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Baixar Zip Baixando... Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name archaeological-gis description Use this Skill for archaeological GIS: site catchment analysis, viewshed computation, kernel density estimation, and spatial statistics with GeoPandas.
tags ["archaeology","gis","spatial-analysis","geopandas","site-analysis"] version 1.0.0 authors [{"name":"Rosetta Skills Contributors","github":"@xjtulyc"}] license MIT platforms ["claude-code","codex","gemini-cli","cursor"] dependencies {"python":["geopandas>=0.14","shapely>=2.0","numpy>=1.24","scipy>=1.11","matplotlib>=3.7","rasterio>=1.3"]} last_updated 2026-03-17 status stable
Archaeological GIS Analysis
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:
$$
R = \frac{\bar{d}{observed}}{\bar{d} {expected}} = \frac{\bar{d}_{obs}}{0.5/\sqrt{n/A}}
$$
$R < 1$: clustered; $R = 1$: random; $R > 1$: dispersed.
Kernel Density Estimation (KDE)
$$
\hat{f}(x) = \frac{1}{nh^2} \sum_{i=1}^n K\left(\frac{x - x_i}{h}\right)
$$
Smoothed density surface showing probability of site occurrence, suitable for predictive modeling.
Environment Setup
Install Dependencies
pip install geopandas>=0.14 shapely>=2.0 numpy>=1.24 scipy>=1.11 \
matplotlib>=3.7 rasterio>=1.3
Verify Installation
import geopandas as gpd
import numpy as np
from shapely.geometry import Point
sites = gpd.GeoDataFrame(
{'name' : ['Site A' , 'Site B' , 'Site C' ]},
geometry=[Point(0
0
1
1
2
0.5
'EPSG:4326'
print
f"GeoPandas: {gpd.__version__} "
print
f"Test GeoDataFrame: {len (sites)} sites"
Core Workflow
Step 1: Site Distribution Mapping and Nearest Neighbor Analysis import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
from scipy.spatial import KDTree
from scipy.stats import norm
from shapely.geometry import Point, Polygon
np.random.seed(42 )
n_sites = 80
study_area_km2 = 2500
c1_x = np.random.normal(120 , 5 , 30 )
c1_y = np.random.normal(45 , 3 , 30 )
c2_x = np.random.normal(140 , 4 , 25 )
c2_y = np.random.normal(55 , 3 , 25 )
rand_x = np.random.uniform(100 , 160 , 25 )
rand_y = np.random.uniform(35 , 65 , 25 )
x_all = np.concatenate([c1_x, c2_x, rand_x])
y_all = np.concatenate([c1_y, c2_y, rand_y])
site_type = np.array(
['settlement' ]*30 + ['ceremonial' ]*10 + ['artifact scatter' ]*15 +
['unknown' ]*15 + ['lithic workshop' ]*10
)[:n_sites]
period = np.random.choice(['Neolithic' , 'Bronze Age' , 'Iron Age' ], n_sites, p=[0.3 , 0.4 , 0.3 ])
n_finds = np.random.randint(5 , 500 , n_sites)
geometry = [Point(x, y) for x, y in zip (x_all[:n_sites], y_all[:n_sites])]
sites_gdf = gpd.GeoDataFrame({
'site_id' : [f"S{i:04d} " for i in range (n_sites)],
'site_type' : site_type,
'period' : period,
'n_finds' : n_finds,
'x_km' : x_all[:n_sites],
'y_km' : y_all[:n_sites],
}, geometry=geometry)
print (f"Archaeological survey: {len (sites_gdf)} sites" )
print (sites_gdf['site_type' ].value_counts())
coords = np.array(list (zip (sites_gdf['x_km' ], sites_gdf['y_km' ])))
tree = KDTree(coords)
nn_dists, _ = tree.query(coords, k=2 )
nn_dists = nn_dists[:, 1 ]
mean_nn = nn_dists.mean()
expected_nn = 0.5 / np.sqrt(n_sites / study_area_km2)
R_statistic = mean_nn / expected_nn
