Take Home Exercise 3

Published

March 26, 2023

Modified

March 25, 2023

1 Setting The Scene

Housing is an essential component of household wealth worldwide. Buying a housing has always been a major investment for most people. The price of housing is affected by many factors. Some of them are global in nature such as the general economy of a country or inflation rate. Others can be more specific to the properties themselves. These factors can be further divided to structural and locational factors. Structural factors are variables related to the property themselves such as the size, fitting, and tenure of the property. Locational factors are variables related to the neighbourhood of the properties such as proximity to childcare centre, public transport service and shopping centre.

Conventional, housing resale prices predictive models were built by using Ordinary Least Square (OLS) method. However, this method failed to take into consideration that spatial autocorrelation and spatial heterogeneity exist in geographic data sets such as housing transactions. With the existence of spatial autocorrelation, the OLS estimation of predictive housing resale pricing models could lead to biased, inconsistent, or inefficient results (Anselin 1998). In view of this limitation, Geographical Weighted Models were introduced for calibrating predictive model for housing resale prices.

2 The Task

In this take-home exercise, you are tasked to predict HDB resale prices at the sub-market level (i.e. HDB 3-room, HDB 4-room and HDB 5-room) for the month of January and February 2023 in Singapore. The predictive models must be built by using by using conventional OLS method and GWR methods. You are also required to compare the performance of the conventional OLS method versus the geographical weighted methods.

3 Importing Required R Packages

The following packages are used in this assignment

  • Building Ordinary Least Square model and performing diagnostics tests

  • Calibrating geographical weighted family of models

  • Multivariate data visualisation and analysis

  • Random Forest model generation

  • Converting JSON objects to R data types

  • Spatial data handling

    • sf

  • Attribute data handling

    • tidyverse, especially readr, ggplot2 and dplyr

  • Choropleth mapping

    • tmap

  • Handling spatial autocorrelation calculations

  • For API queries calls for Singapore specific data

  • Summarising model statistics

pacman::p_load(sf, tidyverse, tmap, jsonlite, rvest, onemapsgapi, olsrr, corrplot, ggpubr, spdep, GWmodel, gtsummary, ggthemes, zoo, SpatialML, tmap, rsample, Metrics, matrixStats, kableExtra)

4 The Data

Structural and Locational factors will be extracted:

  • Structural factors

    • Area of the unit

    • Floor level

    • Remaining lease

    • Age of the unit

  • Locational factors

    • Proxomity to CBD

    • Proximity to eldercare

    • Proximity to hawker centres

    • Proximity to MRT

    • Proximity to park

    • Proximity to good primary school

    • Proximity to shopping mall

    • Proximity to supermarket

    • Number of kindergartens within 350m

    • Number of childcare centres within 350m

    • Number of bus stop within 350m

    • Number of primary schools within 1km

The following table describes the data sources used in this assignment.

Show Code 
datasets <- data.frame(
  Type=c("Aspatial",
         "Geospatial",
         "Geospatial - Extracted",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial - Extracted",
         "Geospatial - Extracted",
         "Geospatial - Extracted",
         "Geospatial - Extracted",
         "Geospatial",
         "Geospatial",
         "Geospatial"
         ),
  
  Name=c("Resale Flat Prices",
         "Master Plan 2019 Subzone Boundary (Web)",
         "Central Business District Coordinates",
         "Eldercare Services",
         "Hawker Centres",
         "MRT Stations",
         "Parks",
         "Good Primary Schools",
         "Shopping Mall",
         "Supermarkets",
         "Kindergartens",
         "Childcare Services",
         "Bus Stops",
         "Primary Schools"),
  
  Format=c(".csv", 
           ".shp", 
           ".shp", 
           ".shp", 
           ".geojson",
           ".shp", 
           ".kml", 
           ".shp", 
           ".geojson",
           ".geojson", 
           ".shp",
           ".geojson",
           ".shp",
           ".xlsx"
           ),
  
  Source=c("[data.gov.sg](https://data.gov.sg/dataset/resale-flat-prices)",
           "[In-Class Ex 9](https://data.gov.sg/dataset/master-plan-2019-subzone-boundary-no-sea)",
           "[latlong.net](https://www.latlong.net/place/downtown-core-singapore-20616.html)",
           "[data.gov.sg](https://data.gov.sg/dataset/eldercare-services)",
           "[data.gov.sg](https://data.gov.sg/dataset/hawker-centres)",
           "[Datamall](https://datamall.lta.gov.sg/content/dam/datamall/datasets/Geospatial/TrainStationExit.zip)",
           "[data.gov.sg](https://data.gov.sg/dataset/parks)",
           
           "[salary.sg](https://www.salary.sg/2022/best-primary-schools-2022-by-popularity/)",
           "[Mall SVY21 Coordinates Web Scaper](https://github.com/ValaryLim/Mall-Coordinates-Web-Scraper)",
           "[data.gov.sg](https://data.gov.sg/dataset/supermarkets)",
           "[OneMap API](https://www.onemap.gov.sg/docs/)",
           "[Hands-on Ex 3](https://annatrw-is415.netlify.app/hands-on_ex/hands-on_ex03/hands-on_ex03)",
           "[Datamall](https://datamall.lta.gov.sg/content/dam/datamall/datasets/Geospatial/BusStopLocation.zip)",
           "[data.gov.sg](https://data.gov.sg/dataset/school-directory-and-information)"
           )
  )

# with reference to this guide on kableExtra:
# https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html
# kable_material is the name of the kable theme
# 'hover' for to highlight row when hovering, 'scale_down' to adjust table to fit page width
library(knitr)
kable(datasets, caption="Datasets Used") %>%
  kable_material("hover", latex_options="scale_down")
Datasets Used
Type Name Format Source
Aspatial Resale Flat Prices .csv [data.gov.sg](https://data.gov.sg/dataset/resale-flat-prices)
Geospatial Master Plan 2019 Subzone Boundary (Web) .shp [In-Class Ex 9](https://data.gov.sg/dataset/master-plan-2019-subzone-boundary-no-sea)
Geospatial - Extracted Central Business District Coordinates .shp [latlong.net](https://www.latlong.net/place/downtown-core-singapore-20616.html)
Geospatial Eldercare Services .shp [data.gov.sg](https://data.gov.sg/dataset/eldercare-services)
Geospatial Hawker Centres .geojson [data.gov.sg](https://data.gov.sg/dataset/hawker-centres)
Geospatial MRT Stations .shp [Datamall](https://datamall.lta.gov.sg/content/dam/datamall/datasets/Geospatial/TrainStationExit.zip)
Geospatial Parks .kml [data.gov.sg](https://data.gov.sg/dataset/parks)
Geospatial - Extracted Good Primary Schools .shp [salary.sg](https://www.salary.sg/2022/best-primary-schools-2022-by-popularity/)
Geospatial - Extracted Shopping Mall .geojson [Mall SVY21 Coordinates Web Scaper](https://github.com/ValaryLim/Mall-Coordinates-Web-Scraper)
Geospatial - Extracted Supermarkets .geojson [data.gov.sg](https://data.gov.sg/dataset/supermarkets)
Geospatial - Extracted Kindergartens .shp [OneMap API](https://www.onemap.gov.sg/docs/)
Geospatial Childcare Services .geojson [Hands-on Ex 3](https://annatrw-is415.netlify.app/hands-on_ex/hands-on_ex03/hands-on_ex03)
Geospatial Bus Stops .shp [Datamall](https://datamall.lta.gov.sg/content/dam/datamall/datasets/Geospatial/BusStopLocation.zip)
Geospatial Primary Schools .xlsx [data.gov.sg](https://data.gov.sg/dataset/school-directory-and-information)

5 Structural Factors

5.1 Import HDB Resale Prices Data

Using read_csv(), import the HDB resale price data.

resale_all <- read_csv("data/aspatial/resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv")

This is how the resale data looks like.

glimpse(resale_all)

Filter out 2021 and 2022 data as per assignment requirement using grepl().

resale2122 <- filter(resale_all, grepl('2021', month)|grepl('2022', month))

Filter out 2023 data for testing.

resale23_test <- filter(resale_all, grepl('2023', month))

For the purposes of this assignment, we will be focusing on 5 Room HDB resale flat prices.

unique(resale2122$flat_type)

resale2122_5r <- filter(resale2122, flat_type == '5 ROOM')
resale23_test <- filter(resale23_test, flat_type == '5 ROOM')

5.2 Geocode Resale Data

Referencing senior’s work, this geocoding function using OneMap API will retrieve the latitude and longitude for resale data.

library(httr)
geocode_function <- function(block, street_name) {
  base_url <- "https://developers.onemap.sg/commonapi/search"
  address <- paste(block, street_name, sep = " ")
  query <- list("searchVal" = address, 
                "returnGeom" = "Y",
                "getAddrDetails" = "N",
                "pageNum" = "1")
  
  res <- GET(base_url, query = query)
  restext<-content(res, as="text")
  
  output <- fromJSON(restext)  %>% 
    as.data.frame %>%
    select(results.LATITUDE, results.LONGITUDE)

  return(output)
}
resale2122_5r$street_name <- gsub("ST\\.", "SAINT", resale2122_5r$street_name)

Execute the geocoding function

resale2122_5r$LATITUDE <- 0
resale2122_5r$LONGITUDE <- 0

for (i in 1:nrow(resale2122_5r)){
  temp_output <- geocode_function(resale2122_5r[i, 4], resale2122_5r[i, 5])
  
  resale2122_5r$LATITUDE[i] <- temp_output$results.LATITUDE
  resale2122_5r$LONGITUDE[i] <- temp_output$results.LONGITUDE
}

We check if there are null values in the coordinate columns, in which there are none.

sum(is.na(resale2122_5r$LATITUDE))
sum(is.na(resale2122_5r$LONGITUDE))
resale23_test$street_name <- gsub("ST\\.", "SAINT", resale23_test$street_name)

Execute the geocoding function

resale23_test$LATITUDE <- 0
resale23_test$LONGITUDE <- 0

for (i in 1:nrow(resale23_test)){
  temp_output <- geocode_function(resale23_test[i, 4], resale23_test[i, 5])
  
  resale23_test$LATITUDE[i] <- temp_output$results.LATITUDE
  resale23_test$LONGITUDE[i] <- temp_output$results.LONGITUDE
}
sum(is.na(resale23_test$LATITUDE))
sum(is.na(resale23_test$LONGITUDE))

5.3 Remaining Lease

Referencing senior’s work, the remaining lease in years is retrieved by converting years and months into years.

str_list <- str_split(resale2122_5r$remaining_lease, " ")

for (i in 1:length(str_list)) {
  if (length(unlist(str_list[i])) > 2) {
      year <- as.numeric(unlist(str_list[i])[1])
      month <- as.numeric(unlist(str_list[i])[3])
      resale2122_5r$remaining_lease[i] <- year + round(month/12, 2)
  }
  else {
    year <- as.numeric(unlist(str_list[i])[1])
    resale2122_5r$remaining_lease[i] <- year
  }
}
str_list <- str_split(resale23_test$remaining_lease, " ")

for (i in 1:length(str_list)) {
  if (length(unlist(str_list[i])) > 2) {
      year <- as.numeric(unlist(str_list[i])[1])
      month <- as.numeric(unlist(str_list[i])[3])
      resale23_test$remaining_lease[i] <- year + round(month/12, 2)
  }
  else {
    year <- as.numeric(unlist(str_list[i])[1])
    resale23_test$remaining_lease[i] <- year
  }
}

5.4 Floor Level

Referencing senior’s work, the floor level is retrieved by first sorting the storeys in ascending order, then assigning a numerical value to each category.

storeys <- sort(unique(resale2122_5r$storey_range))
storey_order <- 1:length(storeys)
storey_range_order <- data.frame(storeys, storey_order)
resale2122_5r <- left_join(resale2122_5r, storey_range_order, by= c("storey_range" = "storeys"))
storeys <- sort(unique(resale23_test$storey_range))
storey_order <- 1:length(storeys)
storey_range_order <- data.frame(storeys, storey_order)
resale23_test <- left_join(resale23_test, storey_range_order, by= c("storey_range" = "storeys"))

5.5 Age of Unit

Convert the date field to a date data type, which assumes the first day of the month since date is in yyyy/mm format; referencing sources online.

date <- as.Date(as.yearmon(resale2122_5r$month))
resale2122_5r$start <- as.numeric(format(date,'%Y'))

Age of unit calculation will only consider the year when the flat was sold.

resale2122_5r$UNIT_AGE <- resale2122_5r$start - resale2122_5r$lease_commence_date
date <- as.Date(as.yearmon(resale23_test$month))
resale23_test$start <- as.numeric(format(date,'%Y'))
resale23_test$UNIT_AGE <- resale23_test$start - resale23_test$lease_commence_date

5.6 Convert Resale Data To sf Object

Convert the dataframe into an sf object with geometry field attached, referencing here.

resale2122_5r_sf <- st_as_sf(resale2122_5r, 
                      coords = c("LONGITUDE", 
                                 "LATITUDE"), 
                      # the geographical features are in longitude & latitude, in decimals
                      # as such, WGS84 is the most appropriate coordinates system
                      crs=4326) %>%
  #afterwards, we transform it to SVY21, our desired CRS
  st_transform(crs = 3414)
resale23_test_sf <- st_as_sf(resale23_test, 
                      coords = c("LONGITUDE", 
                                 "LATITUDE"), 
                      # the geographical features are in longitude & latitude, in decimals
                      # as such, WGS84 is the most appropriate coordinates system
                      crs=4326) %>%
  #afterwards, we transform it to SVY21, our desired CRS
  st_transform(crs = 3414)

6 Locational Factors

As mentioned, the following locational factors will be used for predictive modelling of HDB resale prices.

