library(tidyverse) 
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library(sf)
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library(tmap)
library(spatstat)
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## For an introduction to spatstat, type 'beginner'
library(maptools)
## Loading required package: sp
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## Please note that 'maptools' will be retired by the end of 2023,
## plan transition at your earliest convenience;
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library(sqldf)
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library(ggplot2)
library(spatstat.geom)
library(leaflet.providers)
library(markdown)
library(polyclip)
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library(ggmap)
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
library(dplyr)
library(jsonlite)
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library(devtools)
## Loading required package: usethis
library(yelpr)
hotel_resp <- read.csv("hotel_response.csv")
grocery_resp <- read.csv("grocery_response.csv") 
#Organize: stack the data & create new column called "term" that's either called hotel or grocery.
hotel_resp$type <- "hotel"
grocery_resp$type <- "grocery"
resp_stack <- rbind(hotel_resp, grocery_resp)

#Convert resp into a spatial file
stack_sf <- st_as_sf(resp_stack, coords = c("coordinates.longitude","coordinates.latitude"), 
                     dim = "XY", crs = 4326) %>% 
                      na.omit()

#Filter out anomaly
stack_sf <- filter(stack_sf, id != "dw0dQNw3d6MlYHO7psLRNA")
tmap_mode('view')
## tmap mode set to interactive viewing
tm_shape(stack_sf) + tm_dots(col = "type", id = "name")
grocery_sf <- filter(stack_sf, type == "grocery")
hotel_sf <- filter(stack_sf, type == "hotel")

grocery.proj <- st_transform(grocery_sf, crs = 26967) 
grocery.sp  <- as(grocery.proj, "Spatial")  # Create Spatial Object
grocery.ppp <- as(grocery.sp, "ppp") 

hotel.proj <- st_transform(hotel_sf, crs = 26967) 
hotel.sp  <- as(hotel.proj, "Spatial")  # Create Spatial Object
hotel.ppp <- as(hotel.sp, "ppp")

#Q1 - Report on the Chi Squared Test results for the hotels and grocery stores

hotel.qc <- quadratcount(hotel.ppp, nx=4, ny=4)
hotel.qt <- quadrat.test(hotel.qc)
print(hotel.qt)
## 
##  Chi-squared test of CSR using quadrat counts
## 
## data:  
## X2 = 375.85, df = 15, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 
## Quadrats: 4 by 4 grid of tiles
grocery.qc <- quadratcount(grocery.ppp, nx=4, ny=4)
grocery.qt <- quadrat.test(grocery.qc)
print(grocery.qt)
## 
##  Chi-squared test of CSR using quadrat counts
## 
## data:  
## X2 = 164.46, df = 15, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 
## Quadrats: 4 by 4 grid of tiles

#Question 2 - Show quadrant maps for Hotel and Grocery Stores

plot(hotel.qc, main = "Quadrant Analysis of POI type : hotel")

plot(grocery.qc, main = "Quadrant Analysis of POI type : grocery")

#Question 3 - What are the three issues with quadrat analysis?

• A change in Quadrat count completely changes the statistical analysis. • It does not take account of the context very well. The formation of the boxes does not follow the constraints, boundaries, forms of cities or regions. • It does not provide information about clustering or dispersal within a box. • It can measure primarily static data. It might not be suitable to analyze parameters like movement.

#Question 4 - Create a Nearest Neighbor Plot (G Plot) for Hotels and Grocery Stores. Describe which is clustered the most and how you know ?

hotel.G <- Gest(hotel.ppp)
grocery.G <- Gest(grocery.ppp)

plot(hotel.G, . ~ r, col = "red", main = "Nearest Neighbor for Hotels in Red and Grocery Stores in Black", legend = FALSE)
plot(grocery.G, . ~ r, col = "black", legend = FALSE, add = TRUE )

#Hotels cluster more than grocery stores (G(r) increases at a greater rate with respect to r for red line (Hotels)