Healthy Cities GIS Assignment

Author

Max Reed

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
library(leaflet)
setwd("~/24X Course Work/DATA110")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

The GeoLocation variable has (lat, long) format

Split GeoLocation (lat, long) into two columns: lat and long

latlong <- cities500|>
  mutate(GeoLocation = str_replace_all(GeoLocation, "[()]", ""))|>
  separate(GeoLocation, into = c("lat", "long"), sep = ",", convert = TRUE)
head(latlong)

# A tibble: 6 × 25
   Year StateAbbr StateDesc  CityName  GeographicLevel DataSource Category      
  <dbl> <chr>     <chr>      <chr>     <chr>           <chr>      <chr>         
1  2017 CA        California Hawthorne Census Tract    BRFSS      Health Outcom…
2  2017 CA        California Hawthorne City            BRFSS      Unhealthy Beh…
3  2017 CA        California Hayward   City            BRFSS      Health Outcom…
4  2017 CA        California Hayward   City            BRFSS      Unhealthy Beh…
5  2017 CA        California Hemet     City            BRFSS      Prevention    
6  2017 CA        California Indio     Census Tract    BRFSS      Health Outcom…
# ℹ 18 more variables: UniqueID <chr>, Measure <chr>, Data_Value_Unit <chr>,
#   DataValueTypeID <chr>, Data_Value_Type <chr>, Data_Value <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
#   Data_Value_Footnote_Symbol <chr>, Data_Value_Footnote <chr>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

Filter the dataset

Remove the StateDesc that includes the United Sates, select Prevention as the category (of interest), filter for only measuring crude prevalence and select only 2017.

latlong_clean <- latlong |>
  filter(StateDesc != "United States") |>
  filter(Category == "Prevention") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2017)
head(latlong_clean)

# A tibble: 6 × 25
   Year StateAbbr StateDesc  CityName   GeographicLevel DataSource Category  
  <dbl> <chr>     <chr>      <chr>      <chr>           <chr>      <chr>     
1  2017 AL        Alabama    Montgomery City            BRFSS      Prevention
2  2017 CA        California Concord    City            BRFSS      Prevention
3  2017 CA        California Concord    City            BRFSS      Prevention
4  2017 CA        California Fontana    City            BRFSS      Prevention
5  2017 CA        California Richmond   Census Tract    BRFSS      Prevention
6  2017 FL        Florida    Davie      Census Tract    BRFSS      Prevention
# ℹ 18 more variables: UniqueID <chr>, Measure <chr>, Data_Value_Unit <chr>,
#   DataValueTypeID <chr>, Data_Value_Type <chr>, Data_Value <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
#   Data_Value_Footnote_Symbol <chr>, Data_Value_Footnote <chr>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

What variables are included? (can any of them be removed?)

names(latlong_clean)

 [1] "Year"                       "StateAbbr"                 
 [3] "StateDesc"                  "CityName"                  
 [5] "GeographicLevel"            "DataSource"                
 [7] "Category"                   "UniqueID"                  
 [9] "Measure"                    "Data_Value_Unit"           
[11] "DataValueTypeID"            "Data_Value_Type"           
[13] "Data_Value"                 "Low_Confidence_Limit"      
[15] "High_Confidence_Limit"      "Data_Value_Footnote_Symbol"
[17] "Data_Value_Footnote"        "PopulationCount"           
[19] "lat"                        "long"                      
[21] "CategoryID"                 "MeasureId"                 
[23] "CityFIPS"                   "TractFIPS"                 
[25] "Short_Question_Text"

Remove the variables that will not be used in the assignment

prevention <- latlong_clean |>
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(prevention)

# A tibble: 6 × 18
   Year StateAbbr StateDesc  CityName  GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>      <chr>     <chr>           <chr>    <chr>    <chr>  
1  2017 AL        Alabama    Montgome… City            Prevent… 151000   Choles…
2  2017 CA        California Concord   City            Prevent… 616000   Visits…
3  2017 CA        California Concord   City            Prevent… 616000   Choles…
4  2017 CA        California Fontana   City            Prevent… 624680   Visits…
5  2017 CA        California Richmond  Census Tract    Prevent… 0660620… Choles…
6  2017 FL        Florida    Davie     Census Tract    Prevent… 1216475… Choles…
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

nj <- prevention |>
  filter(StateAbbr=="NJ")
head(nj)

# A tibble: 6 × 18
   Year StateAbbr StateDesc  CityName  GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>      <chr>     <chr>           <chr>    <chr>    <chr>  
1  2017 NJ        New Jersey Camden    Census Tract    Prevent… 3410000… "Curre…
2  2017 NJ        New Jersey Clifton   Census Tract    Prevent… 3413690… "Takin…
3  2017 NJ        New Jersey Newark    Census Tract    Prevent… 3451000… "Curre…
4  2017 NJ        New Jersey Jersey C… Census Tract    Prevent… 3436000… "Chole…
5  2017 NJ        New Jersey Paterson  Census Tract    Prevent… 3457000… "Chole…
6  2017 NJ        New Jersey Union Ci… Census Tract    Prevent… 3474630… "Curre…
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

md <- prevention |>
  filter(StateAbbr=="MD")
head(md)

# A tibble: 6 × 18
   Year StateAbbr StateDesc CityName  GeographicLevel Category  UniqueID Measure
  <dbl> <chr>     <chr>     <chr>     <chr>           <chr>     <chr>    <chr>  
1  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Chole…
2  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Visit…
3  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Visit…
4  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Curre…
5  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Curre…
6  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Visit…
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

The new dataset “Prevention” is a manageable dataset now.

