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129 changes: 68 additions & 61 deletions README.md
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# South Gloucestershire heat and flooding hazard and vulnerability mapping
# Mapping vulnerability to climate-related hazards to inform local authority action in adaptation: a feasibility study

James Thomas, Josh Howkins, Eunice Lo, Jeffrey Neal\
March 2023
[James Thomas](https://github.com/jatonline), Josh Howkins, Eunice Lo, Jeffrey Neal\
August 2023

This repository contains the code used in the analysis for:

* Howkins, J. et al. "Mapping vulnerability to climate-related hazards to inform local authority
action in adaptation," [In Prep.]
> Howkins, J. et al. "Mapping vulnerability to climate-related hazards to inform local authority action in adaptation: a feasibility study," Submitted for publication.
## Overview

Expand All @@ -24,60 +23,6 @@ estimates.
We perform these calculations at the level of Lower Layer Super Output Areas (LSOAs). These are UK
statistical geographies covering 1,000-3,000 people each.

## Data sources

### Heat hazard exposure

* **Met Office HadUK‐Grid** - historical daily maximum and minimum temperature observations at 1 km
grid resolution.

* Hollis, D. et al. (2019) "HadUK‐Grid—A new UK dataset of gridded climate observations,"
Geoscience Data Journal, 6(2), 151–159. doi:10.1002/gdj3.78

* Met Office et al. (2022) "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK,"
v1.1.0.0 (1836-2021). NERC EDS Centre for Environmental Data Analysis, 26 May 2022.
doi:10.5285/bbca3267dc7d4219af484976734c9527

* **OS OpenMap - Local** - building footprints and centroids.

* Contains Ordnance Survey data © Crown copyright and database right 2022

* **Office for National Statistics geospatial data** - LSOA extents.

* Office for National Statistics licensed under the Open Government Licence v.3.0

### Flooding hazard exposure

* **Fathom flood level predictions** - coastal, pluvial and fluvial flooding under various climate
models, date ranges and return periods (probabilities) at 10 m grid resolution.

* Bates, P.D. et al. "A climate-conditioned catastrophe risk model for UK flooding," Natural
Hazards and Earth System Sciences. 2023;23(2):891-908.

* **OS OpenMap - Local** - building footprints and centroids.

* Contains Ordnance Survey data © Crown copyright and database right 2022

* **Office for National Statistics geospatial data** - LSOA extents.

* Office for National Statistics licensed under the Open Government Licence v.3.0

### Vulnerability

* **Census 2021** - TS001, 006, 007, 008, 017, 029, 037, 038, 039, 044, 045, 054, 069.

* Office for National Statistics licensed under the Open Government Licence v.3.0

* **English indices of deprivation** - IMD domains and underlying indicators.

* Ministry of Housing, Communities & Local Government licensed under the Open Government Licence
v.3.0

* **Office for National Statistics geospatial data** - LSOA and MSOA extents, LSOA to MSOA lookup,
LSOA 2011 to 2021 lookup.

* Office for National Statistics licensed under the Open Government Licence v.3.0

## High-level flow diagram

```mermaid
Expand Down Expand Up @@ -118,12 +63,68 @@ flowchart TD
```
[Open flowchart as an image](high-level-flow-diagram.png)

## More detail on the pipeline stages

For more detailed explanations for the pipeline, check the `README` files in the `hazard` and
`vulnerability` directories, and the embedded documentation with the Python scripts and Jupyter
Notebooks.

* [Hazard README](hazard/README.md)
* [Vulnerability README](vulnerability/README.md)
* [**Hazard README**](hazard/README.md)
* [**Vulnerability README**](vulnerability/README.md)

## Data sources

### Heat hazard exposure

* [**Met Office HadUK‐Grid**](https://www.metoffice.gov.uk/research/climate/maps-and-data/data/haduk-grid/datasets) - historical daily maximum and minimum temperature observations at 1 km
grid resolution.

* Hollis, D. et al. (2019) "HadUK‐Grid—A new UK dataset of gridded climate observations,"
Geoscience Data Journal, 6(2), 151–159. doi:10.1002/gdj3.78

* Met Office et al. (2022) "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK,"
v1.1.0.0 (1836-2021). NERC EDS Centre for Environmental Data Analysis, 26 May 2022.
doi:10.5285/bbca3267dc7d4219af484976734c9527

* [**OS OpenMap - Local**](https://osdatahub.os.uk/downloads/open/OpenMapLocal) - building footprints and centroids.

* Contains Ordnance Survey data © Crown copyright and database right 2022

* [**Office for National Statistics geospatial data**](https://geoportal.statistics.gov.uk) - LSOA extents.

* Office for National Statistics licensed under the Open Government Licence v.3.0

### Flooding hazard exposure

* [**Fathom flood level predictions**](https://www.fathom.global) - coastal, pluvial and fluvial flooding under various climate
models, date ranges and return periods (probabilities) at 10 m grid resolution.

