.Rhistory

inner_join(pct_female_headed_households, by="neighborhood")
top_pcts <- distinct(top_10 %>%
select(neighborhood)) %>%
inner_join(female_cmbd, by = "neighborhood") %>%
select(femhhs21)
mean(top_pcts$femhhs21)
bottom_pcts <- distinct(bottom_10 %>%
select(neighborhood)) %>%
inner_join(female_cmbd, by = "neighborhood") %>%
select(femhhs21)
mean(bottom_pcts$femhhs21)
# As the code above shows, the percentage of female-headed households in Baltimore’s top 10% wealthiest neighborhoods is 22.06%. The percentage of female-headed households in Baltimore’s bottom 10% wealthiest neighborhoods is 66.82%.
#C - load Lobraries
library(tidyverse)
library(rio)
library(janitor)
#intro
#For my notebook I wanted to test if there was correlation between race and housing prices in Baltimore City. In the data sets we have looked at previously we were able to identify regional disparities in median income and race. We have also analyzed population ethnic percentages in these areas. The missing piece that could tie the data notebook together would be the housing prices data. With the regional data median housing price data of Baltimore we can compare the housing value and race population. This would indicate the value of the neighborhood and we can see if there are trends with neighborhood value, race and income.
#this is the Median housing data of cities in Baltimore MD. I will create table to assess which cities in MD had the highest and lowest value of homes and then assess my findings with the other data. I will be looking at the home value of the most current year in each city. So i will sort the data according to the 2019 home value median price.
Median_Price_of_Homes_Sold <- read_csv("~/Documents/GitHub/abbey_weltman_jour472/abbeyweltman_notebook/Median_Price_of_Homes_Sold.csv")
Median_Price_of_Homes_Sold <- read_csv("Median_Price_of_Homes_Sold.csv")
high_home_pr <- Median_Price_of_Homes_Sold %>%
select(CSA2010,salepr19) %>%
arrange(desc(salepr19)) %>%
slice_max(salepr19, n = 5)
low_home_pr <-Median_Price_of_Homes_Sold %>%
select(CSA2010,salepr19) %>%
arrange(salepr19) %>%
slice_max(salepr19, n = 5)
View(Median_Price_of_Homes_Sold)
View(high_home_pr)
View(low_home_pr)
View(high_home_pr)
View(low_home_pr)
View(high_home_pr)
Racial_Diversity_Index_1_ <- read_csv("Racial_Diversity_Index (1).csv")
diverse_CSA <- Racial_Diversity_Index_1_ %>%
filter(CSA2020 == c("North Baltimore/Guilford/Homeland")) %>%
select(racdiv21,CSA2020) %>%
arrange(desc(racdiv21)) %>%
slice_max(racdiv21, n = 10)
View(diverse_CSA)
diverse_CSA <- Racial_Diversity_Index_1_ %>%
filter(CSA2020 == c("North Baltimore/Guilford/Homeland"))
city_md_income <- read_csv("city_md_income.csv")
top_income <- city_md_income %>%
select(place ,median_inc_2020)%>%
arrange(desc(median_inc_2020)) %>%
slice_max(median_inc_2020, n = 20)
low_income <- city_md_income %>%
select(place, median_inc_2020)%>%
arrange(median_inc_2020) %>%
slice_max(median_inc_2020, n = 20)
View(top_income)
View(low_income)
View(top_income)
View(low_income)
#remotes::install_github("walkerke/tidycensus")
library(tidyverse)
library(tidycensus)
#a = get_decennial(geography = "state", variables = "P1_001N", year = 2020)
# head(a, 5)
#install.packages("formattable")
library(formattable)
census_api_key("9cabe8a191a1f824755d4a1845f13cb08faa2c5f", install = TRUE)
#2020 census tract
#P1_003N  !!Total:!!Population of one race:!!White alone
