Region and Time Conversion

Region conversion

In this section, we convert a dataset containing the GDP of all countries into a 17-region mapping of the WITCH model, then into the 5-region mapping used in the SSP database.

First, let’s create the initial database, we will extract the GDP in 2005 from the default_weights. The column names have to be renamed. By convention, column names must be “iso3” and “value”

library(data.table)
library(witchtools)

# Creation of the initial dataset
# Note the column names: iso3 and value
gdp_iso3 <- copy(default_weights[['gdp']])
setnames(gdp_iso3,'weight','value')
gdp_iso3
#>      iso3        value
#>   1:  AFG 7.543866e+01
#>   2:  ALB 3.861787e+01
#>   3:  DZA 4.673124e+02
#>   4:  AGO 1.979695e+02
#>   5:  ARG 8.905475e+02
#>  ---                  
#> 246:  BLM 1.000000e-10
#> 247:  CYM 1.000000e-10
#> 248:  KNA 1.000000e-10
#> 249:  MAF 1.000000e-10
#> 250:  MSR 1.000000e-10

Now, using the function convert_region, we will aggregate this dataset at country-level (ISO3) to the 17-region mapping of the WITCH model, available as a default mapping.

# Aggregate value at WITCH regional scale (17 regions)
gdp_witch17 <- convert_region(gdp_iso3, to_reg = 'witch17')
gdp_witch17
#>         witch17      value
#>  1:        laca  3811.0076
#>  2:       sasia  2616.5509
#>  3:         ssa  3281.0841
#>  4:      europe 22137.4325
#>  5:        mena  7253.0736
#>  6:          te  8156.9139
#>  7:     oceania  1501.8456
#>  8:      brazil  2969.7366
#>  9:      seasia  4873.5174
#> 10:      canada  1746.1213
#> 11:       china 22961.7494
#> 12:   indonesia  3118.3992
#> 13:       india  8377.0372
#> 14:      jpnkor  7243.6577
#> 15:      mexico  2300.2179
#> 16:         usa 19910.1964
#> 17: southafrica   749.2962

In a final step, we will convert the 17-region data into the 5-region of the SSP database. The dataset is first disaggregated at iso3 level then it is aggregated into 5 regions, automatically.

# Aggregate value at WITCH regional scale (17 regions)
gdp_r5 <- convert_region(gdp_witch17, to_reg = 'r5')
gdp_r5
#>        r5     value
#> 1:  r5lam  8973.866
#> 2: r5oecd 52788.486
#> 3: r5asia 44228.718
#> 4:  r5maf 11283.454
#> 5:  r5ref  5733.314

Time conversion

In this section, we convert a yearly time-serie into the default time period of the WITCH model, 5 year-periods from 2005 to 2150, using the function convert_time_period.


# Time-serie
dd <- data.table(tech = c("tech1","tech2"), year = 2005:2050, value = 1:46)

# Aggregate values to the default WITCH time period mapping
res <- convert_time_period(dd, 't30')
print(nrow(res))
#> [1] 20
print(res, nrows = 20)
#>      tech  t value
#>  1: tech1  1     2
#>  2: tech2  1     2
#>  3: tech2  2     6
#>  4: tech1  2     6
#>  5: tech1  3    11
#>  6: tech2  3    11
#>  7: tech2  4    16
#>  8: tech1  4    16
#>  9: tech1  5    21
#> 10: tech2  5    21
#> 11: tech2  6    26
#> 12: tech1  6    26
#> 13: tech1  7    31
#> 14: tech2  7    31
#> 15: tech2  8    36
#> 16: tech1  8    36
#> 17: tech1  9    41
#> 18: tech2  9    41
#> 19: tech2 10    45
#> 20: tech1 10    45

The function convert_time_period can also extrapolate the missing periods.


# Aggregate values to the default WITCH time period mapping with extrapolation
res <- convert_time_period(dd, 't30', do_extrap = TRUE)
print(nrow(res))
#> [1] 60
print(tail(res))
#>     tech  t value
#> 1: tech2 25    46
#> 2: tech2 26    46
#> 3: tech2 27    46
#> 4: tech2 28    46
#> 5: tech2 29    46
#> 6: tech2 30    46

L. Drouet

2023-09-21

Region conversion

Time conversion