* Bates, P.D. et al. "A climate-conditioned catastrophe risk model for UK flooding," Natural
Hazards and Earth System Sciences. 2023;23(2):891-908.

* [**OS OpenMap - Local**](https://osdatahub.os.uk/downloads/open/OpenMapLocal) - building footprints and centroids.

* Contains Ordnance Survey data © Crown copyright and database right 2022

* [**Office for National Statistics geospatial data**](https://geoportal.statistics.gov.uk) - LSOA extents.

* Office for National Statistics licensed under the Open Government Licence v.3.0

### Vulnerability

* [**Census 2021**](https://www.nomisweb.co.uk/sources/census_2021) - TS001, 006, 007, 008, 017, 029, 037, 038, 039, 044, 045, 054, 069.

* Office for National Statistics licensed under the Open Government Licence v.3.0

* [**English indices of deprivation**](https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019) - IMD domains and underlying indicators.

* Ministry of Housing, Communities & Local Government licensed under the Open Government Licence
v.3.0

* [**Office for National Statistics geospatial data**](https://geoportal.statistics.gov.uk) - LSOA and MSOA extents, LSOA to MSOA lookup,
LSOA 2011 to 2021 lookup.

* Office for National Statistics licensed under the Open Government Licence v.3.0

## Setup

Expand All @@ -147,6 +148,12 @@ To run the entire processing pipeline, you will need to:
ESRI® Sharefile covering at least the area you are interested in. Only the `ST_Building` layer is
required.

All other data files specified in [`data_sources.yaml`](vulnerability/data_sources.yaml)
will be downloaded automatically. However note that the URLs for the data
sources are as they were when the analysis for the publication was performed.
They may have changed as new versions of the data sources are released are
updated, and so may need to be updated before the pipeline will run.

## Instructions

The outline process is as follows:
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Expand Up @@ -32,59 +32,53 @@ summer daytime temperatures, as well as the proportion of days where the average
exceeded 15°C, the mean temperature above which mortality risks in Southwest England
increase[^4][^5].

In each case, LSOAs were ranked as deciles by the average of these metrics, weighted by the
locations of buildings.
In each case, LSOAs were ranked as deciles by the weighted average of these
metrics, weighted according to the density of buildings within each LSOA.

## Key files and directories

This directory contains code and data for the heat and flooding hazard exposure analysis.

* `code/`
* [`code/`](code/)

* `calculate_heat_hazard_indicators.py` - Generates gridded input data for the heat hazard
* [`calculate_heat_hazard_indicators.py`](code/calculate_heat_hazard_indicators.py) - Generates gridded input data for the heat hazard
analysis (average maximum temperature and days exceeding threshold metrics) from HadUK-Grid
data. Designed to be run in advance on a high performance computing (HPC) cluster that
provides access to the HadUK-Grid dataset.

* `notebooks/`
* [`notebooks/`](notebooks/)

* `Heat_hazard_analysis.ipynb` - Produce LSOA-level heat hazard data (average maximum
* [`Heat_hazard_analysis.ipynb`](notebooks/Heat_hazard_analysis.ipynb) - Produce LSOA-level heat hazard data (average maximum
temperature and days exceeding threshold metrics in each LSOA) from the gridded input data derived from HadUK-Grid (see above).

* `Flooding_hazard_analysis.ipynb` and `Flooding_hazard_analysis_disaster.ipynb` - Produce
* [`Flooding_hazard_analysis.ipynb`](notebooks/Flooding_hazard_analysis.ipynb) and [`Flooding_hazard_analysis_disaster.ipynb`](notebooks/Flooding_hazard_analysis_disaster.ipynb) - Produce
LSOA-level flooding hazard data (proportion of buildings flooded in typical and disaster
scenarios in each LSOA) using the Fathom data.

* `data/`
* [`data/`](data/)

* `input/`
* [`input/`](data/input/)

* `buildings/` - The Building layer from OS OpenMap - Local dataset.
* [`buildings/`](data/input/buildings/) - The Building layer from OS OpenMap - Local dataset.

```
ST_Building.dbf
ST_Building.prj
ST_Building.shp
ST_Building.shx
```
* `ST_Building.dbf`
* `ST_Building.prj`
* `ST_Building.shp`
* `ST_Building.shx`

