method. This method is used to measure park accessibility in Wuhan, China. The results show that the spatial distribution of park accessibility under the proposed method is variant. High accessibility areas are clustered near the Third Ring Road with strong supply capacity parks, and low accessibility areas are distributed in the western and southern regions. Compared with the single-model accessibility (bicycling, driving, and public transit) method, we found that the multimodal spatial accessibility, combining the characteristics of three single transportations, can provide a more realistic evaluation. We also explore the spatial relationship between park accessibility and population density by bivariate local Moran’s I statistic and find that the Low Ai-High Pi area is located in the center of the study area, and the Low Ai-Low Pi area is located at the edge of the study area, with a relatively discrete distribution of parks and weak supply capacity. These findings may provide some insights for urban planners to formulate effective policies and strategies to ease the spatial inequity of urban parks.

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.

The shape pattern recognition of building footprints stands as a pivotal concern within GIS spatial cognition. In this study, we introduce a novel approach for the shape recognition of building footprints, leveraging t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction visualization. First, the Canonical Time Warping (CTW) algorithm is employed to gauge the shape similarity distance of building footprints. Subsequently, the t-SNE model is utilized to map the building footprints, featuring varying numbers of coordinate vertices, onto points within the Cartesian coordinate system. The shape similarity distance serves as the input to the t-SNE model for parameter optimization. Lastly, building footprint shapes are identified through the inherent clustering patterns of points using a Gaussian Mixture Model (GMM). Experimental results demonstrate the method’s robustness to the translation, rotation, scaling, and mirroring of geometric objects, while effectively measuring shape similarity between building footprints. Furthermore, diverse types of building footprints are discernible through natural clustering in low-dimensional spaces, aligning closely with human visual perception.