Abstract
In the urban tourism and service industry, the POI data with coordinate and attribute information of the major map platforms constitute one of the important data sources of the urban tourism and service industry. In this paper, the spatial data transaction database under four distances was established based on the gate buffer of 3A and above scenic spots in Beijing. The Apriori algorithm was used to calculate the lifting degree to obtain the distance for mining the best association features of 3A, 4A and 5A scenic spots, and then the association features of the three scenic spots in different directions were analysed.
Highlights
With the acceleration of modernization process and the development of mobile portable devices, LBS acquired by mobile devices provides massive data, which provides a strong theoretical basis and data support for the mining of spatial knowledge and spatial relations hidden in spatial data
Spatial association rules
After the concept and algorithm of association rules [4] were proposed, scholars conducted a large number of researches, which mainly focused on the application field and the mining algorithm
Summary
With the acceleration of modernization process and the development of mobile portable devices, LBS acquired by mobile devices provides massive data, which provides a strong theoretical basis and data support for the mining of spatial knowledge and spatial relations hidden in spatial data. After the concept and algorithm of association rules [4] were proposed, scholars conducted a large number of researches, which mainly focused on the application field and the mining algorithm. There are few research examples of spatial association analysis of urban facilities, and the current research is mainly based on the fuzzy perspective to study the association characteristics of urban services. In this paper, using POI point data of urban public facilities and considering the influence of distance and orientation, the spatial association characteristics of facilities in urban scenic spots are analysed. The main research contents are as follows :(1) Calculate the average promotion degree and analyse the optimal distance for mining the optimal association characteristics in different levels of scenic spots. The main research contents are as follows :(1) Calculate the average promotion degree and analyse the optimal distance for mining the optimal association characteristics in different levels of scenic spots. (2) Calculate the similarities and differences of the association features of different levels of scenic spots in different directions and corresponding distances
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