gnnwr-spatial-analysis

name	gnnwr-spatial-analysis
description	Use when analyzing spatial or spatiotemporal data with geographic non-stationarity, building GNNWR/GTNNWR models, generating spatial coefficient maps, or interpreting geographically varying regression results. Triggers on keywords like spatial regression, GWR, GNNWR, spatial non-stationarity, geographic weighting, coefficient mapping, PM2.5 spatial modeling, land price spatial analysis.
license	MIT
metadata	{"author":"SteadfastAsArt","version":"1.0.0"}

GNNWR Spatial Intelligent Analysis

Spatial/spatiotemporal regression with GNNWR (Geographically Neural Network Weighted Regression). Produces publication-ready coefficient maps and diagnostic reports.

Quick Reference

from gnnwr import models, datasets, utils
import pandas as pd

Data → Model → Results (Minimal)

data = pd.read_csv("data.csv")

train, val, test = datasets.init_dataset(
    data=data, test_ratio=0.2, valid_ratio=0.1,
    x_column=["x1", "x2", "x3"], y_column=["y"],
    spatial_column=["lon", "lat"],  # REQUIRED: geographic coords
    batch_size=32, process_fn="minmax_scale"
)

model = models.GNNWR(train, val, test, use_gpu=True, optimizer="Adam", start_lr=0.01)
model.run(max_epoch=200, early_stop=30)

result = model.reg_result(only_return=True)  # DataFrame: coef_x1, coef_x2, ..., Pred_y, denormalized_pred_result
print(model.result())                         # R², AIC, RMSE, F-tests summary

Spatiotemporal (GTNNWR)

train, val, test = datasets.init_dataset(
    data=data, ...,
    spatial_column=["lon", "lat"],
    temp_column=["year", "month"],  # add temporal coords
    use_model="gtnnwr"
)
model = models.GTNNWR(train, val, test, use_gpu=True)

Large-Scale (N > 10k) — KNN Mode

train, val, test = datasets.init_dataset(
    data=data, ..., knn_k=500  # only k nearest neighbor distances
)
# Memory: N=100k full=55GB → knn_k=2000 only 763MB

API Essentials

init_dataset Key Parameters

Parameter	Default	Notes
knn_k	None	KNN sparse distance; None=full matrix
process_fn	"minmax_scale"	or "standard_scale"
spatial_fun	BasicDistance	Euclidean; or ManhattanDistance
Reference	None	"train", "train_val", or custom DataFrame
sample_seed	42	Reproducibility

Model Hyperparameters

Parameter	Recommended	Notes
optimizer	"Adam"	Also: SGD, AdamW, Adagrad, RMSprop
start_lr	0.01–0.1	Critical tuning point
drop_out	0.2	0.0–0.5
dense_layers	None (auto)	Auto: power-of-2 sequence from input_dim to n_coef
early_stop	20–50	Patience; -1=disabled
batch_norm	True	Stabilizes training
use_ols	True	OLS-initialized output layer

Diagnostics (DIAGNOSIS)

diag = model._test_diagnosis
diag.R2()           # always available
diag.RMSE()         # always available
diag.AIC()          # needs lite=False (auto for N<10k)
diag.AICc()         # corrected AIC
diag.F1_Global()    # GNNWR vs OLS significance
diag.F2_Global()    # spatial weight significance
diag.F3_Local()     # per-variable significance → (dict1, dict2)

lite=True (auto when N>10k): only R²/RMSE; Hat-matrix diagnostics skipped.

