ppgrid — Pull-Push Scattered-Data Interpolation

Fast, continent-scale raster interpolation for scattered point data. Turns tens of millions of geolocated points into a pair of GeoTIFFs in minutes on a single machine. No GPU needed.

What it does

You have N points (N ~ 10^7) with (longitude, latitude, value). You want a raster of M cells where every cell within a defensible distance of real data carries an interpolated value, and everything else is nodata.

Standard IDW in QGIS/ArcGIS is O(N×M) — ~14 hours for 16M points. This tool uses pull-push mipmap interpolation to reduce cost to O(M), independent of N.

How it works

Pull-Push (mipmap) Interpolation

From Gortler et al. 1996 (Lumigraph) and Kraus 2009.

Once points are snapped to a grid, IDW is exactly a normalised convolution:

z = (S ⊛ K) / (C ⊛ K)   K(r) = r^-p

Pull-push evaluates this across a mipmap pyramid so cost is O(M), independent of N.

Steps:

1. Points are binned into S (sum of transformed values) and C (count) grids
2. A mipmap pyramid is built by repeated 2x2 block sums
3. The coarsest level seeds the interpolation
4. Descending the pyramid, each level blends local estimate vs upsampled parent
5. Local confidence is min(C/saturation, 1) — dense cells trust themselves, sparse cells inherit
6. A summed-area table (box_count) provides an exact radius fill cap

Output bands:

• Value: int16, 0-100 percentile, percentile = DN/scale
• Support km: int16, support_km = 2^(DN/8), effective spatial scale of estimate

Working CRS: EPSG:6933 (Wagner VII) by default — global equal-area, metres are true. Configurable via --work-crs.

Output CRS: EPSG:3857 (Web Mercator) by default. Configurable via --out-crs.

Calibration (optional)

Before interpolation, the tool can:

1. Choose a transform — tests identity/log10/sqrt/percentile and picks the one with highest intraclass correlation across coarse scales
2. Derive a fill cap — spatially blocked cross-validation to find the honest distance beyond which interpolation has no skill
3. Save calibration.json — contains the percentile-to-value lookup table for decoding the output raster back to real units

Project Structure

ppgrid/
  __init__.py    — package init
  __main__.py    — entry point for `python -m ppgrid`
  pullpush.py    — mipmap kernels (downsample, upsample, pull_push, box_count)
  calibrate.py   — transform selection + blocked CV for fill cap
  idwgrid.py     — CLI driver with block parallel processing
examples/
  test_run/
    value.tif       — interpolated value band (percentile)
    support_km.tif  — effective support scale
    calibration.json — percentile-to-value lookup table

Benchmarks

The tool was benchmarked using the Melbourne Housing dataset (13,580 points) at various resolutions on a single machine.

Resolution	Wall Time	File Size
10m	26.6s	27.3 MB
25m	4.0s	6.8 MB
50m	1.3s	2.3 MB
100m	0.8s	749 KB
250m	0.6s	159 KB
500m	0.6s	49 KB

Install

uv sync

Or manually:

pip install numpy pandas pyproj rasterio

Quick Start

# Interpolate Melbourne house prices
ppgrid data/melb_houses.csv --value-col price --res 500 --cap-km 10 --skip-calibration -o examples/melb/

Example Outputs

The Melbourne Housing dataset (13,580 points) interpolated at 10m resolution:

Cropped to the CBD to show resolution differences:

10m	25m	50m

100m	250m	500m

Usage

# Quick run (skip calibration)
python -m ppgrid data.csv --value-col premium --res 500 --cap-km 64 --skip-calibration

# Full run with calibration (saves calibration.json)
python -m ppgrid data.csv --value-col premium --res 100 --cap-km auto

# Custom projection and params
python -m ppgrid data.csv --value-col premium --res 100 --cap-km 25 --transform log10 --workers 8 --work-crs 3857 --out-crs 3857

# Reuse existing calibration
python -m ppgrid data.csv --value-col premium --calibration examples/previous/calibration.json

CLI Options

Flag	Default	Description
input	(required)	CSV or Parquet input path
-o, --out	examples/	Output directory
--value-col	value	Value column name
--lng-col	longitude	Longitude column name
--lat-col	latitude	Latitude column name
--res	500.0	Cell size in metres
--cap-km	auto	Fill cap km, or 'auto' (blocked-CV derived)
--transform	auto	auto | identity | log10 | sqrt | percentile
--saturation	1.0	Counts for a cell to fully self-trust
--block	8192	Block size in cells
--workers	4	Number of parallel workers
--scale	100.0	DN = percentile × scale
--compress	ZSTD	GeoTIFF compression
--calibration	(none)	Path to existing calibration.json
--calib-max-points	2000000	Max points to use for calibration
--src-crs	4326	Input coordinate reference system
--work-crs	6933	Working CRS for interpolation (equal-area)
--out-crs	3857	Output CRS for final GeoTIFF
--skip-calibration	false	Skip calibration, use defaults

Decoding the output

The value band stores percentiles, not raw values. To convert back to real units, use the calibration.json saved in the output folder:

import json, numpy as np, rasterio

with open("examples/test_run/calibration.json") as f:
    quantiles = np.array(json.load(f)["percentile_quantiles"])

with rasterio.open("examples/test_run/value.tif") as r:
    percentiles = np.array(r.read(1), copy=True) / 100.0

real_values = np.interp(percentiles, np.linspace(0, 100, len(quantiles)), quantiles)

Data

data/melb_houses.csv — 13,580 Melbourne property sales with latitude, longitude, and price. Sourced from the Melbourne Housing Snapshot (CC BY-NC-SA 4.0).

data/all_equakes.csv — 44,376 earthquake events from Jan–Aug 2026, mag ≥ 1.5.

Data Lineage

Step	Description
Source	USGS Earthquake Hazards Program
API	FDSN Event Web Service
Download	Batched CSV requests by month, minmagnitude=1.5, starttime=2026-01-01, endtime=2026-08-08
Processing	Concatenated monthly CSVs (deduplicated header) into single file
License	Public Domain (USGS federal data)

Columns Used

• latitude / longitude — spatial coordinates (WGS 84)
• mag — earthquake magnitude (continuous, for interpolation)
• depth — focal depth in km (optional value layer)
• time — event timestamp (ISO 8601)