Examples Of Raster Calculation Expression In Arcgis

Compute raster analysis results using common ArcGIS spatial analyst expressions. Select your operation and input parameters below.

Comprehensive Guide to Raster Calculation Expressions in ArcGIS

Raster calculation expressions in ArcGIS provide powerful tools for spatial analysis, enabling GIS professionals to derive meaningful information from raster datasets. This guide explores practical examples, syntax rules, and advanced techniques for working with raster calculator expressions in ArcGIS Spatial Analyst.

1. Understanding Raster Calculator Basics

The Raster Calculator in ArcGIS allows you to create and run Map Algebra expressions to perform spatial analysis. Map Algebra uses a simple but powerful syntax where:

• Operators include arithmetic (+, -, *, /), logical (&&, ||, !), and mathematical functions
• Rasters are referenced by their layer names in the Table of Contents
• Tools can be incorporated directly in expressions (e.g., Slope(), Aspect())
• Constants are numeric values used in calculations

Basic expression syntax:
[raster1] + [raster2] * 2
Slope(“elevation”)
Con(IsNull(“landuse”), 0, “landuse”)

2. Common Raster Calculation Examples

2.1 Terrain Analysis Expressions

Slope Calculation: Measures the rate of change in elevation

Slope(“dem”, “DEGREE”)
Slope(“dem”, “PERCENT_RISE”)

Aspect Calculation: Identifies the downhill direction of maximum rate of change

Aspect(“dem”)

Hillshade: Creates a shaded relief effect

HillShade(“dem”, 315, 45)

2.2 Vegetation Indices

NDVI (Normalized Difference Vegetation Index):

Float(“nir_band” – “red_band”) / Float(“nir_band” + “red_band”)

SAVI (Soil-Adjusted Vegetation Index):

1.5 * (Float(“nir_band” – “red_band”)) / (Float(“nir_band” + “red_band” + 0.5))

2.3 Reclassification Operations

Simple Reclass:

Con(“landuse” == 1, 10,
  Con(“landuse” == 2, 20,
    Con(“landuse” == 3, 30, 0)))

Reclass by Range:

Con(“elevation” > 1000 && “elevation” <= 2000, 1,
  Con(“elevation” > 2000, 2, 0))

3. Advanced Raster Calculation Techniques

3.1 Conditional Logic with Con()

The Con() function (conditional evaluation) is one of the most powerful tools in raster calculations:

Con(condition, true_expression, false_expression)

Example: Classify elevation into 3 categories
Con(“dem” < 500, 1,
  Con(“dem” < 1000, 2, 3))

3.2 Mathematical Functions

ArcGIS supports numerous mathematical functions:

Function	Description	Example
Abs()	Absolute value	Abs(“raster” – 100)
Exp()	Exponential	Exp(“raster”)
Log()	Natural logarithm	Log(“raster”)
Power()	Exponentiation	Power(“raster”, 2)
SquareRoot()	Square root	SquareRoot(“raster”)

3.3 Focal Statistics

Apply neighborhood operations:

FocalStatistics(“elevation”, NbrRectangle(3,3), “MEAN”)
FocalStatistics(“temperature”, NbrCircle(5), “MAJORITY”)

4. Performance Optimization

When working with large raster datasets:

1. Set appropriate environments: Define processing extent, cell size, and mask to control output
2. Use temporary rasters: For intermediate steps to save memory
3. Batch process: Break large areas into tiles when possible
4. Simplify expressions: Combine operations when possible to reduce processing steps
5. Use 32-bit float: For continuous data to maintain precision

Example with environment settings:
# Set environments first
arcpy.env.extent = “study_area”
arcpy.env.cellSize = “dem”
arcpy.env.mask = “boundary”

# Then run calculation
result = Slope(“dem”, “PERCENT_RISE”) * 100

5. Common Errors and Solutions

Error Type	Common Causes	Solution
Syntax Errors	Missing parentheses, incorrect operators, undefined variables	Check expression structure carefully, use valid layer names
Data Type Mismatch	Mixing integer and float operations without conversion	Use Float() or Int() to convert explicitly
Extent Mismatch	Input rasters have different extents or cell sizes	Set analysis environment to control extent and cell size
NoData Handling	Unexpected NoData values in output	Use IsNull() or Con() to handle NoData explicitly
Memory Errors	Dataset too large for available memory	Process in tiles, use 32-bit processing, increase virtual memory

6. Real-World Application Examples

6.1 Flood Risk Assessment

Combine multiple factors to create a flood risk index:

# Normalize each factor to 0-1 range
slope_norm = Float(“slope” – 0) / (10 – 0)
precip_norm = Float(“precipitation” – 500) / (1500 – 500)
soil_norm = Con(“soil_type” == 1, 0.9, Con(“soil_type” == 2, 0.5, 0.1))

# Combine with weights
flood_risk = 0.4*slope_norm + 0.3*precip_norm + 0.3*soil_norm

6.2 Urban Heat Island Analysis

Calculate temperature difference between urban and rural areas:

# Classify land cover
urban = Con(“landcover” == 1, 1, 0)
rural = Con(“landcover” == 2, 1, 0)

# Calculate mean temperatures
urban_temp = ZonalStatistics(urban, “VALUE”, “temperature”, “MEAN”)
rural_temp = ZonalStatistics(rural, “VALUE”, “temperature”, “MEAN”)

# Calculate difference
heat_island = urban_temp – rural_temp

7. Best Practices for Raster Calculations

1. Document your expressions: Keep records of all calculations for reproducibility
2. Validate inputs: Check raster properties before processing
3. Test with subsets: Verify expressions on small areas before full processing
4. Use meaningful names: Name output rasters descriptively
5. Maintain data provenance: Track data sources and processing steps
6. Consider projections: Ensure all data is in the same coordinate system
7. Handle edge effects: Be aware of artifacts at raster boundaries

Additional Resources

For more advanced techniques and official documentation, consult these authoritative sources:

• ESRI Documentation: How Raster Calculator Works
• USGS National Map: Raster Data Sources
• Humboldt State University: Raster Calculator Tutorial