Overlaying Layers With Different Coordinate Systems
The Problem of Mismatched Coordinate Systems
A key issue that arises when analyzing spatial data from different sources is that the layers may not share a common coordinate reference system (CRS). For example, one layer could be in a geographic CRS using latitude and longitude, while another is in a projected CRS using metric units. If the layers are visualized or analyzed without properly accounting for these CRS differences, it can lead to inaccurate, misaligned, or erroneous results.
Some examples of errors that can occur when overlaying mismatched data layers include: false positives/negatives in overlay analysis, improper alignment or distortion in map visualizations, incorrect measurements and statistics, and topology errors during geoprocessing workflows. These problems stem from the mathematical transformations that must occur to convert data points from one CRS to another. Without understanding these potential pitfalls, it’s easy to improperly overlay spatial data layers and undermine analysis.
Understanding Coordinate Reference Systems
A coordinate reference system (CRS) defines the translation between locations on the Earth’s surface and numeric coordinates. It provides a frame of reference and standard units to encode locations. There are two overarching categories of CRSs used in GIS:
- Geographic CRS – Based on a spheroid/ellipsoid model of the Earth’s surface. Coordinates are angles in degrees such as latitude and longitude. Example: WGS 84.
- Projected CRS – Based on a planar projection with Cartesian coordinates. Uses linear units such as meters or feet. Designed for mapping limited regions. Example: UTM Zones.
Using a consistent CRS is fundamental for overlaying layers and performing accurate GIS analysis. Software can reproject data “on the fly” between certain CRSs using transformation algorithms. But the best practice is to standardize all data layers to one well-documented CRS at the beginning of a project to avoid cumulative numerical errors.
Transforming Layer Coordinate Systems
GIS software packages provide built-in functionality to reproject vector and raster data sets between different coordinate systems. For example, in QGIS this can be achieved with the “Reproject Layer” tool. In Python, the pyproj library and GeoPandas DataFrames have .to_crs() methods to reproject datasets.
On-the-fly reprojection shows layers in their correct relative positions by temporarily displaying them in one CRS defined for the map canvas or project. Permanent reprojection creates new output dataset files transformed to the desired CRS. Permanent reprojection is better for repeated overlay analysis to avoid compounding numerical errors.
import geopandas
df = geopandas.read_file('layer.shp')
# Reproject to ESPG 4326
df = df.to_crs(epsg=4326)
df.to_file('layer_4326.shp')
Best Practices For Managing Coordinate Systems
Here are some best practices to effectively manage coordinate reference systems in GIS workflows:
- Identify CRS – Determine layer CRSs when data is acquired and document for later reference.
- Set project CRS – Standardize all data to one CRS at the start.
- Detect mismatches – Visually check for alignment issues when overlaying layers.
- Metadata standards – Record CRS info and transformations in metadata.
Following these practices prevents accumulation of numeric errors from unnecessary data transformations and supports geospatial analysis transparency/reproducibility.
Performing Spatial Analysis Across Transformed Layers
When analyzing multiple data layers in a GIS, it’s imperative that their CRSs properly line up after any necessary transformations. Certain types of vector overlay analysis are particularly sensitive.
For example, an intersection operation determines the mutual polygons, lines, or points present in both layers. If one layer is inaccurately registered due to CRS differences, it can result in incorrect geometric intersections or topologies. This subsequently impacts statistics calculated on attribute joins and spatial queries.
# Open two feature classes with different initial CRSs
roads_df = geopandas.read_file('roads_3035.shp')
parcels_df = geopandas.read_file('parcels_4326.shp')
# Reproject parcels to match roads CRS before intersection
parcels_df = parcels_df.to_crs(epsg=3035)
# Perform intersection to find parcels touching roads
intersections = geopandas.overlay(parcels_df, roads_df,
how='intersection')
Careful management of CRS transformations prior to analysis helps avoid erroneous outputs and metrics.
Troubleshooting Issues Related to Coordinate System Mismatches
Some common symptoms that spatial data layers have incompatible coordinate reference systems include:
- Obviously misaligned features when visualized
- Error messages during geoprocessing/analysis workflows
- “Artifacts” near layer boundaries after transformations
- Inaccurate or missing intersection features
Steps to troubleshoot these potential issues include:
- Check layer CRS parameters and documentation
- Explicitly reproject one layer to match the other’s CRS
- Modify analysis environment settings to enforce uniform CRSs
- Split processing into geographic sub-areas to minimize distortion
Tracking coordinate transformations, visually inspecting outputs, and controlling analysis environments are key tactics for resolving mismatches. Consulting geospatial metadata documentation is also essential to identify the proper CRS specifications for each dataset.
