GIS Assignment 500_Healthy_Cities

Author

Mamokotjo Letjama

Introduction

The aim is to explore how Maryland communities engage in key preventive health measures, use visualizations to uncover patterns in participation and highlight potential gaps in public health outreach.

caption = “Source: Centers for Disease Control and Prevention (CDC)” https://www.cdc.gov/places/about/500-cities-2016-2019/ ## Load the libraries and set the working directory

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(viridis)

Loading required package: viridisLite

setwd("C:/Users/tmats/OneDrive/DATA110/Working Directories")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

Rows: 810103 Columns: 24
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (17): StateAbbr, StateDesc, CityName, GeographicLevel, DataSource, Categ...
dbl  (6): Year, Data_Value, Low_Confidence_Limit, High_Confidence_Limit, Cit...
num  (1): PopulationCount

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data(cities500)

Warning in data(cities500): data set 'cities500' not found

The GeoLocation variable has (lat, long) format

split GeoLocation(lat, long) into two columns: lat and long

lat_long <- cities500 |>
  mutate(GeoLocation = str_replace_all(GeoLocation, "[()]", "")) |>
  separate(GeoLocation, into = c("lat", "long"), sep = ",", convert = TRUE)
head(lat_long)

# 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.

lat_long_md <- lat_long |>
  filter(StateDesc != "United State") |>
  filter(Data_Value_Type == "Crude prevalence") |>
  filter(Year == 2017) |>
  filter(StateAbbr == "MD") |>
  filter(Category == "Prevention")
head(lat_long_md)

# A tibble: 6 × 25
   Year StateAbbr StateDesc CityName  GeographicLevel DataSource Category  
  <dbl> <chr>     <chr>     <chr>     <chr>           <chr>      <chr>     
1  2017 MD        Maryland  Baltimore Census Tract    BRFSS      Prevention
2  2017 MD        Maryland  Baltimore Census Tract    BRFSS      Prevention
3  2017 MD        Maryland  Baltimore Census Tract    BRFSS      Prevention
4  2017 MD        Maryland  Baltimore Census Tract    BRFSS      Prevention
5  2017 MD        Maryland  Baltimore Census Tract    BRFSS      Prevention
6  2017 MD        Maryland  Baltimore 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>

md_clean1 <- lat_long_md |>
  select(-DataSource, -Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote,)
head(md_clean1)

# A tibble: 6 × 19
   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 11 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   Data_Value_Footnote_Symbol <chr>, PopulationCount <dbl>, lat <dbl>,
#   long <dbl>, CategoryID <chr>, MeasureId <chr>, CityFIPS <dbl>,
#   TractFIPS <dbl>, Short_Question_Text <chr>

Create subset not more than 900 observations

md_clean1 |>
  filter(!is.na(PopulationCount), !is.na(Data_Value))

# A tibble: 800 × 19
    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… "Chole…
 2  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Visit…
 3  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Visit…
 4  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
 5  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
 6  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Visit…
 7  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
 8  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Takin…
 9  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
10  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Chole…
# ℹ 790 more rows
# ℹ 11 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   Data_Value_Footnote_Symbol <chr>, PopulationCount <dbl>, lat <dbl>,
#   long <dbl>, CategoryID <chr>, MeasureId <chr>, CityFIPS <dbl>,
#   TractFIPS <dbl>, Short_Question_Text <chr>

md_clean1 |>
  filter(!is.na(Data_Value))

# A tibble: 800 × 19
    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… "Chole…
 2  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Visit…
 3  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Visit…
 4  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
 5  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
 6  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Visit…
 7  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
 8  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Takin…
 9  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Curre…
10  2017 MD        Maryland  Baltimore Census Tract    Prevent… 2404000… "Chole…
# ℹ 790 more rows
# ℹ 11 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   Data_Value_Footnote_Symbol <chr>, PopulationCount <dbl>, lat <dbl>,
#   long <dbl>, CategoryID <chr>, MeasureId <chr>, CityFIPS <dbl>,
#   TractFIPS <dbl>, Short_Question_Text <chr>

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

ggplot(md_clean1, aes(x = Short_Question_Text, y = Data_Value, fill = Short_Question_Text)) +
  geom_bar(stat = "identity", width = 0.7) +
  geom_text(aes(label=Data_Value), vjust=2.5, hjust=0.5, size = 3, color="white") +
  scale_fill_viridis(discrete = TRUE, option = "D") +
  labs( x = NULL, 
        caption = 'Source:Centers for Disease Control and Prevention (CDC)')

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_bar()`).

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_text()`).

  theme_minimal() +
  ggtitle("Health Prevention Behaviors Among Maryland Adults")

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##Now create a map of your subsetted dataset. First map chunk here Annual Checkup subset

library(maps)


Attaching package: 'maps'

The following object is masked from 'package:viridis':

    unemp

The following object is masked from 'package:purrr':

    map

library(sf)

Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUE

library(leaflet)

Subset_prevention <- md_clean1 |>
  filter(Short_Question_Text == "Annual Checkup")

leaflet(data = Subset_prevention) |>
  addProviderTiles("OpenStreetMap") |>
  setView(lng = -76.6413, lat = 39.0458, zoom = 11) |>
            addCircleMarkers(lng = ~long,
    lat = ~lat,
    radius = ~sqrt(PopulationCount) / 10,  # Adjust denominator as needed for better scaling
    color = "dodgerblue",stroke = TRUE,
    weight = 1,
    fillOpacity = 0.7)

Refine your map to include a mouse-click tooltip

leaflet(data = Subset_prevention) |>
  addProviderTiles("OpenStreetMap") |>
  setView(lng = -76.6413, lat = 39.0458, zoom = 11) |>
            addCircleMarkers(lng = ~long,
    lat = ~lat,
    radius = ~sqrt(PopulationCount) / 10,  
    color = "blue",
    fillColor = "yellow",
    stroke = TRUE,
    weight = 1,
    fillOpacity = 0.35,
    popup = ~ paste0(
      "<strong>Population:</strong> ", PopulationCount, "<br/>",
      "<strong>Category:</strong> ", Short_Question_Text))

Brief Essay

The bar chart compares the prevalence of four preventive health measures, namely, annual Checkup, Cholesterol Screening, Health Insurance Coverage, and Blood Pressure Medication use. The tallest bar appeared to be Cholesterol screening followed by BP Medication usage, signaling strong participation in these essential health behaviors. In contrast, Health Insurance coverage, while fundamental, shows comparatively lower representation, raising questions about accessibility or reporting within this subset.Maps paint a vivid picture of how different communities across Maryland are participating in annual checkups. Each circle represents a population cluster, with larger circles indicating higher numbers of individuals reporting annual medical visits. By visualizing these categories side by side, the graph shines a light on where public health outreach might be thriving and where it could be reinforced. By visualizing these categories side by side, the graph shines a light on where public health outreach might be thriving and where it could be reinforced.