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Shapely and geometric objects

Sources:

These materials are partly based on Shapely-documentation and Westra E. (2013), Chapter 3.

Spatial data model

Spatial data model

Fundamental geometric objects that can be used in Python with Shapely.

The most fundamental geometric objects are Points, Lines and Polygons which are the basic ingredients when working with spatial data in vector format. Python has a specific module called Shapely for doing various geometric operations. There are many useful functionalities that you can do with Shapely such as:

  • Create a Line or Polygon from a Collection of Point -geometries
  • Calculate areas/length/bounds etc. of input geometries
  • Conduct geometric operations based on the input geometries such as Union, Difference, Distance etc.
  • Conduct spatial queries between geometries such as Intersects, Touches, Crosses, Within etc.

Geometric Objects consist of coordinate tuples where:

  • Point -object represents a single point in space. Points can be either two-dimensional (x, y) or three dimensional (x, y, z).
  • LineString -object (i.e. a line) represents a sequence of points joined together to form a line. Hence, a line consist of a list of at least two coordinate tuples
  • Polygon -object represents a filled area that consists of a list of at least three coordinate tuples that forms the outerior ring and a (possible) list of hole polygons.

It is also possible to have a collection of geometric objects (e.g. Polygons with multiple parts):

  • MultiPoint -object represents a collection of points and consists of a list of coordinate-tuples
  • MultiLineString -object represents a collection of lines and consists of a list of line-like sequences
  • MultiPolygon -object represents a collection of polygons that consists of a list of polygon-like sequences that construct from exterior ring and (possible) hole list tuples

Tuple

Tuple is a Python data structure that consists of a nuber of values separated by commas. Coordinate pairs are often represented as a tuple. For example:

(60.192059, 24.945831)

Tuples belong to sequence data types in Python. Other sequence data types are lists and ranges. Tuples have many similarities with lists and ranges, but they are often used for different purposes. The main difference between tuples and lists is that tuples are immutable, which means that the contents of a tuple cannot be altered (while lists are mutable; you can, for example, add and remove values from lists).

Point

Creating point is easy, you pass x and y coordinates into Point() -object (+ possibly also z -coordinate):

[1]:
# Import necessary geometric objects from shapely module
from shapely.geometry import Point, LineString, Polygon

# Create Point geometric object(s) with coordinates
point1 = Point(2.2, 4.2)
point2 = Point(7.2, -25.1)
point3 = Point(9.26, -2.456)
point3D = Point(9.26, -2.456, 0.57)

Let’s see what the variables look like:

[2]:
point1
[2]:
../../_images/notebooks_L1_geometric-objects_5_0.svg

Jupyter notebook is able to display the shape directly on the screen.

We can also print out the points to see the actual definition:

[3]:
print(point1)
print(point3D)
POINT (2.2 4.2)
POINT Z (9.26 -2.456 0.57)

3D-point can be recognized from the capital Z -letter in front of the coordinates.

Let’s also check the data type of a point:

[4]:
type(point1)
[4]:
shapely.geometry.point.Point

We can see that the type of the point is shapely’s Point which is represented in a specific format that is based on GEOS C++ library that is one of the standard libraries in GIS. It runs under the hood e.g. in QGIS.

We can also access the geometry type of the object using Point.geom_type:

[5]:
point1.geom_type
[5]:
'Point'

Point attributes and functions

Points and other shapely objects have useful built-in attributes and methods. One of the most useful ones are the ability to extract the coordinates of a Point and calculate the Euclidian distance between points.

Extracting the coordinates of a Point can be done in a couple of different ways:

[6]:
# Get the coordinates
point_coords = point1.coords
print(point_coords)
<shapely.coords.CoordinateSequence object at 0x000002E363D8BF98>
[7]:
# What is the data type?
type(point_coords)
[7]:
shapely.coords.CoordinateSequence

As we can see, the data type of our point_coords variable is a Shapely CoordinateSequence.

Let’s see how we can get out the actual coordinates from this object:

[8]:
# Get x and y coordinates
xy = point_coords.xy

# Get only x coordinates of Point1
x = point1.x

# Whatabout y coordinate?
y = point1.y

Print out the coordinates:

[9]:
print("xy:", xy, "\n")
print("x:", x, "\n")
print("y:", y)
xy: (array('d', [2.2]), array('d', [4.2]))

x: 2.2

y: 4.2

As we can see from above the xy -variable contains a tuple where x and y coordinates are stored inside numpy arrays. Using the attributes point1.x and point1.y it is possible to get the coordinates directly as plain decimal numbers.

It is also possible to calculate the distance between two objects using shapely. The returned distance is based on the projection of the points (e.g. degrees in WGS84, meters in UTM).