* `fathom/` - Flood level data provided under licence by Fathom.
* [`fathom/`](data/input/fathom/) - Flood level data provided under licence by Fathom.

```
UK_1.1_Bristol/10m/coastal/rcp85_2050/1in100/n51w3_C.tif
UK_1.1_Bristol/10m/coastal/rcp85_2050/1in1000/n51w3_C.tif
UK_1.1_Bristol/10m/fluvial/rcp85_2050/1in100/n51w3_F.tif
UK_1.1_Bristol/10m/fluvial/rcp85_2050/1in1000/n51w3_F.tif
UK_1.1_Bristol/10m/pluvial/rcp85_2050/1in100/n51w3_P.tif
UK_1.1_Bristol/10m/pluvial/rcp85_2050/1in1000/n51w3_P.tif
```
* `UK_1.1_Bristol/10m/coastal/rcp85_2050/1in100/n51w3_C.tif`
* `UK_1.1_Bristol/10m/coastal/rcp85_2050/1in1000/n51w3_C.tif`
* `UK_1.1_Bristol/10m/fluvial/rcp85_2050/1in100/n51w3_F.tif`
* `UK_1.1_Bristol/10m/fluvial/rcp85_2050/1in1000/n51w3_F.tif`
* `UK_1.1_Bristol/10m/pluvial/rcp85_2050/1in100/n51w3_P.tif`
* `UK_1.1_Bristol/10m/pluvial/rcp85_2050/1in1000/n51w3_P.tif`

* `hadukgrid/` - Gridded input data derived from HadUK-Grid (see above).
* [`hadukgrid/`](data/input/hadukgrid/) - Gridded input data derived from HadUK-Grid (see above).

```
average_daily_maximum.tif
days_over_threshold.tif
```
* `average_daily_maximum.tif`
* `days_over_threshold.tif`


[^1]: Bates, P.D. et al. "A climate-conditioned catastrophe risk model for UK flooding," Natural
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Expand Up @@ -116,57 +116,53 @@ The numerical stages of the methodology were:

This directory contains code and data for the vulnerability analysis.

* `data_sources.yaml` - Defines all the data sources that need to be downloaded and defines how they
* [`data_sources.yaml`](data_sources.yaml) - Defines all the data sources that need to be downloaded and defines how they
should be processed.

* `code/`
* [`code/`](code/)

* The initial **stages 1-5** of the analysis:

```
01_download_data.py
02_extract_and_subset_data.py
03_join_tables.py
04_calculate_indicators.py
05_resample_and_join.py
```
* [`01_download_data.py`](code/01_download_data.py)
* [`02_extract_and_subset_data.py`](code/02_extract_and_subset_data.py)
* [`03_join_tables.py`](code/03_join_tables.py)
* [`04_calculate_indicators.py`](code/04_calculate_indicators.py)
* [`05_resample_and_join.py`](code/05_resample_and_join.py)

* Common configuration for the analysis, including data directories:

```
config.py
```
* [`config.py`](code/config.py)

* `notebooks/`
* [`notebooks/`](notebooks/)

* `Developing_vulnerability_indicators.ipynb` - Calculates of domain and sub-domain scores as
* [`Developing_vulnerability_indicators.ipynb`](notebooks/Developing_vulnerability_indicators.ipynb) - Calculates of domain and sub-domain scores as
well as a combined vulnerability index (**stage 6** of the analysis).

* `Overall_risk_assessment.ipynb` - An example of an overall risk assessment, for demonstration
* [`Overall_risk_assessment.ipynb`](notebooks/Overall_risk_assessment.ipynb) - An example of an overall risk assessment, for demonstration
purposes (**stage 7** of the analysis).

* `Exploratory_data_analysis.ipynb` - Initial data exploration (not used in the analysis
* [`Exploratory_data_analysis.ipynb`](notebooks/Exploratory_data_analysis.ipynb) - Initial data exploration (not used in the analysis
pipeline).

* `data/`
* [`data/`](data/)

* `01_raw/` - The raw data automatically downloaded from the internet (in the original file
* [`01_raw/`](data/01_raw/) - The raw data automatically downloaded from the internet (in the original file
format).

* `02_subset/` - The data for our area of interest (South Gloucestershire) in either csv or
* [`02_subset/`](data/02_subset/) - The data for our area of interest (South Gloucestershire) in either csv or
Shapefile format.

* `03_combined/` - Data for the same geographic level combined into combined into the same file:
* [`03_combined/`](data/03_combined/) - Data for the same geographic level combined into combined into the same file:
2021 Census LSOA, 2021 Census MSOA and 2011 Census LSOA (the latter used in the IMD data).

* `04_indicators/` - Metrics a.k.a. indicators variables calculated from the combined data,
using the formulae specified in `data_sources.yaml`.
* [`04_indicators/`](data/04_indicators/) - Metrics a.k.a. indicators variables calculated from the combined data,
using the formulae specified in [`data_sources.yaml`](data_sources.yaml).

* `05_resampled/` - Data converted to 2021 LSOA-level and combined into one file.
* [`05_resampled/`](data/05_resampled/) - Data converted to 2021 LSOA-level and combined into one file.

* `06_index/` - Calculation of a combined vulnerability index.
* [`06_index/`](data/06_index/) - Calculation of a combined vulnerability index.

* `07_overall/` - Calculation of an overall risk assessment, for demonstration purposes.
* [`07_overall/`](data/07_overall/) - Calculation of an overall risk assessment, for demonstration purposes.


[^1]: Tate E. Social vulnerability indices: a comparative assessment using uncertainty and
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