When you work with geospatial data, every measurement you make depends on how Earth’s curved surface gets flattened into a 2D map. This transformation process, called map projection, affects every spatial analysis you perform. Small projection choices can lead to significant errors in distance calculations, area measurements, and spatial relationships.

Understanding map projections isn’t just academic theory. It directly impacts the accuracy of your spatial analysis results. Whether you’re calculating service areas for utilities, measuring land parcels, or analysing network coverage, the wrong projection can skew your data and lead to poor decisions.

This guide explains how different projections affect your spatial analysis accuracy and shows you practical ways to choose and manage coordinate systems for reliable results.

Why map projections matter for accurate spatial analysis #

Map projections transform Earth’s three-dimensional surface onto a flat map, but this transformation always introduces distortions. These distortions directly affect the accuracy of your spatial analysis calculations.

Consider measuring the area of a utility service territory. Using the wrong projection can make the same polygon appear 20% larger or smaller than its actual size. For distance measurements, projection errors compound over longer distances. A 100-kilometre pipeline measured in an inappropriate projection might show as 95 kilometres or 105 kilometres, depending on the distortion pattern.

**Direction measurements** suffer similar problems. Network analysis for telecommunications or utility routing relies on accurate bearing calculations. When your projection distorts angles, shortest-path algorithms can select suboptimal routes, increasing infrastructure costs and reducing service efficiency.

Real-world examples highlight these issues. Water utilities using global projections for local network analysis often discover significant discrepancies when field measurements don’t match their GIS calculations. The problem stems from using coordinate systems designed for world maps rather than local analysis.

These projection-related errors cascade through complex spatial analysis workflows. Buffer zones around infrastructure become inaccurate. Proximity analysis gives wrong results. Service area calculations mislead capacity planning decisions.

Common projection distortions that skew your data #

Four main types of distortion occur when transforming 3D Earth data to 2D maps. Understanding these helps you recognise when projection issues might affect your analysis.

Area distortion #

Area distortion changes the relative size of geographic features. Some projections preserve area relationships (equal-area projections) while others significantly distort them. The Mercator projection, commonly used in web mapping, makes polar regions appear much larger than they actually are relative to equatorial areas.

For spatial analysis, area distortion affects density calculations, service territory planning, and resource allocation studies. If you’re calculating population density or infrastructure coverage ratios, area distortion leads to incorrect conclusions.

Distance distortion #

Distance distortion affects linear measurements and calculations. Most projections only preserve accurate distances along specific lines (like the central meridian). Distances measured elsewhere contain errors that increase with distance from these reference lines.

This impacts network analysis, buffer calculations, and proximity studies. Telecommunications companies planning coverage areas need accurate distance measurements to determine optimal tower placement and signal reach.

Direction distortion #

Direction distortion changes angular relationships between geographic features. Some projections (conformal projections) preserve local angles, while others distort them significantly. This affects bearing calculations and navigation analysis.

**Direction accuracy** matters for infrastructure routing, emergency response planning, and asset management. When your projection distorts directions, automated routing algorithms make suboptimal decisions.

Shape distortion #

Shape distortion changes how geographic features appear. Circular features might become elliptical, straight lines might curve, and regular patterns might appear irregular.

Shape distortion affects visual analysis and pattern recognition. It can make regular infrastructure grids appear irregular or cause misinterpretation of spatial relationships during visual inspection.

Choosing the right coordinate system for your project #

Selecting appropriate coordinate systems requires matching projection characteristics to your analysis requirements. Different analysis types prioritise different accuracy aspects.

Geographic extent considerations #

Local analysis projects benefit from coordinate systems designed for specific regions. National mapping agencies typically provide optimised coordinate systems for their territories. In the Netherlands, the Rijksdriehoekscoördinaten (RD) system provides excellent accuracy for local analysis.

For continental or global analysis, universal systems like UTM (Universal Transverse Mercator) offer reasonable accuracy across large areas. UTM divides the world into zones, each optimised for its specific longitude range.

Analysis type requirements #

Area-based analysis requires equal-area projections. If you’re calculating service territories, land use patterns, or resource distributions, choose projections that preserve area relationships. Lambert Equal Area projections work well for regional area analysis.

