Gambling neighbourhoods using an hybrid approach with HDBSCAN and eps=500m
This vignette demonstrates how to create interactive maps using the
leaflet package in R to visualize the gambling
neighbourhoods identified using an hybrid approach combining HDBSCAN
clustering with a fixed eps=500m. In here we will load the geographical
data containing the coordinates of gambling premises and the clustering
results from HDBSCAN with eps=500m for different values
of MinPts (4,5,10,15,20). We will create a separate map for each value
of MinPts, showing the cluster centers and the number of gambling
premises in each cluster. Clusters are in blue and represented by
polygons, each with a popup displaying the cluster ID and the number of
gambling premises within that cluster. Each polygon is sized according
to the number of gambling premises it contains. Gambling premises inside
each cluster are shown also in blue, while unclustered
(noise) points are shown in black. Each point (a
gambling premise GP) has a popup displaying its ID.
Load data
Load the geographical data (spatial points dataframe) containing the coordinates of gambling premises.
xy <- readRDS("data/xy.rds")MinPts=4 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 131 7929 4
#> 3 -2.10 57.1 131 4660 4
#> 4 -2.11 57.1 130 6787 9
#> 5 -2.11 57.1 130 5866 9
#> 6 -2.11 57.1 130 4658 9
#> [1] 8761 5Number of clusters (excluding noise):
#> [1] 131Number of noise points:
#> [1] 260Leaflet map for MinPts=4, eps=500m
MinPts=5 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 512 7246 56
#> 2 -2.10 57.1 512 7929 56
#> 3 -2.10 57.1 512 4660 56
#> 4 -2.11 57.1 512 6787 56
#> 5 -2.11 57.1 512 5866 56
#> 6 -2.11 57.1 512 4658 56
#> [1] 8761 5Number of clusters (excluding noise):
#> [1] 512Number of noise points:
#> [1] 1808Leaflet map for MinPts=5, eps=500m
MinPts=10 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 245 7246 56
#> 2 -2.10 57.1 245 7929 56
#> 3 -2.10 57.1 245 4660 56
#> 4 -2.11 57.1 245 6787 56
#> 5 -2.11 57.1 245 5866 56
#> 6 -2.11 57.1 245 4658 56
#> [1] 8761 5Number of clusters (excluding noise):
#> [1] 245Number of noise points:
#> [1] 3119Leaflet map for MinPts=10, eps=500m
MinPts=15 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 143 7246 56
#> 2 -2.10 57.1 143 7929 56
#> 3 -2.10 57.1 143 4660 56
#> 4 -2.11 57.1 143 6787 56
#> 5 -2.11 57.1 143 5866 56
#> 6 -2.11 57.1 143 4658 56
#> [1] 8761 5Number of clusters (excluding noise):
#> [1] 143Number of noise points:
#> [1] 3236Leaflet map for MinPts=15, eps=500m
MinPts=20 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 83 7246 56
#> 2 -2.10 57.1 83 7929 56
#> 3 -2.10 57.1 83 4660 56
#> 4 -2.11 57.1 83 6787 56
#> 5 -2.11 57.1 83 5866 56
#> 6 -2.11 57.1 83 4658 56
#> [1] 8761 5Number of clusters (excluding noise):
#> [1] 83Number of noise points:
#> [1] 2646Leaflet map for MinPts=20, eps=500m