race_dec <- tidycensus::get_decennial( geography = "tract", geometry = TRUE, state = "MD", year = 2020, variables = "P1_003N", summary_var = "P1_002N")
#Calls variables for the 2020 decennial census
v20 <- load_variables(2020, "pl", cache = TRUE)
v16 <- load_variables(2016, "acs5", cache = TRUE)
v19 <- load_variables(2019, "acs5", cache = TRUE)
#vars10 <- c("P005003", "P005004", "P005006", "P004003")
# P1_003N
#  !!Total:!!Population of one race:!!White alone
# P1_004N
#  !!Total:!!Population of one race:!!Black or African American alone
# P1_005N
#  !!Total:!!Population of one race:!!American Indian and Alaska Native alone
# P1_006N
#  !!Total:!!Population of one race:!!Asian alone
# P2_002N
#  !!Total:!!Hispanic or Latino
vars20 <- c("P1_003N", "P1_004N", "P1_005N", "P1_006N", "P2_002N")
#Race by Maryland County, 2020
MD_race <- get_decennial(geography = "county", geometry = TRUE, state = "MD", year = 2020, variables = c(white = "P1_003N", black = "P1_004N", asian = "P1_006N", nativeam = "P1_005N", hispanic = "P2_002N"), summary_var = "P1_002N") %>%
mutate(pct_race = (value / summary_value)) %>%
rename(race = variable, population = value, county_pop = summary_value)
MD_race$pct_race <- round(MD_race$pct_race, 2)
MD_race$pct_race <- percent(MD_race$pct_race, 0)
#using the rename variables feature
#https://walker-data.com/census-r/an-introduction-to-tidycensus.html#renaming-variable-ids
MD_race <- as.data.frame(MD_race)
MD_race <- MD_race %>%
select(NAME, race, pct_race, population, county_pop)
#write.csv (MD_race, "MD_2020_counties_race.csv")
#B19001 COUNTS THE NUMBER OF HOUSEHOLDS
md_income1 <- get_acs(geography = "county",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2016)
md_income1
#B19001 COUNTS THE NUMBER OF HOUSEHOLDS
md_income2016 <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2016) %>%
mutate(year=("2016"))
md_income2016
#B19001 COUNTS THE NUMBER OF HOUSEHOLDS
md_income2020 <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2020) %>%
mutate(year=("2020"))
md_income2020
#B19001 COUNTS THE NUMBER OF HOUSEHOLDS
md_income2010 <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2010) %>%
mutate(year=("2010"))
md_income2010
md_income_all <- rbind(md_income2010, md_income2016, md_income2020)
md_income_all <- separate(data = md_income_all, col = NAME, into = c("Census_Tract", "County", "State"), sep = ",", extra = "merge", fill = "right")
#write.csv(md_income_all, "md_income_all.csv")
balt_income_all <- filter(md_income_all, grepl ("Baltimore", County))
#write.csv(balt_income_all, "balt_income_all.csv")
baltcity_income <- filter(balt_income_all, grepl ("Baltimore city", County))
baltcity_income <- baltcity_income %>%
filter(variable=="median_income")
View(baltcity_income)
# fish_encounters %>%
#   pivot_wider(names_from = station, values_from = seen)
baltcity_income2 <- baltcity_income %>%
select(!(moe)) %>%
pivot_wider(names_from = year, values_from = estimate)
baltcity_income2 <- janitor::clean_names(baltcity_income2)
baltcity_income2 <- baltcity_income2 %>%
mutate(Diff_2020_2010 = (x2020-x2010)) %>%
mutate(Diff_2020_2016 = (x2020-x2016))
# write.csv(baltcity_income2, "baltcity_income2.csv")
# write_rds(baltcity_income2, "baltcity_income2.rds")
state_list <- fips_codes %>%
distinct(state) %>%
as.list()
state_list <-state_list$state[1:51]
decennial_vars <- load_variables(2020, dataset="pl")
