Measures the degree to which features are concentrated or dispersed around the geometric mean center.
Learn about how Standard Distance works
Measures the degree to which features are concentrated or dispersed around the geometric mean center.
Learn about how Standard Distance works
This tool honors the environment output coordinate system. Feature geometry is projected to the output coordinate system prior to analysis. All mathematical computations are based on the output coordinate system spatial reference.
Calculations based on either Euclidean or Manhattan distance require projected data to accurately measure distances.
Standard Distance measures the degree to which features are concentrated or dispersed around their geometric mean or median center.
The standard distance calculation may be based on an optional weight (to get the standard distance of businesses weighted by employees, for example).
The standard distance is a useful statistic, as it provides a single summary measure of feature distribution around their center (similar to the way a standard deviation measures the distribution of data values around the statistical mean).
Standard Distance creates a new feature class containing a circle polygon centered on the mean or median for each case. Each circle polygon is drawn with a radius equal to the standard distance. The attribute value for each circle polygon is its standard distance value.
If the underlying spatial pattern of the input features is concentrated in the center with fewer features toward the periphery (spatial normal distribution), a one standard deviation circle polygon will cover approximately 68 percent of the features; a two standard deviation circle will contain approximately 95 percent of the features; and three standard deviations will cover approximately 99 percent of the features in the cluster.
For line and polygon features, feature true geometric centroids are used in the computations.
Whenever using shapefiles keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from nonshapefile inputs may store or interpret null values as zero. This can lead to unexpected results.
Current map layers may be used to define the input feature class. When using layers, only the currently selected features are included in the analysis.
Parameter  Explanation  Datatype 

Input Feature Class (Required) 
A feature class containing a distribution of features for which the standard distance will be calculated.

Feature Layer 
Output Standard Distance Feature Class (Required) 
A polygon feature class that will contain a circle polygon for each input center. These circle polygons graphically portray the standard distance at each center point.

Feature Class 
Circle Size (Required) 
The size of output circles in standard deviations. The default circle size is 1; valid choices are 1, 2, or 3 standard deviations.

String 
Weight Field (Optional) 
The numeric field used to weight locations according to their relative importance.

Field 
Case Field (Optional) 
Field used to group features for separate standard distance calculations. The case field can be of numeric, date, or string type.

Field 
# Measure the geographic distribution of auto thefts # Import system modules import arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create() # Local variables... workspace = "C:/chris/data/" auto_theft_locations = "AutoTheft.shp" auto_theft_links = "AutoTheft_links.shp" auto_theft_sd = "auto_theft_SD.shp" auto_theft_se = "auto_theft_SE.shp" auto_theft_ldm = "auto_theft_LDM.shp" try: # Set the workspace (to avoid having to type in the full path to the data every time) gp.Workspace = workspace# Process: Standard Distance of auto theft locations...gp.StandardDistance_stats(auto_theft_locations, auto_theft_sd, "1 Standard Deviation", "#", "#") # Process: Directional Distribution (Standard Deviational Ellipse) of auto theft locations... gp.DirectionalDistribution_stats(auto_theft_locations, auto_theft_se, "1 Standard Deviation", "#", "#") # Process: Linear Directional Mean of auto thefts... gp.DirectionalMean_stats(auto_theft_links, auto_theft_ldm, "false", "#") except: # If an error occurred while running a tool, print the messages print gp.GetMessages()