  • Proximity to CBD
  • Proximity to eldercare services
  • Proximity to hawker centres
  • Proximity to MRT stations
  • Proximity to parks
  • Proximity to good primary schools
  • proximity to shopping malls
  • proximity to supermarkets
  • Number of kindergarten within 350m
  • Number of child care services within 350m
  • Number of bus stops within 350m
  • Number of primary schools within 1km

6.1 Factors With Geographic Coordinates

6.1.1 Self-sourced and referenced

6.1.1.1 Kindergartens

Using OneMap API retrieval methods, my personal token was pre-loaded into the token variable and used to call the OneMap API to retrieve location of kindergartens in Singapore as an sf object.

search_themes(token, "kindergarten")
get_theme_status(token, "kindergartens")
themetibble <- get_theme(token, "kindergartens")
kindergarten_sf <- st_as_sf(themetibble, coords=c("Lng", "Lat"), crs=4326)

6.1.1.2 Shopping Malls

With reference to a previously done project to scrape the locations of shopping malls in Singapore, read in the csv and convert it to an sf object with appropriate projection system.

mall_csv <- read_csv("data/geospatial/mall_coordinates_updated.csv")
glimpse(mall_csv)
malls_sf <- st_as_sf(mall_csv,
                    coords = c("longitude", 
                               "latitude"),
                    crs=4326) %>%
  st_transform(crs = 3414)

6.1.2 Geojson Sources

Import the geojson files.

elder_sf <- st_read(dsn = "data/geospatial/ElderCare", layer="ELDERCARE")
mrt_sf <- st_read(dsn = "data/geospatial/TrainStationExit", layer="Train_Station_Exit_Layer")
bus_sf <- st_read(dsn = "data/geospatial/BusStop_Feb2023", layer="BusStop")

hawker_sf <- st_read("data/geospatial/hawker-centres-geojson.geojson") 
parks_sf <- st_read("data/geospatial/parks.kml") 
supermkt_sf <- st_read("data/geospatial/supermarkets-geojson.geojson") 
childcare_sf <- st_read("data/geospatial/child-care-services-geojson.geojson") 

mpsz_sf <- st_read(dsn="data/geospatial/MPSZ2019", layer = "MPSZ-2019")

6.1.2.1 CRS

Check the CRS projection for the layers.

st_crs(elder_sf)
st_crs(mrt_sf)
st_crs(bus_sf)
st_crs(hawker_sf)
st_crs(parks_sf)
st_crs(supermkt_sf)
st_crs(childcare_sf)
st_crs(mpsz_sf)
st_crs(kindergarten_sf)
st_crs(malls_sf)

They are projected wrongly - using EPSG 9001 - when it should be projected to EPSG 4326 instead.

Set correct CRS except for malls_sf which is already EPSG 3414 done in previous section when transforming to sf object.

mpsz_sf <- st_set_crs(mpsz_sf, 3414)
elder_sf <- st_set_crs(elder_sf, 3414)
mrt_sf <- st_set_crs(mrt_sf, 3414)
bus_sf <- st_set_crs(bus_sf, 3414)

hawker_sf <- hawker_sf %>%
  st_transform(crs = 3414)
parks_sf <- parks_sf %>%
  st_transform(crs = 3414)
supermkt_sf <- supermkt_sf %>%
  st_transform(crs = 3414)
childcare_sf <- childcare_sf %>%
  st_transform(crs = 3414)
kindergarten_sf <- kindergarten_sf %>%
  st_transform(crs = 3414)

Verify that there are no invalid geometries:

length(which(st_is_valid(mpsz_sf) == FALSE))
length(which(st_is_valid(elder_sf) == FALSE))
length(which(st_is_valid(mrt_sf) == FALSE))
length(which(st_is_valid(bus_sf) == FALSE))
length(which(st_is_valid(hawker_sf) == FALSE))
length(which(st_is_valid(supermkt_sf) == FALSE))
length(which(st_is_valid(parks_sf) == FALSE))
length(which(st_is_valid(childcare_sf) == FALSE))
length(which(st_is_valid(kindergarten_sf) == FALSE))
length(which(st_is_valid(malls_sf) == FALSE))

There are invalid geometries for mpsz_sf; which can be rectified with st_make_valid().

mpsz_sf <- sf::st_make_valid(mpsz_sf)
length(which(st_is_valid(mpsz_sf) == FALSE))

6.1.3 Calculate Proximity

Using a proximity function from senior’s work, st_distance is used to compute a distance matrix which will retrieve the minimum distance from origin to destination.

get_prox <- function(origin_df, dest_df, col_name){
  
  # creates a matrix of distances
  dist_matrix <- st_distance(origin_df, dest_df)           
  
  # find the nearest location_factor and create new data frame
  near <- origin_df %>% 
    mutate(PROX = apply(dist_matrix, 1, function(x) min(x)) / 1000) 
  
  # rename column name according to input parameter
  names(near)[names(near) == 'PROX'] <- col_name

  # Return df
  return(near)
}
resale2122_5r_sf <- get_prox(resale2122_5r_sf, elder_sf, "PROX_ELDERCARE") 
resale2122_5r_sf <- get_prox(resale2122_5r_sf, mrt_sf, "PROX_MRT")
resale2122_5r_sf <- get_prox(resale2122_5r_sf, hawker_sf, "PROX_HAWKER") 
resale2122_5r_sf <- get_prox(resale2122_5r_sf, parks_sf, "PROX_PARK") 
resale2122_5r_sf <- get_prox(resale2122_5r_sf, supermkt_sf, "PROX_SUPERMARKET")
resale2122_5r_sf <- get_prox(resale2122_5r_sf, malls_sf, "PROX_MALL")
resale23_test_sf <- get_prox(resale23_test_sf, elder_sf, "PROX_ELDERCARE") 
resale23_test_sf <- get_prox(resale23_test_sf, mrt_sf, "PROX_MRT")
resale23_test_sf <- get_prox(resale23_test_sf, hawker_sf, "PROX_HAWKER") 
resale23_test_sf <- get_prox(resale23_test_sf, parks_sf, "PROX_PARK") 
resale23_test_sf <- get_prox(resale23_test_sf, supermkt_sf, "PROX_SUPERMARKET")
resale23_test_sf <- get_prox(resale23_test_sf, malls_sf, "PROX_MALL")

6.2 Calculate Number of Amenities

Using a function to get the number of amenities within a certain distance from senior’s work, the get within function counts the number of amenities within the threshold distance.

get_within <- function(origin_df, dest_df, threshold_dist, col_name){
  
  # creates a matrix of distances
  dist_matrix <- st_distance(origin_df, dest_df)   
  
  # count the number of location_factors within threshold_dist and create new data frame
  wdist <- origin_df %>% 
    mutate(WITHIN_DT = apply(dist_matrix, 1, function(x) sum(x <= threshold_dist)))
  
  # rename column name according to input parameter
  names(wdist)[names(wdist) == 'WITHIN_DT'] <- col_name

  # Return df
  return(wdist)
}

6.2.0.1 Number of Kindergartens Within 350m

resale2122_5r_sf <- get_within(resale2122_5r_sf, kindergarten_sf, 350, "WITHIN_350M_KINDERGARTEN")

6.2.0.2 Number of Childcare Centres Within 350m

resale2122_5r_sf <- get_within(resale2122_5r_sf, childcare_sf, 350, "WITHIN_350M_CHILDCARE")

6.2.0.3 Number of Bus Stops Within 350m

resale2122_5r_sf <- get_within(resale2122_5r_sf, bus_sf, 350, "WITHIN_350M_BUS")

6.2.0.4 Number of Kindergartens Within 350m

resale23_test_sf <- get_within(resale23_test_sf, kindergarten_sf, 350, "WITHIN_350M_KINDERGARTEN")

6.2.0.5 Number of Childcare Centres Within 350m

resale23_test_sf <- get_within(resale23_test_sf, childcare_sf, 350, "WITHIN_350M_CHILDCARE")

6.2.0.6 Number of Bus Stops Within 350m

resale23_test_sf <- get_within(resale23_test_sf, bus_sf, 350, "WITHIN_350M_BUS")

6.3 Factors Without Geographic Coordinates

6.3.1 CBD

Get the coordinates of Singapore’s Central Business District in Downtown Core using latlong.net: 1.287953, 103.851784

name <- c('CBD')
latitude= c(1.287953)
longitude= c(103.851784)
cbd <- data.frame(name, latitude, longitude)
cbd_sf <- st_as_sf(cbd, coords = c("longitude", "latitude"), crs = 4326) %>%
  st_transform(crs = 3414)
st_crs(cbd_sf)

Upon checking, the CBD data is in the correct CRS.

6.3.1.1 Get Proximity To CBD

This is done by calling the earlier defined get_prox function.

resale2122_5r_sf <- get_prox(resale2122_5r_sf, cbd_sf, "PROX_CBD") 
resale23_test_sf <- get_prox(resale23_test_sf, cbd_sf, "PROX_CBD")

6.3.2 Primary Schools

Read the csv of schools and filter out the primary schools for study.

pri_sch <- read_csv("data/geospatial/general-information-of-schools.csv")
pri_sch <- pri_sch %>%
  filter(mainlevel_code == "PRIMARY") %>%
  select(school_name, address, postal_code, mainlevel_code)
glimpse(pri_sch)

6.3.2.1 Geocode

Referencing this link, define a get_coords() function that utilises the OneMap API, feeding the postal codes through it to get latitude and longitude.

get_coords <- function(add_list){
  
  # Create a data frame to store all retrieved coordinates
  postal_coords <- data.frame()
    
  for (i in add_list){
    #print(i)

    r <- GET('https://developers.onemap.sg/commonapi/search?',
           query=list(searchVal=i,
                     returnGeom='Y',
                     getAddrDetails='Y'))
    data <- fromJSON(rawToChar(r$content))
    found <- data$found
    res <- data$results
    
    # Create a new data frame for each address
    new_row <- data.frame()
    
    # If single result, append 
    if (found == 1){
      postal <- res$POSTAL 
      lat <- res$LATITUDE
      lng <- res$LONGITUDE
      new_row <- data.frame(address= i, postal = postal, latitude = lat, longitude = lng)
    }
    
    # If multiple results, drop NIL and append top 1
    else if (found > 1){
      # Remove those with NIL as postal
      res_sub <- res[res$POSTAL != "NIL", ]
      
      # Set as NA first if no Postal
      if (nrow(res_sub) == 0) {
          new_row <- data.frame(address= i, postal = NA, latitude = NA, longitude = NA)
      }
      
      else{
        top1 <- head(res_sub, n = 1)
        postal <- top1$POSTAL 
        lat <- top1$LATITUDE
        lng <- top1$LONGITUDE
        new_row <- data.frame(address= i, postal = postal, latitude = lat, longitude = lng)
      }
    }

    else {
      new_row <- data.frame(address= i, postal = NA, latitude = NA, longitude = NA)
    }
    
    # Add the row
    postal_coords <- rbind(postal_coords, new_row)
  }
  return(postal_coords)
}

We extract the unique postal codes and feed them through the get_coords function.

prisch_postal <- sort(unique(pri_sch$postal_code))
prisch_coords <- get_coords(prisch_postal)

This code checks for any null values in the retrieved coordinates data.

prisch_coords[(is.na(prisch_coords$postal) | is.na(prisch_coords$latitude) | is.na(prisch_coords$longitude)), ]
glimpse(prisch_coords)

We can safely combine the retrieved coordinates with the original primary schools csv data.

prisch_coords = prisch_coords[c("postal","latitude", "longitude")]
pri_sch <- left_join(pri_sch, prisch_coords, by = c('postal_code' = 'postal'))

Lastly, we convert to it to an sf object with the correct projection.

prisch_sf <- st_as_sf(pri_sch,
                    coords = c("longitude", 
                               "latitude"),
                    crs=4326) %>%
  st_transform(crs = 3414)

6.3.2.2 Number of Primary Schools Within 1km

Run the primary schools data through the get_within function to get the number of primary schools within 1km.

resale2122_5r_sf <- get_within(resale2122_5r_sf, prisch_sf, 1000, "WITHIN_1KM_PRISCH")
resale23_test_sf <- get_within(resale23_test_sf, prisch_sf, 1000, "WITHIN_1KM_PRISCH")

6.4 Good Primary Schools

With reference to salary.sg, we can extract the top 10 primary schools in 2022. Using the method employed by senior, the following web crawling method is used to search for the html element - which is a list item - on the web page to retrieve the top 10 primary schools.

