Summary
Visualizes the variables stored in a netCDF cube and the results generated by the Space Time Pattern Mining tools. Output from this tool is a two-dimensional representation uniquely rendered based on the variable and theme chosen.
Illustration
Usage
This tool can only accept netCDF files created by the Create Space Time Cube By Aggregating Points tool.
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The LOCATIONS_WITH_DATA option allows you to see all locations that contain data for the chosen variable, and the TRENDS option shows you where values have been increasing or decreasing over time (the results of the Mann-Kendall statistic run on the chosen Cube Variable for each location). Both LOCATIONS_WITH_DATA and TRENDS are always available.
HOT_AND_COLD_SPOT_TRENDS shows you where hot and cold spot z-scores are increasing or decreasing over time (the results of the Mann-Kendall statistic run on the z-scores of the space-time hot spot analysis for the chosen Cube Variable), and EMERGING_HOT_SPOT_ANALYSIS_RESULTS re-creates the results you saw when you ran the Emerging Hot Spot Analysis tool. Both HOT_AND_COLD_SPOT_TRENDS and EMERGING_HOT_SPOT_ANALYSIS are only available when Emerging Hot Spot Analysis has been run on the chosen Cube Variable.
PERCENTAGE_OF_LOCAL_OUTLIERS, LOCAL_OUTLIER_IN_MOST_RECENT_TIME_PERIOD, LOCAL_OUTLIER_ANALYSIS_RESULTS, and LOCATIONS_WITHOUT_SPATIAL_NEIGHBORS are only available when you have run the Local Outlier Analysis tool. PERCENTAGE_OF_LOCAL_OUTLIERS shows you the proportion of total outliers at each location, and LOCAL_OUTLIER_IN_MOST_RECENT_TIME_PERIOD shows you all of the outliers that occurred in the most recent time step of your space-time cube. LOCAL_OUTLIER_ANALYSIS_RESULTS re-creates the results you saw when you initially ran the Local Outlier Analysis tool. LOCATIONS_WITHOUT_SPATIAL_NEIGHBORS displays all locations that have no spatial neighbors within the Neighborhood Distance that was chosen when you ran Local Outlier Analysis. As a result, these locations are relying only on temporal neighbors for analysis calculations.
NUMBER_OF_ESTIMATED_BINS shows how many bins were estimated at each unique location, allowing you to see if there is a spatial pattern of places with missing values. If entire sections of the map have high numbers of estimated bins, that area might be best left out of the analysis. LOCATIONS_EXCLUDED_FROM_ANALYSIS shows those places that had data, but had empty bins that could not be filled because they did not meet the criteria for estimation. Both NUMBER_OF_ESTIMATED_BINS and LOCATIONS_EXCLUDED_FROM_ANALYSIS are only available for Summary Fields.
Syntax
VisualizeSpaceTimeCube2D_stpm (in_cube, cube_variable, display_theme, output_features)
| Parameter | Explanation | Data Type |
in_cube | The netCDF cube that contains the variable to be displayed. This file must have an .nc extension and must have been created using the Create Space Time Cube By Aggregating Points tool. | File |
cube_variable | The numeric variable in the netCDF cube that you want to explore. The cube will always contain the COUNT variable. Any Summary Fields in the cube will also be available if they were included in the cube creation process. | String |
display_theme |
The characteristic of the Cube Variable that you want to display. LOCATIONS_WITH_DATA and TRENDS will always be available with every cube. HOT_AND_COLD_SPOT_TRENDS and EMERGING_HOT_SPOT_ANALYSIS_RESULTS will only be available after Emerging Hot Spot Analysis has been run on the selected Cube Variable. NUMBER_OF_ESTIMATED_BINS and LOCATIONS_EXCLUDED_FROM_ANALYSIS will only be available for those Summary Fields that were included in the cube creation process.
| String |
output_features | The output feature class results. This feature class will be a two-dimensional map representation of the display variable chosen. | Feature Class |
Code sample
VisualizeSpaceTimeCube2D example 1 (Python window)
The following Python window script demonstrates how to use the VisualizeSpaceTimeCube2D tool.
arcpy.env.workspace = r"C:\STPM"
arcpy.VisualizeSpaceTimeCube2D_stpm("Homicides.nc", "AGE_STD_ZEROS", "LOCATIONS_EXCLUDED_FROM_ANALYSIS", "Homicides_Age_LocExc.shp")
VisualizeSpaceTimeCube2D example 2 (stand-alone script)
The following stand-alone Python script demonstrates how to use the VisualizeSpaceTimeCube2D tool.
# Display Space Time Cube of homicide incidents in a metropolitan area
# Import system modules
import arcpy
# Set geoprocessor object property to overwrite existing output, by default
arcpy.overwriteOutput = True
# Local variables...
workspace = r"C:\STPM"
try:
# Set the current workspace (to avoid having to specify the full path to the feature classes each time)
arcpy.env.workspace = workspace
# Display Space Time Cube of homicide with the standard deviation of victim’s age, fill no-data as 0
# Only display the locations excluded from analysis.
# Process: Visualize Space Time Cube in 2D
cube = arcpy.VisualizeSpaceTimeCube2D_stpm("Homicides.nc", "AGE_STD_ZEROS", "LOCATIONS_EXCLUDED_FROM_ANALYSIS", "Homicides_Age_LocExc.shp")
except:
# If any error occurred when running the tool, print the messages
print(arcpy.GetMessages())
Environments
Licensing information
- ArcGIS Desktop Basic: Yes
- ArcGIS Desktop Standard: Yes
- ArcGIS Desktop Advanced: Yes