Healthy Cities GIS Assignment

Author

Your Name

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)

setwd("C:/Users/mafok/OneDrive/Desktop/Data 110")
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

latlong_clean <- latlong %>%
  filter(StateDesc %in% c("California", "Texas", "New York", "Illinois", "Florida")) %>%
  filter(Data_Value_Type == "Crude prevalence") %>%
  filter(Year == 2017)
latlong_clean2 <- latlong_clean %>%
  select(-DataSource, -Data_Value_Unit, -DataValueTypeID, 
         -Low_Confidence_Limit, -High_Confidence_Limit, 
         -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
subset_data <- latlong_clean2 %>% sample_n(900)

head(subset_data)
# A tibble: 6 × 18
   Year StateAbbr StateDesc  CityName  GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>      <chr>     <chr>           <chr>    <chr>    <chr>  
1  2017 CA        California Orange    Census Tract    Health … 0653980… Arthri…
2  2017 NY        New York   New York  Census Tract    Health … 3651000… Corona…
3  2017 TX        Texas      Plano     Census Tract    Prevent… 4858016… Choles…
4  2017 CA        California San Bern… Census Tract    Health … 0665000… Physic…
5  2017 CA        California Los Ange… Census Tract    Health … 0644000… Cancer…
6  2017 IL        Illinois   Bolingbr… Census Tract    Prevent… 1707133… 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>

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

First plot chunk here

prevalence_hist <- ggplot(subset_data, aes(x = Data_Value)) +
  geom_histogram(fill = "#66c3a5", color = "pink", bins = 30) +
  labs(title = "Distribution of Crude Prevalence Rates (2017)",
       x = "Crude Prevalence",
       y = "Count") +
  theme_minimal()
prevalence_hist
Warning: Removed 31 rows containing non-finite outside the scale range
(`stat_bin()`).

3. Now create a map of your subsetted dataset.

First map chunk here

map_plot <- ggplot(subset_data, aes(x = long, y = lat)) +
  geom_point(color = "#fc8d92", size = 2) +
  coord_quickmap() +
  labs(title = "Geographic Distribution of Crude Prevalence (2017)",
       x = "Longitude",
       y = "Latitude") +
  theme_minimal()
map_plot

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

Refined map chunk here

5. Write a paragraph

In a paragraph, describe the plots you created and what they show. the first plot (a histogram) shows how the crude prevalence values are distributed among the cities in our subset. This helps us see the frequency of different prevalence rates. The basic map created with ggplot2 shows the geographic locations of the cities using their longitude and latitude. Finally, the refined interactive map using leaflet adds clickable circle markers to each location. When you click on a marker, a small popup appears displaying the city name and the corresponding crude prevalence value. Together, these visuals help us understand both the statistical and spatial distribution of a health indicator (crude prevalence) in the selected states.