Journal of Geo-information Science ›› 2018, Vol. 20 ›› Issue (1): 28-36.doi: 10.12082/dqxxkx.2018.170266

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The Discovery of Spatial Association Patterns of Resource and Environment Information Based on Grid Data

XU Zhen(), JING Yaodong, BI Rutian*(), GAO Yang, WANG Peng   

  1. College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, China
  • Received:2017-06-13 Revised:2017-09-23 Online:2018-01-20 Published:2018-01-20
  • Contact: BI Rutian;
  • Supported by:
    Foundation item: Public Welfare Profession Project of Ministry of Land and Resources of the People's Republic of China, No.201411007.


Spatial association patterns include location patterns of spatial association which emphasize on spatial data and structure patterns of the spatial association, which emphasize on attribute data. However, traditional methods were based on traditional spatial data and used spatial predicates as the logic in the process of mining. This would lead to the following problems: Firstly, it relied on the boundaries of spatial phenomenon and didn’t take account in the area of spatial phenomenon. Secondly, the results were restricted strongly by the table of spatial predicate built before data mining. Based on The Tobler’s First Law of Geography, this research proposed a new method of extracting spatial association patterns without using spatial predicate. According to specific data content and data format, this method converted spatial data into grid data which has the same spatial coordinate and the same size of each grid. Then, the method used a smooth moving-mask to get the transaction database from the grid data. Apriori algorithm without self-connection of attributes was adopted to explore the latent association patterns in transaction database. Finally, an experiment was conducted to verify the accuracy of this method. The experiment data included the data of coal mining area, land use data, water system data and terrain data in Changhe basin of Jincheng City in Shanxi Province. In the experiment, the error of grid transformation of each data layer was controlled within 5% and the accuracy of transaction was verified in co-location pattern. Grid transformation generated 28 434 grids and the size of each grid was 64 meters. After setting cultivated land as main factor, there were 38 310 records in transaction database. Through the study on some association patterns with higher confidence, it showed that the results were consistent with the prior knowledge related to cultivated land in ore-agricultural area. Therefore, this method can effectively extract the meaning association patterns and improve the interestingness of the results. This method improves the degree of freedom of the data mining by setting different sizes of the grid, main factors and mask sizes. Based on grid data instead of traditional spatial data, this method doesn’t rely on the boundaries of spatial phenomenon and takes into account the area factor.

Key words: spatial data mining, grid data, spatial association patterns, Apriori algorithm