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Evacuation_MassachusettsPDF.Rmd
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Evacuation_MassachusettsPDF.Rmd
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---
title: "Evacuation Risks in Massachusetts"
author: "Marcos Luna and Neenah Estrella-Luna"
date: "`r Sys.Date()`"
output:
bookdown::pdf_document2:
toc: true
toc_depth: 3
number_sections: true
fig_caption: yes
includes:
in_header: my_header.tex
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, message=FALSE, warning=FALSE)
```
\pagebreak
# Analysis of evacuation-related risks in Massachusetts
This is an analysis of flood or storm surge-related evacuation risks for priority populations in Massachusetts. In the event of significant inland or coastal flooding, evacuation may be required. For individuals and households with limited mobility, either due to inadequate access to transportation options or because of physical limitations, evacuation presents heightened risk. Evacuation may also prove especially difficult for individuals and households due to limited economic resources, difficulty understanding or accessing information, or low trust in official sources of information.
## Analysis of Flood Hazard Exposure in Massachusetts
```{r NFHZAdata, include=FALSE}
# Analysis of flooding and hurricane evacuation risks for priority populations in Massachusetts
library(tidyverse)
library(sf)
library(tmap)
library(tmaptools)
library(lwgeom)
library(tigris)
options(tigris_use_cache = TRUE, tigris_class = "sf")
load("DATA/ne_layers.rds")
# Extract state census units and convert to projected local CRS EPSG:2805: NAD83(HARN) / Massachusetts See https://spatialreference.org/ref/epsg/2805/
ma_blkgrp_2805 <- ne_blkgrp_sf %>%
filter(STATE == "Massachusetts") %>%
st_transform(., crs = 2805)
ma_tracts_2805 <- ne_tracts_sf %>%
filter(STATE == "Massachusetts") %>%
st_transform(., crs = 2805)
# Get rid of empty geometries
empty_geo <- st_is_empty(ma_blkgrp_2805)
ma_blkgrp_2805 <- ma_blkgrp_2805[!empty_geo,]
empty_geo <- st_is_empty(ma_tracts_2805)
ma_tracts_2805 <- ma_tracts_2805[!empty_geo,]
# Extract state boundary
ma_state_sf <- ne_states_sf_cb %>%
filter(NAME == "Massachusetts") %>%
st_transform(., crs = 2805)
# #### Processing in ArcGIS #####
# # Write out block groups for processing in ArcGIS
# ma_blkgrp_2805 %>%
# dplyr::select(GEOID) %>%
# st_write(., "DATA/FEMA/MA/ma_blkgrp_2805.shp", delete_layer = TRUE)
# #
# # repeat for tracts
# ma_tracts_2805 %>%
# dplyr::select(GEOID) %>%
# st_write(., "DATA/FEMA/MA/ma_tracts_2805.shp", delete_layer = TRUE)
#
# # repeat for state boundary
# ma_state_sf %>%
# st_write(., "DATA/FEMA/MA/ma_state_2805.shp", delete_layer = TRUE)
# Use dasymetric mapping to calculate populations within flood zones. Approach follows method used by Qiang (2019) to eliminate unpopulated areas of census polygons and then reallocate populations to developed areas as identified in National Land Cover Dataset (NLCD).
# Perform NLCD raster-to-vector conversion, vector erase/difference, and vector intersections in ArcMap because it takes too long in R.
# In ArcMap:
# Convert NLCD raster to shapefile. Clip to state.
# Isolate undeveloped areas (gridcode NOT 22 - 24).
# Erase areas of ma_blkgrp_2805 and ma_tracts_2805 that overlap with undeveloped areas in NLCD shapefiles. Compute OldArea of erased polygons in sqm to identify area of developed polygons remaining.
# Intersect erased ma_blkgrps and erased ma_tracts with NFHZA and Hurricane evacuation zones. Read back into R.
##### Return to working in R ######
# # read in processed ma_blkgrps and ma_tracts
# st_layers(dsn = "DATA/FEMA/MA")
ma_blkgrps_developed <- st_read(dsn = "DATA/FEMA/MA",
layer = "ma_blkgrps_developed") %>%
left_join(., as.data.frame(ma_blkgrp_2805), by = "GEOID") %>%
st_transform(., crs = 2805) %>%
mutate(NewArea = st_area(.)) %>%
st_make_valid() %>%
mutate(Proportion = as.numeric(NewArea/OldArea),
NewPop = totalpopE*Proportion,
NewMinority = minorityE*Proportion,
NewUnder5 = under5E*Proportion,
NewOver64 = over64E*Proportion,
NewUnder18 = under18E*Proportion,
NewEng_limit = eng_limitE*Proportion,
NewPov = num2povE*Proportion,
NewLths = lthsE*Proportion)
ma_blkgrps_nfhza <- st_read(dsn = "DATA/FEMA/MA",
layer = "ma_blkgrps_nfhza") %>%
left_join(., as.data.frame(ma_blkgrp_2805), by = "GEOID") %>%
st_transform(., crs = 2805) %>%
mutate(NewArea = st_area(.)) %>%
st_make_valid()
ma_tracts_nfhza <- st_read(dsn = "DATA/FEMA/MA",
layer = "ma_tracts_nfhza") %>%
left_join(., as.data.frame(ma_tracts_2805), by = "GEOID") %>%
st_transform(., crs = 2805) %>%
mutate(NewArea = st_area(.)) %>%
st_make_valid()
# Apportion populations based on geographic proportion of intersect
ma_blkgrps_nfhza <- ma_blkgrps_nfhza %>%
mutate(MA_LOWINC = if_else(MA_INCOME == "I",totalpopE,0)) %>%
mutate(MA_LOWINC = replace_na(MA_LOWINC,0)) %>%
mutate(MA_MINORITIES = if_else(MA_MINORITY == "M", totalpopE,0)) %>%
mutate(MA_MINORITIES = replace_na(MA_MINORITIES,0)) %>%
mutate(MA_NOENGLISH = if_else(MA_ENGLISH == "E", totalpopE,0)) %>%
mutate(MA_NOENGLISH = replace_na(MA_NOENGLISH,0)) %>%
mutate(Proportion = as.numeric(NewArea/OldArea),
NewPop = totalpopE*Proportion,
NewMinority = minorityE*Proportion,
NewUnder5 = under5E*Proportion,
NewOver64 = over64E*Proportion,
