TROPICAL GEOGRAPHY ›› 2018, Vol. 38 ›› Issue (6): 751-758.doi: 10.13284/j.cnki.rddl.003087

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Spatial Rough Association Rules Method from the Perspective of Urban Computing

LIAO Weihuaa, NIE Xinb   

  1. (a. College of Mathematics and Information Science; b. School of Public Administration, Guangxi University, Nanning 530004, China)
  • Online:2018-11-30 Published:2018-11-30


With the rapid development of E-commerce and smart city in china, it produces the explosive growth of the urban data. These data is an important data source for urban calculation. This study introduced rough sets to compute urban service entities with spatial reference coordinates. This study has done following studies in this paper. 1) It got distance near table in a given distance value for every spatial urban service entities, then related spatial entities table with distance near table. Then it got every spatial near entities of urban service and spatial urban service transaction database. 2) It got information decision table from spatial transaction database using SQL technology. It used every urban service name as attribute name in decision table. It used any service as decision attribute, others as conditional attribute, then it can get spatial association rules for this decision service and others’ service. 3) It got attribute core and attribute reduction of spatial decision attribute using rough sets concept and method, then got spatial urban service association rules based on its reduction. The main contributions of this study are as follows: 1) By introducing rough sets, the complex geospatial association problem is transformed into information decision problem, and the spatial association and other topological relations between urban entities are calculated in the information decision table. The calculation process and results can mine the spatial aggregation and association problems between urban industries. 2) Attribute kernel can reduce the dimension of high-dimensional spatial data and find the important factors affecting spatial association. 3) Broaden the theoretical methods of urban computing and the application of rough set method. Through Nanning City service industry data from Python crawling to verify the method, the results of the calculation to the mature Apriori algorithm results, as well as the actual situation of Nanning City service industry spatial association is basically consistent, proving the feasibility and correctness of the rough spatial association method.

Key words: spatial association, rough set, urban computing, urban service, Nanning City