DOI: 10.5061/dryad.1jwstqk3g
title: Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs

description: Community engagement in planning is essential for effective and just
 climate adaptation. However, historically underserved communities are
 often difficult to reach through traditional means of soliciting public
 input. The Climate Adaptation Solutions Accelerator (CASA) through
 School-Community Hubs project identifies public schools as promising sites
 for building both community engagement and community capacity for climate
 adaptation. To serve in this role, schools need information about the
 intersecting threats climate change poses to the communities they serve.
 The Climate Hazard Dashboard for California Schools is a platform that
 maps the current and future risks associated with five climate hazards,
 including wildfire, extreme heat days, wildfire extreme precipitation,
 flooding, and sea level rise, for the nearly 10,000 public schools serving
 Kindergarten through Grade 12 students in California. Each hazard is
 mapped and visualized at the school level, providing an accessible way for
 administrators, teachers, students, and neighborhoods to explore data
 reflecting the climate hazards they face, at a scale relevant to their
 communities. The dashboard also provides an aggregate summary hazard
 metric.
description: Data for extreme heat and extreme precipitation were retrieved
 using API requests from the caladaptr package. The data retrieved to
 calculate extreme heat days were historical observed daily maximum
 temperature for 1961-2005 and projected daily maximum temperature for
 2006-2064. The data retrieved to calculate extreme precipitation days were
 historical observed daily precipitation totals for 1961-2005 and projected
 daily precipitation totals for 2006-2064. Data for wildfire, flooding, and
 sea level rise were downloaded directly from their sources and stored in a
 remote server for use. All data were processed in R
 Studio using Quarto Docs. Tabular data for extreme heat and precipitation
 first used the retrieved historical data to calculate a threshold value to
 classify an extreme event. The threshold was determined to be the 98th
 percentile value of observed historical data for California. For extreme
 heat, this is 98°F. For extreme precipitation, this is 0.73 inches. Then,
 projected daily values exceeding these thresholds were assigned a 1, and
 those not exceeding assigned a 0. The count of projected extreme days
 within each year from 2005-2064 was then assigned to each California
 public school. Spatial data was mainly processed to
 serve mapping purposes in the dashboard. The wildfire raster was clipped
 to the boundaries of California and reclassified. The mean wildfire hazard
 potential score for each school area was also derived and attached to each
 school. The original FEMA flood polygons have many different
 classifications, which were reclassified into three categories: high risk,
 moderate to low risk, and undetermined risk. The percentage of each school
 area that falls within a high risk flood zone was also attached to each
 school. The sea level rise polygons simply describe the extent of flooding
 under a 0.8 feet sea level rise scenario and a 100-year coastal storm. The
 polygons were simplified to decrease map load times in the dashboard. The
 percentage of each school area affected by the sea level rise scenario and
 a 100-year coastal storm were also attached to each school.
 To read a more detailed description of data processing, please
 refer to the "Summary of Solution Design" section in the
 CASAschools Technical Documentation: https://bren.ucsb.edu/projects/climate-hazards-data-integration-and-visualization-climate-adaptation-solutions  
