Assigning From And To Nodes For Polygon Grouping Analysis In Arcgis

Defining the Problem: Why Group Polygons?

Geographic information systems (GIS) software allows users to store, display, and analyze spatial data. A common spatial data type is the polygon, which represents an area on a map such as a county, census tract, or land parcel. When analyzing a collection of polygons in GIS, it is often necessary to group related polygons together for more effective analysis. Reasons for grouping polygons include:

Calculating aggregated statistics: Grouping related polygons allows quick summarization of their total area, population, etc.
Modeling interactions: Grouping polygons by proximity or similarity allows analyzing how they potentially affect each other.
Simplifying a complex dataset: Analyzing thousands of individual polygons can be unwieldy, grouping them makes the data more tractable.
Performing regional analyses: Polygons belonging to a defined region (e.g. watershed) must be identified for specialized analysis.
Managing data more efficiently: Storing, editing, and analyzing grouped polygons reduces workload.

The most common way of grouping polygons in GIS is by spatial or attribute relationships – either grouping polygons that are near each other geographically or those that have similar attributes. This allows creating polygon groupings that make sense for the particular analysis being performed.

Setting Up the Geodatabase for Polygon Grouping

When aggregating a collection of polygons into logical groupings, proper data setup is imperative for an efficient workflow. The ArcGIS geodatabase provides an optimal container for housing input polygons, manipulating groupings, viewing spatial relationships, and enabling data sharing. Geodatabase topology rules can also be set to validate polygon groups and relationships.

Table of Contents

To get started, create a file geodatabase and import the raw polygon dataset with features needing grouping analysis. Ensure polygon features encompass the full extent needing analysis, with clean, closed boundaries and no gaps or overlaps between features. If necessary, create topology rules to enforce no gaps or overlaps.

Next, add unique IDs to each polygon for easier manipulation based on defined grouping rules. Also include desired grouping attributes like land use type, watershed codes, population sectors, etc. Make sure attributes are properly defined and uniformly populated throughout the dataset.

Now the geodatabase has been optimized for applying advanced spatial operations to assign groupings and derive intelligence. The imported polygon features can be manipulated as needed while maintaining integrity through validation rules.

Assigning Unique IDs to Polygons with ArcPy

ArcPy is a Python library for scripting and automating ArcGIS geoprocessing workflows. To systematically assign unique ID values to input polygons, an ArcPy cursor can iterate through each feature and update a designated “PolyID” field, for example:

import arcpy

# Path to polygons feature class  
inputFC = r"C:\Project\Grouping.gdb\Polygons"

# Field name to assign IDs
idField = "PolyID"

# Start ID number
idNum = 1  

# Create update cursor
with arcpy.da.UpdateCursor(inputFC, [idField]) as cursor:
    for row in cursor:
        row[0] = idNum 
        idNum += 1
        cursor.updateRow(row)

Now each polygon feature will have a unique integer ID for easier handling in grouping procedures. By running this script, manual one-by-one assignment is avoided even for thousands of polygons. The automated workflow also eliminates risks like duplicate IDs or gaps in numbering sequence.

Connecting Polygons by Proximity with Spatial Joins

A common way of aggregating polygons is by spatial proximity – grouping together polygons within a defined distance threshold. The Spatial Join geoprocessing tool sets up these proximity-based linkages.

By spatially joining the polygon layer to itself, an output table can be generated with source polygon IDs assigned to each proximate “neighbor” polygon falling within the specified distance. Additional attributes from the input features can also be transferred to the output table.

# Set local variables
inFeatures = "Polygons"
outTable = r"C:\Project\Grouping.gdb\PolygonNeighbors"
searchRadius = "100 Meters"
distanceField = "prox_dist"

# Perform spatial join 
arcpy.SpatialJoin_analysis(inFeatures, inFeatures, outTable, 
                           "JOIN_ONE_TO_MANY", "KEEP_ALL", 
                           searchRadius, distanceField)

Now table PolygonNeighbors links each polygon to its proximate neighbors, allowing further processing to define official groupings based on the proximity network.

Creating Adjacency Matrices to Finalize Connections

An adjacency matrix can supplement proximity-based spatial joins by crisply defining which polygons neighbor one another. This square {n x n} matrix for a dataset with n polygons has cells where a “1” signifies polygons share a boundary and “0” means no shared boundary.