NewUnder18 = under18E*Proportion,
NewEng_limit = eng_limitE*Proportion,
NewPov = num2povE*Proportion,
NewLths = lthsE*Proportion,
NewMA_LOWINC = MA_LOWINC*Proportion,
NewMA_MINORITY = MA_MINORITIES*Proportion,
NewMA_NOENGLISH = MA_NOENGLISH*Proportion)
ma_tracts_nfhza <- ma_tracts_nfhza %>%
mutate(Proportion = as.numeric(NewArea/OldArea),
NewDisabled = disabledOver18E*Proportion,
NewNoCar = HHnoCarE*Proportion)
# Compute total block group populations within flood zones
ma_flood_blkgrp_df <- ma_blkgrps_nfhza %>%
as.data.frame() %>%
summarize(`Total Pop` = as.integer(sum(NewPop)),
Minority = as.integer(sum(NewMinority)),
`Under 5` = as.integer(sum(NewUnder5)),
`Over 64` = as.integer(sum(NewOver64)),
`Under 18` = as.integer(sum(NewUnder18)),
`Limited English HH` = as.integer(sum(NewEng_limit)),
`Low Income` = as.integer(sum(NewPov)),
`No HS Dip` = as.integer(sum(NewLths)),
`MA Low Income` = as.integer(sum(NewMA_LOWINC)),
`MA Minority` = as.integer(sum(NewMA_MINORITY)),
`MA Limited English HH` = as.integer(sum(NewMA_NOENGLISH))) %>%
gather(key = Group, value = FloodPop)
ma_flood_tracts_df <- ma_tracts_nfhza %>%
as.data.frame() %>%
summarize(`Disabled` = as.integer(sum(NewDisabled)),
`No Car HH` = as.integer(sum(NewNoCar))) %>%
gather(key = Group, value = FloodPop)
# Compute total tract populations within the state for same groups
ma_tract_flood_pops_df <- ma_tracts_2805 %>%
as.data.frame() %>%
summarize(`Disabled` = sum(disabledOver18E),
`No Car HH` = sum(HHnoCarE)) %>%
gather(key = Group, value = MAPop) %>%
left_join(.,ma_flood_tracts_df, by = "Group")
# Compute populations for state,and join with flood pops
ma_FloodPops_df <- ma_blkgrp_2805 %>%
as.data.frame() %>%
mutate(MA_LOWINC = if_else(MA_INCOME == "I",totalpopE,0)) %>%
mutate(MA_LOWINC = replace_na(MA_LOWINC,0)) %>%
mutate(MA_MINORITIES = if_else(MA_MINORITY == "M", totalpopE,0)) %>%
mutate(MA_MINORITIES = replace_na(MA_MINORITIES,0)) %>%
mutate(MA_NOENGLISH = if_else(MA_ENGLISH == "E", totalpopE,0)) %>%
mutate(MA_NOENGLISH = replace_na(MA_NOENGLISH,0)) %>%
summarize(`Total Pop` = sum(totalpopE),
Minority = sum(minorityE),
`Under 5` = sum(under5E),
`Over 64` = sum(over64E),
`Under 18` = sum(under18E),
`Limited English HH` = sum(eng_limitE),
`Low Income` = sum(num2povE),
`No HS Dip` = sum(lthsE),
`MA Low Income` = sum(MA_LOWINC, na.rm = TRUE),
`MA Minority` = sum(MA_MINORITIES, na.rm = TRUE),
`MA Limited English HH` = sum(MA_NOENGLISH, na.rm = TRUE)) %>%
gather(key = Group, value = MAPop) %>%
left_join(., ma_flood_blkgrp_df, by = "Group") %>%
rbind(.,ma_tract_flood_pops_df) %>%
mutate(PctFlood = FloodPop/MAPop*100)
ma_towns_sf <- county_subdivisions(state = "MA", cb = TRUE) %>%
st_transform(., crs = 2805)
town_tracts_df <- ma_tracts_2805 %>%
dplyr::select(-GEOID) %>%
st_centroid(.) %>%
st_intersection(ma_towns_sf,.) %>%
as.data.frame() %>%
group_by(NAME) %>%
summarize(TotalDisabled = sum(disabledOver18E, na.rm = TRUE),
TotalOver18 = sum(Over18E, na.rm = TRUE),
TotalNoCar = sum(HHnoCarE, na.rm = TRUE))
townpopsdf <- ma_blkgrp_2805 %>%
dplyr::select(-GEOID) %>%
st_centroid(.) %>%
st_intersection(ma_towns_sf,.) %>%
as.data.frame() %>%
mutate(MA_LOWINC = if_else(MA_INCOME == "I", totalpopE, 0)) %>%
mutate(MA_LOWINC = replace_na(MA_LOWINC,0)) %>%
mutate(MA_MINORITY = if_else(MA_MINORITY == "M", totalpopE,0)) %>%
mutate(MA_MINORITY = replace_na(MA_MINORITY,0)) %>%
mutate(MA_ENGLISH = if_else(MA_ENGLISH == "E", totalpopE,0)) %>%
mutate(MA_ENGLISH = replace_na(MA_ENGLISH,0)) %>%
group_by(NAME) %>%
summarize(GEOID = unique(GEOID),
TotalPop = sum(totalpopE, na.rm = TRUE),
TotalHH = sum(householdsE, na.rm = TRUE),
TotalLEH = sum(eng_limitE, na.rm = TRUE),
TotalMin = sum(minorityE, na.rm = TRUE),
TotalLowInc = sum(num2povE, na.rm = TRUE),
TotalNoHS = sum(lthsE, na.rm = TRUE),
TotalOver64 = sum(over64E, na.rm = TRUE),
TotalUnder5 = sum(under5E, na.rm = TRUE),
TotalUnder18 = sum(under18E, na.rm = TRUE),
TotalMA_LOWINC = sum(MA_LOWINC, na.rm = TRUE),
TotalMA_MINORITY = sum(MA_MINORITY, na.rm = TRUE),
TotalMA_ENGLISH = sum(MA_ENGLISH, na.rm = TRUE)) %>%
left_join(., town_tracts_df, by = "NAME")
ma_state_sf_cb <- ne_states_sf_cb %>%
filter(NAME == "Massachusetts") %>%
st_transform(., crs = st_crs(ma_blkgrp_2805))
# # save data for later analysis
# save(ma_blkgrps_nfhza,ma_tracts_nfhza,ma_FloodPops_df,
# file = "DATA/FEMA/MA/nfhza_census.Rds")
```
As a humid, coastal state, a significant portion of Massachusetts's land area and population are exposed to the risk of flooding from overbanking of inland water bodies (e.g., ponds and rivers) or from coastal storm surge and sea level rise.
The analysis of flood exposure presented here is based on the Federal Emergency Management Agency's (FEMA) National Flood Hazard Layer (NFHL), a digital version of FEMA's most recent flood maps.[^NFHL] FEMA identifies areas subject to varying levels of flood risk through its Flood Insurance Rate Maps (FIRMs), which are produced for most parts of the country to identify areas subject to flooding. FEMA's FIRMs are a national standard used by all federal agencies for the purposes of requiring and rating the purchase of flood insurance and regulating new development. Population data comes from the American Community Survey (ACS) 2018 5-year estimates at the census tract and block group levels.
