How to calculate the area of a polygon on the earth's surface using python?
Question:
The title basically says it all. I need to calculate the area inside a polygon on the Earth’s surface using Python. Calculating area enclosed by arbitrary polygon on Earth's surface says something about it, but remains vague on the technical details:
If you want to do this with a more
“GIS” flavor, then you need to select
an unit-of-measure for your area and
find an appropriate projection that
preserves area (not all do). Since you
are talking about calculating an
arbitrary polygon, I would use
something like a Lambert Azimuthal
Equal Area projection. Set the
origin/center of the projection to be
the center of your polygon, project
the polygon to the new coordinate
system, then calculate the area using
standard planar techniques.
So, how do I do this in Python?
Answers:
The easiest way to do this (in my opinion), is to project things into (a very simple) equal-area projection and use one of the usual planar techniques for calculating area.
First off, I’m going to assume that a spherical earth is close enough for your purposes, if you’re asking this question. If not, then you need to reproject your data using an appropriate ellipsoid, in which case you’re going to want to use an actual projection library (everything uses proj4 behind the scenes, these days) such as the python bindings to GDAL/OGR or (the much more friendly) pyproj.
However, if you’re okay with a spherical earth, it quite simple to do this without any specialized libraries.
The simplest equal-area projection to calculate is a sinusoidal projection. Basically, you just multiply the latitude by the length of one degree of latitude, and the longitude by the length of a degree of latitude and the cosine of the latitude.
def reproject(latitude, longitude):
"""Returns the x & y coordinates in meters using a sinusoidal projection"""
from math import pi, cos, radians
earth_radius = 6371009 # in meters
lat_dist = pi * earth_radius / 180.0
y = [lat * lat_dist for lat in latitude]
x = [long * lat_dist * cos(radians(lat))
for lat, long in zip(latitude, longitude)]
return x, y
Okay… Now all we have to do is to calculate the area of an arbitrary polygon in a plane.
There are a number of ways to do this. I’m going to use what is probably the most common one here.
def area_of_polygon(x, y):
"""Calculates the area of an arbitrary polygon given its verticies"""
area = 0.0
for i in range(-1, len(x)-1):
area += x[i] * (y[i+1] - y[i-1])
return abs(area) / 2.0
Hopefully that will point you in the right direction, anyway…
Because the earth is a closed surface a closed polygon drawn on its surface creates TWO polygonal areas. You also need to define which one is inside and which is outside!
Most times people will be dealing with small polygons, and so it’s ‘obvious’ but once you have things the size of oceans or continents, you better make sure you get this the right way round.
Also, remember that lines can go from (-179,0) to (+179,0) in two different ways. One is very much longer than the other. Again, mostly you’ll make the assumption that this is a line that goes from (-179,0) to (-180,0) which is (+180,0) and then to (+179,0), but one day… it won’t.
Treating lat-long like a simple (x,y) coordinate system, or even neglecting the fact that any coordinate projection is going to have distortions and breaks, can make you fail big-time on spheres.
Let’s say you have a representation of the state of Colorado in GeoJSON format
{"type": "Polygon",
"coordinates": [[
[-102.05, 41.0],
[-102.05, 37.0],
[-109.05, 37.0],
[-109.05, 41.0]
]]}
All coordinates are longitude, latitude. You can use pyproj to project the coordinates and Shapely to find the area of any projected polygon:
co = {"type": "Polygon", "coordinates": [
[(-102.05, 41.0),
(-102.05, 37.0),
(-109.05, 37.0),
(-109.05, 41.0)]]}
lon, lat = zip(*co['coordinates'][0])
from pyproj import Proj
pa = Proj("+proj=aea +lat_1=37.0 +lat_2=41.0 +lat_0=39.0 +lon_0=-106.55")
That’s an equal area projection centered on and bracketing the area of interest. Now make new projected GeoJSON representation, turn into a Shapely geometric object, and take the area:
x, y = pa(lon, lat)
cop = {"type": "Polygon", "coordinates": [zip(x, y)]}
from shapely.geometry import shape
shape(cop).area # 268952044107.43506
It’s a very close approximation to the surveyed area. For more complex features, you’ll need to sample along the edges, between the vertices, to get accurate values. All caveats above about datelines, etc, apply. If you’re only interested in area, you can translate your feature away from the dateline before projecting.
A bit late perhaps, but here is a different method, using Girard’s theorem. It states that the area of a polygon of great circles is R**2 times the sum of the angles between the polygons minus (N-2)*pi where N is number of corners.
