![]() Table 17 Simple feature geometry types and WKT examples # WKT strings of the geometries shown in Fig. WKT is understood by many programs, including shapely, the sf package in R, and PostGIS. Well Known Text (WKT) is a plain text format for representing Simple Feature geometries, and part of the Simple Feature standard. The seventh type ( "Geometr圜ollection") is intended for any combination of the other six types. The multi-part versions are intended for geometries that contain more than one single-part geometry at once. Note that there are separate geometry types for points ( "Point"), lines ( "LineString"), and polygons ( "Polygon"), as well are their multi-part versions ( "MultiPoint", "MultiLineString", "MultiPolygon"), which sums up to the first six geometry types. The Simple Features defines at least 17 geometry types, but only seven types are commonly used in spatial analysis. The geometry type specification and functions may be familiar to you, since both the GEOS software and the Simple Features standard are widely used in open-source GIS software and in other programming languages, such as R’s sf package and the PostGIS spatial database. ![]() The GEOS software, and accordingly also the shapely package, implements the Simple Features specification of geometry types. 26 The Shapely User Manual # What are Simple Features? # 26) is quite detailed, and has many illustrations which are useful to understand how the various functions work.įig. 5), to provide more high-level methods to work with complete vector layers. These are accomlished with geopandas, which builds on top of pandas and shapely ( Fig. shapely does not include any functions to read or write geometries to or from files, or more complex data structures to represent collections of geometries with or without non-spatial attributes (i.e., vector layers). GEOS is used in numerous open-source programs other than shapely, such as QGIS, and interfaced in many programming languages, such as the sf package in R.īy design, shapely only deals with individual geometries, their creation, their derived properties, and spatial operation applied to them. Technically, shapely is a Python interface to the Geometry Engine Open Source (GEOS) software. ![]() shapely includes functions for creating geometries, as well as functions for applying geometric operations on geometries, such as calculating the centroid of a polygon. Shapely is a Python package for working with vector geometries, that is, the geometric component of vector layers (the other component being non-spatial attributes). Transforming points to a line (see Points to line (shapely)) Pairs of geometries (see New geometries 2), e.g., calculating the intersection (i.e., shared area) of two geometriesĮvaluating boolean relations between geometries (see Boolean functions), e.g., whether one geometry intersects with another or notĬalculating distances (see Distance (shapely)) Individual geometries (see New geometries 1), e.g., calculating geometry centroid Several methods of creating geometry objects (see Creating geometries)ĭerived properties, such as length and area (see Derived properties) Specifically, in this chapter we are going to cover the following topics: Therefore it is essential to be familiar with the shapely package, first, before moving on geopandas. The individual geometries within a vector layer are stored as shapely geometries. The next two chapters (see Vector layers (geopandas) and Geometric operations) deal with another package, called geopandas, which is used to represent an work with vector layers. In this chapter, we cover the shapely package, which is used to represent and work with individual vector geometries. In the next three chapters, we go through the basics of working with the first type, vector layers, in Python. Rasters-numeric arrays representing a regular grid over a rectangular area ![]() Vector layers-points, lines, and polygons, associated with attributes Spatial data can be divided into two categories: Now, we move on to the main topic of this book, working with spatial data. In the previous chapters we covered the basics of working with Python, starting with the working environment setup (see Setting up the environment) to advanced methods of working with tables ( Table reshaping and joins). ![]()
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