For your assignment, work with the cleaned “Prevention” dataset

1. Once you run the above code, filter this dataset one more time for any particular subset.

Filter chunk here

unique(latlong_clean$StateAbbr)

 [1] "AL" "CA" "FL" "CT" "IL" "MN" "NY" "PA" "NC" "OH" "OK" "OR" "TX" "RI" "SC"
[16] "SD" "TN" "UT" "VA" "WA" "AK" "WI" "AZ" "AR" "CO" "DE" "NV" "DC" "GA" "ID"
[31] "HI" "MA" "MI" "IN" "KS" "KY" "IA" "LA" "MD" "ME" "NH" "NJ" "NM" "MO" "MS"
[46] "NE" "MT" "ND" "WV" "VT" "WY"

njLL <- latlong |>
  filter(StateAbbr=="NJ") |>
  filter(Category == "Health Outcomes") |>
  filter(Year == 2016)
head(njLL)

# A tibble: 6 × 25
   Year StateAbbr StateDesc  CityName    GeographicLevel DataSource Category    
  <dbl> <chr>     <chr>      <chr>       <chr>           <chr>      <chr>       
1  2016 NJ        New Jersey Trenton     Census Tract    BRFSS      Health Outc…
2  2016 NJ        New Jersey Paterson    Census Tract    BRFSS      Health Outc…
3  2016 NJ        New Jersey Jersey City Census Tract    BRFSS      Health Outc…
4  2016 NJ        New Jersey Camden      Census Tract    BRFSS      Health Outc…
5  2016 NJ        New Jersey Newark      Census Tract    BRFSS      Health Outc…
6  2016 NJ        New Jersey Trenton     Census Tract    BRFSS      Health Outc…
# ℹ 18 more variables: UniqueID <chr>, Measure <chr>, Data_Value_Unit <chr>,
#   DataValueTypeID <chr>, Data_Value_Type <chr>, Data_Value <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
#   Data_Value_Footnote_Symbol <chr>, Data_Value_Footnote <chr>,
#   PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.

First plot chunk here

ggplot(njLL, aes(x=CityName, y=Data_Value, fill = CityName)) +
  geom_boxplot() +
  theme_bw() +
  xlab("City in NJ") +
  ylab ("Teeth Loss Score") +
  labs(title = "Teeth Loss Metrics in various Cities in NJ in 2016",
       caption = "Source: USGS")

3. Now create a map of your subsetted dataset.

First map chunk here

nj_LONG <- -74.4057
nj_LAT <- 40.40

njPal <- colorNumeric(
  palette = "OrRd",
  domain = njLL$Data_Value
)

leaflet() |>
  setView(lng = nj_LONG, lat = nj_LAT, zoom = 8) |>
  addProviderTiles("OpenStreetMap.HOT") |>
  addCircles(
    data=njLL,
    radius= (njLL$Data_Value)^2.25,
    color= njPal(njLL$Data_Value),
    fillOpacity = 0.33
  )

Assuming "long" and "lat" are longitude and latitude, respectively

4. Refine your map to include a mouse-click tooltip

Refined map chunk here

njpopup <- paste0(
      "<b>City: </b>", njLL$CityName, "<br>",
      "<b>Teeth Loss Score: </b>", njLL$Data_Value, "<br>",
      "<b>Population: </b>", njLL$PopulationCount, "<br>",
      "<b>Data Value Type: </b>", njLL$Data_Value_Type, "<br>"
)

leaflet() |>
  setView(lng = nj_LONG, lat = nj_LAT, zoom = 8) |>
  addProviderTiles("OpenStreetMap.HOT") |>
  addCircles(
    data=njLL,
    radius= (njLL$Data_Value)^2.25,
    color= njPal(njLL$Data_Value),
    fillOpacity = 0.33,
    popup = njpopup
  )

Assuming "long" and "lat" are longitude and latitude, respectively

5. Write a paragraph

In a paragraph, describe the plots you created and what they show.

For this assignment, I decided to focus on Teeth Loss and how it’s felt differently between various cities in New Jersey (my home state). The first plot directly showcases the differences in Teeth Loss measured, setting each recorded city side by side. Camden, near Philadelphia, seems to have a much higher score than other recorded cities. Some cities with particularly low teeth loss scores are Jersey City, Union City, and Clifton, which can be better explained in the mapped visualization. We could draw a conclusion, if given access to income levels to this cities, that perhaps better access to healthcare with wealth directly impacts how teeth loss affects Adults in these regions. Other statistics, such as drug use in these areas, dentistry access, etc. may also inform why this is.

In our mapped visualizations, it seems like a proximity to NYC has an increase in teeth retention. As previously mentioned, cities like Union City, Jersey City, and Clifton have much lower teeth loss scores. These areas are known for being more affluent than those compared and reside just outside New York City. Given the cost of living in these areas, we can infer that income levels directly impact access to healthcare and dentistry, which can affect whether or not teeth are well maintained or given treatment as needed.