Simple features or simple feature access refers to a formal standard that describes how objects in the real world can be represented in computers, with emphasis on the spatial geometry of these objects. It also describes how such objects can be stored in and retrieved from databases, and which geometrical operations should be defined for them.
The standard is widely implemented in spatial databases (such as PostGIS), commercial GIS (e.g., ESRI ArcGIS), forms the vector data basis for libraries such as GDAL, and a subset of simple features forms the GeoJSON standard.
R has well-supported classes for storing spatial data (sp) and interfacing to the above mentioned environments (rgdal, rgeos), but has so far lacked a complete implementation of simple features, making conversions at times convoluted, inefficient or incomplete. The package sf tries to fill this gap, and aims at succeeding sp in the long term.
A feature is thought of as a thing, or an object in the real world, examples would be a building or a tree. As is the case with objects, they often consist of other objects. This is the case with features too: a set of features can form a single feature. A forest stand can be a feature, a forest can be a feature, a city can be a feature. A satellite image pixel can be a feature, a complete image can be a feature too.
Features have a geometry describing where on Earth the feature is located, and they have attributes, which describe other properties. The geometry of a tree can be the delineation of its crown, of its stem, or the point indicating its centre. Other properties may include its height, color, diameter at breast height at a particular date, and so on.
The standard says: “A simple feature is defined by the OpenGIS Abstract specification to have both spatial and non-spatial attributes. Spatial attributes are geometry valued, and simple features are based on 2D geometry with linear interpolation between vertices.” We will see soon that the same standard will extend its coverage beyond 2D and beyond linear interpolation. Here, we take simple features as the data structures and operations described in the standard.
All geometries are composed of points. Points are coordinates in a 2-, 3- or 4-dimensional space. All points in a geometry have the same dimensionality. In addition to X and Y coordinates, there are two optional additional dimensions:
The four possible cases then are:
The following seven simple feature types are the most common, and are for instance the only ones used for GeoJSON:
||zero-dimensional geometry containing a single point|
||sequence of points connected by straight, non-self intersecting line pieces; one-dimensional geometry|
||geometry with a positive area (two-dimensional); sequence of points form a closed, non-self intersecting ring; the first ring denotes the exterior ring, zero or more subsequent rings denote holes in this exterior ring|
||set of points; a MULTIPOINT is simple if no two Points in the MULTIPOINT are equal|
||set of linestrings|
||set of polygons|
||set of geometries of any type except GEOMETRYCOLLECTION|
Each of the geometry types can also be a (typed) empty set, containing zero coordinates (for
POINT the standard is not clear how to represent the empty geometry). Empty geometries can be thought of being the analogue to missing (
NA) attributes, NULL values or empty lists.
The remaining geometries 10 are more rare, but increasingly find implementations:
||The CIRCULARSTRING is the basic curve type, similar to a LINESTRING in the linear world. A single segment requires three points, the start and end points (first and third) and any other point on the arc. The exception to this is for a closed circle, where the start and end points are the same. In this case the second point MUST be the center of the arc, ie the opposite side of the circle. To chain arcs together, the last point of the previous arc becomes the first point of the next arc, just like in LINESTRING. This means that a valid circular string must have an odd number of points greated than 1.|
||A compound curve is a single, continuous curve that has both curved (circular) segments and linear segments. That means that in addition to having well-formed components, the end point of every component (except the last) must be coincident with the start point of the following component.|
||Example compound curve in a curve polygon: CURVEPOLYGON(COMPOUNDCURVE(CIRCULARSTRING(0 0,2 0, 2 1, 2 3, 4 3),(4 3, 4 5, 1 4, 0 0)), CIRCULARSTRING(1.7 1, 1.4 0.4, 1.6 0.4, 1.6 0.5, 1.7 1) )|
||A MultiCurve is a 1-dimensional GeometryCollection whose elements are Curves, it can include linear strings, circular strings or compound strings.|
||A MultiSurface is a 2-dimensional GeometryCollection whose elements are Surfaces, all using coordinates from the same coordinate reference system.|
||A Curve is a 1-dimensional geometric object usually stored as a sequence of Points, with the subtype of Curve specifying the form of the interpolation between Points|
||A Surface is a 2-dimensional geometric object|
||A PolyhedralSurface is a contiguous collection of polygons, which share common boundary segments|
||A TIN (triangulated irregular network) is a PolyhedralSurface consisting only of Triangle patches.|
||A Triangle is a polygon with 3 distinct, non-collinear vertices and no interior boundary|
CURVEPOLYGON are not described in the SFA standard, but in the SQL-MM part 3 standard. The descriptions above were copied from the postgis docs.
Numbers of coordinates only have a meaning as places on the Earth when their coordinate reference system is known; this may be an ellipsoidal reference system such as WGS84, a flat, two-dimensional coordinate reference system such as a UTM zone or Web Mercator, or a three-dimensional reference system. Similarly, M-coordinates need an attribute reference system, e.g. a measurement unit.
sf represents simple features as native R objects. All functions and methods in
sf are prefixed by
st_, so that they are easily found by command-line completion. Simple features are implemented as R native data, using simple data structures (S3 classes, lists, matrix, vector). Typical use involves reading, manipulating and writing of sets of features, with attributes and geometries.