nation_geometries <- get_decennial(year=2020, geography="tract",state=state_list, variables = "P3_001N", sumfile = "pl", geometry = TRUE)
clean_dec_nation_geometries <- nation_geometries %>%
clean_names() %>%
separate(name, into = c("tract","county","state"), sep=", ") %>%
mutate(total_pop=value) %>%
select(-variable,-value)
clean_dec_nation_geometries <- nation_geometries %>%
janitor::clean_names() %>%
separate(name, into = c("tract","county","state"), sep=", ") %>%
mutate(total_pop=value) %>%
select(-variable,-value)
baltcity_income_map <- nation_geometries  %>%
right_join(baltcity_income2, by=c("GEOID"="geoid"))
View(baltcity_income_map)
clean_dec_nation_geometries <- readRDS("dec_nation_geometries.rds")
clean_dec_nation_geometries <- readRDS("data\dec_nation_geometries.rds")
clean_dec_nation_geometries <- readRDS("..\data\dec_nation_geometries.rds")
getwd()
setwd("~/Code/Baltimore/code")
clean_dec_nation_geometries <- readRDS("..\data\dec_nation_geometries.rds")
clean_dec_nation_geometries <- readRDS("data\dec_nation_geometries.rds")
clean_dec_nation_geometries <- readRDS("../Baltimore/data/dec_nation_geometries.rds")
getwd()
setwd("~/Code/Baltimore")
clean_dec_nation_geometries <- readRDS("../Baltimore/data/dec_nation_geometries.rds")
clean_dec_nation_geometries <- readRDS("/Users/robwells/Code/Baltimore/data/dec_nation_geometries.rds")
MD_dec_geometries <- clean_dec_nation_geometries %>%
filter(county== "Baltimore city")
pal <- colorNumeric(
palette = "inferno",
domain = MD_dec_geometries$total_pop
)
library(leaflet)
MD_dec_geometries <- clean_dec_nation_geometries %>%
filter(county== "Baltimore city")
pal <- colorNumeric(
palette = "inferno",
domain = MD_dec_geometries$total_pop
)
leaflet() %>%
addProviderTiles(providers$Stamen.TonerLite) %>%
addPolygons(data = MD_dec_geometries,
color = ~pal(total_pop),
weight = 0.5,
smoothFactor = 0.2,
fillOpacity = 0.75,
label = ~total_pop) %>%
addLegend(
position = "bottomright",
pal = pal,
values = MD_dec_geometries$total_pop,
title = "total pop <br>per census tract"
)
#remotes::install_github("walkerke/tidycensus")
library(tidyverse)
library(tidycensus)
#a = get_decennial(geography = "state", variables = "P1_001N", year = 2020)
# head(a, 5)
#install.packages("formattable")
library(formattable)
library(htmlwidgets)
library(leaflet)
library(sf)
library(formattable)
library(dplyr)
library(tidyr)
library(janitor)
#install.packages("leaflet.extras")
library(leaflet.extras)
library(googlesheets4)
census_api_key("9cabe8a191a1f824755d4a1845f13cb08faa2c5f", install = TRUE, overwrite = TRUE)
googlesheets4::gs4_deauth()
smith <- read_sheet("https://docs.google.com/spreadsheets/d/1RheKDdtsToCcSLnZoZBYwtN_ISw6E2kgPIQpDQE_BYw/edit?usp=sharing") %>%
as.data.frame()
smith <- smith %>%
clean_names()
#join Community Statistical Area names
#associates neighborhood names to Census Tracts using Community Statistical Areas, 2010 https://bniajfi.org/mapping-resources/
#Baltimore has 56 neighborhoods as measured by Community Statistical Areas
csa <- read_csv("../data/balt_census_crosswalks_2020.csv") %>%
rename(
geoid = GEOID_Tract_2020
) %>%
clean_names()
#smith$tract <- as.character(smith$tract)
smith1 <- smith  %>%
right_join(csa, by=c("tract"="geoid")) %>%
distinct()
#Rename Columns
smith1<- smith1 %>%
rename(neighborhood = community_statistical_area_2020,
census = tract_2020)
#write.csv(smith1, "smith1_balt.csv")
