500 Healthy Cities GIS Assignment

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
library(leaflet)
library(webshot2)
setwd("~/Documents/Data 110")
cities500 <- read_csv("500LocalCities.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

Official Subsetted Data Set

latlong_5 <- latlong |>
  filter(!is.na(latlong$PopulationCount) & !is.na(latlong$Data_Value)) |>
  filter(StateDesc != "United States") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2016) |>
  filter(Category == "Prevention") |>
  filter(GeographicLevel == "Census Tract") |>
  filter(Short_Question_Text %in% c("Mammography", "Pap Smear Test")) |>
  filter(StateDesc %in% c("Texas"))|>
  head(900) |>
mutate(prev_level = case_when(
    Data_Value >= 67.8 & Data_Value <= 77.8 ~ "Low",
    Data_Value > 77.8 & Data_Value <= 79.9 ~ "Medium",
    Data_Value > 79.9 ~ "High"))

latlong_5 <- latlong_5 |> 
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote, -MeasureId, -StateAbbr, -UniqueID, -CityFIPS, -CategoryID, -TractFIPS, -Measure)

fivenum(latlong_5$Data_Value)

[1] 67.8 77.8 79.9 81.7 86.8

head(latlong_5)

# A tibble: 6 × 12
   Year StateDesc CityName   GeographicLevel Category Data_Value_Type Data_Value
  <dbl> <chr>     <chr>      <chr>           <chr>    <chr>                <dbl>
1  2016 Texas     Houston    Census Tract    Prevent… Crude prevalen…       83.6
2  2016 Texas     Houston    Census Tract    Prevent… Crude prevalen…       75.8
3  2016 Texas     Amarillo   Census Tract    Prevent… Crude prevalen…       74.7
4  2016 Texas     Arlington  Census Tract    Prevent… Crude prevalen…       78.7
5  2016 Texas     Beaumont   Census Tract    Prevent… Crude prevalen…       80.2
6  2016 Texas     Brownsvil… Census Tract    Prevent… Crude prevalen…       76.4
# ℹ 5 more variables: PopulationCount <dbl>, lat <dbl>, long <dbl>,
#   Short_Question_Text <chr>, prev_level <chr>

latlong_4 <- latlong |>
  filter(!is.na(latlong$PopulationCount) & !is.na(latlong$Data_Value)) |>
  filter(StateDesc != "United States") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2016) |>
  filter(Category == "Prevention") |>
  filter(GeographicLevel == "Census Tract") |>
  filter(Short_Question_Text %in% c("Mammography", "Pap Smear Test")) |>
  filter(StateDesc %in% c("California"))|>
  head(900) |>
  mutate(prev_level = case_when(
    Data_Value >= 72.50 & Data_Value < 79 ~ "Low",
    Data_Value >= 79 & Data_Value < 83.15 ~ "Medium",
    Data_Value >=83.15 ~ "High"))

latlong_4 <- latlong_4 |> 
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote, -MeasureId, -StateAbbr, -UniqueID, -CityFIPS, -CategoryID, -TractFIPS, -Measure)


latlong_4

# A tibble: 900 × 12
    Year StateDesc  CityName GeographicLevel Category Data_Value_Type Data_Value
   <dbl> <chr>      <chr>    <chr>           <chr>    <chr>                <dbl>
 1  2016 California Buena P… Census Tract    Prevent… Crude prevalen…       78.1
 2  2016 California Concord  Census Tract    Prevent… Crude prevalen…       79.7
 3  2016 California Richmond Census Tract    Prevent… Crude prevalen…       80.8
 4  2016 California Tracy    Census Tract    Prevent… Crude prevalen…       80.7
 5  2016 California Bakersf… Census Tract    Prevent… Crude prevalen…       80.8
 6  2016 California Anaheim  Census Tract    Prevent… Crude prevalen…       80  
 7  2016 California Anaheim  Census Tract    Prevent… Crude prevalen…       80.4
 8  2016 California Alameda  Census Tract    Prevent… Crude prevalen…       82.5
 9  2016 California Anaheim  Census Tract    Prevent… Crude prevalen…       77.5
10  2016 California Anaheim  Census Tract    Prevent… Crude prevalen…       82.4
# ℹ 890 more rows
# ℹ 5 more variables: PopulationCount <dbl>, lat <dbl>, long <dbl>,
#   Short_Question_Text <chr>, prev_level <chr>

