planx_geostats

 # PlanX GeoStats Lab **Spatial statistics for planners — inside the QGIS Processing Toolbox.** [](https://github.com/YusufEminoglu/planx_geostats/releases) [](LICENSE) [](https://qgis.org) [](https://docs.qgis.org/latest/en/docs/user_manual/processing/index.html) [](https://github.com/YusufEminoglu/PlanX) [](https://yusufeminoglu.github.io/planx_geostats/GEOSTATS_REFERENCE_MANUAL.html) From *"is this clustered?"* to *"which model explains it?"* — **30+ Processing algorithms covering the full spatial-statistics workflow, with guidance built in at every step.** ## 📖 Documentation **[Comprehensive Academic Reference Manual](https://yusufeminoglu.github.io/planx_geostats/GEOSTATS_REFERENCE_MANUAL.html)** — 34 algorithms, 6 groups, 215+ DOI-verified academic references. Every algorithm documented with theoretical background, mathematical formulation (numbered display equations), complete parameter and output tables, interpretation guides, and literature. Hosted on GitHub Pages with deep-link per-algorithm anchors accessible directly from the QGIS Processing dialog (Help button). [Install](#-installation) · [Tool catalog](#-tool-catalog) · [Guided workflow](#-a-lab-not-a-toolbox-dump) · [Optional libraries](#-optional-libraries) · [Sample data](#-bundled-sample-data) · [Türkçe](#-türkçe-özet)

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✨ Why GeoStats Lab?

🧭 It guides, not just computes	A Workflow Advisor recommends a tool sequence for your analysis goal; a Data Readiness Audit checks geometry validity, CRS risk, outliers and multicollinearity before you model. Every report explains assumptions, pitfalls and safer moves.
📊 Full method ladder	Global pattern scans → local hot spots/outliers → centers & direction → OLS/GLR → spatial lag & error models → GWR/MGWR → model comparison → Monte Carlo sensitivity. One provider, one consistent reporting style.
🧪 Reproducible & honest	Permutation inference where it matters, CSV/JSON exports for audit handoffs, and HTML analyst guidance attached to results — interpretation included, not implied.
🏙 Planner-first	Bundled İzmir neighbourhoods dataset (237 polygons, heat/vegetation/population/park/street-network indicators) so every tool is try-able in one click.
🔌 Honest dependencies	Core tools run on pure QGIS. Advanced methods (PySAL/MGWR/scikit-learn) are optional — a Library Status tool diagnoses the QGIS Python environment and a transparent installer previews the exact pip command before touching anything.

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🛠 Tool Catalog

All tools live under Processing Toolbox → PlanX GeoStats Lab, organised as a numbered workflow:

00 Setup & Diagnostics

GeoStats Library Status · Install/Update GeoStats Libraries · Sample Dataset Guide · GeoStats Workflow Advisor · Data Readiness Audit

01 Data Preparation & Neighborhoods

Export Attributes · Calculate Distance Band (neighbour-distance selection)

02 Urban Pattern Scan (global statistics)

| Tool | Question it answers | |—|—| | Average Nearest Neighbor | Are my points clustered or dispersed? | | Ripley’s K | …and at which distances? | | Global Moran’s I | Is the attribute spatially autocorrelated? | | Incremental Spatial Autocorrelation | At what scale does clustering peak? | | Getis-Ord General G | Do high or low values dominate the clustering? | | Bivariate Lee’s L | Do two indicators co-cluster in space? | | Spatial Inequality (Gini + Spatial Gini) | How unequal is the distribution — and how much of that inequality is spatial? |

03 Hot Spots & Spatial Outliers (local statistics)

| Tool | Output | |—|—| | Hot Spot Analysis (Getis-Ord Gi*) | Statistically significant hot/cold spots | | Cluster & Outlier Analysis (Local Moran’s I / LISA) | HH·LL clusters, HL·LH outliers | | Multivariate Clustering | K-means feature groups across several indicators | | Similarity Search | Features most similar to your reference feature |

