GIS project

Author

Aminata Diatta

load the libraries

library(tidyverse)
library(tidyr)
setwd("C:/Users/satad/Downloads")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

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 = T)
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>

Tenesse will the first state that I will work on

tn <- Prevention |> 
  filter(StateAbbr == "TN")
head(tn)
# A tibble: 6 × 18
   Year StateAbbr StateDesc CityName   GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>     <chr>      <chr>           <chr>    <chr>    <chr>  
1  2017 TN        Tennessee Clarksvil… Census Tract    Prevent… 4715160… Choles…
2  2017 TN        Tennessee Knoxville  City            Prevent… 4740000  Choles…
3  2017 TN        Tennessee Knoxville  Census Tract    Prevent… 4740000… Choles…
4  2017 TN        Tennessee Memphis    Census Tract    Prevent… 4748000… Taking…
5  2017 TN        Tennessee Memphis    Census Tract    Prevent… 4748000… Choles…
6  2017 TN        Tennessee Memphis    Census Tract    Prevent… 4748000… 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>

1. Filtering the dataset for Blood Pressure Medication in Tenesse in 2017

Health_insurance_tn <- tn |>
 filter(Short_Question_Text == "Health Insurance" & PopulationCount>20)
head(Health_insurance_tn)
# A tibble: 6 × 18
   Year StateAbbr StateDesc CityName   GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>     <chr>      <chr>           <chr>    <chr>    <chr>  
1  2017 TN        Tennessee Clarksvil… Census Tract    Prevent… 4715160… "Curre…
2  2017 TN        Tennessee Knoxville  Census Tract    Prevent… 4740000… "Curre…
3  2017 TN        Tennessee Clarksvil… Census Tract    Prevent… 4715160… "Curre…
4  2017 TN        Tennessee Knoxville  Census Tract    Prevent… 4740000… "Curre…
5  2017 TN        Tennessee Knoxville  Census Tract    Prevent… 4740000… "Curre…
6  2017 TN        Tennessee Chattanoo… Census Tract    Prevent… 4714000… "Curre…
# 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 my subsetted dataset.

First plot chunk here

library(ggplot2)
ggplot(Health_insurance_tn, aes(x= Data_Value,y= PopulationCount, color= MeasureId))+ geom_point()+ scale_color_brewer()+
  scale_y_continuous(limits = c(0, 10000)) +
  labs(title = "Health Insurance provided in 2017 in Tenesse",
       x= "Health Insurance",
      y= "Population Count")+
  theme_dark()
Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_point()`).

options(scipen = 999)

Comments :

Upon reviewing the visualization, the lack of health insurance is not a lot , so it becomes apparent that a substantial number of Tenesse residents acquired health insurance in 2017. This year emerges as a significant milestone in the healthcare domain of Tenesse.I believe this development signifies a positive outcome for Tenesse residents. Enhanced health insurance coverage is poised to improve the overall well-being of the population, potentially leading to longer and healthier lives. With expanded access to healthcare services, individuals can expect to receive greater attention and care from doctors and nurses.

3. Now create a map of my dataset.

long_tn <- -86.58
lat_tn <- 35.52

#Health_insurance_tn$lat <- as.numeric(Health_insurance_tn$lat)
#Health_insurance_tn$long <- as.numeric(Health_insurance_tn$long)
library(leaflet)
Warning: package 'leaflet' was built under R version 4.3.3
map1<- Health_insurance_tn
leaflet() |>
  setView(lng = long_tn, lat = lat_tn, zoom = 5) |>
  addProviderTiles("Esri.WorldPhysical") |>
  addCircles( data = Health_insurance_tn,
    radius = (Health_insurance_tn$PopulationCount),
    color = "green",
    fillColor = "green",
    fillOpacity = 0.21)
Assuming "long" and "lat" are longitude and latitude, respectively

4. Refine your map to include a mousover tooltip

Refined map chunk here

map2<- map1
leaflet() |>
  setView(lng = long_tn, lat = lat_tn, zoom = 6) |>
  addProviderTiles("Esri.WorldPhysical") |>
  addCircles( data = Health_insurance_tn,
    radius = (Health_insurance_tn$PopulationCount),
    color = "green",
    fillOpacity = 0.21,
    popup = ~paste("City: ", Health_insurance_tn$CityName, "<br>",
                   "Health Insurance: ", Health_insurance_tn$Data_Value, "%"))
Assuming "long" and "lat" are longitude and latitude, respectively

5 Paragraph :

According to the map, Memphis, one of the largest cities in Tennessee, had the highest percentage of residents who enrolled for health insurance in 2017. This project aimed to provide insights into Tennessee, a city I have never visited. However, it was challenging to find all the relevant information in one place. Exploring the map was intriguing, but it proved to be difficult to navigate and obtain comprehensive information.