url <- "https://www.salary.sg/2022/best-primary-schools-2022-by-popularity/"

good_pri <- data.frame()

schools <- read_html(url) %>%
  html_nodes(xpath = paste('//*[@id="post-33132"]/div[3]/div/div/ol/li') ) %>%
  html_text() 

for (i in (schools)){
  sch_name <- toupper(gsub(" – .*","",i))
  sch_name <- gsub("\\(PRIMARY SECTION)","",sch_name)
  sch_name <- trimws(sch_name)
  new_row <- data.frame(pri_sch_name=sch_name)
  # Add the row
  good_pri <- rbind(good_pri, new_row)
}

top_good_pri <- head(good_pri, 10)
top_good_pri

Check which top schools are not in prisch_sf.

top_good_pri$pri_sch_name[!top_good_pri$pri_sch_name %in% prisch_sf$school_name]

Get the coordinates list of good primary schools and check for null values, upon which we see that St Hilda’s primary school does not exist in the prisch_sf data.

good_pri_list <- unique(top_good_pri$pri_sch_name)
good_pri_list
goodprisch_coords <- get_coords(good_pri_list)
goodprisch_coords
goodprisch_coords[(is.na(goodprisch_coords$postal) | is.na(goodprisch_coords$latitude) | is.na(goodprisch_coords$longitude)), ]

To rectify this, change the apostrophe symbol for the school title “ST HILDA’S PRIMARY SCHOOL”.

top_good_pri$pri_sch_name[top_good_pri$pri_sch_name == "ST. HILDA’S PRIMARY SCHOOL"] <- "ST. HILDA'S PRIMARY SCHOOL"

Here, we check once again for null values in the new coordinates list.

good_pri_list <- unique(top_good_pri$pri_sch_name)
good_pri_list
goodprisch_coords <- get_coords(good_pri_list)
goodprisch_coords
goodprisch_coords[(is.na(goodprisch_coords$postal) | is.na(goodprisch_coords$latitude) | is.na(goodprisch_coords$longitude)), ]

Finally, convert the layer into an sf object with the correct projection.

good_pri_sf <- st_as_sf(goodprisch_coords,
                    coords = c("longitude", 
                               "latitude"),
                    crs=4326) %>%
  st_transform(crs = 3414)
resale2122_5r_sf <- get_prox(resale2122_5r_sf, good_pri_sf, "PROX_GOOD_PRISCH")
resale23_test_sf <- get_prox(resale23_test_sf, good_pri_sf, "PROX_GOOD_PRISCH")

6.5 Other Data Processing

For the last steps of data processing, we retain necessary columns by dropping unnecessary one using subset() as well as convert the remaining lease field from a string data type to numeric.

resale2122_5r_sf <- subset(resale2122_5r_sf, select = -c(month, town, flat_type, storey_range, block, street_name, flat_model, lease_commence_date, start)) %>% select(resale_price, everything())
resale2122_5r_sf$remaining_lease <- as.numeric(resale2122_5r_sf$remaining_lease)
glimpse(resale2122_5r_sf)
resale23_test_sf <- subset(resale23_test_sf, select = -c(month, town, flat_type, storey_range, block, street_name, flat_model, lease_commence_date, start)) %>% select(resale_price, everything())
resale23_test_sf$remaining_lease <- as.numeric(resale23_test_sf$remaining_lease)

6.6 Write Data To rds File

Save the train and test data that are sf objects into an rds file to reduce the computation and render time, as well as for easy access.

train <- write_rds(resale2122_5r_sf, "data/rds/factors.rds")
test <- write_rds(resale23_test_sf, "data/rds/factors_test.rds")

7 EDA

Since we are building a predictive model, load the train and test data separately.

train <- read_rds("data/rds/factors.rds")
test <- read_rds("data/rds/factors_test.rds")

Our training data has 14,519 rows and 18 columns as shown below.

glimpse(train)
Rows: 14,519
Columns: 18
$ resale_price             <dbl> 483000, 590000, 629000, 670000, 680000, 76000…
$ floor_area_sqm           <dbl> 118, 123, 118, 128, 133, 133, 110, 110, 110, …
$ remaining_lease          <dbl> 59.08, 55.58, 58.67, 74.25, 71.25, 74.50, 84.…
$ storey_order             <int> 1, 5, 6, 3, 1, 5, 8, 5, 6, 6, 5, 5, 8, 1, 2, …
$ UNIT_AGE                 <dbl> 40, 44, 41, 25, 28, 25, 15, 19, 15, 10, 10, 1…
$ geometry                 <POINT [m]> POINT (30820.82 39547.58), POINT (29412…
$ PROX_ELDERCARE           <dbl> 1.0646617, 0.1908834, 0.7891907, 0.1476040, 0…
$ PROX_MRT                 <dbl> 1.0808607, 0.5243923, 0.4159309, 0.2427019, 0…
$ PROX_HAWKER              <dbl> 0.4828156, 0.3317637, 0.3792242, 0.5884497, 0…
$ PROX_PARK                <dbl> 0.7359373, 0.5808933, 0.3087999, 0.2838337, 0…
$ PROX_SUPERMARKET         <dbl> 0.41991387, 0.24554343, 0.31805791, 0.3135757…
$ PROX_MALL                <dbl> 1.2132871, 0.4416229, 0.5494572, 1.5369839, 0…
$ WITHIN_350M_KINDERGARTEN <int> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, …
$ WITHIN_350M_CHILDCARE    <int> 2, 5, 3, 3, 2, 3, 5, 2, 5, 4, 5, 4, 4, 2, 2, …
$ WITHIN_350M_BUS          <int> 2, 8, 6, 11, 6, 8, 4, 9, 5, 9, 7, 9, 9, 7, 6,…
$ PROX_CBD                 <dbl> 9.537543, 8.663223, 9.449166, 9.211988, 8.935…
$ WITHIN_1KM_PRISCH        <int> 1, 3, 2, 3, 3, 3, 4, 2, 2, 2, 2, 2, 2, 3, 2, …
$ PROX_GOOD_PRISCH         <dbl> 2.6201989, 1.2626796, 1.8786592, 0.4463958, 1…

View the summary of the training data to get a quick sense of the data spread and values contained in it.

summary(train)
  resale_price     floor_area_sqm  remaining_lease  storey_order   
 Min.   : 350000   Min.   : 99.0   Min.   :49.08   Min.   : 1.000  
 1st Qu.: 528000   1st Qu.:112.0   1st Qu.:69.92   1st Qu.: 2.000  
 Median : 595000   Median :115.0   Median :77.83   Median : 3.000  
 Mean   : 627297   Mean   :117.4   Mean   :77.61   Mean   : 3.514  
 3rd Qu.: 690000   3rd Qu.:122.0   3rd Qu.:89.08   3rd Qu.: 5.000  
 Max.   :1418000   Max.   :167.0   Max.   :96.75   Max.   :17.000  
    UNIT_AGE              geometry     PROX_ELDERCARE      PROX_MRT      
 Min.   : 2.00   POINT        :14519   Min.   :0.0000   Min.   :0.02293  
 1st Qu.:10.00   epsg:3414    :    0   1st Qu.:0.3561   1st Qu.:0.25435  
 Median :21.00   +proj=tmer...:    0   Median :0.6375   Median :0.43919  
 Mean   :21.45                         Mean   :0.8264   Mean   :0.53943  
 3rd Qu.:29.00                         3rd Qu.:1.1393   3rd Qu.:0.72288  
 Max.   :50.00                         Max.   :8.2660   Max.   :2.12909  
  PROX_HAWKER        PROX_PARK       PROX_SUPERMARKET      PROX_MALL     
 Min.   :0.04949   Min.   :0.06004   Min.   :0.0000007   Min.   :0.0000  
 1st Qu.:0.44137   1st Qu.:0.48334   1st Qu.:0.1808138   1st Qu.:0.3669  
 Median :0.76583   Median :0.71014   Median :0.2784712   Median :0.5474  
 Mean   :0.88310   Mean   :0.79447   Mean   :0.2984345   Mean   :0.6301  
 3rd Qu.:1.17913   3rd Qu.:1.01433   3rd Qu.:0.3934945   3rd Qu.:0.8079  
 Max.   :6.63383   Max.   :6.00302   Max.   :1.6713100   Max.   :4.0092  
 WITHIN_350M_KINDERGARTEN WITHIN_350M_CHILDCARE WITHIN_350M_BUS 
 Min.   :0.000            Min.   : 0.000        Min.   : 0.000  
 1st Qu.:0.000            1st Qu.: 3.000        1st Qu.: 6.000  
 Median :1.000            Median : 4.000        Median : 8.000  
 Mean   :1.075            Mean   : 3.952        Mean   : 8.141  
 3rd Qu.:1.000            3rd Qu.: 5.000        3rd Qu.:10.000  
 Max.   :8.000            Max.   :20.000        Max.   :19.000  
    PROX_CBD      WITHIN_1KM_PRISCH PROX_GOOD_PRISCH  
 Min.   : 1.611   Min.   :0.000     Min.   : 0.07958  
 1st Qu.:10.637   1st Qu.:2.000     1st Qu.: 2.05408  
 Median :13.509   Median :3.000     Median : 3.17117  
 Mean   :12.704   Mean   :3.419     Mean   : 3.37962  
 3rd Qu.:15.250   3rd Qu.:4.000     3rd Qu.: 4.60464  
 Max.   :23.277   Max.   :9.000     Max.   :13.01618

7.1 EDA with Statistical Graphs

summary(train$resale_price)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 350000  528000  595000  627297  690000 1418000 
ggplot(data=train, aes(x=`resale_price`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

From the histogram, We can observe a right skewed distribution of HDB resale prices in 2021 to 2022.

To visualise the shape of independent variables, use a multiple histogram plot below.

FLOOR_AREA_SQM <- ggplot(data=train, aes(x= `floor_area_sqm`)) + 
  geom_histogram(bins=20, color="black", fill="light blue")

LEASE_YRS <- ggplot(data=train, aes(x= `remaining_lease`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

STOREY_ORDER <- ggplot(data=train, aes(x= `storey_order`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

UNIT_AGE <- ggplot(data=train, aes(x= `UNIT_AGE`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

PROX_ELDERCARE <- ggplot(data=train, aes(x= `PROX_ELDERCARE`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

PROX_MRT <- ggplot(data=train, aes(x= `PROX_MRT`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

PROX_HAWKER <- ggplot(data=train, aes(x= `PROX_HAWKER`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

PROX_PARK <- ggplot(data=train, aes(x= `PROX_PARK`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

PROX_SPM <- ggplot(data=train, aes(x= `PROX_SUPERMARKET`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

PROX_MALL <- ggplot(data=train, aes(x= `PROX_MALL`)) +
  geom_histogram(bins=20, color="black", fill="light blue")


PROX_CBD <- ggplot(data=train, aes(x= `PROX_CBD`)) +
  geom_histogram(bins=20, color="black", fill="light blue")


PROX_TOP_SCH <- ggplot(data=train, 
                               aes(x= `PROX_GOOD_PRISCH`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

ggarrange(FLOOR_AREA_SQM, LEASE_YRS, STOREY_ORDER, UNIT_AGE, PROX_ELDERCARE, PROX_MRT, PROX_HAWKER, PROX_PARK, PROX_SPM, PROX_MALL, PROX_CBD, PROX_TOP_SCH,  
          ncol = 3, nrow = 4 )

We can observe a generally right skewed nature of the above variables.

NUM_KG <- ggplot(data=train, aes(x= `WITHIN_350M_KINDERGARTEN`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

NUM_CC <- ggplot(data=train, aes(x= `WITHIN_350M_CHILDCARE`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

NUM_BUS <- ggplot(data=train, aes(x= `WITHIN_350M_BUS`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

NUM_PRISCH <- ggplot(data=train, aes(x= `WITHIN_1KM_PRISCH`)) +
  geom_histogram(bins=20, color="black", fill="light blue")

ggarrange(NUM_KG, NUM_CC, NUM_BUS, NUM_PRISCH, ncol= 2, nrow= 2)

We can observe the right skewed data from ‘WITHIN_350M_KINDERGARTEN’, while the other 3 factors indicate normally distributed childcare, bus stops and primary schools.

7.2 Statistical Point Map

tmap_mode("view")
tmap_options(check.and.fix = TRUE)
Reading layer `MPSZ-2019' from data source 
  `C:\annatrw\IS415\Take-home_Ex\Take-home_Ex03\data\geospatial\MPSZ2019' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84
tm_shape(mpsz_sf)+
  tm_polygons() +
tm_shape(train) +  
  tm_dots(col = "resale_price",
          alpha = 0.6,
          style="quantile") +
  tm_view(set.zoom.limits = c(11, 12))
tmap_mode("plot")

We can observe that the HDB resale prices are high in the CBD, Southern and central areas of Singapore, reaching to $720,000 to $1,418,000 range. Resale flats in the North East (Punggol, Sengkang etc) are beginning to get more expensive, reaching the $625,000 range. The West and Northern districts have a larger majority of cheaper resale HDB flats that are in the $350,000 to $625,000 range.