A significant proportion of Massachusetts's population lives near the coast or near inland water bodies. As a result, a significant proportion of the population lives near or within known flood zones. Figure \@ref(fig:mapNFHZAPop) below shows the population distribution and areas subject to floods with an Annual Exceedance Probability (AEP) of 1% (also known as a '100-year' flood) and areas subject to 0.2% AEP (also known as a '500-year' flood). Areas within the 1% AEP flood zone are designated by FEMA as Special Flood Hazard Areas, and development within those zones must be covered by flood insurance. Areas within the 0.2% AEP are not currently regulated, but these areas are nevertheless subject to flood risk under more extreme, albeit less frequent, flooding circumstances. Coastal areas within the 0.2% AEP zone are a reasonable proxy measure for the risks of sea level rise. Note that portions of northwest and northeast Massachusetts are not currently mapped for flooding risk by FEMA.
```{r mapNFHZAdata, include=FALSE}
# Process data for mapping
# Create point layer of major cities for context
# Note cb=FALSE is necessary for extracting centroids from town polygons. Otherwise, if cb=TRUE, cannot extract centroids from multipolygon features.
# Read in NFHL for MA. Data comes from FEMA.
# List available layers in geodatabase
# # st_layers("DATA/FEMA/MA/NFHL_25_20200109.gdb")
# # Read in flood hazard areas
# ma_nfhza_2805 <- st_read(dsn = "DATA/FEMA/MA/NFHL_25_20200109.gdb", layer = "S_FLD_HAZ_AR", quiet = TRUE) %>%
# filter(!FLD_ZONE %in% c("OPEN WATER","AREA NOT INCLUDED","D") &
# !ZONE_SUBTY %in% c("AREA OF MINIMAL FLOOD HAZARD",
# "AREA WITH REDUCED FLOOD RISK DUE TO LEVEE")) %>%
# mutate(FLD_ZONE = as.character(FLD_ZONE)) %>% # omit unused factor levels
# st_transform(., crs = 2805) %>%
# st_make_valid() %>%
# group_by(FLD_ZONE) %>% # aggregate flood zone polygons
# summarize(count = n()) %>%
# mutate(Area = st_area(.),
# Interval = case_when(
# FLD_ZONE == "A" ~ "100-year",
# FLD_ZONE == "AE" ~ "100-year",
# FLD_ZONE == "AH" ~ "100-year",
# FLD_ZONE == "AO" ~ "100-year",
# FLD_ZONE == "VE" ~ "100-year",
# FLD_ZONE == "X" ~ "500-year"))
#
# # get rid of empty geometries
# empty_geo <- st_is_empty(ma_nfhza_2805)
# ma_nfhza_2805 <- ma_nfhza_2805[!empty_geo,]
#
# # save this layer since it takes more than 30 mins to load
# saveRDS(ma_nfhza_2805,"DATA/FEMA/MA/ma_nfhza_2805.Rds")
# load previously process flood layer
ma_nfhza_2805 <- readRDS("DATA/FEMA/MA/ma_nfhza_2805.Rds")
# download Census TIGERLine hydrography for MA
## First, extract list of county names to use with tigris::water
# ma_counties <- counties("MA") %>%
# pull(NAME)
# # Next, download water features for each county and rbind to one layer
# ma_awater_sf <- rbind_tigris(
# lapply(
# ma_counties, function(x) area_water(state = "MA", county = x)
# )
# ) %>%
# st_union() %>%
# st_as_sf() %>%
# st_transform(., crs = 2805)
#
# # save time by loading file
# saveRDS(ma_awater_sf, "DATA/FEMA/MA/ma_awater_sf.Rds")
# load water features
ma_awater_sf <- readRDS("DATA/FEMA/MA/ma_awater_sf.Rds")
# # crop flood zones to land areas only
# start_time <- Sys.time()
# ma_nfhza_2805_land <- ma_nfhza_2805 %>%
# crop_shape(., ma_state_sf, polygon = TRUE) %>%
# st_difference(., ma_awater_sf) %>%
# mutate(Area = st_area(.)) %>%
# st_make_valid() # takes about 1.5 hours
# end_time <- Sys.time()
# end_time - start_time
# # save time by just loading file
# saveRDS(ma_nfhza_2805_land, file = "DATA/FEMA/MA/ma_nfhza_2805_land.Rds")
# load cropped flood zones
ma_nfhza_2805_land <- readRDS("DATA/FEMA/MA/ma_nfhza_2805_land.Rds")
# Create a dot density map of total populations and overlay on flood zones
# Create point layer of major cities for context
# Note cb=FALSE is necessary for extracting centroids from town polygons. Otherwise, if cb=TRUE, cannot extract centroids from multipolygon features.