Distance-based analysis needs projections that minimise distance distortion in your study area. Equidistant projections preserve distances from specific points, making them useful for accessibility analysis and service radius calculations.

Direction-sensitive analysis requires conformal projections that preserve angular relationships. Network routing, navigation analysis, and bearing calculations perform best with projections like Transverse Mercator or Lambert Conformal Conic.

Popular systems for different applications #

Utility companies often use local coordinate systems optimised for their service areas. These provide the highest accuracy for infrastructure management and asset mapping.

**Regional analysis** typically uses continental coordinate systems like European Terrestrial Reference System 1989 (ETRS89) for European projects or North American Datum 1983 (NAD83) for North American work.

Web mapping applications commonly use Web Mercator (EPSG:3857) for display purposes, but this projection introduces significant distortions for analytical work, especially at high latitudes.

How projection errors compound in multi-layer analysis #

Combining datasets with different coordinate systems creates alignment issues that invalidate analysis results. These problems multiply when working with multiple data layers from different sources.

Spatial data integration requires all layers to share the same coordinate system. When layers use different projections, features that should align perfectly appear offset. A utility pole might appear to be in the middle of a road instead of alongside it, simply due to projection misalignment.

**Measurement errors compound** when calculating relationships between misaligned layers. Distance measurements between features in different coordinate systems produce meaningless results. Overlay analysis generates incorrect intersection patterns.

These issues become particularly problematic in infrastructure analysis where precise spatial relationships matter. Gas pipeline analysis might combine cadastral data, geological surveys, and infrastructure records. If these datasets use different coordinate systems without proper transformation, the analysis produces unreliable results.

Temporal analysis suffers when historical data uses different coordinate systems than current data. Comparing infrastructure changes over time requires consistent spatial reference systems. Coordinate system changes between historical and current datasets can make stable features appear to move.

The solution involves establishing a single project coordinate system and transforming all data layers to match. However, coordinate system transformations introduce their own small errors. Multiple transformations compound these errors, reducing overall accuracy.

Best practices for projection management in GIS workflows #

Effective projection management starts with project planning and continues through every analysis stage. Establishing clear protocols prevents projection-related errors and ensures consistent results.

Project setup protocols #

Define your project coordinate system before importing any data. Choose the system that best matches your analysis requirements and geographic extent. Document this choice and ensure all team members understand the selected system.

Create a data transformation log that records the original coordinate system of each dataset and any transformations applied. This documentation helps identify potential accuracy issues and supports quality control processes.

Data validation procedures #

Verify coordinate system information for all incoming datasets. Many data sources have incorrect or missing coordinate system metadata. Visual inspection can reveal obvious projection problems, but systematic validation requires checking known reference points.

**Test spatial relationships** between datasets before performing analysis. Overlay datasets and verify that features align correctly. Infrastructure data should align with base mapping, and administrative boundaries should match between different data sources.

Transformation management #

Use appropriate transformation parameters when converting between coordinate systems. Generic transformations might not provide sufficient accuracy for precise analysis. National mapping agencies often provide optimised transformation parameters for their regions.

Minimise the number of coordinate system transformations. Each transformation introduces small errors that compound over multiple operations. Transform data to your project coordinate system once, then perform all analysis in that system.

Quality assurance measures #

Implement systematic accuracy checks throughout your analysis workflow. Compare calculated distances and areas with known reference measurements. Check that spatial relationships make sense in the real world.

Document accuracy requirements for your analysis and verify that your chosen coordinate system meets these requirements. Different applications have different accuracy needs, and your coordinate system choice should reflect these requirements.

Regular validation against field measurements helps identify systematic errors. When GIS calculations consistently differ from field measurements, coordinate system issues are often the cause.

Understanding and managing map projections forms the foundation of accurate spatial analysis. The projection choices you make affect every measurement and calculation in your geospatial workflows. By selecting appropriate coordinate systems, validating data alignment, and maintaining consistent projection management practices, you ensure your spatial analysis produces reliable results that support confident decision-making. At Spatial Eye, we build these projection management principles into all our spatial analysis solutions, helping utilities and infrastructure organisations achieve the accuracy they need for critical operational decisions.