Defining Vs Projecting Coordinate Systems In Arcgis

Defining Coordinate Systems

Coordinate systems are fundamental to working with spatial data in ArcGIS. They define the reference framework for plotting locations on the earth’s surface. Understanding coordinate systems is critical for visualizing, analyzing, and integrating spatial data.

There are two main categories of coordinate systems in ArcGIS – geographic coordinate systems and projected coordinate systems. Geographic coordinate systems use a spheroid or ellipsoid to define locations on the earth. These are best suited for data that covers large areas. Common geographic systems include latitude/longitude, World Geodetic System 1984 (WGS84), and North American Datum 1983 (NAD83).

Projected coordinate systems are derived from geographic systems using a map projection. Map projections transform the earth’s curved surface onto a flat plane. This introduces some distortion but allows for more accurate measurements and analysis. There are many different projections to choose from depending on the region and purpose of your data.

Table of Contents

It’s important to understand the difference between defining and projecting coordinate systems in ArcGIS. Defining sets the native system for the dataset while projecting displays data from one coordinate system in another system. Defining maintains the original coordinates while projecting creates new coordinates. Mixing up these concepts can lead to issues visualizing, analyzing, and integrating spatial data.

Geographic Coordinate Systems

Geographic coordinate systems geo-reference data using a spherical model of the earth. Locations are defined using angular units – latitude and longitude. Latitude lines run east-west while longitude lines run north-south, together creating a graticule crisscrossing the globe. Where these lines intersect identifies a unique location. Latitude values range from -90 degrees at the South Pole to 90 degrees at the North Pole. Longitude values range from -180 to 180 degrees, with 0 degrees passing through Greenwich, England.

Geographic coordinates take earth’s curvature into account, allowing for data to be visualized on a globe. This preserves relative positions and distances across larger areas but can result in distortions on flat maps. There is no perfect geographic system – each makes compromises in flatten the earth’s surface or adjusting positioning. Commonly used geographic systems include WGS84 and NAD83 based on different spheroids and datums.

Geographic systems form the underlying framework for projected systems. It’s essential to define data in the appropriate native geographic system first before projecting into planar coordinates. This preserves accuracy for coordinate transformations and analysis.

Projected Coordinate Systems

Projected coordinate systems represent the earth on a two-dimensional flat surface rather than a spheroid. Locations are defined with linear units such as meters or feet instead of angular degrees. This allows for more accurate area, distance, and direction calculations. Projected systems achieve this by mathematically transforming geographic coordinates using a map projection.

There are many possible map projections, each with specific properties and use cases. Common projections include Transverse Mercator, Albers Equal Area, Robinson, and Web Mercator. Choosing the right projection involves tradeoffs between accuracy, area, shape, distance, and scale. No single projection is optimal for all spatial analysis tasks. Most countries and regions have defined standard projected systems tailored to their geography and applications.

Projected coordinates introduce distortion compared to the curved surface of geographic systems. This distortion increases towards the edges of the defined projection region. Accurately visualizing and analyzing data may require multiple projected systems for different areas. Coordinates may need to be transformed between projected systems using coordinate conversion tools.

It’s essential to understand the properties and intended usage for any projected system. Projections optimized for area analysis will differ from projections designed for navigation or web mapping. Selecting the appropriate predefined projection can simplify workflows in ArcGIS.

Differences between Defining and Projecting Coordinate Systems

Defining a dataset’s coordinate system records its native reference system on disk. This specifies the original spatial location and units for features in the dataset. Defining the coordinate system does not actually transform feature coordinates. Defining an incorrect system will result in coordinates being interpreted incorrectly, leading to issues visualizing and analyzing data.

In contrast, projecting displays data from one coordinate system in terms of another target system. This dynamically converts coordinates “on-the-fly” for rendering maps and performing analysis. Projection re-projects feature locations from the defined system to new locations in the target system. This introduces distortion but allows combining data with different native systems.

In most workflows, datasets first need their coordinate system defined accurately to match how data was originally collected and stored. Projection can then transform coordinates as needed for specific use cases. Defining registers the source coordinate system while projecting displays coordinates in terms of user-specified systems.

