Healthy Cities GIS Assignment

Author

Oliver Kronen

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
setwd("C:/Users/MyPC/Downloads/Data 110")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")
data(cities500)

“C:/Users/rsaidi/Dropbox/Rachel/MontColl/Datasets/Datasets”)

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)

clean_oliver <- latlong |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(StateAbbr == "CO") |>
  filter(Category == "Prevention") |>
  filter(Year == "2016") |>
  filter(GeographicLevel == "Census Tract") |>
  filter(CityName == "Denver")
head(clean_oliver)
# A tibble: 6 × 25
   Year StateAbbr StateDesc CityName GeographicLevel DataSource Category  
  <dbl> <chr>     <chr>     <chr>    <chr>           <chr>      <chr>     
1  2016 CO        Colorado  Denver   Census Tract    BRFSS      Prevention
2  2016 CO        Colorado  Denver   Census Tract    BRFSS      Prevention
3  2016 CO        Colorado  Denver   Census Tract    BRFSS      Prevention
4  2016 CO        Colorado  Denver   Census Tract    BRFSS      Prevention
5  2016 CO        Colorado  Denver   Census Tract    BRFSS      Prevention
6  2016 CO        Colorado  Denver   Census Tract    BRFSS      Prevention
# 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>
cleaner_oliver <- clean_oliver |>
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(cleaner_oliver)
# A tibble: 6 × 18
   Year StateAbbr StateDesc CityName GeographicLevel Category   UniqueID Measure
  <dbl> <chr>     <chr>     <chr>    <chr>           <chr>      <chr>    <chr>  
1  2016 CO        Colorado  Denver   Census Tract    Prevention 0820000… "Visit…
2  2016 CO        Colorado  Denver   Census Tract    Prevention 0820000… "Fecal…
3  2016 CO        Colorado  Denver   Census Tract    Prevention 0820000… "Papan…
4  2016 CO        Colorado  Denver   Census Tract    Prevention 0820000… "Older…
5  2016 CO        Colorado  Denver   Census Tract    Prevention 0820000… "Fecal…
6  2016 CO        Colorado  Denver   Census Tract    Prevention 0820000… "Older…
# 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

graph <- ggplot(cleaner_oliver, aes(x = MeasureId, y = Data_Value, color = PopulationCount)) +
  geom_jitter(alpha = 0.9) +
  scale_color_gradient(low = "lightgreen", high = "darkgreen") +
  theme_minimal() +
  labs(x = "Measure of Health", y = "Frequency in Percentage", title = "Percentage of Health Conditions in Denver Colorado", color = "Population Size", caption = "Source : 500 Cities Project 2016-2019")
graph

3. Now create a map of your subsetted dataset.

First map chunk here

library(leaflet)
Warning: package 'leaflet' was built under R version 4.5.3
one_option <- cleaner_oliver |> # Filter for only one Measure ID
  filter(MeasureId == "DENTAL")

leaflet() |>
  setView(lng = -104.9903, lat = 39.7392, zoom = 11) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data = one_option,
    radius = one_option$PopulationCount/10,
    color = "darkgreen",
    opacity = .5
  )
Assuming "long" and "lat" are longitude and latitude, respectively

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

Refined map chunk here

popper <- paste0(
  "<b> Dental Health Percentage:", "", one_option$Data_Value, "%", "<br>"
)

leaflet() |>
  setView(lng = -104.9903, lat = 39.7392, zoom = 11) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data = one_option,
    radius = one_option$PopulationCount/10,
    color = "darkgreen",
    opacity = .5,
    popup = popper
  )
Assuming "long" and "lat" are longitude and latitude, respectively

5. Write a paragraph

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

In the first graph, I made a scatter plot showcasing the percentage of health conditions seen across different population sizes in Denver Colorado. Using the jitter function, we can see that the dental condition has the widest range of values among all the different factors. Knowing this, I will explore this further in the next two graphs. The second graph is a map showcasing the population size of dental conditions in different areas around Denver Colorado. This graph highlights how the density of populations increases as you progress closer and closer to the center of the city. On the other hand, as we move away from the center, the population size increases, but becomes less clustered. In the third graph, we can hover over the dots to view the percentage of dental health problems faced by citizens in that specific area. The percentage value tells us that x% of individuals in that region deal with some form of dental problem. For example, the very bottom left circle has a percentage of 70.8%. This means 70.8% of individuals in that area suffer from dental issues.