description: --- editor\_options: markdown: wrap: 72 -------- This README.txt file was
 generated on 2024-05-23 by Liane Chen, Charlie Curtin, Kristina Glass, and
 Hazel Vaquero. It is associated with the data archival on this project
 through Dryad. To view the data archival and download datasets, please
 visit
 [https://doi.org/10.5061/dryad.1jwstqk3g](https://doi.org/10.5061/dryad.1jwstqk3g). Recommended citation: Curtin, Charles; Glass, Kristina; Chen, Liane; Vaquero, Hazel (Forthcoming 2024). Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs [Dataset]. Dryad. [https://doi.org/10.5061/dryad.1jwstqk3g](https://doi.org/10.5061/dryad.1jwstqk3g) **GENERAL INFORMATION** 1\. Title of the Project: Climate Adaptation Solutions Accelerator through School Community Hubs (alias CASAschools) 2\. Author Information A. Principal Investigator Contact Information Name: Liane Chen, Charlie Curtin, Kristina Glass, and Hazel Vaquero Institution: University of California Santa Barbara, Bren School of Environmental Science & Management Address: Bren Hall, 2400 University of California, Santa Barbara, CA 93117 Email: [liane@ucsb.edu](mailto:liane@ucsb.edu){.email}, [charlescurtin@ucsb.edu](mailto:charlescurtin@ucsb.edu){.email}, [kristinaglass@ucsb.edu](mailto:kristinaglass@ucsb.edu){.email}, [hazelvaquero@ucsb.edu](mailto:hazelvaquero@ucsb.edu){.email} B. Associate or Co-investigator Contact Information Name: Dr. Simone Pulver Institution: University of California Santa Barbara, Environmental Studies Program Address: 4001 Bren Hall, 2400 University of California, Santa Barbara, CA 93117 Email: [pulver@ucsb.edu](mailto:pulver@ucsb.edu){.email} C. Alternate Contact Information Name: Dr. Sarah Anderson Institution: University of California Santa Barbara Bren School of Environmental Science & Management Address: 4510 Bren Hall, 2400 University of California, Santa Barbara, CA 93117 Email: [sanderson@bren.ucsb.edu](mailto:sanderson@bren.ucsb.edu){.email} D. Alternate Contact Information Name: Dr. Danielle Harlow Institution: University of California Santa Barbara The Gevirtz School Address: Education 3129 Gevirtz Graduate School of Education, University of California-Santa Barbara, CA 93106 Email: [danielle.harlow@ucsb.edu](mailto:danielle.harlow@ucsb.edu){.email} 3\. Date of data collection or obtaining Hazards Data * FEMA Flood NFHL V3.2: 2024-03-13 * Wildfire Hazard Potential for the United States (270-m) version 2023 (4th Edition): 2024-04-12 * Coastal Storm Modeling System: 2024-04-02 * Cal-Adapt: * Maximum Daily Temperature: 2024-03-13 * Maximum Daily Precipitation: 2024-03-13 California Department of Education School Boundaries Data * California Schools 2022-2023: 2024-03-05 * California School District Area 2022-2023 * CalMatters Disaster Days: 2024-04-1 4\. Geographic location of data collection: California, CA: * Cal-Adapt * Coastal Storm Modeling * California Schools 2022-2023 * California School District Areas * CalMatters Disaster Days * FEMA NFHL National: * Wildfire Hazard Potential for the United States (270-m) version 2023 (4th Edition): 5\. Information about funding sources that supported the collection of the data: **SHARING/ACCESS INFORMATION** 1\. Licenses/restrictions placed on the data: Open Source: * FEMA Flood NFHL V3.2 * Wildfire Hazard Potential for the United States (270-m) version 2023 (4th Edition) * Coastal Storm Modeling System * Cal-Adapt: * Maximum Daily Temperature * Maximum Daily Precipitation School Boundaries Data * California Schools 2022-2023 * California School District Area 2022-2023 * CalMatters Disaster Days 2\. Links to location of data: * FEMA Flood NFHL V3.2: [https://msc.fema.gov/portal/advanceSearch](https://msc.fema.gov/portal/advanceSearch) * Wildfire Hazard Potential for the United States (270-m) version 2023 (4th Edition): [https://www.fs.usda.gov/rds/archive/catalog/RDS-2015-0047-4](https://www.fs.usda.gov/rds/archive/catalog/RDS-2015-0047-4) * Coastal Storm Modeling System: [https://ourcoastourfuture.org/hazard-map](https://ourcoastourfuture.org/hazard-map) * Cal-Adapt: [https://ucanr-igis.github.io/caladaptr](https://ucanr-igis.github.io/caladaptr) * Maximum Daily Temperature * Maximum Daily Precipitation School Boundaries Data * California Schools 2022-2023: [https://gis.data.ca.gov/datasets/CDEGIS::california-schools-2022-23/about](https://gis.data.ca.gov/datasets/CDEGIS::california-schools-2022-23/about) * California School District Area 2022-2023: [https://gis.data.ca.gov/maps/CDEGIS::california-school-district-areas-2022-23/about](https://gis.data.ca.gov/maps/CDEGIS::california-school-district-areas-2022-23/about) * CalMatters Disaster Days: [https://disasterdays.calmatters.org/california-school-closures](https://disasterdays.calmatters.org/california-school-closures) 6\. Recommended citation for the project: Curtin, Charles; Glass, Kristina; Chen, Liane; Vaquero, Hazel (Forthcoming 2024). Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs [Dataset]. Dryad. [https://doi.org/10.5061/dryad.1jwstqk3g](https://doi.org/10.5061/dryad.1jwstqk3g) **DATA & FILE OVERVIEW** 1\. File List: relevant files contained in the CASAschools GitHub organization: [https://github.com/CASAschools](https://github.com/CASAschools) ├── climate_hazards │ ├── calmatters │ │ └── calmatter_cleaning.qmd: *Cleaning Calmatters Disaster day .csv to be displayed on dashboard* │ ├── climate_hazard_summary │ │ └── hazard_summary.qmd: *Normalizes variables on a scale from 0-5 for use in the hazard summary scoring, contains code to recreate the hazard summary plot in the dashboard* │ ├── extreme_heat │ │ ├── extreme_heat_threshold.qmd: *Determine maximum daily temperature 98th percentile threshold* │ │ ├── dashboard_prep.qmd: *Joined RCP4.5 & RCP8.5 and prepped the dataframe for usage in daashboard* │ │ ├── schools_extreme_heat_days_rcp45.qmd: *Extreme heat days calculated for all 10,008 schools in a RCP 4.5 scenario* │ │ └── schools_extreme_heat_days_rcp85.qmd: *Extreme heat days calculated for all 10,008 schools in a RCP 8.5 scenario* │ ├── flooding-FEMA │ │ ├── FEMA_schools.qmd: *Joined high risk with schools to find percentage of area* │ │ └── flooding_mapping.qmd: *Binned risk categories into high, moderate to low, and undetermined and plotted leaflet* │ ├── precipitation │ │ ├── caladapt_precip_r.qmd: *Determine maximum daily precipitation 98th percentile threshold* │ │ ├── joining_data.qmd: *Joined RCP4.5 & RCP8.5 and prepped the dataframe for usage in daashboard* │ │ ├── schools_extreme_precip_days_rcp45.qmd: *Extreme precipitation days calculated for all 10,008 schools in a RCP 4.5 scenario* │ │ └── schools_extreme_precip_days_rcp85.qmd: *Extreme precipitation days calculated for all 10,008 schools in a RCP 4.5 scenario* │ ├── sea_level_rise │ │ └── sea_level_rise.qmd: *cleans sea level rise data and prepares it for use in the hazard summary score and mapping in the dashboard* │ └── wildfire │ ├── wildfire_mapping.qmd: *prepares wildfire data for mapping in the dashboard* │ └── wildfire_prep.qmd: *cleans wildfire data and prepares it for use in the hazard summary score* ├── shiny_dashboard: *all of the code needed to run the California Schools Climate Hazards Dashboard* │ ├── global.R: *datasets, packages, and global options needed to run the dashboard* │ ├── R: *scripts that build plots, maps, and reactive text* │ ├── server.R: *outputs plots, maps, and connects the buttons based on user input* │ ├── shiny_dashboard.Rproj │ ├── text: *markdown files that contain the text for each page* │ ├── ui.R: *layout elements