ma_towns_sf_pts <- county_subdivisions(state = "MA", cb = TRUE) %>%
filter(NAME %in% c("Boston",
"Salem",
"Lawrence",
"Lowell",
"Newburyport",
"Rockport",
"Brockton",
"New Bedford",
"Plymouth",
"Worcester",
"Springfield",
"Pittsfield",
"Athol")) %>%
st_transform(., crs = 2805) %>%
st_centroid(of_largest_polygon = TRUE)
# Create road layer for context
ma_highways <- primary_roads() %>%
filter(FULLNAME %in% c("I- 84","I- 90","I- 91","I- 95","I- 190","I- 195","I- 290","I- 395","I- 495","US Hwy 6","US Hwy 202","Mohawk Trl","George W Stanton Hwy","State Rte 2","Mass State Hwy","Concord Tpke","State Rte 25")) %>%
tmaptools::crop_shape(., ne_states_sf_cb) %>%
st_transform(., crs = 2805)
ma_highways2nd <- primary_secondary_roads("MA") %>%
filter(FULLNAME %in% c("US Hwy 6","Mohawk Trl","State Rte 2","Cambridge Tpke")) %>%
st_transform(., crs = 2805)
# Extract highway segments for labeling
I90roadSegment <- ma_highways %>%
filter(LINEARID == "1103745154991")
I90roadSegment2 <- ma_highways %>%
filter(LINEARID == "110340769311")
I91roadSegment <- ma_highways %>%
filter(LINEARID == "1104748241453")
I95roadSegment <- ma_highways %>%
filter(LINEARID == "1105569136116")
I95roadSegment2 <- ma_highways %>%
filter(LINEARID == "1103737956638")
I195roadSegment2 <- ma_highways %>%
filter(LINEARID == "1101922014382")
I395roadSegment <- ma_highways %>%
filter(LINEARID == "1104259933162")
I495roadSegment <- ma_highways %>%
filter(LINEARID == "1103745404033")
I495roadSegment2 <- ma_highways %>%
filter(LINEARID == "1105589457557")
I495roadSegment3 <- ma_highways %>%
filter(LINEARID == "1101922014436")
StRt2Segment <- ma_highways2nd %>%
filter(LINEARID == "1106087431756")
USHwy6Segment <- ma_highways %>%
filter(LINEARID == "1109096413415")
# Create custom icons of highway shields
I90 <- tmap_icons(file = "https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/I-90.svg/200px-I-90.svg.png")
I95 <- tmap_icons(file = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/61/I-95.svg/200px-I-95.svg.png")
I395 <- tmap_icons("https://upload.wikimedia.org/wikipedia/commons/thumb/1/16/I-395.svg/200px-I-395.svg.png")
I91 <- tmap_icons("https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/I-91.svg/200px-I-91.svg.png")
I495 <- tmap_icons("https://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/I-495.svg/200px-I-495.svg.png")
Hwy2 <- tmap_icons("https://upload.wikimedia.org/wikipedia/commons/thumb/5/54/MA_Route_2.svg/240px-MA_Route_2.svg.png")
Hwy6 <- tmap_icons("https://upload.wikimedia.org/wikipedia/commons/thumb/e/ef/US_6.svg/200px-US_6.svg.png")
# Bring in outline of New York for western boundary
ny_state_sf <- tigris::states(cb = TRUE) %>%
filter(NAME == "New York")
# # Create polygons to show where there are no FEMA maps
# start_time <- Sys.time()
# nofemapolys <- st_read(dsn = "DATA/FEMA/MA/NFHL_25_20200109.gdb",
# layer = "S_FLD_HAZ_AR", quiet = TRUE) %>%
# filter(FLD_ZONE != "AREA NOT INCLUDED") %>%
# group_by() %>%
# summarize() %>% # aggregate all flood zone polygons into one
# st_transform(., crs = st_crs(ma_state_sf)) %>%
# st_make_valid() %>%
# st_difference(ma_state_sf,.) %>% # extract areas with no flood coverage
# st_cast(., "POLYGON") %>% # disaggregate multipolygon feature
# mutate(Area = st_area(.),
# LABEL = "No FEMA\nFlood Data") %>% # calc area of each polygon
# filter(as.numeric(Area)/10^6 > 1000) # remove polygons < 1000km2
# end_time <- Sys.time()
# end_time - start_time #takes about 9.2mins
#
# # save time by loading previously processed file
# saveRDS(nofemapolys,"DATA/FEMA/MA/nofemapolys.Rds")
# load data
nofemapolys <- readRDS("DATA/FEMA/MA/nofemapolys.Rds")
# # Create random points, with 1 point for every 1,000 people
# ma_totalpop_pts <- ma_blkgrps_developed %>%
# select(NewPop) %>%
# filter(NewPop >= 1000) %>%
# st_sample(., size = round(.$NewPop/1000)) %>% # create 1 random point for every 1000 people
# st_sf(.) %>%
# group_by() %>%
# summarize() # reduce to one multipoint object
# Create a simplified version of NFHZA polygons to speed up mapping
# start_time <- Sys.time()
ma_nfhza_2805_land_simple <- ma_nfhza_2805_land %>%
select(Interval) %>%
st_cast(., "MULTIPOLYGON") %>% # homogenize type
st_cast(., "POLYGON") %>%
st_simplify(., dTolerance = 100) %>% # reduce vertices
st_make_valid() %>%
group_by(Interval) %>%
summarize() %>%
mutate(Area = st_area(.))
# end_time <- Sys.time()
# end_time - start_time
# Create a simplified version of ma_awater_sf to speed up mapping
ma_awater_sf_simple <- ma_awater_sf %>%
st_cast(., "MULTIPOLYGON") %>% # homogenize type
st_cast(., "POLYGON") %>%
st_simplify(., dTolerance = 100) %>% # reduce vertices
st_make_valid() %>%
group_by() %>%
summarize()
```
```{r mapNFHZAPop, fig.align = "center", fig.cap="FEMA flood zones across Massachusetts."}
# Create a dot density map of total populations and overlay on flood zones
# Map totalpop and flood zones
m1 <- tm_layout(bg.color = "#e6f3f7") +
tm_shape(ma_blkgrp_2805, unit = "mi") + tm_fill(col = "white") +
tm_shape(ne_states_sf_cb) + tm_fill(col="white") +
tm_shape(ny_state_sf) + tm_fill(col="white") +
tm_shape(ma_awater_sf_simple) + tm_fill(col = "#e6f3f7") +
# tm_shape(ne_states_sf_cb) + tm_borders() +
# tm_shape(ny_state_sf) + tm_borders() +
# tm_shape(ma_totalpop_pts) + tm_dots(col = "darkgoldenrod3",
# labels = "1 dot = 1,000 people",
# alpha = 0.6) +
tm_shape(ma_nfhza_2805_land_simple) +
tm_fill(col = "Interval",
palette = c("dodgerblue", "blueviolet"),
labels = c("1% AEP (100-year)", "0.2% AEP (500-year)"),
title = "FEMA Flood Zones",
alpha = 0.6,
border.alpha = 0) +
tm_shape(ne_states_sf_cb) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ny_state_sf) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ma_highways) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(ma_highways2nd) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(I95roadSegment) +
tm_symbols(shape = I95, border.lwd = NA, size = .1) +
tm_shape(I95roadSegment2) +
tm_symbols(shape = I95, border.lwd = NA, size = .1) +
tm_shape(I395roadSegment) +
tm_symbols(shape = I395, border.lwd = NA, size = .1) +
tm_shape(I91roadSegment) +
tm_symbols(shape = I91, border.lwd = NA, size = .1) +
tm_shape(I495roadSegment) +
tm_symbols(shape = I495, border.lwd = NA, size = .1) +
tm_shape(I495roadSegment2) +
tm_symbols(shape = I495, border.lwd = NA, size = .1) +
tm_shape(I495roadSegment3) +
tm_symbols(shape = I495, border.lwd = NA, size = .1) +
tm_shape(I90roadSegment) +
tm_symbols(shape = I90, border.lwd = NA, size = .1) +
tm_shape(I90roadSegment2) +
tm_symbols(shape = I90, border.lwd = NA, size = .1) +
# tm_shape(StRt2Segment) +
# tm_symbols(shape = Hwy2, border.lwd = NA, size = 0.2) +
# tm_shape(USHwy6Segment) +
# tm_symbols(shape = Hwy6, border.lwd = NA, size = 0.2) +
tm_shape(ma_towns_sf_pts) + tm_dots() +
tm_text("NAME", size = 0.5, col = "black",
xmod = 0.7, ymod = 0.2, shadow = TRUE) +
tm_shape(nofemapolys) + tm_fill(col = "gray", alpha = 0.4) +
tm_text("LABEL", alpha = 0.5) +
tm_scale_bar(breaks = c(0, 10, 20), text.size = 0.5,
position = c(0.6,0.005)) +
# tm_add_legend(type = "fill", col = "darkgoldenrod3",
# alpha = 0.6,
# border.col = "white", border.alpha = 0,
# labels = "1 dot = 1,000 people",
# title = "Total Population") +
tm_layout(title = "FEMA\nFlood Zones",
frame = TRUE, main.title.size = 0.8,
legend.outside = TRUE,
legend.title.size = 0.8,
legend.outside.position = c("right", "top"))
tmap_save(m1, "DATA/FEMA/MA/ma_flood.png",
height = 4, width = 8, units = "in", dpi = 600)
knitr::include_graphics("DATA/FEMA/MA/ma_flood.png")
```
Approximately 9.2% of Massachusetts's land area falls within FEMA flood zones. The breakdown by type of flood zone is presented in Table \@ref(tab:tabNFHZAarea) below.