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

First plot chunk here

  ggplot(latlong_4, aes(x = Data_Value, y = PopulationCount, color = Short_Question_Text)) +
  scale_color_manual(values = c("Mammography" = "purple", "Pap Smear Test" = "hotpink")) +
  geom_point(alpha = 0.3, size = 5) +
  labs(title = "Prevalence of Preventative Healthcare for Women, by Census Tracts in California",
       subtitle = "In the Year 2016",
       caption = "Source: CDC",
       x = "Preventative Care Prevalence (%)",
       y = "Population",
       color = "Type of Preventative Care") +
  theme_bw()

ggplot(latlong_5, aes(x = Data_Value, y = PopulationCount, color = Short_Question_Text)) +
  scale_color_manual(values = c("Mammography" = "green", "Pap Smear Test" = "pink")) +
  geom_point(alpha = 0.3, size = 5) +
  labs(title = "Prevalence of Preventative Healthcare for Women, by Census Tracts in Texas",
       subtitle = "In the Year 2016",
       caption = "Source: CDC",
       x = "Preventative Care Prevalence (%)",
       y = "Population",
       color = "Type of Preventative Care") +
  theme_bw()

For Fun

latlong_me <- latlong |>
  filter(StateDesc != "United States") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2016) |>
  filter(Category == "Prevention") |>
  filter(GeographicLevel == "Census Tract") |>
  filter(Short_Question_Text %in% c("Mammography", "Pap Smear Test")) 

latlong_me <- latlong_me |> 
  select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote, -MeasureId, -StateAbbr, -UniqueID, -CityFIPS, -CategoryID, -TractFIPS, -Measure)
   

latlong_me

# A tibble: 55,635 × 11
    Year StateDesc  CityName GeographicLevel Category Data_Value_Type Data_Value
   <dbl> <chr>      <chr>    <chr>           <chr>    <chr>                <dbl>
 1  2016 Alabama    Hoover   Census Tract    Prevent… Crude prevalen…       81.9
 2  2016 Alaska     Anchora… Census Tract    Prevent… Crude prevalen…       74  
 3  2016 Arizona    Glendale Census Tract    Prevent… Crude prevalen…       81.2
 4  2016 Arizona    Phoenix  Census Tract    Prevent… Crude prevalen…       78.7
 5  2016 California Buena P… Census Tract    Prevent… Crude prevalen…       78.1
 6  2016 California Concord  Census Tract    Prevent… Crude prevalen…       79.7
 7  2016 California Richmond Census Tract    Prevent… Crude prevalen…       80.8
 8  2016 California Tracy    Census Tract    Prevent… Crude prevalen…       80.7
 9  2016 Indiana    Bloomin… Census Tract    Prevent… Crude prevalen…       75  
10  2016 Indiana    Muncie   Census Tract    Prevent… Crude prevalen…       73.2
# ℹ 55,625 more rows
# ℹ 4 more variables: PopulationCount <dbl>, lat <dbl>, long <dbl>,
#   Short_Question_Text <chr>

latlong_top <- latlong_me |>
  group_by(StateDesc) |>
  slice_max(order_by = PopulationCount, n = 1, with_ties = FALSE) |>
  arrange(-PopulationCount) |>
  head(5)

latlong_top

# A tibble: 5 × 11
# Groups:   StateDesc [5]
   Year StateDesc   CityName GeographicLevel Category Data_Value_Type Data_Value
  <dbl> <chr>       <chr>    <chr>           <chr>    <chr>                <dbl>
1  2016 California  Chula V… Census Tract    Prevent… Crude prevalen…       80.6
2  2016 New York    New York Census Tract    Prevent… Crude prevalen…       87.7
3  2016 Florida     Miramar  Census Tract    Prevent… Crude prevalen…       84.7
4  2016 Virginia    Norfolk  Census Tract    Prevent… Crude prevalen…       83.8
5  2016 South Caro… Mount P… Census Tract    Prevent… Crude prevalen…       84.8
# ℹ 4 more variables: PopulationCount <dbl>, lat <dbl>, long <dbl>,
#   Short_Question_Text <chr>

latlong_bott <- latlong_me |>
  filter(PopulationCount > 4000)|>
  group_by(StateDesc) |>
  slice_min(order_by = PopulationCount, n = 1, with_ties = FALSE) |>
  arrange(PopulationCount)|>
  head(5)

