Healthy Cities GIS Assignment

Author

Your Name

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
library(leaflet)
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")
data(cities500)

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.

write.csv(latlong_clean2, "latlong_clean2.csv")

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)

# Filter for Obesity only, and remove city level data to keep only Census Tracts
subset_data <- latlong_clean2 |>
  filter(Short_Question_Text == "Obesity") |>
  filter(GeographicLevel == "Census Tract")

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

First plot chunk here

ggplot(subset_data, aes(x = reorder(CityName, Data_Value, FUN = median), y = Data_Value, fill = CityName)) +
  geom_boxplot() +
  coord_flip() +
  labs(title = "Distribution of Obesity Rates by CT City (2017)",
       x = "City",
       y = "Crude Prevalence (%)") +
  theme_minimal() +
  theme(legend.position = "none") # Hiding the redundant legend # Obesity boxplot city

3. Now create a map of your subsetted dataset.

First map chunk here

leaflet(subset_data) |>
  addTiles() |>
  addCircleMarkers(
    lng = ~long,
    lat = ~lat,
    radius = 3,
    color = "blue",
    stroke = FALSE,
    fillOpacity = 0.6
  )

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

Refined map chunk here

pal <- colorNumeric(palette = "YlOrRd", domain = subset_data$Data_Value)

leaflet(subset_data) |>
  addTiles() |>
  addCircleMarkers(
    lng = ~long,
    lat = ~lat,
    radius = 5,
    color = ~pal(Data_Value),
    stroke = FALSE,
    fillOpacity = 0.8,
    popup = ~paste0("<b>City:</b> ", CityName, "<br>", # mouse click tips
                    "<b>Tract FIPS:</b> ", TractFIPS, "<br>",
                    "<b>Obesity Rate:</b> ", Data_Value, "%")
  ) |>
  addLegend("bottomright", pal = pal, values = ~Data_Value,
            title = "Obesity Rate (%)",
            opacity = 1)

5. Write a paragraph

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

My analysis focused on the prevalence of obesity among adults (over or 18) in Connecticut during 2017, my subset is based on the Census Tract level to identify localized disparities. The non-map boxplot reveals a pretty significant variation in obesity rates between cities; Hartford has the highest median obesity rate (like 38.9%), while Stamford and Norwalk have the lowest (like 22%). The refined map further highlights these disparities geographically. By coloring the census tracts based on prevalence, we can see dense clusters of high obesity (indicated by darker red markers) primarily located within Hartford, New Haven, and Bridgeport. And the single highest recorded tract prevalence (45.6%) is located in Hartford, while the lowest (16.8%) is found in a specific tract in New Haven, demonstrating the health inequalities that can exist even within the same state or regions.