## 1 Introduction  
The overlay analysis of vector polygons is one of the basic functions of geographic information system (GIS) spatial analysis [1], and it is an important and difficult problem in the field of geographic data processing [2; 3]. Vector polygon overlay analysis is not only complicated in algorithm implementation but is also becoming increasingly computationally intensive and data-intensive with the rapid growth of spatial data volumes [4; 5; 6]. The computing performance of serial algorithms in dealing with large-scale spatial data superposition is showing increasing difficulty in meeting application requirements [7]. The development of computer hardware technology and programming models has given parallel computing an important role in solving intensive problems in the field of geoscience computing. The ability of a CPU to directly and quickly access internal storage also allows data sharing between tasks to be fast and unified. The OpenMP programmingmodel in a multicore environment is an effective way to perform parallel polygon overlay analysis [8; 9; 10]. In parallel spatial analysis, the primary task of the parallel processing of spatial data is to reasonably divide complex data and store them evenly and in a dispersed manner among multiple computing nodes [11]. [PERSON] et al., divided vector data in accordance with the storage order of the polygon IDs, with the same number of polygons in each group [12]. [PERSON], based on the spatial range division method of regular strips, extended the grid division method for raster data division to the division of vector data and divided the data in accordance with the spatial position of the data set [13]. [PERSON] et al. proposed a quadtree partitioning method based on the spatial locations of data sets [14]. [PERSON] et al., proposed a heuristic partitioning method using equal areas as the criterion for partitioning [15]. [PERSON] et al., proposed a data partitioning method based on the Hilbert curve [16]. [PERSON] et al. proposed a data partitioning method that amounts to the topological relationships of polygons and achieved good parallel efficiency [17]. From the perspective of multicore data parallelism, [PERSON] et al. analyzed the similarities and differences of the parallel implementation methods of eight polygon overlay analysis tools and proposed an improved grouping association minimization method to achieve data partitioning. Based on a load-balancing calculation strategy with the number of vertices as an indicator and a variety of parallel optimization methods and strategies, a parallel polygon overlay analysis toolset, including eight operations, was implemented [10]. [PERSON] et al. proposed a parallel filling and digging algorithm for large-scale DEM data based on strip division [18; 19]. These scholars' data partitioning methods have improved the efficiency of parallel spatial data processing, but these partitioning methods cannot fully consider the impacts of polygon shape, size, and structure, which can easily lead to an imbalance in the amount of calculation needed among different groups. The data structures of the geographic polygons used in overlay analysis can greatly differ. These polygons include not only simple convex polygons but also complex concave polygons, polygons with hole islands, and self-intersecting polygons [20]. To quantitatively measure the complexity of polygons, the concept of shape complexity has been proposed [21]. [PERSON] first proposed constructing a shape complexity model based on geometric features such as the number of vertices, symmetry, turning number, and angular variability, but this model is effective only for concave polygons [22]. [PERSON] et al. used global distance entropy, local angle entropy, and polygon randomness to measure the shape complexity of two-dimensional graphs [23]. [PERSON] et al. used a combination of the area, perimeter ratio, and number of blocks of a graph to represent shape complexity [24]. [PERSON] et al.