```{r tabNFHZAarea, fig.align = "center", fig.cap="Massachusetts Land Area within Flood Zones."}
# Total land in MA
ma_area <- as.numeric(ma_state_sf$ALAND)
# Total and percentage of land area of RI within flood zones
ma_nfhza_2805_land %>%
as.data.frame() %>%
group_by(Interval) %>%
summarize(SqKm = round(as.numeric(sum(Area)/10^6),1),
SqMi = round(as.numeric(SqKm/2.59),1),
PctArea = paste0(as.character(round(as.numeric(sum(Area)/ma_area*100),1)),"%")) %>%
kableExtra::kable(longtable = T, booktabs = T,
format.args = list(big.mark = ','),
caption = "Massachusetts Land Area within Flood Zones",
digits = 0, align = "r") %>%
kableExtra::kable_styling(latex_options = c("repeat_header"))
```
These percentages approximate the proportion of the total population in the state that are living within these flood zones. However, exposure varies significantly by population subgroup. Table \@ref(tab:tabNFHZApop) below shows the total and percentages of the general population and various subgroups living within flood zones. Aside from the total population, the largest absolute numbers of subpopulations living within a flood zone are low income persons followed by People of Color The latter groups are priority populations who may have limited mobility in the event of an evacuation.
```{r tabNFHZApop, echo=FALSE, message=FALSE, warning=FALSE, fig.align = "center", fig.cap="Massachusetts Populations Living within Flood Zones."}
# Show table of pops within flood zones
ma_FloodPops_df %>%
mutate(PctFlood = paste0(as.character(round(PctFlood,1)),"%")) %>%
arrange(-FloodPop) %>%
mutate(Group = recode(Group, "Minority" = "People of Color",
"MA Minority" = "MA POC",
"No HS Dip" = "No HS Diploma")) %>%
kableExtra::kable(longtable = T, booktabs = T,
format.args = list(big.mark = ','),
caption = "Massachusetts Populations Living within Flood Zones",
digits = 0, align = "r") %>%
kableExtra::kable_styling(latex_options = c("repeat_header"))
```
Some groups are disproportionately exposed to these flood hazards. While approximately 5.4% of the general population are living within flood zones, limited English speaking households, households without access to a car, people in low income households, and other priority populations, exceed the population average living within flood zones. These priority populations are disproportionately exposed to flood risk (see Figure \@ref(fig:chartNFHZApop) below).
```{r chartNFHZApop, echo=FALSE, message=FALSE, warning=FALSE, fig.align = "center", fig.cap="Massachusetts Populations Living within Flood Zones."}
# Create lollipop plot of pops within flood zones
ma_FloodPops_df %>%
mutate(Group = recode(Group, "Minority" = "People of Color",
"MA Minority" = "MA POC",
"No HS Dip" = "No HS Diploma")) %>%
ggplot(aes(x = reorder(Group,PctFlood),
y = PctFlood)) +
geom_segment(aes(x = reorder(Group,PctFlood),
xend = reorder(Group,PctFlood),
y = ma_FloodPops_df[1,4], yend = PctFlood),
color = "skyblue") +
geom_point(color = "blue", size = 4, alpha = 0.8) +
coord_flip() + xlab("") + ylab("") +
ggtitle("Massachusetts Populations Living within Flood Zones") + theme_light() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank()) +
geom_text(aes(x = Group, y = PctFlood + 0.2 * sign(PctFlood),
label = paste0(round(PctFlood,1),"%")),
hjust = 1.5, vjust = -.8, size = 3,
color=rgb(100,100,100, maxColorValue=255)) +
scale_y_continuous(labels = function(x) paste0(x, "%")) +
geom_hline(yintercept = ma_FloodPops_df[1,4], linetype = "dashed") +
geom_text(aes(x = "Over 64", y = 7, label = "Above state\naverage"),
color = "gray48") +
geom_text(aes(x = "Over 64", y = 4, label = "Below state\naverage"),
color = "gray48") +
expand_limits(y = c(3.5,10.5))
ggsave("images/MA_FEMA_graph.png")
# geom_segment(aes(x = "Total Pop", xend = "Total Pop", y = 5.6, yend = 6.6),
# arrow = arrow(length = unit(0.3,"cm")))
```
The maps below (Figures \@ref(fig:mapNFHZAPopConcern1) and \@ref(fig:mapNFHZAPopConcern2)) show those populations disproportionately exposed to flood risk. Maps for other priority populations can be found in Figures \@ref(fig:mapNFHZAPopConcern3) to \@ref(fig:mapNFHZAPopConcern12) and breakdowns by municipality in Tables \@ref(tab:tabNFHZAMALEH) to \@ref(tab:tabNFHZAU18) in Appendix B.
