miami

library(ggplot2)
library(dplyr)
library(flexdashboard)
library(tidyverse)
library(janitor)
library(usethis)
library(devtools)
library(plotly)
library(rlang)
library(sf)
library(sp)
library(spdep)
library(tmap)
library(tmaptools)
library(mapdeck)
library(cartography)
library(igraph)
library(rgdal)
library(shp2graph)
library(leaflet)
library(mapproj)
library(DCluster)
library(ggmap)
library(tidyverse)

miami_grid_all <- st_read("D:/google_drive/00_city_logistics/01_production/shapefiles/eua/miami_grid_all.shp")
miami_ware <- st_read("D:/google_drive/00_city_logistics/01_production/shapefiles/eua/miami_ware.shp")
miami_poi <- st_read("D:/google_drive/00_city_logistics/01_production/shapefiles/eua/miami_poi.shp")
miami_road <- st_read("D:/google_drive/00_city_logistics/01_production/shapefiles/eua/miami_road.shp")

miami_grid_all <- miami_grid_all[,-c(1,2,3, 5, 6, 7, 8)]

names(miami_grid_all) <- c("id", "poi", "km", "wh", "avg_price", "sum_size", "avg_size", "geometry")

miami_grid_all <- mutate(miami_grid_all, ipoi = poi/12188)
miami_grid_all <- mutate(miami_grid_all, ikm = km/89618.46)
miami_grid_all <- mutate(miami_grid_all, iwh = wh/701)
miami_grid_all <- mutate(miami_grid_all, iprice = avg_price/1.111669)
miami_grid_all <- mutate(miami_grid_all, isize = avg_size/59538.14)
miami_grid_all <- mutate(miami_grid_all, wh_per_poi = wh/poi)
miami_grid_all <- mutate(miami_grid_all, wh_per_km = wh/km)

library(corrplot)
#> corrplot 0.84 loaded
data <- miami_grid_all
st_geometry(data) <- NULL
data <- as.matrix(data)
str(data)
#>  num [1:825, 1:14] 752 1002 328 1175 782 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : chr [1:825] "1" "2" "3" "4" ...
#>   ..$ : chr [1:14] "id" "poi" "km" "wh" ...
corrplot(cor(data))

lm1 <- lm(wh ~ km + avg_price, data=miami_grid_all)
summary(lm1)
#> 
#> Call:
#> lm(formula = wh ~ km + avg_price, data = miami_grid_all)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -10.527  -0.282   0.189   0.335  55.668 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -0.334888   0.176964  -1.892   0.0588 .  
#> km           0.005187   0.001157   4.485 8.32e-06 ***
#> avg_price    6.314092   0.558799  11.299  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 4.146 on 822 degrees of freedom
#> Multiple R-squared:  0.2851, Adjusted R-squared:  0.2833 
#> F-statistic: 163.9 on 2 and 822 DF,  p-value: < 2.2e-16

lm2 <- lm(wh ~ avg_price, data=miami_grid_all)
summary(lm2)
#> 
#> Call:
#> lm(formula = wh ~ avg_price, data = miami_grid_all)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#> -10.82  -0.08  -0.08  -0.08  56.74 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   0.0801     0.1526   0.525      0.6    
#> avg_price     7.8238     0.4512  17.340   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 4.193 on 823 degrees of freedom
#> Multiple R-squared:  0.2676, Adjusted R-squared:  0.2667 
#> F-statistic: 300.7 on 1 and 823 DF,  p-value: < 2.2e-16

lm3 <- lm(iwh ~ iprice, data=miami_grid_all)
summary(lm3)
#> 
#> Call:
#> lm(formula = iwh ~ iprice, data = miami_grid_all)
#> 
#> Residuals:
#>       Min        1Q    Median        3Q       Max 
#> -0.015429 -0.000114 -0.000114 -0.000114  0.080944 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 0.0001143  0.0002177   0.525      0.6    
#> iprice      0.0124073  0.0007155  17.340   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.005982 on 823 degrees of freedom
#> Multiple R-squared:  0.2676, Adjusted R-squared:  0.2667 
#> F-statistic: 300.7 on 1 and 823 DF,  p-value: < 2.2e-16

lm4 <- lm(iprice ~ ipoi, data=miami_grid_all)
summary(lm4)
#> 
#> Call:
#> lm(formula = iprice ~ ipoi, data = miami_grid_all)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1.13059 -0.05052 -0.04776 -0.04776  1.43216 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  0.04776    0.01001   4.773 2.14e-06 ***
#> ipoi        33.58136    2.85586  11.759  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.2696 on 823 degrees of freedom
#> Multiple R-squared:  0.1438, Adjusted R-squared:  0.1428 
#> F-statistic: 138.3 on 1 and 823 DF,  p-value: < 2.2e-16

lm5 <- lm(iprice ~ ikm, data=miami_grid_all)
summary(lm5)
#> 
#> Call:
#> lm(formula = iprice ~ ikm, data = miami_grid_all)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -0.62298 -0.06074  0.01541  0.03334  1.28336 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  -0.033337   0.009862   -3.38 0.000758 ***
#> ikm         100.501564   4.642392   21.65  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.2326 on 823 degrees of freedom
#> Multiple R-squared:  0.3628, Adjusted R-squared:  0.3621 
#> F-statistic: 468.7 on 1 and 823 DF,  p-value: < 2.2e-16

lm6 <- lm(iprice ~ iwh + ikm +ipoi, data=miami_grid_all)
summary(lm6)
#> 
#> Call:
#> lm(formula = iprice ~ iwh + ikm + ipoi, data = miami_grid_all)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -0.87382 -0.05358  0.00778  0.02341  1.24779 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -0.023415   0.009261  -2.528   0.0116 *  
#> iwh         13.502295   1.189419  11.352   <2e-16 ***
#> ikm         82.579687   5.969938  13.833   <2e-16 ***
#> ipoi        -3.764524   3.025416  -1.244   0.2137    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.2165 on 821 degrees of freedom
#> Multiple R-squared:  0.4495, Adjusted R-squared:  0.4475 
#> F-statistic: 223.5 on 3 and 821 DF,  p-value: < 2.2e-16

lm7 <- lm(iprice ~ iwh + ikm , data=miami_grid_all)
summary(lm7)
#> 
#> Call:
#> lm(formula = iprice ~ iwh + ikm, data = miami_grid_all)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -0.86080 -0.05277  0.00754  0.02244  1.25512 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -0.022441   0.009231  -2.431   0.0153 *  
#> iwh         13.426614   1.188259  11.299   <2e-16 ***
#> ikm         78.085924   4.755255  16.421   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.2166 on 822 degrees of freedom
#> Multiple R-squared:  0.4485, Adjusted R-squared:  0.4472 
#> F-statistic: 334.2 on 2 and 822 DF,  p-value: < 2.2e-16