latlong_bott

# A tibble: 5 × 11
# Groups:   StateDesc [5]
   Year StateDesc   CityName GeographicLevel Category Data_Value_Type Data_Value
  <dbl> <chr>       <chr>    <chr>           <chr>    <chr>                <dbl>
1  2016 Arizona     Mesa     Census Tract    Prevent… Crude prevalen…       78.7
2  2016 California  Los Ang… Census Tract    Prevent… Crude prevalen…       84.1
3  2016 North Caro… Charlot… Census Tract    Prevent… Crude prevalen…       81.1
4  2016 Florida     Miami    Census Tract    Prevent… Crude prevalen…       86  
5  2016 Illinois    Evanston Census Tract    Prevent… Crude prevalen…       78.9
# ℹ 4 more variables: PopulationCount <dbl>, lat <dbl>, long <dbl>,
#   Short_Question_Text <chr>

combined_latlong <- rbind(latlong_bott, latlong_top) 


  combined_latlong <- combined_latlong |>
    mutate(rep_lib = "Character") 
  
  combined_latlong[1,12] = "Republican" 
  combined_latlong[2,12] = "Liberal" 
  combined_latlong[3,12] = "Republican" 
  combined_latlong[4,12] = "Republican" 
  combined_latlong[5,12] = "Liberal" 
  combined_latlong[6,12] = "Liberal" 
  combined_latlong[7,12] = "Liberal" 
  combined_latlong[8,12] = "Republican" 
  combined_latlong[9,12] = "Liberal" 
  combined_latlong[10,12] = "Republican" 
  
  combined_latlong <- combined_latlong |>
  mutate(CityName=recode(CityName,
                         'Mesa' = 'Mesa, Arizona',
                         'Los Angeles' = 'Los Angeles, California',
                         'Charlotte' = 'Charlotte, North Carolina',
                         'Miami' = 'Miami, Florida',
                         'Evanston' = 'Evanston, Illinois',
                         'Chula Vista' = 'Chula Vista, California',
                         'New York' = 'New York, New York',
                         'Miramar' = 'Miramar, Florida',
                         'Norfolk' = 'Norfolk, Virginia',
                         'Mount Pleasant' = 'Mount Pleasant, South Carolina'))

head(combined_latlong)

# A tibble: 6 × 12
# Groups:   StateDesc [5]
   Year StateDesc   CityName GeographicLevel Category Data_Value_Type Data_Value
  <dbl> <chr>       <chr>    <chr>           <chr>    <chr>                <dbl>
1  2016 Arizona     Mesa, A… Census Tract    Prevent… Crude prevalen…       78.7
2  2016 California  Los Ang… Census Tract    Prevent… Crude prevalen…       84.1
3  2016 North Caro… Charlot… Census Tract    Prevent… Crude prevalen…       81.1
4  2016 Florida     Miami, … Census Tract    Prevent… Crude prevalen…       86  
5  2016 Illinois    Evansto… Census Tract    Prevent… Crude prevalen…       78.9
6  2016 California  Chula V… Census Tract    Prevent… Crude prevalen…       80.6
# ℹ 5 more variables: PopulationCount <dbl>, lat <dbl>, long <dbl>,
#   Short_Question_Text <chr>, rep_lib <chr>

ggplot(combined_latlong, aes(x=Data_Value, y=PopulationCount, color =CityName, shape = rep_lib)) +
  scale_color_viridis_d()+
  geom_point(alpha = 0.6, size = 5) +  
  labs(title = "Prevalence of Preventative healthcare for women, by census tract population size",
       caption = "Source: CDC, nytimes",
       color = "City",
       x = "Preventative Care prevalence(%)",
       y = "Population",
       shape = "Party in the 2016 Presidental Election")  +
  theme_bw()

3. Now create a map of your subsetted dataset.

First map chunk here

pal <- colorFactor(palette = c("hotpink", "yellow", "green"), 
               levels = c("Low", "Medium", "High"), latlong_4$prev_level)
leaflet(latlong_4) |>
  setView(lng = -119.417931, lat = 36.778259, zoom = 5) |>
  addProviderTiles("Esri.NatGeoWorldMap") |>
  addCircles(
    stroke = FALSE,
    fillColor = ~pal(prev_level),
    radius = (latlong_4$PopulationCount)*4,
    fillOpacity = 0.4
    )

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

leaflet(latlong_5) |>
  setView(lng = -99.9018, lat = 31.9686, zoom = 5.5) |>
  addProviderTiles("Esri.NatGeoWorldMap") |>
  addCircles(
    stroke = FALSE,
    fillColor = ~pal(prev_level),
    radius = (latlong_5$PopulationCount)*3,
    fillOpacity = 0.4
    )