smith <- read.csv("../data/smith1_balt.csv")
#199 census tracts
#25 zip codes (totalling 199 areas, but need to check boundaries)
x<- smith %>%
group_by(zip) %>%
count ()
sum(x$n)
#per capita income seems way too low
median(smith$income_per_capita, na.rm = TRUE) #22789.76
median(smith$pct_anymembershp_zip, na.rm = TRUE) #0.455563
summary(smith$pct_anymembershp_zip, na.rm = TRUE)
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
#  0.3353  0.4444  0.4556  0.4613  0.4752  0.5241       3
median(smith$nbanks_zip, na.rm = TRUE) #5
summary(smith$nbanks_zip, na.rm = TRUE)
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
# 0.000   2.000   5.000   5.847   8.000  14.000       3
median(smith$census_response_rate2020, na.rm = TRUE) #0.564
summary(smith$census_response_rate2020, na.rm = TRUE)
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
#  0.2830  0.4753  0.5640  0.5594  0.6522  0.8360       3
baltcity_income_clean <- read.csv("baltcity_income_clean.csv")
baltcity_income_clean <- read.csv("data/baltcity_income_clean.csv")
getwd()
baltcity_income_clean <- read.csv("../data/baltcity_income_clean.csv")
baltcity_percap_income <- read_csv("../data/baltcity_percap_income.csv") %>%
as.data.frame()
median(baltcity_income_clean$x2020, na.rm = TRUE)
baltcity_percap_income <- read_csv("../data/baltcity_percap_income.csv") %>%
as.data.frame()
median(baltcity_percap_income$x2020, na.rm = TRUE)
#Calls variables for the 2020 decennial census
v20_dec <- load_variables(2020, "pl", cache = TRUE)
#Calls variables for the 2020 ACS census
v20_acs <- load_variables(2020, "acs5", cache = TRUE)
#2020 Median Income By Census Tract ACS
#2016-2020
#B19001 COUNTS THE NUMBER OF HOUSEHOLDS
#B19013_001 is the household median income
md_income2020 <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2020) %>%
mutate(year=("2020"))
md_income2020
#B19013 defined: https://www.socialexplorer.com/data/ACS2010_5yr/metadata/?ds=ACS10_5yr&var=B19013001
#2016 Median Income By Census Tract ACS
#2016-2012
md_income2016 <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2016) %>%
mutate(year=("2016"))
md_income2016
#2010 Median Income By Census Tract 2010
#2006-2010 sample
md_income2010 <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2010) %>%
mutate(year=("2010"))
md_income2010
#Bind together 2010, 2016, 2020 files
md_income_all <- rbind(md_income2010, md_income2016, md_income2020)
md_income_all <- separate(data = md_income_all, col = NAME, into = c("Census_Tract", "County", "State"), sep = ",", extra = "merge", fill = "right")
#write.csv(md_income_all, "md_income_all.csv")
#Subset Baltimore city-county, city files
balt_income_all <- filter(md_income_all, grepl ("Baltimore", County))
#write.csv(balt_income_all, "balt_income_all.csv")
#Baltimore city median income, 2010, 2016, 2020
baltcity_income <- filter(balt_income_all, grepl ("Baltimore city", County)) %>%
filter(variable=="median_income")
#reshape baltcity_income table
baltcity_income <- baltcity_income %>%
dplyr::select(-moe) %>%
pivot_wider(names_from = "year", values_from = "estimate")
baltcity_income <- janitor::clean_names(baltcity_income)
baltcity_income <- baltcity_income %>%
mutate(diff_2020_2010 = (x2020-x2010)) %>%
mutate(diff_2020_2016 = (x2020-x2016))
# write.csv(baltcity_income, "baltcity_income.csv")
# write_rds(baltcity_income, "baltcity_income.rds")
member_all <- smith %>%
group_by(neighborhood) %>%
