├── data/
│ ├── tree_list_GiGL_Pre2023_2050.csv # tree specices will be at risk by 2050
│ ├── GiGL_GLATrees_Pre2023_risk_2050.shp # at-risk tree shapefile data
│ └──
│
├── code/
| |
| |__ gcm-data-clip-stats-viz.Rmd # fig. 1a
| |
| |
│ ├── lc_scenarios/
| | | # for the manuscript
| | |__ scenario_1_pavement_and_2_opportunity_trees.ipynb # scenario 1 and 2
| | |__ london-climate-scenario.Rmd # scenario 3 - filter global tree-climate-scenario pairs for London
| | |__ london-tree-climate-risk.Rmd # scenario 3 - match London tree data to the scenario pairs
| | |__ london-tree-climate-risk-GiGL.ipynb # scenario 3 - link at-risk list to London tree shapefile for map
| | |__ # scenario 4 - InVEST Scenario Generator (Proximity Based) model rather than script
| | |__ tree_equity_1_number_of_trees_to_polygon.py # scenario 5 - planting equity trees (10%, 20%, 30% relative increase in TCC)
| | |__ tree_equity_2_scenario_engine.py
| | |__ tree_equity_3_lulc_stats.py
| | |
| | |
| |
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│ ├── health_assessment/ # Health outcome estimates based on lc and climate scenarios
| | |__ health-model-01-prep-input-ONS-mortality-data.Rmd # baseline data
| | |__ health-modeling.py # main code for modeling
| | |__ health-modeling_*.bat # batch run for each scenario
| | |__ ~~health-modeling-output-plot.ipynb # (not updated)~~
| | |__ health-modeling-output-plot-city.Rmd # fig.4c: overall stats at city level
| | |__ health-modeling-zonal-stats.ipynb # zonal stats at borough or LSOA level
| | |__ health-modeling-zonal-stats-viz-borough.Rmd # viz zonal stats: bar plots + maps
│ |
| |
│ | # visualize results
│ | ## --- step 1. zonal stats ---
│ ├── invest_result_zonal_stats_temp.ipynb
│ ├── invest_result_zonal_stats_energy.ipynb # stats by borough vs lsoa
│ ├── invest_result_zonal_stats_productivity.ipynb # stats by borough vs lsoa (be sure to run a separate updated productivity function)
│ |
│ | ## --- step 2. viz ---
│ ├── invest_result_zonal_viz_0_data_prep.Rmd
│ ├── invest_result_zonal_viz_1_temp.Rmd # fig.3a, fig.3b
│ ├── invest_result_zonal_viz_2_energy.Rmd # fig.4a
│ ├── invest_result_zonal_viz_3_pd_NEW.Rmd # fig.4b
│ ├── invest_result_zonal_viz_pd_model_data.ipynb #
│ ├── viz-es-change-due-to-lc.Rmd # fig.5 - map of borough-scale changes in co-benefits
| |
| |
| | # equity analysis
│ ├── socio-economic-data.Rmd #
│ ├── equity-health.Rmd # fig.6 - Non-linear associations between SVI and prev. mortality
| | # fig.7 - bicolor maps
| | # SM table 1. Comparison of linear and non-linear model performance
│ ├── equity-temp-tcc.Rmd # fig.2 - spatial inequality on temp, and TCC
│ ├──
│ ├──
│ |
│ | # functions
│ ├── func_*.py # Various data processing functions in Python
│ ├── func_*.R # Various data processing functions in R
│