In-Class Exercise 3.1: Kernel Density Estimation

In-class exercise 3.1

0.0 Class Notes

Spatial Point Patterns

• Points as events, relates to activities

• Issues with points: Highly affected by the size of the ‘dots’. KDE is more consistent

• The points MUST be a population. Cannot be a sample or we will have sample bias.

KDE Setup

• Choose bandwidth
• Choose a kernel

NKDE: Network-Constrained Kernel Density Estimation

• Constrained by network

TNKDE: Temporal Network Kernel Density Estimate

• Accounts for both time and location

1.0 Setup

1.1 Dependencies

pacman::p_load(sf, spNetwork, tmap, classInt, viridis, tidyverse)

1.2 Importing and Changing CRS

network <- st_read(dsn="data/geospatial", layer="Punggol_St")

Reading layer `Punggol_St' from data source 
  `/Users/matthewho/Work/Y3S2/IS415/Website/IS415/InClassEx/ICE3/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 2642 features and 2 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 34038.56 ymin: 40941.11 xmax: 38882.85 ymax: 44801.27
Projected CRS: SVY21 / Singapore TM

childcare <- st_read(dsn="data/geospatial", layer="Punggol_CC")

Reading layer `Punggol_CC' from data source 
  `/Users/matthewho/Work/Y3S2/IS415/Website/IS415/InClassEx/ICE3/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 61 features and 1 field
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 34423.98 ymin: 41503.6 xmax: 37619.47 ymax: 44685.77
z_range:       zmin: 0 zmax: 0
Projected CRS: SVY21 / Singapore TM

tmap_mode('view')
tm_shape(childcare) +
  tm_dots() +
  tm_shape(network) +
  tm_lines()

tmap_mode('plot')

Mindist = 0.5 * first param

lixels <- lixelize_lines(network, 750, mindist = 375)

samples <- lines_center(lixels)

densities <- nkde(network, 
                  events=childcare, 
                  w=rep(1, nrow(childcare)), 
                  samples=samples, 
                  kernel_name="quartic", 
                  bw=300, div="bw", 
                  method="simple", 
                  digits=1,
                  tol=1,
                  grid_shape=c(1,1), 
                  max_depth = 8, 
                  agg=5, 
                  sparse=TRUE, 
                  verbose = FALSE)

samples$density <- densities
lixels$density <- densities

samples$density <- samples$density*1000
lixels$density <- lixels$density*1000

tmap_mode('view')
tm_shape(lixels) + 
  tm_lines(col="density") + 
tm_shape(childcare) + 
  tm_dots()

tmap_mode('plot')