Healthy Cities GIS Assignment

Load the libraries and set the working directory

getwd()

[1] "C:/Users/Nathaniel/DATA110"

library(tidyverse)
library(tidyr)
library(knitr)
library(leaflet)
library(sf)
setwd("C:/Users/Nathaniel/DATA110")
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.

Filter chunk here

unique(latlong_clean$StateAbbr)

 [1] "AL" "CA" "FL" "CT" "IL" "MN" "NY" "PA" "NC" "OH" "OK" "OR" "TX" "RI" "SC"
[16] "SD" "TN" "UT" "VA" "WA" "AK" "WI" "AZ" "AR" "CO" "DE" "NV" "DC" "GA" "ID"
[31] "HI" "MA" "MI" "IN" "KS" "KY" "IA" "LA" "MD" "ME" "NH" "NJ" "NM" "MO" "MS"
[46] "NE" "MT" "ND" "WV" "VT" "WY"

filter(prevention, Data_Value > 70) |>
filter(StateAbbr == "MD")

# A tibble: 521 × 18
    Year StateAbbr StateDesc CityName  GeographicLevel Category UniqueID Measure
   <dbl> <chr>     <chr>     <chr>     <chr>           <chr>    <chr>    <chr>  
 1  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Choles…
 2  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Visits…
 3  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Visits…
 4  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Taking…
 5  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Choles…
 6  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Taking…
 7  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Visits…
 8  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Choles…
 9  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Taking…
10  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… Taking…
# ℹ 511 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>

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

First plot chunk here

ggplot(prevention, aes(x = PopulationCount, y = Data_Value, color = StateDesc)) +
  geom_point() +
  scale_color_viridis_d() +
  geom_jitter() +
  labs(title = "Data Value and Population for Each State",
       xlabs = "Population",
       ylabs = "Data Value") +
  theme_bw()

Warning: Removed 3144 rows containing missing values (`geom_point()`).
Removed 3144 rows containing missing values (`geom_point()`).

3. Now create a map of your subsetted dataset.

First map chunk here

strong <- prevention |>
  filter(Data_Value > 50)

leaflet() |>
  setView(lng = -76.6, lat = 39.0, zoom = 6) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data = strong,
    radius = strong$Data_Value
  )

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

4. Refine your map to include a mousover tooltip

Refined map chunk here

pop <- paste0(
 "<b>Population: </b>", strong$PopulationCount, "<br>",
 "<b>DataValue: </b>", strong$Data_Value, "<br>",
 "<b>State: </b>", strong$StateDesc, "<br>"
 )

leaflet() |>
  setView(lng = -76.6, lat = 39.0, zoom = 6) |>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    data = strong,
    radius = strong$Data_Value,
    color = "#14010d",
    fillColor = "#f2079c",
    popup = pop
  )

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

5. Write a paragraph

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

In this project I used the tutorial provided by Professor Saidi and the csv file also provided by Professor Saidi to create a visualization and practice my mapping skills. In this mapping exercise, I chose to use the PopulationCount, Data_Value, and StateDesc variables to create my visualization. In the scatter plot, it shows the data values and population per state. In the mapping visualization, it shows the locations of all the observations were present.