sigma_nn = 0.26136 / np.sqrt(n_sites**2 / study_area_km2)
z_score = (mean_nn - expected_nn) / sigma_nn
p_value = 2 * norm.sf(abs (z_score))
print (f"\nNearest Neighbor Analysis:" )
print (f" Mean observed NN distance: {mean_nn:.3 f} km" )
print (f" Expected (random): {expected_nn:.3 f} km" )
print (f" R statistic: {R_statistic:.4 f} ({'clustered' if R_statistic < 1 else 'dispersed' if R_statistic > 1 else 'random' } )" )
print (f" Z-score: {z_score:.3 f} , p-value: {p_value:.4 f} " )
fig, axes = plt.subplots(1 , 2 , figsize=(14 , 6 ))
type_colors = {'settlement' :'#e74c3c' , 'ceremonial' :'#9b59b6' ,
'artifact scatter' :'#f39c12' , 'unknown' :'#95a5a6' , 'lithic workshop' :'#27ae60' }
for site_type_val, color in type_colors.items():
mask = sites_gdf['site_type' ] == site_type_val
subset = sites_gdf[mask]
axes[0 ].scatter(subset['x_km' ], subset['y_km' ],
c=color, s=subset['n_finds' ]/10 + 20 , alpha=0.7 ,
edgecolors='black' , linewidths=0.5 , label=f"{site_type_val} (n={mask.sum ()} )" )
axes[0 ].set_xlabel("Easting (km)" ); axes[0 ].set_ylabel("Northing (km)" )
axes[0 ].set_title("Archaeological Site Distribution" )
axes[0 ].legend(fontsize=7 , loc='upper left' ); axes[0 ].grid(True , alpha=0.3 )
axes[1 ].hist(nn_dists, bins=20 , color='steelblue' , edgecolor='black' , linewidth=0.5 , alpha=0.7 )
axes[1 ].axvline(mean_nn, color='blue' , linewidth=2 , linestyle='-' , label=f'Observed mean={mean_nn:.2 f} km' )
axes[1 ].axvline(expected_nn, color='red' , linewidth=2 , linestyle='--' , label=f'Expected (random)={expected_nn:.2 f} km' )
axes[1 ].set_xlabel("Nearest Neighbor Distance (km)" ); axes[1 ].set_ylabel("Frequency" )
axes[1 ].set_title(f"NN Distance Distribution\nR={R_statistic:.3 f} , p={p_value:.4 f} " )
axes[1 ].legend(); axes[1 ].grid(True , alpha=0.3 )
plt.tight_layout()
plt.savefig("site_distribution.png" , dpi=150 )
plt.show()
Step 2: Kernel Density Estimation and Hotspot Analysis import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
from scipy.ndimage import gaussian_filter
x = sites_gdf['x_km' ].values
y = sites_gdf['y_km' ].values
grid_res = 0.5
x_grid = np.arange(x.min ()-2 , x.max ()+2 , grid_res)
y_grid = np.arange(y.min ()-2 , y.max ()+2 , grid_res)
X_grid, Y_grid = np.meshgrid(x_grid, y_grid)
xy = np.vstack([x, y])
kde = gaussian_kde(xy, bw_method='scott' )
density = kde(np.vstack([X_grid.ravel(), Y_grid.ravel()])).reshape(X_grid.shape)
hotspot_threshold = np.percentile(density, 75 )
hotspot_mask = density > hotspot_threshold
fig, axes = plt.subplots(1 , 2 , figsize=(14 , 6 ))
cf = axes[0 ].contourf(X_grid, Y_grid, density, levels=20 , cmap='hot_r' , alpha=0.8 )
plt.colorbar(cf, ax=axes[0 ], label='Site density' )
axes[0 ].scatter(x, y, c='white' , s=15 , edgecolors='black' , linewidths=0.5 , zorder=5 , alpha=0.7 )
axes[0 ].set_xlabel("Easting (km)" ); axes[0 ].set_ylabel("Northing (km)" )
axes[0 ].set_title("Kernel Density Estimation — Archaeological Sites" )
axes[0 ].grid(True , alpha=0.2 )
axes[1 ].contourf(X_grid, Y_grid, density, levels=20 , cmap='YlOrRd' , alpha=0.7 )
axes[1 ].contour(X_grid, Y_grid, hotspot_mask.astype(float ), levels=[0.5 ],
colors='red' , linewidths=2 )
axes[1 ].scatter(x, y, c='black' , s=15 , zorder=5 , alpha=0.5 )
