Geo-parcel-based geographical thematic mapping using C5.0 decision tree: a case study of evaluating sugarcane planting suitability

Geographical thematic mapping based on spatial information can effectively support scientific decision-making in Geosciences. To obtain finer spatial decision information, this paper proposes a geo-parcel-based thematic mapping methodology for evaluating cash crop planting suitability using C5.0 decision tree (DT). In this study, geo-parcels are utilized as basic mapping units. Multi-source data are firstly employed to increase geo-parcel units’ attributes and a decision table then is constructed under a multi-attribute index system. Next, rules are mined using a C5.0 DT algorithm according to local geo-parcels in this decision table. Finally, rules are referred as thematic-distinguishing knowledge for inferential mapping in global geo-parcels. A case study of sugarcane planting suitability evaluation is conduct based on the proposed methodology. The experimental results showed that the cross-validation accuracy of the rules is 81.34% and the sum of the very suitable area and suitable area in the generated evaluation map is close to that of historical selected high-yield and high-sugar-content sugarcane bases, which indicated that the mapping result is in good agreement with the actual selection situation. These also demonstrate the effectiveness of our method and thus may be extended to other domains requiring fine geographical thematic mapping of cash crop planting suitability.

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