Abstract: Public participatory map making is prone to visual problems such as visual coalescence, overcrowding, and crowdedness, which are only solved by automatic map generalization. Since both the original map scale and the target map scale are sometimes difficult to quantify accurately in the map, it is no longer applicable that the conventional map generalization method is based on the "original-target map scale" to judge whether or not the map generalization is needed. After visualizing the vector data, it will produce visual coalescence, the more noticeable the coalescence is, the worse the map representation is, and the more comprehensive the generalization demand is. Based on this rule, this paper proposes a quantitative description of visual coalescence and judges whether or not map generalization is needed. First of all, from the perspective of human visual perception, we designed a quantitative indicator of visual coalescence of vector curves-visual clarity. Then, based on the "pyramid" scale space, the clarity of the curve expressed in multiple scales is calculated, and the change function of the clarity is fitted. The experiment applies this function to web map generalization decisions for VGI geographic data. Experimental results show that this method can accurately determine whether each vector curve needs to be generalized, and can effectively solve the visual problems brought by the heterogeneity of geographic scale. At the same time, the clarity change function expands the scale description of the curve from a static value to a continuous function, which is expected to better support multi-scale spatial data processing and web map generalization.

Key words: web map generalization, coalescence, visual clarity, clarity function, spatial granularit