Let’s calculate the distance between point1 and point2:

[10]:
# Calculate the distance between point1 and point2
point_dist = point1.distance(point2)

print("Distance between the points is {0:.2f} decimal degrees".format(point_dist))
Distance between the points is 29.72 decimal degrees

LineString

Creating LineString -objects is fairly similar to creating Shapely Points.

Now instead using a single coordinate-tuple we can construct the line using either a list of shapely Point -objects or pass the points as coordinate-tuples:

[11]:
# Create a LineString from our Point objects
line = LineString([point1, point2, point3])

# It is also possible to produce the same outcome using coordinate tuples
line2 = LineString([(2.2, 4.2), (7.2, -25.1), (9.26, -2.456)])

Let’s see how our line looks like:

[12]:
line
[12]:
../../_images/notebooks_L1_geometric-objects_24_0.svg
[13]:
print("line: \n", line, "\n")
print("line2: \n", line2, "\n")
line:
 LINESTRING (2.2 4.2, 7.2 -25.1, 9.26 -2.456)

line2:
 LINESTRING (2.2 4.2, 7.2 -25.1, 9.26 -2.456)

As we can see from above, the line -variable constitutes of multiple coordinate-pairs.

Check also the data type:

[14]:
print("Object data type:", type(line))
print("Geometry type as text:", line.geom_type)
Object data type: <class 'shapely.geometry.linestring.LineString'>
Geometry type as text: LineString

LineString attributes and functions

LineString -object has many useful built-in attributes and functionalities. It is for instance possible to extract the coordinates or the length of a LineString (line), calculate the centroid of the line, create points along the line at specific distance, calculate the closest distance from a line to specified Point and simplify the geometry. See full list of functionalities from Shapely documentation. Here, we go through a few of them.

We can extract the coordinates of a LineString similarly as with Point

[15]:
# Get x and y coordinates of the line
lxy = line.xy
[16]:
print(lxy)
(array('d', [2.2, 7.2, 9.26]), array('d', [4.2, -25.1, -2.456]))

As we can see, the coordinates are again stored as a numpy arrays where first array includes all x-coordinates and the second one all the y-coordinates respectively.

We can extract only x or y coordinates by referring to those arrays using indices (same way you would access values in a list):

[17]:
# Extract x coordinates
line_xcoords = lxy[0]

# Extract y coordinates straight from the LineObject by referring to a array at index 1
line_ycoords = line.xy[1]
[18]:
print('line_x:\n', line_xcoords, '\n')

print('line_y:\n', line_ycoords)
line_x:
 array('d', [2.2, 7.2, 9.26])

line_y:
 array('d', [4.2, -25.1, -2.456])

It is possible to retrieve specific attributes such as lenght of the line and center of the line (centroid) straight from the LineString object itself:

[19]:
# Get the lenght of the line
l_length = line.length

# Get the centroid of the line
l_centroid = line.centroid
[20]:
# Print the outputs
print("Length of our line: {0:.2f}".format(l_length))
print("Centroid of our line: ", l_centroid)
print("Type of the centroid:", type(l_centroid))
Length of our line: 52.46
Centroid of our line:  POINT (6.229961354035622 -11.89241115757239)
Type of the centroid: <class 'shapely.geometry.point.Point'>

Nice! These are already fairly useful information for many different GIS tasks, and we didn’t even calculate anything yet! These attributes are built-in in every LineString object that is created.

Notice that the centroid that is returned is Point -object that has its own functions as was described earlier.

Polygon

Creating a Polygon -object continues the same logic of how Point and LineString were created but Polygon object only accepts a sequence of coordinates as input.

Polygon needs at least three coordinate-tuples (that basically forms a triangle):

[21]:
# Create a Polygon from the coordinates
poly = Polygon([(2.2, 4.2), (7.2, -25.1), (9.26, -2.456)])

# It is also possible to produce the same outcome using a list of lists which contain the point coordinates.
# We can do this using the point objects we created before and a list comprehension:
# --> here, we pass a list of lists as input when creating the Polygon (the linst comprehension generates this list: [[2.2, 4.2], [7.2, -25.1], [9.26, -2.456]]):
poly2 = Polygon([[p.x, p.y] for p in [point1, point2, point3]])

Let’s see how our Polygon looks like

[22]:
poly
[22]:
../../_images/notebooks_L1_geometric-objects_42_0.svg
[23]:
print('poly:', poly)
print('poly2:', poly2)
poly: POLYGON ((2.2 4.2, 7.2 -25.1, 9.26 -2.456, 2.2 4.2))
poly2: POLYGON ((2.2 4.2, 7.2 -25.1, 9.26 -2.456, 2.2 4.2))

Notice that Polygon representation has double parentheses around the coordinates (i.e. POLYGON ((<values in here>)) ). This is because Polygon can also have holes inside of it.

