Healthy Cities GIS Assignment

Load the libraries and set the working directory

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidyr)
library(leaflet)

Warning: package 'leaflet' was built under R version 4.5.3

setwd("C:/Users/SwagD/Downloads/Data 110")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

Rows: 810103 Columns: 24
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (17): StateAbbr, StateDesc, CityName, GeographicLevel, DataSource, Categ...
dbl  (6): Year, Data_Value, Low_Confidence_Limit, High_Confidence_Limit, Cit...
num  (1): PopulationCount

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data(cities500)

Warning in data(cities500): data set 'cities500' not found

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(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2017) |>
  filter(StateAbbr == "CT") |>
  filter(Category == "Unhealthy Behaviors")
head(latlong_clean)

# A tibble: 6 × 25
   Year StateAbbr StateDesc   CityName   GeographicLevel DataSource Category    
  <dbl> <chr>     <chr>       <chr>      <chr>           <chr>      <chr>       
1  2017 CT        Connecticut Bridgeport Census Tract    BRFSS      Unhealthy B…
2  2017 CT        Connecticut Danbury    City            BRFSS      Unhealthy B…
3  2017 CT        Connecticut Norwalk    Census Tract    BRFSS      Unhealthy B…
4  2017 CT        Connecticut Bridgeport Census Tract    BRFSS      Unhealthy B…
5  2017 CT        Connecticut Hartford   Census Tract    BRFSS      Unhealthy B…
6  2017 CT        Connecticut Waterbury  Census Tract    BRFSS      Unhealthy B…
# 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

latlong_clean2 <- latlong_clean |>
  select(-DataSource, -Data_Value_Unit, -DataValueTypeID,
         -Low_Confidence_Limit, -High_Confidence_Limit,
         -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(latlong_clean2)

# A tibble: 6 × 18
   Year StateAbbr StateDesc   CityName GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>       <chr>    <chr>           <chr>    <chr>    <chr>  
1  2017 CT        Connecticut Bridgep… Census Tract    Unhealt… 0908000… Obesit…
2  2017 CT        Connecticut Danbury  City            Unhealt… 918430   Obesit…
3  2017 CT        Connecticut Norwalk  Census Tract    Unhealt… 0955990… Obesit…
4  2017 CT        Connecticut Bridgep… Census Tract    Unhealt… 0908000… Curren…
5  2017 CT        Connecticut Hartford Census Tract    Unhealt… 0937000… Obesit…
6  2017 CT        Connecticut Waterbu… Census Tract    Unhealt… 0980000… Obesit…
# 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 “latlong_clean2” is a manageable dataset now.

For your assignment, work with a cleaned dataset where you perform your own cleaning and filtering.

1. Once you run the above code and filter this complicated dataset, perform your own investigation by filtering this dataset however you choose so that you have a subset with no more than 900 observations through some inclusion/exclusion criteria.

Filter chunk here (you may need multiple chunks)

southeast_health <- latlong |>
  filter(StateDesc != "United States") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2017) |>
  filter(StateAbbr %in% c("FL", "GA", "NC", "SC")) |>
  filter(Category == "Health Outcomes") |>
  filter(GeographicLevel == "City") |>
  filter(!is.na(Data_Value)) |>
  select(-DataSource, -Data_Value_Unit, -DataValueTypeID,
         -Low_Confidence_Limit, -High_Confidence_Limit,
         -Data_Value_Footnote_Symbol, -Data_Value_Footnote)

nrow(southeast_health)

[1] 756

head(southeast_health)

# A tibble: 6 × 18
   Year StateAbbr StateDesc CityName   GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>     <chr>      <chr>           <chr>    <chr>    <chr>  
1  2017 FL        Florida   Boca Raton City            Health … 1207300  Chroni…
2  2017 FL        Florida   Gainesvil… City            Health … 1225175  Stroke…
3  2017 FL        Florida   Lauderhill City            Health … 1239550  Stroke…
4  2017 FL        Florida   Melbourne  City            Health … 1243975  Curren…
5  2017 FL        Florida   Miami      City            Health … 1245000  Chroni…
6  2017 FL        Florida   Miramar    City            Health … 1245975  Stroke…
# 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>

southeast_health |>
  summarize(
    n_observations = n(),
    n_cities       = n_distinct(CityName),
    n_states       = n_distinct(StateAbbr),
    n_measures     = n_distinct(MeasureId)
  )