```{r mapNFHZAPopConcern, include=FALSE}
# Create a dot density map of transit-dependent populations in flood zone
# # create a function to select variable, filter records, and create sample points
# # pop2points <- function(sf, x){
# # x <- enquo(x)
# # sf %>%
# # select(!!x) %>%
# # filter(!!x >= 10) %>%
# # st_sample(., size = round(.[[as_label(x)]]/10)) %>%
# # st_sf(.) %>%
# # mutate(Group = quo_name(x))
# # }
#
# pop2points <- function(sf, x, group){
# x <- enquo(x)
# sf %>%
# select(!!x) %>%
# filter(!!x >= 10) %>%
# st_sample(., size = round(.[[as_label(x)]]/10)) %>%
# st_sf(.) %>%
# mutate(Group = group)
# }
#
# MANoEngHH_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewMA_NOENGLISH,"MA Limited English HH")
#
# LimitEngHH_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewEng_limit,"Limited English HH")
#
# NoCarHH_nfhza_pts <- pop2points(ma_tracts_nfhza,NewNoCar,"No Car HH")
#
# NoHSdip_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewLths,"No HS Dip")
#
# LowInc_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewPov,"Low Income")
#
# Disabled_nfhza_pts <- pop2points(ma_tracts_nfhza,NewDisabled,"Disabled")
#
# MAMinority_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewMA_MINORITY,"MA Minority")
#
# MALowInc_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewMA_LOWINC,"MA Low Income")
#
# Minor_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewMinority,"Minority")
#
# Under5_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewUnder5,"Under 5")
#
# Over64_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewOver64,"Over 64")
#
# Under18_nfhza_pts <- pop2points(ma_blkgrps_nfhza,NewUnder18,"Under 18")
#
# # Bring them together
# ma_nfhza_vulnerable <- rbind(NoCarHH_nfhza_pts,
# MALowInc_nfhza_pts,
# MAMinority_nfhza_pts,
# MANoEngHH_nfhza_pts,
# LimitEngHH_nfhza_pts,
# Disabled_nfhza_pts,
# LowInc_nfhza_pts,
# Minor_nfhza_pts,
# Over64_nfhza_pts,
# Under5_nfhza_pts,
# Under18_nfhza_pts,
# NoHSdip_nfhza_pts) %>%
# # slice(sample(1:n())) %>% # randomise order to avoid bias in plotting order
# group_by(Group) %>%
# summarize()
#
# # clean up pts layers
# rm(list = ls(pattern = "_nfhza_pts"))
#
# # save time by just loading file
# saveRDS(ma_nfhza_vulnerable, file = "DATA/FEMA/MA/ma_nfhza_vulnerable.Rds")
# load vulnerable point file
ma_nfhza_vulnerable <- readRDS("DATA/FEMA/MA/ma_nfhza_vulnerable.Rds")
```
```{r mapNFHZAPopConcern1, echo=FALSE, message=FALSE, warning=FALSE, fig.align = "center", fig.cap="Limited English Speaking Households, as Defined by the Massachusetts Environmental Justice Policy, Living within Flood Zones."}
# Create data subset
nfhza_sub <- ma_nfhza_vulnerable %>%
filter(Group == "MA Limited English HH")
# Map over-represented pops and flood zones
m <- tm_layout(bg.color = "#e6f3f7") +
tm_shape(ma_blkgrp_2805, unit = "mi") + tm_fill(col = "white") +
tm_shape(ne_states_sf_cb) + tm_fill(col="white") +
tm_shape(ny_state_sf) + tm_fill(col="white") +
tm_shape(ma_awater_sf_simple) + tm_fill(col = "#e6f3f7") +
tm_shape(nfhza_sub) +
tm_dots(col = "darkgoldenrod3",
labels = "1 dot = 10 people",
alpha = 0.6) +
# tm_facets(by = "Group",
# ncol = 2,
# free.coords = FALSE) +
tm_shape(ma_nfhza_2805_land_simple) +
tm_fill(col = "Interval",
palette = c("dodgerblue", "blueviolet"),
labels = c("1% AEP (100-year)", "0.2% AEP (500-year)"),
title = "FEMA Flood Zones",
alpha = 0.6,
border.alpha = 0) +
tm_shape(ne_states_sf_cb) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ny_state_sf) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ma_highways) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(ma_highways2nd) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(I95roadSegment) +
tm_symbols(shape = I95, border.lwd = NA, size = 0.1) +
tm_shape(I95roadSegment2) +
tm_symbols(shape = I95, border.lwd = NA, size = 0.1) +
tm_shape(I395roadSegment) +
tm_symbols(shape = I395, border.lwd = NA, size = 0.1) +
tm_shape(I91roadSegment) +
tm_symbols(shape = I91, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment2) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment3) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I90roadSegment) +
tm_symbols(shape = I90, border.lwd = NA, size = 0.1) +
tm_shape(I90roadSegment2) +
tm_symbols(shape = I90, border.lwd = NA, size = 0.1) +
tm_shape(ma_towns_sf_pts) + tm_dots() +
tm_text("NAME", size = 0.5, col = "black",
xmod = 0.7, ymod = 0.2, shadow = TRUE) +
tm_shape(nofemapolys) +
tm_fill(col = "gray", alpha = 0.4) +
tm_text("LABEL", alpha = 0.5, size = 0.6) +
tm_scale_bar(breaks = c(0, 10, 20), text.size = 0.5,
position = c(0.6,0.005)) +
tm_add_legend(type = "fill", col = "darkgoldenrod3",
alpha = 0.6,
border.col = "white", border.alpha = 0,
labels = "1 dot = 10 people") +
tm_layout(title = "MA Limited\nEnglish\nSpeaking\nHouseholds\nLiving within\nFlood Zones",
frame = TRUE, main.title.size = 0.8,
legend.outside = TRUE,
legend.title.size = 0.8,
legend.outside.position = c("right", "top"))
tmap_save(m, "DATA/FEMA/MA/MA_nhfza_vulnerable1.png",
height = 4, width = 8, units = "in", dpi = 600)
knitr::include_graphics("DATA/FEMA/MA/MA_nhfza_vulnerable1.png")
```
```{r mapNFHZAPopConcern2, echo=FALSE, message=FALSE, warning=FALSE, fig.align = "center", fig.cap="Households without a Car Living within Flood Zones."}
# Create data subset
nfhza_sub <- ma_nfhza_vulnerable %>%
filter(Group == "No Car HH")
# Map over-represented pops and flood zones
m <- tm_layout(bg.color = "#e6f3f7") +
tm_shape(ma_blkgrp_2805, unit = "mi") + tm_fill(col = "white") +
tm_shape(ne_states_sf_cb) + tm_fill(col="white") +
tm_shape(ny_state_sf) + tm_fill(col="white") +
tm_shape(ma_awater_sf_simple) + tm_fill(col = "#e6f3f7") +
tm_shape(nfhza_sub) +
tm_dots(col = "darkgoldenrod3",
labels = "1 dot = 10 people",
alpha = 0.6) +
# tm_facets(by = "Group",
# ncol = 2,
# free.coords = FALSE) +
tm_shape(ma_nfhza_2805_land_simple) +
tm_fill(col = "Interval",
palette = c("dodgerblue", "blueviolet"),
labels = c("1% AEP (100-year)", "0.2% AEP (500-year)"),
title = "FEMA Flood Zones",
alpha = 0.6,
border.alpha = 0) +
tm_shape(ne_states_sf_cb) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ny_state_sf) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ma_highways) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(ma_highways2nd) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(I95roadSegment) +
tm_symbols(shape = I95, border.lwd = NA, size = 0.1) +
tm_shape(I95roadSegment2) +
tm_symbols(shape = I95, border.lwd = NA, size = 0.1) +
tm_shape(I395roadSegment) +
tm_symbols(shape = I395, border.lwd = NA, size = 0.1) +
