Update FEMA NRI analysis with new version of the data#879
Update FEMA NRI analysis with new version of the data#879pmarchand1 wants to merge 1 commit intodevelopfrom
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request updates the exploratory data analysis of FEMA's National Risk Index for San Francisco, incorporating a newer version of the data and providing a more detailed explanation of the earthquake risk estimation methodology. It also introduces a new analysis that aggregates earthquake risk data by San Francisco neighborhoods, correlating it with renter occupancy rates to offer a more localized and socially nuanced perspective on earthquake vulnerability. Highlights
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Code Review
This pull request significantly updates the EDA of FEMA earthquake risk index for San Francisco. The eda_fema_nri.Rmd document now includes more detailed documentation on the Hazus model for earthquake EAL estimation, introduces new per-capita and per-building-value loss ratios (EALP_ratio, EALB_ratio), and refines the commentary. A new R Markdown document, nri_neighborhoods.Rmd, has been added to aggregate the NRI data by San Francisco neighborhoods, integrate renter/owner occupancy data, and visualize the relationship between building loss ratio and renter percentage. The review comments point out several potential division-by-zero issues in the calculations for EALP_ratio, EALB_ratio, rent_pct, and EALB_PCT that could lead to Inf or NaN values, and suggest making the neighborhood data filename more maintainable.
| ### Expected damage to people | ||
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| ```{r, fig.width = 10} | ||
| nri <- mutate(nri, EALP_ratio = ERQK_EALP / ERQK_EXPP) |
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The calculation of EALP_ratio could result in division by zero if ERQK_EXPP is 0 for any census tract (e.g., for parks). This would produce Inf values, which might cause issues in plotting or subsequent analyses. It's safer to handle this case explicitly.
nri <- mutate(nri, EALP_ratio = if_else(ERQK_EXPP > 0, ERQK_EALP / ERQK_EXPP, 0))| ### Expected damage to buildings | ||
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| ```{r, fig.width = 10} | ||
| nri <- mutate(nri, EALB_ratio = ERQK_EALB / ERQK_EXPB) |
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The calculation of EALB_ratio could result in division by zero if ERQK_EXPB (building exposure value) is 0 for any census tract. This would produce Inf values. To prevent potential issues in subsequent steps, it's better to handle this division by zero.
nri <- mutate(nri, EALB_ratio = if_else(ERQK_EXPB > 0, ERQK_EALB / ERQK_EXPB, 0))| The file matching each census tract to one of 42 analysis neighborhoods was downloaded from DataSF ([link](https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods-2020-census-tracts-assigned/sevw-6tgi)). | ||
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| ```{r, message = FALSE} | ||
| neigh <- read_csv("Analysis_Neighborhoods_-_2020_census_tracts_assigned_to_neighborhoods_20251118.csv") |
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Hardcoding a filename with a date makes the script less maintainable. When the data is updated, this filename will need to be manually changed. It would be better to define the filename as a variable at the top of the script for easier updates.
| "own", "rent")) %>% | ||
| select(TRACT, variable, estimate) %>% | ||
| pivot_wider(names_from = variable, values_from = estimate) %>% | ||
| mutate(rent_pct = rent / (rent + own) * 100) |
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The calculation for rent_pct could result in division by zero if rent + own is 0 (i.e., a tract with no households). Although you filter for population, this doesn't guarantee households are present. It's safer to add a check to prevent NaN values.
mutate(rent_pct = if_else(rent + own > 0, rent / (rent + own) * 100, 0))| nri_neigh <- inner_join(nri, neigh) %>% | ||
| inner_join(own_rent) %>% | ||
| select(TRACT, neighborhood, POPULATION, RENT_PCT = rent_pct, ERQK_EALB, ERQK_EXPB) %>% | ||
| mutate(EALB_PCT = ERQK_EALB / ERQK_EXPB * 100) |
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The calculation of EALB_PCT could lead to division by zero if ERQK_EXPB is 0 for any tract. Even with a population filter, building exposure might be zero. It's safer to handle this case to prevent Inf or NaN values.
mutate(EALB_PCT = if_else(ERQK_EXPB > 0, ERQK_EALB / ERQK_EXPB * 100, 0))| group_by(neighborhood) %>% | ||
| summarize(TOTAL_POP = sum(POPULATION), | ||
| RENT_PCT = weighted.mean(RENT_PCT, POPULATION), | ||
| EALB_PCT = sum(ERQK_EALB) / sum(ERQK_EXPB) * 100) |
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When summarizing by neighborhood, sum(ERQK_EXPB) could be zero if a neighborhood consists of tracts with no building value (e.g., a neighborhood that is entirely a park). This would cause a division-by-zero error. It's safer to add a check for this possibility.
EALB_PCT = if_else(sum(ERQK_EXPB) > 0, sum(ERQK_EALB) / sum(ERQK_EXPB) * 100, 0))1 participant
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