Mixing up defining and projecting systems is a common source of problems in ArcGIS. Data displayed in the wrong location or at the wrong scale often indicates incorrectly defined coordinate systems. Errors calculating measurements frequently occur because data was not projected prior to analysis. Understanding this key distinction is essential to working with spatial data.

Projecting Data in ArcGIS

ArcGIS contains tools to define, project, transform, inspect and set the coordinate systems used in spatial data and maps. Properly projecting data ensures features align and calculate accurately during analysis. There are several approaches to managing coordinate systems within ArcGIS Desktop and ArcGIS Pro.

Setting the Coordinate System of a Dataset

The first step working with any spatial dataset should be verifying and correctly defining its coordinate system. Most data contains metadata or properties recording the native system it was collected and stored in. Identifying the original system allows ArcGIS to interpret coordinates and render maps correctly.

The Define Projection tool assigns a coordinate system when none exists or fixes incorrectly defined systems. This writes the selected system into dataset properties without transforming any coordinates. Define Projection is important for georeferencing untagged or incorrectly tagged data sources.

The Project tool actually transforms coordinates from one system to another. This projects data “on-the-fly” for display and analysis purposes without persisting re-projected coordinates. Project should be used when source data needs to be viewed or analyzed in systems different than its native defined system.

On-the-Fly Projection

By default, ArcMap uses on-the-fly projection to dynamically transform coordinates to the data frame’s system. This allows visualizing, querying and analyzing layers with different defined systems together in one map. On-the-fly projection reprojects data only temporarily for the current session without changing coordinate values stored on disk.

The data frame’s coordinate system determines the target projection when using on-the-fly capabilities. Setting the appropriate data frame system is critical to ensure accurate feature alignment and measurement. On-the-fly projection introduces distortion which can accumulate if coordinating multiple transformations across layers.

In ArcGIS Pro, the map’s coordinate system defines the target projection system instead of the data frame properties. ArcGIS Pro introduces enhanced on-the-fly capabilities through the Transformations properties panel controlling how layers are displayed. This provides additional options to manage distortion and alignment issues when visualizing data together.

Project Tool

The Project tool creates a new output dataset with all coordinates permanently transformed to a different system. This reprojects source features to write new feature classes or rasters with coordinates in the specified target system. The Project tools offers more control and accuracy for one-time reprojection tasks compared to temporary on-the-fly transformations.

Project will correctly handle datum transformations along with projecting coordinates. This maintains spatial accuracy for source layers stored with geographic coordinates based on older datums. Project is also required to permanently reproject GIS resources to standardized systems for sharing public-facing maps and apps.

The resulting projected dataset will contain distortions relative to the original defined system unless projecting from one projected system to another similar system. As a rule, project data into the appropriate system only at the stage in analysis or for end-use where distortion introduced is acceptable.

Example Code for Projecting Data

Automating and scripting projection tasks is a common activity. ArcPy contains a full suite of coordinate system functions for code workflows. Below demonstrates typical logic for projecting feature class data programmatically:

import arcpy
from arcpy import env
env.workspace = “C:/data”

#Define input/output  
inFeatures = "cities.shp"
outFeatureClass = “cities_projected.shp"
coordinateSystem = "PROJCS['NAD_1983_UTM_Zone_10N',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983'"

#Execute project tool
arcpy.Project_management(inFeatures, outFeatureClass, coordinateSystem)  

#Overwrites existing output feature class
arcpy.Project_management(inFeatures, outFeatureClass, coordinateSystem, "#", "#", "PROJCS['NAD_1983_Albers',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983’”)

Structured code practices make coordinate system transformations easier to maintain and optimize:

Store common systems as variables for reference across tasks
Utilize describe functions to validate defined coordinate systems on inputs
Wrap project workflows into reusable functions/methods
Output exclusively into file geodatabases or databases to preserve projections

Programming best practices combined with understanding ArcGIS projection tools creates streamlined, scalable projection code.

Common Issues and Solutions

Handling coordinate systems incorrectly in ArcGIS remains a primary source of problems for GIS analysts. Symptoms include layers not aligning, features appearing at wrong scales, attribute data falling in incorrect locations, and output measurements varying significantly from expectations.

Learning to recognize and resolve these kinds of issues involves deeper understanding of principles guiding coordinate system definitions, on-the-fly projection, and coordinate transformation workflows.

