Healthy Cities GIS Assignment

Author

Your Name

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
#setwd("C:/Users/leahm/OneDrive/Desktop/Data 110")
setwd("C:/Users/rsaidi/Dropbox/Rachel/MontColl/Datasets/Datasets")
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)

# Check what values are available before filtering

unique(latlong_clean2$Category)

[1] "Unhealthy Behaviors"

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"

my_subset <- latlong_clean2 |>
  filter(StateAbbr == "CT",
         Category == "Unhealthy Behaviors")
nrow(my_subset)

[1] 912

head(my_subset)

# 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>

unique(my_subset$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"

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

First plot chunk here

# non map plot

my_subset |>
  group_by(CityName) |>
  summarise(mean_value = mean(Data_Value, na.rm = TRUE)) |>
  ggplot(aes(x = reorder(CityName, -mean_value), y = mean_value, fill = CityName)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  labs(
    title = "Average Rate of Unhealthy Behaviors Across Connecticut Cities (2017)",
    x = "City",
    y = "Percentage of Adults With this Behavior (%)"
    ) +
  theme_minimal()

3. Now create a map of your subsetted dataset.

First map chunk here

# leaflet()

#| message: false
#| warning: false
library(leaflet)

Warning: package 'leaflet' was built under R version 4.5.1

# Create a leaflet map

leaflet(data = my_subset) |>
  addTiles() |> 
  addCircleMarkers(
    ~long, ~lat, 
    radius = ~Data_Value / 2, 
    color = "red",
    stroke = FALSE,
    fillOpacity = 0.6,
    popup = ~paste0(
      "", CityName, "",
      "Average Rate: ", round(Data_Value, 1), "%"
      )
    ) |>
  setView(lng = -72.7, lat = 41.6, zoom = 8)

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

Refined map chunk here

library(leaflet)

# Refined leaflet map with tooltip

leaflet(data = my_subset) |>
  addTiles() |> 
  addCircleMarkers(
    ~long, ~lat, 
    radius = ~Data_Value / 2, 
    color = "red",
    stroke = FALSE,
    fillOpacity = 0.6,
    popup = ~paste0(
      "", CityName, "",
      "Average Rate: ", round(Data_Value, 1), "%"
      ),
    label = ~paste0(CityName, ": ", round(Data_Value, 1), "%"), 
    labelOptions = labelOptions(
      direction = "auto",
      textsize = "14px",
      opacity = 0.8,
      offset = c(0, -5)
      )
    ) |>
  setView(lng = -72.7, lat = 41.6, zoom = 8)

5. Write a paragraph

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

The two plots provide complementary ways of examining unhealthy behaviors in Connecticut cities. The bar chart makes it easy to see which cities have higher or lower rates of the selected behavior, allowing for straightforward comparisons across the state. For example, it highlights which cities have the highest prevalence and which have lower rates, giving a sense of the overall distribution. The map adds a geographic perspective, showing each city in its actual location and using the size of the circle markers to reflect the prevalence. Hovering over a city displays a tooltip with the exact percentage, and clicking on a marker provides more detailed information. Together, these visualizations not only show the differences in behavior rates between cities but also reveal the geographic patterns, helping to understand where certain unhealthy behaviors are more common and potentially guiding public health efforts.

refrence : textbook and https://rstudio.github.io/leaflet/articles/markers.html