1 Load CentralaGBG.shp file

CentralaGBG = readOGR("CentralaGBG/CentralaGBG.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\Heatfront\Desktop\blibtexfolder\javainRstdio\CentralaGBG\CentralaGBG.shp", layer: "CentralaGBG"
## with 8534 features
## It has 8 fields

## Warning in readOGR("CentralaGBG/CentralaGBG.shp"): Z-dimension discarded

crs(CentralaGBG)

## CRS arguments: NA

extent(CentralaGBG)

## class      : Extent 
## xmin       : 38550 
## xmax       : 39950 
## ymin       : 27443 
## ymax       : 28843

2 Import csv file and view data structure

Temperatur22APRIL =read.csv("CentralaGBG/Temperatur22APRIL.txt", header=T,sep="\t")

str(Temperatur22APRIL)

## 'data.frame':    57 obs. of  3 variables:
##  $ X         : int  39477 39425 39332 39260 39405 39521 39554 39640 39716 39747 ...
##  $ Y         : int  27543 27588 27678 27830 27874 27902 27809 27819 27899 27977 ...
##  $ Temperatur: num  11.1 11 10.8 11.1 11.5 ...

class(Temperatur22APRIL)

## [1] "data.frame"

3 Create spatial object

coordinates(Temperatur22APRIL) = ~X + Y
class(Temperatur22APRIL)

## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"

4 Interactive Actual Temperature Map : Central Gothenburg City

5 IDW interpolation

max_temp = max(Temperatur22APRIL$Temperatur)
min_temp = min(Temperatur22APRIL$Temperatur)

max_X = max(Temperatur22APRIL$X)
min_X = min(Temperatur22APRIL$X)

max_Y = max(Temperatur22APRIL$Y)
min_Y = min(Temperatur22APRIL$Y)

x_range = as.numeric(c(min_X, max_X))
y_range = as.numeric(c(min_Y, max_Y))

6 Create an empty grid of values ranging from the xmin-xmax, ymin-ymax

grd = expand.grid(x = seq(from = x_range[1],to = x_range[2],  by = 4),
                  y = seq(from = y_range[1],to = y_range[2],  by = 4))
class(grd)

## [1] "data.frame"

# Convert grd object to a matrix and then turn into a spatial
# points object
coordinates(grd) = ~x + y
# turn into a spatial pixels object

gridded(grd) =  TRUE
# 
# plot(grd, cex = 1.5, col = "grey")
# 
# plot(Temperatur22APRIL,pch = 15,col = "red",cex = 1,add = TRUE)

7 Set CRS to shape file

CentralaGBG = readOGR("CentralaGBG/CentralaGBG.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\Heatfront\Desktop\blibtexfolder\javainRstdio\CentralaGBG\CentralaGBG.shp", layer: "CentralaGBG"
## with 8534 features
## It has 8 fields

## Warning in readOGR("CentralaGBG/CentralaGBG.shp"): Z-dimension discarded

crs(CentralaGBG)

## CRS arguments: NA

extent(CentralaGBG)

## class      : Extent 
## xmin       : 38550 
## xmax       : 39950 
## ymin       : 27443 
## ymax       : 28843

proj4string(CentralaGBG) = CRS("+init=epsg:3847")
proj4string(CentralaGBG)

## [1] "+init=epsg:3847 +proj=tmerc +lat_0=0 +lon_0=15.80628452944445 +k=1.00000561024 +x_0=1500064.274 +y_0=-667.711 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

proj4string(Temperatur22APRIL) = CRS("+init=epsg:3847")
proj4string(Temperatur22APRIL)

## [1] "+init=epsg:3847 +proj=tmerc +lat_0=0 +lon_0=15.80628452944445 +k=1.00000561024 +x_0=1500064.274 +y_0=-667.711 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

grd_ref = expand.grid(x = seq(from = x_range[1],to = x_range[2],  by = 4),
                  y = seq(from = y_range[1],to = y_range[2],  by = 4))
class(grd_ref)

## [1] "data.frame"

coordinates(grd_ref) = ~x + y
gridded(grd_ref) =  TRUE
proj4string(grd_ref) = CRS("+init=epsg:3847")
idw_Temperatur_ref = idw(formula = Temperatur ~ 1, locations = Temperatur22APRIL, newdata = grd_ref,idp = 1)

## [inverse distance weighted interpolation]

idw_Temperatur_ras_ref = raster(idw_Temperatur_ref)

8 Interactive Temperature Prediction Map : Central Gothenburg City

mapview(CentralaGBG, alpha.regions = 0.6 ) + mapview(idw_Temperatur_ras_ref, col = heat.colors(40), alpha.regions = 0.8  )