Healthy Cities GIS Assignment

Author

Ronie N

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
setwd("C:/Users/ronal/OneDrive/Documents/DATA 110")
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.

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)

names(latlong_clean2)

 [1] "Year"                "StateAbbr"           "StateDesc"          
 [4] "CityName"            "GeographicLevel"     "Category"           
 [7] "UniqueID"            "Measure"             "Data_Value_Type"    
[10] "Data_Value"          "PopulationCount"     "lat"                
[13] "long"                "CategoryID"          "MeasureId"          
[16] "CityFIPS"            "TractFIPS"           "Short_Question_Text"

unique(latlong_clean2$Measure)

[1] "Obesity among adults aged >=18 Years"                          
[2] "Current smoking among adults aged >=18 Years"                  
[3] "Binge drinking among adults aged >=18 Years"                   
[4] "No leisure-time physical activity among adults aged >=18 Years"

unique(latlong_clean2$StateAbbr)

[1] "CT"

# Filter: Maryland and Unhealthy Behaviors
subset_data <- latlong_clean2 |>
  filter(StateAbbr == "CT",
         Category == "Unhealthy Behaviors",
         Measure %in% c("Current smoking among adults aged >=18 Years",
                        "No leisure-time physical activity among adults aged >=18 Years"))

# Check number of observations
nrow(subset_data)

[1] 456

#View first few rows
head(subset_data)

# 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… Curren…
2  2017 CT        Connecticut Danbury  Census Tract    Unhealt… 0918430… No lei…
3  2017 CT        Connecticut New Bri… Census Tract    Unhealt… 0950370… No lei…
4  2017 CT        Connecticut New Hav… Census Tract    Unhealt… 0952000… No lei…
5  2017 CT        Connecticut Bridgep… Census Tract    Unhealt… 0908000… No lei…
6  2017 CT        Connecticut Waterbu… Census Tract    Unhealt… 0980000… Curren…
# 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>

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

First plot chunk here

# non map plot
subset_data |>
  group_by(Measure) |>
  summarise(avg_value = mean(Data_Value, na.rm = TRUE)) |>
  ggplot(aes(x = Measure, y = avg_value, fill = Measure)) +
  geom_col() +
  labs(title = "Average Crude Prevalence of Unhealthy Behaviors in Connecticut (2017)",
       x = "Behavior Type",
       y = "Average Prevalence (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 20, hjust = 1),
        legend.position = "none")

3. Now create a map of your subsetted dataset.

First map chunk here

# leaflet()
library(leaflet)

Warning: package 'leaflet' was built under R version 4.5.2

leaflet(subset_data) |>
  addTiles() |>
  addCircleMarkers(
    lng = ~long,
    lat = ~lat,
    radius = ~Data_Value / 2,
    color = "blue",
    fillOpacity = 0.6
  )

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

Refined map chunk here

leaflet(subset_data) |>
  addTiles() |>
  addCircleMarkers(
    lng = ~long,
    lat = ~lat,
    radius = ~Data_Value / 2,
    color = "blue",
    fillOpacity = 0.7,
    popup = ~paste0(
      "<b>City:</b> ", CityName, "<br>",
      "<b>Behavior:</b> ", Measure, "<br>",
      "<b>Prevalence:</b> ", Data_Value, "%"
    )
  )

5. Write a paragraph

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

In this analysis, I examined two unhealthy behaviors in Connecticut; current smoking and physical inactivity, using CDC 500 Cities data for 2017. The bar chart shows that physical inactivity has a slightly higher average prevalence than smoking across Connecticut cities. The interactive map reveals that higher rates for both behaviors cluster around more urbanareas like New Haven- while smaller towns show lower prevalence. These visualizations imply that lifestyle and environment may influence health behavior patterns across the state.