Interpolate Points (Standard Feature Analysis)

Summary

Predicts values at new locations based on measurements from a collection of points. The tool uses point data with values at each point as input and creates areas classified by predicted values.

Examples include the following:

• An air quality management district has sensors that measure pollution levels. This tool can be used to predict pollution levels at locations that don't have sensors, such as locations with at-risk populations, for example, schools or hospitals.
• Predict heavy metal concentrations in crops based on samples taken from individual plants.
• Predict soil nutrient levels (nitrogen, phosphorus, potassium, and so on) and other indicators (such as electrical conductivity) to study their relationships to crop yield and prescribe precise amounts of fertilizer for each location in the field.
• Meteorological applications include the prediction of temperatures, rainfall, and associated variables (such as acid rain).

Illustration

Usage

• A point layer is used as the input. The input layer must have a numeric field to serve as the basis of the interpolation. This tool is designed to work with data that changes slowly and smoothly over the landscape, such as temperature and pollution levels. It is not appropriate for data such as population or median income that changes abruptly over short distances.

• This tool can be set to optimize speed, accuracy, or a balanced combination of the two. The more accurate the predictions, the slower the results take to calculate and vice versa.

• A layer of standard errors can be created by this tool using the output prediction error option. A 95 percent confidence interval can be calculated for the interpolated layer by taking the interpolation value and adding two standard errors for the upper limit and subtracting two standard errors from the lower limit.

• This tool uses the Esri Empirical Bayesian Kriging method to perform the interpolation. The parameters that are supplied to this method are controlled by the Interpolate Option parameter. The parameters are outlined below.

  Parameter	Speed	Default	Accuracy
  Data transformation type	NONE	NONE	EMPIRICAL
  Semivariogram model type	POWER	POWER	K_BESSEL
  Maximum number of points in each local model	50	75	200
  Local model area overlap factor	1	1.5	3
  Number of simulated semivariograms	30	100	200
  Minimum neighbors	8	10	15
  Maximum neighbors	8	10	15

Parameters

Dialog

Label	Explanation	Data Type
Input Features	The point features that will be interpolated to a continuous surface layer.	Feature Set
Output Name	The name of the output layer that will be created on your portal.	String
Interpolation Field (Optional)	The numeric field containing the values that will be interpolated.	Field
Interpolate Option (Optional)	Specifies whether speed, accuracy, or a balanced combination of the two will be used. More accurate predictions take longer to calculate. Speed—Speed will be used. Balanced—A balanced combination of speed and accuracy will be used. This is the default. Accuracy—Accuracy will be used.	String
Output prediction error (Optional)	Specifies whether a prediction error output layer will be created. If checked, a polygon layer of standard errors for the interpolation predictions will be output. Standard errors are useful because they provide information about the reliability of the predicted values. A simple rule of thumb is that the true value will fall within two standard errors of the predicted value 95 percent of the time. For example, a new location has a predicted value of 50 with a standard error of 5. This means that this task's best prediction is that the true value at that location is 50, but it reasonably could be as low as 40 or as high as 60. To calculate this range of reasonable values, multiply the standard error by 2, add this value to the predicted value to get the upper end of the range, and subtract it from the predicted value to get the lower end of the range. Unchecked—A prediction error output layer will not be created. This is the default. Checked—A prediction error output layer will be created.	Boolean
Classification Type (Optional)	Specifies how predicted values will be classified into polygons. Equal interval— Polygons will be created so that the range of density values is equal for each area. Geometric interval— Polygons will be created based on class intervals that have a geometric series. This ensures that each class range has approximately the same number of values in each class and that the change between intervals is consistent. This is the default. Equal area— Polygons will be created so that the size of each area is equal. For example, if the result has more high-density values than low-density values, more polygons will be created for high densities. Enter class breaks manually—A custom range of values for areas will be used. These values must be provided as class breaks.	String
Number of Classes (Optional)	The range of predicted values that will be divided into distinct classes. The range of values in each class is determined by the classification type. Each class defines the boundaries of the result polygons. The default is 10 and the maximum is 32.	Long
Class Breaks (Optional)	The class break values that will be used for a manual classification. These values define the upper limit of each class, so the number of classes will equal the number of values provided. Areas will not be created for any locations with predicted values above the largest break value provided. You must provide at least 2 and no more than 32 values.	Double
Bounding Polygons (Optional)	The layer containing the polygons where values will be interpolated. For example, if you are interpolating densities of fish in a lake, you can use the boundary of the lake in this parameter, and the output will only contain polygons within the boundary of the lake.	Feature Set
Predict At Point Layer (Optional)	An optional layer containing point locations that will be used to calculate prediction values. This allows you to make predictions at specific locations of interest. For example, if the input layer represents measurements of pollution levels, you can use this parameter to predict the pollution levels of locations with large at-risk populations, such as schools or hospitals. You can then use this information to give recommendations to health officials in those locations.	Feature Set