8 Hedonic Pricing Model

Before we begin building the pricing model for HDB resale prices, we will use the package corrplot to check for multicolinearity. The code chunk below drops the geometry field to plot correlation matrix. It uses the ‘AOE’ order, ordering variables by the eigenvectors method suggested by Michael Friendly.

train_nogeom <- train %>% st_drop_geometry()

corrplot(cor(train_nogeom[, 2:17]), diag = FALSE, order = "AOE",
         tl.pos = "td", tl.cex = 0.5, method = "number", type = "upper")

The correlation matrix above shows that all values are below 0.8 apart from UNIT_AGE and remaining_lease that has a value of 1, indicating they are highly correlated.

Therefore, the UNIT_AGE variable will be excluded from subsequent analysis.

train <- subset(train, select= -c(UNIT_AGE))
test <- subset(test, select= -c(UNIT_AGE))

8.1 Ordinary Least Squares (OLS) Method

Compute the regression model using the lm() function below.

train_nogeom <- train %>% st_drop_geometry()

price_mlr <- lm(resale_price ~ .,
                data=train_nogeom)
summary(price_mlr)

Call:
lm(formula = resale_price ~ ., data = train_nogeom)

Residuals:
    Min      1Q  Median      3Q     Max 
-324299  -54097   -2735   48780  715869 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)              -165691.0    19744.8  -8.392  < 2e-16 ***
floor_area_sqm              5530.3      126.6  43.675  < 2e-16 ***
remaining_lease             5588.6       79.5  70.295  < 2e-16 ***
storey_order               19992.5      352.5  56.723  < 2e-16 ***
PROX_ELDERCARE              1002.0     1180.4   0.849  0.39595    
PROX_MRT                  -25078.5     2016.2 -12.439  < 2e-16 ***
PROX_HAWKER               -32340.1     1382.1 -23.399  < 2e-16 ***
PROX_PARK                   7980.0     1752.5   4.554 5.32e-06 ***
PROX_SUPERMARKET           -8458.9     4651.4  -1.819  0.06900 .  
PROX_MALL                 -23894.5     2090.7 -11.429  < 2e-16 ***
WITHIN_350M_KINDERGARTEN    8418.9      668.8  12.588  < 2e-16 ***
WITHIN_350M_CHILDCARE      -3877.4      390.7  -9.924  < 2e-16 ***
WITHIN_350M_BUS              534.2      245.4   2.177  0.02952 *  
PROX_CBD                  -19925.7      239.9 -83.054  < 2e-16 ***
WITHIN_1KM_PRISCH         -14105.9      502.4 -28.078  < 2e-16 ***
PROX_GOOD_PRISCH           -1322.9      418.2  -3.163  0.00156 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 83390 on 14503 degrees of freedom
Multiple R-squared:  0.6746,    Adjusted R-squared:  0.6742 
F-statistic:  2004 on 15 and 14503 DF,  p-value: < 2.2e-16

8.1.1 Publication Quality Table

Using gt summary package, it produces a summary table for the OLS model output for HDB resale price data.

tbl_regression(price_mlr, intercept = TRUE) %>% 
  add_glance_source_note(
    label = list(sigma ~ "\U03C3"),
    include = c(r.squared, adj.r.squared, 
                AIC, statistic,
                p.value, sigma))
Characteristic Beta 95% CI1 p-value
(Intercept) -165,691 -204,393, -126,989 <0.001
floor_area_sqm 5,530 5,282, 5,778 <0.001
remaining_lease 5,589 5,433, 5,744 <0.001
storey_order 19,992 19,302, 20,683 <0.001
PROX_ELDERCARE 1,002 -1,312, 3,316 0.4
PROX_MRT -25,079 -29,030, -21,127 <0.001
PROX_HAWKER -32,340 -35,049, -29,631 <0.001
PROX_PARK 7,980 4,545, 11,415 <0.001
PROX_SUPERMARKET -8,459 -17,576, 658 0.069
PROX_MALL -23,894 -27,993, -19,796 <0.001
WITHIN_350M_KINDERGARTEN 8,419 7,108, 9,730 <0.001
WITHIN_350M_CHILDCARE -3,877 -4,643, -3,112 <0.001
WITHIN_350M_BUS 534 53, 1,015 0.030
PROX_CBD -19,926 -20,396, -19,455 <0.001
WITHIN_1KM_PRISCH -14,106 -15,091, -13,121 <0.001
PROX_GOOD_PRISCH -1,323 -2,143, -503 0.002
R² = 0.675; Adjusted R² = 0.674; AIC = 370,258; Statistic = 2,004; p-value = <0.001; σ = 83,388
1 CI = Confidence Interval

8.1.2 Check Non-linearity

ols_plot_resid_fit(price_mlr)

The points are scattered around 0 line, indicating that the dependent variable (price) and independent variables have a linear relationship.

8.1.3 Check Multicolinearity

vif <- ols_vif_tol(price_mlr)
vif
                  Variables Tolerance      VIF
1            floor_area_sqm 0.5598668 1.786139
2           remaining_lease 0.5253518 1.903486
3              storey_order 0.8703427 1.148973
4            PROX_ELDERCARE 0.8084843 1.236882
5                  PROX_MRT 0.8038367 1.244034
6               PROX_HAWKER 0.7643724 1.308263
7                 PROX_PARK 0.8262207 1.210330
8          PROX_SUPERMARKET 0.8722162 1.146505
9                 PROX_MALL 0.7694176 1.299684
10 WITHIN_350M_KINDERGARTEN 0.9239515 1.082308
11    WITHIN_350M_CHILDCARE 0.7381697 1.354702
12          WITHIN_350M_BUS 0.8500367 1.176420
13                 PROX_CBD 0.5009505 1.996205
14        WITHIN_1KM_PRISCH 0.6576783 1.520500
15         PROX_GOOD_PRISCH 0.8104372 1.233902

We can observe that all vif values are well below 5, indicating there is no multicolinearity between the independent variables.

8.1.4 Check Normality Assumption

From the ols_plot_resid_hist package, the residuals are resembling a normal distribution.

ols_plot_resid_hist(price_mlr)

8.1.5 Check Spatial Autocorrelation

Since we are building a predictive geographically weighted regression model that accounts for spatial distribution, we check for spatial autocorrelation to understand the relationship data points have with neighbouring points.

Extract out the residuals and convert it into a dataframe.

mlr.output <- as.data.frame(price_mlr$residuals)
price_resale.res.sf <- cbind(train, 
                        price_mlr$residuals) %>%
rename(`MLR_RES` = `price_mlr.residuals`)

Next, convert it into an sp spatial object to prepare the residuals for the spatial autocorrelation test.

price_resale.sp <- as_Spatial(price_resale.res.sf)
price_resale.sp
class       : SpatialPointsDataFrame 
features    : 14519 
extent      : 6958.193, 42645.18, 28157.26, 48741.06  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 17
names       : resale_price, floor_area_sqm, remaining_lease, storey_order,       PROX_ELDERCARE,           PROX_MRT,        PROX_HAWKER,          PROX_PARK,     PROX_SUPERMARKET,            PROX_MALL, WITHIN_350M_KINDERGARTEN, WITHIN_350M_CHILDCARE, WITHIN_350M_BUS,         PROX_CBD, WITHIN_1KM_PRISCH, ... 
min values  :       350000,             99,           49.08,            1, 3.24852165967354e-07, 0.0229291315265009, 0.0494878683203785, 0.0600396043825887, 7.47984318619627e-07, 2.21189111471176e-12,                        0,                     0,               0,  1.6109563636452,                 0, ... 
max values  :      1418000,            167,           96.75,           17,     8.26597091415839,   2.12908590009577,   6.63382585101601,   6.00301683925507,     1.67131003223853,     4.00920093399656,                        8,                    20,              19, 23.2773731998479,                 9, ... 
tmap_mode("view")
tm_shape(mpsz_sf)+
  tmap_options(check.and.fix = TRUE) +
  tm_polygons(alpha = 0.4) +
tm_shape(price_resale.res.sf) +  
  tm_dots(col = "MLR_RES",
          alpha = 0.6,
          style="quantile") +
  tm_view(set.zoom.limits = c(11,14))
tmap_mode("plot")

From the tmap plot of residuals, we can observe signs of spatial clustering, hence we can perform Moran I test to confirm this.

Using the dnearneigh function from spdep package, the following code chunk identifies neighbours that are euclidean distance away between the lower and upper bounds specified.

nb <- dnearneigh(coordinates(price_resale.sp), 0, 1500, longlat = FALSE)
summary(nb)
Neighbour list object:
Number of regions: 14519 
Number of nonzero links: 10417942 
Percentage nonzero weights: 4.942066 
Average number of links: 717.5385 
Link number distribution:

   4    5    6   13   21   29   30   32   54   55   56   57   59   62   64   67 
   5    6    6    8    6    6    3    7   14   52    2    4    2    2   10    2 
  71   72   73   74   81   84   85   86   88   89   90   91   92   93   94   95 
   1    3    2    1    6    8    9    1    6   13    5   11    5    7    2   11 
  96   98   99  100  101  102  103  104  105  106  108  109  111  113  114  115 
  21    3   11   17    1    5   10    8    5    4    8    4    6   10   12   23 
 116  117  118  119  120  121  122  123  124  125  126  127  128  129  131  132 
   9    6   16    5   32    4    4   11   10   16    4   23  134   44   38    7 
 133  134  135  136  137  138  139  140  141  142  143  144  145  146  147  148 
   4   17   16   12    5   10   16    1    1    8   13   28    9    8    4   10 
 149  150  151  152  153  154  155  156  157  158  159  160  161  162  163  166 
  10   16   24   11    6   11   57    5   12    3   26   11   24    8   16    6 
 167  168  170  171  172  173  174  175  176  177  178  179  180  181  182  183 
  13   21   10   24   12   11    5    4   18   11    3   18   12   12    6    1 
 184  185  186  187  188  189  190  191  192  193  194  195  197  198  199  200 
  31   15    4   23   33    3    5    3    9   26    3   16   27    1    2    6 
 201  202  203  204  205  206  207  208  209  210  211  212  213  214  215  216 
  14    1   10   10    6    3   14   13   14   16   17   13   16   14   11   15 
 217  218  219  220  221  222  223  224  225  226  227  228  229  230  231  232 
  14    8    3   29   18   12   48   16   13    4   14    3    3    6   20   23 
 233  234  235  236  237  238  239  240  241  242  243  244  245  246  247  248 
   6    8   44   30    5   12   18   34   32   19    6    8    7    5   11    5 
 249  250  251  252  253  254  255  256  257  258  259  260  261  262  263  264 
   6   17    6    8   54    1    8    7    4    3    6   25    8    1   25    3 
 265  266  267  268  269  270  271  272  273  274  275  276  277  278  279  280 
  11   17    4   17   21   12   12   31   15    6   29   26   20   49   47   16 
 281  282  283  284  285  286  287  288  289  290  291  292  293  294  295  296 
   4   11   10   21   20    6   23   14   27   15   16   14   41   10   10   12 
 297  298  299  300  301  302  303  304  305  306  307  308  309  310  311  312 
  22   34   16   21   19   24   15   24   15    4   21   18    8   20    9   33 
 313  314  315  316  317  318  319  320  321  322  323  324  325  326  327  328 
  13    2   26   16   14   18    6   21   18   18    6   25   47    5   25   15 
 329  330  331  332  333  334  335  336  337  338  339  340  341  342  343  344 
  19   27   38   22   35   38   38   24   16   78   43   23   21   42   46   26 
 345  346  347  348  349  350  351  352  353  354  355  356  357  358  359  360 
  32    6   33   37   27   13   14    7   14   16   31   21   18   13   16   25 
 361  362  363  364  365  366  367  368  369  370  372  373  374  375  376  377 
  23   49   11    1    6   20   17   38   16   23   17   11   36   17    7    4 
 378  379  380  381  382  383  384  385  386  387  388  389  390  391  392  393 
  13    4   19   17   14    6   12    5   53   27    6   31   27   20    4   46 
 394  395  396  397  398  399  400  401  402  403  404  405  406  407  408  409 
  30    6   31    1   20    3    8   23   26   11   23   38    8   17    4   20 
 410  411  412  413  414  415  416  417  418  419  420  421  422  423  424  425 
  45   18   13   22   20   28   15   16    9   11   13   23   24   48    3   22 
 426  427  428  429  430  431  432  433  434  435  436  437  438  439  440  441 
   8   21   20   49   21   25   17    6    6   10   40   14   12   38    4   10 
 442  443  444  445  446  447  448  449  450  451  452  453  454  455  456  457 
   9   25    8   26    7   30   10   11   17   17   20   34   12   22   11   13 
 458  459  460  461  462  463  464  465  466  467  468  469  470  471  472  473 
  13   17   17    8   37   17    6   14    8   25   20   13    2   13   14    7 
 474  475  476  477  478  479  480  481  482  483  484  485  486  487  488  489 
   2   10   20   15   10   19   20    6   11   25   19    6   10   12    8   13 
 490  491  492  493  494  495  496  497  498  499  500  501  502  503  504  505 
  20   13    6   17    6   23    9    5   28    6    6   30   16   25   36   20 
 506  507  508  509  510  511  512  513  514  515  516  517  518  519  520  521 
   6   29    7    3    9   14    1    7   23    2   20   12   16    6   11   33 
 522  523  524  525  526  527  528  529  530  531  532  533  534  535  536  537 
  12   26    8    5   31    9    7   10   17   10   14   10   14   15   32    5 
 538  539  540  542  543  544  545  547  548  549  550  551  553  554  555  556 
   5   19   22   14   11    1   14    4   10   10   11   16   10    5   28    4 
 557  558  559  560  561  562  563  564  565  566  567  568  569  570  571  572 
   7   14   21    8    9    6    6    2    5   11    8   12    4   36   20    1 
 573  574  575  576  577  578  579  580  581  582  583  584  585  586  587  588 
  13    6   29    4   13    7    6   10   16    7   23    3   17   10   31   17 
 589  590  591  592  593  595  596  597  598  599  600  601  602  603  604  605 
   8   13    8    3   19    7   21    8   11   19    9   28    1   19   10   20 
 606  607  608  609  610  611  612  613  614  615  616  617  618  619  620  621 
   1   21   16   14   14    6   17   16   17   22   21   10   28   33   40   45 
 622  623  624  625  626  627  628  629  630  631  632  633  634  635  636  637 
  22   11   12   14   44   27   16    2   18   56   24    9   12   15   23   33 
 638  639  640  641  642  643  644  645  646  647  648  649  650  651  652  653 
  46   20    6   25   21   15   23    8    8   18   12   16   11   10    4   27 
 654  655  656  657  658  659  660  661  662  663  664  665  666  667  668  669 
  21   16   24   13   13   19   25   18    7   15    6    6   25    2   20   14 
 670  671  672  673  674  675  676  677  678  679  680  682  683  684  685  686 
   8   34    2   10    9   35   31    6    7   13   19    5    7    4   51   15 
 687  688  689  691  692  693  694  695  696  697  698  699  700  701  702  703 
  10    9   20    5   31    6   29    9   18   19   26   11    9    5   13   13 
 704  705  707  708  709  710  711  712  713  714  715  716  717  718  719  720 
   3   25   10   14    8   10   26   19   23   32   46   70    2   18    9   16 
 721  722  723  724  725  726  727  728  729  730  731  732  733  734  735  736 
   6   18   15    8   11    1   16   20   14   13   14   10   34    3   11   22 
 737  738  739  740  741  742  743  744  745  746  747  748  749  750  751  752 
  13   11    3    6    8    8    8    8   27    4   11   35   27    8   32    4 
 753  754  755  756  757  758  759  760  761  762  763  764  765  766  767  768 
   1   12    2    8   17    1   14    3    1    8    3   14   16    8   10    6 
 769  770  771  772  773  774  775  776  777  778  779  780  781  782  783  784 
   7   33   19   15    6    3    4   22    3   10    9    3    1    9   11    3 
 785  786  787  788  789  790  791  792  793  794  795  796  797  798  799  800 
   4    7   26    3    3    8    6   17    1    8    2   10   13   12    1    2 
 801  802  803  804  805  806  807  808  809  810  811  813  815  819  820  821 
   8    2    6    4    8    1    7    4    8    5   12    5   10    8   11    4 
 823  824  827  828  829  830  831  832  833  834  835  838  839  840  842  843 
   3    3    7    3    1    7    4    4    1    6    1   12    7    1    3   16 
 845  846  847  848  849  850  852  854  856  857  858  861  862  863  865  866 
   3    1    4    3    4    8    3    6   11    1    5    4    2    4    3    4 
 867  868  870  871  872  873  874  875  878  879  880  883  884  885  889  890 
  11    5    2    1    3    1    3    7    7    3    3    1    6    1    4   10 
 892  893  894  896  897  898  899  900  901  903  904  907  908  909  910  912 
   5    7    1    4    4    1    2    4   10    4    7   10    5    1    2    7 
 913  914  929  933  935  942  949  950  964  967  971  973  985  997 1001 1006 
  10   21    6    3    1    5    6    3    1   11    6    1   34   10    8    8 
1009 1022 1026 1027 1030 1040 1045 1051 1054 1079 1092 1093 1095 1097 1101 1102 
   2   17    7    3   12   10   17    2    1   15    5   13   34    8    6    6 
1120 1124 1132 1137 1144 1148 1151 1152 1156 1160 1167 1170 1173 1175 1183 1186 
   7    2    7    2    3   10    4   19   12    7    3    3    2   22    3    2 
1195 1199 1204 1212 1217 1220 1223 1226 1239 1243 1247 1250 1252 1254 1259 1261 
  12   21    7    1    4    5   38   16    4    8    2    1    5    2    7    4 
1263 1264 1266 1267 1268 1273 1276 1280 1285 1291 1292 1294 1295 1296 1297 1298 
   3    3    1    5    3    5    7   36    1    5   11    8    7    3    4    2 
1300 1305 1309 1313 1316 1317 1320 1324 1333 1335 1336 1344 1348 1352 1354 1356 
   3    5    4    8    1    4    9    3    4    2    3    5    5   11    3    5 
1357 1359 1364 1366 1367 1368 1369 1379 1383 1384 1390 1392 1396 1399 1401 1402 
   7    6   10    2    4    4    4    4   12    4   11   17    7    5    2   13 
1403 1405 1407 1408 1410 1411 1413 1415 1416 1417 1422 1423 1424 1426 1429 1431 
   9    5   10    4    8    8    6    5   10    1    3    3   17    9    8   12 
1432 1433 1435 1436 1437 1438 1441 1442 1444 1447 1448 1451 1453 1455 1456 1458 
   6    4    4   11   11    7    9    6    2    5    4   10    9   20    6   16 
1462 1463 1464 1467 1472 1474 1479 1482 1483 1486 1487 1489 1492 1493 1496 1497 
   8   15   12    8   10    3   20    5    8    5    8   16    3    9    4    6 
1498 1499 1501 1506 1510 1513 1514 1519 1524 1525 1526 1528 1529 1531 1532 1533 
   7    7    3   12    4    8    3    2    2    4   13    3    3   10    3   42 
1534 1535 1538 1539 1540 1541 1542 1543 1544 1545 1548 1549 1550 1551 1552 1556 
  15    7    5    9   16    6    2    9    5    2    3    4   12    4    3    2 
1557 1558 1559 1561 1562 1565 1566 1568 1569 1572 1573 1577 1579 1580 1583 1584 
   4    8    9    4    1    2    2   18   20    8    5    8    9    4    5    2 
1586 1587 1589 1590 1598 1600 1601 1603 1609 1610 1611 1612 1615 1616 1617 1620 
   5   16    5   14    1    3    7    2    5    9    5   13   11   25   13    2 
1624 1629 1633 1635 1636 1638 1641 1642 1647 1648 1650 1652 1654 1655 1656 1657 
   7    7    9    4    3   17   11    4    5    6    6   13    3    5   12    6 
1658 1660 1661 1662 1666 1667 1668 1671 1676 1679 1682 1683 1684 1685 1689 1690 
   7    9   14    9    2    9   15    6    4    6    4    3    3   17    9    1 
1691 1692 1694 1707 1709 1712 1713 1717 1718 1719 1720 1721 1730 1731 1732 1734 
   2    8    4   15   12    7    2    2    6    3   12    6    1   11    7    8 
1736 1738 1739 1741 1743 1744 1745 1746 1747 1759 1762 1765 1771 1773 1775 1786 
   8    5   12    3    6   12    8    7   10    5    2    4    3    1    2   10 
1794 1803 1805 1808 1810 1813 1815 1817 1821 1823 1828 1831 1832 1844 1853 1856 
  18    3    3   11    6    7    1    2    8    8    9    3    1    5    4    2 
1862 1865 1871 1878 1880 1883 1884 1886 1888 1889 1891 1892 1893 1898 1902 1907 
   4    6    7   14    2    4    7    5    8    3    9    6    9    4    2    2 
1909 1910 1920 1921 1924 1933 1935 1937 1938 1940 1941 1942 1945 1949 1952 1955 
   5   11    6    2    2    6    3    1    2    4    1    6    7    2    6    2 
1959 1961 1964 1965 1969 1974 1976 1977 1980 1988 1991 1993 1996 1999 2005 2010 
   5    3    4    4    3    3   10    4    6    3    5    9    3   12    6    5 
2012 2014 2017 2024 2027 2029 2033 2036 2042 2043 2045 2046 2052 2053 2055 2061 
   5    4    7   15    4   15    9    4    1    2    5    3    5   13    1   19 
2063 2066 2067 2069 2071 2077 2081 2084 2092 2093 2097 2098 2100 2109 2110 2112 
  15    2    1    6    6    6    9    1    6    3    2    6    2   12   15   11 
2115 2117 2118 2119 2138 2142 2143 2145 2148 2154 2158 2164 2165 2170 2171 2172 
   3    2    3    2    8    1    2   15    1    4    1   10    3    4    2    6 
2186 2188 2190 2194 2198 2200 2204 2208 2216 2218 2227 2229 2233 2236 2237 2240 
   4    4    6    2    5    8    5   16    2    9    6    4    4    5    3    8 
2244 2245 2248 2249 2251 2253 2256 2260 2263 2264 2265 2275 2276 2280 2290 2303 
   3    4    3    3    5    5    3   13    1   10    6    2    4   11    2    4 
2304 2305 2306 2310 2313 2314 2316 2317 2319 2320 2321 2322 2326 2327 2335 2338 
   5    7    5    4    3    8    6    4    4    2    2    4    2    5    5    4 
2345 2346 2347 2350 2351 2354 2356 2358 2372 2375 2378 2387 2388 2392 2393 2398 
   6    2    3    5    7    1    2    3    4    5    4    7    5    5    1    4 
2403 2404 2406 2407 2409 2412 2413 2417 2418 2419 2426 2430 2431 2434 2435 2439 
   4    1    5    5    4    1    5    5    2    4    4   10    5    7    5    6 
2444 2445 2457 2462 2464 2474 2479 2490 2493 2494 2496 2502 2508 2509 2516 2517 
   6    1    2    7    6    4    8    7   15    8    4    5    1   11    2    5 
2519 2552 
   2    5 
5 least connected regions:
918 6125 10704 10705 10706 with 4 links
5 most connected regions:
4165 6296 6974 7602 12525 with 2552 links

The next code chunk below converts the neighbours list into spatial weights using nb2listw function.

nb_lw <- nb2listw(nb, style = 'W')
summary(nb_lw)
Characteristics of weights list object:
Neighbour list object:
Number of regions: 14519 
Number of nonzero links: 10417942 
Percentage nonzero weights: 4.942066 
Average number of links: 717.5385 
Link number distribution:

   4    5    6   13   21   29   30   32   54   55   56   57   59   62   64   67 
   5    6    6    8    6    6    3    7   14   52    2    4    2    2   10    2 
  71   72   73   74   81   84   85   86   88   89   90   91   92   93   94   95 
   1    3    2    1    6    8    9    1    6   13    5   11    5    7    2   11 
  96   98   99  100  101  102  103  104  105  106  108  109  111  113  114  115 
  21    3   11   17    1    5   10    8    5    4    8    4    6   10   12   23 
 116  117  118  119  120  121  122  123  124  125  126  127  128  129  131  132 
   9    6   16    5   32    4    4   11   10   16    4   23  134   44   38    7 
 133  134  135  136  137  138  139  140  141  142  143  144  145  146  147  148 
   4   17   16   12    5   10   16    1    1    8   13   28    9    8    4   10 
 149  150  151  152  153  154  155  156  157  158  159  160  161  162  163  166 
  10   16   24   11    6   11   57    5   12    3   26   11   24    8   16    6 
 167  168  170  171  172  173  174  175  176  177  178  179  180  181  182  183 
  13   21   10   24   12   11    5    4   18   11    3   18   12   12    6    1 
 184  185  186  187  188  189  190  191  192  193  194  195  197  198  199  200 
  31   15    4   23   33    3    5    3    9   26    3   16   27    1    2    6 
 201  202  203  204  205  206  207  208  209  210  211  212  213  214  215  216 
  14    1   10   10    6    3   14   13   14   16   17   13   16   14   11   15 
 217  218  219  220  221  222  223  224  225  226  227  228  229  230  231  232 
  14    8    3   29   18   12   48   16   13    4   14    3    3    6   20   23 
 233  234  235  236  237  238  239  240  241  242  243  244  245  246  247  248 
   6    8   44   30    5   12   18   34   32   19    6    8    7    5   11    5 
 249  250  251  252  253  254  255  256  257  258  259  260  261  262  263  264 
   6   17    6    8   54    1    8    7    4    3    6   25    8    1   25    3 
 265  266  267  268  269  270  271  272  273  274  275  276  277  278  279  280 
  11   17    4   17   21   12   12   31   15    6   29   26   20   49   47   16 
 281  282  283  284  285  286  287  288  289  290  291  292  293  294  295  296 
   4   11   10   21   20    6   23   14   27   15   16   14   41   10   10   12 
 297  298  299  300  301  302  303  304  305  306  307  308  309  310  311  312 
  22   34   16   21   19   24   15   24   15    4   21   18    8   20    9   33 
 313  314  315  316  317  318  319  320  321  322  323  324  325  326  327  328 
  13    2   26   16   14   18    6   21   18   18    6   25   47    5   25   15 
 329  330  331  332  333  334  335  336  337  338  339  340  341  342  343  344 
  19   27   38   22   35   38   38   24   16   78   43   23   21   42   46   26 
 345  346  347  348  349  350  351  352  353  354  355  356  357  358  359  360 
  32    6   33   37   27   13   14    7   14   16   31   21   18   13   16   25 
 361  362  363  364  365  366  367  368  369  370  372  373  374  375  376  377 
  23   49   11    1    6   20   17   38   16   23   17   11   36   17    7    4 
 378  379  380  381  382  383  384  385  386  387  388  389  390  391  392  393 
  13    4   19   17   14    6   12    5   53   27    6   31   27   20    4   46 
 394  395  396  397  398  399  400  401  402  403  404  405  406  407  408  409 
  30    6   31    1   20    3    8   23   26   11   23   38    8   17    4   20 
 410  411  412  413  414  415  416  417  418  419  420  421  422  423  424  425 
  45   18   13   22   20   28   15   16    9   11   13   23   24   48    3   22 
 426  427  428  429  430  431  432  433  434  435  436  437  438  439  440  441 
   8   21   20   49   21   25   17    6    6   10   40   14   12   38    4   10 
 442  443  444  445  446  447  448  449  450  451  452  453  454  455  456  457 
   9   25    8   26    7   30   10   11   17   17   20   34   12   22   11   13 
 458  459  460  461  462  463  464  465  466  467  468  469  470  471  472  473 
  13   17   17    8   37   17    6   14    8   25   20   13    2   13   14    7 
 474  475  476  477  478  479  480  481  482  483  484  485  486  487  488  489 
   2   10   20   15   10   19   20    6   11   25   19    6   10   12    8   13 
 490  491  492  493  494  495  496  497  498  499  500  501  502  503  504  505 
  20   13    6   17    6   23    9    5   28    6    6   30   16   25   36   20 
 506  507  508  509  510  511  512  513  514  515  516  517  518  519  520  521 
   6   29    7    3    9   14    1    7   23    2   20   12   16    6   11   33 
 522  523  524  525  526  527  528  529  530  531  532  533  534  535  536  537 
  12   26    8    5   31    9    7   10   17   10   14   10   14   15   32    5 
 538  539  540  542  543  544  545  547  548  549  550  551  553  554  555  556 
   5   19   22   14   11    1   14    4   10   10   11   16   10    5   28    4 
 557  558  559  560  561  562  563  564  565  566  567  568  569  570  571  572 
   7   14   21    8    9    6    6    2    5   11    8   12    4   36   20    1 
 573  574  575  576  577  578  579  580  581  582  583  584  585  586  587  588 
  13    6   29    4   13    7    6   10   16    7   23    3   17   10   31   17 
 589  590  591  592  593  595  596  597  598  599  600  601  602  603  604  605 
   8   13    8    3   19    7   21    8   11   19    9   28    1   19   10   20 
 606  607  608  609  610  611  612  613  614  615  616  617  618  619  620  621 
   1   21   16   14   14    6   17   16   17   22   21   10   28   33   40   45 
 622  623  624  625  626  627  628  629  630  631  632  633  634  635  636  637 
  22   11   12   14   44   27   16    2   18   56   24    9   12   15   23   33 
 638  639  640  641  642  643  644  645  646  647  648  649  650  651  652  653 
  46   20    6   25   21   15   23    8    8   18   12   16   11   10    4   27 
 654  655  656  657  658  659  660  661  662  663  664  665  666  667  668  669 
  21   16   24   13   13   19   25   18    7   15    6    6   25    2   20   14 
 670  671  672  673  674  675  676  677  678  679  680  682  683  684  685  686 
   8   34    2   10    9   35   31    6    7   13   19    5    7    4   51   15 
 687  688  689  691  692  693  694  695  696  697  698  699  700  701  702  703 
  10    9   20    5   31    6   29    9   18   19   26   11    9    5   13   13 
 704  705  707  708  709  710  711  712  713  714  715  716  717  718  719  720 
   3   25   10   14    8   10   26   19   23   32   46   70    2   18    9   16 
 721  722  723  724  725  726  727  728  729  730  731  732  733  734  735  736 
   6   18   15    8   11    1   16   20   14   13   14   10   34    3   11   22 
 737  738  739  740  741  742  743  744  745  746  747  748  749  750  751  752 
  13   11    3    6    8    8    8    8   27    4   11   35   27    8   32    4 
 753  754  755  756  757  758  759  760  761  762  763  764  765  766  767  768 
   1   12    2    8   17    1   14    3    1    8    3   14   16    8   10    6 
 769  770  771  772  773  774  775  776  777  778  779  780  781  782  783  784 
   7   33   19   15    6    3    4   22    3   10    9    3    1    9   11    3 
 785  786  787  788  789  790  791  792  793  794  795  796  797  798  799  800 
   4    7   26    3    3    8    6   17    1    8    2   10   13   12    1    2 
 801  802  803  804  805  806  807  808  809  810  811  813  815  819  820  821 
   8    2    6    4    8    1    7    4    8    5   12    5   10    8   11    4 
 823  824  827  828  829  830  831  832  833  834  835  838  839  840  842  843 
   3    3    7    3    1    7    4    4    1    6    1   12    7    1    3   16 
 845  846  847  848  849  850  852  854  856  857  858  861  862  863  865  866 
   3    1    4    3    4    8    3    6   11    1    5    4    2    4    3    4 
 867  868  870  871  872  873  874  875  878  879  880  883  884  885  889  890 
  11    5    2    1    3    1    3    7    7    3    3    1    6    1    4   10 
 892  893  894  896  897  898  899  900  901  903  904  907  908  909  910  912 
   5    7    1    4    4    1    2    4   10    4    7   10    5    1    2    7 
 913  914  929  933  935  942  949  950  964  967  971  973  985  997 1001 1006 
  10   21    6    3    1    5    6    3    1   11    6    1   34   10    8    8 
1009 1022 1026 1027 1030 1040 1045 1051 1054 1079 1092 1093 1095 1097 1101 1102 
   2   17    7    3   12   10   17    2    1   15    5   13   34    8    6    6 
1120 1124 1132 1137 1144 1148 1151 1152 1156 1160 1167 1170 1173 1175 1183 1186 
   7    2    7    2    3   10    4   19   12    7    3    3    2   22    3    2 
1195 1199 1204 1212 1217 1220 1223 1226 1239 1243 1247 1250 1252 1254 1259 1261 
  12   21    7    1    4    5   38   16    4    8    2    1    5    2    7    4 
1263 1264 1266 1267 1268 1273 1276 1280 1285 1291 1292 1294 1295 1296 1297 1298 
   3    3    1    5    3    5    7   36    1    5   11    8    7    3    4    2 
1300 1305 1309 1313 1316 1317 1320 1324 1333 1335 1336 1344 1348 1352 1354 1356 
   3    5    4    8    1    4    9    3    4    2    3    5    5   11    3    5 
1357 1359 1364 1366 1367 1368 1369 1379 1383 1384 1390 1392 1396 1399 1401 1402 
   7    6   10    2    4    4    4    4   12    4   11   17    7    5    2   13 
1403 1405 1407 1408 1410 1411 1413 1415 1416 1417 1422 1423 1424 1426 1429 1431 
   9    5   10    4    8    8    6    5   10    1    3    3   17    9    8   12 
1432 1433 1435 1436 1437 1438 1441 1442 1444 1447 1448 1451 1453 1455 1456 1458 
   6    4    4   11   11    7    9    6    2    5    4   10    9   20    6   16 
1462 1463 1464 1467 1472 1474 1479 1482 1483 1486 1487 1489 1492 1493 1496 1497 
   8   15   12    8   10    3   20    5    8    5    8   16    3    9    4    6 
1498 1499 1501 1506 1510 1513 1514 1519 1524 1525 1526 1528 1529 1531 1532 1533 
   7    7    3   12    4    8    3    2    2    4   13    3    3   10    3   42 
1534 1535 1538 1539 1540 1541 1542 1543 1544 1545 1548 1549 1550 1551 1552 1556 
  15    7    5    9   16    6    2    9    5    2    3    4   12    4    3    2 
1557 1558 1559 1561 1562 1565 1566 1568 1569 1572 1573 1577 1579 1580 1583 1584 
   4    8    9    4    1    2    2   18   20    8    5    8    9    4    5    2 
1586 1587 1589 1590 1598 1600 1601 1603 1609 1610 1611 1612 1615 1616 1617 1620 
   5   16    5   14    1    3    7    2    5    9    5   13   11   25   13    2 
1624 1629 1633 1635 1636 1638 1641 1642 1647 1648 1650 1652 1654 1655 1656 1657 
   7    7    9    4    3   17   11    4    5    6    6   13    3    5   12    6 
1658 1660 1661 1662 1666 1667 1668 1671 1676 1679 1682 1683 1684 1685 1689 1690 
   7    9   14    9    2    9   15    6    4    6    4    3    3   17    9    1 
1691 1692 1694 1707 1709 1712 1713 1717 1718 1719 1720 1721 1730 1731 1732 1734 
   2    8    4   15   12    7    2    2    6    3   12    6    1   11    7    8 
1736 1738 1739 1741 1743 1744 1745 1746 1747 1759 1762 1765 1771 1773 1775 1786 
   8    5   12    3    6   12    8    7   10    5    2    4    3    1    2   10 
1794 1803 1805 1808 1810 1813 1815 1817 1821 1823 1828 1831 1832 1844 1853 1856 
  18    3    3   11    6    7    1    2    8    8    9    3    1    5    4    2 
1862 1865 1871 1878 1880 1883 1884 1886 1888 1889 1891 1892 1893 1898 1902 1907 
   4    6    7   14    2    4    7    5    8    3    9    6    9    4    2    2 
1909 1910 1920 1921 1924 1933 1935 1937 1938 1940 1941 1942 1945 1949 1952 1955 
   5   11    6    2    2    6    3    1    2    4    1    6    7    2    6    2 
1959 1961 1964 1965 1969 1974 1976 1977 1980 1988 1991 1993 1996 1999 2005 2010 
   5    3    4    4    3    3   10    4    6    3    5    9    3   12    6    5 
2012 2014 2017 2024 2027 2029 2033 2036 2042 2043 2045 2046 2052 2053 2055 2061 
   5    4    7   15    4   15    9    4    1    2    5    3    5   13    1   19 
2063 2066 2067 2069 2071 2077 2081 2084 2092 2093 2097 2098 2100 2109 2110 2112 
  15    2    1    6    6    6    9    1    6    3    2    6    2   12   15   11 
2115 2117 2118 2119 2138 2142 2143 2145 2148 2154 2158 2164 2165 2170 2171 2172 
   3    2    3    2    8    1    2   15    1    4    1   10    3    4    2    6 
2186 2188 2190 2194 2198 2200 2204 2208 2216 2218 2227 2229 2233 2236 2237 2240 
   4    4    6    2    5    8    5   16    2    9    6    4    4    5    3    8 
2244 2245 2248 2249 2251 2253 2256 2260 2263 2264 2265 2275 2276 2280 2290 2303 
   3    4    3    3    5    5    3   13    1   10    6    2    4   11    2    4 
2304 2305 2306 2310 2313 2314 2316 2317 2319 2320 2321 2322 2326 2327 2335 2338 
   5    7    5    4    3    8    6    4    4    2    2    4    2    5    5    4 
2345 2346 2347 2350 2351 2354 2356 2358 2372 2375 2378 2387 2388 2392 2393 2398 
   6    2    3    5    7    1    2    3    4    5    4    7    5    5    1    4 
2403 2404 2406 2407 2409 2412 2413 2417 2418 2419 2426 2430 2431 2434 2435 2439 
   4    1    5    5    4    1    5    5    2    4    4   10    5    7    5    6 
2444 2445 2457 2462 2464 2474 2479 2490 2493 2494 2496 2502 2508 2509 2516 2517 
   6    1    2    7    6    4    8    7   15    8    4    5    1   11    2    5 
2519 2552 
   2    5 
5 least connected regions:
918 6125 10704 10705 10706 with 4 links
5 most connected regions:
4165 6296 6974 7602 12525 with 2552 links

Weights style: W 
Weights constants summary:
      n        nn    S0       S1       S2
W 14519 210801361 14519 80.30014 58598.26

The Moran I test is performed below.

lm.morantest(price_mlr, nb_lw)

    Global Moran I for regression residuals

data:  
model: lm(formula = resale_price ~ ., data = train_nogeom)
weights: nb_lw

Moran I statistic standard deviate = 534.39, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Observed Moran I      Expectation         Variance 
    2.939151e-01    -4.904277e-04     3.035164e-07

Since the p-value is less than 2.2e-16 which is smaller than the alpha value of 0.05, there is evidence to conclude the residuals are not randomly distributed. the Observed Moran I value is 0.29395 which is larger than 0, indicating the residuals resemble a clustered distribution.

