GIS AlgorithmsSAGE, 09/11/2015 - 336 páginas Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises. Using Python code throughout, Xiao breaks the subject down into three fundamental areas:
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Palavras e frases frequentes
adjacent branch calculate central meridian chapter child nodes circle code in Listing compute create data set data structure DCEL discussed dist edges endpoints example Figure fºr frºm function called GDAL greedy algorithm halfedges halfline implement important indexing input insert interpolation intersection point inverse distance weighted iteration Kfunction kriging lag distance latitude leaf node line segments list comprehension Matplotlib matrix MBRs Mollweide projection Moran’s nearest neighbor distance node line number of points NumPy objective function ºil optimal solution overlay pairs partitioning pattern pmedian problem pºint point quadtree polygon PR kD tree PySAL Python program quadrants quadtree random range query raster rectangle result Robinson projection root Rtree running search algorithm semivariance shapefile shortest path simple simulated annealing spatial data spatial indexing specifically variable vertices weight Write a Python