and structure of the dashboard* │ └── www: *any images used in the dashboard* 2\. Relationship between files, if important: All of the repositories up to shiny_dashboard contain the code needed to create final outputs for use in the California Schools Climate Hazards Dashboard. The shiny_dashboard uses these datasets, which are included as a part of this submission. **METHODOLOGICAL INFORMATION** 1\. Description of methods used for collection/generation of data: Data for extreme heat and extreme precipitation were retrieved using API requests from the caladaptr package. The data retrieved to calculate extreme heat days were historical observed daily maximum temperature for 1961-2005 and projected daily maximum temperature for 2006-2064. The data retrieved to calculate extreme precipitation days were historical observed daily precipitation totals for 1961-2005 and projected daily precipitation totals for 2006-2064. Data for wildfire, flooding, and sea level rise were downloaded directly from their sources and stored in a remote server for use. 2\. Methods for processing the data: All data were processed in R Studio using Quarto Docs. Tabular data for extreme heat and precipitation first used the retrieved historical data to calculate a threshold value to classify an extreme event. The threshold was determined to be the 98th percentile value of observed historical data for California. For extreme heat, this is 98°F. For extreme precipitation, this is 0.73 inches. Then, projected daily values exceeding these thresholds were assigned a 1, and those not exceeding assigned a 0. The count of projected extreme days within each year from 2005-2064 was then assigned to each California public school. Spatial data was mainly processed to serve mapping purposes in the dashboard. The wildfire raster was clipped to the boundaries of California and reclassified. The mean wildfire hazard potential score for each school area was also derived and attached to each school. The original FEMA flood polygons have many different classifications, which were reclassified into three categories: high risk, moderate to low risk, and undetermined risk. The percentage of each school area that falls within a high risk flood zone was also attached to each school. The sea level rise polygons simply describe the extent of flooding under a 0.8 feet sea level rise scenario and a 100-year coastal storm. The polygons were simplified to decrease map load times in the dashboard. The percentage of each school area affected by the sea level rise scenario and a 100-year coastal storm were also attached to each school. To read a more detailed description of data processing, please refer to the "Summary of Solution Design" section in the CASAschools Technical Documentation: [https://bren.ucsb.edu/projects/climate-hazards-data-integration-and-visualization-climate-adaptation-solutions](https://bren.ucsb.edu/projects/climate-hazards-data-integration-and-visualization-climate-adaptation-solutions) 3\. Instrument- or software-specific information needed to interpret the data: * Software: R Studio version 2022.12.0.353.20 * R version 4.2.2 (2022-10-31) * Packages used, versions, and source ``` abind 1.4-5 2016-07-21 [2] CRAN (R 4.2.2) backports 1.4.1 2021-12-13 [2] CRAN (R 4.2.2) base64enc 0.1-3 2015-07-28 [2] CRAN (R 4.2.2) broom 1.0.5 2023-06-09 [2] CRAN (R 4.2.2) bslib 0.7.0 2024-03-29 [1] CRAN (R 4.2.2) cachem 1.0.8 2023-05-01 [2] CRAN (R 4.2.2) class 7.3-20 2022-01-16 [2] CRAN (R 4.2.2) classInt 0.4-10 2023-09-05 [2] CRAN (R 4.2.2) cli 3.6.2 2023-12-11 [1] CRAN (R 4.2.2) cluster 2.1.4 2022-08-22 [2] CRAN (R 4.2.2) codetools 0.2-18 2020-11-04 [2] CRAN (R 4.2.2) colorspace * 2.1-0 2023-01-23 [2] CRAN (R 4.2.2) countrycode * 1.6.0 2024-03-22 [1] CRAN (R 