04 Centers, Direction & Dispersion

Mean Center · Median Center · Central Feature · Standard Distance · Standard Deviational Ellipse · Linear Directional Mean

05 Models & Scenarios

| Tool | Method | |—|—| | Ordinary Least Squares | Baseline regression + residual diagnostics | | Generalized Linear Regression | Gaussian/binary/count families | | Exploratory Regression | Search candidate variable combinations | | Spatial Lag Regression | Spatial dependence in the outcome | | Spatial Error Regression | Spatial dependence in the residuals | | GWR / MGWR | Local — and multiscale local — relationships | | Model Comparison | Score competing models side by side | | Monte Carlo Sensitivity Test | How robust is the result to perturbation? |

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🧭 A lab, not a toolbox dump

The intended session is itself a method:

00 Data Readiness Audit  →  02 pattern scan      →  03 hot spots / LISA
        ↓                        (is it clustered?)       (where exactly?)
   Workflow Advisor                                            ↓
   (pick the goal,        05 OLS → spatial lag/error → GWR/MGWR → comparison → sensitivity
    get the sequence)        (why? and is the "why" stable across space and noise?)

Each report ends with interpretation guidance — what the statistic assumes, what commonly goes wrong, and which tool to run next. The decision logic lives in QGIS-independent core helpers, so it is unit-tested headlessly on every release.

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🔌 Optional Libraries

Core tools are pure QGIS. Advanced methods use, when present:

libpysal · esda · spreg · mgwr · scikit-learn · numba

The honest installer: QGIS plugins run inside QGIS’s own Python — installing into Anaconda or a system Python won’t help. GeoStats Library Status shows exactly which interpreter QGIS uses and what’s missing; Install/Update GeoStats Libraries previews the full pip command and only runs it after an explicit confirmation checkbox. Restart QGIS afterwards.

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🗂 Bundled Sample Data

Dataset	Contents	Use it for
İzmir neighbourhoods (planx_geostats_izmir_neighborhoods.gpkg)	237 polygons; heat, vegetation, population, parks, street-network structure, building form, model-QA fields — English schema	Realistic end-to-end workflow practice
Synthetic QA fixture (planx_geostats_synthetic_qa.gpkg)	Deterministic point/line/polygon + model-output layers	Edge cases: KNN weights, multipart lines, binary/count models

Load either (or both) via 00 → Sample Dataset Guide, then run Data Readiness Audit for suggested analysis roles and starter sequences.

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📦 Installation

From QGIS Plugin Hub (recommended)

Plugins → Manage and Install Plugins… → search PlanX GeoStats Lab → Install. Tools appear in the Processing Toolbox (no toolbar/menu clutter — this plugin is Processing-only by design).

From ZIP

Download the latest zip from Releases → Plugins → Install from ZIP.

Requirement	Value
QGIS	3.28 LTR → 4.x (validated on both runtimes)
Hard dependencies	None — pure QGIS for core tools
Optional	PySAL stack + scikit-learn via the built-in guided installer
License	GPL-3.0

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🧪 Quality

• Headless smoke tests (tests/smoke_core.py, smoke_sample_data.py, smoke_provider_catalog.py) run without QGIS and gate every release. The report decision logic is intentionally kept in QGIS-independent core helpers, so workflow advising, model-comparison scoring, Monte Carlo sensitivity interpretation, Global Moran’s I report interpretation and Spatial Gini inequality decomposition are unit-tested without launching QGIS.
• A full QGIS runtime matrix executes every algorithm against the bundled sample data on QGIS 3 LTR and QGIS 4.
• A manual QA test matrix (QA_MANUAL_TEST_MATRIX.md) covers setup, statistics, symbology, report interpretation and release gates.
• The release-zip verifier asserts that developer-only paths are absent, algorithm icons are present, metadata points to a packaged icon, and the plugin remains Processing-only with no menu or toolbar UI hooks.

