From ade9b22dfbe9fefab9e5e913284c33a91c9ecf15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Erick=20Hecht=20L=C3=B3pez?= Date: Mon, 28 Sep 2026 23:12:52 -0300 Subject: [PATCH] feat: publish LAB 02 spatial EDA --- .gitattributes | 2 + README.md | 94 +- labs/lab02_spatial_eda/02_spatial_eda.ipynb | 1589 +++++++++++++++++ labs/lab02_spatial_eda/02_spatial_eda.py | 221 +++ labs/lab02_spatial_eda/README.md | 239 +++ labs/lab02_spatial_eda/data/context/SOURCE.md | 70 + .../data/context/chile_context.geojson | 8 + .../results/experiment_metadata.json | 48 + .../results/monte_carlo_by_realization.csv | 1201 +++++++++++++ .../results/resolution_summary.csv | 4 + src/geoai/spatial.py | 412 +++++ tests/test_spatial.py | 149 ++ 12 files changed, 4021 insertions(+), 16 deletions(-) create mode 100644 labs/lab02_spatial_eda/02_spatial_eda.ipynb create mode 100644 labs/lab02_spatial_eda/02_spatial_eda.py create mode 100644 labs/lab02_spatial_eda/README.md create mode 100644 labs/lab02_spatial_eda/data/context/SOURCE.md create mode 100644 labs/lab02_spatial_eda/data/context/chile_context.geojson create mode 100644 labs/lab02_spatial_eda/results/experiment_metadata.json create mode 100644 labs/lab02_spatial_eda/results/monte_carlo_by_realization.csv create mode 100644 labs/lab02_spatial_eda/results/resolution_summary.csv create mode 100644 src/geoai/spatial.py create mode 100644 tests/test_spatial.py diff --git a/.gitattributes b/.gitattributes index ba01027..e10a834 100644 --- a/.gitattributes +++ b/.gitattributes @@ -5,6 +5,7 @@ # Normalize text files in the repository * text=auto +.gitattributes text eol=lf # Source code *.py text eol=lf @@ -15,6 +16,7 @@ *.yaml text eol=lf *.toml text eol=lf *.json text eol=lf +*.geojson text eol=lf # Documentation *.md text eol=lf diff --git a/README.md b/README.md index 550748c..93bca11 100644 --- a/README.md +++ b/README.md @@ -63,6 +63,49 @@ A spatial operation requires geometries to use a compatible spatial reference sy --- +### LAB 02 - Spatial Exploratory Data Analysis + +[Open the recruiter-facing notebook](labs/lab02_spatial_eda/02_spatial_eda.ipynb) + +**Problem:** an apparent spatial hotspot can reflect uneven sampling intensity rather than a strong underlying spatial signal. + +The walkthrough demonstrates: + +- synthetic spatial sampling with regional and campaign components +- projected analysis in WGS 84 / UTM Zone 19S (`EPSG:32719`) +- nearest-neighbour sampling-density diagnostics +- regular-grid aggregation +- separation of sampling density from underlying synthetic signal +- grid-resolution sensitivity using persisted Monte Carlo results +- geographic context with documented Natural Earth provenance +- reusable spatial utilities +- automated tests + +Key numerical evidence: + +| Metric | Result | +| --- | ---: | +| Observations | 800 | +| Regional mean NN distance | 1807.35 m | +| Campaign mean NN distance | 502.73 m | +| Campaign / regional NN ratio | 0.278 | +| Primary descriptive grid | 10 km | +| Densest cell | `c2_r3` | +| Highest observed-signal cell | `c6_r6` | +| Highest true-signal cell | `c6_r6` | + +The densest cell differs from both signal hotspots, while the highest observed-signal cell agrees with the highest true-signal cell in this synthetic realization. + +The 10 km grid is used only as the primary descriptive resolution for this experiment. It is not presented as a universal optimum. + +**Core lesson:** + +```text +sampling density is not underlying spatial signal +``` + +--- + ## Why this matters for GeoAI Spatial errors can silently propagate into features such as: @@ -102,14 +145,16 @@ technical interpretation The current public portfolio includes: -- 2 recruiter-facing geospatial notebooks -- reusable CRS utilities under `src/geoai/` +- 3 recruiter-facing geospatial notebooks +- reusable CRS and spatial-analysis utilities under `src/geoai/` - automated pytest validation -- 15 passing CRS-related tests +- 24 passing automated tests +- persisted synthetic Monte Carlo result artifacts for LAB 02 +- documented provenance for the LAB 02 geographic context layer - reproducible Conda environment definition - pre-commit quality controls - Gitleaks secret scanning -- synthetic and redistribution-safe examples +- synthetic and redistribution-safe analytical examples ## Repository structure @@ -117,19 +162,34 @@ The current public portfolio includes: geoai-mineral-portfolio/ | |-- labs/ -| `-- lab01_geospatial_foundations/ -| |-- 01a_coordinate_foundations.ipynb -| |-- 01b_cross_zone_crs.ipynb -| `-- README.md +| |-- lab01_geospatial_foundations/ +| | |-- 01a_coordinate_foundations.ipynb +| | |-- 01b_cross_zone_crs.ipynb +| | `-- README.md +| | +| `-- lab02_spatial_eda/ +| |-- 02_spatial_eda.ipynb +| |-- 02_spatial_eda.py +| |-- README.md +| |-- data/ +| | `-- context/ +| | |-- chile_context.geojson +| | `-- SOURCE.md +| `-- results/ +| |-- experiment_metadata.json +| |-- monte_carlo_by_realization.csv +| `-- resolution_summary.csv | |-- src/ | `-- geoai/ | |-- __init__.py -| `-- crs.py +| |-- crs.py +| `-- spatial.py | |-- tests/ | |-- test_crs.py -| `-- test_cross_zone_crs.py +| |-- test_cross_zone_crs.py +| `-- test_spatial.py | |-- environment.yml |-- pyproject.toml @@ -202,10 +262,10 @@ python -m pytest -q Current validated result: ```text -15 passed +24 passed ``` -The reusable CRS logic is tested independently from the explanatory notebooks. +Reusable CRS and spatial-analysis logic is tested independently from the explanatory notebooks. ## Data strategy @@ -218,14 +278,16 @@ This approach keeps the experiments: - independent of proprietary exploration datasets - focused on the spatial concept being demonstrated +LAB 02 additionally uses a Natural Earth country boundary only for geographic context. Its source, version, license status, and integrity hashes are documented alongside the local context file. The analytical experiment itself remains entirely synthetic. + Future external datasets will require explicit review of source, license, attribution, CRS, resolution, redistribution rights, and known limitations. ## Current roadmap | Lab | Topic | Status | | --- | --- | --- | -| LAB 01 | Geospatial Python Foundations | In progress | -| LAB 02 | Spatial Exploratory Data Analysis | Planned | +| LAB 01 | Geospatial Python Foundations | Complete | +| LAB 02 | Spatial Exploratory Data Analysis | Complete | | LAB 03 | Spatial Cross-Validation | Planned | | LAB 04 | Geochemical CoDA | Planned | | LAB 05 | Mineral Prospectivity Mapping | Planned | @@ -235,9 +297,9 @@ Future external datasets will require explicit review of source, license, attrib | LAB 09 | Deep Learning for GeoAI | Planned | | LAB 10 | Graph & Multimodal GeoAI | Planned | -LAB 01A and LAB 01B are completed and validated. +LAB 01 and LAB 02 are completed and validated. -Additional LAB 01 work will progressively cover geometry validation, spatial operations, raster foundations, and raster/vector integration. +LAB 03 will extend the portfolio from exploratory spatial reasoning into spatial cross-validation, with explicit attention to spatial leakage and the limitations of conventional random train/test splits. ## Technical principles diff --git a/labs/lab02_spatial_eda/02_spatial_eda.ipynb b/labs/lab02_spatial_eda/02_spatial_eda.ipynb new file mode 100644 index 0000000..a491f5d --- /dev/null +++ b/labs/lab02_spatial_eda/02_spatial_eda.ipynb @@ -0,0 +1,1589 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d72af043", + "metadata": {}, + "source": [ + "# LAB 02 - Spatial Exploratory Data Analysis\n", + "\n", + "## Sampling density is not spatial signal\n", + "\n", + "This laboratory examines a common problem in geospatial analysis:\n", + "\n", + "> A location with many observations is not necessarily the location with the\n", + "> strongest underlying spatial signal.\n", + "\n", + "The experiment uses synthetic data so that the underlying spatial process is\n", + "known. This makes it possible to compare sampling density, observed signal,\n", + "and synthetic ground truth before predictive modelling.\n", + "\n", + "The workflow follows:\n", + "\n", + "**CRS validation -> sampling-density analysis -> spatial aggregation ->\n", + "resolution sensitivity -> interpretation**\n" + ] + }, + { + "cell_type": "markdown", + "id": "04d20164", + "metadata": {}, + "source": [ + "## Experimental design\n", + "\n", + "Each synthetic realization contains:\n", + "\n", + "- 500 broadly distributed regional observations;\n", + "- 300 observations from an intensive local campaign;\n", + "- 800 observations in total.\n", + "\n", + "The intensive campaign and the strongest underlying signal are deliberately\n", + "located in different parts of the study region.\n", + "\n", + "All spatial calculations use **EPSG:32719 - WGS 84 / UTM zone 19S**, so\n", + "distances and grid dimensions are expressed in metres.