pyutm 0.1

Creates standardized grid references and unique identifiers for point data.

PyUTM

PyUTM is a Python package that creates standardized grid references for point data. It can also use those grid references to create spatially meaningful unique identifiers for tasks such as asset naming.

Grid references can be created in the following formats:

• United States National Grid (USNG)
• Military Grid Reference System (MGRS)
• Universal Transverse Mercator (UTM) (in progress)

For a discussion regarding the benefits of using the US National Grid, see this awesome document.

Version 0.1

The following features are currently in development, but not yet supported in the current version:

• UTM coordinates
• Locations requiring UPS grid references
• Exceptions to grid zones in UTM

Table of Contents

What is a grid reference?

• Reading a USNG or MGRS grid reference

What is a unique identifier?

Examples

• Create a Grid object
• Call the write_refs() method
• Call the write_uids() method
• Call the write_utm() method

References

What is a grid reference?

Based on the Universal Transverse Mercator (UTM) and Universal Polar Stereographic (UPS) map projections, a grid reference allows any location in the world to be described by a string of up to 15 characters. The precision of a location increases as characters are added to its grid reference and decreases as characters are removed.

The following are all valid examples of grid references at different levels of precision:

• 18LVJ
• 12JXQ69
• 30UWB8270
• 36RUU203175
• 33MXS97396762
• 44RKR0691609280

The first set of characters in a grid reference describes its Grid Zone Designation (GZD).

• This is usually three characters long and is comprised of a number between 1 and 60 followed by a letter: 33M.
• By default, PyUTM adds a leading zero to numbers less than 10: 02U.
• With some exceptions in the northern latitudes and at the poles, the GZD describes a standard area encompassing 6° of longitude and 8° of latitude.
• The letters 'I' and 'O' are omitted to avoid confusion with the numbers '1' and '0'.

The second set of characters in a grid reference describes its 100 kilometer square within the GZD.

• This is always two characters long and is comprised of two letters, again omitting 'I' and 'O': XS.
• Though the majority of squares within a GZD are 100 km on each side, those located on the edges of the GZD can be smaller in area, due to the conformal nature of the projections from which the grid references are derived.

The third and fourth sets of characters in a grid reference describe its distance in meters from the lower left corner of the 100 km square. The third set of characters describes the location's distance east of the lower left corner (its easting), while the fourth set of characters describes the location's distance north of the lower left corner (its northing).

• Both sets can be between zero and five characters long and are comprised entirely of digits; they must have the same number of characters and are not separated by a space: 97396762 represents a location of 9739 easting and 6762 northing.
• The number of digits used in each set determines the number of meters that those digits represent. As the number of digits increases from zero to five, so does the precision with which a location can be established within the 100 km square:

Number of Digits	Precision in Meters	Size of Location
Zero	100 000	Regional Area
One	10 000	Local Area
Two	1 000	Neighborhood
Three	100	Football Field
Four	10	House
Five	1	Bath towel

Reading a USNG or MGRS grid reference

To locate the area described by a grid reference, break the grid reference down into its component sets then read from left to right.

For example, the grid reference 52JGS0495 contains the following sets:

• Grid Zone Designation: 52J
• 100 km square: GS
• Distance: 0495
  • Easting: 04
  • Northing: 95

This grid reference therefore describes a location...

• ...within grid zone 52J...

• ...that is within the 100 km square GS.

• Because the easting and northing contain two digits, they describe a location with a precision of 1,000 meters (1 km), found 4 km east and 95 km north of the 100 km square's lower left corner:

• Here we see the location described by 52JGS0495: Uluru.

Note that the grid reference does not describe the point where the easting and northing cross, but rather the entire 1 km square to the northeast of that point. To describe a smaller area, we would add digits to the distance, thereby increasing its precision.

What is a unique identifier?

A unique identifier (UID) modifies a point's grid reference by adding a numeric suffix to insure that each reference-based ID within the dataset is unique, even when two points may share the same grid reference. It can add prefixes to the reference to make the UID more meaningful or remove character sets from the standard grid reference to make a UID shorter; it also adds a delimiter between the character sets to improve readability.

• M-XS-9739-6762-3 is a UID containing both a custom prefix and numeric suffix; the GZD has not been used and delimiters have been added to separate the character sets.

Examples

To install PyUTM, use PyPI:*

pip install pyutm

1. Create a Grid() object

Grid(data, columns=None, epsg=4326)

• data: the point data used to construct the grid references and UIDs
  • The data can be either a list of coordinates or a file path to a CSV or shapefile
  • Lists should be in the following format: [(X coordinate, Y coordinate), ...]
• columns: if the data is in CSV format, these are the columns that hold the coordinates
  • The argument should be passed in the following format: [X coordinate, Y coordinate]
• epsg: the EPSG number of the data's coordinate reference system
  • The default EPSG is 4326 (WGS 84)

>>> import pyutm
# Create a grid object from a list of longitudes and latitudes
>>> lonlat = [(16.776031, -3.005612), (16.772291, -3.007136)]
>>> grid_from_list = pyutm.Grid(lonlat)
# Create a grid object from a CSV file
>>> grid_from_csv = pyutm.Grid('my_points.csv', columns=['LON', 'LAT'])
# Create a grid object from a shapefile in WGS 84 Web Mercator (Auxiliary Sphere)
>>> grid_from_shp = pyutm.Grid('my_points.shp', epsg=3857)

2. Call the write_refs() method

If a file name is specified, this function writes the grid reference of each point to the file. If the input data is in the CSV or shapefile format, the grid references are added as a new column to the file.

