Evaluating an existing site combination

Lokigi can also help you to evaluate your current setup, helping you to provide data that supports a case for a wider programme of re-evaluation.

It’s also useful to be able to provide a baseline for comparison with any solutions created.

from lokigi.site import SiteProblem

problem_public_transport_extended = SiteProblem()

problem_public_transport_extended.add_sites(
    "../../../sample_data/devon_mius.geojson",
    candidate_id_col="Facility_Name"
    )

problem_public_transport_extended.add_region_geometry_layer(
    "https://github.com/hsma-programme/h6_3c_interactive_plots_travel/raw/main/h6_3c_interactive_plots_travel/example_code/LSOA_2011_Boundaries_Super_Generalised_Clipped_BSC_EW_V4.geojson",
    common_col="LSOA11NM"
    )

problem_public_transport_extended.add_travel_matrix(
    travel_matrix_df="../../../sample_data/devon_miu_travel_matrix_public_transport_extended.csv",
    source_col="from_id",
    unit="minutes",
    )

In this instance, we have 15 sites.

problem_public_transport_extended.show_sites()
canonical_site_index Facility_Name Latitude Longitude geometry
0 0 North Devon District Hospital 51.09217 -4.05043 POINT (256506.101 134540.134)
1 1 Honiton Hospital 50.79492 -3.18659 POINT (316466.043 100155.33)
2 2 Tiverton & District Hospital 50.90933 -3.49308 POINT (295122.857 113268.884)
3 3 Exmouth Minor Injury Unit 50.62083 -3.40198 POINT (300919.29 81063.23)
4 4 Victoria Hospital (Sidmouth) 50.68161 -3.23966 POINT (312514.661 87617.001)
5 5 Newton Abbot Community Hospital 50.53926 -3.61224 POINT (285848.833 72295.946)
6 6 Totnes Community Hospital 50.43283 -3.68406 POINT (280491.309 60575.271)
7 7 NHS Walk in Centre (Exeter) 50.72658 -3.52521 POINT (292444.533 92994.08)
8 8 Tavistock Hospital 50.54708 -4.15376 POINT (247503.629 74139.474)
9 9 South Hams Hospital (Kingsbridge) 50.28929 -3.78143 POINT (273194.03 44777.085)
10 10 Cumberland Centre (Plymouth) 50.37004 -4.16873 POINT (245868.211 54486.789)
11 11 Derriford Hospital (UTC) 50.41802 -4.11890 POINT (249563.619 59719.094)
12 12 Ilfracombe & District Tyrrell Hospital 51.20468 -4.12454 POINT (251678.187 147197.738)
13 13 South Molton Hospital 51.01681 -3.83823 POINT (271156.007 125767.813)
14 14 Dawlish Community Hospital 50.58057 -3.47482 POINT (295677.679 76686.654)

So when we solve, we simply solve for p=15.

In this instance, we’re passing in an optional ‘threshold_for_coverage’, which will flag if any regions have a travel time of more than 60 minutes from their nearest MIU.

Since every one of these 15 sites is already open, this isn’t really a “solve” in the optimisation sense – we already know which sites we want evaluated. evaluate_baseline() does exactly that: it evaluates one named set of sites directly, without brute-forcing through every other combination of 15 sites (of which there’s only one anyway). It returns the same kind of SiteSolutionSet that solve() does, so everything below – plotting, equity checks – works identically either way.

solution = problem_public_transport_extended.evaluate_baseline(
    site_names=problem_public_transport_extended.show_sites()["Facility_Name"].tolist(),
    threshold_for_coverage=60, # OPTIONAL
    )
/__w/lokigi/lokigi/lokigi/site.py:647: UserWarning:

No demand data was provided. Demand from all regions has been assumed to be equal. If you wish to override this, run .add_demand() to add your demand dataframe before calling evaluate_baseline() again.

solution.show_solutions()
site_names site_indices unselected_site_names coverage_threshold weighted_average unweighted_average 90th_percentile max weighted_average_for_ranking unweighted_average_for_ranking ... gap_absolute_weighted gap_relative_weighted avg_lower_third_bins avg_middle_third_bins avg_upper_third_bins inter_tertile_ratio gap_absolute_description gap_relative_description inter_tertile_description problem_df
0 [North Devon District Hospital, Honiton Hospit... [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... [] 60 56.03 56.03 96.4 262.0 56.03 56.03 ... None None None None None None N/A (No equity data) N/A (No equity data) N/A (No equity data) from_id  North Devon District Hos...

1 rows × 36 columns

solution.plot_best_combination(
        title = """Baseline - Current Configuration
        Coverage within threshold of {solution['coverage_threshold'].values[0]} minutes: {solution['proportion_within_coverage_threshold'].values[0]:.1%}
        Unweighted Average: {solution['unweighted_average'].values[0]:.1f} minutes
        Maximum: {solution['max'].values[0]:.1f} minutes""",
        height=16,
        width=9,
        annotation_size=6
        );
/__w/lokigi/lokigi/lokigi/mixins/site_solution_plots.py:1216: UserWarning:

The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.


solution.plot_best_combination(
    plot_regions_not_meeting_threshold=True,
    title = """Baseline - Current Configuration
    Coverage within threshold of {solution['coverage_threshold'].values[0]} minutes: {solution['proportion_within_coverage_threshold'].values[0]:.1%}
    Unweighted Average: {solution['unweighted_average'].values[0]:.1f} minutes
    Maximum: {solution['max'].values[0]:.1f} minutes"""
    );
/__w/lokigi/lokigi/lokigi/mixins/site_solution_plots.py:1216: UserWarning:

The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.


solution.plot_best_combination(
    plot_site_allocation=True,
    legend_bbox_to_anchor=(1.65, 1),
    title = """Baseline - Current Configuration
    Coverage within threshold of {solution['coverage_threshold'].values[0]} minutes: {solution['proportion_within_coverage_threshold'].values[0]:.1%}
    Unweighted Average: {solution['unweighted_average'].values[0]:.1f} minutes
    Maximum: {solution['max'].values[0]:.1f} minutes"""
    );
/__w/lokigi/lokigi/lokigi/mixins/site_solution_plots.py:1216: UserWarning:

The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.

We might also want to explore the solution equity.

problem_public_transport_extended.add_equity_data(
    "../../../sample_data/Index_of_Multiple_Deprivation_(Dec_2015)_Lookup_in_England.csv",
    equity_col="IMD15",
    common_col="LSOA11NM",
    label="Indices of Multiple Deprivation",
    continuous_measure=True,
    reverse=False
    )
problem_public_transport_extended.plot_region_geometry_layer(
    plot_equity=True,
    plot_region_of_interest_only=True
    )

Let’s re-evaluate the baseline, overwriting our previous object.

solution = problem_public_transport_extended.evaluate_baseline(
    site_names=problem_public_transport_extended.show_sites()["Facility_Name"].tolist(),
    threshold_for_coverage=60, # OPTIONAL
    )
solution.check_solution_equity(return_plot=True)
groupings = {
    "IMD15 Highest Deprivation": [1, 2],
    "IMD15 Lowest Deprivation": [9, 10],
    "IMD15 Mid-High": [3, 4, 5],
    "IMD15 Mid-Low": [6, 7, 8],
}

solution.plot_combination_by_equity(figsize_multiplier=8, groupings=groupings, ncols=2);

Once we have a baseline, we can start using our comparison tools, or measure how many people’s journeys would actually change if we opened a new site, in Population Impact vs Baseline.

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