SAGE, 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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CHAPTER 9 SPATIAL PATTERN AND ANALYSIS
CHAPTER 10 NETWORK ANALYSIS
CHAPTER 11 SPATIAL OPTIMIZATION
CHAPTER 12 HEURISTIC SEARCH ALGORITHMS
APPENDIX B GDALOGR AND PYSAL
APPENDIX C CODE LIST
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__main__ __name__ adjacent append calculate called chapter child nodes circle code in Listing compute coordinates create data set data structure data1 DCEL dist edges elif endpoints entries example extent Figure Floyd–Warshall algorithm GDAL greedy algorithm half-edges half-line implement import numpy indexing input insert interpolation intersection point inverse distance weighted iteration k-D tree K-function kriging latitude leaf node line segments list comprehension Matplotlib matrix MBRs median method Mollweide projection Moran’s nearest neighbor distance number of points NumPy numpy as np objective function optimal solution Output overlay p-median problem point import point quadtree polygon PR k-D tree Python program quadrants quadtree R-tree random range query range(n region result Robinson projection root semivariance shapefile shortest path simulated annealing specifically update variable vertices weight Write a Python xmax xmin ymax ymin