Check also the data type:

[24]:
print("Object data type:", type(poly))
print("Geometry type as text:", poly.geom_type)
Object data type: <class 'shapely.geometry.polygon.Polygon'>
Geometry type as text: Polygon
[25]:
# Check the help for Polygon objects:
#help(Polygon)

As the help of Polygon -object tells, a Polygon can be constructed using exterior coordinates and interior coordinates (optional) where the interior coordinates creates a hole inside the Polygon:

Help on Polygon in module shapely.geometry.polygon object:
     class Polygon(shapely.geometry.base.BaseGeometry)
      |  A two-dimensional figure bounded by a linear ring
      |
      |  A polygon has a non-zero area. It may have one or more negative-space
      |  "holes" which are also bounded by linear rings. If any rings cross each
      |  other, the feature is invalid and operations on it may fail.
      |
      |  Attributes
      |  ----------
      |  exterior : LinearRing
      |      The ring which bounds the positive space of the polygon.
      |  interiors : sequence
      |      A sequence of rings which bound all existing holes.

Let’s see how we can create a Polygon with a hole inside; lLet’s create a bounding box of the world and make a whole in it:

[26]:
# First we define our exterior
world_exterior = [(-180, 90), (-180, -90), (180, -90), (180, 90)]

# Let's create a single big hole where we leave ten decimal degrees at the boundaries of the world
# Notice: there could be multiple holes, thus we need to provide a list of holes
hole = [[(-170, 80), (-170, -80), (170, -80), (170, 80)]]
[27]:
# World without a hole
world = Polygon(shell=world_exterior)

# Now we can construct our Polygon with the hole inside
world_has_a_hole = Polygon(shell=world_exterior, holes=hole)

Let’s see what we have now:

[28]:
world_has_a_hole
[28]:
../../_images/notebooks_L1_geometric-objects_54_0.svg
[29]:
print('world:', world)
print('world_has_a_hole:', world_has_a_hole)
print('type:', type(world_has_a_hole))
world: POLYGON ((-180 90, -180 -90, 180 -90, 180 90, -180 90))
world_has_a_hole: POLYGON ((-180 90, -180 -90, 180 -90, 180 90, -180 90), (-170 80, -170 -80, 170 -80, 170 80, -170 80))
type: <class 'shapely.geometry.polygon.Polygon'>

As we can see the Polygon has now two different tuples of coordinates. The first one represents the outerior and the second one represents the hole inside of the Polygon.

Polygon attributes and functions

We can again access different attributes directly from the Polygon object itself that can be really useful for many analyses, such as area, centroid, bounding box, exterior, and exterior-length. See a full list of methods in the Shapely User Manual.

Here, we can see a few of the available attributes and how to access them:

[30]:
# Get the centroid of the Polygon
world_centroid = world.centroid

# Get the area of the Polygon
world_area = world.area

# Get the bounds of the Polygon (i.e. bounding box)
world_bbox = world.bounds

# Get the exterior of the Polygon
world_ext = world.exterior

# Get the length of the exterior
world_ext_length = world_ext.length
[31]:
# Print the outputs
print("Poly centroid: ", world_centroid)
print("Poly Area: ", world_area)
print("Poly Bounding Box: ", world_bbox)
print("Poly Exterior: ", world_ext)
print("Poly Exterior Length: ", world_ext_length)
Poly centroid:  POINT (-0 -0)
Poly Area:  64800.0
Poly Bounding Box:  (-180.0, -90.0, 180.0, 90.0)
Poly Exterior:  LINEARRING (-180 90, -180 -90, 180 -90, 180 90, -180 90)
Poly Exterior Length:  1080.0

As we can see above, it is again fairly straightforward to access different attributes from the Polygon -object. Notice, that the extrerior lenght is given here with decimal degrees because we passed latitude and longitude coordinates into our Polygon.

Geometry collections (optional)

In some occassions it is useful to store e.g. multiple lines or polygons under a single feature (i.e. a single row in a Shapefile represents more than one line or polygon object). Collections of points are implemented by using a MultiPoint -object, collections of curves by using a MultiLineString -object, and collections of surfaces by a MultiPolygon -object. These collections are not computationally significant, but are useful for modeling certain kinds of features. A Y-shaped line feature (such as road), or multiple polygons (e.g. islands on a like), can be presented nicely as a whole by a using MultiLineString or MultiPolygon accordingly. Creating and visualizing a minimum bounding box e.g. around your data points is a really useful function for many purposes (e.g. trying to understand the extent of your data), here we demonstrate how to create one using Shapely.