tm_shape(I91roadSegment) +
tm_symbols(shape = I91, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment2) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment3) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I90roadSegment) +
tm_symbols(shape = I90, border.lwd = NA, size = 0.1) +
tm_shape(I90roadSegment2) +
tm_symbols(shape = I90, border.lwd = NA, size = 0.1) +
tm_shape(ma_towns_sf_pts) + tm_dots() +
tm_text("NAME", size = 0.5, col = "black",
xmod = 0.7, ymod = 0.2, shadow = TRUE) +
tm_shape(nofemapolys) +
tm_fill(col = "gray", alpha = 0.4) +
tm_text("LABEL", alpha = 0.5, size = 0.6) +
tm_scale_bar(breaks = c(0, 10, 20), text.size = 0.5,
position = c(0.6,0.005)) +
tm_add_legend(type = "fill", col = "darkgoldenrod3",
alpha = 0.6,
border.col = "white", border.alpha = 0,
labels = "1 dot = 10 people") +
tm_layout(title = "Households\nwithout\na Car\nLiving within\nFlood Zones",
frame = TRUE, main.title.size = 0.8,
legend.outside = TRUE,
legend.title.size = 0.8,
legend.outside.position = c("right", "top"))
tmap_save(m, "DATA/FEMA/MA/MA_nhfza_vulnerable2.png",
height = 4, width = 8, units = "in", dpi = 600)
knitr::include_graphics("DATA/FEMA/MA/MA_nhfza_vulnerable2.png")
```
\pagebreak
## Analysis of Hurricane Evacuation Risk
```{r HEVACdata, echo=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# read in hurricane evacuation zone layer
ma_hea_sf <- st_read(dsn = "DATA/FEMA/MA",layer = "ma_hevac2") %>%
mutate(EVACZONE = as.character(EVACZONE)) %>%
filter(EVACZONE %in% c("A","B")) %>%
st_transform(., crs = 2805) %>%
st_make_valid() %>%
mutate(Area = st_area(.))
# read in processed ma_blkgrps and ma_tracts
ma_blkgrps_hevac <- st_read(dsn = "DATA/FEMA/MA",
layer = "ma_blkgrps_hevac") %>%
left_join(., as.data.frame(ma_blkgrp_2805), by = "GEOID") %>%
st_transform(., crs = 2805) %>%
mutate(NewArea = st_area(.)) %>%
st_make_valid()
ma_tracts_hevac <- st_read(dsn = "DATA/FEMA/MA",
layer = "ma_tracts_hevac") %>%
left_join(., as.data.frame(ma_tracts_2805), by = "GEOID") %>%
st_transform(., crs = 2805) %>%
mutate(NewArea = st_area(.)) %>%
st_make_valid()
# Apportion populations based on geographic proportion of intersect
ma_blkgrps_hevac <- ma_blkgrps_hevac %>%
mutate(MA_LOWINC = if_else(MA_INCOME == "I",totalpopE,0)) %>%
mutate(MA_LOWINC = replace_na(MA_LOWINC,0)) %>%
mutate(MA_MINORITIES = if_else(MA_MINORITY == "M", totalpopE,0)) %>%
mutate(MA_MINORITIES = replace_na(MA_MINORITIES,0)) %>%
mutate(MA_NOENGLISH = if_else(MA_ENGLISH == "E", totalpopE,0)) %>%
mutate(MA_NOENGLISH = replace_na(MA_NOENGLISH,0)) %>%
mutate(Proportion = as.numeric(NewArea/OldArea),
NewPop = totalpopE*Proportion,
NewMinority = minorityE*Proportion,
NewUnder5 = under5E*Proportion,
NewOver64 = over64E*Proportion,
NewUnder18 = under18E*Proportion,
NewEng_limit = eng_limitE*Proportion,
NewPov = num2povE*Proportion,
NewLths = lthsE*Proportion,
NewMA_LOWINC = MA_LOWINC*Proportion,
NewMA_MINORITY = MA_MINORITIES*Proportion,
NewMA_NOENGLISH = MA_NOENGLISH*Proportion)
ma_tracts_hevac <- ma_tracts_hevac %>%
mutate(Proportion = as.numeric(NewArea/OldArea),
NewDisabled = disabledOver18E*Proportion,
NewNoCar = HHnoCarE*Proportion)
# Compute total populations within hurricane evac zones
ma_hevac_blkgrp_df <- ma_blkgrps_hevac %>%
as.data.frame() %>%
summarize(`Total Pop` = as.integer(sum(NewPop)),
Minority = as.integer(sum(NewMinority)),
`Under 5` = as.integer(sum(NewUnder5)),
`Over 64` = as.integer(sum(NewOver64)),
`Under 18` = as.integer(sum(NewUnder18)),
`Limited English HH` = as.integer(sum(NewEng_limit)),
`Low Income` = as.integer(sum(NewPov)),
`No HS Dip` = as.integer(sum(NewLths)),
`MA Low Income` = as.integer(sum(NewMA_LOWINC)),
`MA Minority` = as.integer(sum(NewMA_MINORITY)),
`MA Limited English HH` = as.integer(sum(NewMA_NOENGLISH))) %>%
gather(key = Group, value = HevacPop)
ma_hevac_tracts_df <- ma_tracts_hevac %>%
as.data.frame() %>%
summarize(`Disabled` = as.integer(sum(NewDisabled)),
`No Car HH` = as.integer(sum(NewNoCar))) %>%
gather(key = Group, value = HevacPop)
# Compute total tract populations within the state for same groups
ma_trama_hevac_pops_df <- ma_tracts_2805 %>%
as.data.frame() %>%
summarize(`Disabled` = sum(disabledOver18E),
`No Car HH` = sum(HHnoCarE)) %>%
gather(key = Group, value = MAPop) %>%
left_join(.,ma_hevac_tracts_df, by = "Group")
# Compute populations for state, and join with hurricane evac pops
ma_HevacPops_df <- ma_blkgrp_2805 %>%
as.data.frame() %>%
mutate(MA_LOWINC = if_else(MA_INCOME == "I",totalpopE,0)) %>%
mutate(MA_LOWINC = replace_na(MA_LOWINC,0)) %>%
mutate(MA_MINORITIES = if_else(MA_MINORITY == "M", totalpopE,0)) %>%
mutate(MA_MINORITIES = replace_na(MA_MINORITIES,0)) %>%
mutate(MA_NOENGLISH = if_else(MA_ENGLISH == "E", totalpopE,0)) %>%
mutate(MA_NOENGLISH = replace_na(MA_NOENGLISH,0)) %>%
summarize(`Total Pop` = sum(totalpopE),
Minority = sum(minorityE),
`Under 5` = sum(under5E),
`Over 64` = sum(over64E),
`Under 18` = sum(under18E),
`Limited English HH` = sum(eng_limitE),
`Low Income` = sum(num2povE),
`No HS Dip` = sum(lthsE),
`MA Low Income` = sum(MA_LOWINC, na.rm = TRUE),
`MA Minority` = sum(MA_MINORITIES, na.rm = TRUE),
`MA Limited English HH` = sum(MA_NOENGLISH, na.rm = TRUE)) %>%
gather(key = Group, value = MAPop) %>%
left_join(., ma_hevac_blkgrp_df, by = "Group") %>%
rbind(.,ma_trama_hevac_pops_df) %>%
mutate(PctHevac = HevacPop/MAPop*100)
# # save data for later analysis
# save(ma_blkgrps_hevac, ma_tracts_hevac,ma_HevacPops_df,
# file = "DATA/FEMA/MA/hevac_census.Rds")
```
Massachusetts is subject to significant hurricane risk. Since 1900, Massachusetts has been struck by hurricanes 8 times, and 3 of those were major hurricanes (i.e., Category 3 or higher). The most recent hurricane to hit Massachusetts directly was [Hurricane Bob in 1991](https://www.mass.gov/service-details/the-worst-massachusetts-hurricanes-of-the-20th-century),[^HurricaneBob] a Category 2 storm when it struck the Cape and Islands and south shore of Massachusetts. The latter caused over $39 million in damage in Massachusetts alone and left 60% of the state without power. Hurricane Sandy in 2012 did not hit Massachusetts directly, but nevertheless resulted in flooding of Massachusetts coastal communities. In general, the Cape and Islands of Massachusetts are subject to a [hurricane return period of approximately 13 - 16 years](https://www.nhc.noaa.gov/climo/#returns),[^HurricaneReturn] a level of risk comparable to the Gulf Coast states including Texas, Louisiana, and Florida. Boston Harbor is subject to a hurricane return period of approximately 30 years.
