Common Pitfalls When Working With Unknown Coordinate Systems

Understanding Coordinate Reference Systems

A coordinate reference system (CRS) is a coordinate-based mapping system that specifies locations on the Earth. It defines a specific map projection, as well as transformations between different spatial references.

Common issues that arise when working with unknown CRSs include inability to identify the projection parameters, improper handling of datum transformations, and distortion effects leading to inaccurate geospatial analysis.

Defining Coordinate Systems and Projections

A coordinate system consists of a set of mathematical rules for specifying locations on the Earth. The coordinate system is the foundation that the map projection is built upon.

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There are two main types of coordinate systems:

Geographic coordinate systems – Based on a spheroidal model of the Earth and use latitude and longitude coordinates
Projected coordinate systems – Defined on a flat, 2D surface and use linear units like meters or feet

A map projection translates the spherical coordinates of the Earth’s surface into planar coordinates. Common map projections include Mercator, Universal Transverse Mercator (UTM), and State Plane Coordinate System (SPCS).

Common Issues with Unknown Systems

Working with geospatial data from an unknown CRS can lead to the following pitfalls:

Inability to accurately georeference and integrate datasets
Distortion effects such as length, angle, area, shape, scale, and distance distortions
Incorrect geospatial analysis output and invalid results
Errors when visualizing multiple datasets in a map

Identifying Projection Parameters

Identifying the projection parameters is crucial for understanding the properties and distortions within an unknown CRS. Parameters can provide insight into the datum, coordinate system, linear units, and projection used.

Gathering Metadata Clues

Examine all available metadata for relevant projection details:

Look for EPSG code, spheroid/datum name, projection name, units, etc.
Review raster dataset headers and bands
Check file properties and Open Geospatial Consortium (OGC) metadata
Identify coordinate bounding extents

Online repositories like epsg.io can be used to search for EPSG codes identifying specific CRSs.

Using Projection Identification Tools

Various utilities and spatial software can suggest the projection from sampling coordinates:

projfinder.com – Online tool suggesting CRS based on input coordinates
PRJ Finder – ArcGIS toolbar to select projections
GDAL srsdetect utility – Reads projection from dataset
QGIS, GeoServer, ArcGIS – May auto-detect projection parameters

Control points can also help narrow down coordinate system candidates by constraining linear units and extents.

Handling Transformations

Converting data between different datums and coordinate reference systems requires specialized transformations. Issues can arise when transformation methods or parameters are unknown.

Choosing Transformation Methods

Common geospatial transformation methods include:

Datum shifts – For converting between datums
Map projections – For converting between coordinate systems
Helmert transforms – Handles datum changes with fewer parameters
NTv2 grids – Datum conversion specific to Canada/North America regions
Geocentric translations – Converts through an intermediary 3D system

Choosing the optimal transformation method requires identifying the initial and destination CRS characteristics.

Dealing with Distortion Effects

Datums and projections have distinct distortion effects that can compound through transformations:

Datum changes cause positional skew and offset
Projections distribute distortion unevenly throughout the map
Choosing unsuitable transformation parameters amplifies distortions

Analyzing control points before and after transformation highlights distortion magnitudes and directionality.

Best Practices

Adopting standard guidelines for documenting and managing coordinate reference systems helps avoid issues with unknown projections.

Documenting Coordinate Systems

Record full projection definition details as metadata
Register spatial datasets with EPSG codes
Log transformation particulars applied to datasets
Note projection limitations, distortions, and zone boundaries

Setting Project Policies

Mandate internal use of standard projections and parameters
Define permitted data sources and acceptable coordinate uncertainties
Perform checks to identify unknown projections on ingest datasets
Require formal projection review before analyzing new data

Leveraging Spatial Libraries

Robust geospatial libraries like Proj, GeoTools, and PROJ.4 abstract complex projections and transformations into simple functions. This avoids needing to handle lower-level complexities.

Tools built atop spatial libraries can take advantage of advanced projection handling capabilities. This enables quickly applying complex transformations safely without heavyweight coding.