Visualizing Layers with Different Coordinate Systems

The most common problem coordinating spatial data involves layers failing to properly align on a map. Attempting to visualize multiple datasets with differing native coordinate systems frequently results in features rendered in incorrect relative locations.

For example, boundary layers often use geographic coordinates, while transportation or infrastructure data features project into planar systems. Drawing such data together using on-the-fly capabilities means reprojecting source coordinates “behind the scenes”.

Indirect projection pathways between the sources can introduce compounding distortions that offset layers from true relative positions. Each successive transformation between non-matching coordinate systems multiplies these distortions.

Solutions include:

Define all source data in the same system before visualizing
Choose an appropriate common projected system for the map area
Project sources permanently into a target system for one-off use cases
Adjust Transformation properties for certain data sources

Taking time to standardize projections ultimately saves effort trying to reconcile incorrect feature alignments.

Dealing with Distortion Effects

Projecting data out of native coordinate systems introduces distortion as the price for viewing information together spatially. Map projections transform the earth’s three-dimensional surface onto a flat grid, skewing shapes, distances, directions, and areas in the process.

The effects intensify further from the defined origin point or outside optimal usage regions for each projection. Some projections preserve area but distort shape and scale (Equal Area family). Other projections maintain accuracy for distance, bearing and scale relationships at the expense of area – like Mercator commonly used in web maps.

Solving distortion effects again relies on selecting appropriate projections for the intended usage:

Use conformal (shape-preserving) projections for navigation
Use equidistant projections for measuring distances
Use equal-area for calculating areas or raster analysis
Use region-specific projections to minimize scale variation locally

There are also techniques to model some distortion effects and transform coordinates to compensate alignments.

Troubleshooting Errors

ArcGIS can raise cryptic errors during workflows transforming and projecting data. Common examples include “Failed to project” warnings when calculating fields or exporting outputs. Such errors point to mismatched coordinate systems upstream that have not been aligned properly.

Strategies to troubleshoot these types of projection issues include:

Explicitly define projections on all inputs, especially old legacy shapefiles
Use describe functions to validate expected projections match prior to operations
Avoid intermediate unprojected outputs when chaining multistep tasks
Double check parameters on projections assigned to output data

Taking extra care to validate systems align resolves many systemic projection issues.

When to Define vs Project Coordinate Systems

With a deeper grasp of coordinate system concepts and functions in ArcGIS, determining optimal workflows becomes more apparent.

In practice, accurately defining a dataset’s native system should take priority over projection tasks. Assigning proper coordinate system definitions should follow directly after creating or acquiring new spatial data.

Projection operations come into play once data needs to be accessed and visualized across multiple defined source systems. Understanding intended analysis and output goals guides projection approach.

Scenarios for Defining Coordinate Systems

Rules of thumb for when defining coordinate systems makes the most sense:

Immediately after digitizing new features or collecting field data
When loading external raw data sources lacking projections
Prior to editing or transforming feature geometry coordinates
Before calculating measurements or spatial analysis
When archiving authoritative GIS datasets externally

Systems should be assigned based on source metadata or spatial references from related data in enterprise geodatabases whenever possible.
Consistently defining accurate coordinate systems cements the critical spatial foundation for downstream workflows.

Scenarios for Projecting Coordinate Systems

Common use cases where projecting makes sense:

Visualizing layers together with mismatched systems
Analyzing layers covering different regional extents
Exporting maps and data to standardized systems
Sharing access to data using web applications
Calibrating analysis locally when working in large geographic systems

Project “on-the-fly” for dynamic visualization and queries. Project permanently to output analysis products and shareable resources with normalized coordinates.

Best Practices

Adopting core best practices helps minimize projection issues:

Define native coordinate systems for sources upfront whenever possible
Investigate metadata and properties to identify defined projections
Use specialized projections aligned to data and analysis contexts
Limit analysis chaining across multiple undefined or misaligned systems
Only project once necessary at the last stage for outputs or web integration

Projection workflows abstract underlying coordinate system complexities away from users. But lacking knowledge of their principles can undermine analysis and weaken confidence in GIS overall. Coordinate systems remain a critical building block within the science and technology of geographic information systems.