Available with Spatial Analyst license.

## Summary

Calculates a statistic on the points in a neighborhood around each output cell.

## Usage

When the specified field is integer, the available overlay statistic choices are Mean, Majority, Maximum, Median, Minimum, Minority, Range, Standard deviation, Sum, and Variety. When the field is floating point, the only allowed statistics are Mean, Maximum, Minimum, Range, Standard deviation, and Sum.

For statistic types Majority, Maximum, Median, Minimum, Minority, Range, and Sum, the output data type of the raster will be the same as the input field type. For statistic types Mean and Standard deviation, the output raster will always be floating point. For Variety, the output raster will always be integer.

If there aren't any points in the neighborhood of a raster cell, the Variety statistic assigns it a value of 0. For the other statistics, NoData is assigned.

See Analysis environments and Spatial Analyst for additional details on the geoprocessing environments that apply to this tool.

## Syntax

PointStatistics (in_point_features, field, {cell_size}, {neighborhood}, {statistics_type})

Parameter | Explanation | Data Type |

in_point_features | The input point features for which to calculate the statistics in a neighborhood around each output cell. The input can be either a point or multipoint feature class. | Feature Layer |

field | The field that the specified statistic will be calculated for. It can be any numeric field of the input features. It can be the Shape field if the input features contain z. | Field |

cell_size (Optional) | Cell size for output raster dataset. This is the value in the environment if specifically set. If not set in the environment, it is the shorter of the width or height of the extent of the input feature dataset, in the output spatial reference, divided by 250. | Analysis Cell Size |

neighborhood (Optional) | The Neighborhood class dictates the shape of the area around each input point used to calculate the statistic. The different types of neighborhood available are NbrAnnulus, NbrCircle, NbrRectangle, and NbrWedge. The following are the forms of the neighborhoods: - NbrAnnulus({innerRadius}, {outerRadius}, {units})
- NbrCircle({radius}, {units}
- NbrRectangle({width}, {height}, {units})
- NbrWedge({radius}, {startAngle}, {endAngle}, {units})
The default neighborhood is a square NbrRectangle with a width and height of 3 cells. | Neighborhood |

statistics_type (Optional) | The statistic type to be calculated. The calculation is performed on the values of the specified field of the point input in the neighborhood of each output raster cell. - MEAN — Calculates the average of the field values in each neighborhood.
- MAJORITY — Determines the most frequently occurring field value in each neighborhood. In the case of a tie, the lower value is used.
- MAXIMUM — Determines the largest field value in each neighborhood.
- MEDIAN — Determines the median field value in each neighborhood. In the case of an even number of points in the neighborhood, the result will be the lower of the two middle values.
- MINIMUM — Determines the smallest field value in each neighborhood.
- MINORITY — Determines the least frequently occurring field value in each neighborhood. In the case of a tie, the lower value is used.
- RANGE — Calculates the range (difference between largest and smallest) of the field values in each neighborhood.
- STD — Calculates the standard deviation of the field values in each neighborhood.
- SUM — Calculates the total of the field values in each neighborhood.
- VARIETY — Calculates the number of unique field values in each neighborhood.
The available choices for the statistics type is determined by the numeric type of the specified field. | String |

### Return Value

Name | Explanation | Data Type |

out_raster | The output point statistics raster. | Raster |

## Code sample

##### PointStatistics example 1 (Python window)

This example determines a statistic (the sum) on the input shapefile point features that fall in a circular neighborhood around each output raster cell.

```
import arcpy
from arcpy import env
from arcpy.sa import *
env.workspace = "C:/sapyexamples/data"
outPointStats = PointStatistics("ca_ozone_pts.shp", "OZONE", 500,
NbrCircle(10000, "MAP"), "SUM")
outPointStats.save("C:/sapyexamples/output/pointstatsout")
```

##### PointStatistics example 2 (stand-alone script)

This example determines a statistic (the average) on the input shapefile point features that fall in a circular neighborhood around each output raster cell.

```
# Name: PointStatistics_Ex_02.py
# Description: Calculates a statistic on points over a specified
# neighborhood outputting a raster.
# Requirements: Spatial Analyst Extension
# Import system modules
import arcpy
from arcpy import env
from arcpy.sa import *
# Set environment settings
env.workspace = "C:/sapyexamples/data"
# Set local variables
inPointFeatures = "ca_ozone_pts.shp"
field = "OZONE"
cellSize = 500
neighborhood = NbrCircle(6000, "MAP")
# Execute PointStatistics
outPointStatistics = PointStatistics(inPointFeatures, field, cellSize,
neighborhood, "MEAN")
# Save the output
outPointStatistics.save("C:/sapyexamples/output/pointstatout")
```

## Environments

## Licensing information

- ArcGIS Desktop Basic: Requires Spatial Analyst
- ArcGIS Desktop Standard: Requires Spatial Analyst
- ArcGIS Desktop Advanced: Requires Spatial Analyst