Derived Output

Label	Explanation	Data Type
Output Layer	The output polygon features, where each polygon surrounds interpolated values based on the classification type and number of classes.	Feature Set
Output Prediction Error Layer	Contains the predicted error for each point in the input layer.	Feature Set
Output Predicted Points Layer	The point layer containing points from the predicted point layer with their predicted values.	Feature Set

Python

arcpy.sfa.InterpolatePoints(inputLayer, outputName, {field}, {interpolateOption}, {outputPredictionError}, {classificationType}, {numClasses}, {classBreaks}, {boundingPolygonLayer}, {predictAtPointLayer})

Name	Explanation	Data Type
inputLayer	The point features that will be interpolated to a continuous surface layer.	Feature Set
outputName	The name of the output layer that will be created on your portal.	String
field (Optional)	The numeric field containing the values that will be interpolated.	Field
interpolateOption (Optional)	Specifies whether speed, accuracy, or a balanced combination of the two will be used. More accurate predictions take longer to calculate. 1—Speed will be used. 5—A balanced combination of speed and accuracy will be used. This is the default. 9—Accuracy will be used.	String
outputPredictionError (Optional)	Specifies whether a prediction error output layer will be created. If OUTPUT_ERROR is specified, a polygon layer of standard errors for the interpolation predictions will be output. Standard errors are useful because they provide information about the reliability of the predicted values. A simple rule of thumb is that the true value will fall within two standard errors of the predicted value 95 percent of the time. For example, a new location has a predicted value of 50 with a standard error of 5. This means that this task's best prediction is that the true value at that location is 50, but it reasonably could be as low as 40 or as high as 60. To calculate this range of reasonable values, multiply the standard error by 2, add this value to the predicted value to get the upper end of the range, and subtract it from the predicted value to get the lower end of the range. NO_ERROR—A prediction error output layer will not be created. This is the default. OUTPUT_ERROR—A prediction error output layer will be created.	Boolean
classificationType (Optional)	Specifies how predicted values will be classified into polygons. EQUALINTERVAL— Polygons will be created so that the range of density values is equal for each area. GEOMETRICINTERVAL— Polygons will be created based on class intervals that have a geometric series. This ensures that each class range has approximately the same number of values in each class and that the change between intervals is consistent. This is the default. EQUALAREA— Polygons will be created so that the size of each area is equal. For example, if the result has more high-density values than low-density values, more polygons will be created for high densities. MANUAL—A custom range of values for areas will be used. These values must be provided as class breaks.	String
numClasses (Optional)	The range of predicted values that will be divided into distinct classes. The range of values in each class is determined by the classification type. Each class defines the boundaries of the result polygons. The default is 10 and the maximum is 32.	Long
classBreaks [classBreaks,...] (Optional)	The class break values that will be used for a manual classification. These values define the upper limit of each class, so the number of classes will equal the number of values provided. Areas will not be created for any locations with predicted values above the largest break value provided. You must provide at least 2 and no more than 32 values.	Double
boundingPolygonLayer (Optional)	The layer containing the polygons where values will be interpolated. For example, if you are interpolating densities of fish in a lake, you can use the boundary of the lake in this parameter, and the output will only contain polygons within the boundary of the lake.	Feature Set
predictAtPointLayer (Optional)	An optional layer containing point locations that will be used to calculate prediction values. This allows you to make predictions at specific locations of interest. For example, if the input layer represents measurements of pollution levels, you can use this parameter to predict the pollution levels of locations with large at-risk populations, such as schools or hospitals. You can then use this information to give recommendations to health officials in those locations.	Feature Set

Derived Output

Name	Explanation	Data Type
outputLayer	The output polygon features, where each polygon surrounds interpolated values based on the classification type and number of classes.	Feature Set
outputPredictionErrorLayer	Contains the predicted error for each point in the input layer.	Feature Set
outputPredictedPointsLayer	The point layer containing points from the predicted point layer with their predicted values.	Feature Set

Environments

Extent

Licensing information

• Basic: Requires your account in ArcGIS Enterprise to have the Perform Analysis privilege
• Standard: Requires your account in ArcGIS Enterprise to have the Perform Analysis privilege
• Advanced: Requires your account in ArcGIS Enterprise to have the Perform Analysis privilege