An adjacency matrix resolves any uncertainties from the distance-based spatial join and gives absolute binary decisions whether polygon groupings are linked. Creating the matrix in Python, for 1000 polygons:

import numpy as np
import arcpy

# Get count of polygons
polygonCount = int(arcpy.GetCount_management("Polygons").getOutput(0)) 

# Initialize empty matrix 
adjMatrix = np.zeros((polygonCount, polygonCount))

# Update matrix diagonal
np.fill_diagonal(adjMatrix, 1)

# Fill cells by checking polygon boundaries
with arcpy.da.SearchCursor("Polygons", ["OID@", "SHAPE@"]) as curs:
    for row in curs:
        feat = row[1] 
        for row2 in curs:
            otherFeat = row2[1]  
            if feat.touches(otherFeat):
                adjMatrix[row[0]-1,row2[0]-1] = 1

print(adjMatrix)

Now the adjacency matrix definitively maps the linkage structure for the polygon grouping analysis. This supplements the proximity-based links already established.

Generating Polygon Groups and Analysis Ready Datasets

With polygon unique IDs captured, proximity neighbors identified, and adjacency matrices built, the pieces are in place to start generating official groupings.

A primary method is assigning a common GroupID attribute to connected polygons. Python code can traverse adjacency matrix rows and proximity links to assign like GroupIDs:

curGroupID = 1

# Process adjacency matrix
for row in adjMatrix:
   polygons = getIDs(row, 1)  
   if polygons not grouped already:
       assignGroupID(polygons, curGroupID) 
       curGroupID += 1
       
# Process spatial join table   
for row in outTable:
   poly = row.getValue('Src_PolyID')
   neighbors = row.getValue('NeighborIDs')
   if poly not grouped already:
       assignGroupID([poly] + neighbors, curGroupID)
       curGroupID += 1

Now each clustered polygon is assigned a GroupID tying it to its neighbors. Additional processes may further refine groupings by attributes or spatial requirements.

Finally, with official groups defined in the GroupID field, summary datasets can be generated for final analysis. Dissolving polygons based on GroupID into aggregated units, statistics like total population and land area sum readily for regions.

Output feature classes can also be created for alternative workflows like defining watershed-based groups. With clean polygon groups, the full power of GIS analytics can be leveraged.

Checking Results and Iterating Groupings

Quality assurance steps should check that groupings meet requirements and make sense for their intended purpose. Visual checks should verify polygons logically cluster together at junction boundaries without odd shapes or disconnected pieces.

Statistics can summarize groupings to check suitable aggregation levels – total group counts seem reasonable and minimum/maximum polygons per group fall within parameters. Compute centroid distances between grouped polygons to verify proximity rules.

If groupings fail validation checks, iteratively adjust parameters like distance thresholds, attribute filters, minimum group sizes, etc. to refine results. Leveraging ArcGIS ModelBuilder streamlines iterating through multiple grouping attempts.

As polygon groupings better capture the true study area structure, they better serve required analyses. But grouping workflows involve experimentation before optimal configurations emerge.

Example Code for Polygon Grouping Workflow

Below provides a sample Python code snippet implementing a basic polygon grouping workflow involving)

Loading input polygons
Executing proximity-based spatial join
Generating adjacency matrix
Traversing links to define groups
Checking results

import arcpy, numpy 

# Paths
inPolys = r"C:\Project.gdb\Polygons"
outSpJoin = r"C:\Project.gdb\SpatialJoin" 
adjMatrix = r"C:\Project\Matrix.csv"
groupedFC = r"C:\Project.gdb\Grouped_Polys"

# Spatial Join 
arcpy.SpatialJoin_analysis(inPolys, inPolys, outSpJoin, 
                           "JOIN_ONE_TO_MANY", "KEEP_ALL", 
                           "30 Meters")

# Adjacency Matrix
polygonCount = arcpy.GetCount_management(inPolys) 
matrix = numpy.zeros((polygonCount, polygonCount))
np.fill_diagonal(matrix, 1)
# ..code to populate matrix based on geometry..
numpy.savetxt(adjMatrix, matrix, delimiter=",")

# Grouping 
curGroupID = 1
for row in matrix:
    polys = getIDs(row, 1) # Get connections
    if polys not assigned already: 
        assignGroupID(polys, curGroupID)
        curGroupID += 1
        
# Check Results
dissolvedPolys = arcpy.Dissolve_management(groupedFC, dissolve_field="GroupID") 
print("Total grouped polygons:", arcpy.GetCount(dissolvedPolys))

This presents a streamlined workflow for leveraging proximity and topology to logically group polygons for more effective analysis.