I thought this would be worth posting, since it doesn’t rely on any other libraries than numpy, and it is a quite different method than the others. Of course, this only works on a sphere, so there will be some inaccuracy when applying it to the Earth.
First, I define a function to compute the bearing angle from point 1 along a great circle to point 2:
import numpy as np
from numpy import cos, sin, arctan2
d2r = np.pi/180
def greatCircleBearing(lon1, lat1, lon2, lat2):
dLong = lon1 - lon2
s = cos(d2r*lat2)*sin(d2r*dLong)
c = cos(d2r*lat1)*sin(d2r*lat2) - sin(lat1*d2r)*cos(d2r*lat2)*cos(d2r*dLong)
return np.arctan2(s, c)
Now I can use this to find the angles, and then the area (In the following, lons and lats should of course be specified, and they should be in the right order. Also, the radius of the sphere should be specified.)
N = len(lons)
angles = np.empty(N)
for i in range(N):
phiB1, phiA, phiB2 = np.roll(lats, i)[:3]
LB1, LA, LB2 = np.roll(lons, i)[:3]
# calculate angle with north (eastward)
beta1 = greatCircleBearing(LA, phiA, LB1, phiB1)
beta2 = greatCircleBearing(LA, phiA, LB2, phiB2)
# calculate angle between the polygons and add to angle array
angles[i] = np.arccos(cos(-beta1)*cos(-beta2) + sin(-beta1)*sin(-beta2))
area = (sum(angles) - (N-2)*np.pi)*R**2
With the Colorado coordinates given in another reply, and with Earth radius 6371 km, I get that the area is 268930758560.74808
Or simply use a library: https://github.com/scisco/area
from area import area
>>> obj = {'type':'Polygon','coordinates':[[[-180,-90],[-180,90],[180,90],[180,-90],[-180,-90]]]}
>>> area(obj)
511207893395811.06
…returns the area in square meters.
Here is a solution that uses basemap, instead of pyproj and shapely, for the coordinate conversion. The idea is the same as suggested by @sgillies though. NOTE that I’ve added the 5th point so that the path is a closed loop.
import numpy
from mpl_toolkits.basemap import Basemap
coordinates=numpy.array([
[-102.05, 41.0],
[-102.05, 37.0],
[-109.05, 37.0],
[-109.05, 41.0],
[-102.05, 41.0]])
lats=coordinates[:,1]
lons=coordinates[:,0]
lat1=numpy.min(lats)
lat2=numpy.max(lats)
lon1=numpy.min(lons)
lon2=numpy.max(lons)
bmap=Basemap(projection='cea',llcrnrlat=lat1,llcrnrlon=lon1,urcrnrlat=lat2,urcrnrlon=lon2)
xs,ys=bmap(lons,lats)
area=numpy.abs(0.5*numpy.sum(ys[:-1]*numpy.diff(xs)-xs[:-1]*numpy.diff(ys)))
area=area/1e6
print area
The result is 268993.609651 in km^2.
UPDATE: Basemap has been deprecated, so you may want to consider alternative solutions first.
You can compute the area directly on the sphere, instead of using an equal-area projection.
Moreover, according to this discussion, it seems that Girard’s theorem (sulkeh’s answer) does not give accurate results in certain cases, for example “the area enclosed by a 30º lune from pole to pole and bounded by the prime meridian and 30ºE” (see here).
A more precise solution would be to perform line integral directly on the sphere. The comparison below shows this method is more precise.
Like all other answers, I should mention the caveat that we assume a spherical earth, but I assume that for non-critical purposes this is enough.
Python implementation
Here is a Python 3 implementation which uses line integral and Green’s theorem:
def polygon_area(lats, lons, radius = 6378137):
"""
Computes area of spherical polygon, assuming spherical Earth.
Returns result in ratio of the sphere's area if the radius is specified.
Otherwise, in the units of provided radius.
lats and lons are in degrees.
