Healthy Cities GIS Assignment

Author

Your Name

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
setwd("~/Data 110 Class Folder")
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)
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 Hawthorne City            BRFSS      Unhealthy Beh…
3  2017 CA        California Hayward   City            BRFSS      Unhealthy Beh…
4  2017 CA        California Indio     Census Tract    BRFSS      Health Outcom…
5  2017 CA        California Inglewood Census Tract    BRFSS      Health Outcom…
6  2017 CA        California Lakewood  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 Census Tract    Health … 0632548… Arthri…
2  2017 CA        California Hawthorne City            Unhealt… 632548   Curren…
3  2017 CA        California Hayward   City            Unhealt… 633000   Obesit…
4  2017 CA        California Indio     Census Tract    Health … 0636448… Arthri…
5  2017 CA        California Inglewood Census Tract    Health … 0636546… Diagno…
6  2017 CA        California Lakewood  City            Unhealt… 639892   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 in this format, filter this dataset however you choose so that you have a subset with no more than 900 observations.

Filter chunk here

#library(dplyr)
midAtlantic <- latlong_clean2 |> 
  filter(StateAbbr == "PA") |>
  filter(Category == "Unhealthy Behaviors") |>
  filter(MeasureId == "CSMOKING")

#n_distinct(midAtlantic$CityName)
#unique(midAtlantic$MeasureId)

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

First plot chunk here

ggplot(midAtlantic, aes(x = CityName, y = Data_Value)) +
  geom_col() +
  labs(title = "Percentage of Smokers by City in PA",
       x = "City",
       y = "Percent Smokers") +
  theme()
Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_col()`).

3. Now create a map of your subsetted dataset.

First map chunk here

library(leaflet)
Warning: package 'leaflet' was built under R version 4.4.3
leaflet(data = midAtlantic) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(lng = ~long, lat = ~lat, radius = 500,
             color = "black", fillOpacity = 0.25)

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

Refined map chunk here

popup_info <- paste0(
  "<b>City: </b>", midAtlantic$CityName, "<br>",
  "<b>State: </b>", midAtlantic$StateDesc, "<br>",
  "<b>Percentage: </b>", midAtlantic$Data_Value, "%"
)

leaflet(data = midAtlantic) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(lng = ~long, lat = ~lat, radius = 500,
             color = "brown", fillOpacity = 0.4,
             popup = popup_info)

5. Write a paragraph

For my assignment I wanted to look at smoking data. I originally had to play around a lot with where I was going to look, because states that had few enough data points to be under the 900 threshold also tended to have only 1 or 2 locations where data was present, making mapping harder. Eventually, I landed on Pennsylvania because it had the most locations with under 900 points. I am still not super happy with the data, because it is only from a few larger cities in the state, and several large communities like Harrisburg, the state capital and 3rd largest metro area, are left out. Another problem with the data is that it is only reported on the city level, not on the neighborhood or even zip code level. I’m sure depending on where you go in a city, especially ones with such high populations like Philadelphia and Pittsburgh, you will get massive disparity in different parts of the city for what percentage of people smoke. Factors such as socioeconomic status of the area, what jobs people have, laws and regulations around the price and purchasing of cigarettes can and are all factors and are hard to disseminate on a city level in many cases.