GIS -Project2
Introduction:
The CDC’s “PLACES: Local Data for Better Health” provides access to detailed local health data. It helps users explore health indicators by county, city, and census areas to improve public health efforts. The platform offers interactive maps, data tools, and resources for understanding local health measures based on CDC and U.S. Census data. For more details, visit the PLACES website.
This dataset is adapted from 500 Cities: Local Data for Better Health, 2017 release.
Project Prompt:
For this project, you will work with a cleaned dataset and conduct an analysis using GIS techniques.
Start by filtering the dataset further to create a subset containing no more than 900 observations. Choose a specific subset based on a meaningful criterion related to your analysis.
Create a plot that visualizes an aspect of your subsetted dataset. This could be a histogram, scatter plot, or line chart, depending on the nature of your data.
Generate a basic GIS map that represents the geographic distribution of your subsetted data points. Ensure that the map clearly conveys relevant spatial patterns.
Refine your GIS map by adding interactive elements, such as a tooltip that displays information when users click on a data point.
Write a paragraph summarizing your visualizations. Explain what your plot and map reveal about your subsetted dataset. Discuss any trends, patterns, or insights gained from your analysis.
This project will help you practice data filtering, visualization, and GIS mapping techniques, reinforcing concepts from the Japan earthquakes tutorial.
Let’s start:
Load the libraries and set the working directory
library(tidyverse)
library(tidyr)
setwd("L:/R_Datasets")
cities500 <- read_csv("L:/R_Datasets/500CitiesLocalHealthIndicators.cdc.csv")
data(cities500)Cleaning the data set:
1. The GeoLocation variable has (lat, long) format. We need to split GeoLocation (lat, long) into two columns: lat and long.
To do so, we will remove the parentheses from a column, then split it into separate latitude and longitude columns.
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>str_replace_all(GeoLocation, "[()]", "") removes any
parentheses from the GeoLocation column.
separate(GeoLocation, into = c("lat", "long"), sep = ",", convert = TRUE)
The separate() function splits the GeoLocation column
into two new columns:
“lat” (latitude) “long” (longitude)
sep = "," specifies that the values are separated by a
comma.
convert = TRUE automatically converts the new columns
into appropriate data types (numeric in this case).
2. 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")
dim(md)## [1] 804 18Your Work
- MD has 804 observations. For this project, start by filtering the dataset further to create a subset containing no more than 900 observations. Choose a specific subset based on a meaningful criterion related to your analysis.
md_cholesterol <- md |>
filter(Measure == "Cholesterol screening among adults aged >=18 Years")
dim(md_cholesterol)## [1] 201 18- Create a plot that visualizes an aspect of your subsetted dataset. This could be a histogram, scatter plot, or line chart, depending on the nature of your data.
library(ggplot2)
# Create a histogram to visualize the distribution of cholesterol screening percentages
ggplot(md_cholesterol, aes(x = Data_Value)) +
geom_histogram(binwidth = 2, fill = "blue", color = "black", alpha = 0.7) +
labs(
title = "Distribution of Cholesterol Screening among Adults in Maryland",
x = "Percentage of Adults with Cholesterol Screening",
y = "Frequency"
) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bin()`).
- Generate a basic GIS map that represents the geographic distribution of your subsetted data points. Ensure that the map clearly conveys relevant spatial patterns.
# Load leaflet
library(leaflet)## Warning: package 'leaflet' was built under R version 4.4.3leaflet(md_cholesterol) |>
addTiles() |> # Adding tiles
addCircles(
lat = ~lat, # Latitude
lng = ~long, # Longitude
weight = 1,
# # Sizing the circles based on screening rate (The larger the screening rate, the larger the circle)
radius = ~Data_Value * 3, # Scale by Data_Value and multiply by 3 to reflect screening rate
color = ~colorNumeric(palette = "inferno", domain = md_cholesterol$Data_Value)(Data_Value), # Color by Data_Value
opacity = 0.7,
fillOpacity = 0.5
)- Refine your GIS map by adding interactive elements, such as a tooltip that displays information when users click on a data point.
leaflet(md_cholesterol) |>
addTiles() |> # Adding tiles
addCircles(
lat = ~lat, # Latitude
lng = ~long, # Longitude
weight = 1,
# Sizing the circles based on screening rate (The larger the screening rate, the larger the circle)
radius = ~Data_Value * 3, # Scale by Data_Value and multiply by 3 to reflect screening rate
color = ~colorNumeric(palette = "inferno", domain = md_cholesterol$Data_Value)(Data_Value), # Color by Data_Value
opacity = 0.7,
fillOpacity = 0.5,
# Adding tooltip which displays information when a user clicks on a data point
popup = ~paste("City: ", CityName, "\n",
"Cholesterol Screening Rate: ", Data_Value, "%", "\n",
"Population: ", PopulationCount)
)- Write a paragraph summarizing your visualizations. Explain what your plot and map reveal about your subsetted dataset. Discuss any trends, patterns, or insights gained from your analysis.
The data collected from Maryland for the percentages of cholesterol screening among adults was only for the city of Baltimore, there were 201 observations providing insight into how different parts of Baltimore have different health trends for the prevalence of cholesterol screening. The range of data shown on the histogram is around slightly under 70% to around 92.5% of adults getting screened for cholesterol. This shows a disparity in healthcare in Baltimore as this gap alone may show us that some citizens lack the necessary insurance, resources, or health literacy needed to get a cholesterol test. A cholesterol test is also important as it can help medical professionals understand what is going on with a patient and can help personalize healthcare.
The GIS maps revealed that the majority of Baltimore is within a similar range to each other regarding the cholesterol testing percentage. However, there are specific areas that are struggling with this statistic. I could not gather any solid trends from the map besides being able to identify outliers that have low percentages of adults who get cholesterol testing. This would be important for the city of Baltimore to address these areas as it could improve the overall health of the city and help increase the lifespan of citizens. A weak generalization could be made that areas with a lower percentage of cholesterol testing tend to be near the center of this city although this is not a general rule as there are many exceptions. The areas with better cholesterol testing percentages are the Northeast of Baltimore and the Northwest.
Overall the city of Baltimore has to work to increase the percentage of adults who get tested for cholesterol, to help protect its citizens from diseases and potential health problems as well as to help health professionals treat citizens. This visualization may prove useful in which areas the city of Baltimore should focus on first.