Chapter 5 Geo-matching the Dataset

5.1 Technical steps - Geo-matching and Rasterize in R

Step 1 - Load the soil data and soil map for NJ

# Generate NJ shapefile '.shp' file using the tigris pakage
library(tigris)
options(tigris_use_cache = TRUE)
library(rgdal)
library(raster) 
library(sp)

nj.shp <- counties(state = 'NJ', cb = TRUE, resolution = '20m') 
# Get the geometry
nj.shp$geometry

## Geometry set for 21 features 
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -75.55945 ymin: 38.92852 xmax: -73.89398 ymax: 41.35742
## Geodetic CRS:  NAD83
## First 5 geometries:

plot(nj.shp$geometry)

# Save the geodata as .SHP

# CHANGE SHAPEFILE PROJECTION
nj.shp <- spTransform(nj.shp, CRS("+proj=longlat +datum=WGS84"))

# Convert the SOC data in nj.Data to log10()
nj.Data$OCSKGM <- log(nj.Data$SOC)

# Promote to SpatialPointsDataFrame
coordinates(nj.Data) <- ~ X + Y

# Project the Data created in Chapter 3 to the Shapefile
nj.Data@proj4string <- nj.shp@proj4string

5.2 Step 2 - Adding raster soilmap to the raster stack

covs <- stack(files)

5.3 Step 3 - Geo-matching

nj.shp$OCSKGM <- over(nj.shp, nj.Data[,"OCSKGM"], fun=mean)[,1]

5.4 Step 4 - Rasterize and export the results as geotiff

# Convert the layer to raster, to comply with the deliverables specifications.
pred.nj <- rasterize(nj.shp, covs, "OCSKGM")

plot(pred.nj)
lines(nj.shp, lwd=0.3)

# extract values from covariates to the soil points
nj.SOC <- extract(x = covs, y = nj.Data, sp = TRUE) 
summary(nj.SOC@data)

