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:  • Geometric Algorithms  • Spatial Indexing  • Spatial Analysis and Modelling With its comprehensive coverage of the many algorithms involved, GIS Algorithms is a key new textbook in this complex and critical area of geography.

Interpolation

Interpolation

The overwhelming thrust of geospatial data in the age of “big data” gives us a false sense that we have obtained complete coverage of data observation around the world. In other words, we may erroneously think that we know everything about everywhere on the earth’s surface. A proof: look at maps.google.com and choose the satellite view. We seem to be able to see everywhere on the map with a bird’s-eye view (Figure 8.1). On the dark side of this, we have heard stories about how an advanced missile can hit almost anywhere on earth, with an accurate digital elevation model that guides the missile to hit the target.

Figure 8.1 Part of the Ohio State University campus shown in satellite view on Google Maps

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