| Title: | Mapping Risk and Resilience to wildfires in the UK |
|---|---|
| Description: | Build an social vulnerability index using PCA and identify areas of high wildfire risk and high social vulnerability. |
| Authors: | Matteo Larrode [aut, cre] |
| Maintainer: | Matteo Larrode <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.9.0 |
| Built: | 2024-12-18 06:26:33 UTC |
| Source: | https://github.com/humaniverse/wildfires |
A dataset containing point data of all wildfires that happened in the UK in the months of March, April and May between 2002 and 2022. From the MODIS Collection 6.1 of the NASA FIRMS Archive
fires_spring_ukfires_spring_uk
A data frame of class "sf" with 9448 rows and 5 variables:
Latitude of the fire
Longitude of the fire
Point coordinates of the fire
Year
Month
Matteo Larrode
https://firms.modaps.eosdis.nasa.gov/download/
A dataset containing point data of all wildfires that happened in the UK in the months of June, July, August, andSeptember between 2002 and 2022. From the MODIS Collection 6.1 of the NASA FIRMS Archive
fires_summer_ukfires_summer_uk
A data frame of class "sf" with 5221 rows and 5 variables:
Latitude of the fire
Longitude of the fire
Point coordinates of the fire
Year
Month
https://firms.modaps.eosdis.nasa.gov/download/
This dataset provides a comprehensive collection of socioeconomic variables for Middle Layer Super Output Areas (MSOAs) in England and Wales. Derived primarily from Census data, these indicators have been used for constructing the Social Vulnerability Index (SoVI).
indic_msoa_eng_walesindic_msoa_eng_wales
A tibble with 7,264 rows and 26 variables:
character MSOA code uniquely identifying each area.
character Descriptive name of the MSOA.
double Percentage of the population aged below 15 years old.
double Percentage of the population aged over 65 years old.
double Percentage of the population without educational qualifications.
Other socioeconomic indicators such as disability, health status, household composition, housing status, employment, and ethnicity, all normalised by MSOA population.
The indicators within this dataset were selected based on a comprehensive literature review that identified key factors contributing to social vulnerability, particularly in the context of wildfire risk. The normalisation process facilitates comparison across different MSOAs and enhances the dataset's utility in spatial analyses.
The selection of variables is based on the methodology and literature review conducted in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset contains socioeconomic indicators from the 2021 Census for MSOAs in Scotland, essential for understanding demographic patterns, social vulnerability, and aiding in socio-economic analyses and policy formulation.
indic_msoa_scotlandindic_msoa_scotland
A tibble with 1,279 rows and 20 variables. Key variables include:
character Unique identifier for each MSOA, known as Intermediate Zone (IZ) code.
character Name of the MSOA, known as Intermediate Zone (IZ) name.
double Normalised proportion of the population under 15 years.
double Normalised proportion of the population over 65 years.
double Normalised proportion of the population without formal qualifications.
Other socioeconomic indicators such as employment status, housing conditions, and ethnic diversity, all normalised for comparative analysis.
The indicators within this dataset were selected based on a comprehensive literature review that identified key factors contributing to social vulnerability, particularly in the context of wildfire risk. The normalisation process facilitates comparison across different areas and enhances the dataset's utility in spatial analyses.
The selection of variables is based on the methodology and literature review conducted in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
Northern Ireland Statistics and Research Agency (NISRA) - Census 2021 data.
This dataset encapsulates a range of socioeconomic indicators derived from the 2021 Census for Super Data Zones (SDZs) in Northern Ireland.
indic_sdz_niindic_sdz_ni
A tibble with 850 rows and 16 variables:
character Unique identifier for each Super Data Zone.
double Proportion of the population under 15 years.
double Proportion of the population over 65 years.
double Proportion of the population without any formal qualifications.
double Proportion of the population with disabilities.
double Proportion of the population with long-term health conditions.
double Proportion of the unemployed population.
double Proportion of the population in skilled occupations.
double Proportion of the population living in privately rented accommodations.
double Proportion of the population living in socially rented accommodations.
double Proportion of households without access to a car.
double Proportion of the population living in caravans or other temporary structures.
double Proportion of single-person households.
double Proportion of the population belonging to ethnic minorities, excluding the major ethnic groups.
double Proportion of the population who migrated from within the UK.
double Proportion of the population who migrated from outside the UK.
The indicators within this dataset were selected based on a comprehensive literature review that identified key factors contributing to social vulnerability, particularly in the context of wildfire risk. The normalisation process facilitates comparison across different areas and enhances the dataset's utility in spatial analyses.
The selection of variables is based on the methodology and literature review conducted in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
Northern Ireland Statistics and Research Agency (NISRA) - Census 2021 data.
