Prehistoric Monument Geophysical Analysis
Budget: £750 – £1,500 GBP
Freelancer Advert: GIS / Spatial Data Analyst for Archaeological-Geology Pilot Study
I am looking for a GIS / spatial data analyst to run a pilot spatial analysis for an archaeological and geophysical research project.
The pilot question is:
Do prehistoric monuments occur on high-resistivity geology, geological boundaries, faults, or other geophysical-coupling features more often than expected by chance?**
This is a neutral hypothesis-testing project. I am not looking for someone to “prove” a theory. I need a competent analyst who can identify suitable open-source datasets, build a clean GIS workflow, run a proper spatial null model, and report the result honestly, whether positive, negative, or inconclusive.
## Core Pilot Task
The minimum pilot test is:
Compare prehistoric monument locations against geological substrate type and random control points.
The analyst should:
1. Recommend a suitable study region outside the UK.
* Possible examples: Brittany, Ireland, France, Spain, Portugal, or another region with strong monument-coordinate data and geological mapping.
2. Source open or publicly accessible monument coordinate data.
3. Source geological map data for the same region.
4. Classify geology into broad resistivity categories:
* High-resistivity: granite, limestone, chalk, sandstone, quartzite, etc.
* Low-resistivity: clay, mudstone, shale, alluvium, peat, etc.
* Intermediate/uncertain: handled separately.
5. Overlay monument points on the geology layer.
6. Generate at least 10,000 random control points within the same valid study area.
7. Compare monument locations against the random controls.
8. Calculate enrichment ratio, confidence interval, and statistical significance.
9. Produce maps, tables, plots, and a short technical report.
10. Provide reproducible workflow files, preferably R/Python code plus GIS project files.
Preferred Extended Analysis
I am especially interested in applicants who can also test a broader geophysical-coupling model, not just simple resistivity.
Where data are available, the extended model should include:
* distance to faults,
* distance to geological contacts or lithological boundaries,
* distance to rivers, springs, wetlands, or coastlines,
* elevation and slope,
* magnetic anomaly strength or magnetic-gradient data,
* bedrock type,
* karst/cave/cavity-prone geology,
* other structural or geophysical lineaments.
The aim is to compare whether monument placement is better explained by:
1. simple high-resistivity geology,
2. proximity to geological boundaries,
3. proximity to faults or lineaments,
4. conventional landscape variables,
5. or a composite geophysical-coupling model.
Required Deliverables
The pilot should produce:
* dataset source list with links/citations,
* cleaned monument coordinate table,
* classified geology layer,
* random control-point layer,
* GIS project file or GeoPackage,
* map of monuments over geology,
* map of random/control points,
* table comparing real monuments versus controls,
* enrichment ratio and statistical significance,
* short technical report,
* reproducible R/Python scripts or clearly documented GIS workflow.
Important Requirements
The analyst must:
* use transparent and reproducible methods,
* cite all datasets,
* avoid cherry-picking,
* include negative or null results if found,
* identify confounding variables,
* distinguish correlation from causation,
* explain limitations clearly,
* produce results that another analyst could check or rerun.
## Useful Skills
Ideal experience includes:
* QGIS, ArcGIS, GRASS GIS, or similar,
* Python: geopandas, rasterio, shapely, scipy, scikit-learn, matplotlib,
* R: sf, terra/raster, spatstat, tidyverse, ggplot2,
* spatial statistics,
* archaeological GIS,
* geological mapping,
* geophysical datasets,
* Monte Carlo null models,
* logistic regression or spatial regression.
Application Questions
Please respond with:
1. Which region you recommend for the pilot and why.
2. What monument dataset you would use.
3. What geology or geophysical dataset you would use.
4. How you would construct the random-point null model.
5. Whether you can include distance to faults/geological contacts.
6. Whether you can provide reproducible R/Python code.
7. Examples of similar GIS, archaeological, geological, ecological, or spatial-statistical work.
8. Estimated fixed price and timescale for the pilot.
Budget
I am initially seeking a fixed-price pilot. Please quote separately for:
1. basic resistivity/geology enrichment test,
2. extended model including faults, geological contacts, and landscape controls,
3. final report and reproducible code.
