Strengths and Weaknesses. Spatial images VS. Non-spatial images

This study used two methods of presenting the data. The first method was using spatial. The strength of using spatial images is that it allows an individual to see the data more accessible. A weakness of using spatial images is that it does not display information like slope, intercept, mean, or median. The strengths and weaknesses of non-spatial images are the opposite of spatial images. Non-spatial images allow people to see intricate patterns. However, non-spatial images may be more difficult to interpret if you have no experience in understanding data.

Question on (cb = TRUE)

In R, when you change the cb = TRUE into cb = False, the resolution of the image changes.

library(readr)
library(dplyr)
library(tidyverse)
library(sf)
library(tmap)
library(tigris)
library(spdep)

options(tigris_use_cache = TRUE)
options(tigris_progress_bar = FALSE)
options(tidycensus_progress_bar = FALSE)
options(tigris_class = "sf")
countyMap=counties(cb = TRUE  )
map <- st_read("Desktop/tl_2016_us_county/tl_2016_us_county.shp",stringsAsFactors = FALSE)
## Reading layer `tl_2016_us_county' from data source `/Users/ariel_rosario_jr./Desktop/tl_2016_us_county/tl_2016_us_county.shp' using driver `ESRI Shapefile'
## Simple feature collection with 3233 features and 17 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -179.2311 ymin: -14.60181 xmax: 179.8597 ymax: 71.44106
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs
Enviormental_Data  <- read_csv("Desktop/Enviormental Data 1.csv")
Enviormental_Data <- Enviormental_Data%>%
  rename("GEOID"="FIPS")
Enviormental_Data$GEOID=as.integer(Enviormental_Data$GEOID)
map$GEOID=as.integer(map$GEOID)
mergedData=left_join(map,Enviormental_Data,by="GEOID")

UnitedStates_map =mergedData %>%
  filter(STATEFP != "02") %>%
  filter(STATEFP != "15") %>%
  filter(STATEFP != "60") %>%
  filter(STATEFP != "66") %>%
  filter(STATEFP != "69") %>%
  filter(STATEFP != "72") %>%
  filter(STATEFP == "36") %>%
  filter(STATEFP != "78")%>%
  mutate(trees=as.integer(`Area Of Land Cover Class 41, Deciduous Forest`))

tm_shape(UnitedStates_map, projection = 2163) + 
  tm_fill("trees",palette="Greens",midpoint=10,border.col = "grey", border.alpha = .3,title='Area Of Land Cover Class 41, Deciduous Forest')+
  tm_borders(lwd = .28, col = "black", alpha = 1)+
  tm_layout(panel.labels=("Deciduous Forest'_ New York State "),legend.position = c("left","top"))