"""
from numpy import arctan2, cos, sin, sqrt, pi, power, append, diff, deg2rad
lats = np.deg2rad(lats)
lons = np.deg2rad(lons)
# Line integral based on Green's Theorem, assumes spherical Earth
#close polygon
if lats[0]!=lats[-1]:
lats = append(lats, lats[0])
lons = append(lons, lons[0])
#colatitudes relative to (0,0)
a = sin(lats/2)**2 + cos(lats)* sin(lons/2)**2
colat = 2*arctan2( sqrt(a), sqrt(1-a) )
#azimuths relative to (0,0)
az = arctan2(cos(lats) * sin(lons), sin(lats)) % (2*pi)
# Calculate diffs
# daz = diff(az) % (2*pi)
daz = diff(az)
daz = (daz + pi) % (2 * pi) - pi
deltas=diff(colat)/2
colat=colat[0:-1]+deltas
# Perform integral
integrands = (1-cos(colat)) * daz
# Integrate
area = abs(sum(integrands))/(4*pi)
area = min(area,1-area)
if radius is not None: #return in units of radius
return area * 4*pi*radius**2
else: #return in ratio of sphere total area
return area
I wrote a somewhat more explicit version (and with many more references and TODOs…) in the sphericalgeometry package there.
Numerical Comparison
Colorado will be the reference, since all previous answers were evaluated on its area. Its precise total area is 104,093.67 square miles (from the US Census Bureau, p. 89, see also here), or 269601367661 square meters. I found no source for the actual methodology of the USCB, but I assume it is based on summing actual measurements on ground, or precise computations using WGS84/EGM2008.
Method | Author | Result | Variation from ground truth
--------------------------------------------------------------------------------
Albers Equal Area | sgillies | 268952044107 | -0.24%
Sinusoidal | J. Kington | 268885360163 | -0.26%
Girard's theorem | sulkeh | 268930758560 | -0.25%
Equal Area Cylindrical | Jason | 268993609651 | -0.22%
Line integral | Yellows | 269397764066 | **-0.07%**
Conclusion: using direct integral is more precise.
Performance
I have not benchmarked the different methods, and comparing pure Python code with compiled PROJ projections would not be meaningful. Intuitively less computations are needed. On the other hand, trigonometric functions may be computationally intensive.
According to Yellows’ assertion, direct integral is more precise.
But Yellows use an earth radius = 6378 137m, which is the WGS-84 ellipsoid, semi-major axis, while Sulkeh use 6371 000 m.
Using a radius = 6378 137 m in the Sulkeh’ method, gives 269533625893 square meters.
Assuming that the true value of Colorado area (from the US Census Bureau) is 269601367661 square meters then the variation from the ground truth of Sulkeh’ method is : -0,025%, better than -0.07 with the Line integral method.
So Sulkeh’ proposal seems to be the more precise so far.
In order to be able to make a numerical comparison of the solutions, with the assumption of a spherical Earth, all calculations must use the same terrestrial radius.
Here is a Python 3 implementation where the function would take a list of tuple-pairs of lats and longs and would return the area enclosed in the projected polygon.It uses pyproj to project the coordinates and then Shapely to find the area of any projected polygon
def calc_area(lis_lats_lons):
import numpy as np
from pyproj import Proj
from shapely.geometry import shape
lons, lats = zip(*lis_lats_lons)
ll = list(set(lats))[::-1]
var = []
for i in range(len(ll)):
var.append('lat_' + str(i+1))
st = ""
for v, l in zip(var,ll):
st = st + str(v) + "=" + str(l) +" "+ "+"
st = st +"lat_0="+ str(np.mean(ll)) + " "+ "+" + "lon_0" +"=" + str(np.mean(lons))
tx = "+proj=aea +" + st
pa = Proj(tx)
x, y = pa(lons, lats)
cop = {"type": "Polygon", "coordinates": [zip(x, y)]}
return shape(cop).area
For a sample set of lats/longs, it gives an area value close to the surveyed approximation value
calc_area(lis_lats_lons = [(-102.05, 41.0),
(-102.05, 37.0),
(-109.05, 37.0),
(-109.05, 41.0)])
Which outputs an area of 268952044107.4342 Sq. Mts.
I know that answering 10 years later has some advantages, but to somebody that looks today at this question it seems fair to provide an updated answer.
pyproj directly calculates areas, without need of calling shapely:
# Modules:
from pyproj import Geod
import numpy as np
# Define WGS84 as CRS:
geod = Geod('+a=6378137 +f=0.0033528106647475126')
# Data for Colorado (no need to close the polygon):
coordinates = np.array([
[-102.05, 41.0],
[-102.05, 37.0],
[-109.05, 37.0],
[-109.05, 41.0]])
lats = coordinates[:,1]
lons = coordinates[:,0]
# Compute:
area, perim = geod.polygon_area_perimeter(lons, lats)
print(abs(area)) # Positive is counterclockwise, the data is clockwise.
The result is: 269154.54988400977 km2, or -0.17% of the reported correct value (269601.367661 km2).
