Query: stroke map generalization

title: Fisheye Map Using Stroke Based Generalization for Web Map Services
abstract: A fisheye map lets users view both detailed and wide areas. The Focus+Glue+Context map is a fisheye map suited for Web map systems; it consists of a detailed map (i.e. Focus) wide area map (i.e. Context) and an area to absorb the difference in scales between Focus and Context (i.e. Glue) Because Glue is compressed, the road density is too high to draw all of the roads in this area. Although existing methods can filter roads to draw, they have problems with rendering the road density and connectivity in Glue. This paper proposes an improved method to filter roads in Glue by applying a generalization method based on weighted strokes. In addition, a technique to speed up the proposed method by using a weighted stroke database is described. A prototype Web map system with a high level of response was developed and evaluated in terms of its connectivity, road density, and response. key words: fisheye view, web map service, stroke

title: A road generalization method using layered stroke networks
abstract: A sketch map is a generalized simple map using abstractive and graphical symbols to provide the information for a specific purpose, e.g. tourist information or a guidance for commercial facilities, and it becomes popular and be widely used due to its good readability. A sketch map consists of the only necessary (or requested) facilities, such as restaurants or parking lots, and their adjacent necessary roads. This implies that to generate a sketch map, we have to select the necessary roads and remove the others, which is called a road generalization. However, it is a difficult problem to automatically select all the necessary roads when the facilities are given. In this paper, we propose a road generalization method based on the strokes [18, 19] to automatically select all the necessary roads in order to support a generation of a sketch map when the facilities are given. The proposed method has several features as follows: (1) classifies the stroke networks into two layers: global stroke networks and local stroke networks to improve the perceptibility (i.e. readability) of a sketch map, (2) guarantees the reachability to the facilities, and (3) avoids a generation of a redundant detour path to the facilities. We implement the prototype system based on the proposed method and compare with the previous method. From the experiment evaluations, we show that the proposed method decreases the average distance to the given facilities by about 60% Moreover, by using local stroke networks, the reachability to the facilities is improved from about 0.888 to over 0.979 (up to 1.0)

title: An Improved Hybrid Method for Enhanced Road Feature Selection in Map Generalization
abstract: Road selection is a critical component of road network generalization that directly affects its accuracy. However, most conventional selection methods are based solely on either a linear or an areal representation mode, often resulting in low selection accuracy and biased structural selection. In this paper we propose an improved hybrid method combining the linear and areal representation modes to increase the accuracy of road selection. The proposed method offers two primary advantages. First, it improves the stroke generation algorithm in a linear representation mode by using an ordinary least square (OLS) model to consider overall information for the roads to be connected. Second, by taking advantage of the areal representation mode, the proposed method partitions road networks and calculates road density based on weighted Voronoi diagrams. Roads were selected using stroke importance and a density threshold. Finally, experiments were conducted comparing the proposed technique with conventional single representation methods. Results demonstrate the increased stroke generation accuracy and improved road selection achieved by this method.

title: A comparative study of various strategies to concatenate road segments into strokes for map generalization
abstract: The study of road networks has been a topic of interest for some time. A road network in a database is often represented by intersections and segments. However, in many cases (e.g. traffic flow analysis and map generalization) one needs to consider individual roads as a whole, instead of individual segments. Thus, it is sometimes very desirable to concatenate road segments into long lines 'strokes' as they are called in the literature. For stroke building, a number of strategies are available and the effectiveness of using these strategies needs to be evaluated. This article presents a comparative analysis of 17 such strategies, including 3 of the geometric approach, 1 of the thematic approach, and 13 of the hybrid approach for road network generalization purposes. Three sets of real life data with different patterns are used to test these strategies. Corresponding road maps at smaller scales are used as benchmarks and a new measure called the accuracy rate is proposed to indicate the correctness of the concatenated strokes. The results show that if only the geometric approach is considered, the every best fit strategy performs best; if thematic attributes are also added, road class can be more effective than road name. Also significance tests (the chi square test and the Marascuilo procedure) are carried out to give all pairwise comparisons of these strategies. The results indicate that 45 of the 136 pairs of strategies have statistically significant differences; the purely geometry based every best fit performs significantly better than the purely geometry based self fit; and the inclusion of thematic attributes, especially road class, sometimes improves the accuracy rate but the improvement is not significant.

title: A Stroke based Approach to Detect Patterns of Discrete Buildings for Generalization Purposes
abstract: Generalization is the prerequisite to derive multiple representation and requires high efforts in case of manual map production. For the buildings in the OpenStreetMap (OSM) data, there can be found no intermediate generalization results between the neighboring zoom levels, which may lead to lower legibility at the smaller scales. In this paper, a stroke based approach is proposed to detect patterns of discrete buildings for the generalization purposes, which aims at meeting this problem. The strokes are structured based on the proximity graph of building. After pruning the strokes by the given rules, five patterns of building are detected based on the related isolated stroke or strokes. Therefore different generalization units are formed on basis of the patterns. Following that, different generalization strategies are developed and applied aiming at preserving the original characteristics of each unit. By this way, generalized buildings satisfy the legibility constraints and the original distribution structures of buildings are also preserved. The advantages and limitations of the proposed approach are discussed based on the experimental results.

