archaeological-gis

Transform archaeological site coordinates into spatial clustering, density, and territory outputs.

33|6|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill archaeological-gis
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: archaeological-gis
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/15-archaeology/archaeological-gis
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill archaeological-gis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you turn raw archaeological site locations into spatial insights, including clustering, density hotspots, territorial catchments, and predictive site-likelihood maps.

Core Features & Use Cases

  • Site catchment analysis: Create resource-availability territories using buffer radii (e.g., 1-hour and 2-hour walking distances).
  • Nearest neighbor & spatial clustering: Quantify whether site distributions are clustered, random, or dispersed using nearest neighbor statistics.
  • Kernel density estimation (KDE) & hotspot mapping: Produce smoothed density surfaces and identify hotspot zones for guiding fieldwork.
  • Thiessen (Voronoi) territories: Generate per-site territorial polygons and compute their area for comparative site landscape analysis.
  • Predictive site location modeling: Build feature-based models (e.g., using environmental covariates) to estimate where sites are likely to occur.

Quick Start

Use the archaeological-gis skill to compute nearest neighbor clustering and KDE hotspots from your archaeological site coordinates, then output catchment buffers and Thiessen territories as GIS-ready results.

Frequently Asked Questions about archaeological-gis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform nearest neighbor analysis on archaeological site coordinates to test for spatial clustering?

Nearest neighbor analysis on archaeological site coordinates quantifies whether distributions are clustered, random, or dispersed using scipy and numpy statistical computations, transforming raw site locations into spatial clustering diagnostics.

What's the best way to generate kernel density estimation hotspots from archaeological site locations?

Kernel density estimation hotspots are generated by producing smoothed density surfaces from archaeological site locations, identifying concentration zones to guide fieldwork using GeoPandas-compatible geometries and matplotlib visualization.

How do I create Thiessen Voronoi polygons to model territorial catchments around archaeological sites?

Thiessen Voronoi polygons model territorial catchments by generating per-site boundary polygons and computing their areas for comparative landscape analysis using spatial coordinate datasets and CRS handling.

Can I use Python and GeoPandas to build predictive models for archaeological site locations?

Python and GeoPandas support predictive archaeological site location modeling by building feature-based models using environmental covariates to estimate site occurrence probability and output likelihood maps.

Does this spatial analysis workflow require specific Python libraries for DEM and raster integration?

Spatial analysis requires Python with GeoPandas for geometries, scipy and numpy for numerical computations, and rasterio and matplotlib to support visualization and raster or DEM integration.

How do I compute site catchment buffers for walking distances around archaeological locations?

Site catchment buffers for walking distances are computed by creating resource-availability territories using specified buffer radii, such as 1-hour and 2-hour walking distances, around archaeological site locations.