The title basically says it all. I need to calculate the area inside a polygon on the Earth’s surface using Python. Calculating area enclosed by arbitrary polygon on Earth's surface says something about it, but remains vague on the technical details:
If you want to do this with a more
“GIS” flavor, then you need to select
an unit-of-measure for your area and
find an appropriate projection that
preserves area (not all do). Since you
are talking about calculating an
arbitrary polygon, I would use
something like a Lambert Azimuthal
Equal Area projection. Set the
origin/center of the projection to be
the center of your polygon, project
the polygon to the new coordinate
system, then calculate the area using
standard planar techniques.
So, how do I do this in Python?
The easiest way to do this (in my opinion), is to project things into (a very simple) equal-area projection and use one of the usual planar techniques for calculating area.
First off, I’m going to assume that a spherical earth is close enough for your purposes, if you’re asking this question. If not, then you need to reproject your data using an appropriate ellipsoid, in which case you’re going to want to use an actual projection library (everything uses proj4 behind the scenes, these days) such as the python bindings to GDAL/OGR or (the much more friendly) pyproj.
However, if you’re okay with a spherical earth, it quite simple to do this without any specialized libraries.
The simplest equal-area projection to calculate is a sinusoidal projection. Basically, you just multiply the latitude by the length of one degree of latitude, and the longitude by the length of a degree of latitude and the cosine of the latitude.
def reproject(latitude, longitude):
"""Returns the x & y coordinates in meters using a sinusoidal projection"""
from math import pi, cos, radians
earth_radius = 6371009 # in meters
lat_dist = pi * earth_radius / 180.0
y = [lat * lat_dist for lat in latitude]
x = [long * lat_dist * cos(radians(lat))
for lat, long in zip(latitude, longitude)]
return x, y
Okay… Now all we have to do is to calculate the area of an arbitrary polygon in a plane.
There are a number of ways to do this. I’m going to use what is probably the most common one here.
def area_of_polygon(x, y):
"""Calculates the area of an arbitrary polygon given its verticies"""
area = 0.0
for i in range(-1, len(x)-1):
area += x[i] * (y[i+1] - y[i-1])
return abs(area) / 2.0
Hopefully that will point you in the right direction, anyway…
Because the earth is a closed surface a closed polygon drawn on its surface creates TWO polygonal areas. You also need to define which one is inside and which is outside!
Most times people will be dealing with small polygons, and so it’s ‘obvious’ but once you have things the size of oceans or continents, you better make sure you get this the right way round.
Also, remember that lines can go from (-179,0) to (+179,0) in two different ways. One is very much longer than the other. Again, mostly you’ll make the assumption that this is a line that goes from (-179,0) to (-180,0) which is (+180,0) and then to (+179,0), but one day… it won’t.
Treating lat-long like a simple (x,y) coordinate system, or even neglecting the fact that any coordinate projection is going to have distortions and breaks, can make you fail big-time on spheres.
Let’s say you have a representation of the state of Colorado in GeoJSON format
{"type": "Polygon",
"coordinates": [[
[-102.05, 41.0],
[-102.05, 37.0],
[-109.05, 37.0],
[-109.05, 41.0]
]]}
All coordinates are longitude, latitude. You can use pyproj to project the coordinates and Shapely to find the area of any projected polygon:
co = {"type": "Polygon", "coordinates": [
[(-102.05, 41.0),
(-102.05, 37.0),
(-109.05, 37.0),
(-109.05, 41.0)]]}
lon, lat = zip(*co['coordinates'][0])
from pyproj import Proj
pa = Proj("+proj=aea +lat_1=37.0 +lat_2=41.0 +lat_0=39.0 +lon_0=-106.55")
That’s an equal area projection centered on and bracketing the area of interest. Now make new projected GeoJSON representation, turn into a Shapely geometric object, and take the area:
x, y = pa(lon, lat)
cop = {"type": "Polygon", "coordinates": [zip(x, y)]}
from shapely.geometry import shape
shape(cop).area # 268952044107.43506
It’s a very close approximation to the surveyed area. For more complex features, you’ll need to sample along the edges, between the vertices, to get accurate values. All caveats above about datelines, etc, apply. If you’re only interested in area, you can translate your feature away from the dateline before projecting.
A bit late perhaps, but here is a different method, using Girard’s theorem. It states that the area of a polygon of great circles is R**2 times the sum of the angles between the polygons minus (N-2)*pi where N is number of corners.
