Healthy Cities GIS Assignment

Author

Michael Desir

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
library(plotly)
setwd("C:/Users/desir_7411ic3/Desktop/Montgomery College/DATA110/DATASETS-20240830T194929Z-001/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(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>

unique(md$CityName)

[1] "Baltimore"

The new dataset “Prevention” is a manageable dataset now.

For your assignment, work with a cleaned dataset.

1. Once you run the above code, filter this dataset one more time for any particular subset with no more than 900 observations.

Filter chunk here

hw1 <- prevention %>%
  filter(StateAbbr == "LA" & Short_Question_Text == "Cholesterol Screening")
hw1

# A tibble: 383 × 18
    Year StateAbbr StateDesc CityName  GeographicLevel Category UniqueID Measure
   <dbl> <chr>     <chr>     <chr>     <chr>           <chr>    <chr>    <chr>  
 1  2017 LA        Louisiana Baton Ro… Census Tract    Prevent… 2205000… Choles…
 2  2017 LA        Louisiana Lake Cha… Census Tract    Prevent… 2241155… Choles…
 3  2017 LA        Louisiana New Orle… Census Tract    Prevent… 2255000… Choles…
 4  2017 LA        Louisiana Baton Ro… Census Tract    Prevent… 2205000… Choles…
 5  2017 LA        Louisiana Baton Ro… Census Tract    Prevent… 2205000… Choles…
 6  2017 LA        Louisiana Kenner    Census Tract    Prevent… 2239475… Choles…
 7  2017 LA        Louisiana New Orle… Census Tract    Prevent… 2255000… Choles…
 8  2017 LA        Louisiana New Orle… Census Tract    Prevent… 2255000… Choles…
 9  2017 LA        Louisiana Lafayette Census Tract    Prevent… 2240735… Choles…
10  2017 LA        Louisiana Lake Cha… Census Tract    Prevent… 2241155… Choles…
# ℹ 373 more rows
# 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>

tract_only <- hw1 %>%
  filter(GeographicLevel == "Census Tract")
city_only <- hw1 %>%
  filter(GeographicLevel == "City")

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

First plot chunk here

hw2 <- ggplot(tract_only, aes(CityName, Data_Value, color = CityName)) +
  geom_point(shape=17, size = 3) +
  scale_color_brewer(palette = "Dark2") +
  labs(x = "City Name",
       y = "% Value",
       title = "Est. % Adult Use of Cholesterol Screening Services",
       subtitle = "Each shape represents a census tract in the noted city. This plot finds that the economically 
       diverse cities of Baton Rouge and New Orleans most likely contained census tracts with low 
       use of cholesterol screening services. Smaller, wealthier cities saw higher utilization rates.",
       color = "City Name") +
  theme(
    plot.background = element_rect(fill = "lightgrey"),
    panel.background = element_rect(fill = "grey"),
    axis.title = element_text(face = 2),
    legend.background = element_rect(fill = "lightgrey"),
    legend.title = element_text(color = "black", size = 14),
    legend.text = element_text(color = "black", size = 11),
    legend.key.size = unit(0.75, units = "cm"),
    panel.grid = element_line(color = "darkgrey")
  )
hw2

Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).

3. Now create a map of your subsetted dataset.

Load libraries for mapping

library(leaflet)
library(sf)
library(knitr)

First map chunk here

map_plot <- leaflet() |>
  setView(lat = 31.18, lng = -91.87, zoom = 7) |>
  addProviderTiles("CartoDB.DarkMatter") |>
  addCircles(
    data = tract_only,
    radius = sqrt(10^(tract_only$Data_Value/22)) *5,
    color = "#ff7f50"
  )

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

map_plot

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

Refined map chunk here

map_popup <- paste0(
  "<b>City: </b>", tract_only$CityName, "<br>",
  "<b>Census Tract: </b>", tract_only$UniqueID, "<br>",
  "<b>Data Value (%): </b>", tract_only$Data_Value, "<br>",
  "<strong>Population: </strong>", tract_only$PopulationCount, "<br>"
)
map_w_popup <- leaflet() |>
  setView(lat = 31.18, lng = -91.87, zoom = 7) |>
  addProviderTiles("CartoDB.DarkMatter") |>
  addCircles(
    data = tract_only,
    radius = sqrt(10^(tract_only$Data_Value/23)) *5,
    color = "#ff7f50",
    popup = map_popup
  )

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

map_w_popup

5. Write a paragraph

I decided to extract from the “Prevention” dataset info from Louisiana as it regards screening for cholesterol. What I noted was that the state’s major cities (Baton Rouge and New Orleans) had a wider range of data values. This indicates that these cities contained census tracts where the population, for reasons unspecified, did not make use of cholesterol screening services. In smaller, more affluent cities, the data notes higher usage of cholesterol screening services. In my mapped plot, I used the equation to do my best to show the differences in data values.