A successful pilot may lead to further work across additional regions and related tests.
I am looking for a GIS / spatial data analyst to run a pilot spatial analysis for an archaeological and geophysical research project.
The pilot question is:
Do prehistoric monuments occur on high-resistivity geology, geological boundaries, faults, or other geophysical-coupling features more often than expected by chance?**
This is a neutral hypothesis-testing project. I am not looking for someone to “prove” a theory. I need a competent analyst who can identify suitable open-source datasets, build a clean GIS workflow, run a proper spatial null model, and report the result honestly, whether positive, negative, or inconclusive.
## Core Pilot Task
The minimum pilot test is:
Compare prehistoric monument locations against geological substrate type and random control points.
The analyst should:
1. Recommend a suitable study region outside the UK.
* Possible examples: Brittany, Ireland, France, Spain, Portugal, or another region with strong monument-coordinate data and geological mapping.
2. Source open or publicly accessible monument coordinate data.
3. Source geological map data for the same region.
4. Classify geology into broad resistivity categories:
* High-resistivity: granite, limestone, chalk, sandstone, quartzite, etc.
* Low-resistivity: clay, mudstone, shale, alluvium, peat, etc.
* Intermediate/uncertain: handled separately.
5. Overlay monument points on the geology layer.
6. Generate at least 10,000 random control points within the same valid study area.
7. Compare monument locations against the random controls.
8. Calculate enrichment ratio, confidence interval, and statistical significance.
9. Produce maps, tables, plots, and a short technical report.
10. Provide reproducible workflow files, preferably R/Python code plus GIS project files.
Preferred Extended Analysis
I am especially interested in applicants who can also test a broader geophysical-coupling model, not just simple resistivity.
Where data are available, the extended model should include:
* distance to faults,
* distance to geological contacts or lithological boundaries,
* distance to rivers, springs, wetlands, or coastlines,
* elevation and slope,
* magnetic anomaly strength or magnetic-gradient data,
* bedrock type,
* karst/cave/cavity-prone geology,
* other structural or geophysical lineaments.
The aim is to compare whether monument placement is better explained by:
1. simple high-resistivity geology,
2. proximity to geological boundaries,
3. proximity to faults or lineaments,
4. conventional landscape variables,
5. or a composite geophysical-coupling model.
Required Deliverables
The pilot should produce:
* dataset source list with links/citations,
* cleaned monument coordinate table,
* classified geology layer,
* random control-point layer,
* GIS project file or GeoPackage,
* map of monuments over geology,
* map of random/control points,
* table comparing real monuments versus controls,
* enrichment ratio and statistical significance,
* short technical report,
* reproducible R/Python scripts or clearly documented GIS workflow.
Important Requirements
The analyst must:
* use transparent and reproducible methods,
* cite all datasets,
* avoid cherry-picking,
* include negative or null results if found,
* identify confounding variables,
* distinguish correlation from causation,
* explain limitations clearly,
* produce results that another analyst could check or rerun.
## Useful Skills
Ideal experience includes:
* QGIS, ArcGIS, GRASS GIS, or similar,
* Python: geopandas, rasterio, shapely, scipy, scikit-learn, matplotlib,
* R: sf, terra/raster, spatstat, tidyverse, ggplot2,
* spatial statistics,
* archaeological GIS,
* geological mapping,
* geophysical datasets,
* Monte Carlo null models,
* logistic regression or spatial regression.
Application Questions
Please respond with:
1. Which region you recommend for the pilot and why.
2. What monument dataset you would use.
3. What geology or geophysical dataset you would use.
4. How you would construct the random-point null model.
5. Whether you can include distance to faults/geological contacts.
6. Whether you can provide reproducible R/Python code.
7. Examples of similar GIS, archaeological, geological, ecological, or spatial-statistical work.
8. Estimated fixed price and timescale for the pilot.
Budget
I am initially seeking a fixed-price pilot. Please quote separately for:
1. basic resistivity/geology enrichment test,
2. extended model including faults, geological contacts, and landscape controls,
3. final report and reproducible code.
A successful pilot may lead to further work across additional regions and related tests.