library(tidyverse)
library(tidyr)
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")
data(cities500)Healthy Cities GIS Assignment
Load the libraries and set the working directory
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(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 CA California Hawthorne Census Tract BRFSS Health Outcom…
2 2017 CA California Hawthorne City BRFSS Unhealthy Beh…
3 2017 CA California Hayward City BRFSS Unhealthy Beh…
4 2017 CA California Indio Census Tract BRFSS Health Outcom…
5 2017 CA California Inglewood Census Tract BRFSS Health Outcom…
6 2017 CA California Lakewood City BRFSS Unhealthy Beh…
# 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
latlong_clean2 <- latlong_clean |>
select(-DataSource,
-Data_Value_Unit,
-DataValueTypeID,
-Low_Confidence_Limit,
-High_Confidence_Limit,
-Data_Value_Footnote_Symbol,
-Data_Value_Footnote)
head(latlong_clean2)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CA California Hawthorne Census Tract Health … 0632548… Arthri…
2 2017 CA California Hawthorne City Unhealt… 632548 Curren…
3 2017 CA California Hayward City Unhealt… 633000 Obesit…
4 2017 CA California Indio Census Tract Health … 0636448… Arthri…
5 2017 CA California Inglewood Census Tract Health … 0636546… Diagno…
6 2017 CA California Lakewood City Unhealt… 639892 Obesit…
# 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 a cleaned dataset.
1. Once you run the above code and learn how to filter in this format, filter this dataset however you choose so that you have a subset with no more than 900 observations.
Filter in Physical Inactivity from updated prevention dataset
lpa <- latlong_clean2 |>
filter(MeasureId == "LPA")Down to 28.5k observations
Filter in Maryland from lpa datset
lpa_md <- lpa |>
filter(StateAbbr == "MD")And here we are with just 201 observations of physical inactivity cases in Maryland.
2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
quakes <- read_csv("japan_quakes_01-18.csv")Rows: 14092 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): magType, net, id, place, type, status, locationSource, magSource
dbl (12): latitude, longitude, depth, mag, nst, gap, dmin, rms, horizontalE...
dttm (2): time, updated
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.gaprmsmwr <- quakes |>
filter(magType == "mwr") |>
filter(!is.na(mag),
!is.na(rms),
!is.na(gap))
ggplot(gaprmsmwr,
aes(x = rms,
y = gap,
size = mag)) +
geom_point(color = "red",
alpha = 0.3) +
scale_color_hue()+
theme_bw() +
labs(title = "Reliability in predictions (RMS) and Informations (GAP) of MWR type Japanese Earthquakes",
caption = "Source: USGS")
3. Now create a map of your subsetted dataset.
library(leaflet)
japan_lon <- 138.129731
japan_lat <- 36.2615855
leaflet() |>
setView(lng = japan_lon,
lat = japan_lat,
zoom = 6) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
addCircles(data = gaprmsmwr,
radius = 5000,
color = "white",
fillColor = "red",
fillOpacity = 0.5)Assuming "longitude" and "latitude" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
popuprmsgap <- paste0("<b>RMS: </b>", gaprmsmwr$rms, "<br>",
"<b>GAP: </b>", gaprmsmwr$gap, "<br>",
"<b>Magnitude: </b>", gaprmsmwr$mag, "<br>",
"<b>Place: </b>", gaprmsmwr$place, "<br>")
leaflet() |>
setView(lng = japan_lon,
lat = japan_lat,
zoom = 6) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
addCircles(data = gaprmsmwr,
radius = 5000,
color = "white",
fillColor = "red",
fillOpacity = 0.5,
popup = popuprmsgap)Assuming "longitude" and "latitude" are longitude and latitude, respectively5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
For this assignment, I focused on understanding how reliable earthquake data can be by looking into the RMS and GAP variables in the dataset. After looking up their definitions, I learned that RMS (Root-Mean-Square) represents the margin of error in the recorded event, while GAP (Azimuthal Gap) tells us how close the surrounding recording stations were. The lower either variable are, the better the event was covered… even more if both are. I filtered the dataset to focus only on MWR magnitude types and removed any missing values. I created a scatterplot to visualize the relationship between RMS and GAP. I sized the dots by magnitude to give extra context, which worked well visually. Originally, I was going to size the points based on RMS or GAP in the leaflet map too, but I wasn’t able to get that part working and that’s something I’d love to get advice on, since I followed the class notes step by step and still couldn’t figure it out. Then I created an interactive leaflet map showing the earthquake locations around Japan. The map includes tooltips with details like RMS, GAP, magnitude, and place. Seeing this all together helped me better understand the quality and reliability of real world data… not just what it says, but how well supported it is. It was also a solid opportunity to apply the mapping and filtering techniques we’ve been learning. I could definitely see myself doing more geography-related data analysis in a future job.