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)
library(sf)
library(leaflet)
setwd("C:/Users/mcuser/Downloads")
cities500 <- read_csv("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.
insurance <- prevention |>
filter(MeasureId == "ACCESS2") |>
filter(StateAbbr == c("NY", "NJ", "PA", "DE", "MD", "VA", "WV")) |>
filter(PopulationCount < 20000) #removing population outliers so they dont skew the actual data
dim(insurance)## [1] 589 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.
insurance |>
ggplot(aes(x = PopulationCount, y = Data_Value, color = StateAbbr)) +
geom_jitter() +
geom_smooth(aes(group = 1), color = "black") +
labs(x = "Population",
y = "Crude Prevelance of adults without health insurance",
title = "Crude Prevelance of adults without health insurance based on Population")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 10 rows containing missing values or values outside the scale range
## (`geom_point()`).
- Generate a basic GIS map that represents the geographic distribution of your subsetted data points. Ensure that the map clearly conveys relevant spatial patterns.
leaflet() |>
setView(lng = -77.93, lat = 40.65, zoom = 6) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles( data = insurance,
radius = ~sqrt(Data_Value) * 5000,
color = "darkred",
fillColor = "red",
opacity = .35)## Assuming "long" and "lat" are longitude and latitude, respectively- Refine your GIS map by adding interactive elements, such as a tooltip that displays information when users click on a data point.
popupins <- paste0(
"<b>City: </b>", insurance$CityName, "<br>",
"<b>State: </b>", insurance$StateAbbr, "<br>",
"<b>Population: </b>", insurance$PopulationCount, "<br>",
"<b>Crude Prevalence: </b>", insurance$Data_Value, "<br>",
"<b>Measure: </b>", insurance$Measure
)
popupins <- iconv(popupins, from = "", to = "UTF-8", sub = "byte") #making the popup UTF-8 type to fix an error I recievedleaflet() |>
setView(lng = -77.93, lat = 40.65, zoom = 6) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles( data = insurance,
radius = ~sqrt(Data_Value) * 5000,
color = "darkred",
fillColor = "red",
opacity = .35,
popup = popupins)## Assuming "long" and "lat" are longitude and latitude, respectively- 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.
My plot shows that access to health insurance is a very independent variable. When comparing the crude prevalence of individuals without health insurance to population of their city I found little to no correlation. the points were rather evenly distributed and when adding a line it showed no trend. The maps show that access to health insurance is not dependent on location either, there is not a significant difference in size throughout the map. I also noticed there aren’t really any outliers suggesting that at least in the mid-atlantic health insurance is rather equally accessible and no city is doing particularly better or worse then the others. When looking at the data set there does appear to be more variation then shown, so for future experiments I would be interested in really diving into this map and trying to weed out any patterns or trends that I may have missed.