CDC 500 Cities/PLACES GIS Data Analysis
Mingzhuo Tian
2023-11-08
knitr::include_graphics("https://www.cdc.gov/places/about/500-cities-2016-2019/images/500-cities-logo.png")
The 500 cities datasets from the Centers for Disease Control and Prevention (CDC), It describes how to load and handle a dataset that includes health indicators from various American cities. The dataset contains variables such as population counts, geographic locations, city names, state abbreviations, measurements of health outcomes and behaviors, and data values with confidence bounds.
Load the libraries and set the working directory
library(tidyverse)
library(tidyr)
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")
str(cities500)## spc_tbl_ [810,103 × 24] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Year : num [1:810103] 2017 2017 2017 2017 2017 ...
## $ StateAbbr : chr [1:810103] "CA" "CA" "CA" "CA" ...
## $ StateDesc : chr [1:810103] "California" "California" "California" "California" ...
## $ CityName : chr [1:810103] "Hawthorne" "Hawthorne" "Hayward" "Hayward" ...
## $ GeographicLevel : chr [1:810103] "Census Tract" "City" "City" "City" ...
## $ DataSource : chr [1:810103] "BRFSS" "BRFSS" "BRFSS" "BRFSS" ...
## $ Category : chr [1:810103] "Health Outcomes" "Unhealthy Behaviors" "Health Outcomes" "Unhealthy Behaviors" ...
## $ UniqueID : chr [1:810103] "0632548-06037602504" "632548" "633000" "633000" ...
## $ Measure : chr [1:810103] "Arthritis among adults aged >=18 Years" "Current smoking among adults aged >=18 Years" "Coronary heart disease among adults aged >=18 Years" "Obesity among adults aged >=18 Years" ...
## $ Data_Value_Unit : chr [1:810103] "%" "%" "%" "%" ...
## $ DataValueTypeID : chr [1:810103] "CrdPrv" "CrdPrv" "AgeAdjPrv" "CrdPrv" ...
## $ Data_Value_Type : chr [1:810103] "Crude prevalence" "Crude prevalence" "Age-adjusted prevalence" "Crude prevalence" ...
## $ Data_Value : num [1:810103] 14.6 15.4 4.8 24.2 78 22 17.7 6 12.7 82.5 ...
## $ Low_Confidence_Limit : num [1:810103] 13.9 15 4.7 24.1 77.6 21.1 17.5 5.8 12 82 ...
## $ High_Confidence_Limit : num [1:810103] 15.2 15.9 4.8 24.4 78.3 22.8 17.9 6.2 13.5 83 ...
## $ Data_Value_Footnote_Symbol: chr [1:810103] NA NA NA NA ...
## $ Data_Value_Footnote : chr [1:810103] NA NA NA NA ...
## $ PopulationCount : num [1:810103] 4407 84293 144186 144186 78657 ...
## $ GeoLocation : chr [1:810103] "(33.905547923, -118.337332298)" "(33.914667701, -118.347667728)" "(37.6329591551, -122.077051051)" "(37.6329591551, -122.077051051)" ...
## $ CategoryID : chr [1:810103] "HLTHOUT" "UNHBEH" "HLTHOUT" "UNHBEH" ...
## $ MeasureId : chr [1:810103] "ARTHRITIS" "CSMOKING" "CHD" "OBESITY" ...
## $ CityFIPS : num [1:810103] 632548 632548 633000 633000 633182 ...
## $ TractFIPS : num [1:810103] 6.04e+09 NA NA NA NA ...
## $ Short_Question_Text : chr [1:810103] "Arthritis" "Current Smoking" "Coronary Heart Disease" "Obesity" ...
## - attr(*, "spec")=
## .. cols(
## .. Year = col_double(),
## .. StateAbbr = col_character(),
## .. StateDesc = col_character(),
## .. CityName = col_character(),
## .. GeographicLevel = col_character(),
## .. DataSource = col_character(),
## .. Category = col_character(),
## .. UniqueID = col_character(),
## .. Measure = col_character(),
## .. Data_Value_Unit = col_character(),
## .. DataValueTypeID = col_character(),
## .. Data_Value_Type = col_character(),
## .. Data_Value = col_double(),
## .. Low_Confidence_Limit = col_double(),
## .. High_Confidence_Limit = col_double(),
## .. Data_Value_Footnote_Symbol = col_character(),
## .. Data_Value_Footnote = col_character(),
## .. PopulationCount = col_number(),
## .. GeoLocation = col_character(),
## .. CategoryID = col_character(),
## .. MeasureId = col_character(),
## .. CityFIPS = col_double(),
## .. TractFIPS = col_double(),
## .. Short_Question_Text = col_character()
## .. )
## - attr(*, "problems")=<externalptr>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>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
str(prevention)## tibble [113,983 × 18] (S3: tbl_df/tbl/data.frame)