Hurricane intensity is ranked from 1 to 5 on the Saffir-Simpson scale, which is based on sustained wind speeds. However the most destructive and deadly aspects of hurricanes are due to storm surge - ocean water pushed inland by the high winds. This coastal storm surge may be combined with intense rainfall which can exacerbate flooding. As a result, coastal evacuation may be necessary as a hurricane approaches.
The analysis of hurricane evacuation risk presented here is based on Hurricane Evacuation Maps produced by the U.S. Army Corps of Engineers for New England. The maps show Hurricane Evacuation Zones that are recommended to be evacuated during potential worst-case hurricane storm surge inundation. These evacuation zones are based on [storm surge modeling by the National Weather Service's National Hurricane Center](https://www.nhc.noaa.gov/nationalsurge/) using the hydrodynamic Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model to simulate storm surge from tropical cyclones.[^NHCsloshMaps] These data products are provided to federal, state, and local government agencies to assist in planning, risk assessment studies, and operational decision-making. Population data comes from the American Community Survey (ACS) 2018 5-year estimates at the census tract and block group levels.
A significant proportion of Massachusetts's population lives near the coast and is therefore within these hurricane evacuation zones. Figure \@ref(fig:mapHEVACPop) below shows the population distribution and areas within Hurricane Evacuation Zones. Evacuation Zone “A” is recommended to be evacuated prior to an expected category 1 or 2 hurricane. Evacuation Zone “B” is recommended to be evacuated prior to an expected category 3 or 4 hurricane.
```{r mapHEVACPop, echo=FALSE, message=FALSE, warning=FALSE, fig.align = "center", fig.cap="Hurricane evacuation zones across Massachusetts."}
# Create a simplified version of hea polygons to speed up mapping
empty_geo <- st_is_empty(ma_hea_sf)
ma_hea_sf <- ma_hea_sf[!empty_geo,]
ma_hea_sf_simple <- ma_hea_sf %>%
select(EVACZONE) %>%
# st_buffer(., dist = 0) %>%
# st_set_precision(1e6) %>%
# st_cast(., "MULTIPOLYGON") %>% # homogenize type
# st_cast(., "POLYGON") %>%
st_simplify(., dTolerance = 100) %>% # reduce vertices
group_by(EVACZONE) %>%
summarize() %>%
st_make_valid() %>%
mutate(Area = st_area(.))
# Map totalpop and hurricane evacuation zones
m2 <- tm_layout(bg.color = "#e6f3f7") +
tm_shape(ma_hea_sf_simple, unit = "mi") + tm_fill(col = "white") +
tm_shape(ma_blkgrp_2805, unit = "mi") + tm_fill(col = "white") +
tm_shape(ne_states_sf_cb) + tm_fill(col="white") +
tm_shape(ny_state_sf) + tm_fill(col="white") +
tm_shape(ma_awater_sf_simple) + tm_fill(col = "#e6f3f7") +
# tm_shape(ne_states_sf_cb) + tm_borders() +
# tm_shape(ny_state_sf) + tm_borders() +
# tm_shape(ma_totalpop_pts) +
# tm_dots(col = "springgreen3",
# labels = "1 dot = 500 people",
# alpha = 0.6) +
tm_shape(ma_hea_sf_simple) +
tm_fill(col = "EVACZONE",
palette = c("deeppink", "purple3"),
labels = c("A: Category 1 - 2",
"B: Category 3 - 4"),
title = "Evacuation Zone and\nHurricane Category",
alpha = 0.6,
border.alpha = 0) +
tm_shape(ne_states_sf_cb) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ny_state_sf) + tm_borders(lwd = 0.2, alpha = 0.8) +
tm_shape(ma_highways) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(ma_highways2nd) + tm_lines(col = "seashell4", lwd = 1) +
tm_shape(I95roadSegment) +
tm_symbols(shape = I95, border.lwd = NA, size = 0.1) +
tm_shape(I95roadSegment2) +
tm_symbols(shape = I95, border.lwd = NA, size = 0.1) +
tm_shape(I395roadSegment) +
tm_symbols(shape = I395, border.lwd = NA, size = 0.1) +
tm_shape(I91roadSegment) +
tm_symbols(shape = I91, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment2) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I495roadSegment3) +
tm_symbols(shape = I495, border.lwd = NA, size = 0.1) +
tm_shape(I90roadSegment) +
tm_symbols(shape = I90, border.lwd = NA, size = 0.1) +
tm_shape(I90roadSegment2) +
tm_symbols(shape = I90, border.lwd = NA, size = 0.1) +
tm_shape(ma_towns_sf_pts) + tm_dots() +
tm_text("NAME", size = 0.5, col = "black",
xmod = 0.7, ymod = 0.2, shadow = TRUE) +
tm_scale_bar(breaks = c(0, 10, 20), text.size = 0.5,