select(pct_anymembershp_zip, income_per_capita, tract) %>%
arrange(desc(pct_anymembershp_zip)) %>%
mutate(pct_anymembershp_zip = formattable::percent(pct_anymembershp_zip))
member_all$income_per_capita <- formattable::currency(member_all$income_per_capita, digits =0L)
library(kableExtra)
member_all %>%
rename(Neighborhood = neighborhood, Membership = pct_anymembershp_zip, Per_Capita_Income = income_per_capita, Census_Tract = tract) %>%
kbl(caption = "All Communities By Civic Membership", font_size = 50, bold=T) %>%
kable_classic(full_width = T, html_font = "Cambria") %>%
column_spec(1, bold = T, border_right = T) %>%
column_spec(2, border_right = T, width = "10em", background = "yellow") %>%
column_spec(3, border_right = T, width = "10em") %>%
save_kable(file = "../output/member_all.html", self_contained = T)
#subset the geometry
md_income2020_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2020, geometry = TRUE) %>%
mutate(year=("2020")) %>%
select(GEOID, NAME)
member_all <- member_all %>%
mutate(GEOID = as.character(tract))
#join geocoordinates with baltcity
member_all2 <- md_income2020_geo  %>%
right_join(member_all, by=c("GEOID")) %>%
distinct()
member_all2$pct_anymembershp_zip <- formattable::percent(member_all2$pct_anymembershp_zip, 1)
# member_all2$income_per_capita <-
#   paste0('$', round(member_all2$income_per_capita,0))
View(member_all2)
View(smith1)
View(md_income2020_geo)
View(member_all)
md_income2020_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2020, geometry = TRUE) %>%
mutate(year=("2020"))
View(baltcity_income_clean)
View(baltcity_income)
View(baltcity_percap_income)
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022")) %>%
select(GEOID, NAME)
View(md_income2022_geo)
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022"))
member_all <- member_all %>%
mutate(GEOID = as.character(tract))
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022")) %>%
rename(hh_med_inc_2022 = estimate)
member_all2 <- md_income2022_geo  %>%
right_join(member_all, by=c("GEOID")) %>%
distinct()
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022")) %>%
rename(hh_med_inc_2022 = estimate) %>%
filter(viable="median_income")
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022")) %>%
rename(hh_med_inc_2022 = estimate) %>%
filter(viable=="median_income")
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022")) %>%
rename(hh_med_inc_2022 = estimate) %>%
filter(variable=="median_income")
member_all2 <- md_income2022_geo  %>%
right_join(member_all, by=c("GEOID")) %>%
distinct()
member_all2$pct_anymembershp_zip <- formattable::percent(member_all2$pct_anymembershp_zip, 1)
#Map by Median Income Difference
member_all2 <- member_all2 %>%
sf::st_transform('+proj=longlat +datum=WGS84')
pal <- colorNumeric(
palette = "inferno",
domain = member_all2$pct_anymembershp_zip
)
#https://leaflet-extras.github.io/leaflet-providers/preview/
leafMap <- leaflet() %>%
addProviderTiles(providers$Esri.WorldStreetMap) %>%
addPolygons(data = member_all2,
color = ~pal(pct_anymembershp_zip),
weight = 2.5,
smoothFactor = 0.2,
fillOpacity = 0.5,
label = paste("Pct membership in civic groups is:",(member_all2$pct_anymembershp_zip), "and household median income is",(member_all2$hh_med_inc_2022), "in", (member_all2$neighborhood))) %>%
# addLegend(
#   position = "bottomright",
#   pal = pal,
#   values = member_all2$pct_anymembershp_zip,
#   title = "Pct Membership <br> 2020"
# ) %>%
addControl(