Developer validation commands

```powershell py -3 planx_geostats\tests\smoke_core.py py -3 planx_geostats\tests\smoke_sample_data.py py -3 planx_geostats\tests\smoke_provider_catalog.py py -3 packaging\test_verify_release_zip.py py -3 packaging\validate_plugin.py planx_geostats --strict powershell -NoProfile -ExecutionPolicy Bypass -File .\packaging\Build-PluginZip.ps1 -PluginDir planx_geostats py -3 packaging\verify_release_zip.py QGIS_Plugin_Releases\planx_geostats.zip --root planx_geostats --version 0.9.23 ```

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🇹🇷 Türkçe Özet

PlanX GeoStats Lab, QGIS İşlem Araç Kutusu (Processing) içinde çalışan, plancılar için tasarlanmış bir mekânsal istatistik laboratuvarıdır:

• 30+ araç, numaralı iş akışı: veri hazırlık ve denetim (00–01) → küresel desen taraması: Ortalama En Yakın Komşu, Ripley K, Global Moran I, General G, İki Değişkenli Lee L, Mekânsal Gini eşitsizliği (02) → sıcak nokta (Getis-Ord Gi*) ve LISA küme/aykırı analizi, çok değişkenli kümeleme, benzerlik araması (03) → merkez/yön/dağılım araçları (04) → EKK, GLR, mekânsal gecikme/hata modelleri, keşifsel regresyon, GWR/MGWR, model karşılaştırma ve Monte Carlo duyarlılık testi (05).
• Yol gösteren laboratuvar: Workflow Advisor analiz hedefinize göre araç sırası önerir; Data Readiness Audit modellemeden önce geometri, CRS, aykırı değer ve çoklu doğrusallık risklerini raporlar. Her raporun sonunda varsayımlar, sık hatalar ve “bundan sonra ne çalıştırmalı” rehberi vardır.
• Örnek veri dahildir: 237 mahalleli İzmir veri seti (ısı, bitki örtüsü, nüfus, park, sokak ağı göstergeleri) ve sentetik QA veri seti — her araç tek tıkla denenebilir.
• Dürüst bağımlılık yönetimi: Çekirdek araçlar saf QGIS ile çalışır; gelişmiş yöntemler için PySAL/MGWR/scikit-learn kurulumunu, çalıştırmadan önce pip komutunu aynen gösteren şeffaf bir kurulum aracı üstlenir.

Kurulum: QGIS → Eklentiler → Eklentileri Yönet ve Kur → PlanX GeoStats Lab aratın; araçlar İşlem Araç Kutusu’nda görünür.

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🧩 Part of the PlanX ecosystem

This plugin is one of 15 open-source QGIS plugins for urban planning by the same author:

Planning & analysis	CAD & production	3D & visualization
PlanX — spatial-planning suite	PlanX CAD Toolset — drafting-grade CAD	PlanX 3D City — Three.js city viewer
GeoStats Lab — spatial statistics	EasyFillet — tangent-arc fillet	3D OSM Model — OSM → 3D city in browser
Suitability Lab — raster MCDA	Settlement Toolset — 9-stage settlement plans	OSM Quick 3D — OSM → native QGIS 3D
DataCube Lab — spatiotemporal cubes	UIP Toolset — Turkish master-plan automation	Urban Procedural 3D — parametric zoning lab
Urban Resilience — 28 resilience tools	ParcelFlux — parcel subdivision	CartoLab — publication cartography

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🤝 Contributing & Support

• 🐛 Bugs / requests → Issues
• 📜 Changelog → CHANGELOG.md follows Keep a Changelog
• ✅ Before a PR: py -3 tests/smoke_core.py && py -3 tests/smoke_sample_data.py && py -3 tests/smoke_provider_catalog.py (headless, no QGIS required)

👤 Author

Yusuf Eminoğlu — urban planner & developer GitHub · yusuf.eminoglu@deu.edu.tr

_{Statistics with interpretation included. If GeoStats Lab sharpens your analysis, a ⭐ helps others find it.}

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