Summarize Percent Change (Crime Analysis and Safety)

Summary

Calculates the percent change for features that correspond with point features representing two equal comparison time periods.

Usage

• The Input Current Period Point Features and Input Previous Period Point Features features can be created by performing date and time selections on a larger dataset and saving the selections to new layers.

• The following fields will be added to the output:

  • CUR_CNT—The number of points from the Input Current Period Point Features parameter values that are coincident with the Input Features parameter values.
  • PREV_CNT—The number of points from the Input Previous Period Point Features parameter values that are coincident with the Input Features parameter values.
  • DIFF_CNT—The difference between the CUR_CNT and PREV_CNT fields.
  • PERC—The percentage of change between the CUR_CNT and PREV_CNT fields.This is calculated using the formula

    ((C - P) / P) * 100

    where C is the current count and P is the previous count. If the current count is 0, the percent change value is null. When the percent change value is null, these features are designated as no percentage in output symbology classification.

Syntax

SummarizePercentChange(in_features, in_current_features, in_previous_features, out_feature_class, {search_radius})

Parameter	Explanation	Data Type
in_features	The coincident features from which comparison time periods will be counted and compared.	Feature Layer
in_current_features	The point features filtered to the most recent comparison time period. For example, crimes from the previous 14 days.	Feature Layer
in_previous_features	The point features filtered to the time period immediately preceding the current period. This time period must beof equal length to the current period, to provide an accurate comparison. For example, if the current period contains features from January 15 to January 28, the previous period would contain features from January 1 to January 14.	Feature Layer
out_feature_class	The output feature class containing difference counts and percent change calculations for the time period comparison.	Feature Class
search_radius (Optional)	The maximum distance from the line or point Input Features that a point feature will be considered coincident. This parameter is only active when point or line features are used as the input features.	Linear Unit

Code sample

SummarizePercentChange example 1 (Python window)

The following Python window script demonstrates how to use the SummarizePercentChange function in immediate mode.

import arcpy
arcpy.env.workspace = r"C:\data\city_pd.gdb"
arcpy.ca.SummarizePercentChange("Precincts", "crimes_q3", "crimes_q2",
                                "crimes_percent_change_by_precinct_q2_to_q3")

SummarizePercentChange example 2 (stand-alone script)

The following Python script demonstrates how to use the SummarizePercentChange function in a stand-alone script.

# Name: SummarizePercentChange.py
# Description: Compare the number of crimes committed in the second quarter to 
#              the third quarter by precincts, and determine the percent 
#              change.

# import system modules 
import arcpy

# Set environment settings
arcpy.env.workspace = r"C:\data\city_pd.gdb"

# Execute SummarizePercentChange
arcpy.ca.SummarizePercentChange("Precincts", "crimes_q3", "crimes_q2",
                                "crimes_percent_change_by_precinct_q2_to_q3")

Environments

Current Workspace, Scratch Workspace, Output Coordinate System, Geographic Transformations, Extent, XY Resolution, XY Tolerance, Output has M values, M Resolution, M Tolerance, Output has Z values, Default Output Z Value, Z Resolution, Z Tolerance, Qualified Field Names, Output CONFIG Keyword, Maintain Attachments, Auto Commit, Output XY Domain, Output M Domain, Output Z Domain

Licensing information

• Basic: Yes
• Standard: Yes
• Advanced: Yes