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I am new to R and (unsupervised) machine learning. I'm trying to find out the best cluster solution for my data in R.

What is my data about?

I have a dataset with +/- 800 long / lat WGS84 coordinates in one city.

Long is in the range 6.90 - 6.95 lat is in the range 52.29 - 52.33

What do I want?

I want to find "hotspots" based on their density. As example: minimum 5 long/lat points in a range of 50 meter. This is a point plot example:

point plot

Why do I want this?

As example: let's assume that every single point is a car accident. By clustering the points I hope to see which areas need attention. (min x points in a range of x meter needs attention)

What have I found?

The following clustering algorithms seems possible for my solution:

  1. DBscan (https://cran.r-project.org/web/packages/dbscan/dbscan.pdf)
  2. HDBscan(https://cran.r-project.org/web/packages/dbscan/vignettes/hdbscan.html)
  3. OPTICS (https://www.rdocumentation.org/packages/dbscan/versions/0.9-8/topics/optics)
  4. City Clustering Algorithm (https://cran.r-project.org/web/packages/osc/vignettes/paper.pdf)

My questions

  1. What is the best solution or algorithm for my case in R?
  2. Is it true that I have to convert my long/lat to a distance / Haversine matrix first?
halfer
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bluewater
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1 Answers1

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Find something interested on: https://gis.stackexchange.com/questions/64392/finding-clusters-of-points-based-distance-rule-using-r

I changed this code a bit, using the outliers as places where a lot happens

# 1. Make spatialpointsdataframe #

xy <- SpatialPointsDataFrame(
  matrix(c(x,y), ncol=2), data.frame(ID=seq(1:length(x))),
  proj4string=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84"))

# 2. Use DISTM function to generate distance matrix.# 
mdist <- distm(xy)

# 3. Use hierarchical clustering with complete methode#
hc <- hclust(as.dist(mdist), method="complete")

# 4. Show dendogram#
plot(hc, labels = input$street, xlab="", sub="",cex=0.7)

# 5. Set distance: in my case 300 meter#
d=300

# 6. define clusters based on a tree "height" cutoff "d" and add them to the SpDataFrame
xy$clust <- cutree(hc, h=d)

# 7. Add clusters to dataset#
input$cluster <- xy@data[["clust"]]

# 8. Plot clusters #
plot(input$long, input$lat, col=input$cluster, pch=20)
text(input$long, input$lat, labels =input$cluster)

300 m cluster

# 9. Count n in cluster#
selection2 <- input %>% count(cluster)

# 10. Make a boxplot #
boxplot(selection2$n)

#11. Get first outlier#
outlier <- boxplot.stats(selection2$n)$out
outlier <- sort(outlier)
outlier <- as.numeric(outlier[1])

#12. Filter clusters greater than outlier#
selectie3 <- as.vector(selection2 %>% filter(selection2$n >= outlier[1]) %>% select(cluster))

#13. Make a new DF with all outlier clusters#
heatclusters <- input %>% filter(cluster%in% c(selectie3$cluster))

#14. Plot outlier clusters#
plot(heatclusters$long, heatclusters$lat, col=heatclusters$cluster)

outlier cluster

#15. Plot on density map ##
googlemap + geom_point(aes(x=long , y=lat), data=heatclusters, color="red", size=0.1, shape=".") +
  stat_density2d(data=heatclusters,
                 aes(x =long, y =lat, fill= ..level..), alpha = .2, size = 0.1,
                 bins = 10, geom = "polygon") + scale_fill_gradient(low = "green", high = "red")

Don't know if this a good solution. But it seems to work. Maybe someone has any other suggestion?

bluewater
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