title: Tree like River Networks Hierarchical Relation Method Establishing and Generalization Considering Stroke Properties
abstract: Tree like river system is one of the key elements on a map, of which the simplification directly determines the quality of cartography generalization. Consideration should be given to the semantics, geometry, topology, structure and other characteristics in the simplification, but most of the existing methods only focus on such restrictive quantitative indicators as length, angle and attribute, making the simplified spatial distribution characteristics be easily damaged. Firstly, the method of intelligently identifying characteristics was studied in this paper on the basis of directed topological tree (DTT) Secondly, according to the "180deg hypothesis" and "acute hypothesis" the hierarchy for tree like river systems was set up. And then, the algorithms of overall quantity determination and stratified selection were put forward, which can simplify tree like river systems automatically. Through actual data tests, this method proved to be rational and valid.

title: Evaluation of Properties to determine the Importance of individual Roads for Map Generalization
abstract: Many researchers have paid much attention to the importance of individual roads for various applications such as traffic flow analysis and road network generalization. This paper gives a comparative analysis of different properties to determine the importance of individual roads for road network generalization purpose. These properties include one geometric property (length) three topological properties (degree, closeness and betweenness) and one thematic property (road class) Two representative selective omission approaches (stroke based selection and mesh densitybased elimination) are implemented to generalize road network. For each approach, different properties are respectively used to determine the importance of individual roads. The road network of Hong Kong Island is used as study area for testing and two measures (similarity and connectivity) are employed to evaluate the selections. Results show that, when the stroke based selection approach is implemented, using length performs best in determining the importance of individual roads, and using betweenness or closeness performs well in preserving the connectivity of the retained network; when the mesh density based elimination approach is implemented, all these properties have quite similar selections.

title: Road selection based on Voronoi diagrams and "strokes" in map generalization
abstract: Road selection is a prerequisite to effective road network generalization. This article introduces a novel algorithm for road network selection in map generalization, which take four types of information into consideration: statistical, metric, topological, and thematic at three spatial scales: macro scale which describes the general pattern of networks, mezzo scale that handles relationships among road segments, and micro scale that focuses on individual roads' properties. A set of measures is selected to quantify these different types of information at various spatial levels. An algorithm is then developed with the extraction of these measures based on Voronoi diagrams and a perceptual grouping method called "stroke" The selection process consists of three consecutive steps: measuring network information based on Voronoi partitioning and stroke generation, selecting roads based on information extraction in the first step with strokes as selection unit, and assessing selection results. The algorithm is further tested with a real world dataset: road network map at 1:10,000 scale and its generalized version at 1:50,000 scale in Wuhan, China. The result reveals that the algorithm can produce reasonable selection results and thus has the potential to be adopted in road selection in map generalization.

title: A Method of Road Network Generalization Considering Stroke Properties of Road Object
abstract: For road feature selection in automated map generalization,the grade in semantic description,the length and shape in geometric characteristics,the distribution density and the accessibility in topological properties are considered.The traditional methods evaluate the road entity importance only based on single or part of these factors,and then the selection process completes by assigning an importance value.Due to the lack of effective approaches to retain the spatial distribution information of road network,it is easy to destroy the original spatial distribution characteristics.A new method based on the stroke properties is presented.Firstly,concatenating the road segments into strokes and evaluating the importance of every stroke;secondly,adding two types of constraint conditions to the process of roads selection by the importance of stroke,including level constraint conditions and spatial proximity constraint conditions.This approach considers not only topological,geometric,semantic but also the spatial distribution information of road network,and the experiments show that this method is convincing.

title: Using stroke and mesh to recognize building group patterns
abstract: Building patterns are crucial structures and should be preserved in map generalization. However, while building patterns are not explicitly described in building datasets, map readers perceive building patterns effortlessly. Hence, to better support map generalization, it is important to automatically recognize building patterns in such datasets. This paper first proposes an extended and integrated typology of different building patterns. Based on the typology, building patterns are recognized using stroke and mesh. This method first structures the proximity graph of buildings, and then introduces six constraints (distance, size, shape, orientation, elongation, and facing ratio) to refine the original proximity graph. Strokes and meshes are derived from the refined proximity graph, and are used to recognize linear and grid building patterns, respectively. The proposed method is tested in four regions that are representative of different pattern types. The recognition results are evaluated in an expert survey and compared with the minimum spanning tree method. Assessment suggests that the linear and grid patterns in suburban and rural areas are recognized with satisfying results.