html = '<div id="legend" style="background-color: white; padding: 10px; border-radius: 5px; border: 1px solid #ccc; text-align: center;">
<p><strong>Pct Membership 2020</strong></p>
<div style="background-color: black; height: 20px; width: 30px; display: inline-block;"></div> 30-39%
<br>
<div style="background-color: #e75480; height: 20px; width: 30px; display: inline-block;"></div> 40-45%
<br>
<div style="background-color: #fa8072; height: 20px; width: 30px; display: inline-block;"></div> 46-50%
<br>
<div style="background-color: #ffffbf; height: 20px; width: 30px; display: inline-block;"></div> 51-60%
</div>',
position = "bottomright"
) %>%
addControl(
html = "<div style='background-color: white; padding: 5px; border-radius: 3px;'><h3>Civil Membership, Household Median Income in Baltimore</h3></div>",
position = "topright"
)
# saveWidget(leafMap, "../output/member_all_map_jan_24.html")
saveWidget(leafMap, "../output/member_all_map_march_6.html")
leafMap
md_income2022_geo <- get_acs(geography = "tract",
variables = c(number_households = "B19001_001", median_income = "B19013_001"),
state = "MD",
year = 2022, geometry = TRUE) %>%
mutate(year=("2022")) %>%
rename(hh_med_inc_2022 = estimate) %>%
filter(variable=="median_income") %>%
mutate(hh_med_inc_2022 = formattable::currency(hh_med_inc_2022, digits =0L))
View(md_income2022_geo)
member_all2 <- md_income2022_geo  %>%
right_join(member_all, by=c("GEOID")) %>%
distinct()
member_all2$pct_anymembershp_zip <- formattable::percent(member_all2$pct_anymembershp_zip, 1)
View(member_all2)
#Map by Median Income Difference
member_all2 <- member_all2 %>%
sf::st_transform('+proj=longlat +datum=WGS84')
pal <- colorNumeric(
palette = "inferno",
domain = member_all2$pct_anymembershp_zip
)
#https://leaflet-extras.github.io/leaflet-providers/preview/
leafMap <- leaflet() %>%
addProviderTiles(providers$Esri.WorldStreetMap) %>%
addPolygons(data = member_all2,
color = ~pal(pct_anymembershp_zip),
weight = 2.5,
smoothFactor = 0.2,
fillOpacity = 0.5,
label = paste("Pct membership in civic groups is:",(member_all2$pct_anymembershp_zip), "and household median income is",(member_all2$hh_med_inc_2022), "in", (member_all2$neighborhood))) %>%
# addLegend(
#   position = "bottomright",
#   pal = pal,
#   values = member_all2$pct_anymembershp_zip,
#   title = "Pct Membership <br> 2020"
# ) %>%
addControl(
html = '<div id="legend" style="background-color: white; padding: 10px; border-radius: 5px; border: 1px solid #ccc; text-align: center;">
<p><strong>Pct Membership 2020</strong></p>
<div style="background-color: black; height: 20px; width: 30px; display: inline-block;"></div> 30-39%
<br>
<div style="background-color: #e75480; height: 20px; width: 30px; display: inline-block;"></div> 40-45%
<br>
<div style="background-color: #fa8072; height: 20px; width: 30px; display: inline-block;"></div> 46-50%
<br>
<div style="background-color: #ffffbf; height: 20px; width: 30px; display: inline-block;"></div> 51-60%
</div>',
position = "bottomright"
) %>%
addControl(
html = "<div style='background-color: white; padding: 5px; border-radius: 3px;'><h3>Civil Membership, Household Median Income in Baltimore</h3></div>",
position = "topright"
)
# saveWidget(leafMap, "../output/member_all_map_jan_24.html")
saveWidget(leafMap, "../output/member_all_map_march_6.html")
leafMap