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A Feasible Method to Find Areas with Constraints Using Hierarchical Depth-First Clustering


Kwang-Su Yang, Ruixin Yang, and Menas Kafatos

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Abstract

This paper addresses a reliable, feasible method to find geographical areas with constraints using hierarchical depth-first clustering. The method involves multi-level hierarchical clustering with depth-first strategy, depending on whether the area of each cluster is satisfying given constraints. The attributes used in hierarchical clustering are coordinates of grid data points. The constraints are an average value range and the minimum size of area with a small proportion of missing data points. Convex hull and point-in-polygon algorithms are involved in examining the constraint satisfaction. The method is implemented for an Earth science data set, NDVI, for vegetation studies.


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