site.SiteProblem
site.SiteProblem(preferred_crs='EPSG:27700', debug_mode=True)Facility location optimization for healthcare site planning.
A comprehensive toolkit for solving spatial optimization problems in healthcare service delivery. This class supports multiple location-allocation models including p-median, p-center, and maximal covering location problems (MCLP), with various solution strategies from exact brute-force to heuristic methods.
The class handles the complete workflow from data ingestion (demand patterns, candidate sites, travel costs) through optimization to solution evaluation, with built-in support for geographic data and spatial visualizations.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| preferred_crs | str | The coordinate reference system for spatial data. All geographic inputs will be transformed to this CRS. Defaults to British National Grid. | "EPSG:27700" |
| debug_mode | bool | If True, enables verbose output during optimization and data processing. | True |
Attributes
| Name | Type | Description |
|---|---|---|
| demand_data | pandas.DataFrame or geopandas.GeoDataFrame or None | Patient or service demand locations with associated weights. |
| candidate_sites | geopandas.GeoDataFrame or None | Potential facility locations available for optimization. |
| travel_matrix | pandas.DataFrame or None | Cost matrix (time/distance) between demand points and candidate sites. |
| region_geometry_layer | geopandas.GeoDataFrame or None | Optional geographic boundaries for visualization (e.g., LSOA polygons). |
| travel_and_demand_df | pandas.DataFrame or geopandas.GeoDataFrame or None | Internal merged dataset combining demand and travel cost data. |
| total_n_sites | int or None | Total number of candidate facilities available for optimization. |
Notes
The class implements three inheritance mixins providing different solution strategies:
- BruteForceMixin: Exhaustive enumeration for small problems
- GreedyMixin: Fast constructive heuristic for larger problems
- GraspMixin: Randomized adaptive search with local optimization
Supported optimization objectives:
- ‘simple_p_median’: Minimize total unweighted travel distance/time
- ‘hybrid_simple_p_median’: Simple p-median with maximum distance/time constraint
- ‘p_median’: Minimize total weighted travel distance/time
- ‘hybrid_p_median’: P-median with maximum distance/time constraint
- ‘p_center’: Minimize maximum travel distance/time
- ‘mclp’: Maximize coverage within a distance/time threshold
Methods
| Name | Description |
|---|---|
| add_additional_data | Append a user-defined dataset to be tracked during optimization. |
| add_demand | Add demand data to the site problem and validate its structure. |
| add_equity_data | Add a dataframe containing equity data into your problem. |
| add_region_geometry_layer | Add a region geodataframe to the site problem and validate its structure. |
| add_secondary_demand | Register an additional demand scenario (e.g. projected future |
| add_secondary_travel_matrix | Register an additional travel/cost matrix for a different mode or |
| add_sites | Add candidate facility sites to the problem and handle spatial alignment. |
| add_travel_matrix | Add a travel cost matrix to the problem and handle unit conversions. |
| describe_models | Prints a menu of available optimization strategies for healthcare. |
| evaluate_baseline | Evaluate the current (“do-nothing”) network as a one-solution |
| evaluate_single_solution_single_objective | Evaluate a specific set of facility sites against a single objective. |
| get_hotspots | Identify statistically significant hotspots and coldspots. |
| plot_accessibility | Map 2SFCA accessibility: a region choropleth of accessibility, |
| plot_hotspots | Plot statistically significant hotspots and coldspots. |
| plot_quadrant_map | Plot the 2×2 attribute typology for a combined demand/deprivation analysis. |
| plot_region_geometry_layer | Visualize the regional geometry layer, optionally overlaid with demand data. |
| plot_site_utilisation | Map each candidate site’s current utilisation ratio. |
| plot_sites | Generate a visualization of the candidate facility sites. |
| show_demand | Returns a loaded demand dataframe |
| show_demand_format | Prints the expected structure for the demand DataFrame. |
| show_region_geometry_layer | Returns a loaded region geometry geodataframe |
| show_secondary_demand | Returns a registered secondary demand scenario dataframe. |
| show_secondary_travel_matrix | Returns a registered secondary travel/cost matrix dataframe. |
| show_sites | Returns a loaded candidate site geodataframe |
| show_travel_format | Prints the expected structure for the travel/cost matrix DataFrame. |
| show_travel_matrix | Returns a loaded travel or cost matrix dataframe |
| site_utilisation_summary | Summarise each candidate site’s real-world current utilisation. |
| solve | Solve the site location problem using the specified objective and strategy. |
| two_step_floating_catchment | Two-step floating catchment area (2SFCA) accessibility score. |