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "952c723b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:35.539928Z", + "iopub.status.busy": "2026-09-28T22:36:35.538929Z", + "iopub.status.idle": "2026-09-28T22:36:37.898138Z", + "shell.execute_reply": "2026-09-28T22:36:37.898138Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primary grid resolution: 10000.0 m\n" + ] + } + ], + "source": [ + "import json\n", + "from pathlib import Path\n", + "import sys\n", + "\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from shapely.geometry import box\n", + "\n", + "\n", + "def find_repo_root(start: Path) -> Path:\n", + " current = start.resolve()\n", + "\n", + " for candidate in (current, *current.parents):\n", + " if (candidate / \"src\" / \"geoai\").exists():\n", + " return candidate\n", + "\n", + " raise RuntimeError(\n", + " \"Repository root containing src/geoai was not found.\"\n", + " )\n", + "\n", + "\n", + "REPO_ROOT = find_repo_root(Path.cwd())\n", + "SRC_DIR = REPO_ROOT / \"src\"\n", + "LAB_DIR = (\n", + " REPO_ROOT\n", + " / \"labs\"\n", + " / \"lab02_spatial_eda\"\n", + ")\n", + "RESULTS_DIR = LAB_DIR / \"results\"\n", + "CONTEXT_DIR = LAB_DIR / \"data\" / \"context\"\n", + "\n", + "if str(SRC_DIR) not in sys.path:\n", + " sys.path.insert(0, str(SRC_DIR))\n", + "\n", + "from geoai.spatial import (\n", + " DEFAULT_BOUNDS,\n", + " DEFAULT_CRS,\n", + " assign_regular_grid,\n", + " generate_synthetic_sampling,\n", + " nearest_neighbor_distances,\n", + " require_metric_projected_crs,\n", + " summarize_grid,\n", + ")\n", + "\n", + "PRIMARY_CELL_SIZE_M = 10000.0\n", + "GRID_ORIGIN = (\n", + " DEFAULT_BOUNDS[0],\n", + " DEFAULT_BOUNDS[2],\n", + ")\n", + "\n", + "print(\"Primary grid resolution:\", PRIMARY_CELL_SIZE_M, \"m\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ed34e862", + "metadata": {}, + "source": [ + "## Geographic context of the synthetic experiment\n", + "\n", + "Before analysing sampling density, the synthetic study domain is placed in a\n", + "real geographic reference frame.\n", + "\n", + "The country outline comes from **Natural Earth Admin 0 - Countries, 1:110m,\n", + "version 5.1.1**. It is used only as a lightweight locator.\n", + "\n", + "The LAB02 study extent, sampling design, spatial signal, observations, and\n", + "analytical results remain entirely synthetic.\n", + "\n", + "The projected study domain uses **EPSG:32719 - WGS 84 / UTM zone 19S** and is\n", + "transformed to EPSG:4326 only for this geographic-context figure.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4a02a0c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:37.900144Z", + "iopub.status.busy": "2026-09-28T22:36:37.900144Z", + "iopub.status.idle": "2026-09-28T22:36:48.564940Z", + "shell.execute_reply": "2026-09-28T22:36:48.564940Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Context source: Natural Earth\n", + "Context version: 5.1.1\n", + "Synthetic study CRS: EPSG:32719\n", + "Study domain within Chile: True\n", + "Study bounds in EPSG:4326: (-70.501432, -26.217861, -69.696498, -25.489572)\n" + ] + } + ], + "source": [ + "context_path = (\n", + " CONTEXT_DIR\n", + " / \"chile_context.geojson\"\n", + ")\n", + "\n", + "if not context_path.exists():\n", + " raise FileNotFoundError(\n", + " f\"Context layer was not found: {context_path}\"\n", + " )\n", + "\n", + "chile_context = gpd.read_file(\n", + " context_path,\n", + " engine=\"pyogrio\",\n", + ")\n", + "\n", + "required_context_columns = {\n", + " \"name\",\n", + " \"iso_a2\",\n", + " \"iso_a3\",\n", + " \"source\",\n", + " \"source_dataset\",\n", + " \"source_scale\",\n", + " \"source_version\",\n", + " \"geometry\",\n", + "}\n", + "\n", + "missing_context_columns = (\n", + " required_context_columns\n", + " - set(chile_context.columns)\n", + ")\n", + "\n", + "if missing_context_columns:\n", + " raise ValueError(\n", + " \"Context layer is missing required columns: \"\n", + " f\"{sorted(missing_context_columns)}\"\n", + " )\n", + "\n", + "if len(chile_context) != 1:\n", + " raise ValueError(\n", + " \"Context layer must contain exactly one country feature.\"\n", + " )\n", + "\n", + "if chile_context.iloc[0][\"iso_a3\"] != \"CHL\":\n", + " raise ValueError(\n", + " \"Context layer must represent Chile (ISO_A3 = CHL).\"\n", + " )\n", + "\n", + "if chile_context.crs is None:\n", + " raise ValueError(\n", + " \"Context layer CRS is missing.\"\n", + " )\n", + "\n", + "if chile_context.crs.to_epsg() != 4326:\n", + " raise ValueError(\n", + " \"Context layer must use EPSG:4326.\"\n", + " )\n", + "\n", + "if not chile_context.geometry.is_valid.all():\n", + " raise ValueError(\n", + " \"Context layer contains invalid geometry.\"\n", + " )\n", + "\n", + "xmin, xmax, ymin, ymax = DEFAULT_BOUNDS\n", + "\n", + "study_domain_utm = gpd.GeoDataFrame(\n", + " {\n", + " \"name\": [\n", + " \"Synthetic LAB02 study domain\",\n", + " ],\n", + " },\n", + " geometry=[\n", + " box(\n", + " xmin,\n", + " ymin,\n", + " xmax,\n", + " ymax,\n", + " )\n", + " ],\n", + " crs=DEFAULT_CRS,\n", + ")\n", + "\n", + "study_domain_wgs84 = (\n", + " study_domain_utm\n", + " .to_crs(chile_context.crs)\n", + ")\n", + "\n", + "chile_geometry = (\n", + " chile_context\n", + " .geometry\n", + " .iloc[0]\n", + ")\n", + "\n", + "study_geometry = (\n", + " study_domain_wgs84\n", + " .geometry\n", + " .iloc[0]\n", + ")\n", + "\n", + "if not study_geometry.within(chile_geometry):\n", + " raise ValueError(\n", + " \"Synthetic study domain must be fully contained \"\n", + " \"within the Chile context geometry.\"\n", + " )\n", + "\n", + "study_bounds = (\n", + " study_domain_wgs84\n", + " .total_bounds\n", + ")\n", + "\n", + "country_bounds = (\n", + " chile_context\n", + " .total_bounds\n", + ")\n", + "\n", + "study_width = (\n", + " study_bounds[2]\n", + " - study_bounds[0]\n", + ")\n", + "\n", + "study_height = (\n", + " study_bounds[3]\n", + " - study_bounds[1]\n", + ")\n", + "\n", + "x_margin = max(\n", + " 1.5 * study_width,\n", + " 1.0,\n", + ")\n", + "\n", + "y_margin = max(\n", + " 1.5 * study_height,\n", + " 1.0,\n", + ")\n", + "\n", + "fig, axes = plt.subplots(\n", + " 1,\n", + " 2,\n", + " figsize=(12, 7),\n", + ")\n", + "\n", + "chile_context.plot(\n", + " ax=axes[0],\n", + " color=\"0.88\",\n", + " edgecolor=\"0.25\",\n", + " linewidth=0.7,\n", + ")\n", + "\n", + "study_domain_wgs84.boundary.plot(\n", + " ax=axes[0],\n", + " color=\"tab:red\",\n", + " linewidth=2.0,\n", + ")\n", + "\n", + "axes[0].set_title(\n", + " \"Chile locator\"\n", + ")\n", + "\n", + "axes[0].set_xlabel(\n", + " \"Longitude\"\n", + ")\n", + "\n", + "axes[0].set_ylabel(\n", + " \"Latitude\"\n", + ")\n", + "\n", + "axes[0].set_xlim(\n", + " country_bounds[0] - 0.5,\n", + " country_bounds[2] + 0.5,\n", + ")\n", + "\n", + "axes[0].set_ylim(\n", + " country_bounds[1] - 0.5,\n", + " country_bounds[3] + 0.5,\n", + ")\n", + "\n", + "chile_context.plot(\n", + " ax=axes[1],\n", + " color=\"0.88\",\n", + " edgecolor=\"0.25\",\n", + " linewidth=0.7,\n", + ")\n", + "\n", + "study_domain_wgs84.boundary.plot(\n", + " ax=axes[1],\n", + " color=\"tab:red\",\n", + " linewidth=2.0,\n", + ")\n", + "\n", + "study_centroid = (\n", + " study_geometry\n", + " .centroid\n", + ")\n", + "\n", + "axes[1].annotate(\n", + " \"Synthetic LAB02 domain\",\n", + " xy=(\n", + " study_centroid.x,\n", + " study_centroid.y,\n", + " ),\n", + " xytext=(8, 8),\n", + " textcoords=\"offset points\",\n", + ")\n", + "\n", + "axes[1].set_title(\n", + " \"Northern Chile context\"\n", + ")\n", + "\n", + "axes[1].set_xlabel(\n", + " \"Longitude\"\n", + ")\n", + "\n", + "axes[1].set_ylabel(\n", + " \"Latitude\"\n", + ")\n", + "\n", + "axes[1].set_xlim(\n", + " study_bounds[0] - x_margin,\n", + " study_bounds[2] + x_margin,\n", + ")\n", + "\n", + "axes[1].set_ylim(\n", + " study_bounds[1] - y_margin,\n", + " study_bounds[3] + y_margin,\n", + ")\n", + "\n", + "fig.suptitle(\n", + " \"Geographic context of the synthetic LAB02 study domain\"\n", + ")\n", + "\n", + "fig.text(\n", + " 0.01,\n", + " 0.01,\n", + " (\n", + " \"Context: Natural Earth 1:110m v5.1.1 | \"\n", + " \"Study domain and analytical data: synthetic\"\n", + " ),\n", + " fontsize=8,\n", + ")\n", + "\n", + "plt.tight_layout(\n", + " rect=(0.0, 0.04, 1.0, 0.95)\n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "print(\n", + " \"Context source:\",\n", + " chile_context.iloc[0][\"source\"],\n", + ")\n", + "\n", + "print(\n", + " \"Context version:\",\n", + " chile_context.iloc[0][\"source_version\"],\n", + ")\n", + "\n", + "print(\n", + " \"Synthetic study CRS:\",\n", + " DEFAULT_CRS,\n", + ")\n", + "\n", + "print(\n", + " \"Study domain within Chile:\",\n", + " study_geometry.within(chile_geometry),\n", + ")\n", + "\n", + "print(\n", + " \"Study bounds in EPSG:4326:\",\n", + " tuple(\n", + " round(float(value), 6)\n", + " for value in study_bounds\n", + " ),\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "36c005c5", + "metadata": {}, + "source": [ + "### Context interpretation\n", + "\n", + "The map provides geographic orientation only.