class: center, middle, inverse, title-slide .title[ # Phase II: Using Our Toolbox ] .subtitle[ ## Module 6: Spatial Awareness ] .author[ ### Dr. Christopher Kenaley ] .institute[ ### Boston College ] .date[ ### 2024/10/28 ] --- class: inverse, top # In class today <!-- Add icon library --> <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.14.0/css/all.min.css"> .pull-left[ Today we'll .... - Intro to Spatial Analysis - Plotting maps in R - Raster vs Shape Data ] .pull-right[ ] --- class: inverse, top <!-- slide 1 --> ## Spatial analysis .pull-left[ - sprawling field in data science. * Ecology, Natural resources, Economics, even Cell biology - more than longitude and latitude (X,Y coords) * generally concerned with patterns that vary with lat/long - Data comes in 2 forms * Raster (pixels) * Shape/vector (lines, polygons, etc.) ] .pull-right[  ] --- class: inverse, top ## Mapping: the first step .pull-left[ - usually done with shape data ``` r library(rnaturalearth) library(sf) library(tidyverse) bermuda <- ne_states(country = "Bermuda") %>% st_as_sf p <- bermuda %>% ggplot() + geom_sf() ``` ] .pull-right[ ``` r print(p) ``` <!-- --> ] --- class: inverse, top ## Raster data .pull-left[ ``` r library(stars) bermuda_pop <- read_stars("https://data.worldpop.org/GIS/Population_Density/Global_2000_2020_1km/2020/BMU/bmu_pd_2020_1km.tif") %>% st_crop(bermuda) p <- ggplot() + geom_stars(data = bermuda_pop) ``` ] .pull-right[ ``` r print(p) ``` <!-- --> ] --- class: inverse, top ## Adding raster to shape map .pull-left[ ``` r bermuda2 <- bermuda_pop %>% setNames("population") %>% st_as_sf() %>% st_join(bermuda) %>% group_by(name) %>% summarise(population = sum(population)) p <- bermuda %>% ggplot() + geom_sf() + geom_sf(data = bermuda2, aes(fill = population)) ``` ] .pull-right[ ``` r print(p) ``` <!-- --> ]