##       X.1              id                 SOC              CRFVOL       
##  Min.   :  1.00   Length:188         Min.   :  22.96   Min.   : 0.0000  
##  1st Qu.: 47.75   Class :character   1st Qu.: 142.07   1st Qu.: 0.0000  
##  Median : 94.50   Mode  :character   Median : 299.02   Median : 0.6363  
##  Mean   : 94.50                      Mean   : 437.62   Mean   : 3.5593  
##  3rd Qu.:141.25                      3rd Qu.: 770.85   3rd Qu.: 2.7634  
##  Max.   :188.00                      Max.   :1000.00   Max.   :45.6198  
##      OCSKGM        annualPET      aridityIndexThornthwaite
##  Min.   :3.134   Min.   : 759.3   Min.   : 8.372          
##  1st Qu.:4.956   1st Qu.: 883.1   1st Qu.:14.620          
##  Median :5.700   Median : 933.8   Median :17.407          
##  Mean   :5.669   Mean   : 928.1   Mean   :17.426          
##  3rd Qu.:6.647   3rd Qu.: 995.1   3rd Qu.:19.996          
##  Max.   :6.908   Max.   :1032.5   Max.   :28.434          
##  climaticMoistureIndex continentality    embergerQ     growingDegDays0
##  Min.   :0.01279       Min.   :21.88   Min.   :104.9   Min.   :4379   
##  1st Qu.:0.15834       1st Qu.:22.96   1st Qu.:126.0   1st Qu.:5115   
##  Median :0.19868       Median :23.57   Median :130.8   Median :5393   
##  Mean   :0.20279       Mean   :23.68   Mean   :131.5   Mean   :5300   
##  3rd Qu.:0.24609       3rd Qu.:24.67   3rd Qu.:134.8   3rd Qu.:5507   
##  Max.   :0.34004       Max.   :25.17   Max.   :158.4   Max.   :5653   
##  growingDegDays5 maxTempColdest  meanTempColdest  meanTempWarmest
##  Min.   :2934    Min.   :3.450   Min.   :-0.425   Min.   :24.48  
##  1st Qu.:3462    1st Qu.:5.650   1st Qu.: 1.900   1st Qu.:26.39  
##  Median :3631    Median :6.688   Median : 3.400   Median :26.61  
##  Mean   :3587    Mean   :6.270   Mean   : 2.871   Mean   :26.55  
##  3rd Qu.:3737    3rd Qu.:7.000   3rd Qu.: 3.800   3rd Qu.:26.96  
##  Max.   :3873    Max.   :7.275   Max.   : 4.575   Max.   :27.75  
##  minTempWarmest  monthCountByTemp10 PETColdestQuarter PETDriestQuarter
##  Min.   :19.67   Min.   :7.000      Min.   :22.56     Min.   :25.87   
##  1st Qu.:21.83   1st Qu.:7.000      1st Qu.:25.41     1st Qu.:37.27   
##  Median :23.20   Median :8.000      Median :25.81     Median :57.12   
##  Mean   :22.71   Mean   :7.739      Mean   :25.84     Mean   :51.05   
##  3rd Qu.:23.48   3rd Qu.:8.000      3rd Qu.:26.52     3rd Qu.:63.02   
##  Max.   :24.20   Max.   :8.000      Max.   :28.34     Max.   :69.67   
##  PETseasonality PETWarmestQuarter PETWettestQuarter thermicityIndex
##  Min.   :3265   Min.   :104.3     Min.   : 69.0     Min.   :184.4  
##  1st Qu.:4097   1st Qu.:125.7     1st Qu.:119.5     1st Qu.:245.2  
##  Median :4497   Median :134.8     Median :131.3     Median :263.1  
##  Mean   :4518   Mean   :134.6     Mean   :126.3     Mean   :257.2  
##  3rd Qu.:5073   3rd Qu.:147.2     3rd Qu.:144.5     3rd Qu.:271.8  
##  Max.   :5257   Max.   :150.8     Max.   :148.4     Max.   :287.0  
##    annualPET      aridityIndexThornthwaite climaticMoistureIndex
##  Min.   : 759.3   Min.   : 8.372           Min.   :0.01279      
##  1st Qu.: 883.1   1st Qu.:14.620           1st Qu.:0.15834      
##  Median : 933.8   Median :17.407           Median :0.19868      
##  Mean   : 928.1   Mean   :17.426           Mean   :0.20279      
##  3rd Qu.: 995.1   3rd Qu.:19.996           3rd Qu.:0.24609      
##  Max.   :1032.5   Max.   :28.434           Max.   :0.34004      
##  continentality    embergerQ     growingDegDays0 growingDegDays5
##  Min.   :21.88   Min.   :104.9   Min.   :4379    Min.   :2934   
##  1st Qu.:22.96   1st Qu.:126.0   1st Qu.:5115    1st Qu.:3462   
##  Median :23.57   Median :130.8   Median :5393    Median :3631   
##  Mean   :23.68   Mean   :131.5   Mean   :5300    Mean   :3587   
##  3rd Qu.:24.67   3rd Qu.:134.8   3rd Qu.:5507    3rd Qu.:3737   
##  Max.   :25.17   Max.   :158.4   Max.   :5653    Max.   :3873   
##  maxTempColdest  meanTempColdest  meanTempWarmest minTempWarmest 
##  Min.   :3.450   Min.   :-0.425   Min.   :24.48   Min.   :19.67  
##  1st Qu.:5.650   1st Qu.: 1.900   1st Qu.:26.39   1st Qu.:21.83  
##  Median :6.688   Median : 3.400   Median :26.61   Median :23.20  
##  Mean   :6.270   Mean   : 2.871   Mean   :26.55   Mean   :22.71  
##  3rd Qu.:7.000   3rd Qu.: 3.800   3rd Qu.:26.96   3rd Qu.:23.48  
##  Max.   :7.275   Max.   : 4.575   Max.   :27.75   Max.   :24.20  
##  monthCountByTemp10 PETColdestQuarter PETDriestQuarter PETseasonality
##  Min.   :7.000      Min.   :22.56     Min.   :25.87    Min.   :3265  
##  1st Qu.:7.000      1st Qu.:25.41     1st Qu.:37.27    1st Qu.:4097  
##  Median :8.000      Median :25.81     Median :57.12    Median :4497  
##  Mean   :7.739      Mean   :25.84     Mean   :51.05    Mean   :4518  
##  3rd Qu.:8.000      3rd Qu.:26.52     3rd Qu.:63.02    3rd Qu.:5073  
##  Max.   :8.000      Max.   :28.34     Max.   :69.67    Max.   :5257  
##  PETWarmestQuarter PETWettestQuarter thermicityIndex
##  Min.   :104.3     Min.   : 69.0     Min.   :184.4  
##  1st Qu.:125.7     1st Qu.:119.5     1st Qu.:245.2  
##  Median :134.8     Median :131.3     Median :263.1  
##  Mean   :134.6     Mean   :126.3     Mean   :257.2  
##  3rd Qu.:147.2     3rd Qu.:144.5     3rd Qu.:271.8  
##  Max.   :150.8     Max.   :148.4     Max.   :287.0

# The points with NA values have to be removed 
# nj.SOC = nj.SOC[complete.cases(nj.SOC), ]

# Export as a *.csv table
write.csv(nj.SOC, "saveLocation", row.names = FALSE)