8.2 Ordinary Least Squares (OLS) Regression Predictive Model

8.2.1 Training Data

price_ols_train <- predict(price_mlr,
             data=train,
             se.fit = TRUE,
             interval = "prediction")
summary(price_ols_train)
               Length Class  Mode   
fit            43557  -none- numeric
se.fit         14519  -none- numeric
df                 1  -none- numeric
residual.scale     1  -none- numeric
price_ols_train$residual.scale
[1] 83388.49

8.2.2 Testing Data

To predict HDB resale prices using the test data, use the predict () function from the car package.

price_ols_test <- predict(price_mlr,
             data=test,
             se.fit = TRUE,
             interval = "prediction")
summary(price_ols_test)
               Length Class  Mode   
fit            43557  -none- numeric
se.fit         14519  -none- numeric
df                 1  -none- numeric
residual.scale     1  -none- numeric
summary(price_ols_test$fit)
      fit               lwr               upr         
 Min.   : 108035   Min.   : -58559   Min.   : 274628  
 1st Qu.: 542859   1st Qu.: 379321   1st Qu.: 706467  
 Median : 612140   Median : 448599   Median : 775661  
 Mean   : 627297   Mean   : 463755   Mean   : 790839  
 3rd Qu.: 685101   3rd Qu.: 521575   3rd Qu.: 848660  
 Max.   :1170458   Max.   :1006807   Max.   :1334109  
price_ols_test$residual.scale
[1] 83388.49

By comparing the residuals from the training and testing data, they have yielded the same value of 83388.49, indicating that the OLS predictive model was able to predict the HDB resale prices.

8.3 Geographically Weighted Regression (GWR) Predictive Model

Convert the training data into an sp object for subsequent analysis.

train_sp <- as_Spatial(train)
train_sp
class       : SpatialPointsDataFrame 
features    : 14519 
extent      : 6958.193, 42645.18, 28157.26, 48741.06  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
variables   : 16
names       : resale_price, floor_area_sqm, remaining_lease, storey_order,       PROX_ELDERCARE,           PROX_MRT,        PROX_HAWKER,          PROX_PARK,     PROX_SUPERMARKET,            PROX_MALL, WITHIN_350M_KINDERGARTEN, WITHIN_350M_CHILDCARE, WITHIN_350M_BUS,         PROX_CBD, WITHIN_1KM_PRISCH, ... 
min values  :       350000,             99,           49.08,            1, 3.24852165967354e-07, 0.0229291315265009, 0.0494878683203785, 0.0600396043825887, 7.47984318619627e-07, 2.21189111471176e-12,                        0,                     0,               0,  1.6109563636452,                 0, ... 
max values  :      1418000,            167,           96.75,           17,     8.26597091415839,   2.12908590009577,   6.63382585101601,   6.00301683925507,     1.67131003223853,     4.00920093399656,                        8,                    20,              19, 23.2773731998479,                 9, ...

8.3.1 Adaptive Bandwidth

First, we will determine the optimal adaptive bandwidth for the gwr model using the code below.

bw_adaptive <- bw.gwr(resale_price ~.,
                  data=train_sp,
                  approach="CV",
                  kernel="gaussian",
                  adaptive=TRUE,
                  longlat=FALSE)

Write the output of the optimal adaptive bandwidth into an rds file.

write_rds(bw_adaptive, "data/rds/bw_adaptive.rds")

The gwr predictive model is computed below using the previously calculated optimal bandwidth:

gwr_adaptive <- gwr.basic(formula = resale_price ~
                            floor_area_sqm + storey_order +
                            remaining_lease + PROX_CBD + 
                            PROX_ELDERCARE + PROX_HAWKER +
                            PROX_MRT + PROX_PARK + PROX_MALL + 
                            PROX_SUPERMARKET + WITHIN_350M_KINDERGARTEN +
                            WITHIN_350M_CHILDCARE + WITHIN_350M_BUS +
                            WITHIN_1KM_PRISCH + PROX_GOOD_PRISCH,
                          data=train_sp,
                          bw=bw_adaptive, 
                          kernel = 'gaussian', 
                          adaptive=TRUE,
                          longlat = FALSE)

Write the output of the gwr.basic model into an rds file.

write_rds(gwr_adaptive, "data/rds/gwr_adaptive.rds")

Display the contents of the model below:

gwr_adaptive <- read_rds("data/rds/gwr_adaptive.rds")
gwr_adaptive
   ***********************************************************************
   *                       Package   GWmodel                             *
   ***********************************************************************
   Program starts at: 2023-03-17 08:52:17 
   Call:
   gwr.basic(formula = resale_price ~ floor_area_sqm + storey_order + 
    remaining_lease + PROX_CBD + PROX_ELDERCARE + PROX_HAWKER + 
    PROX_MRT + PROX_PARK + PROX_MALL + PROX_SUPERMARKET + WITHIN_350M_KINDERGARTEN + 
    WITHIN_350M_CHILDCARE + WITHIN_350M_BUS + WITHIN_1KM_PRISCH + 
    PROX_GOOD_PRISCH, data = train_sp, bw = bw_adaptive, kernel = "gaussian", 
    adaptive = TRUE, longlat = FALSE)

   Dependent (y) variable:  resale_price
   Independent variables:  floor_area_sqm storey_order remaining_lease PROX_CBD PROX_ELDERCARE PROX_HAWKER PROX_MRT PROX_PARK PROX_MALL PROX_SUPERMARKET WITHIN_350M_KINDERGARTEN WITHIN_350M_CHILDCARE WITHIN_350M_BUS WITHIN_1KM_PRISCH PROX_GOOD_PRISCH
   Number of data points: 14519
   ***********************************************************************
   *                    Results of Global Regression                     *
   ***********************************************************************

   Call:
    lm(formula = formula, data = data)

   Residuals:
    Min      1Q  Median      3Q     Max 
-324299  -54097   -2735   48780  715869 

   Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
   (Intercept)              -165691.0    19744.8  -8.392  < 2e-16 ***
   floor_area_sqm              5530.3      126.6  43.675  < 2e-16 ***
   storey_order               19992.5      352.5  56.723  < 2e-16 ***
   remaining_lease             5588.6       79.5  70.295  < 2e-16 ***
   PROX_CBD                  -19925.7      239.9 -83.054  < 2e-16 ***
   PROX_ELDERCARE              1002.0     1180.4   0.849  0.39595    
   PROX_HAWKER               -32340.1     1382.1 -23.399  < 2e-16 ***
   PROX_MRT                  -25078.5     2016.2 -12.439  < 2e-16 ***
   PROX_PARK                   7980.0     1752.5   4.554 5.32e-06 ***
   PROX_MALL                 -23894.5     2090.7 -11.429  < 2e-16 ***
   PROX_SUPERMARKET           -8458.9     4651.4  -1.819  0.06900 .  
   WITHIN_350M_KINDERGARTEN    8418.9      668.8  12.588  < 2e-16 ***
   WITHIN_350M_CHILDCARE      -3877.4      390.7  -9.924  < 2e-16 ***
   WITHIN_350M_BUS              534.2      245.4   2.177  0.02952 *  
   WITHIN_1KM_PRISCH         -14105.9      502.4 -28.078  < 2e-16 ***
   PROX_GOOD_PRISCH           -1322.9      418.2  -3.163  0.00156 ** 

   ---Significance stars
   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
   Residual standard error: 83390 on 14503 degrees of freedom
   Multiple R-squared: 0.6746
   Adjusted R-squared: 0.6742 
   F-statistic:  2004 on 15 and 14503 DF,  p-value: < 2.2e-16 
   ***Extra Diagnostic information
   Residual sum of squares: 1.008486e+14
   Sigma(hat): 83348.27
   AIC:  370258.4
   AICc:  370258.5
   BIC:  356031.2
   ***********************************************************************
   *          Results of Geographically Weighted Regression              *
   ***********************************************************************

   *********************Model calibration information*********************
   Kernel function: gaussian 
   Adaptive bandwidth: 69 (number of nearest neighbours)
   Regression points: the same locations as observations are used.
   Distance metric: Euclidean distance metric is used.

   ****************Summary of GWR coefficient estimates:******************
                                   Min.     1st Qu.      Median     3rd Qu.
   Intercept                -3.7946e+08 -9.6724e+05 -1.4552e+05  3.6217e+06
   floor_area_sqm           -1.6034e+05  1.1165e+03  3.2034e+03  5.8176e+03
   storey_order              3.3585e+03  1.1693e+04  1.4186e+04  1.7944e+04
   remaining_lease          -7.3105e+04 -2.1272e+03  4.0938e+03  6.6691e+03
   PROX_CBD                 -8.8341e+07 -2.8883e+05 -2.0156e+04  9.7493e+04
   PROX_ELDERCARE           -1.6279e+07 -4.7141e+04  1.3052e+04  9.1405e+04
   PROX_HAWKER              -6.7649e+06 -5.5437e+04  3.6489e+03  1.1450e+05
   PROX_MRT                 -7.8455e+06 -1.1892e+05 -4.9072e+04  5.2506e+04
   PROX_PARK                -3.5965e+07 -9.6154e+04 -1.0960e+04  6.3723e+04
   PROX_MALL                -2.8128e+07 -1.0545e+05 -2.3200e+04  5.9033e+04
   PROX_SUPERMARKET         -6.7266e+06 -9.3833e+04 -1.8712e+04  5.2123e+04
   WITHIN_350M_KINDERGARTEN -3.5303e+05 -9.4771e+03 -6.3515e+02  7.4898e+03
   WITHIN_350M_CHILDCARE    -9.7906e+04 -3.6575e+03  4.6350e+02  5.2338e+03
   WITHIN_350M_BUS          -4.0644e+04 -2.6840e+03  7.9049e+01  2.7765e+03
   WITHIN_1KM_PRISCH        -1.5107e+06 -6.7399e+03  6.6886e+01  7.2958e+03
   PROX_GOOD_PRISCH         -3.8384e+07 -1.0992e+05  1.7128e+04  2.4181e+05
                                 Max.
   Intercept                791294700
   floor_area_sqm              553894
   storey_order                 28264
   remaining_lease              12809
   PROX_CBD                  37653414
   PROX_ELDERCARE             9015002
   PROX_HAWKER               36079132
   PROX_MRT                  13087576
   PROX_PARK                 12916427
   PROX_MALL                 15349887
   PROX_SUPERMARKET           4282686
   WITHIN_350M_KINDERGARTEN    222801
   WITHIN_350M_CHILDCARE        62437
   WITHIN_350M_BUS              25374
   WITHIN_1KM_PRISCH           190039
   PROX_GOOD_PRISCH          97952173
   ************************Diagnostic information*************************
   Number of data points: 14519 
   Effective number of parameters (2trace(S) - trace(S'S)): 1454.963 
   Effective degrees of freedom (n-2trace(S) + trace(S'S)): 13064.04 
   AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 353806.5 
   AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 352433.6 
   BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 347927.4 
   Residual sum of squares: 2.732835e+13 
   R-square value:  0.9118132 
   Adjusted R-square value:  0.901991 

   ***********************************************************************
   Program stops at: 2023-03-17 08:56:48

From the above output and referencing this link for interpreting GWR outputs, we can observe that the GWR model has a lower AIC value of 352433.6 compared to the Global Regression model which has a value of 370258.4, indicating that the GWR model is a better fit for the data compared to the global regression model. The Residual sum of squares for the GWR model is also smaller than that compared to the global regression model, indicating the GWR model has a closer fit to the observed data.

