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書名： Python Geospatial Development（Second Edition）
作者名： Erik Westra
本章字?jǐn)?shù)： 258字
更新時(shí)間： 2021-08-20 16:55:19

Summary

In this chapter, we discussed many of the core concepts that underlie GIS development, looked briefly at the history of GIS, examined some of the more common GIS data formats, and got our hands dirty exploring US state maps downloaded from the US Census Bureau website. We have learned the following:

Locations are often, but not always, represented using coordinates
Calculating the distance between two points requires you to take into account the curvature of the earth's surface
You must be aware of the units used in geospatial data
Map projections represent the three-dimensional shape of the earth's surface as a two-dimensional map
There are three main classes of map projections: cylindrical, conic and azimuthal
Datums are mathematical models of the earth's shape
The three most common datums in use are called NAD 27, NAD 83, and WGS 84
Coordinate systems describe how coordinates relate to a given point on the earth's surface
Unprojected coordinate systems directly represent points on the earth's surface
Projected coordinate systems use a map projection to represent the earth as a two-dimensional Cartesian plane, onto which coordinates are then placed
Geospatial data can represent shapes in the form of points, linestrings, and polygons
There are a number of standard GIS data formats you might encounter. Some data formats work with raster data, while others use vector data
Using Python to manually perform various geospatial calculations on Shapefile data

In the next chapter, we will look in more detail at the various Python libraries which can be used for working with geospatial data.

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Python Geospatial Development（Second Edition）

Summary