Optimizing Polygon Grouping In Arcgis With Networkx And Arcpy

The Problem of Slow Spatial Joins

Performing spatial joins between polygon layers in ArcGIS can be computationally expensive, especially when working with complex or high-resolution geographic data. The more polygon features that need to be compared and joined, the longer the spatial join tool takes to execute. Slow spatial joins can severely impact workflows and analysis involving polygon neighborhood or adjacency relationships.

The computational complexity stems from the fact that the spatial join tool must compare each polygon to every other polygon in the layers being joined to identify adjacency and build the desired output. With thousands or millions of polygon features, this process can be extremely slow even on powerful hardware.

Understanding Polygon Adjacency

Determining whether polygons share borders or points (adjacency) requires intensive geometric comparisons. The Spatial Join tool iterates through all feature combinations, computes envelopes and buffers, and performs intersection tests to ascertain adjacency.

Optimizing these comparisons is key to accelerating spatial joins for polygon neighborhood analysis. The connections between nearby or adjacent polygons can be pre-computed using computational geometry libraries like NetworkX.

Pre-processing the layers with NetworkX identifies polygon adjacencies via efficient spatial indexing and line intersection tests. This adjacency information can then help optimize ArcGIS spatial joins by reducing the number of on-the-fly geometric comparisons needed.

Using NetworkX to Find Adjacent Polygons

NetworkX is a commonly used Python library for analyzing graph and network structures. The networks it creates can include advanced spatial data types – including polygons and points.

NetworkX has highly optimized functions to test if lines and polygons intersect each other. This capability can be leveraged to efficiently find all polygons that are adjacent to one another in a layer.

The steps to analyze polygon adjacency with NetworkX area:

1. Load polygon geometry into Python as Shapely objects
2. Create a NetworkX graph object to store connections
3. Iterate through polygon edges to find intersections
4. Add intersections as connections between polygons in graph
5. Analyze graph to study spatial adjacencies

The NetworkX graph will contain nodes representing polygons, connected by edges where adjacency was found. This model of spatial relationships can then be applied in ArcGIS.

Example Code for NetworkX Polygon Analysis

Here is example code for finding adjacent polygons with NetworkX:

“`python
import networkx as nx
import geopandas as gpd
from shapely.geometry import LineString

# Load polygons as Shapely geometries
df = gpd.read_file(‘polygons.shp’)

# Create graph
G = nx.Graph()

# Iterate polygon edges
for index, row in df.iterrows():
geom = row[‘geometry’]
for i in range(len(geom.exterior.coords)-1):
line = LineString([geom.exterior.coords[i], geom.exterior.coords[i+1] ])
for index2, row2 in df.iterrows():
if index == index2:
continue

geom2 = row2[‘geometry’]

# Check if polygons share edge
if line.intersects(geom2):
G.add_edge(index, index2)

print(G.adj)
“`

This outputs a NetworkX graph with polygon adjacencies pre-computed prior to spatial analysis in ArcGIS. This effectively indexes polygon neighborhoods to optimize subsequent geoprocessing.

Leveraging ArcPy to Optimize Polygon Groups

The ArcPy module is a powerful Python library for automating and scripting tasks in ArcGIS. It allows direct control of geoprocessing tools via Python.

By leveraging ArcPy scripts, we can harness NetworkX’s pre-computed polygon adjacencies to optimize ArcGIS spatial joins.

The key goals are to:

– Group polygons based on NetworkX adjacency graph
– Perform spatial joins on groups rather than entire dataset
– Combine group results to get overall solution

This focused subsetting and parallel processing of the spatial join accelerates the overall workflow.

Example Code for ArcPy Spatial Joins

Here is sample code applying the NetworkX adjacency graph to optimize ArcPy spatial joins:

“`python
import networkx as nx
import arcpy

# Load NetworkX graph
G = nx.read_gpickle(“polygons_adj.gpickle”)

# Group polygon FIDs into subgraphs
subgraphs = nx.connected_components(G)

# Iterate each subgraph
for i, sg in enumerate(subgraphs):

# Spatial join subgroup
arcpy.SpatialJoin_analysis(polygons, parcels, “join_”+str(i))

# Print progress
print(“Joined subgroup”, i)

# Merge intermediate results
arcpy.Merge_management([“join_1”,”join_2″…, “output”])
“`

This processes adjacencies in focused batches rather than exhaustively trying every single polygon-polygon combination across potentially millions of features. Dramatic performance gains can be achieved.

Analyzing Results and Iterating

After completing the optimized spatial joins, analyze the output polygons compared to a standard brute-force spatial join. Compute statistics on adjacency, attribute transfer, and differences between the approaches.

Key questions to ask:

– Were any adjacencies or attributes missed relative to standard join?
– What performance gains resulted from the optimization?
– How did intermediate merge impact data integrity?

Additionally, experiment with tweaks such as graph node thresholds, polygon simplification, and alternative geoemtry intersection tests in NetworkX. Try indexing the geometry once rather than per-join iteration.

Incrementally improve the implementation based on diagnostic analytics to further speed workflows. The optimal recipe depends greatly on data volumes, attribute needs, and output data quality requirements.

Iteratively refine the methodology based on usage requiring polygon neighborhood analysis within real-world ArcGIS workflows at your organization.