Automating Dominant Zone Identification Workflows In Arcgis

Defining Dominant Zones for Spatial Analysis

Understanding Dominant Zones

A dominant zone refers to a contiguous geographic area that exhibits attributes most representative of a region. Specifically, a dominant zone captures the modal characteristics of spatial phenomena across measurements like land use type, socioeconomic indicators, terrain morphology, and land cover. Gaining insight into dominant zones facilitates more informed decision-making and planning for resource allocation, infrastructure development, environmental regulation, and other spatially-oriented initiatives.

The process of dominant zone identification involves using GIS tools and statistical analysis to determine areas of homogeneity and spatial autocorrelation. This reveals zones within a study area that reflect dominant trends and distributions of target attributes. Automating dominant zone mapping workflows improves efficiency and standardization.

Common Uses of Dominant Zones

Dominant zone delineation provides value across many domains including:

Urban planning – guiding infrastructure development, zoning policy, and service provisioning
Hazard mapping – concentrating mitigation spending based on risk patterns
Environmental analysis – focusing conservation efforts on representative habitats
Agricultural land management – aligning crop selection and farm subsidies spatially
Epidemiology – predicting disease spread vectors based on population geography

These examples demonstrate how dominant zone identification concentrates efforts in signal-rich areas while minimizing resource expenditure in outliers. Automation makes regularly updating dominant zones viable to reflect current conditions.

Challenges in Manual Identification

Attempting to manually delineate dominant zones across regional scales poses multiple challenges including:

Labor intensity – GIS processing and statistical testing requires extensive analyst time
Consistency – output zones vary based on individual judgment
Reproducibility – undocumented workflows impede periodic regeneration
Accuracy – zones reflect dated input conditions unless iterated regularly

By scripting geoprocessing workflows for automated dominant zone mapping, organizations can overcome these challenges and gain efficiency.

Automating Dominant Zone Detection

Leveraging ArcGIS Geoprocessing Tools

ArcGIS provides a robust set of geoprocessing tools and statistical models accessible through Python that can identify dominant zones based on patterns in spatiotemporal input data.
Key capabilities include:

Overlay analysis – combine multiple input datasets with location-based context
Spatial autocorrelation – calculate statistical similarity of proximate features
Hot spot analysis – identify clustering patterns and intensities
Segmentation and clustering – group cells into zones of homogeneity

Chaining ArcGIS tools into a geospatial model streamlines dominant zone mapping. Containerizing the model within a Python script enables scheduling recurring execution as new input data becomes available.

Workflow for Automated Dominant Zone Identification

A best practice workflow for scripting dominant zone identification in ArcGIS involves:

Preparing Input Data

Raw geospatial data, including vector features, tabular records, raster datasets, and time series, are collated into a consolidated geodatabase. Input processing steps transform the source data into normalized features classes for synoptic input into the model. This processing may involve clipping, projecting, resampling, interpolating, aggregating, and filtering operations to produce consistent inputs. Metadata helps track model versions and input lineages.

Configuring Environment Settings

The script sets model parameters, output schemas, and result handling options through script arguments or configuration files. Key settings define snap intervals, statistics fields, band thresholds, segmentation clustering distances, and scoring precedence. Environments control temporary file management, output spatial references, and data sharing across tools.

Executing Tools and Models

The script sequences calls to ArcGIS tools including Intersect, Collect Events, Cluster and Outlier Analysis, Grouping Analysis, and Polygon Neighbors to process inputs into dominant zones. Intermediate data products feed into subsequent operations. Default settings streamline processing, while parameters provide tuning capacity.

Example Python Script for Automation

The following Python code demonstrates a scripted workflow automating dominant zone identification through ArcGIS geoprocessing functions:

# Import modules
import arcpy
from arcpy import env
from arcpy.sa import *

# Set environment
env.workspace = r"C:\GIS\Temp"  

# Local variables
inZoneFC = "studyArea.shp"
inLandcoverRaster = "landcover.img" 
inPopulationTable = "populationData.csv"
outDominantZones = "dominantZonesOutput"
tempWorkspace = "in_memory"

# Set snap interval, statistics, thresholds
cellSize = 100 
statsType = "MEAN"
threshold = 1.0

# Process inputs
zoneRaster = FeatureToRaster(inZoneFC, cellSize) 
popRaster = TabulateArea(zoneRaster, "POP_FIELD", inPopulationTable)  

# Execute model
outRas = SegmentMeanShift(inLandcoverRaster, popRaster, tempWorkspace, cellSize, threshold)
outShp = RasterToPolygon(outRas, "SIMPLIFY", "VALUE")

# Set output  
outShp.save(outDominantZones)

This script demonstrates how Python enables chaining geoprocessing functions into an automated workflow for repeated dominant zone mapping.

Visualizing and Validating Results

Inspecting Identified Dominant Zones

GIS desktop software provides rich capabilities for symbolizing and inspecting output dominant zones. Styling polygons based on domain values reveals spatial patterns. Swiping before and after layer views validates script repeatability. Feature reporting quantifies zone statistics. Interactive selection filters outliers. Data reviewers provide feedback through markup tools to guide script adjustments.

Statistical Validation of Outputs

Comparing statistical distributions between raw input data and dominant zones validates model efficacy. Target metrics include homogeneity of variance, spatial autocorrelation, and feature densities. Assessing statistical similarities across cycles with varied input data tests model robustness. Python notebooks help construct validation framework to profile outputs. Cross-validation against external benchmarks also helps gauge model performance.

Fine-tuning Parameters for Accuracy

Optimizing model parameters is key to achieving accurate, reliable outputs. Incrementally adjusting variables like snapping intervals, statistical fields, cluster thresholds, scoring weights, and zoning hierarchy structures improves done granularity. Comparing iteration results guides parameter selection. Sensitivity analysis identifies which levers provide the greatest performance gains. Validation metrics steer the tuning process towards optimal configurations.

Integrating Automated Workflows into Operations

Batch Processing for Multiple Areas

While scripts automate dominant zone mapping for individual study areas, batch processing capabilities from tools like ArcGIS Pro enable running workflows across multiple regions in succession. Federated geodatabases and enterprise servers help disseminate input data and collect outputs. Distributed processing harnesses infrastructure scalability.

Scheduling and Monitoring Jobs

Server engines like ArcGIS Enterprise GeoEvent Processor enable registering scripts as regularly scheduled services triggered by temporal events or data updates. Dashboards help monitor job queues, resource allocation, and results volumes. Email/text alerts notify administrators of failures while automated retries improve reliability. Usage metrics measure throughput to right-size infrastructure.

Managing Results as Information Products

Treating script outputs as enterprise information products improves discoverability, governance, and lifecycle management. Cataloguing dominant zone datasets with metadata aids in searchability. Hosting results in centralized data lakes with backups and versioning maintains integrity while supporting access. Whitelisting downstream applications assures security. Automated reporting, dashboarding, and alerting unlocks value from output data.