4.2.2) crosstalk 1.2.1 2023-11-23 [1] CRAN (R 4.2.2) data.table 1.14.8 2023-02-17 [2] CRAN (R 4.2.2) DBI 1.2.2 2024-02-16 [1] CRAN (R 4.2.2) DEoptimR 1.0-11 2022-04-03 [2] CRAN (R 4.2.2) devtools * 2.4.5 2022-10-11 [2] CRAN (R 4.2.2) dichromat * 2.0-0.1 2022-05-02 [2] CRAN (R 4.2.2) digest 0.6.35 2024-03-11 [1] CRAN (R 4.2.2) diptest 0.77-1 2024-04-10 [1] CRAN (R 4.2.2) dplyr * 1.1.3 2023-09-03 [2] CRAN (R 4.2.2) e1071 1.7-14 2023-12-06 [1] CRAN (R 4.2.2) ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.2.2) fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.2) fastmap 1.1.1 2023-02-24 [2] CRAN (R 4.2.2) flexmix 2.3-19 2023-03-16 [1] CRAN (R 4.2.2) fontawesome * 0.5.2 2023-08-19 [2] CRAN (R 4.2.2) forcats * 1.0.0 2023-01-29 [2] CRAN (R 4.2.2) fpc 2.2-12 2024-04-30 [1] CRAN (R 4.2.2) fs 1.6.4 2024-04-25 [1] CRAN (R 4.2.2) generics 0.1.3 2022-07-05 [2] CRAN (R 4.2.2) ggplot2 * 3.5.0 2024-02-23 [1] CRAN (R 4.2.2) glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.2) gridExtra * 2.3 2017-09-09 [2] CRAN (R 4.2.2) gtable 0.3.4 2023-08-21 [2] CRAN (R 4.2.2) hms 1.1.3 2023-03-21 [2] CRAN (R 4.2.2) htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.2.2) htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.2.2) httpuv 1.6.9 2023-02-14 [2] CRAN (R 4.2.2) httr 1.4.5 2023-02-24 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[2] CRAN (R 4.2.2) Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.2.2) readr * 2.1.4 2023-02-10 [2] CRAN (R 4.2.2) regressinator * 0.1.3 2024-01-11 [1] CRAN (R 4.2.2) remotes 2.4.2.1 2023-07-18 [2] CRAN (R 4.2.2) rlang 1.1.3 2024-01-10 [1] CRAN (R 4.2.2) rlist * 0.4.6.2 2021-09-03 [1] CRAN (R 4.2.2) robustbase 0.95-0 2022-04-02 [2] CRAN (R 4.2.2) rstudioapi 0.15.0 2023-07-07 [2] CRAN (R 4.2.2) sass 0.4.9 2024-03-15 [1] CRAN (R 4.2.2) scales 1.3.0 2023-11-28 [1] CRAN (R 4.2.2) sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.2.2) sf * 1.0-14 2023-07-11 [2] CRAN (R 4.2.2) shiny * 1.7.4 2022-12-15 [2] CRAN (R 4.2.2) shinycssloaders * 1.0.0 2020-07-28 [1] CRAN (R 4.2.2) shinydashboard * 0.7.2 2021-09-30 [1] CRAN (R 4.2.2) shinyWidgets * 0.8.6 2024-04-24 [1] CRAN (R 4.2.2) snakecase 0.11.1 2023-08-27 [2] CRAN (R 4.2.2) sp 2.1-4 2024-04-30 [1] CRAN (R 4.2.2) stars 0.6-4 2023-09-11 [2] CRAN (R 4.2.2) stringi 1.7.12 2023-01-11 [2] CRAN (R 4.2.2) stringr * 1.5.0 2022-12-02 [2] CRAN (R 4.2.2) terra * 1.7-55 2023-10-13 [2] CRAN (R 4.2.2) tibble * 3.2.1 2023-03-20 [2] CRAN (R 4.2.2) tidyr * 1.3.0 2023-01-24 [2] CRAN (R 4.2.2) tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.2.2) tidyverse * 2.0.0 2023-02-22 [2] CRAN (R 4.2.2) timechange 0.2.0 2023-01-11 [2] CRAN (R 4.2.2) tmap * 3.3-3 2024-05-06 [1] Github (mtennekes/tmap@a4e9fc9) tmaptools 3.1-1 2023-10-19 [2] Github (r-tmap/tmaptools@0c8b0b1) tzdb 0.3.0 2022-03-28 [2] CRAN (R 4.2.2) units * 0.8-5 2023-11-28 [1] CRAN (R 4.2.2) urlchecker 1.0.1 2021-11-30 [2] CRAN (R 4.2.2) usethis * 2.1.6 2022-05-25 [2] CRAN (R 4.2.2) utf8 1.2.4 2023-10-22 [1] CRAN (R 4.2.2) vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.2) viridisLite 0.4.2 2023-05-02 [2] CRAN (R 4.2.2) withr 3.0.0 2024-01-16 [1] CRAN (R 4.2.2) xfun 0.43 2024-03-25 [1] CRAN (R 4.2.2) XML 3.99-0.16.1 2024-01-22 [1] CRAN (R 4.2.2) xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.2) zoo * 1.8-12 2023-04-13 [2] CRAN (R 4.2.2) ``` **DATA-SPECIFIC INFORMATION FOR:** These are the datasets associated with this submission. They are the tables and spatial data needed for the California Schools Climate Hazards Dashboard. 