I thought this would be worth posting, since it doesn’t rely on any other libraries than numpy, and it is a quite different method than the others. Of course, this only works on a sphere, so there will be some inaccuracy when applying it to the Earth.
First, I define a function to compute the bearing angle from point 1 along a great circle to point 2:
import numpy as np
from numpy import cos, sin, arctan2
d2r = np.pi/180
def greatCircleBearing(lon1, lat1, lon2, lat2):
dLong = lon1 - lon2
s = cos(d2r*lat2)*sin(d2r*dLong)
c = cos(d2r*lat1)*sin(d2r*lat2) - sin(lat1*d2r)*cos(d2r*lat2)*cos(d2r*dLong)
return np.arctan2(s, c)
Now I can use this to find the angles, and then the area (In the following, lons and lats should of course be specified, and they should be in the right order. Also, the radius of the sphere should be specified.)
N = len(lons)
angles = np.empty(N)
for i in range(N):
phiB1, phiA, phiB2 = np.roll(lats, i)[:3]
LB1, LA, LB2 = np.roll(lons, i)[:3]
# calculate angle with north (eastward)
beta1 = greatCircleBearing(LA, phiA, LB1, phiB1)
beta2 = greatCircleBearing(LA, phiA, LB2, phiB2)
# calculate angle between the polygons and add to angle array
angles[i] = np.arccos(cos(-beta1)*cos(-beta2) + sin(-beta1)*sin(-beta2))
area = (sum(angles) - (N-2)*np.pi)*R**2
With the Colorado coordinates given in another reply, and with Earth radius 6371 km, I get that the area is 268930758560.74808
Or simply use a library: https://github.com/scisco/area
from area import area
>>> obj = {'type':'Polygon','coordinates':[[[-180,-90],[-180,90],[180,90],[180,-90],[-180,-90]]]}
>>> area(obj)
511207893395811.06
…returns the area in square meters.
Here is a solution that uses basemap, instead of pyproj and shapely, for the coordinate conversion. The idea is the same as suggested by @sgillies though. NOTE that I’ve added the 5th point so that the path is a closed loop.
import numpy
from mpl_toolkits.basemap import Basemap
coordinates=numpy.array([
[-102.05, 41.0],
[-102.05, 37.0],
[-109.05, 37.0],
[-109.05, 41.0],
[-102.05, 41.0]])
lats=coordinates[:,1]
lons=coordinates[:,0]
lat1=numpy.min(lats)
lat2=numpy.max(lats)
lon1=numpy.min(lons)
lon2=numpy.max(lons)
bmap=Basemap(projection='cea',llcrnrlat=lat1,llcrnrlon=lon1,urcrnrlat=lat2,urcrnrlon=lon2)
xs,ys=bmap(lons,lats)
area=numpy.abs(0.5*numpy.sum(ys[:-1]*numpy.diff(xs)-xs[:-1]*numpy.diff(ys)))
area=area/1e6
print area
The result is 268993.609651 in km^2.
UPDATE: Basemap has been deprecated, so you may want to consider alternative solutions first.
You can compute the area directly on the sphere, instead of using an equal-area projection.
Moreover, according to this discussion, it seems that Girard’s theorem (sulkeh’s answer) does not give accurate results in certain cases, for example “the area enclosed by a 30º lune from pole to pole and bounded by the prime meridian and 30ºE” (see here).
A more precise solution would be to perform line integral directly on the sphere. The comparison below shows this method is more precise.
Like all other answers, I should mention the caveat that we assume a spherical earth, but I assume that for non-critical purposes this is enough.
Python implementation
Here is a Python 3 implementation which uses line integral and Green’s theorem:
def polygon_area(lats, lons, radius = 6378137):
"""
Computes area of spherical polygon, assuming spherical Earth.
Returns result in ratio of the sphere's area if the radius is specified.
Otherwise, in the units of provided radius.
lats and lons are in degrees.
