Urban data lab - day 2 - explorative analysis - charging stations HH
Sven Lautenbach
10/18/2020
1 Preparation
Load data from geopackage
## Reading layer `chargingStations' from data source `/home/slautenb/Documents/lehre/HD/ws_2020_21/heikaLab/R4UrbanDataLab_2020/data/HH.gpkg' using driver `GPKG'
## Simple feature collection with 1061 features and 12 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 552777.7 ymin: 5921782 xmax: 581876.5 ymax: 5954451
## projected CRS: ETRS89 / UTM zone 32N2 Explorative analysis
2.1 Kernel density map
We need to creat/define an empty raster that will store the results of the kernel density estimation
Get bounding box of the data
## xmin ymin xmax ymax
## 552777.7 5921782.1 581876.5 5954451.3gridsize=c(500,500)
range_x=list(c(bboxHH[1], bboxHH[3]),c(bboxHH[2], bboxHH[4]))
#552378.61, 5921782.142, 582209.533, 5956674.616
kdeMat <- bkde2D(st_coordinates(charging_stations), bandwidth=c(500,500), gridsize=gridsize, range.x=range_x)Since bkde2D returns a matrix we need to convert it to a raster:
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum European_Terrestrial_Reference_System_1989 in CRS
## definitionScale the raster so that units are more meaningful and create countour lines that we will use in plotting
kdeRas_scaled <- kdeRas*xres(kdeRas)*yres(kdeRas)* nrow(charging_stations)
cellStats(kdeRas_scaled$layer, stat=sum )## [1] 1051.721## tmap mode set to interactive viewing2.2 How far are the stations away from each other?
We use a for loop to calculate the distance to the clostet charging station in the data set. Therefore, we loop over all points and calculate the distance between the point i and all points including itself. This rather brute force approache returns the whole distance matrix which when can be used to calculate differences.
n <- nrow(charging_stations)
distMat <- matrix(data=0, nrow=n, ncol=n)
for(i in seq_len(n))
{
allDists <- st_distance(charging_stations, charging_stations[i,])
distMat[i,] <- allDists
}In many cases several charging stations are at the same point, rendering a distance of zero.
getSecondSmallest <- function(x, pos=2)
{
sort(x)[pos]
}
dist2Shortest <- apply(distMat, MARGI=1, FUN= getSecondSmallest, pos=2)
summary(dist2Shortest)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5075 0.0000 328.8055We can force the analysis to deliver us the shortest distance to a neighbor not at the same location. Therefore, we: * sort values * get the indices of all values larger than zero * get the first element of the indices that is larger then zero
getSecondSmallestNotAtSame <- function(x, pos=1)
{
sortedValues <- sort(x)
idx <- which(sortedValues > 0)
return(sortedValues[idx][pos])
}
dist2ShortestNotAtSame <- apply(distMat, MARGI=1, FUN= getSecondSmallestNotAtSame)
summary(dist2ShortestNotAtSame)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 19.93 201.27 287.89 407.80 456.17 5425.73Or we could get the average distance to the closest ten charging stations
getMeanDistToClostest <- function(x, n=10)
{
sortedValues <- sort(x)
return(mean(sortedValues[1:n+1]))
}
distMean10 <- apply(distMat, MARGI=1, FUN= getMeanDistToClostest)
summary(distMean10)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 180.7 300.6 386.0 602.1 764.3 5300.0If we want to show distances in the map we can simply add the vectors to the simple features object and use tmap afterwards.
## tmap mode set to plottingtm_shape(osm_hh) + tm_rgb() +
tm_shape(charging_stations) +
tm_dots(size="meanDist10", legend.size.show = TRUE, title.size = "Mean distance to closest 10 stations" ) +
tm_basemap(server= "OpenStreetMap.DE") +
tm_layout(legend.outside=TRUE) +
tm_scale_bar()
We see that distances are (not unexpectedly increaing from the city center).
A convenient and fast imlementations is kNNdist from the dbscan package:
## 1 2 3 4
## 1 0 425.1543 425.1543 425.1543
## 2 0 571.8326 571.8326 1319.5587
## 3 0 293.6338 293.6338 312.8868
## 4 0 139.8384 139.8384 196.5883
## 5 0 0.0000 382.7148 382.7148
## 6 0 0.0000 393.3024 393.3024It returns the distance matrix for the k nearest neighbors.
2.3 Identify spatial clusters using dbscan
DBSCAN estimates the density around each data point by counting the number of points in a user-specified eps-neighborhood and applies a used-specified minPts thresholds to identify core, border and noise points. In a second step, core points are joined into a cluster if they are density-reachable (i.e., there is a chain of core points where one falls inside the eps-neighborhood of the next). Finally, border points are assigned to clusters. The algorithm only needs parameters eps and minPts.
## DBSCAN clustering for 1061 objects.
## Parameters: eps = 650, minPts = 7
## The clustering contains 8 cluster(s) and 308 noise points.
##
## 0 1 2 3 4 5 6 7 8
## 308 639 42 27 10 9 9 10 7
##
## Available fields: cluster, eps, minPts## tmap mode set to interactive viewing3 Exercise
Perform the same anlysis with the dataset chargingStations_berlin.shp. You can load the shapefile by simply passing the folder name and the name of the shapefile (including the .shp extension) to st_read. The data are in ETRS89 / UTM zone 33N (use a web search to get the EPSG number)