This function always returns a list in the form [[X, Y, 'Grid Reference'], ...].

write_refs(fname=None, ref_column='GRID_REFS', precision=10)

• fname (optional): the name of the file to which the grid references are written
  • The output file must be in the same format as the input data
• column: the header of the column to which the grid references are written
  • The default column name is GRID_REFS
• precision: the desired precision of the grid references, in meters
  • The default precision is 10 meters

>>> lon_lat = (16.776031, -3.005612)
>>> my_grid = pyutm.Grid(lon_lat)
# Create grid references
>>> my_ref = my_grid.write_refs()
>>> my_ref
[[16.776031, -3.005612, '33MXS97386762']]
# Specify the precision of the grid reference
>>> my_1m_ref = my_grid.write_refs(precision=1)
>>> my_1m_ref
[[16.776031, -3.005612, '33MXS9738967626']]
# Create grid references from a CSV file
>>> grid_from_csv = pyutm.Grid('my_points.csv', column=['LON', 'LAT'])
# Write the grid references to a new CSV with a custom column name
>>> grid_from_csv.write_refs('my_refs.csv', column='MY_REFS')
# Create grid references from a shapefile
>>> grid_from_shp = pyutm.Grid('my_points.shp')
# Write the grid references to a new shapefile with 10 km precision
>>> grid_from_shp.write_refs('my_refs.shp', precision=10000)

3. Call the write_uids() method

If a file name is specified, this function writes the unique identifiers of each point to the file. If the input data is in the CSV or shapefile format, the UIDs are added as a new column to the file.

This function always returns a list in the form [[X, Y, 'UID'], ...].

write_uids(fname=None, uid_column='UID_REFS', precision=10, prefix=None, prefix_column=None, gzd=True, k100=True, delimiter='-')

• fname (optional): the name of the file to which the UIDs are written
  • The output file must be in the same format as the input data
• column: the header of the column to which the UIDs are written
  • The default column name is UID_REFS
• precision: the desired precision of the UIDs, in meters
  • The default precision is 10 meters
• prefix: the prefix to be added to all UIDs
• prefix_column: the name of the data column containing prefix characters
• gzd: whether to include the Grid Zone Designation in the UIDs
  • The default is True
• k100: whether to include the 100 kilometer square in the UIDs
  • The default is True
  • If k100 is set to False, gzd is also set to False
• delimiter: the character(s) used to separate the character sets
  • The default character is a dash: -

>>> lon_lat = (16.776031, -3.005612)
>>> my_grid = pyutm.Grid(lon_lat)
# Create unique identifiers
>>> my_uid = my_grid.write_uids()
>>> my_uid
[[16.776031, -3.005612, '33M-XS-9738-6762-1']]
# Increase the precision, add a prefix to the UID, remove the GZD and change the delimiter
>>> my_prefix = my_grid.write_uids(precision=1, prefix='m', gzd=False, delimiter=':')
>>> my_prefix
[[16.776031, -3.005612, 'm:XS:97389:67626:1']]
# Create UIDs from a CSV file
>>> grid_from_csv = pyutm.Grid('my_points.csv', column=['LON', 'LAT'])
# Add prefixes to the UIDs using values in a column of the input CSV,
# then write the UIDs to a new CSV with a custom column name
>>> grid_from_csv.write_refs('my_uids.csv', column='MY_UIDS', prefix_column='MY_PREFIXES')
# Create UIDs from a shapefile
>>> grid_from_shp = pyutm.Grid('my_points.shp')
# Add prefixes to the UIDs using values in a column of the input shapefile
# and decrease the precision, then write the UIDs to a new shapefile.
>>> grid_from_shp.write_refs('my_uids.shp', precision=1000)

4. Call the write_utm() method

:hammer_and_wrench: Coming soon :hammer_and_wrench:

N.B.

Python 2.7 users must install the Microsoft Visual C++ Compiler for Python 2.7 before using this package.

References

National Geospatial-Intelligence Agency, The Universal Grids and the Transverse Mercator and Polar Stereographic Map Projections, NGA Standardization Document NGA.SIG.0012_2.0.0_UTMUPS. Washington, D.C.: Office of Geomatics, 2014.

• PyUTM will eventually implement the logic found in Sections 11, 12 and 14 of this document.

National Geospatial-Intelligence Agency, Universal Grids and Grid Reference Systems, NGA Standardization Document NGA.STND.0037_2.0.0_GRIDS. Washington, D.C.: Office of Geomatics, 2014.

• Though broader in scope than the previous document, Chapter 3 and Appendices A and B are particularly helpful.
• PyUTM currently implements a version of the logic found in Appendix B of this document.

National Geospatial-Intelligence Agency, Office of Geomatics, Coordinate Systems Data.

• A repository of worldwide GIS layers for the UTM and MGRS coordinate systems