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Point-feature cartographic label placement (PFCLP) involves placing labels adjacent to their corresponding point features on a map. A widely accepted goal of PFCLP is to maximize the number of conflict-free labels. This paper presents an algorithm for PFCLP based on the four-slider (4S) model. The algorithm is composed of two phases: an initializat...
Citations
... Most scholars mainly focus on improving these metaheuristic algorithms or adopting hybrid metaheuristic algorithms. Rabello et al. [16] presented a Clustering Search (CS) metaheuristic algorithm to solve the PFLP problem; Araujo et al. [17] proposed a CS-based Density Search Clustering (DCS) metaheuristic algorithm as a new alternative to PFLP; Ding [18] et al. combined exact and heuristic algorithms to construct the initial solution and used a backward greedy approach to improve the initial solution to obtain maximally conflict-free labels. Li et al [19]. ...
The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index.
... Feature label placement is a fundamental step in map production that can precisely visualize the information and has a significant influence on reader perception [1]. Geographical features are classified into three categories: point features, line features, and area features [2]. Hence, map labels are required to be clear, aesthetic, and legible to present geographical information accurately [3]. ...
... Therefore, the probability of finding the optimal solution is almost zero in polynomial time due to the complexity of the automatic label placement. However, extensive algorithms are able to exploit part of the optimal solution in some cases [2]. ...
... Map labeling is a combinatorial optimization problem, which is proven to be an NPhard problem, and its computational time is (2 ). The complexity of the correspondent problems is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. ...
Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm.
... Another common model for point feature labeling is the slider model, which can make better use of the blank area of the map through the continuous sliding strategy, but the position of the label is limited to this trajectory line. Recently, Ding et al. [9] proposed an algorithm based on a four-slider model to place the label of point features. ...
Automatic multiple geographical feature label placement (MGFLP) is a combinatorial optimization problem shown to be an NP-hard problem, and it is a challenge in automatic cartography. Many automatic label placement algorithms for point, line, and area features were put forward. It is a common way to use multiple candidate positions (MCP) for label placement, but the research in this way mostly focuses on point features and does not take all three types of features and all the possible candidate positions into account on the map. Therefore, in this paper, the concept of degrees of spatial freedom for feature label placement is proposed based on the idea of degrees of freedom of mechanical motion. We define the degrees of freedom (DOF) and its space for feature labels on a planar map so as the potential space, including all the optional candidate positions of each feature label, can be standardized. Based on two degrees of freedom (2-DOF) space, feature reference position (FRP), and certain buffer distance (CBD) from FRP, we studied the methods including generating, calculating, evaluating, and selecting MCP for feature label. By using and improving the discrete differential evolution genetic algorithm (DDEGA), we carried out MGFLP experiments on the same dataset used by DDEGA algorithm. The results show that: 1) although the MCP based on the 2-DOF space increase the complexity of the NP-hard problem, however, the obtained results by optimizing the performance of the algorithm and increasing the number of candidate positions are still better than the traditional 8-candidate positions model. 2) In the same 2-DOF space, increasing the candidate positions from less to more along each direction of the 2-DOF space improves the quality of label placement.
... The GUI of the application has 21 different parts and all these 21 parts are 13) and (14) are number of placed labels, processing time and number of missed labels for previous algorithm respectively. (15), (16) and (17) (20) and (21) show the name of labels that can not be placed into the display. ...
... Since this modified algorithm has been showed better results for large number point-features, the most suitable field to apply this algorithm is cartography. This algorithm may be implemented alone or integrated with other algorithms like 'A two-phase algorithm for point-feature cartographic label placement' [16] or with some generic approach like 'A Constructive Genetic Approach to Point-Feature Cartographic Label Placement' [51] or can be incorporated with map browsing service based on service like 'Determining means, terminal device, system and method for point label placement on a map' [12]. It also can be integrated with different simulated annealing [56] [14]. ...
Label placement is an important and major part of Information Visualization. Point-based label placement is the most challenging among different types of label placement. Point-based label placement without overlapping is becoming more popular due to its numerous applications on different fields. The necessity of even faster algorithm for such label placement has greatly increased because of its applications specially in interactive and dynamic cartography. The foremost focus of this dissertation is the improvement of processing speed for point-based label placement without overlapping point-features and other labels. After profound research on the previous related works, the improvement scopes have been specified. After that, proper planning and design for the implementation of modified (advanced) algorithm has been presented including the improvement of space sampling function and existing pseudo-code. Afterwards, a desktop application has been developed to justify the higher processing speed of new modified algorithm with the comparison of previous existing algorithm. The processing speed and number of placed labels for both algorithms have been calculated using the implemented application with several variations. These data have been analyzed and several observations has been stated based on the analysis. Moreover, an efficiency equation has been formulated to measure the combinations of variables for best output while considering lower processing time and higher number of placed labels. Afterwards, the heat-map chart of overlapped label-placement positions for both algorithm have been compared to investigate further about the new modified algorithm. From the previous data analysis and the comparison of heat-map charts, it has been found that the modified algorithm is faster than the previous existing algorithm for large number of point-features.