axes[1 ].set_xlabel("Easting (km)" ); axes[1 ].set_ylabel("Northing (km)" )
axes[1 ].set_title("Site Hotspot Zones\n(75th percentile density threshold)" )
axes[1 ].grid(True , alpha=0.2 )
plt.tight_layout()
plt.savefig("kde_hotspots.png" , dpi=150 )
plt.show()
n_hotspot = np.sum (density[np.round (
(y[:, None ] - y_grid[0 ]) / grid_res).astype(int ).clip(0 , len (y_grid)-1 ),
np.round ((x[:, None ] - x_grid[0 ]) / grid_res).astype(int ).clip(0 , len (x_grid)-1 )
[:, 0 ]] > hotspot_threshold)
print (f"\nSite density summary:" )
print (f" Grid resolution: {grid_res} km" )
print (f" Hotspot threshold: {hotspot_threshold:.4 e} km⁻²" )
print (f" Hotspot area: {hotspot_mask.sum () * grid_res**2 :.1 f} km² ({hotspot_mask.sum ()/hotspot_mask.size*100 :.1 f} % of study area)" )
Step 3: Site Catchment and Thiessen Polygon Analysis import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi, voronoi_plot_2d
from shapely.geometry import Point, Polygon, MultiPoint
from shapely.ops import unary_union
import geopandas as gpd
WALK_1H = 5.0
WALK_2H = 10.0
settlements = sites_gdf[sites_gdf['site_type' ] == 'settlement' ].copy()
settlements_1h = settlements.copy()
settlements_1h['geometry' ] = settlements_1h.geometry.buffer(WALK_1H)
settlements_2h = settlements.copy()
settlements_2h['geometry' ] = settlements_2h.geometry.buffer(WALK_2H)
x_min, x_max = x.min ()-5 , x.max ()+5
y_min, y_max = y.min ()-5 , y.max ()+5
study_area_polygon = Polygon([(x_min,y_min),(x_max,y_min),(x_max,y_max),(x_min,y_max)])
points = np.array(list (zip (x, y)))
n_bound = 50
angle = np.linspace(0 , 2 *np.pi, n_bound, endpoint=False )
R_bound = 200
bound_pts = np.column_stack([R_bound*np.cos(angle) + points[:,0 ].mean(),
R_bound*np.sin(angle) + points[:,1 ].mean()])
all_points = np.vstack([points, bound_pts])
vor = Voronoi(all_points)
voronoi_polys = []
for i, pt in enumerate (points):
region_idx = vor.point_region[i]
region = vor.regions[region_idx]
if -1 in region or len (region) == 0 :
voronoi_polys.append(None )
continue
poly = Polygon(vor.vertices[region])
clipped = poly.intersection(study_area_polygon)
voronoi_polys.append(clipped)
thiessen_gdf = gpd.GeoDataFrame({
'site_id' : sites_gdf['site_id' ].values,
'site_type' : sites_gdf['site_type' ].values,
'period' : sites_gdf['period' ].values,
}, geometry=voronoi_polys)
thiessen_gdf = thiessen_gdf[thiessen_gdf.geometry.notna()]
thiessen_gdf['area_km2' ] = thiessen_gdf.geometry.area
print ("\nThiessen polygon territory sizes:" )
print (thiessen_gdf['area_km2' ].describe().round (2 ))
fig, axes = plt.subplots(1 , 2 , figsize=(14 , 6 ))
settlements_1h.plot(ax=axes[0 ], color='blue' , alpha=0.1 , edgecolor='blue' , linewidth=0.5 )
settlements_2h.plot(ax=axes[0 ], color='green' , alpha=0.05 , edgecolor='green' , linewidth=0.5 )
sites_gdf.plot(ax=axes[0 ], column='site_type' , cmap='tab10' , markersize=15 , legend=True ,
legend_kwds={'fontsize' : 7 , 'loc' : 'upper left' })
axes[0 ].set_title("Site Catchment Analysis\n(Blue=1hr, Green=2hr walking radius)" )
axes[0 ].set_xlabel("Easting (km)" ); axes[0 ].set_ylabel("Northing (km)" )
axes[0 ].grid(True , alpha=0.3 )
thiessen_gdf.plot(ax=axes[1 ], column='area_km2' , cmap='Blues' , edgecolor='black' ,
