Healthy Cities GIS Assignment

Author

Carla

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
setwd("C:/Users/misst/OneDrive/Documents")
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 “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)

cleanedupdata <- latlong |>  
  filter(StateAbbr == "CT") |>  
  filter(Year == 2017) |> 
  filter(Data_Value_Type == "Crude prevalence") |>  
  filter(Measure == "Diagnosed diabetes among adults aged >=18 Years") |>
  filter(GeographicLevel == "Census Tract")|>
  filter(!is.na(Data_Value)) |>  
  filter(CityName %in% c("Bridgeport", "Danbury", "Norwalk", "Hartford", "Waterbury")) |>  
  select(CityName, PopulationCount, Data_Value, lat, long, Category, Measure) 
head(cleanedupdata)

# A tibble: 6 × 7
  CityName   PopulationCount Data_Value   lat  long Category        Measure     
  <chr>                <dbl>      <dbl> <dbl> <dbl> <chr>           <chr>       
1 Bridgeport            1693        6.5  41.2 -73.2 Health Outcomes Diagnosed d…
2 Bridgeport            4113       10.9  41.2 -73.2 Health Outcomes Diagnosed d…
3 Waterbury             6221       10.2  41.5 -73.1 Health Outcomes Diagnosed d…
4 Danbury               6046        8.1  41.4 -73.5 Health Outcomes Diagnosed d…
5 Hartford              5998       11.8  41.7 -72.7 Health Outcomes Diagnosed d…
6 Hartford              1855       13.5  41.8 -72.7 Health Outcomes Diagnosed d…

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

First plot chunk here

# non map plot

ggplot(cleanedupdata, aes(x = CityName, y = Data_Value, fill = CityName)) +
  geom_boxplot() +
  scale_fill_manual(values = c(
    "Bridgeport" = "red",
    "Danbury" = "orange",
    "Norwalk" = "yellow",
    "Hartford" = "green",
    "Waterbury" = "blue"
  )) +
  labs(
    title = "Comparison of Diabetes Prevalence Across Cities In Conneticut",
    x = "City",
    y = "Prevalence (%)"
  ) +
  theme_minimal()

3. Now create a map of your subsetted dataset.

First map chunk here

# leaflet()

library(leaflet)

leaflet(cleanedupdata) |>
  setView(lng = mean(cleanedupdata$long, na.rm = TRUE),
          lat = mean(cleanedupdata$lat, na.rm = TRUE),
          zoom = 5) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data=cleanedupdata,
    radius = ~Data_Value * 2500,
    color = "purple",
    fillColor = "blue",
    fillOpacity = 0.5
  )

Assuming "long" and "lat" are longitude and latitude, respectively

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

Refined map chunk here

popupdata <- paste0(
  "<b>City: </b>", cleanedupdata$CityName, "<br>",
  "<b>Diabetes (%): </b>", cleanedupdata$Data_Value, "<br>",
  "<b>Category: </b>", cleanedupdata$Category, "<br>",
  "<b>Measure: </b>", cleanedupdata$Measure, "<br>"
)

# leaflet()

finishedleaflet <- leaflet(cleanedupdata) |>
  setView(lng = mean(cleanedupdata$long, na.rm = TRUE),
          lat = mean(cleanedupdata$lat, na.rm = TRUE),
          zoom = 5) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data=cleanedupdata,
    radius = ~Data_Value * 2500,
    color = "purple",
    fillColor = "blue",
    fillOpacity = 0.5,
    popup = popupdata
  )

Assuming "long" and "lat" are longitude and latitude, respectively

finishedleaflet

5. Write a paragraph

I chose to analyze the state of Connecticut since in a previous research in a different class I previously took I discovered this state is known for having Food Deserts. Food Deserts are areas known to have scarce shopping areas like grocery stores where people can access fresh healthy foods, which limits people from having a stable access of beneficial dietary food. Consequently, forces the people in these areas to search for other areas which they can access food even if it means resorting to eating unhealthy food which is the case for many families living in these areas, resulting in children and adults developing diseases like diabetes. I chose to filter this data using the prevalence of Diabetes in the cities of Bridgeport, Danbury, Norwalk, Hartford, and Waterbury for people above the age of 18 that reported population developing Diabetes. The graph highlights high concentrations of reports of Diabetes across the cities that I chose to filter, showing there is a correlation between the amount of diabetes reported in this state and the cities in which are being compared as states in the southern areas of Connecticut have a lower percentage in comparison to the northern states that have a higher percentage.