\n", + "\n", + "The Chile outline is real public-domain contextual cartography, while the\n", + "highlighted LAB02 domain is a synthetic methodological extent.\n", + "\n", + "Its position must not be interpreted as an actual exploration project, mining\n", + "property, concession, prospect, deposit, mineral occurrence, or field campaign.\n", + "\n", + "No geographic context layer contributes to the synthetic signal, sampling\n", + "density, Monte Carlo experiment, or subsequent spatial analysis.\n" + ] + }, + { + "cell_type": "markdown", + "id": "157a2d50", + "metadata": {}, + "source": [ + "## 1. Generate the controlled spatial experiment\n", + "\n", + "The default seed is fixed at `12345` for the baseline walkthrough.\n", + "\n", + "The synthetic dataset retains both:\n", + "\n", + "- `signal_true`: the known underlying spatial process;\n", + "- `signal`: the observed value after random noise is added.\n", + "\n", + "This distinction would not normally be available with real exploration data,\n", + "but it is useful here because it lets us validate the behaviour of the\n", + "exploratory workflow.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9a5e41ff", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:48.567116Z", + "iopub.status.busy": "2026-09-28T22:36:48.567116Z", + "iopub.status.idle": "2026-09-28T22:36:48.581558Z", + "shell.execute_reply": "2026-09-28T22:36:48.580775Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observations: 800\n", + "CRS: EPSG:32719\n", + "\n", + "sampling_component\n", + "regional 500\n", + "campaign 300\n", + "Name: count, dtype: int64\n", + "\n", + " sample_id sampling_component signal_true signal \\\n", + "0 S0001 regional 10.000000 15.480627 \n", + "1 S0002 regional 10.000011 6.262295 \n", + "2 S0003 regional 10.000000 12.893035 \n", + "3 S0004 regional 10.000000 9.663387 \n", + "4 S0005 regional 10.006110 7.958912 \n", + "\n", + " geometry \n", + "0 POINT (368186.882 7156688.989) \n", + "1 POINT (375340.667 7178094.878) \n", + "2 POINT (413789.237 7110619.583) \n", + "3 POINT (404100.374 7107708.607) \n", + "4 POINT (381288.764 7166538.805) \n" + ] + } + ], + "source": [ + "samples = generate_synthetic_sampling(seed=12345)\n", + "\n", + "crs = require_metric_projected_crs(samples)\n", + "\n", + "print(\"Observations:\", len(samples))\n", + "print(\"CRS:\", crs.to_string())\n", + "print()\n", + "print(samples[\"sampling_component\"].value_counts())\n", + "print()\n", + "print(samples.head())\n" + ] + }, + { + "cell_type": "markdown", + "id": "467d2a86", + "metadata": {}, + "source": [ + "## 2. Visualize the sampling pattern\n", + "\n", + "The first map shows **where observations were collected**, not the magnitude of\n", + "the spatial signal.\n", + "\n", + "A dense cluster can therefore represent sampling effort rather than geology.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "158677bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:48.582495Z", + "iopub.status.busy": "2026-09-28T22:36:48.582495Z", + "iopub.status.idle": "2026-09-28T22:36:48.769111Z", + "shell.execute_reply": "2026-09-28T22:36:48.769111Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "regional = samples[\n", + " samples[\"sampling_component\"] == \"regional\"\n", + "].copy()\n", + "\n", + "campaign = samples[\n", + " samples[\"sampling_component\"] == \"campaign\"\n", + "].copy()\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 7))\n", + "\n", + "regional.plot(\n", + " ax=ax,\n", + " marker=\"o\",\n", + " markersize=18,\n", + " alpha=0.65,\n", + " label=\"Regional\",\n", + ")\n", + "\n", + "campaign.plot(\n", + " ax=ax,\n", + " marker=\"^\",\n", + " markersize=22,\n", + " alpha=0.65,\n", + " label=\"Intensive campaign\",\n", + ")\n", + "\n", + "ax.set_title(\"Sampling pattern\")\n", + "ax.set_xlabel(\"Easting (m)\")\n", + "ax.set_ylabel(\"Northing (m)\")\n", + "ax.legend()\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "61b06c6d", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "The intensive campaign creates an obvious cluster of observations.\n", + "\n", + "If sampling density were interpreted visually without further analysis, this\n", + "area could incorrectly appear to be the most important spatial zone.\n", + "\n", + "We therefore quantify sampling density rather than relying only on the map.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a899378", + "metadata": {}, + "source": [ + "## 3. Nearest-neighbour sampling density\n", + "\n", + "Nearest-neighbour distance provides a simple descriptive measure of how tightly\n", + "observations are spatially packed.\n", + "\n", + "Shorter distances indicate denser local sampling.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9174ca26", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:48.771117Z", + "iopub.status.busy": "2026-09-28T22:36:48.770117Z", + "iopub.status.idle": "2026-09-28T22:36:48.785412Z", + "shell.execute_reply": "2026-09-28T22:36:48.785412Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Sampling componentMean NN distance (m)
0Regional1807.35
1Intensive campaign502.73
\n", + "
" + ], + "text/plain": [ + " Sampling component Mean NN distance (m)\n", + "0 Regional 1807.35\n", + "1 Intensive campaign 502.73" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Campaign / regional NN ratio: 0.278\n" + ] + } + ], + "source": [ + "regional_nn = nearest_neighbor_distances(regional)\n", + "campaign_nn = nearest_neighbor_distances(campaign)\n", + "\n", + "regional_mean_nn = float(regional_nn.mean())\n", + "campaign_mean_nn = float(campaign_nn.mean())\n", + "nn_ratio = campaign_mean_nn / regional_mean_nn\n", + "\n", + "nn_summary = pd.DataFrame(\n", + " {\n", + " \"Sampling component\": [\n", + " \"Regional\",\n", + " \"Intensive campaign\",\n", + " ],\n", + " \"Mean NN distance (m)\": [\n", + " regional_mean_nn,\n", + " campaign_mean_nn,\n", + " ],\n", + " }\n", + ")\n", + "\n", + "display(nn_summary.round(2))\n", + "\n", + "print(\n", + " \"Campaign / regional NN ratio:\",\n", + " round(nn_ratio, 3),\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dd4f5b57", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:48.786417Z", + "iopub.status.busy": "2026-09-28T22:36:48.786417Z", + "iopub.status.idle": "2026-09-28T22:36:48.854840Z", + "shell.execute_reply": "2026-09-28T22:36:48.854840Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "\n", + "ax.bar(\n", + " nn_summary[\"Sampling component\"],\n", + " nn_summary[\"Mean NN distance (m)\"],\n", + ")\n", + "\n", + "ax.set_title(\"Mean nearest-neighbour distance\")\n", + "ax.set_ylabel(\"Distance (m)\")\n", + "ax.set_xlabel(\"Sampling component\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f3e590a1", + "metadata": {}, + "source": [ + "### Baseline result\n", + "\n", + "For seed `12345`, the intensive campaign has a much smaller mean\n", + "nearest-neighbour distance than the regional observations.\n", + "\n", + "The campaign/regional ratio is approximately **0.278**.\n", + "\n", + "This confirms quantitatively that the campaign area is much more densely\n", + "sampled.\n" + ] + }, + { + "cell_type": "markdown", + "id": "69ad3ace", + "metadata": {}, + "source": [ + "## 4. Visualize the observed spatial signal\n", + "\n", + "The next map changes the question.\n", + "\n", + "Instead of showing **where sampling is dense**, it shows the magnitude of the\n", + "observed synthetic signal.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6a1e3b09", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:48.855845Z", + "iopub.status.busy": "2026-09-28T22:36:48.855845Z", + "iopub.status.idle": "2026-09-28T22:36:48.996934Z", + "shell.execute_reply": "2026-09-28T22:36:48.996934Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 7))\n", + "\n", + "scatter = ax.scatter(\n", + " samples.geometry.x,\n", + " samples.geometry.y,\n", + " c=samples[\"signal\"],\n", + " s=28,\n", + " alpha=0.8,\n", + ")\n", + "\n", + "ax.set_title(\"Observed synthetic spatial signal\")\n", + "ax.set_xlabel(\"Easting (m)\")\n", + "ax.set_ylabel(\"Northing (m)\")\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "colorbar = plt.colorbar(scatter, ax=ax)\n", + "colorbar.set_label(\"Observed signal\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4eee435", + "metadata": {}, + "source": [ + "## 5. Aggregate observations using the selected grid\n", + "\n", + "Grid aggregation requires a spatial-resolution decision.\n", + "\n", + "LAB 02 evaluates 5 km, 10 km, and 20 km grids using repeated Monte Carlo\n", + "realizations and multiple grid origins.