Healthy Cities GIS Assignment

Author

Bryan Argueta

Load the libraries and set the working directory

library(tidyverse)
library(RColorBrewer)
library(leaflet)
setwd("/Users/bryana/Documents/Data110/Datasets")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

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(Category == "Prevention") |>
  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 AL        Alabama    Montgomery City            BRFSS      Prevention
2  2017 CA        California Concord    City            BRFSS      Prevention
3  2017 CA        California Concord    City            BRFSS      Prevention
4  2017 CA        California Fontana    City            BRFSS      Prevention
5  2017 CA        California Richmond   Census Tract    BRFSS      Prevention
6  2017 FL        Florida    Davie      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>

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

prevention <- latlong_clean |>
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(prevention)

# A tibble: 6 × 18
   Year StateAbbr StateDesc  CityName  GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>      <chr>     <chr>           <chr>    <chr>    <chr>  
1  2017 AL        Alabama    Montgome… City            Prevent… 151000   Choles…
2  2017 CA        California Concord   City            Prevent… 616000   Visits…
3  2017 CA        California Concord   City            Prevent… 616000   Choles…
4  2017 CA        California Fontana   City            Prevent… 624680   Visits…
5  2017 CA        California Richmond  Census Tract    Prevent… 0660620… Choles…
6  2017 FL        Florida    Davie     Census Tract    Prevent… 1216475… Choles…
# 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>

md <- prevention |>
  filter(StateAbbr=="MD")
head(md)

# A tibble: 6 × 18
   Year StateAbbr StateDesc CityName  GeographicLevel Category  UniqueID Measure
  <dbl> <chr>     <chr>     <chr>     <chr>           <chr>     <chr>    <chr>  
1  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Chole…
2  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Visit…
3  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Visit…
4  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Curre…
5  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Curre…
6  2017 MD        Maryland  Baltimore Census Tract    Preventi… 2404000… "Visit…
# 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 the cleaned “Prevention” dataset

1. Once you run the above code, filter this dataset one more time for any particular subset.

I want to explore unhealthy behaviors in the DMV area

latlong_clean2 <- latlong |>
  filter(StateAbbr == "MD" | StateAbbr == "DC" | StateAbbr == "VA") |>
  filter(Category == "Unhealthy Behaviors") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2017)

I’m going to clean up the unnecessary columns from the data frame

unhealthy <- latlong_clean2 |>
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote, -CityFIPS, -TractFIPS, -UniqueID)

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

To create the bar graph I need to figure out how many people in the population do the unhealthy behavior. I can use the percentage and population count to figure this out.

unhealthy$NumberOfPeople <- unhealthy$PopulationCount * (unhealthy$Data_Value / 100)

ggplot(unhealthy, aes(x = StateAbbr, y = NumberOfPeople, fill = Short_Question_Text)) +
  geom_bar(stat = "identity", position = "dodge") +
  theme_classic() +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "Unhealthy Behaviors in the DMV during 2017", x = "US State", y = "Number of People", fill = "Unhealthy Behavior")

Warning: Removed 16 rows containing missing values (`geom_bar()`).

3. Now create a map of your subsetted dataset.

I want to plot the obesity unhealthy behavior but only focus on Maryland.

md_unhealthy <- unhealthy |>
  filter(StateAbbr == "MD") |>
  filter(MeasureId == "OBESITY")

leaflet() |>
  setView(lng = -76.6, lat = 39.3, zoom = 11.5) |>
  addProviderTiles("Esri.NatGeoWorldMap") |>
  addCircles(
  data = md_unhealthy,
  radius = md_unhealthy$Data_Value * 5,
  color = "#14010d",
  fillColor = "#7393B3",
  fillOpacity = 0.25
)

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

4. Refine your map to include a mouseover tooltip

Refined map chunk here

popupmd <- paste0(
  "<b>Year:  </b>", md_unhealthy$Year, "<br>",
  "<b>Unhealthy Behavior:  </b>", md_unhealthy$Measure, "<br>",
  "<b>Population:  </b>", md_unhealthy$PopulationCount, "<br>",
  "<b>People Particpiating in Bad Behavior:  </b>", md_unhealthy$NumberOfPeople, "<br>",
  "<b>Percentage Particpiating in Bad Behavior:  </b>", md_unhealthy$Data_Value, "<br>"

)

leaflet() |>
  setView(lng = -76.6, lat = 39.3, zoom = 11.5) |>
  addProviderTiles("Esri.NatGeoWorldMap") |>
  addCircles(
  data = md_unhealthy,
  radius = md_unhealthy$Data_Value * 4,
  color = "#14010d",
  fillColor = "#7393B3",
  fillOpacity = 0.25,
  popup = popupmd
)

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

5. Write a paragraph

In my first plot, I created a bar graph illustrating the differences in unhealthy behaviors for the DMV. The graph shows that across the DMV, there are more obese people than binge drinkers, physically inactive people, and smokers. Maryland also has the highest number of obese people, physically inactive people, and smokers when compared to the DC and Virginia In my second plot, I created a map plotting where the highest number of obese people are in Baltimore, Marlyland. The map shows that the city is where the highest concentration of obese people are. This is to be expected since there are more people in the city than in the areas surrounding it.