## 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 or the Create Space Time Cube From Defined Locations tool.

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 results are only available when Emerging Hot Spot Analysis has been run on the chosen Cube Variable.

Time Series Clustering results re-creates the results you saw when you initially ran the Time Series Clustering tool.

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(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 either the Create Space Time Cube By Aggregating Points or Create Space Time Cube From Defined Locations 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 or Variables will also be available if they were included in the cube creation process. | String |

display_theme |
The characteristic of the Cube Variable you want to display. Options will vary depending on how the cube was created and the analyses that were run. If your cube was created by aggregating points, Locations with data and Trends will always be available. 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. If your cube was created from defined locations, Trends will be available for those Summary Fields or Variables that were included in the cube creation process. 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. Percentage of local outliers, Local outlier in the 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. - LOCATIONS_WITH_DATA —Displays all locations that contain data for the chosen variable
- TRENDS —The trend of values at each location using the Mann-Kendall statistic
- HOT_AND_COLD_SPOT_TRENDS —The trend of z-scores at each location using the Mann-Kendall statistic
- EMERGING_HOT_SPOT_ANALYSIS_RESULTS —Displays the results of Emerging Hot Spot Analysis for the Cube Variable chosen
- LOCAL_OUTLIER_ANALYSIS_RESULTS —Displays the results of Local Outlier Analysis for the Cube Variable chosen
- PERCENTAGE_OF_LOCAL_OUTLIERS —Displays the total percentage of outliers at each location
- LOCAL_OUTLIER_IN_MOST_RECENT_TIME_PERIOD —Displays all outliers that are present in the most recent time period
- TIME_SERIES_CLUSTERING_RESULTS —Displays the results of Time Series Clustering for the Cube Variable chosen
- LOCATIONS_WITHOUT_SPATIAL_NEIGHBORS —For the last analysis run, displays all locations that have no spatial neighbors and, as a result, are relying on temporal neighbors for analysis
- NUMBER_OF_ESTIMATED_BINS —The number of bins that were estimated for each location
- LOCATIONS_EXCLUDED_FROM_ANALYSIS —The locations that were excluded from analysis because they had empty bins that did not meet the criteria for estimation
| 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

The following Python window script demonstrates how to use the VisualizeSpaceTimeCube2D tool.

```
import arcpy
arcpy.env.workspace = r"C:\STPM"
arcpy.VisualizeSpaceTimeCube2D_stpm("Homicides.nc", "AGE_STD_ZEROS", "LOCATIONS_EXCLUDED_FROM_ANALYSIS", "Homicides_Age_LocExc.shp")
```

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 environment property to overwrite existing output, by default
arcpy.env.overwriteOutput = True
# Local variables...
workspace = r"C:\STPM"
# 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")
```

## Environments

## Licensing information

- Basic: Yes
- Standard: Yes
- Advanced: Yes