Visualization Patterns

1. Folium Interactive Maps (built-in)

viz = utils.Visualize(model, lon_lat_columns=["lon", "lat"], zoom=5)

# Dataset distribution
m1 = viz.display_dataset(name="all", y_column="y")
m1.save("dataset_map.html")

# Coefficient spatial variation — one map per variable
for col in [c for c in result.columns if c.startswith("coef_")]:
    m = viz.coefs_heatmap(data_column=col, steps=20)
    m.save(f"map_{col}.html")

# Custom dot map for any DataFrame
m3 = viz.dot_map(result, "lon", "lat", "denormalized_pred_result", zoom=5)

2. Matplotlib Static Maps (publication-ready)

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np

result = model.reg_result(only_return=True)

fig, axes = plt.subplots(2, 3, figsize=(18, 12))
coef_cols = [c for c in result.columns if c.startswith("coef_")]

for ax, col in zip(axes.flat, coef_cols):
    sc = ax.scatter(
        result["lon"], result["lat"],
        c=result[col], cmap="RdYlBu_r", s=5, alpha=0.8,
        vmin=result[col].quantile(0.02), vmax=result[col].quantile(0.98)
    )
    ax.set_title(col.replace("coef_", "β_"), fontsize=14)
    ax.set_xlabel("Longitude")
    ax.set_ylabel("Latitude")
    plt.colorbar(sc, ax=ax, shrink=0.8)

plt.suptitle("Spatially Varying Coefficients (GNNWR)", fontsize=16)
plt.tight_layout()
plt.savefig("coefficients_map.png", dpi=300, bbox_inches="tight")

3. Residual Spatial Distribution

result["residual"] = result["denormalized_pred_result"] - result[y_column]

fig, ax = plt.subplots(figsize=(10, 8))
sc = ax.scatter(
    result["lon"], result["lat"],
    c=result["residual"], cmap="coolwarm", s=5,
    vmin=-result["residual"].abs().quantile(0.95),
    vmax=result["residual"].abs().quantile(0.95)
)
ax.set_title("Spatial Residual Distribution")
plt.colorbar(sc, ax=ax, label="Residual")
plt.savefig("residuals_map.png", dpi=300, bbox_inches="tight")

4. Prediction vs Observed Scatter

fig, ax = plt.subplots(figsize=(8, 8))
ax.scatter(result[y_column], result["denormalized_pred_result"], s=3, alpha=0.5)
lim = [result[y_column].min(), result[y_column].max()]
ax.plot(lim, lim, "r--", linewidth=2, label="1:1 line")
ax.set_xlabel("Observed"); ax.set_ylabel("Predicted")
ax.set_title(f"GNNWR: R²={model._test_diagnosis.R2().item():.4f}")
ax.legend()
plt.savefig("pred_vs_obs.png", dpi=300, bbox_inches="tight")

5. GeoPandas + Contextily (with basemap)

import geopandas as gpd
import contextily as ctx

gdf = gpd.GeoDataFrame(result, geometry=gpd.points_from_xy(result.lon, result.lat), crs="EPSG:4326")
gdf_web = gdf.to_crs(epsg=3857)

fig, ax = plt.subplots(figsize=(12, 10))
gdf_web.plot(column="coef_x1", ax=ax, cmap="RdYlBu_r", legend=True,
             markersize=5, alpha=0.7, legend_kwds={"shrink": 0.6})
ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron)
ax.set_title("β_x1 Spatial Variation")
ax.set_axis_off()
plt.savefig("coef_basemap.png", dpi=300, bbox_inches="tight")

Workflow Checklist

1. EDA: Check spatial distribution, feature correlations, OLS baseline
2. Data split: init_dataset with appropriate ratios and sample_seed=42
3. Train: Start with defaults, tune start_lr and early_stop
4. Diagnose: R², RMSE, F1 (GNNWR vs OLS), F2 (spatial weight significance)
5. Visualize: Coefficient maps (spatial non-stationarity), residual maps (model adequacy), pred vs obs
6. Interpret: Where do coefficients vary most? Which variables show strongest non-stationarity? (F3_Local)
7. Report: Model summary table + coefficient maps + diagnostic statistics

Common Pitfalls

• Forgot spatial_column: Model degenerates to global regression
• N > 10k without knn_k: OOM on distance matrix; use knn_k=500–2000
• start_lr too high: Loss explodes; start with 0.01
• No early_stop: Overfitting; always set early_stop=20–50
• Interpreting normalized coefficients: Use reg_result() which returns denormalized predictions; coefficients are on normalized scale
• GTNNWR without temp_column: Silently falls back to GNNWR behavior