## $ Year : num [1:113983] 2017 2017 2017 2017 2017 ...
## $ StateAbbr : chr [1:113983] "AL" "CA" "CA" "CA" ...
## $ StateDesc : chr [1:113983] "Alabama" "California" "California" "California" ...
## $ CityName : chr [1:113983] "Montgomery" "Concord" "Concord" "Fontana" ...
## $ GeographicLevel : chr [1:113983] "City" "City" "City" "City" ...
## $ Category : chr [1:113983] "Prevention" "Prevention" "Prevention" "Prevention" ...
## $ UniqueID : chr [1:113983] "151000" "616000" "616000" "624680" ...
## $ Measure : chr [1:113983] "Cholesterol screening among adults aged >=18 Years" "Visits to doctor for routine checkup within the past Year among adults aged >=18 Years" "Cholesterol screening among adults aged >=18 Years" "Visits to doctor for routine checkup within the past Year among adults aged >=18 Years" ...
## $ Data_Value_Type : chr [1:113983] "Crude prevalence" "Crude prevalence" "Crude prevalence" "Crude prevalence" ...
## $ Data_Value : num [1:113983] 80.2 64.5 80.1 66.1 81.8 84.5 73.5 81 79.1 19.7 ...
## $ PopulationCount : num [1:113983] 205764 122067 122067 196069 5706 ...
## $ lat : num [1:113983] 32.3 38 38 34.1 37.9 ...
## $ long : num [1:113983] -86.3 -122 -122 -117.5 -122.3 ...
## $ CategoryID : chr [1:113983] "PREVENT" "PREVENT" "PREVENT" "PREVENT" ...
## $ MeasureId : chr [1:113983] "CHOLSCREEN" "CHECKUP" "CHOLSCREEN" "CHECKUP" ...
## $ CityFIPS : num [1:113983] 151000 616000 616000 624680 660620 ...
## $ TractFIPS : num [1:113983] NA NA NA NA 6.01e+09 ...
## $ Short_Question_Text: chr [1:113983] "Cholesterol Screening" "Annual Checkup" "Cholesterol Screening" "Annual Checkup" ...# Check types of screens
prevention |>
select(Short_Question_Text) |>
distinct()## # A tibble: 4 × 1
## Short_Question_Text
## <chr>
## 1 Cholesterol Screening
## 2 Annual Checkup
## 3 Health Insurance
## 4 Taking BP Medication# Check types of categories
prevention |>
select(CategoryID) |>
distinct()## # A tibble: 1 × 1
## CategoryID
## <chr>
## 1 PREVENT# Filter Cholesterol Screening
cholesterol_subset <- prevention |>
filter(Short_Question_Text == "Cholesterol Screening")
head(cholesterol_subset)## # 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 Choles…
## 3 2017 CA California Richmond Census Tract Prevent… 0660620… Choles…
## 4 2017 FL Florida Davie Census Tract Prevent… 1216475… Choles…
## 5 2017 FL Florida Hialeah Census Tract Prevent… 1230000… Choles…
## 6 2017 FL Florida Miami Be… Census Tract Prevent… 1245025… 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
cholesterol_subset$lat <- as.numeric(cholesterol_subset$lat)
cholesterol_subset$long <- as.numeric(cholesterol_subset$long)
cholesterol_subset <- na.omit(cholesterol_subset)
str(cholesterol_subset)## tibble [27,210 × 18] (S3: tbl_df/tbl/data.frame)
## $ Year : num [1:27210] 2017 2017 2017 2017 2017 ...
## $ StateAbbr : chr [1:27210] "CA" "FL" "FL" "FL" ...
## $ StateDesc : chr [1:27210] "California" "Florida" "Florida" "Florida" ...
## $ CityName : chr [1:27210] "Richmond" "Davie" "Hialeah" "Miami Beach" ...
## $ GeographicLevel : chr [1:27210] "Census Tract" "Census Tract" "Census Tract" "Census Tract" ...
## $ Category : chr [1:27210] "Prevention" "Prevention" "Prevention" "Prevention" ...
## $ UniqueID : chr [1:27210] "0660620-06013380000" "1216475-12011070210" "1230000-12086000605" "1245025-12086004403" ...
## $ Measure : chr [1:27210] "Cholesterol screening among adults aged >=18 Years" "Cholesterol screening among adults aged >=18 Years" "Cholesterol screening among adults aged >=18 Years" "Cholesterol screening among adults aged >=18 Years" ...
## $ Data_Value_Type : chr [1:27210] "Crude prevalence" "Crude prevalence" "Crude prevalence" "Crude prevalence" ...
## $ Data_Value : num [1:27210] 81.8 84.5 81 79.1 83.7 75.6 77.1 73.7 83.6 76.8 ...
## $ PopulationCount : num [1:27210] 5706 3228 4806 2944 2348 ...
## $ lat : num [1:27210] 37.9 26.1 25.9 25.8 37.3 ...
## $ long : num [1:27210] -122.3 -80.3 -80.3 -80.1 -122 ...
## $ CategoryID : chr [1:27210] "PREVENT" "PREVENT" "PREVENT" "PREVENT" ...
## $ MeasureId : chr [1:27210] "CHOLSCREEN" "CHOLSCREEN" "CHOLSCREEN" "CHOLSCREEN" ...
## $ CityFIPS : num [1:27210] 660620 1216475 1230000 1245025 669084 ...
## $ TractFIPS : num [1:27210] 6.01e+09 1.20e+10 1.21e+10 1.21e+10 6.09e+09 ...
## $ Short_Question_Text: chr [1:27210] "Cholesterol Screening" "Cholesterol Screening" "Cholesterol Screening" "Cholesterol Screening" ...