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

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

Refined map chunk here

popuptexas <- paste0(
      "<b>Population: </b>", latlong_5$PopulationCount, "<br>",
      "<b>Women's Prevention Care Prevalence (%): </b>", latlong_5$Data_Value, "<br>",
      "<b>Prevention Care Prevalence (comparative rating): </b>", latlong_5$prev_level, "<br>",
      "<b> City Name: <b>", latlong_5$CityName, "<br>",
      "<b> Geographic Level: <b>", latlong_5$GeographicLevel, "<br>",
      "<b> Data Type: <b>" , latlong_5$Data_Value_Type, "<br>",
      "<b> Preventative Care Type: <b>" , latlong_5$Short_Question_Text
    )
leaflet(latlong_5) |>
  setView(lng = -99.9018, lat = 31.9686, zoom = 5.5) |>
  addProviderTiles("Esri.NatGeoWorldMap") |>
  addCircles(
    stroke = FALSE,
    fillColor = ~pal(prev_level),
    radius = (latlong_5$PopulationCount)*3,
    fillOpacity = 0.4,
    popup = popuptexas
    )

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

popupcali <- paste0(
      "<b>Population: </b>", latlong_4$PopulationCount, "<br>",
      "<b>Women's Prevention Care Prevalence (%): </b>", latlong_4$Data_Value, "<br>",
      "<b>Prevention Care Prevalence (comparative rating): </b>", latlong_4$prev_level, "<br>",
      "<b> City Name: <b>", latlong_4$CityName, "<br>",
      "<b> Geographic Level: <b>", latlong_4$GeographicLevel, "<br>",
      "<b> Data Type: <b>" , latlong_4$Data_Value_Type, "<br>",
      "<b> Preventative Care Type: <b>" , latlong_4$Short_Question_Text
    )
leaflet(latlong_4) |>
  setView(lng = -119.417931, lat = 36.778259, zoom = 5) |>
  addProviderTiles("Esri.NatGeoWorldMap") |>
  addCircles(
    stroke = FALSE,
    fillColor = ~pal(prev_level),
    radius = (latlong_5$PopulationCount)*5,
    fillOpacity = 0.4,
    popup = popupcali
    )

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.

The first scatterplot I created aimed to show if there was any relationship between population size in California census tracts, and women’s preventative health care prevalence (pap smears, mammograms). I used census tracts because I researched their use, and it seemed that was the best option, and would have less smoothing of potential trends. It seems that, at least in california, people tend to get a lot more pap smears than mamograms, which makes sense considering pap smears are a more ruitine form of screening than mammograms, as it is recommended women begin getting mammograms starting at 40, while pap smears start at 21, and California’s population is pretty young. Population doesn’t seem to be much of an influence however, which I find interesting, because I would have thought there would be more of a relationship. My mother works for NIH, specifically in cancer screening, so this was all very interesting to me. I also created a second scatterplot, looking into the same variabels, but with Texas. I wanted to compare the difference between a historically liberal and a historically republican state, in the year of the first Trump election, specifically Texas because I know it does not do well with reproductive care for women. With that plot we can see that more women get mammograms, which probably just has to do with the age of Texas residents. It also shows less care overall than california, but that could be due to a number of different factors. My third plot, the first map, I plotted something very similar to my first scatterplot, but I added a variable ranking the care prevalence, either low, medium, or high, according to the 5 number summary of preventative care prevalence, and made a custom pallet to illustrate that, using a mapping style that had labled and outlined cities because I liked that context. I also plotted the same informatuon for Texas. For my last maps, I added the tool tip for both, which included Population, Women’s Prevention Care Prevalence (%), Prevention Care Prevalence (comparative rating), City Name, Geographic Level, Data Type, and Preventative Care Type. When origionally starting out on this project, I wanted to add some outside data from a different source to see if, since the data is from 2016/2017, a state’s political party in the 2016 presidental election was a relevent predictor as to the prevalence of womens preventative healthcare, but I could not figure out a good way to add that varibale to a significant amount of data, enough to show any potential trends. The plot I did make, was both messy and uninformative, especially since it had only 10 census tracts as I only knew how to manually input the republican and liberal values for each row. It was also when I was still considering whether population was a factor in preventative health care, which I now know, it isn’t really. I kept the plot in this assignment because, while the execution is not good, I did spend a lot of time on it, and I still think it’s some what of an interesting lense. Perhaps it would have been more appropriate using data from this past year and a half, sadly.