"""
from numpy import arctan2, cos, sin, sqrt, pi, power, append, diff, deg2rad
lats = np.deg2rad(lats)
lons = np.deg2rad(lons)
# Line integral based on Green's Theorem, assumes spherical Earth
#close polygon
if lats[0]!=lats[-1]:
lats = append(lats, lats[0])
lons = append(lons, lons[0])
#colatitudes relative to (0,0)
a = sin(lats/2)**2 + cos(lats)* sin(lons/2)**2
colat = 2*arctan2( sqrt(a), sqrt(1-a) )
#azimuths relative to (0,0)
az = arctan2(cos(lats) * sin(lons), sin(lats)) % (2*pi)
# Calculate diffs
# daz = diff(az) % (2*pi)
daz = diff(az)
daz = (daz + pi) % (2 * pi) - pi
deltas=diff(colat)/2
colat=colat[0:-1]+deltas
# Perform integral
integrands = (1-cos(colat)) * daz
# Integrate
area = abs(sum(integrands))/(4*pi)
area = min(area,1-area)
if radius is not None: #return in units of radius
return area * 4*pi*radius**2
else: #return in ratio of sphere total area
return area
I wrote a somewhat more explicit version (and with many more references and TODOs…) in the sphericalgeometry package there.
Numerical Comparison
Colorado will be the reference, since all previous answers were evaluated on its area. Its precise total area is 104,093.67 square miles (from the US Census Bureau, p. 89, see also here), or 269601367661 square meters. I found no source for the actual methodology of the USCB, but I assume it is based on summing actual measurements on ground, or precise computations using WGS84/EGM2008.
Method | Author | Result | Variation from ground truth
--------------------------------------------------------------------------------
Albers Equal Area | sgillies | 268952044107 | -0.24%
Sinusoidal | J. Kington | 268885360163 | -0.26%
Girard's theorem | sulkeh | 268930758560 | -0.25%
Equal Area Cylindrical | Jason | 268993609651 | -0.22%
Line integral | Yellows | 269397764066 | **-0.07%**
Conclusion: using direct integral is more precise.
Performance
I have not benchmarked the different methods, and comparing pure Python code with compiled PROJ projections would not be meaningful. Intuitively less computations are needed. On the other hand, trigonometric functions may be computationally intensive.
According to Yellows’ assertion, direct integral is more precise.
But Yellows use an earth radius = 6378 137m, which is the WGS-84 ellipsoid, semi-major axis, while Sulkeh use 6371 000 m.
Using a radius = 6378 137 m in the Sulkeh’ method, gives 269533625893 square meters.
Assuming that the true value of Colorado area (from the US Census Bureau) is 269601367661 square meters then the variation from the ground truth of Sulkeh’ method is : -0,025%, better than -0.07 with the Line integral method.
So Sulkeh’ proposal seems to be the more precise so far.
In order to be able to make a numerical comparison of the solutions, with the assumption of a spherical Earth, all calculations must use the same terrestrial radius.
Here is a Python 3 implementation where the function would take a list of tuple-pairs of lats and longs and would return the area enclosed in the projected polygon.It uses pyproj to project the coordinates and then Shapely to find the area of any projected polygon
def calc_area(lis_lats_lons):
import numpy as np
from pyproj import Proj
from shapely.geometry import shape
lons, lats = zip(*lis_lats_lons)
ll = list(set(lats))[::-1]
var = []
for i in range(len(ll)):
var.append('lat_' + str(i+1))
st = ""
for v, l in zip(var,ll):
st = st + str(v) + "=" + str(l) +" "+ "+"
st = st +"lat_0="+ str(np.mean(ll)) + " "+ "+" + "lon_0" +"=" + str(np.mean(lons))
tx = "+proj=aea +" + st
pa = Proj(tx)
x, y = pa(lons, lats)
cop = {"type": "Polygon", "coordinates": [zip(x, y)]}
return shape(cop).area
For a sample set of lats/longs, it gives an area value close to the surveyed approximation value
calc_area(lis_lats_lons = [(-102.05, 41.0),
(-102.05, 37.0),
(-109.05, 37.0),
(-109.05, 41.0)])
Which outputs an area of 268952044107.4342 Sq. Mts.
I know that answering 10 years later has some advantages, but to somebody that looks today at this question it seems fair to provide an updated answer.
pyproj directly calculates areas, without need of calling shapely:
# Modules:
from pyproj import Geod
import numpy as np
# Define WGS84 as CRS:
geod = Geod('+a=6378137 +f=0.0033528106647475126')
# Data for Colorado (no need to close the polygon):
coordinates = np.array([
[-102.05, 41.0],
[-102.05, 37.0],
[-109.05, 37.0],
[-109.05, 41.0]])
lats = coordinates[:,1]
lons = coordinates[:,0]
# Compute:
area, perim = geod.polygon_area_perimeter(lons, lats)
print(abs(area)) # Positive is counterclockwise, the data is clockwise.
The result is: 269154.54988400977 km2, or -0.17% of the reported correct value (269601.367661 km2).