Take Home Exercise 5 - GeoVisual Analysis

This exercise is to visualise and analyse geographic and movement Data, including: - social areas of the city of Engagement, Ohio USA. - visualising and analysing locations with traffic bottleneck of the city of Engagement, Ohio USA.

1. Load necessary packages

packages = c('tidyverse', 'sf', 'tmap', 'sftime', 'rmarkdown','lubridate', 'clock')
for (p in packages){
  if (!require(p, character.only = T)){
    install.packages(p)
  }
  library(p, character.only = T)
}

2. Load data for Buidlings, Pubs, Restaurants and Empoyers

pubs <- read_sf("data/th_5_data/Pubs.csv", 
                options = "GEOM_POSSIBLE_NAMES=location")
employers <- read_sf("data/th_5_data/Employers.csv", 
                     options = "GEOM_POSSIBLE_NAMES=location")
restaurants <- read_sf("data/th_5_data/Restaurants.csv", 
                       options = "GEOM_POSSIBLE_NAMES=location")
bldgs <- read_sf("data/th_5_data/Buildings.csv", 
                 options = "GEOM_POSSIBLE_NAMES=location")

3. Plot Map of Pubs, Restaurants, and Employers on Buildings

tmap_mode("plot")
tm_shape(bldgs)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(pubs)+
  tm_dots(col = "red",
          size = 0.15)+
tm_shape(restaurants)+
  tm_dots(col = "yellow",
          size = 0.15)+
tm_shape(employers)+
  tm_dots(col = "blue",
          size = 0.15)

4. Load data of CheckinJournal, data wrangling

Number_of_Visits <- read_csv("data/th_5_data/CheckinJournal.csv")

Number_of_Visits <- Number_of_Visits %>% 
  filter (venueType == "Pub" | venueType == "Restaurant" | venueType == "Workplace") %>% 
  group_by (venueId) %>% 
  mutate(count = n()) %>% 
  select(venueId, count) %>% 
  distinct() %>% 
  mutate(venueId = as.character(venueId))
write_rds(Number_of_Visits, "data/rds/Number_of_Visits.rds")

4.1. Join pub, restaurants, employers with the No. of visits

Number_of_Visits <- read_rds("data/rds/Number_of_Visits.rds")

pub_visits <- pubs %>% 
  left_join(Number_of_Visits,
            by = c("pubId" = "venueId"))
write_rds(pub_visits, 
          "data/rds/pub_visits.rds")

restaurant_visits <- restaurants %>% 
  left_join(Number_of_Visits, 
            by = c("restaurantId" ="venueId"))
write_rds(restaurant_visits,
          "data/rds/restaurant_visits.rds")

employer_visits<- employers %>% 
  left_join(Number_of_Visits, 
            by = c("employerId" ="venueId"))
write_rds(employer_visits,
          "data/rds/employer_visits.rds")

5. Visualize the No. of visits to pubs

tmap_mode("plot")
tm_shape(bldgs)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(pub_visits)+
  tm_dots(col = "red",
          size = "count")

6. Visualize the No. of visits to restaurants

tmap_mode("plot")
tm_shape(bldgs)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(restaurant_visits) +
  tm_bubbles(col = "yellow",
             size="count")

7. Visualize the No. of visits to employers

tmap_mode("plot")
tm_shape(bldgs)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(employer_visits) +
  tm_bubbles(col = "blue",
             size="count")