Given a binary 'worst-decile' type variable at the MSOA level or equivalent, aggregates it to the Lower Tier Local Authority level and maps it. In this function, the theme is designed for the mapping of a 'worst quintile' binary variable for the wildfire risk and social vulnerability index.
map_worst_decile_ltla(df, nation = "All")map_worst_decile_ltla(df, nation = "All")
df |
Dataset including a 'is_worst_deciles' column |
nation |
(default is "All"). Nation to be mapped: one of "All", "England", "Wales", "Scotland", or "Northern Ireland" |
An image (png) with the map (not yet)
Maps a binary variable at the MSOA level or equivalent for the UK or a given nation. In this function, the theme is designed for the mapping of a 'worst quintile' binary variable for the wildfire risk and social vulnerability index.
map_worst_decile_msoa(df, nation = "All")map_worst_decile_msoa(df, nation = "All")
df |
Dataset including a 'is_worst_deciles' column |
nation |
(default is "All"). Nation to be mapped: one of "All", "England", "Wales", "Scotland", or "Northern Ireland" |
An image (png) with the map (not yet)
This dataset quantifies the Social Vulnerability Index (SoVI) across Middle Layer Super Output Areas (MSOAs) in England. The SoVI is a composite measure derived from various socio-economic and demographic variables, providing insights into relative vulnerability across the UK.
sovi_englandsovi_england
A tibble with 6,856 rows and 4 columns:
character MSOA code.
character MSOA name.
double Social Vulnerability Index score.
double Standardised Social Vulnerability Index score.
The SoVI is constructed using Principal Component Analysis (PCA) on a set of socio-economic and demographic variables sourced from the Census, as detailed in the referenced study. This index provides insights into the relative vulnerability of communities to social and environmental hazards, with higher scores indicating greater vulnerability.
The methodology for constructing the SoVI is detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility," by Hasan Guler.
This dataset quantifies the Social Vulnerability Index (SoVI) across Super Data Zones (SDZs) in Northern Ireland. The SoVI is a composite measure derived from various socio-economic and demographic variables, providing insights into relative vulnerability across the UK.
sovi_nisovi_ni
A tibble with 850 rows and 4 columns:
character Super Data Zone code.
character Super Data Zone name.
double Social Vulnerability Index score.
double Standardised Social Vulnerability Index score.
The SoVI is constructed using Principal Component Analysis (PCA) on a set of socio-economic and demographic variables sourced from the Census, as detailed in the referenced study. This index provides insights into the relative vulnerability of communities to social and environmental hazards, with higher scores indicating greater vulnerability.
The methodology for constructing the SoVI is detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility," by Hasan Guler.
This dataset presents the Social Vulnerability Index (SoVI) for Intermediate Zones (IZs) in Scotland; The SoVI is a composite measure derived from various socio-economic and demographic variables, providing insights into relative vulnerability across the UK.
sovi_scotlandsovi_scotland
A tibble with 1,279 rows and 4 columns:
character Intermediate Zone code.
character Intermediate Zone name.
double Social Vulnerability Index score.
double Standardised Social Vulnerability Index score.
The SoVI is constructed using Principal Component Analysis (PCA) on a set of socio-economic and demographic variables sourced from the Census, as detailed in the referenced study. This index provides insights into the relative vulnerability of communities to social and environmental hazards, with higher scores indicating greater vulnerability.
The methodology for constructing the SoVI is detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility," by Hasan Guler.
This dataset indicates the Social Vulnerability Index (SoVI) for Middle Layer Super Output Areas (MSOAs) in Wales. The SoVI is a composite measure derived from various socio-economic and demographic variables, providing insights into relative vulnerability across the UK.
sovi_walessovi_wales
A tibble with 408 rows and 4 columns:
character MSOA code.
character MSOA name.
double Social Vulnerability Index score.
double Standardised SoVI score.
The SoVI is constructed using Principal Component Analysis (PCA) on a set of socio-economic and demographic variables sourced from the Census, as detailed in the referenced study. This index provides insights into the relative vulnerability of communities to social and environmental hazards, with higher scores indicating greater vulnerability.
The methodology for constructing the SoVI is detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility," by Hasan Guler.
This RasterStack object contains a collection of raster layers representing various environmental predictors related to spring wildfires in the UK.
spring_independent_var_stackspring_independent_var_stack
A RasterStack object with the following layers:
Raster layer representing the slope of the terrain.
Raster layer representing the aspect of the terrain.
Raster layer representing the average temperature during spring.
Raster layer representing precipitation during spring.
Raster layer representing average wind speed during spring.
Raster layer representing proximity to major roads.
Raster layer representing population counts in the UK.
The RasterStack has the following properties:
class: RasterStack
dimensions: 263 rows, 250 columns, 65750 cells, 7 layers
resolution: 0.04166667 x 0.04166667 (x, y)
extent: -8.666667, 1.75, 49.875, 60.83333 (xmin, xmax, ymin, ymax)
crs: +proj=longlat +datum=WGS84 +no_defs
names: Slope, Aspect, Average.Temperature, Precipitation, Wind.Speed, Proximity.to.Major.Roads, Population.Counts
min values: 0.00000, 0.00000, 0.00000, 33.96667, 2.93600, -0.09200, 0.00000
max values: 0.0930622, 6.2831853, 10.5779605, 193.2136383, 10.3826666, 68.3430023, 441.3430481
Matteo Larrode
This RasterStack object contains a collection of raster layers representing various environmental predictors related to summer wildfires in the UK.
summer_independent_var_stacksummer_independent_var_stack
A RasterStack object with the following layers:
Raster layer representing the slope of the terrain.