\n", + "\n", + "The sensitivity experiment selected **10 km x 10 km** as the primary\n", + "descriptive resolution for this synthetic experiment.\n", + "\n", + "This is not a universal recommendation for mineral exploration.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "596a438b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:48.999199Z", + "iopub.status.busy": "2026-09-28T22:36:48.999199Z", + "iopub.status.idle": "2026-09-28T22:36:49.022009Z", + "shell.execute_reply": "2026-09-28T22:36:49.022009Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populated cells: 64\n", + "Observations preserved: 800\n" + ] + } + ], + "source": [ + "gridded = assign_regular_grid(\n", + " samples,\n", + " cell_size_m=PRIMARY_CELL_SIZE_M,\n", + " origin=GRID_ORIGIN,\n", + ")\n", + "\n", + "observed_summary = summarize_grid(\n", + " gridded,\n", + " signal_column=\"signal\",\n", + ")\n", + "\n", + "true_summary = summarize_grid(\n", + " gridded,\n", + " signal_column=\"signal_true\",\n", + ")\n", + "\n", + "print(\"Populated cells:\", len(observed_summary))\n", + "print(\n", + " \"Observations preserved:\",\n", + " int(observed_summary[\"observation_count\"].sum()),\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dbcbf3a", + "metadata": {}, + "source": [ + "## 6. Compare sampling density and spatial signal\n", + "\n", + "For the selected 10 km grid we identify:\n", + "\n", + "1. the cell with the greatest number of observations;\n", + "2. the cell with the highest observed mean signal;\n", + "3. the cell with the highest known true mean signal.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6977f9af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:49.023346Z", + "iopub.status.busy": "2026-09-28T22:36:49.023346Z", + "iopub.status.idle": "2026-09-28T22:36:49.030351Z", + "shell.execute_reply": "2026-09-28T22:36:49.030351Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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FeatureGrid IDObservationsMean signal
0Densest sampling cellc2_r317810.26
1Highest observed-signal cellc6_r6659.19
2Highest true-signal cellc6_r6659.89
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" + ], + "text/plain": [ + " Feature Grid ID Observations Mean signal\n", + "0 Densest sampling cell c2_r3 178 10.26\n", + "1 Highest observed-signal cell c6_r6 6 59.19\n", + "2 Highest true-signal cell c6_r6 6 59.89" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "densest_cell = observed_summary.loc[\n", + " observed_summary[\"observation_count\"].idxmax()\n", + "]\n", + "\n", + "observed_hotspot = observed_summary.loc[\n", + " observed_summary[\"mean_signal\"].idxmax()\n", + "]\n", + "\n", + "true_hotspot = true_summary.loc[\n", + " true_summary[\"mean_signal\"].idxmax()\n", + "]\n", + "\n", + "comparison = pd.DataFrame(\n", + " [\n", + " {\n", + " \"Feature\": \"Densest sampling cell\",\n", + " \"Grid ID\": densest_cell[\"grid_id\"],\n", + " \"Observations\": int(\n", + " densest_cell[\"observation_count\"]\n", + " ),\n", + " \"Mean signal\": float(\n", + " densest_cell[\"mean_signal\"]\n", + " ),\n", + " },\n", + " {\n", + " \"Feature\": \"Highest observed-signal cell\",\n", + " \"Grid ID\": observed_hotspot[\"grid_id\"],\n", + " \"Observations\": int(\n", + " observed_hotspot[\"observation_count\"]\n", + " ),\n", + " \"Mean signal\": float(\n", + " observed_hotspot[\"mean_signal\"]\n", + " ),\n", + " },\n", + " {\n", + " \"Feature\": \"Highest true-signal cell\",\n", + " \"Grid ID\": true_hotspot[\"grid_id\"],\n", + " \"Observations\": int(\n", + " true_hotspot[\"observation_count\"]\n", + " ),\n", + " \"Mean signal\": float(\n", + " true_hotspot[\"mean_signal\"]\n", + " ),\n", + " },\n", + " ]\n", + ")\n", + "\n", + "display(comparison.round(2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bffff2d5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:49.031627Z", + "iopub.status.busy": "2026-09-28T22:36:49.031627Z", + "iopub.status.idle": "2026-09-28T22:36:49.034626Z", + "shell.execute_reply": "2026-09-28T22:36:49.034626Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Densest cell differs from observed hotspot: True\n", + "Densest cell differs from true hotspot: True\n", + "Observed hotspot agrees with true hotspot: True\n" + ] + } + ], + "source": [ + "print(\n", + " \"Densest cell differs from observed hotspot:\",\n", + " densest_cell[\"grid_id\"] != observed_hotspot[\"grid_id\"],\n", + ")\n", + "\n", + "print(\n", + " \"Densest cell differs from true hotspot:\",\n", + " densest_cell[\"grid_id\"] != true_hotspot[\"grid_id\"],\n", + ")\n", + "\n", + "print(\n", + " \"Observed hotspot agrees with true hotspot:\",\n", + " observed_hotspot[\"grid_id\"] == true_hotspot[\"grid_id\"],\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "64275aa2", + "metadata": {}, + "source": [ + "### Baseline interpretation\n", + "\n", + "The densest sampling cell is not the strongest spatial-signal cell.\n", + "\n", + "For the baseline realization, the highest observed-signal cell also agrees with\n", + "the known true-signal cell.\n", + "\n", + "This illustrates the main methodological point:\n", + "\n", + "> Sampling effort and spatial signal must be analysed separately.\n" + ] + }, + { + "cell_type": "markdown", + "id": "066e2326", + "metadata": {}, + "source": [ + "## 7. Resolution sensitivity\n", + "\n", + "The primary resolution was not chosen from the baseline map.\n", + "\n", + "A separate confirmatory Monte Carlo experiment produced the persisted public artifacts under `results/`. Its design metadata and summary statistics are loaded below.\n", + "\n", + "Grid origin and resolution are repeated evaluations within each realization,\n", + "not independent observations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3511f351", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:49.035641Z", + "iopub.status.busy": "2026-09-28T22:36:49.035641Z", + "iopub.status.idle": "2026-09-28T22:36:49.048958Z", + "shell.execute_reply": "2026-09-28T22:36:49.048958Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Independent spatial realizations: 400\n", + "Grid resolutions: 3\n", + "Offsets per resolution: 4\n", + "Total grid evaluations: 4800\n" + ] + }, + { + "data": { + "text/html": [ + "
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Grid (km)Mean offset match rate (%)All 4 offsets matching (%)Median n at true hotspotPeak retention (%)
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220.098.995.831.545.7
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" + ], + "text/plain": [ + " Grid (km) Mean offset match rate (%) All 4 offsets matching (%) \\\n", + "0 5.0 87.4 63.0 \n", + "1 10.0 98.8 95.8 \n", + "2 20.0 98.9 95.8 \n", + "\n", + " Median n at true hotspot Peak retention (%) \n", + "0 2.0 94.3 \n", + "1 7.5 78.8 \n", + "2 31.5 45.7 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary_path = (\n", + " RESULTS_DIR\n", + " / \"resolution_summary.csv\"\n", + ")\n", + "\n", + "metadata_path = (\n", + " RESULTS_DIR\n", + " / \"experiment_metadata.json\"\n", + ")\n", + "\n", + "if not summary_path.exists():\n", + " raise FileNotFoundError(\n", + " \"Monte Carlo summary not found. \"\n", + " \"Expected persisted public artifact under results/.\"\n", + " )\n", + "\n", + "if not metadata_path.exists():\n", + " raise FileNotFoundError(\n", + " \"Experiment metadata not found. \"\n", + " \"Expected persisted public artifact under results/.\"\n", + " )\n", + "\n", + "resolution_results = pd.read_csv(\n", + " summary_path\n", + ")\n", + "\n", + "with metadata_path.open(\n", + " \"r\",\n", + " encoding=\"utf-8\",\n", + ") as handle:\n", + " experiment_metadata = json.load(handle)\n", + "\n", + "monte_carlo = experiment_metadata[\n", + " \"monte_carlo\"\n", + "]\n", + "\n", + "expected_resolutions = {\n", + " int(value)\n", + " for value in monte_carlo[\n", + " \"grid_resolutions_m\"\n", + " ]\n", + "}\n", + "\n", + "observed_resolutions = {\n", + " int(value)\n", + " for value in resolution_results[\n", + " \"cell_size_m\"\n", + " ]\n", + "}\n", + "\n", + "if observed_resolutions != expected_resolutions:\n", + " raise ValueError(\n", + " \"Resolution summary does not match \"\n", + " \"the experiment metadata.\"\n", + " )\n", + "\n", + "expected_realizations = int(\n", + " monte_carlo[\n", + " \"independent_realizations\"\n", + " ]\n", + ")\n", + "\n", + "if not (\n", + " resolution_results[\"n_realizations\"]\n", + " == expected_realizations\n", + ").all():\n", + " raise ValueError(\n", + " \"Resolution summary contains an \"\n", + " \"unexpected realization count.