# A tibble: 1 × 4
  n_observations n_cities n_states n_measures
           <int>    <int>    <int>      <int>
1            756       62        4         12

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

First plot chunk here

southeast_health |>
  group_by(Short_Question_Text) |>
  summarize(avg_prevalence = mean(Data_Value, na.rm = TRUE)) |>
  arrange(desc(avg_prevalence)) |>
  ggplot(aes(x = reorder(Short_Question_Text, avg_prevalence),
             y = avg_prevalence,
             fill = avg_prevalence)) +
  geom_col() +
  coord_flip() +
  scale_fill_gradient(low = "orange", high = "red") +
  labs(
    title    = "Average Crude Prevalence of Health Outcomes",
    subtitle = "Southeastern U.S. cities — FL, GA, NC, SC (2017)",
    x        = "Health Outcome Measure",
    y        = "Average Crude Prevalence (%)",
    fill     = "Prevalence (%)",
    caption  = "Source: CDC 500 Cities Project, 2017 release"
  ) +
  theme_minimal(base_size = 12) +
  theme(plot.title = element_text(face = "bold"))

3. Now create a map of your subsetted dataset.

First map chunk here

# Filter to high blood pressure only
hbp_data <- southeast_health |>
  filter(MeasureId == "BPHIGH") |>
  filter(!is.na(lat) & !is.na(long))

# Color palette scaled to prevalence range
pal <- colorNumeric(
  palette = c("yellow", "orange", "red"),
  domain  = hbp_data$Data_Value
)

leaflet(hbp_data) |>
  addTiles() |>
  addCircleMarkers(
    lng         = ~long,
    lat         = ~lat,
    radius      = 7,
    fillColor   = ~pal(Data_Value),
    stroke      = FALSE,
    fillOpacity = 0.85
  ) |>
  addLegend(
    position = "bottomright",
    pal      = pal,
    values   = ~Data_Value,
    title    = "High Blood Pressure<br>Prevalence (%)",
    opacity  = 0.9
  )

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

Refined map chunk here

leaflet(hbp_data) |>
  addProviderTiles(providers$CartoDB.Positron) |>
  addCircleMarkers(
    lng         = ~long,
    lat         = ~lat,
    radius      = ~sqrt(Data_Value) * 1.5,
    fillColor   = ~pal(Data_Value),
    stroke      = TRUE,
    weight      = 1,
    color       = "white",
    fillOpacity = 0.85,
    popup = ~paste0(
      "<strong>", CityName, ", ", StateAbbr, "</strong><br>",
      "High Blood Pressure Prevalence: <b>", Data_Value, "%</b>"
    )
  ) |>
  addLegend(
    position = "bottomright",
    pal      = pal,
    values   = ~Data_Value,
    title    = "High Blood Pressure<br>Prevalence (%)",
    opacity  = 0.9
  ) |>
  addControl(
    html     = "<b>CDC 500 Cities — High Blood Pressure (2017)</b><br><i>Click a circle for details</i>",
    position = "topright"
  )

5. Write a paragraph

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

The maps I created looked at rates of 12 health conditions across Florida, Georgia, North Carolina, and South Carolina using CDC 500 Cities data. The data used comes from survey responses, not any diagnoses, so it follows the population trends instead of being an exact counts.The bar chart shows that high blood pressure and arthritis are the most common conditions in the regions we are looking at, affecting more than a third of the residents, while kidney disease and cancer are the least common. The maps also reveals that inland cities in Georgia and the Carolinas have higher rates of high blood pressure than cities in Florida. Clicking on any city in the map would allow us to see its exact rate, making it a lot easier to spot which areas may need more resources.