# geometrics

> Regional growth, convergence, and inequality analysis on the PySAL stack
> (libpysal, esda, giddy, inequality, mapclassify, spreg, mgwr) with Plotly
> figures, Great Tables, and plain-language interpretation on every result.

Three inputs: gdf (geometry with ONLY the entity ID; shapefile / zipped
shapefile / GeoJSON / GeoPackage via read_gdf), df (long-form panel declared
with set_panel / set_labels), df_dict (6-column data dictionary: var_name,
var_def, label, type, role, can_be_na). Every public function returns a frozen
result dataclass with .df, .fig and/or .gt, .interpret() and .explain().

Install: pip install geometrics  (extras: [dynamics] for Markov via giddy,
[png] for static export, [all]).

## Docs

- [Explore](https://quarcs-lab.github.io/geometrics/explore.html): maps, weights, Moran/LISA, space-time views — India
- [Analyze](https://quarcs-lab.github.io/geometrics/analyze.html): beta/sigma/club convergence, spatial models with impacts, Markov, inequality — Bolivia
- [Learn](https://quarcs-lab.github.io/geometrics/learn.html): the learn_* concept sandboxes and the explainer registry
- [The data model](https://quarcs-lab.github.io/geometrics/articles/data-model.html): the (gdf, df, df_dict) contract
- [Convergence](https://quarcs-lab.github.io/geometrics/articles/convergence.html): beta/sigma convergence and clubs
- [Spatial dependence](https://quarcs-lab.github.io/geometrics/articles/spatial-dependence.html): weights, Moran, LISA
- [Spatial spillovers](https://quarcs-lab.github.io/geometrics/articles/spillovers.html): the spreg suite and impacts
- [Regional inequality](https://quarcs-lab.github.io/geometrics/articles/inequality.html): Gini/Theil and decompositions
- [Distribution dynamics](https://quarcs-lab.github.io/geometrics/articles/dynamics.html): Markov and spatial Markov
- [The India case study](https://quarcs-lab.github.io/geometrics/articles/india-case-study.html): the full replication arc
- [The Bolivia dataset](https://quarcs-lab.github.io/geometrics/articles/bolivia-dataset.html): PWT-anchored local GDP at three scales
- [For AI / LLMs](https://quarcs-lab.github.io/geometrics/use-with-llms.html): how AI agents should install and call geometrics
- [Changelog](https://quarcs-lab.github.io/geometrics/changelog.html): release notes

## API

- explore_*: explore_choropleth_map, explore_connectivity_map, explore_moran_plot, explore_lisa_cluster_map, explore_moran_over_time, explore_distribution_over_time, explore_spacetime_heatmap
- analyze_*: analyze_beta_convergence, analyze_sigma_convergence, analyze_convergence_clubs, analyze_spatial_model, analyze_spatial_diagnostics, analyze_spatial_model_by_weights, analyze_markov_transitions, analyze_spatial_markov, analyze_inequality_over_time, analyze_theil_decomposition, analyze_gwr, analyze_mgwr
- learn_*: learn_spatial_autocorrelation, learn_spatial_weights, learn_lisa_clusters, learn_spatial_spillovers, learn_omitted_spatial_lag, learn_beta_convergence, learn_sigma_convergence, learn_convergence_clubs, learn_markov_chains, learn_spatial_markov, learn_theil_decomposition
- utilities: read_gdf, make_weights, growth_cross_section, set_panel, resolve_panel, set_labels, resolve_label, set_roles, build_data_dict, set_palette, get_palette, explain, list_topics
- geometrics.data: load_india, load_india_states, load_india_raw, load_bolivia, load_bolivia_departments, load_bolivia_grid, load_bolivia_raw, clear_cache

## Source

- [Repository](https://github.com/quarcs-lab/geometrics)
- [API reference](https://quarcs-lab.github.io/geometrics/reference/index.html)
- [llms-full.txt](https://quarcs-lab.github.io/geometrics/llms-full.txt): full docs text + signatures