Tree-based approach for exploring marine spatial patterns with raster datasets

PLoS One. 2017 May 16;12(5):e0177438. doi: 10.1371/journal.pone.0177438. eCollection 2017.


From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the "Spatial node→Pattern node" from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

MeSH terms

  • Algorithms
  • Data Mining / methods*
  • Databases, Factual*
  • Models, Theoretical*
  • Oceans and Seas*
  • Pacific Ocean
  • Spatial Analysis

Grant support

This study has been funded by National Natural Science Foundation of China with No.41671401 and No. 41371385, by Youth Innovation Promotion Association of Chinese Academy of Science with No.2013113, and by National key research and development program of China with No. 2016YFA0600304. CX received all funding. There was no additional external funding received for this study.