As attributes are typically stored in
data.frame objects (or the very similar
tbl_df), we will also store feature geometries in in a
data.frame column. Since geometries are not single-valued, they are put in a list-column, a list of length equal to the number of records in the
data.frame, with each list element holding the simple feature geometry of that feature. The three classes used to represent simple features are:
sf, the table (
data.frame) with feature attributes and feature geometries, which contains
sfc, the list-column with the geometries for each feature (record), which is composed of
sfg, the feature geometry of an individual simple feature.
We will now discuss each of these three classes.
As we usually do not work with geometries of single simple features, but with datasets consisting of sets of features with attributes, the two are put together in
sf (simple feature) objects. The following command reads the
nc dataset from a file that is contained in the
library(sf) ## Linking to GEOS 3.5.0, GDAL 2.1.0 filename <- system.file("gpkg/nc.gpkg", package="sf") nc <- st_read(filename, "nc.gpkg", crs = 4267) ## Reading layer nc.gpkg from data source /home/edzer/R/x86_64-pc-linux-gnu-library/3.3/sf/gpkg/nc.gpkg using driver "GPKG" ## features: 100 ## fields: 14 ## proj4string: +proj=longlat +datum=NAD27 +no_defs
The short report printed gives the file name, the driver (GPKG: GeoPackage), mentions that there are 100 features (records) and 14 fields (attributes). This object is of class
class(nc) ##  "sf" "data.frame"
meaning it extends (and “is” a)
data.frame, but with a single list-column with geometries, which is held in the column with name
attr(nc, "sf_column") ##  "geom"
If we print the first three features, we see their attribute values and an abridged version of the geometry
print(nc[9:15], n = 3)
which would give the following output:
In the output we see:
data.framerow, consisting of attributes and geometry
sfc, which is a column in the
sf objects are
methods(class = "sf") ##  coerce initialize plot print [ ##  show slotsFromS3 st_bbox st_crs<- st_crs ##  st_drop_zm st_geometry<- st_geometry st_transform ## see '?methods' for accessing help and source code
It is also possible to create
data.frame objects with geometry list-columns that are not of class
sf, e.g. by
nc.no_sf <- as.data.frame(nc) class(nc.no_sf) ##  "data.frame"
However, such objects:
The column in the
sf data.frame that contains the geometries is a list, of class
sfc. We can retrieve the geometry list-column in this case by
nc[], but the more general way uses
(nc.geom <- st_geometry(nc)) ## Geometry set for 100 features ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 ## epsg (SRID): 4267 ## proj4string: +proj=longlat +datum=NAD27 +no_defs ## First 5 geometries: ## MULTIPOLYGON(((-81.4727554321289 36.23435592651... ## MULTIPOLYGON(((-81.2398910522461 36.36536407470... ## MULTIPOLYGON(((-80.4563446044922 36.24255752563... ## MULTIPOLYGON(((-76.0089721679688 36.31959533691... ## MULTIPOLYGON(((-77.2176666259766 36.24098205566...
Geometries are printed in abbreviated form, but we can can view a complete geometry by selecting it, e.g. the first one by
nc.geom[] ## MULTIPOLYGON(((-81.4727554321289 36.2343559265137, -81.5408401489258 36.2725067138672, -81.5619812011719 36.2735939025879, -81.6330642700195 36.3406867980957, -81.7410736083984 36.3917846679688, -81.6982803344727 36.4717788696289, -81.7027969360352 36.5193405151367, -81.6699981689453 36.5896492004395, -81.3452987670898 36.5728645324707, -81.347541809082 36.537914276123, -81.3247756958008 36.5136795043945, -81.3133239746094 36.4806976318359, -81.2662353515625 36.4372062683105, -81.2628402709961 36.4050407409668, -81.2406921386719 36.3794174194336, -81.2398910522461 36.365364074707, -81.2642440795898 36.3524131774902, -81.3289947509766 36.3635025024414, -81.3613739013672 36.3531608581543, -81.3656921386719 36.3390502929688, -81.354133605957 36.2997169494629, -81.3674545288086 36.2786979675293, -81.4063873291016 36.2850532531738, -81.4123306274414 36.2672920227051, -81.431037902832 36.2607192993164, -81.4528884887695 36.2395858764648, -81.4727554321289 36.2343559265137)))
The way this is printed is called well-known text, and is part of the standards. The word
MULTIPOLYGON is followed by three parenthesis, because it can consist of multiple polygons, in the form of
POL1 might consist of an exterior ring and zero or more interior rings, as of
(EXT1,HOLE1,HOLE2). Sets of coordinates are held together with parenthesis, so we get
crds_ is a comma-separated set of coordinates of a ring. This leads to the case above, where
MULTIPOLYGON(((crds_ext))) refers to the exterior ring (1), without holes (2), of the first polygon (3) - hence three parentheses.
We can see there is a single polygon with no rings:
par(mar = rep(0,4)) plot(nc) plot(nc[1,], col = 'red', add = TRUE)