1. extreme_heat.csv: *contains the total number of projected extreme heat days for every California public school between 2006-2064* 1. number of variables: 7 2. number of cases/rows: 1180944 3. relevant variables: 1. CDSCode: *unique school identifier* 2. year: *year from 2006-2064* 3. total: *total number of extreme heat days in a given year for a given RCP scenario* 4. scenario: *Representative Concentration Pathway (RCP), either 4.5 or 8.5* 5. DistrictNa: *school district* 6. SchoolName: *school name* 4. missing data code: NA 2. extreme_precipitation.csv: *contains the total number of projected extreme precipitation days for every California public school between 2006-2064* 1. number of variables: 9 2. number of cases/rows: 1180944 3. relevant variables: 1. CDSCode: *unique school identifier* 2. year: *year from 2006-2064* 3. total: *total number of extreme precipitation days in a given year for a given RCP scenario* 4. scenario: *Representative Concentration Pathway (RCP), either 4.5 or 8.5* 5. City: *city that the school is in* 6. DistrictNa: *school district* 7. SchoolName: *school name* 4. missing data code: NA 3. schools_hazards_intervals.csv: *contains a single number summary of each hazard for each school, the normalized score from 0-5 of each hazard for each school, and the total score* 1. number of variables: 13 2. number of cases/rows: 10008 3. relevant variables: 1. CDSCode: *unique school identifier* 2. SchoolName: *school name* 3. DistrictNa: *school district* 4. slr_percent: *percentage of school area affected by sea level rise and a 100-year coastal storm* 5. FEMA_percent: *percentage of school area within a high risk flood zone* 6. schools_heat: *total number of extreme heat days between 2025-2030* 7. schools_precip: *total number of extreme precipitation days between 2025-2030* 8. whp: *mean wildfire hazard potential score from 0-5* 9. flood_score: *FEMA_percent normalized from 0-5* 10. slr_score: *slr_percent normalized from 0-5* 11. heat_score: *schools_heat normalized from 0-5* 12. precip_score: *schools_precip normalized from 0-5* 13. hazard_score: *the total hazard score, or the sum of whp, flood_score, slr_score, heat_score, and precip_score* 4. missing data code: NA 4. ca_slr_simple.shp: *simplified polygons describing flooding extent under 0.8 feet of sea level rise (projected 2050 sea levels) and a 100-year coastal storm for California* 1. number of variables: 2 2. number of features: 82 3. variable list: FID, geometry 4. missing data code: NA 5. coordinate reference system: EPSG 4326 5. ca_slr_2000_simple.shp: *simplified polygons describing flooding extent with no sea level rise (2000 sea levels) and a 100-year coastal storm for California* 1. number of variables: 2 2. number of features: 95 3. variable list: FID, geometry 4. missing data code: NA 5. coordinate reference system: EPSG 4326 6. whp_reclass2012.tif: *a raster of wildfire hazard potential for California from 2012 reclassified to very low, low, moderate, high, and very high* 1. raster file format: GeoTIFF 2. raster dimensions: 3871 rows, 3334 columns 3. spatial resolution: 270 meters 4. coordinate reference system: EPSG 3857 5. missing data code: NA 7. whp_reclass.tif: *a raster of wildfire hazard potential for California from 2023 reclassified to very low, low, moderate, high, and very high* 1. raster file format: GeoTIFF 2. raster dimensions: 3871 rows, 3334 columns 3. spatial resolution: 270 meters 4. coordinate reference system: EPSG 3857 5. missing data code: NA 8. fema_reclass_simple.shp: *simplified polygons describing flood risk for California reclassified to high risk, moderate to low risk, and undetermined risk* 1. number of variables: 24 2. number of features: 57136 3. variable list: DFIRM_I, VERSION, FLD_AR_, STUDY_T, FLD_ZON, ZONE_SU, SFHA_TF, STATIC_, V_DATUM, DEPTH, LEN_UNI, VELOCIT, VEL_UNI, AR_REVE, AR_SUBT, BFE_REV, DEP_REV, DUAL_ZO, SOURCE_, GFID, SHAPE_L, SHAPE_A, fld_risk, geometry 4. missing data codes: NA 5. coordinate reference system: EPSG 4269