## - attr(*, "na.action")= 'omit' Named int [1:1294] 1 2 7 11 15 16 19 26 30 31 ...
## ..- attr(*, "names")= chr [1:1294] "1" "2" "7" "11" ...p1 <- ggplot(cholesterol_subset, aes(x = long, y = lat, color = Data_Value)) +
geom_point() +
labs(title = "Cholesterol Screening Prevalence by City",
x = "Longitude",
y = "Latitude",
color = "Prevalence Rate (%)")
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Each point on the plot corresponds to a city, with the location of the point representing the city’s geographic location within the United States.
The color intensity of each point reflects the prevalence rate (%) of cholesterol screening in that city, with darker shades likely indicating higher prevalence rates.
data_bar <- prevention|>
filter(Data_Value_Type=="Crude prevalence" & StateAbbr==c("DC","MD") & Year=="2017" & GeographicLevel=="City" & MeasureId %in% c("ACCESS2", "CHECKUP", "CHOLSCREEN", "BPMED"))## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `&...`.
## Caused by warning in `StateAbbr == c("DC", "MD")`:
## ! longer object length is not a multiple of shorter object lengthp2 <- ggplot(data=data_bar, aes(x=CityName, y=Data_Value, fill=CityName)) +
geom_bar(stat = "identity") +
labs(x= "City Name",
y = "Crude Prevalence (%)",
title = "Preventive Health Metrics: Baltimore vs. Washington",
caption = "Source: Centers for Disease Control and Prevention (CDC)")+
geom_text(aes(label=paste(Data_Value,"%", sep="")),
size=3,
hjust=.5,
vjust=2.5,
color="white") +
facet_wrap(~factor(Short_Question_Text,
levels = c("Annual Checkup", "Cholesterol Screening","Taking BP Medication")),
nrow=1)+
scale_fill_brewer(name= "City Name", palette = "Dark2")
theme_bw()+
theme(legend.position="top",
legend.title = element_text(size=10),
legend.key.size = unit(2, 'mm'),
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3. Now create a map of your subsetted dataset.
First map chunk here
library(leaflet)## Warning: package 'leaflet' was built under R version 4.3.2us_lat <- 37.09
us_lon <- -95.71
# Create the map
leaflet(cholesterol_subset) |>
addProviderTiles(providers$OpenStreetMap) |>
setView(lng = us_lon, lat = us_lat, zoom = 4) |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = ~log10(Data_Value),
label = ~paste(CityName, ":", Data_Value),
labelOptions = labelOptions(
direction = "auto",
noHide = F,
textOnly = TRUE
))4. Refine your map to include a mouseover tooltip
Refined map chunk here
The purpose of the visualizations is to shed light on the geographic distribution and prevalence of health-related behaviors, particularly cholesterol screening, among adults in various cities who are 18 years of age or older.
leaflet(cholesterol_subset) |>
setView(lng = us_lon, lat = us_lat, zoom = 4) |> # Set the view to the center of the US
addTiles() |> # Add the default OpenStreetMap map tiles
addMarkers(
lng = ~long, lat = ~lat,
popup = ~paste(CityName, UniqueID, "Cholesterol Level:", Data_Value),
clusterOptions = markerClusterOptions()
)5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
the scatter plot shown below:
The points are dispersed over the United States’ geographic map, with a noteworthy concentration of points in the regions that correspond to the Midwest, the East Coast, the Gulf Coast, and the West Coast. The mountain states have fewer points, which might be because of lower population densities or a dearth of data points from these regions.
The color scale shows that different cities have different prevalence rates for cholesterol screening. The difference in hue indicates that some cities have significantly greater rates of cholesterol screening than others.
Particularly in areas with high population densities, there appear to be clusters of data points. This may suggest that access to healthcare facilities and services may be better in more populated areas, which could result in higher screening rates.
The bar chart shown below:
The crude prevalence of specific health-related activities in Baltimore and Washington are shown in the bar chart. Taking blood pressure medication, screening for cholesterol, and doing an annual checkup are among the tasks.
The following percentages are displayed:
Baltimore has a 74.5% annual checkup prevalence, whereas Washington has a 77% prevalence.
Baltimore has an 82.7% cholesterol screening rate, whereas Washington has a 79.9% rate.
Baltimore has a 76.9% prescription drug taker rate, whereas Washington has a 73.4% rate.
The graph shows that while Baltimore has greater percentages for blood pressure medication and cholesterol screening, Washington has a slightly larger percentage of residents getting yearly exams.