Geometry collections can be constructed in a following manner:

[32]:
# Import collections of geometric objects + bounding box
from shapely.geometry import MultiPoint, MultiLineString, MultiPolygon, box

# Create a MultiPoint object of our points 1,2 and 3
multi_point = MultiPoint([point1, point2, point3])

# It is also possible to pass coordinate tuples inside
multi_point2 = MultiPoint([(2.2, 4.2), (7.2, -25.1), (9.26, -2.456)])

# We can also create a MultiLineString with two lines
line1 = LineString([point1, point2])
line2 = LineString([point2, point3])
multi_line = MultiLineString([line1, line2])

# MultiPolygon can be done in a similar manner
# Let's divide our world into western and eastern hemispheres with a hole on the western hemisphere
# --------------------------------------------------------------------------------------------------

# Let's create the exterior of the western part of the world
west_exterior = [(-180, 90), (-180, -90), (0, -90), (0, 90)]

# Let's create a hole --> remember there can be multiple holes, thus we need to have a list of hole(s).
# Here we have just one.
west_hole = [[(-170, 80), (-170, -80), (-10, -80), (-10, 80)]]

# Create the Polygon
west_poly = Polygon(shell=west_exterior, holes=west_hole)

# Let's create the Polygon of our Eastern hemisphere polygon using bounding box
# For bounding box we need to specify the lower-left corner coordinates and upper-right coordinates
min_x, min_y = 0, -90
max_x, max_y = 180, 90

# Create the polygon using box() function
east_poly_box = box(minx=min_x, miny=min_y, maxx=max_x, maxy=max_y)

# Let's create our MultiPolygon. We can pass multiple Polygon -objects into our MultiPolygon as a list
multi_poly = MultiPolygon([west_poly, east_poly_box])

# Print outputs
print("MultiPoint:", multi_point)
print("MultiLine: ", multi_line)
print("Bounding box: ", east_poly_box)
print("MultiPoly: ", multi_poly)
MultiPoint: MULTIPOINT (2.2 4.2, 7.2 -25.1, 9.26 -2.456)
MultiLine:  MULTILINESTRING ((2.2 4.2, 7.2 -25.1), (7.2 -25.1, 9.26 -2.456))
Bounding box:  POLYGON ((180 -90, 180 90, 0 90, 0 -90, 180 -90))
MultiPoly:  MULTIPOLYGON (((-180 90, -180 -90, 0 -90, 0 90, -180 90), (-170 80, -170 -80, -10 -80, -10 80, -170 80)), ((180 -90, 180 90, 0 90, 0 -90, 180 -90)))

Check the shapes:

[33]:
multi_point
[33]:
../../_images/notebooks_L1_geometric-objects_64_0.svg
[34]:
multi_line
[34]:
../../_images/notebooks_L1_geometric-objects_65_0.svg
[35]:
west_poly
[35]:
../../_images/notebooks_L1_geometric-objects_66_0.svg
[36]:
east_poly_box
[36]:
../../_images/notebooks_L1_geometric-objects_67_0.svg
[37]:
multi_poly
[37]:
../../_images/notebooks_L1_geometric-objects_68_0.svg

We can see that the outputs are similar to the basic geometric objects that we created previously but now these objects contain multiple features of those points, lines or polygons.

Geometry collection -objects’ attributes and functions

Convex Hull:

[38]:
# Convex Hull of our MultiPoint --> https://en.wikipedia.org/wiki/Convex_hull
convex = multi_point.convex_hull
[39]:
print("Convex hull of the points: ", convex)
convex
Convex hull of the points:  POLYGON ((7.2 -25.1, 2.2 4.2, 9.26 -2.456, 7.2 -25.1))
[39]:
../../_images/notebooks_L1_geometric-objects_71_1.svg

Other useful attributes of geometry collections:

[40]:
# How many lines do we have inside our MultiLineString?
lines_count = len(multi_line)

# Print output:
print("Number of lines in MultiLineString:", lines_count)
Number of lines in MultiLineString: 2
[41]:
# Let's calculate the area of our MultiPolygon
multi_poly_area = multi_poly.area

We can also access different items inside our geometry collections. We can e.g. access a single polygon from our MultiPolygon -object by referring to the index:

[42]:
# Let's calculate the area of our Western hemisphere (with a hole) which is at index 0
west_area = multi_poly[0].area

# Print outputs:
print("Area of our MultiPolygon:", multi_poly_area)
print("Area of our Western Hemisphere polygon:", west_area)
Area of our MultiPolygon: 39200.0
Area of our Western Hemisphere polygon: 6800.0

From the above we can see that MultiPolygons have exactly the same attributes available as single geometric objects but now the information such as area calculates the area of ALL of the individual -objects combined.

We can also check if we have a “valid” MultiPolygon. MultiPolygon is thought as valid if the individual polygons does notintersect with each other. Here, because the polygons have a common 0-meridian, we should NOT have a valid polygon. We can check the validity of an object from the is_valid -attribute that tells if the polygons or lines intersect with each other. This can be really useful information when trying to find topological errors from your data:

[43]:
valid = multi_poly.is_valid
print("Is polygon valid?: ", valid)
Is polygon valid?:  False