8.3.2 Visualise GWR output

resale.sf.adaptive <- st_as_sf(gwr_adaptive$SDF) %>%
  st_transform(crs=3414)
resale.sf.adaptive.svy21 <- st_transform(resale.sf.adaptive, 3414)
resale.sf.adaptive.svy21  
Simple feature collection with 14519 features and 54 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 6958.193 ymin: 28157.26 xmax: 42645.18 ymax: 48741.06
Projected CRS: SVY21 / Singapore TM
First 10 features:
     Intercept floor_area_sqm storey_order remaining_lease    PROX_CBD
1  -543922.315       5798.420     19921.33        9898.314   -858.6719
2  -455784.609       4884.513     19901.94        7705.634   2266.5729
3  -512073.474       4490.400     22228.63        7884.370  38683.0292
4   -25274.000       1843.624     24463.34        7990.419 -11223.3874
5  -626888.561       4668.305     21565.30        8089.424  42245.2023
6     1719.449       2941.478     23222.81        8571.491 -24507.6269
7  -557333.913       5302.043     19486.07        7505.329  11773.1036
8  -415412.903       4491.243     21907.71        7744.784  27745.8712
9  -337832.948       4516.634     20322.93        7771.730  -7123.5491
10 -910529.170       4195.171     21988.23        8439.962  71590.8110
   PROX_ELDERCARE PROX_HAWKER  PROX_MRT   PROX_PARK  PROX_MALL PROX_SUPERMARKET
1       -13072.20   -45495.08 -75797.72   76873.781  -93706.87         46355.87
2       -34378.12   134933.71 101221.84 -198916.246  -56710.20        -52827.67
3      -131693.52   -69535.73  51143.76 -153356.715 -114055.72        -19813.92
4        19579.72    77084.86  28637.83  -34017.472  -13903.67         20859.65
5      -172555.91    17625.06 -35577.00 -159535.592   39733.35        -47020.34
6        54207.70    10721.96 -43423.70   -6239.236  -49549.46         41126.84
7       -54903.67   171371.68 140386.22 -260819.748  -68210.93        -70784.58
8      -133868.63   -43581.03  56678.64 -164278.720 -103482.58        -47493.21
9       -37380.73   145880.12  82402.56 -211756.073  -42026.48        -55373.41
10     -168225.94   -84533.06 -86020.14  -80346.241   21808.80         84107.22
   WITHIN_350M_KINDERGARTEN WITHIN_350M_CHILDCARE WITHIN_350M_BUS
1                 -3702.856              4396.732      -3620.1390
2                 12899.984              7369.359       8876.7757
3                 10702.397              7523.916      -2211.8918
4                 18586.932             -1397.015       1121.0196
5                 15323.347             11072.893       2617.3135
6                 20746.176             -3234.304       3210.2936
7                  8886.546              8369.911       5682.4560
8                 12755.269              6366.276        419.2191
9                 12148.770              5232.924       8253.1675
10                -1405.150             13991.956      -2885.0542
   WITHIN_1KM_PRISCH PROX_GOOD_PRISCH      y     yhat   residual CV_Score
1         -19651.954       -14798.835 483000 520774.3  -37774.29        0
2         -22308.331        -4372.036 590000 678469.4  -88469.37        0
3         -52699.212        19801.413 629000 705712.4  -76712.40        0
4          -1117.886       -35827.116 670000 812096.3 -142096.27        0
5         -60158.652         5470.719 680000 732430.0  -52430.00        0
6          -4990.206       -47860.821 760000 847500.1  -87500.08        0
7         -23742.899        10531.956 768000 871399.0 -103398.99        0
8         -56144.137        20399.417 816800 847604.5  -30804.54        0
9         -18145.095         2667.099 820000 869514.4  -49514.42        0
10        -33729.036        22267.276 830000 931320.1 -101320.12        0
   Stud_residual Intercept_SE floor_area_sqm_SE storey_order_SE
1     -0.8742109    158542.20          844.4323        1457.736
2     -2.0152387    126071.62          559.7671        1303.619
3     -1.7477976    259802.12          946.9022        1739.495
4     -3.2541165    112750.46          619.1971        1592.735
5     -1.3833479    199621.63          721.5796        1436.238
6     -2.1093872    120678.76          550.8411        1347.255
7     -2.4053112    146846.13          631.9891        1401.630
8     -0.7453731    212127.87          806.7247        1605.774
9     -1.1171237     96618.68          523.2458        1182.313
10    -2.2650800    282399.65          927.5423        1583.970
   remaining_lease_SE PROX_CBD_SE PROX_ELDERCARE_SE PROX_HAWKER_SE PROX_MRT_SE
1            351.3113   12710.022          20457.23       29693.67    19004.44
2            318.8009   10002.732          15566.87       27512.51    21093.18
3            490.2410   24085.776          44560.68       67283.18    43678.88
4            381.0479    8815.157          18407.69       27153.58    20167.38
5            350.8117   16475.946          33591.89       46095.21    34232.89
6            318.0177    9926.553          17975.10       23531.16    22137.01
7            353.2831   11466.768          18170.32       31947.06    22842.01
8            420.3836   19064.802          34241.56       51584.02    35408.10
9            299.4600    6553.453          14554.28       22167.24    17944.33
10           444.1576   26387.322          50554.34       75087.50    44581.31
   PROX_PARK_SE PROX_MALL_SE PROX_SUPERMARKET_SE WITHIN_350M_KINDERGARTEN_SE
1      35935.86     32703.81            45755.20                    8479.763
2      31876.20     14970.65            30519.60                    3540.085
3      60862.84     29512.47            62214.35                   19820.525
4      39333.19     15078.13            25270.32                    3531.364
5      38123.20     38653.62            48824.18                    9332.557
6      34130.16     13448.84            23398.72                    3764.996
7      37662.12     18007.13            36581.97                    4217.026
8      47536.44     24376.65            44675.07                   14818.855
9      30100.09     13352.78            26761.09                    3203.425
10     62048.51     67936.07            76298.99                   19576.334
   WITHIN_350M_CHILDCARE_SE WITHIN_350M_BUS_SE WITHIN_1KM_PRISCH_SE
1                  4264.484           2371.936            10270.238
2                  2819.247           1885.707             5254.453
3                  5666.858           3444.424            17149.171
4                  2386.609           1704.021             4857.579
5                  4526.847           2575.145            12136.390
6                  2553.898           1622.145             5134.633
7                  3211.516           2288.978             6346.090
8                  4879.843           2957.016            13329.807
9                  2524.873           1693.603             4260.269
10                 6537.305           3417.585            19005.268
   PROX_GOOD_PRISCH_SE Intercept_TV floor_area_sqm_TV storey_order_TV
1            15694.359  -3.43077316          6.866649        13.66594
2            11375.964  -3.61528316          8.725974        15.26669
3            20174.625  -1.97101346          4.742200        12.77878
4            13683.786  -0.22415874          2.977442        15.35933
5            13881.186  -3.14038396          6.469563        15.01513
6            11969.928   0.01424815          5.339976        17.23714
7            12524.443  -3.79535979          8.389453        13.90243
8            15800.972  -1.95831363          5.567256        13.64309
9             9994.823  -3.49655945          8.631954        17.18914
10           22951.966  -3.22425741          4.522889        13.88172
   remaining_lease_TV PROX_CBD_TV PROX_ELDERCARE_TV PROX_HAWKER_TV PROX_MRT_TV
1            28.17534 -0.06755865        -0.6390015     -1.5321476   -3.988420
2            24.17068  0.22659538        -2.2084151      4.9044485    4.798794
3            16.08264  1.60605286        -2.9553749     -1.0334786    1.170904
4            20.96959 -1.27319197         1.0636710      2.8388472    1.420007
5            23.05917  2.56405316        -5.1368318      0.3823622   -1.039264
6            26.95287 -2.46889602         3.0157100      0.4556496   -1.961589
7            21.24452  1.02671508        -3.0216126      5.3642390    6.145966
8            18.42314  1.45534534        -3.9095370     -0.8448552    1.600725
9            25.95248 -1.08699173        -2.5683676      6.5808891    4.592123
10           19.00218  2.71307606        -3.3276259     -1.1257940   -1.929511
   PROX_PARK_TV PROX_MALL_TV PROX_SUPERMARKET_TV WITHIN_350M_KINDERGARTEN_TV
1     2.1391943   -2.8653201           1.0131280                 -0.43666972
2    -6.2402747   -3.7880914          -1.7309428                  3.64397544
3    -2.5197101   -3.8646617          -0.3184783                  0.53996538
4    -0.8648541   -0.9221085           0.8254601                  5.26338535
5    -4.1847374    1.0279334          -0.9630545                  1.64192364
6    -0.1828071   -3.6842937           1.7576531                  5.51027800
7    -6.9252545   -3.7879953          -1.9349579                  2.10730174
8    -3.4558481   -4.2451518          -1.0630808                  0.86074597
9    -7.0350651   -3.1473957          -2.0691763                  3.79243154
10   -1.2948939    0.3210195           1.1023372                 -0.07177801
   WITHIN_350M_CHILDCARE_TV WITHIN_350M_BUS_TV WITHIN_1KM_PRISCH_TV
1                 1.0310116         -1.5262378           -1.9134857
2                 2.6139454          4.7074005           -4.2456050
3                 1.3277050         -0.6421659           -3.0729889
4                -0.5853557          0.6578671           -0.2301324
5                 2.4460497          1.0163752           -4.9568819
6                -1.2664185          1.9790428           -0.9718720
7                 2.6062185          2.4825301           -3.7413429
8                 1.3046069          0.1417710           -4.2119241
9                 2.0725495          4.8731405           -4.2591426
10                2.1403248         -0.8441792           -1.7747203
   PROX_GOOD_PRISCH_TV  Local_R2                  geometry
1           -0.9429398 0.9475410 POINT (30820.82 39547.58)
2           -0.3843222 0.9241404 POINT (29412.84 38680.21)
3            0.9815009 0.9331871 POINT (29982.66 39489.71)
4           -2.6182165 0.9254545  POINT (28149.18 39053.5)
5            0.3941104 0.9317547 POINT (30053.55 38976.12)
6           -3.9984217 0.9056712  POINT (28526.9 39944.36)
7            0.8409121 0.9290346 POINT (29507.16 38473.85)
8            1.2910230 0.9270069  POINT (29872.38 39327.2)
9            0.2668480 0.9318115 POINT (29333.46 38563.53)
10           0.9701686 0.9440498 POINT (30264.97 39315.69)
tmap_options(check.and.fix = TRUE)
tmap_mode("view")
tm_shape(mpsz_sf)+
  tm_polygons(alpha = 0.1) +
tm_shape(resale.sf.adaptive) +  
  tm_dots(col = "Local_R2",
          border.col = "gray60",
          border.lwd = 1) +
  tm_view(set.zoom.limits = c(11,14))

From the map showing the outputs of the local R2 values, the Eastern, Central and Northern areas of Singapore are not able to have good fit of the GWR model for observed values as these areas have low R2 values. As for the rest of Singapore, the model is able to predict HDB resale prices well.

8.4 GWR Random Forest Predictive Model

Using the ranger package Spatial ML, it allows for the computation of geographically weighted models using random forest regression.

8.4.1 Preparing Coordinates Data

coords_train <- st_coordinates(train)
coords_test <- st_coordinates(test)
coords_train <- write_rds(coords_train, "data/rds/coords_train.rds" )
coords_test <- write_rds(coords_test, "data/rds/coords_test.rds" )

Using the grf() function from Spatial ML, compute the GWR random forest model using the adaptive bandwidth derived previously in the GWR regression model. The number of trees is limited to 30 for computational efficiency.

set.seed(1234)
gwRF_adaptive <- grf(formula = resale_price ~
                            floor_area_sqm + storey_order +
                            remaining_lease + PROX_CBD + 
                            PROX_ELDERCARE + PROX_HAWKER +
                            PROX_MRT + PROX_PARK + PROX_MALL + 
                            PROX_SUPERMARKET + WITHIN_350M_KINDERGARTEN +
                            WITHIN_350M_CHILDCARE + WITHIN_350M_BUS +
                            WITHIN_1KM_PRISCH + PROX_GOOD_PRISCH,
                     dframe=train_nogeom, 
                     bw=69,
                     kernel="adaptive",
                     coords=coords_train,
                     ntree=30)

Write and save the gwr adaptive model output into an rds file.

write_rds(gwRF_adaptive, "data/rds/gwRF_adaptive.rds")
gwRF_adaptive <- read_rds("data/rds/gwRF_adaptive.rds")

To predict using random forest methods on the test data, use the predict.grf() function below.

test <- cbind(test, coords_test) %>%
  st_drop_geometry()
gwRF_pred <- predict.grf(gwRF_adaptive, 
                           test, 
                           x.var.name="X",
                           y.var.name="Y", 
                           local.w=1,
                           global.w=0)
GRF_PRED <- write_rds(gwRF_pred, "data/rds/GRF_PRED.rds")
GRF_pred <- read_rds("data/rds/GRF_pred.rds")
GRF_pred_df <- as.data.frame(GRF_pred)

Interpreting results of random forest predictive model

test_data_p <- cbind(test, GRF_pred_df)
write_rds(test_data_p,"data/rds/test_data_p.rds")
rmse(test_data_p$resale_price, 
     test_data_p$GRF_pred)
[1] 56716.64
ggplot(data = test_data_p,
       aes(x = GRF_pred,
           y = resale_price)) +
  geom_point()

The above graph indicates a good fit of the GWR random forest model as points are distributed along the diagonal line.

9 Conclusion

Various methods and models - OLS, GWR and random forest - were used to derive a predictive model, revealing that the GWR model had performed better than the global regression model. The random forest model also performed well with test data as seen from section 8.4. The model was derived for 5 room HDB resale flats in Singapore based on the following independent variables:

  • Structural factors

    • Area of the unit

    • Floor level

    • Remaining lease

  • Locational factors

    • Proxomity to CBD

    • Proximity to eldercare

    • Proximity to hawker centres

    • Proximity to MRT

    • Proximity to park

    • Proximity to good primary school

    • Proximity to shopping mall

    • Proximity to supermarket

    • Number of kindergartens within 350m

    • Number of childcare centres within 350m

    • Number of bus stop within 350m

    • Number of primary schools within 1km