\"\n", + " )\n", + "\n", + "print(\n", + " \"Independent spatial realizations:\",\n", + " expected_realizations,\n", + ")\n", + "print(\n", + " \"Grid resolutions:\",\n", + " len(expected_resolutions),\n", + ")\n", + "print(\n", + " \"Offsets per resolution:\",\n", + " monte_carlo[\"offsets_per_resolution\"],\n", + ")\n", + "print(\n", + " \"Total grid evaluations:\",\n", + " monte_carlo[\"total_grid_evaluations\"],\n", + ")\n", + "\n", + "all_offsets_label = (\n", + " \"All \"\n", + " + str(\n", + " monte_carlo[\n", + " \"offsets_per_resolution\"\n", + " ]\n", + " )\n", + " + \" offsets matching (%)\"\n", + ")\n", + "\n", + "resolution_summary = pd.DataFrame(\n", + " {\n", + " \"Grid (km)\": (\n", + " resolution_results[\n", + " \"grid_km\"\n", + " ]\n", + " ),\n", + " \"Mean offset match rate (%)\": (\n", + " 100.0\n", + " * resolution_results[\n", + " \"mean_offset_match_rate\"\n", + " ]\n", + " ),\n", + " all_offsets_label: (\n", + " 100.0\n", + " * resolution_results[\n", + " \"all_offsets_match_rate\"\n", + " ]\n", + " ),\n", + " \"Median n at true hotspot\": (\n", + " resolution_results[\n", + " \"median_true_hotspot_n\"\n", + " ]\n", + " ),\n", + " \"Peak retention (%)\": (\n", + " 100.0\n", + " * resolution_results[\n", + " \"peak_retention_rate\"\n", + " ]\n", + " ),\n", + " }\n", + ")\n", + "\n", + "display(\n", + " resolution_summary.round(\n", + " {\n", + " \"Grid (km)\": 0,\n", + " \"Mean offset match rate (%)\": 1,\n", + " all_offsets_label: 1,\n", + " \"Median n at true hotspot\": 1,\n", + " \"Peak retention (%)\": 1,\n", + " }\n", + " )\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "824fcbca", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:49.050196Z", + "iopub.status.busy": "2026-09-28T22:36:49.050196Z", + "iopub.status.idle": "2026-09-28T22:36:49.115496Z", + "shell.execute_reply": "2026-09-28T22:36:49.115496Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "\n", + "ax.plot(\n", + " resolution_summary[\"Grid (km)\"],\n", + " resolution_summary[\n", + " all_offsets_label\n", + " ],\n", + " marker=\"o\",\n", + ")\n", + "\n", + "ax.set_title(\"Hotspot stability by grid resolution\")\n", + "ax.set_xlabel(\"Grid resolution (km)\")\n", + "ax.set_ylabel(\"Realizations with all offsets matching (%)\")\n", + "ax.set_xticks(\n", + " resolution_summary[\"Grid (km)\"]\n", + ")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "755934d0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-28T22:36:49.116502Z", + "iopub.status.busy": "2026-09-28T22:36:49.116502Z", + "iopub.status.idle": "2026-09-28T22:36:49.175189Z", + "shell.execute_reply": "2026-09-28T22:36:49.175189Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "\n", + "ax.plot(\n", + " resolution_summary[\"Grid (km)\"],\n", + " resolution_summary[\"Peak retention (%)\"],\n", + " marker=\"o\",\n", + ")\n", + "\n", + "ax.set_title(\"Synthetic peak retention by grid resolution\")\n", + "ax.set_xlabel(\"Grid resolution (km)\")\n", + "ax.set_ylabel(\"Peak retention (%)\")\n", + "ax.set_xticks(\n", + " resolution_summary[\"Grid (km)\"]\n", + ")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "92d6c054", + "metadata": {}, + "source": [ + "## 8. Why 10 km was selected\n", + "\n", + "The tested resolutions show a clear trade-off.\n", + "\n", + "### 5 km\n", + "\n", + "Preserves local signal well, but the hotspot contains only about two\n", + "observations and interpretation is sensitive to grid origin.\n", + "\n", + "### 10 km\n", + "\n", + "Provides high hotspot stability, better local sample support, and retains\n", + "approximately 79% of the theoretical spatial peak.\n", + "\n", + "### 20 km\n", + "\n", + "Provides high stability and sample support, but retains only about 46% of the\n", + "spatial peak, indicating substantial smoothing.\n", + "\n", + "Therefore, **10 km is used as the primary descriptive resolution for this\n", + "synthetic experiment**.\n", + "\n", + "It is not proposed as a universal grid size.\n" + ] + }, + { + "cell_type": "markdown", + "id": "a16b1a5a", + "metadata": {}, + "source": [ + "## 9. GeoAI relevance\n", + "\n", + "This experiment demonstrates a problem that becomes more important when\n", + "predictive models are introduced.\n", + "\n", + "A model such as Random Forest, gradient boosting, XGBoost, or a neural network\n", + "may partially learn **where observations were collected most intensively**\n", + "instead of learning the underlying geological process.\n", + "\n", + "This is one reason why ordinary random train/test splitting can be misleading\n", + "for spatial data.\n", + "\n", + "The next stage of the roadmap therefore introduces **spatial validation and\n", + "spatial cross-validation** before predictive mineral-prospectivity modelling.\n" + ] + }, + { + "cell_type": "markdown", + "id": "42db19e2", + "metadata": {}, + "source": [ + "## 10. Limitations\n", + "\n", + "This laboratory deliberately isolates one methodological problem.\n", + "\n", + "It does not yet include:\n", + "\n", + "- real geological observations;\n", + "- lithological or structural controls;\n", + "- real geochemical assays;\n", + "- compositional-data analysis;\n", + "- anisotropy;\n", + "- fitted spatial autocorrelation models;\n", + "- censoring or analytical detection limits;\n", + "- preferential-sampling correction;\n", + "- predictive modelling;\n", + "- mineral-prospectivity ranking.\n", + "\n", + "These elements are deferred intentionally so that sampling-density and\n", + "aggregation effects are understood first.\n" + ] + }, + { + "cell_type": "markdown", + "id": "afe305cc", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "The controlled experiment supports the main conclusion of LAB 02:\n", + "\n", + "> A spatially dense cluster of samples is not automatically a hotspot of the\n", + "> underlying spatial signal.\n", + "\n", + "The workflow therefore separates:\n", + "\n", + "**sampling density -> spatial signal -> aggregation scale -> validation**\n", + "\n", + "before moving to predictive GeoAI.\n", + "\n", + "This extends the lesson from LAB 01:\n", + "\n", + "> Spatial analysis can be computationally correct while still being\n", + "> scientifically misleading if spatial assumptions are not validated.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (geoai-mineral)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/labs/lab02_spatial_eda/02_spatial_eda.py b/labs/lab02_spatial_eda/02_spatial_eda.py new file mode 100644 index 0000000..a7d29bf --- /dev/null +++ b/labs/lab02_spatial_eda/02_spatial_eda.py @@ -0,0 +1,221 @@ +from __future__ import annotations + +from geoai.spatial import ( + assign_regular_grid, + generate_synthetic_sampling, + nearest_neighbor_distances, + require_metric_projected_crs, + summarize_grid, +) + + +PRIMARY_CELL_SIZE_M = 10000.0 + +GRID_ORIGIN = ( + 350000.0, + 7100000.0, +) + + +def main() -> None: + samples = generate_synthetic_sampling() + + crs = require_metric_projected_crs(samples) + + regional = samples[ + samples["sampling_component"] == "regional" + ].copy() + + campaign = samples[ + samples["sampling_component"] == "campaign" + ].copy() + + regional_nn = nearest_neighbor_distances(regional) + campaign_nn = nearest_neighbor_distances(campaign) + + regional_mean_nn = float(regional_nn.mean()) + campaign_mean_nn = float(campaign_nn.mean()) + + nn_ratio = campaign_mean_nn / regional_mean_nn + + gridded = assign_regular_grid( + samples, + cell_size_m=PRIMARY_CELL_SIZE_M, + origin=GRID_ORIGIN, + ) + + observed_summary = summarize_grid( + gridded, + signal_column="signal", + ) + + true_summary = summarize_grid( + gridded, + signal_column="signal_true", + ) + + densest_cell = observed_summary.loc[ + observed_summary["observation_count"].idxmax() + ] + + highest_observed_signal_cell = observed_summary.loc[ + observed_summary["mean_signal"].idxmax() + ] + + highest_true_signal_cell = true_summary.loc[ + true_summary["mean_signal"].idxmax() + ] + + observations_preserved = int( + observed_summary["observation_count"].sum() + ) + + densest_differs_from_observed = ( + densest_cell["grid_id"] + != highest_observed_signal_cell["grid_id"] + ) + + densest_differs_from_true = ( + densest_cell["grid_id"] + != highest_true_signal_cell["grid_id"] + ) + + observed_agrees_with_true = ( + highest_observed_signal_cell["grid_id"] + == highest_true_signal_cell["grid_id"] + ) + + print("=" * 72) + print("LAB 02 - SPATIAL EXPLORATORY DATA ANALYSIS") + print("=" * 72) + + print("\nDATASET") + print(f"Observations: {len(samples)}") + print(f"CRS: {crs.to_string()}") + + print("\nSAMPLING COMPONENTS") + print(f"Regional observations: {len(regional)}") + print(f"Campaign observations: {len(campaign)}") + + print("\nNEAREST-NEIGHBOUR DISTANCE") + print( + "Regional mean NN distance (m): " + f"{regional_mean_nn:.2f}" + ) + print( + "Campaign mean NN distance (m): " + f"{campaign_mean_nn:.2f}" + ) + print( + "Campaign/regional NN ratio: " + f"{nn_ratio:.3f}" + ) + print( + "Campaign more densely sampled: " + f"{campaign_mean_nn < regional_mean_nn}" + ) + + print("\nREGULAR GRID") + print( + "Cell size (m): " + f"{PRIMARY_CELL_SIZE_M:.0f}" + ) + print( + "Grid origin: " + f"{GRID_ORIGIN}" + ) + print( + "Populated cells: " + f"{len(observed_summary)}" + ) + print( + "Observations preserved: " + f"{observations_preserved}" + ) + + print("\nDENSEST CELL") + print( + "Grid ID: " + f"{densest_cell['grid_id']}" + ) + print( + "Observation count: " + f"{int(densest_cell['observation_count'])}" + ) + print( + "Mean observed signal: " + f"{float(densest_cell['mean_signal']):.2f}" + ) + + print("\nHIGHEST OBSERVED-SIGNAL CELL") + print( + "Grid ID: " + f"{highest_observed_signal_cell['grid_id']}" + ) + print( + "Observation count: " + f"{int(highest_observed_signal_cell['observation_count'])}" + ) + print( + "Mean observed signal: " + f"{float(highest_observed_signal_cell['mean_signal']):.2f}" + ) + + print("\nHIGHEST TRUE-SIGNAL CELL") + print( + "Grid ID: " + f"{highest_true_signal_cell['grid_id']}" + ) + print( + "Observation count: " + f"{int(highest_true_signal_cell['observation_count'])}" + ) + print( + "Mean true signal: " + f"{float(highest_true_signal_cell['mean_signal']):.2f}" + ) + + print("\nKEY RESULTS") + print( + "Densest cell differs from highest observed-signal cell: " + f"{densest_differs_from_observed}" + ) + print( + "Densest cell differs from highest true-signal cell: " + f"{densest_differs_from_true}" + ) + print( + "Highest observed-signal cell agrees with true-signal cell: " + f"{observed_agrees_with_true}" + ) + + print("\nINTERPRETATION") + print( + "The synthetic sampling campaign produces a clear " + "sampling-density hotspot." + ) + print( + "The densest grid cell is separated from the strongest " + "underlying synthetic signal." + ) + print( + "A 10 km grid is used as the primary descriptive resolution " + "for this experiment." + ) + print( + "Monte Carlo sensitivity analysis showed that this resolution " + "provides a useful compromise between hotspot stability, " + "local sample support, and preservation of spatial signal." + ) + print( + "The selected resolution is specific to this synthetic " + "experiment and is not presented as a universal optimum." + ) + print( + "Sampling density must therefore be distinguished from " + "underlying spatial signal before predictive modelling." + ) + + +if __name__ == "__main__": + main() diff --git a/labs/lab02_spatial_eda/README.md b/labs/lab02_spatial_eda/README.md new file mode 100644 index 0000000..b5dd6c2 --- /dev/null +++ b/labs/lab02_spatial_eda/README.md @@ -0,0 +1,239 @@ +# LAB 02 - Spatial Exploratory Data Analysis + +## Purpose + +This laboratory examines how uneven spatial sampling can distort the interpretation of exploratory geospatial data. + +Central question: + +> Can an apparent spatial hotspot be explained by sampling intensity rather than by the underlying spatial signal? + +A controlled synthetic experiment is used so that the underlying spatial signal is known. The goal is to compare sampling density, observed signal, and synthetic ground truth before predictive modelling. + +## Experimental setting + +All observations are generated in **EPSG:32719 (WGS 84 / UTM zone 19S)** over an approximately 80 km x 80 km synthetic domain. + +Each independent spatial realization contains: + +- 500 regional observations; +- 300 intensive-campaign observations; +- 800 observations in total. + +The intensive campaign is deliberately concentrated in a different area from the strongest underlying synthetic signal. + +Campaign centre: + +- Easting: 376,500 m +- Northing: 7,138,500 m + +Signal centre: + +- Easting: 411,500 m +- Northing: 7,162,500 m + +The baseline demonstration uses seed `12345`. + +## Main variables + +The main variables are: + +- `sample_id`: observation identifier; +- `sampling_component`: regional or intensive campaign; +- `geometry`: projected point coordinates; +- `signal_true`: known underlying synthetic spatial signal; +- `signal`: observed signal after random noise is added. + +Derived variables include nearest-neighbour distance, grid identifiers, grid centres, observation counts, and mean signal by grid cell. + +The synthetic signal is a teaching variable. It is not a real ore grade, geochemical assay, resource estimate, or prospectivity score. + +## Geographic context and provenance + +The notebook includes a lightweight geographic locator so that the synthetic +study domain can be understood in a real coordinate reference context. + +The country geometry is derived from **Natural Earth Admin 0 - Countries, +1:110m, version 5.1.1** and is stored locally as +`data/context/chile_context.geojson` in **EPSG:4326**. + +The highlighted LAB 02 domain is not real exploration data. It is generated +from `DEFAULT_BOUNDS` in `DEFAULT_CRS` (**EPSG:32719**) and is reprojected to +EPSG:4326 only for the contextual figure. + +The location does not represent an actual exploration project, mining property, +concession, prospect, deposit, mineral occurrence, or field campaign. + +`data/context/SOURCE.md` documents the source, derivation procedure, +public-domain distribution status, and SHA-256 integrity hashes for both the +downloaded Natural Earth archive and the derived GeoJSON artifact. + +The context layer is an auxiliary cartographic input only. It does not +contribute to the synthetic signal, sampling process, Monte Carlo experiment, +grid selection, or analytical results. + +## Reusable code + +Reusable spatial functions are implemented in `src/geoai/spatial.py`: + +- `require_metric_projected_crs` +- `generate_synthetic_sampling` +- `nearest_neighbor_distances` +- `assign_regular_grid` +- `summarize_grid` + +The baseline exploratory script is `labs/lab02_spatial_eda/02_spatial_eda.py`. + +## Published results and reproducibility + +The public portfolio includes persisted outputs from the confirmatory +Monte Carlo experiment under `labs/lab02_spatial_eda/results/`: + +- `resolution_summary.csv`: summary metrics by grid resolution; +- `monte_carlo_by_realization.csv`: one record per independent realization + and grid resolution; +- `experiment_metadata.json`: experiment design, CRS, sampling counts, + Monte Carlo configuration, and nearest-neighbour robustness metadata. + +The recruiter-facing notebook reads these persisted CSV and JSON artifacts +directly. This keeps the published analysis inspectable and reproducible while +release-build and orchestration tooling remain outside the public portfolio. + +The public analytical lineage is: + +`synthetic experiment -> persisted results -> notebook -> figures and interpretation` + +The geographic context layer follows a separate provenance path and is consumed +only by the notebook visualization stage. + +## Baseline result + +For seed `12345`: + +- observations: 800; +- regional observations: 500; +- campaign observations: 300; +- regional mean nearest-neighbour distance: 1807.35 m; +- campaign mean nearest-neighbour distance: 502.73 m; +- campaign/regional nearest-neighbour ratio: 0.278. + +The intensive campaign is therefore substantially more spatially concentrated than the regional component. + +Using the selected 10 km grid, the densest sampling cell differs from the cell containing the strongest underlying synthetic signal. + +## Why grid resolution matters + +Spatial aggregation creates a scale trade-off. + +A small grid preserves local detail but may contain very few observations per cell. A large grid increases local sample support but may smooth or attenuate the underlying spatial signal. Results can also change when the grid origin is shifted. + +For this reason, grid resolution is evaluated with repeated simulations and multiple grid origins rather than with a single map or random seed. + +## Monte Carlo sensitivity design + +The confirmatory design uses: + +- **400 independent spatial realizations**; +- 3 grid resolutions: 5 km, 10 km, and 20 km; +- 4 grid-origin configurations per resolution. + +This produces **4,800 grid evaluations**. + +The statistical replication unit is the spatial realization. Grid resolution and grid origin are repeated evaluations within each realization and are not treated as independent observations. + +The four offsets are: + +- `(0, 0)` +- `(0.5 cell, 0)` +- `(0, 0.5 cell)` +- `(0.5 cell, 0.5 cell)` + +## Nearest-neighbour robustness + +Across the 400 independent spatial realizations: + +- campaign denser than regional sampling: 100.0%; +- median campaign/regional NN ratio: 0.261; +- 95th percentile NN ratio: 0.284. + +This confirms that the intensive campaign is consistently more spatially concentrated than the regional sampling component. + +## Resolution sensitivity + +### 5 km + +- mean offset match rate: 87.4%; +- all four offsets matching: 63.0%; +- Wilson 95% CI: 58.2% to 