Healthy Cities GIS Assignment

Author

Jhonathan Urquilla

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
library(leaflet)
setwd("C:/Users/ubjho/Downloads")
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 == "CA") |>
  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 CA        California Hawthorne City            BRFSS      Unhealthy Beh…
2  2017 CA        California Hayward   City            BRFSS      Unhealthy Beh…
3  2017 CA        California Lakewood  City            BRFSS      Unhealthy Beh…
4  2017 CA        California Alhambra  Census Tract    BRFSS      Unhealthy Beh…
5  2017 CA        California Antioch   City            BRFSS      Unhealthy Beh…
6  2017 CA        California Chino     City            BRFSS      Unhealthy Beh…
# 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 CA        California Hawthorne City            Unhealt… 632548   Curren…
2  2017 CA        California Hayward   City            Unhealt… 633000   Obesit…
3  2017 CA        California Lakewood  City            Unhealt… 639892   Obesit…
4  2017 CA        California Alhambra  Census Tract    Unhealt… 0600884… Obesit…
5  2017 CA        California Antioch   City            Unhealt… 602252   Binge …
6  2017 CA        California Chino     City            Unhealt… 613210   Binge …
# 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 a cleaned dataset.

1. Once you run the above code and learn how to 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.

Filter chunk here (you may need multiple chunks)

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.
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>
latlong_clean <- latlong |>
  filter(StateDesc != "United States") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2017) |>
  filter(StateAbbr == "CA") |>
  filter(Category == "Health Outcomes")

head(latlong_clean)
# 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 Indio     Census Tract    BRFSS      Health Outcom…
3  2017 CA        California Inglewood Census Tract    BRFSS      Health Outcom…
4  2017 CA        California Livermore City            BRFSS      Health Outcom…
5  2017 CA        California Antioch   Census Tract    BRFSS      Health Outcom…
6  2017 CA        California Berkeley  City            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>

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

First plot chunk here

latlong_plot <- latlong_clean |>
  group_by(Measure) |>
  summarize(mean_value = mean(Data_Value, na.rm = TRUE)) |>
  arrange(desc(mean_value))

ggplot(latlong_plot, aes(x = reorder(Measure, mean_value), y = mean_value)) +
  geom_col(fill = "forestgreen") +
  coord_flip() +
  labs(title = "Average Crude\nPrevalence by\nHealth Outcome\n(California, 2017)",
       x = "Health Outcome Measure",
       y = "Mean Crude Prevalence (%)") +
  theme_minimal()

3. Now create a map of your subsetted dataset.

First map chunk here

ca_lat <- 36.7783
ca_lon <- -119.4179

leaflet() |>
  setView(lng = ca_lon, lat = ca_lat, zoom = 6) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data = latlong_clean,
    lng = ~long,
    lat = ~lat,
    radius = ~Data_Value * 100,
    color = "violet",
    fillColor = "maroon",
    fillOpacity = 0.4
  )

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

Refined map chunk here

pal <- colorFactor(
  palette = c("blueviolet", "chartreuse", "darkred", "deeppink", "orchid", "coral", "darkslateblue", "darkslategrey", "yellowgreen", "tomato", "plum4", "khaki"),
  levels = unique(latlong_clean$Measure),
  latlong_clean$Measure
)

popup_health <- paste0(
  "<b>City:</b> ", latlong_clean$CityName, "<br>",
  "<b>Measure:</b> ", latlong_clean$Measure, "<br>",
  "<b>Value:</b> ", latlong_clean$Data_Value, "%"
)
map1 <- leaflet(latlong_clean) |>
  setView(lng = -119.4179, lat = 36.7783, zoom = 6) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    fillColor = ~pal(Measure),
    radius = ~Data_Value * 100,
    fillOpacity = 0.8,
    color = ~pal(Measure),
    popup = popup_health
  )
Assuming "long" and "lat" are longitude and latitude, respectively
map1 |>
  addLegend("topleft",
            pal = pal,
            values = latlong_clean$Measure,
            title = "Health Outcome",
            opacity = .7)

5. Write a paragraph

The dataset focuses on Health Outcomes in California in 2017, measured by crude prevalence rates. The bar plot reveals which health issues were most common, with “High blood pressure” and “Arthritis” showing the highest mean prevalence across cities. The interactive map displays the geographical spread of these conditions, with tooltips providing detailed information for each city. This visualization allows for quick identification of areas with higher burdens of chronic illness and supports targeted public health interventions.