Spatial information is linked to the position and a system of reference. There are many coordinate systems worldwide with different length units, projections and origins. Somehow, spatial analysis is always linked to information stored on different coordinate systems and we have to provide effective ways to translate them to a specific CRS (coordinate reference system).

We have developed a tutorial for the coordinate system translation of XY point location stored in tables. The tutorial shows the procedure to change coordinate systems from geographic and planar coordinates using the Pyproj library over a Pandas dataframe on a Jupyter notebook.

Input data

You can download the input data for this tutorial from this link.

Script

This is the complete Python script:

Original data from: Hydrogeologic Framework of the Treasure Valley and Surrounding Area, Idaho and Oregon; Well Data https://www.sciencebase.gov/catalog/item/5d83cf8ce4b0c4f70d06f0d3

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Open input files

wellLoc = pd.read_csv('../inputData/TV-HFM_Wells_1Location.csv',header=1,index_col=0,usecols=[0,2,3,4,5])
wellLoc.head()

	Easting	Northing	dd_lon	dd_lat
Bore
A. Isaac	2333140.95	1372225.65	-116.065033	43.532316
A. Woodbridge	2321747.00	1360096.95	-116.201976	43.420553
A.D. Watkins	2315440.16	1342141.86	-116.273786	43.257477
A.L. Clark; 1	2276526.30	1364860.74	-116.761968	43.451284
A.L. Clark; 2	2342620.87	1362980.46	-115.945106	43.451176

Transform coordinate system - Geographical

from pyproj import Transformer
transformer = Transformer.from_crs('epsg:4269','epsg:4326',always_xy=True)
points = list(zip(wellLoc.dd_lon,wellLoc.dd_lat))
coordsWgs = np.array(list(transformer.itransform(points)))
coordsWgs[:5,:]

array([[-116.065033,   43.532316],
       [-116.201976,   43.420553],
       [-116.273786,   43.257477],
       [-116.761968,   43.451284],
       [-115.945106,   43.451176]])

wellLoc['lonWgs']=coordsWgs[:,0]
wellLoc['latWgs']=coordsWgs[:,1]
wellLoc.head()

	Easting	Northing	dd_lon	dd_lat	lonWgs	latWgs
Bore
A. Isaac	2333140.95	1372225.65	-116.065033	43.532316	-116.065033	43.532316
A. Woodbridge	2321747.00	1360096.95	-116.201976	43.420553	-116.201976	43.420553
A.D. Watkins	2315440.16	1342141.86	-116.273786	43.257477	-116.273786	43.257477
A.L. Clark; 1	2276526.30	1364860.74	-116.761968	43.451284	-116.761968	43.451284
A.L. Clark; 2	2342620.87	1362980.46	-115.945106	43.451176	-115.945106	43.451176

Plot transformed coordinates

# with matplotlib
figure = plt.figure(figsize=(10,12))
plt.scatter(wellLoc.lonWgs, wellLoc.latWgs, s=15, c='goldenrod')
plt.show()

# with mplleaflet over a section of points
wellLocCut = wellLoc[wellLoc.lonWgs.between(-116.4,-116.0)][wellLoc.latWgs.between(43.4,43.8)]

import mplleaflet
tiles=('http://mt0.google.com/vt/lyrs=s&hl=en&x=', 
       '<a href=https://www.openstreetmap.org/about">© OpenStreetMap</a>')
wellCrs = 
figure = plt.figure(figsize=(10,12))
plt.scatter(wellLocCut.lonWgs, wellLocCut.latWgs, s=30, c='orangered')
mplleaflet.display(crs=wellCrs,tiles=tiles)

C:\Users\Gida\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

C:\Users\Gida\Anaconda3\lib\site-packages\IPython\core\display.py:694: UserWarning: Consider using IPython.display.IFrame instead
  warnings.warn("Consider using IPython.display.IFrame instead")

Transform coordinate system - Transverse Mercator

from pyproj import Transformer
transformer = Transformer.from_crs('esri:102605','epsg:32611',always_xy=True)
points = list(zip(wellLoc.Easting,wellLoc.Northing))
coordsWgsUTM = np.array(list(transformer.itransform(points)))
coordsWgsUTM[:5,:]

array([[ 575546.62883411, 4820354.91361594],
       [ 564600.3665819 , 4807827.44742112],
       [ 558944.8434037 , 4789663.81991337],
       [ 519259.00615917, 4810958.61748012],
       [ 585351.15027036, 4811459.50309812]])

wellLoc['EastingUTM']=coordsWgsUTM[:,0]
wellLoc['NorthingUTM']=coordsWgsUTM[:,1]

figure = plt.figure(figsize=(10,12))
plt.scatter(wellLoc.EastingUTM, wellLoc.NorthingUTM, s=30, c='orangered')

<matplotlib.collections.PathCollection at 0x1ee319cd198>

Export CSV with transformed coordinates

### Export dataframe as CSV
wellLoc.to_csv('../outputData/TV-HFM_Wells_1Location_trans.csv')

Video

Projection check

All original and translated coordinates were plotted on QGIS to show the accuracy / suitability of the coordinate system translation. The following screenshots show that the point location is the same independent from the coordinate system.