linewidth=0.5 , alpha=0.7 , legend=True ,
legend_kwds={'label' : 'Territory (km²)' })
sites_gdf.plot(ax=axes[1 ], color='red' , markersize=10 , zorder=5 )
axes[1 ].set_title("Thiessen Polygon Territories" )
axes[1 ].set_xlabel("Easting (km)" ); axes[1 ].grid(True , alpha=0.3 )
plt.tight_layout()
plt.savefig("catchment_thiessen.png" , dpi=150 )
plt.show()
Advanced Usage
Predictive Site Location Modeling import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
np.random.seed(42 )
n_total = 300
elevation = np.concatenate([np.random.normal(200 , 50 , n_sites),
np.random.normal(400 , 100 , n_total-n_sites)])
water_dist = np.concatenate([np.random.exponential(2 , n_sites),
np.random.exponential(8 , n_total-n_sites)])
slope = np.concatenate([np.random.uniform(0 , 5 , n_sites),
np.random.uniform(0 , 25 , n_total-n_sites)])
soil_type = np.concatenate([np.random.choice([1 ,2 ], n_sites, p=[0.7 ,0.3 ]),
np.random.choice([1 ,2 ], n_total-n_sites, p=[0.3 ,0.7 ])])
y_label = np.array([1 ]*n_sites + [0 ]*(n_total-n_sites))
X_pred = pd.DataFrame({
'elevation' : elevation,
'water_dist' : water_dist,
'slope' : slope,
'soil_type' : soil_type,
})
rf = RandomForestClassifier(n_estimators=100 , random_state=42 , max_depth=5 )
cv_scores = cross_val_score(rf, X_pred, y_label, cv=5 , scoring='roc_auc' )
print (f"Cross-validated AUC: {cv_scores.mean():.4 f} ± {cv_scores.std():.4 f} " )
rf.fit(X_pred, y_label)
fi = pd.Series(rf.feature_importances_, index=X_pred.columns).sort_values(ascending=False )
print ("\nFeature importances:" )
print (fi.round (4 ))
fig, ax = plt.subplots(figsize=(7 , 4 ))
fi.plot(kind='bar' , ax=ax, color='steelblue' , edgecolor='black' , linewidth=0.7 )
ax.set_ylabel("Importance" ); ax.set_title("Predictive Site Model — Feature Importance" )
ax.tick_params(axis='x' , rotation=0 ); ax.grid(axis='y' , alpha=0.3 )
plt.tight_layout(); plt.savefig("predictive_model.png" , dpi=150 ); plt.show()
Troubleshooting
Error: CRSError: Input does not contain CRS Fix : Always set CRS when creating GeoDataFrame:
gdf = gpd.GeoDataFrame(df, geometry=geometry, crs='EPSG:4326' )
gdf_projected = gdf.to_crs('EPSG:32633' )
Buffer distances are in degrees (not km) Cause : Data is in geographic (lat/lon) CRS.
gdf_m = gdf.to_crs('EPSG:3857' )
gdf_m['geometry' ] = gdf_m.geometry.buffer(5000 )
Voronoi regions extend to infinity Fix : Add bounding box points before Voronoi computation (as shown above), then clip to study area.
Version Compatibility Package Tested versions Notes geopandas 0.14 Requires shapely ≥ 2.0 for performance shapely 2.0, 2.1 Major API change from 1.x to 2.x rasterio 1.3 For DEM reading
External Resources
Official Documentation
Key Papers / Books
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
def morans_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 in range (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:.4 f} " )
print ("(>0 = spatially clustered; <0 = dispersed; 0 = random)" )
Example 2: Chronological Phase Mapping import pandas as pd
import matplotlib.pyplot as plt
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 in zip (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: @xjtulyc
Issues: GitHub Issues
