Land Use Regression (LUR) is a widely used approach for modeling environmental exposure, such as air pollution or noise, by linking observations at specific locations to surrounding built and natural environment characteristics. In practice, LUR models rely on spatial predictors that summarize what is near a location (e.g., road density, land use composition, vegetation) and how close a location is to major sources (e.g., distance to highways or airports).

In this post, I describe a production-oriented workflow implemented with ArcPy to prepare large-scale LUR predictors. The goal is to extract thousands of features around a large number of observation points (e.g., GPS-derived locations) and prediction grid cells, across multiple spatial scales (buffers from 25 m to 500 m). Because this requires millions of overlay and summarization operations, performance and stability become primary design constraints.

Overview of the Workflow

The feature engineering pipeline is organized into two computation types:

1) Within-Buffer Predictors

These summarize attributes inside circular neighborhoods around each point:

This family is typically implemented as: Buffer → Overlay → Geometry calculation → Aggregation.

2) Nearest-Feature Predictors

These compute proximity to the closest relevant feature:

This family is implemented using Near analysis, which is faster and cleaner than building many buffers for proximity tasks.

Considering the typical size of different features (100+ points, 10,000 unique line strings, and 10,000 unique polygons) over 20+ different buffer ranges (25 m to 500 m). Computing and organizing the data raises a significant challenge.

A typical quick solution for this is GeoPandas. However, GeoPandas is mostly an in-memory workflow: it loads geometries and attributes into RAM, and overlay steps like intersection/overlay can create huge intermediate geometries. In a multi-scale LUR setup (many points × many buffer distances × multiple large layers), the intermediate results can grow very quickly and lead to slowdowns, heavy disk swapping, or out-of-memory errors. Also, most geometry operations are effectively single-threaded by default, so getting full CPU speed usually requires manual chunking and multiprocessing, which adds engineering overhead. Finally, accurate distance/area calculations often require careful projection choices and extra re-projection steps. As a result, GeoPandas is great for prototyping and small-to-medium jobs, but it can become fragile and hard to scale for large, multi-buffer feature extraction pipelines.

ArcPy solves these scaling issues mainly by running geoprocessing “out-of-core” against a File Geodatabase (FGDB) (so you don’t have to hold everything in RAM) and by providing the Pairwise toolset, which is optimized for large overlay workloads and can take advantage of multi-core processing internally. In a multi-buffer LUR pipeline, the expensive steps are usually “buffer everything” and “overlay everything” (roads × buffers, land use × buffers).

Replacing standard tools with PairwiseBuffer and PairwiseIntersect typically reduces runtime substantially while keeping the script simple—no manual chunking, no custom multiprocessing, and fewer memory blow-ups from enormous intermediate geometries.

Code example of using Pairwise tools in ArcPy:

import arcpy
import os

# Inputs
points_fc = r"C:\data\lur.gdb\obs_points"     # must have a unique ID field (e.g., OGF_ID)
roads_fc  = r"C:\data\lur.gdb\roads"          # has ROAD_CLASS
out_root  = r"C:\outputs\lur_features"

# Optional: let ArcGIS use available cores where supported
# arcpy.env.parallelProcessingFactor = "100%"

buffer_distances = [25 * i for i in range(1, 21)]  # 25, 50, ..., 500

for dist in buffer_distances:
    # Isolated workspace per distance (reduces locks + keeps outputs organized)
    out_dir = os.path.join(out_root, f"buf_{dist}m")
    os.makedirs(out_dir, exist_ok=True)

    gdb_path = os.path.join(out_dir, f"buf_{dist}m.gdb")
    if not arcpy.Exists(gdb_path):
        arcpy.management.CreateFileGDB(out_dir, f"buf_{dist}m.gdb")

    arcpy.env.workspace = gdb_path

    # 1) Fast buffer generation for all points
    buf_fc = f"buffers_{dist}m"
    arcpy.analysis.PairwiseBuffer(
        in_features=points_fc,
        out_feature_class=buf_fc,
        buffer_distance_or_field=f"{dist} Meters",
        dissolve_option="NONE"
    )

    # 2) Fast overlay: keep only road segments that fall inside each buffer
    roads_in_buf_fc = f"roads_in_{dist}m"
    arcpy.analysis.PairwiseIntersect(
        in_features=[buf_fc, roads_fc],
        out_feature_class=roads_in_buf_fc,
        join_attributes="ALL",
        output_type="LINE"
    )

    # Next step would be: CalculateGeometryAttributes + summarize by point ID and ROAD_CLASS
    print(f"Completed {dist}m: {roads_in_buf_fc}")

AIn ArcGIS Pro, Pairwise Intersect may provide substantial performance gains when inputs overlap heavily (which is common when thousands of buffers overlap the same road network or land-use polygons). Pairwise tools also enable parallel processing by default in ways that classic overlay tools may not, so you often get multi-core speedups without writing your own chunking/multiprocessing logic.

Example (illustration only): on a typical city-scale workload (thousands of buffers × large road network), a classic Buffer + Intersect run might take ~3h 20m, while PairwiseBuffer + PairwiseIntersect finishes in ~45m—about 4.4× faster. Esri also notes that parallel processing is not always faster if the dataset is small (parallel overhead can dominate), so it’s worth timing both approaches on your own dataset.