67.6%; +- median observations at true hotspot: 2.0; +- median true hotspot signal: 84.89; +- peak retention: 94.3%; +- 95th percentile worst-case normalized displacement: 1.414 cell widths. + +The 5 km grid preserves local signal well but has low local sample support and is sensitive to grid origin. + +### 10 km + +- mean offset match rate: 98.8%; +- all four offsets matching: 95.8%; +- Wilson 95% CI: 93.3% to 97.3%; +- median observations at true hotspot: 7.5; +- median true hotspot signal: 70.93; +- peak retention: 78.8%; +- 95th percentile worst-case normalized displacement: 0.000 cell widths. + +The **10 km x 10 km** grid is selected as the primary descriptive resolution for this synthetic experiment because it provides high hotspot stability while retaining substantially more spatial signal than the 20 km grid. + +### 20 km + +- mean offset match rate: 98.9%; +- all four offsets matching: 95.8%; +- Wilson 95% CI: 93.3% to 97.3%; +- median observations at true hotspot: 31.5; +- median true hotspot signal: 41.12; +- peak retention: 45.7%; +- 95th percentile worst-case normalized displacement: 0.000 cell widths. + +The 20 km grid provides strong local support and stability but substantially attenuates the spatial peak, illustrating over-smoothing. + +## Interpretation + +The 10 km resolution is not a universal recommendation for mineral exploration. It is specific to this synthetic design. + +The central result is: + +> Sampling density and spatial signal are different properties of a geospatial dataset and should be evaluated separately before predictive modelling. + +Without spatial validation, a predictive model may partly learn where data were sampled most intensively instead of where the underlying geological signal is strongest. + +This laboratory therefore prepares the foundation for spatial validation and spatial cross-validation before predictive mineral-exploration modelling. + +## Limitations + +This laboratory deliberately uses a simplified synthetic process. It does not yet include real geological units, structural geology, real geochemical assays, compositional-data analysis, anisotropy, a fitted spatial autocorrelation model, measurement error, detection limits, preferential-sampling correction, predictive modelling, or mineral-prospectivity ranking. + +These elements are deferred so that sampling-density and aggregation effects can first be studied in isolation. + +## Current validation status + +- LAB 02 automated tests: 9 passed +- Full project test suite: **24 passed** +- Monte Carlo realizations: 400 +- Grid evaluations: 4,800 +- Persisted realization-resolution records: 1,200 +- Resolution-summary records: 3 +- Primary descriptive resolution: 10 km x 10 km +- Notebook cells: 31 +- Notebook code cells executed: 13 / 13 +- Notebook error outputs: 0 +- Embedded PNG figures: 6 +- Notebook schema validation: passed +- Geographic context feature: 1 valid Chile MultiPolygon +- Geographic context CRS: EPSG:4326 +- Geographic context provenance: documented in `data/context/SOURCE.md` +- Notebook encoding: UTF-8 without BOM +- Notebook line endings: LF +- Pre-commit validation: passed +- Gitleaks secret scan: passed + +The public release combines reusable spatial code, persisted synthetic result artifacts, an executed analytical notebook, and automated tests. diff --git a/labs/lab02_spatial_eda/data/context/SOURCE.md b/labs/lab02_spatial_eda/data/context/SOURCE.md new file mode 100644 index 0000000..9e7d583 --- /dev/null +++ b/labs/lab02_spatial_eda/data/context/SOURCE.md @@ -0,0 +1,70 @@ +# Context data provenance + +## Purpose + +This directory contains a lightweight geographic context layer used only to +locate the synthetic LAB02 study domain within Chile. + +The contextual country geometry is real cartographic reference data. The LAB02 +study area, sampling locations, signal, campaign design, and analytical results +are entirely synthetic. + +## Source + +- Provider: Natural Earth +- Dataset: Admin 0 - Countries +- Scale: 1:110m +- Version: 5.1.1 +- Source archive: `ne_110m_admin_0_countries.zip` +- Source CRS: EPSG:4326 +- Distribution: Natural Earth public-domain vector data + +## Derived artifact + +File: + +`chile_context.geojson` + +The derived layer was created from the Natural Earth country dataset using the +following procedure: + +1. Read the original archive with GeoPandas using the Pyogrio engine. +2. Select exactly one feature using `ISO_A3 == "CHL"`. +3. Retain only the country name, ISO alpha-2 code, ISO alpha-3 code, and geometry. +4. Normalize the output CRS to EPSG:4326. +5. Validate the resulting geometry. +6. Add minimal provenance metadata. +7. Write the derived feature as GeoJSON using Pyogrio. + +The resulting geometry is a valid MultiPolygon containing two polygon parts. + +## Integrity + +Original Natural Earth archive SHA-256: + +`0F243AEAC8AC6CF26F0417285B0BD33AC47F1B5BDB719FD3E0DF37D03EA37110` + +Derived `chile_context.geojson` SHA-256: + +`4B36F55393CB3DED799C6AF73664A7531A90B97580D2B55250C2134F4EA61BE7` + +These hashes identify the exact files used during preparation of this LAB02 +context layer. + +## Analytical scope + +The country geometry is used only as a geographic locator. + +It is not used to derive the synthetic signal, sampling density, Monte Carlo +experiment, spatial statistics, or any mineral prospectivity result. + +The synthetic LAB02 domain is fully contained within the Chile context geometry, +but its location does not represent an actual exploration project, mining +property, prospect, concession, deposit, mineral occurrence, or field campaign. + +## Reproducibility note + +The contextual layer is stored locally so the LAB02 notebook can be executed +without requiring a web-map service, tile provider, or Internet connection. + +No Cartopy, Contextily, Fiona, or external basemap service is required. diff --git a/labs/lab02_spatial_eda/data/context/chile_context.geojson b/labs/lab02_spatial_eda/data/context/chile_context.geojson new file mode 100644 index 0000000..de511dc --- /dev/null +++ b/labs/lab02_spatial_eda/data/context/chile_context.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"name": "chile_context", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{"type":"Feature","properties":{"name":"Chile","iso_a2":"CL","iso_a3":"CHL","source":"Natural Earth","source_dataset":"Admin 0 - 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+5000.000000,5.000000,400,0.874375,0.630000,0.581659,0.675868,1.000000,0.990488,1.000000,0.000000,1.414214,2.000000,84.886447,0.943183 +10000.000000,10.000000,400,0.988125,0.957500,0.932998,0.973298,1.000000,0.990488,1.000000,0.000000,0.000000,7.500000,70.929118,0.788101 +20000.000000,20.000000,400,0.989375,0.957500,0.932998,0.973298,1.000000,0.990488,1.000000,0.000000,0.000000,31.500000,41.124309,0.456937 diff --git a/src/geoai/spatial.py b/src/geoai/spatial.py new file mode 100644 index 0000000..d382e44 --- /dev/null +++ b/src/geoai/spatial.py @@ -0,0 +1,412 @@ +from __future__ import annotations + +from typing import Final + +import geopandas as gpd +import numpy as np +import pandas as pd +from pyproj import CRS + + +DEFAULT_CRS: Final[str] = "EPSG:32719" + +DEFAULT_BOUNDS: Final[tuple[float, float, float, float]] = ( + 350000.0, + 430000.0, + 7100000.0, + 7180000.0, +) + +DEFAULT_CAMPAIGN_CENTER: Final[tuple[float, float]] = ( + 376500.0, + 7138500.0, +) + +DEFAULT_SIGNAL_CENTER: Final[tuple[float, float]] = ( + 411500.0, + 7162500.0, +) + + +def require_metric_projected_crs(gdf: gpd.GeoDataFrame) -> CRS: + """Validate that a GeoDataFrame uses projected metre-based coordinates.""" + if gdf.crs is None: + raise ValueError("GeoDataFrame must define a CRS.") + + crs = CRS.from_user_input(gdf.crs) + + if not crs.is_projected: + raise ValueError("Metric spatial analysis requires a projected CRS.") + + axis_info = crs.axis_info + + if len(axis_info) < 2: + raise ValueError("Projected CRS must expose two coordinate axes.") + + for axis in axis_info[:2]: + factor = axis.unit_conversion_factor + + if factor is None or not np.isclose(float(factor), 1.0): + raise ValueError( + "Projected CRS axes must use metre-based units." + ) + + return crs + + +def _validate_point_geometries(gdf: gpd.GeoDataFrame) -> None: + if gdf.empty: + raise ValueError( + "GeoDataFrame must contain at least one observation." + ) + + if gdf.geometry.isna().any(): + raise ValueError("Geometry column contains missing values.") + + if gdf.geometry.is_empty.any(): + raise ValueError("Geometry column contains empty geometries.") + + if not gdf.geometry.geom_type.eq("Point").all(): + raise ValueError("This operation requires Point geometries.") + + +def generate_synthetic_sampling( + *, + seed: int = 12345, + n_regional: int = 500, + n_campaign: int = 300, + bounds: tuple[float, float, float, float] = DEFAULT_BOUNDS, + campaign_center: tuple[float, float] = DEFAULT_CAMPAIGN_CENTER, + campaign_sigma_m: float = 3500.0, + signal_center: tuple[float, float] = DEFAULT_SIGNAL_CENTER, + signal_sigma_m: float = 7000.0, + signal_baseline: float = 10.0, + signal_amplitude: float = 80.0, + noise_sd: float = 3.0, + crs: str = DEFAULT_CRS, +) -> gpd.GeoDataFrame: + """Generate a reproducible synthetic mineral exploration dataset.""" + if n_regional <= 0 or n_campaign <= 0: + raise ValueError("Sample counts must be positive.") + + if campaign_sigma_m <= 0 or signal_sigma_m <= 0: + raise ValueError("Spatial scales must be positive.") + + if noise_sd < 0: + raise ValueError("noise_sd must be non-negative.") + + xmin, xmax, ymin, ymax = bounds + + if not (xmin < xmax and ymin < ymax): + raise ValueError( + "Bounds must satisfy xmin < xmax and ymin < ymax." + ) + + rng = np.random.default_rng(seed) + + regional_x = rng.uniform( + xmin, + xmax, + n_regional, + ) + regional_y = rng.uniform( + ymin, + ymax, + n_regional, + ) + + campaign_x = np.clip( + rng.normal( + campaign_center[0], + campaign_sigma_m, + n_campaign, + ), + xmin, + xmax, + ) + campaign_y = np.clip( + rng.normal( + campaign_center[1], + campaign_sigma_m, + n_campaign, + ), + ymin, + ymax, + ) + + x = np.concatenate( + ( + regional_x, + campaign_x, + ) + ) + y = np.concatenate( + ( + regional_y, + campaign_y, + ) + ) + + component = np.concatenate( + ( + np.repeat("regional", n_regional), + np.repeat("campaign", n_campaign), + ) + ) + + scaled_x = ( + x - signal_center[0] + ) / signal_sigma_m + + scaled_y = ( + y - signal_center[1] + ) / signal_sigma_m + + signal_true = ( + signal_baseline + + signal_amplitude + * np.exp( + -0.5 + * ( + scaled_x**2 + + scaled_y**2 + ) + ) + ) + + signal = ( + signal_true + + rng.normal( + 0.0, + noise_sd, + x.size, + ) + ) + + data = pd.DataFrame( + { + "sample_id": [ + f"S{i:04d}" + for i in range( + 1, + x.size + 1, + ) + ], + "sampling_component": component, + "signal_true": signal_true, + "signal": signal, + } + ) + + gdf = gpd.GeoDataFrame( + data, + geometry=gpd.points_from_xy( + x, + y, + ), + crs=crs, + ) + + require_metric_projected_crs(gdf) + + return gdf + + +def nearest_neighbor_distances( + gdf: gpd.GeoDataFrame, +) -> pd.Series: + """Calculate Euclidean nearest-neighbour distances in metres.""" + require_metric_projected_crs(gdf) + _validate_point_geometries(gdf) + + if len(gdf) < 2: + raise ValueError( + "At least two observations are required." + ) + + coordinates = np.column_stack( + ( + gdf.geometry.x.to_numpy( + dtype=float + ), + gdf.geometry.y.to_numpy( + dtype=float + ), + ) + ) + + deltas = ( + coordinates[:, np.newaxis, :] + - coordinates[np.newaxis, :, :] + ) + + squared_distances = np.einsum( + "ijk,ijk->ij", + deltas, + deltas, + ) + + np.fill_diagonal( + squared_distances, + np.inf, + ) + + distances = np.sqrt( + np.min( + squared_distances, + axis=1, + ) + ) + + return pd.Series( + distances, + index=gdf.index, + name="nearest_neighbor_m", + dtype=float, + ) + + +def assign_regular_grid( + gdf: gpd.GeoDataFrame, + *, + cell_size_m: float = 5000.0, + origin: tuple[float, float] | None = None, +) -> gpd.GeoDataFrame: + """Assign point observations to a regular metric grid.""" + require_metric_projected_crs(gdf) + _validate_point_geometries(gdf) + + if cell_size_m <= 0: + raise ValueError( + "cell_size_m must be positive." + ) + + if origin is None: + minx, miny, _, _ = gdf.total_bounds + + origin_x = ( + np.floor( + minx / cell_size_m + ) + * cell_size_m + ) + + origin_y = ( + np.floor( + miny / cell_size_m + ) + * cell_size_m + ) + else: + origin_x, origin_y = origin + + result = gdf.copy() + + result["grid_col"] = np.floor( + ( + result.geometry.x.to_numpy( + dtype=float + ) + - origin_x + ) + / cell_size_m + ).astype(int) + + result["grid_row"] = np.floor( + ( + result.geometry.y.to_numpy( + dtype=float + ) + - origin_y + ) + / cell_size_m + ).astype(int) + + result["grid_id"] = ( + "c" + + result["grid_col"].astype(str) + + "_r" + + result["grid_row"].astype(str) + ) + + result["grid_center_x"] = ( + origin_x + + ( + result["grid_col"] + + 0.5 + ) + * cell_size_m + ) + + result["grid_center_y"] = ( + origin_y + + ( + result["grid_row"] + + 0.5 + ) + * cell_size_m + ) + + return result + + +def summarize_grid( + gridded: gpd.GeoDataFrame, + *, + signal_column: str = "signal", +) -> pd.DataFrame: + """Summarize sampling intensity and mean signal by grid cell.""" + required = { + "grid_id", + "grid_col", + "grid_row", + "grid_center_x", + "grid_center_y", + signal_column, + } + + missing = required.difference( + gridded.columns + ) + + if missing: + missing_text = ", ".join( + sorted(missing) + ) + + raise ValueError( + f"Missing required grid columns: {missing_text}" + ) + + summary = ( + gridded.groupby( + [ + "grid_id", + "grid_col", + "grid_row", + "grid_center_x", + "grid_center_y", + ], + as_index=False, + sort=True, + ) + .agg( + observation_count=( + signal_column, + "size", + ), + mean_signal=( + signal_column, + "mean", + ), + ) + .sort_values( + [ + "grid_row", + "grid_col", + ], + ignore_index=True, + ) + ) + + return summary diff --git a/tests/test_spatial.py b/tests/test_spatial.py new file mode 100644 index 0000000..1471346 --- /dev/null +++ b/tests/test_spatial.py @@ -0,0 +1,149 @@ +import geopandas as gpd +import pytest +from shapely.geometry import Point + +from geoai.spatial import ( + assign_regular_grid, + generate_synthetic_sampling, + nearest_neighbor_distances, + require_metric_projected_crs, + summarize_grid, +) + + +@pytest.fixture(scope="module") +def synthetic_samples() -> gpd.GeoDataFrame: + return generate_synthetic_sampling() + + +def test_generate_synthetic_sampling_has_expected_structure( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + assert len(synthetic_samples) == 800 + assert synthetic_samples.crs.to_epsg() == 32719 + + counts = synthetic_samples["sampling_component"].value_counts() + + assert counts["regional"] == 500 + assert counts["campaign"] == 300 + + expected_columns = { + "sample_id", + "sampling_component", + "signal_true", + "signal", + "geometry", + } + + assert expected_columns.issubset(synthetic_samples.columns) + + +def test_generate_synthetic_sampling_is_reproducible() -> None: + first = generate_synthetic_sampling(seed=12345) + second = generate_synthetic_sampling(seed=12345) + + assert first.equals(second) + + +def test_require_metric_projected_crs_accepts_utm( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + crs = require_metric_projected_crs(synthetic_samples) + + assert crs.is_projected + assert crs.to_epsg() == 32719 + + +def test_require_metric_projected_crs_rejects_geographic() -> None: + geographic = gpd.GeoDataFrame( + {"sample_id": ["A", "B"]}, + geometry=[ + Point(-70.0, -30.0), + Point(-70.1, -30.1), + ], + crs="EPSG:4326", + ) + + with pytest.raises( + ValueError, + match="projected CRS", + ): + require_metric_projected_crs(geographic) + + +def test_nearest_neighbor_distances_are_positive( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + distances = nearest_neighbor_distances(synthetic_samples) + + assert len(distances) == len(synthetic_samples) + assert distances.notna().all() + assert (distances > 0).all() + + +def test_campaign_is_more_dense_than_regional( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + regional = synthetic_samples[ + synthetic_samples["sampling_component"] == "regional" + ] + + campaign = synthetic_samples[ + synthetic_samples["sampling_component"] == "campaign" + ] + + regional_nn = nearest_neighbor_distances(regional) + campaign_nn = nearest_neighbor_distances(campaign) + + assert campaign_nn.mean() < regional_nn.mean() + + +def test_regular_grid_preserves_all_observations( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + gridded = assign_regular_grid( + synthetic_samples, + cell_size_m=5000.0, + ) + + summary = summarize_grid(gridded) + + assert len(gridded) == len(synthetic_samples) + assert gridded["grid_id"].notna().all() + assert summary["observation_count"].sum() == len( + synthetic_samples + ) + + +def test_densest_cell_differs_from_highest_signal_cell( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + gridded = assign_regular_grid( + synthetic_samples, + cell_size_m=5000.0, + ) + + summary = summarize_grid(gridded) + + densest = summary.loc[ + summary["observation_count"].idxmax() + ] + + highest_signal = summary.loc[ + summary["mean_signal"].idxmax() + ] + + assert densest["grid_id"] != highest_signal["grid_id"] + + +def test_assign_regular_grid_rejects_non_positive_cell_size( + synthetic_samples: gpd.GeoDataFrame, +) -> None: + with pytest.raises( + ValueError, + match="cell_size_m must be positive", + ): + assign_regular_grid( + synthetic_samples, + cell_size_m=0.0, + )