Raster layer representing the aspect of the terrain.
Raster layer representing the average temperature during summer.
Raster layer representing precipitation during summer.
Raster layer representing average wind speed during summer.
Raster layer representing proximity to major roads.
Raster layer representing population counts in the UK.
The RasterStack has the following properties:
class: RasterStack
dimensions: 263 rows, 250 columns, 65750 cells, 7 layers
resolution: 0.04166667 x 0.04166667 (x, y)
extent: -8.666667, 1.75, 49.875, 60.83333 (xmin, xmax, ymin, ymax)
crs: +proj=longlat +datum=WGS84 +no_defs
names: Slope, Aspect, Average.Temperature, Precipitation, Wind.Speed, Proximity.to.Major.Roads, Population.Counts
min values: 0.000000, 0.000000, 8.392517, 44.140907, 2.506667, -0.092000, 0.000000
max values: 0.0930622, 6.2831853, 18.0772878, 203.4393921, 8.6173331, 68.3430023, 441.3430481
Matteo Larrode
This dataset integrates the Social Vulnerability Index (SoVI) and summer wildfire risk predictions across Middle Layer Super Output Areas (MSOAs) in the UK. It includes a binary indicator identifying MSOAs within the worst deciles (8th, 9th, or 10th) for both SoVI and wildfire risk.
w_sovi_ukw_sovi_uk
A tibble with 9,393 rows and 5 columns:
character MSOA (or equivalent) code.
character Local Authority code.
double Standardised Social Vulnerability Index score.
double Standardised wildfire risk score.
character Indicator for MSOAs in the worst deciles (8th, 9th, or 10th) for both SoVI and wildfire risk. 'yes' indicates presence in the worst deciles, 'NA' denotes otherwise.
The approach for combining SoVI and wildfire risk scores and the methodology for determining the worst deciles are based on principles outlined in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset provides predicted wildfire risk levels for Middle Layer Super Output Areas (MSOAs) in England in the spring, based on a Random Forest model incorporating various environmental and anthropogenic factors.
wildfire_risk_spring_englandwildfire_risk_spring_england
A tibble with 6,856 rows and 5 columns:
character MSOA name.
character MSOA code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset outlines predicted wildfire risk levels for Super Data Zones (SDZs) in Northern Ireland in the spring, derived from a Random Forest analysis that integrates environmental and anthropogenic variables.
wildfire_risk_spring_niwildfire_risk_spring_ni
A tibble with 850 rows and 5 columns:
character SDZ name.
character SDZ code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
Predicted wildfire risk levels for Intermediate Zones (IZs) in Scotland in the spring, utilizing a Random Forest model that integrates environmental and anthropogenic variables.
wildfire_risk_spring_scotlandwildfire_risk_spring_scotland
A tibble with 1,279 rows and 5 columns:
character IZ name.
character IZ code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset assesses wildfire risk across Middle Layer Super Output Areas (MSOAs) in Wales in the spring, derived from a Random Forest analysis that integrates environmental and anthropogenic variables.
wildfire_risk_spring_waleswildfire_risk_spring_wales
A tibble with 408 rows and 5 columns:
character MSOA name.
character MSOA code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset provides predicted wildfire risk levels for Middle Layer Super Output Areas (MSOAs) in England in the summer, based on a Random Forest model incorporating various environmental and anthropogenic factors.
wildfire_risk_summer_englandwildfire_risk_summer_england
A tibble with 6,856 rows and 5 columns:
character MSOA name.
character MSOA code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset outlines predicted wildfire risk levels for Super Data Zones (SDZs) in Northern Ireland in the summer, derived from a Random Forest analysis that integrates environmental and anthropogenic variables.
wildfire_risk_summer_niwildfire_risk_summer_ni
A tibble with 850 rows and 5 columns:
character SDZ name.
character SDZ code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
Predicted wildfire risk levels for Intermediate Zones (IZs) in Scotland in the summer, utilizing a Random Forest model that integrates environmental and anthropogenic variables.
wildfire_risk_summer_scotlandwildfire_risk_summer_scotland
A tibble with 1,279 rows and 5 columns:
character IZ name.
character IZ code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.
This dataset assesses wildfire risk across Middle Layer Super Output Areas (MSOAs) in Wales in the summer, derived from a Random Forest analysis that integrates environmental and anthropogenic variables.
wildfire_risk_summer_waleswildfire_risk_summer_wales
A tibble with 408 rows and 5 columns:
character MSOA name.
character MSOA code.
double Predicted wildfire risk score. Higher values signify greater risk.
character Local Authority code (higher level geography).
double Standardised wildfire risk score.
Wildfire risk predictions are generated using a Random Forest model, considering climatological, topographical, and land use variables, as detailed in "Spatial Assessment of Wildfire Vulnerability in England and Wales: Coupling Social Vulnerability with Predicted Wildfire Susceptibility" by Hasan Guler.