Readable information measurement model based on map symbol similarity

Ensuring the readability of Mobile terminal and Internet online map was one of the important practical problem in the field of cartography today. Based on the map information theory， the amount of readable map information was defined to evaluate the map readability. The amount of readable map information was defined as the difference between the map input information and the map confusion information. Map confusion information was based on the probability of misinterpreting map symbols， which was converted through map symbol similarity. The similarity graph distance model and RGB-weighted Euclidean color distance were used to construct a map chunk similarity measurement model based on visual variables， the map chunk similarity was converted into the probability of map symbol interpretation error to measure the amount of map confusion information based on the conditional probability. The experiments were designed to verify the feasibility of the model， and the analysis was conducted using the thermal map of taxi trajectory density in the main urban area of Beijing and the land use map of a certain remote sensing image as examples. The experimental results showed that， the amount of map confusion information modified by map complexity was close to the amount of map confusion information in map test statistics； The amount of correct interpretation information of map could be augmented by increasing the dimension of visual variables. The proposed method provides a new approach for map symbol design and quality assessment， which can effectively improve map readability by reducing symbol similarity.