Use this Skill for archaeological GIS: site catchment analysis, viewshed computation, kernel density estimation, and spatial statistics with GeoPandas.
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Use this Skill for archaeological GIS: site catchment analysis, viewshed computation, kernel density estimation, and spatial statistics with GeoPandas.
One-line summary: Analyze archaeological site distributions with GeoPandas: kernel density estimation, nearest neighbor analysis, catchment areas, Thiessen polygons, and predictive site modeling.
When to Use This Skill
When mapping and analyzing spatial distributions of archaeological sites
When computing site catchment analysis (resource accessibility areas)
When detecting spatial clustering patterns (K-function, nearest neighbor)
When building predictive site location models from environmental variables
When creating Thiessen (Voronoi) polygons for territory analysis
When overlaying sites with DEM, soil, and land cover data
Trigger keywords: archaeological GIS, site distribution, catchment analysis, kernel density, site prediction, Thiessen polygon, Voronoi, nearest neighbor analysis, K-function, predictive modeling, spatial archaeology, site location model, viewshed, survey data
Background & Key Concepts
Site Catchment Analysis
Resource territory of a site defined by walking time or buffer radius. Typical thresholds: 1-hour walk (~5 km for flat terrain), 2-hour walk (~10 km).
Nearest Neighbor Analysis
Average nearest neighbor distance vs. expected random distance:
$$
R = \frac{\bar{d}{observed}}{\bar{d}{expected}} = \frac{\bar{d}_{obs}}{0.5/\sqrt{n/A}}
$$
Wheatley, D. & Gillings, M. (2002). Spatial Technology and Archaeology. Taylor & Francis.
Verhagen, P. (2007). Case Studies in Archaeological Predictive Modelling. Leiden University Press.
Examples
Example 1: Spatial Autocorrelation (Moran's I)
import numpy as np
from scipy.spatial.distance import cdist
defmorans_i_sites(values, coords, k=8):
"""Spatial autocorrelation of an attribute across sites."""
n = len(values)
y = values - values.mean()
dist = cdist(coords, coords)
W = np.zeros((n, n))
for i inrange(n):
nn = np.argsort(dist[i])[1:k+1]
W[i, nn] = 1
W /= W.sum(axis=1, keepdims=True)
I = n * np.sum(W * np.outer(y, y)) / (W.sum() * np.sum(y**2))
return I
coords = np.array(list(zip(sites_gdf['x_km'], sites_gdf['y_km'])))
I_finds = morans_i_sites(sites_gdf['n_finds'].values, coords)
print(f"Moran's I — number of finds: {I_finds:.4f}")
print("(>0 = spatially clustered; <0 = dispersed; 0 = random)")
Example 2: Chronological Phase Mapping
import pandas as pd
import matplotlib.pyplot as plt
# Sites by period
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
periods = ['Neolithic', 'Bronze Age', 'Iron Age']
colors_per = ['#2ecc71', '#f39c12', '#e74c3c']
for ax, period_name, color inzip(axes, periods, colors_per):
subset = sites_gdf[sites_gdf['period'] == period_name]
ax.scatter(subset['x_km'], subset['y_km'],
c=color, s=50, edgecolors='black', linewidths=0.5, alpha=0.8)
ax.set_title(f"{period_name} sites (n={len(subset)})")
ax.set_xlabel("Easting (km)"); ax.set_ylabel("Northing (km)")
ax.set_xlim(95, 165); ax.set_ylim(30, 70)
ax.grid(True, alpha=0.3)
plt.suptitle("Chronological Site Distribution by Period"); plt.tight_layout()
plt.savefig("chronological_phases.png", dpi=150); plt.show()
Last updated: 2026-03-17 | Maintainer: @xjtulycIssues: GitHub Issues
