This function computes the overall suitability scores and class of the land units.
overall_suit(suit, method = NULL, interval = NULL)an object of class suitability.
a character for the method for computing the overall suitability, choices are:
"minimum", "maximum", and
"average". If NULL, method is set to "minimum".
if NULL, the interval of the suitability class are the following: 0% - 25% (Not
suitable, N), 25% - 50% (Marginally Suitable, S3), 50% - 75% (Moderately Suitable, S2), and
75% - 100% (Highly Suitable, S1). But users can assign custom intervals by specifying
the values of the end points of the intervals. Say for intervals: 0% - 20% (Not
suitable, N), 20% - 50% (Marginally Suitable, S3), 50% - 80% (Moderately Suitable, S2), and
80% - 100% (Highly Suitable, S1), is equivalent to interval = c(0, 0.2, 0.5, 0.8, 1).
A data frame with columns:
Score - the overall suitability scores
Class - the overall suitability classes
https://alstat.github.io/ALUES/; suit
# The overall suitability can be computed using the `overall_suit`
# function, which takes an object of class suitability. For example,
library(ALUES)
banana_suit <- suit("banana", terrain=MarinduqueLT)
#> Warning: maximum is set to 16 for factor CECc since all parameter intervals are equal.
class(banana_suit[["terrain"]])
#> [1] "character"
class(banana_suit[["soil"]])
#> [1] "suitability"
# If we take a look at the output of both terrain and soil
# characteristics, we have:
# lapply is used to show the head of each item in the list
lapply(banana_suit[["terrain"]], function(x) head(x))
#> [[1]]
#> [1] "Error: No factor(s) to be evaluated, since none matches with the crop requirements. If water or temp characteristics was specified then maybe you forgot to specify the sow_month argument, read doc for suit."
#>
lapply(banana_suit[["soil"]], function(x) head(x))
#> $`Factors Evaluated`
#> [1] "CFragm" "CECc" "pHH2O" "SoilTe"
#>
#> $`Suitability Score`
#> CFragm CECc pHH2O SoilTe
#> 1 0.8533333 0.7500 0 0.96
#> 2 0.8800000 0.7500 0 0.96
#> 3 0.8666667 0.7500 0 0.96
#> 4 0.8666667 0.7500 0 0.96
#> 5 0.8400000 0.7500 0 0.96
#> 6 0.8533333 0.8125 0 0.96
#>
#> $`Suitability Class`
#> CFragm CECc pHH2O SoilTe
#> 1 S1 S1 N S1
#> 2 S1 S1 N S1
#> 3 S1 S1 N S1
#> 4 S1 S1 N S1
#> 5 S1 S1 N S1
#> 6 S1 S1 N S1
#>
#> $`Factors' Minimum Values`
#> CFragm CECc pHH2O SoilTe
#> 0 0 0 0
#>
#> $`Factors' Maximum Values`
#> CFragm CECc pHH2O SoilTe
#> 75.00 16.00 8.94 12.50
#>
#> $`Factors' Weights`
#> [1] 3 3 3 2
#>
#> $`Crop Evaluated`
#> [1] "BANANASoil"
#>
#> $Warning
#> [1] "maximum is set to 16 for factor CECc since all parameter intervals are equal."
#>
# There are no factors targetted for the terrain
# characteristics, that is why the returned value is a
# string error. Thus, only the soil characteristics can
# have an overall suitability, and is computed as follows:
ovsuit <- overall_suit(banana_suit[["soil"]])
head(ovsuit)
#> Score Class
#> 1 0 N
#> 2 0 N
#> 3 0 N
#> 4 0 N
#> 5 0 N
#> 6 0 N
# By default, the `overall_suit` function uses minimum
# as a summary statistics, hence the 0 scores and N
# classes across land units. To adjust this to average
# aggregation, use the `method` argument to specify.
ovsuit <- overall_suit(banana_suit[["soil"]], method="average")
head(ovsuit)
#> Score Class
#> 1 0.6505051 S2
#> 2 0.6569697 S2
#> 3 0.6537374 S2
#> 4 0.6537374 S2
#> 5 0.6472727 S2
#> 6 0.6656566 S2
## Intervals
# By default, the `overall_suit` uses an equally spaced
# interval for the suitability classes, that is,
# N [0, 0.25), S3 [0.25, 0.50), S2 [0.50, 0.75),
# and S1 [0.75, 1]. This can be changed using the
# `interval` argument, for example
ovsuit <- overall_suit(banana_suit[["soil"]], method="average",
interval=c(0, 0.6, 0.7, 0.9, 1))
head(ovsuit)
#> Score Class
#> 1 0.6505051 S3
#> 2 0.6569697 S3
#> 3 0.6537374 S3
#> 4 0.6537374 S3
#> 5 0.6472727 S3
#> 6 0.6656566 S3
# The above code sets the suitability class intervals
# into: N [0, 0.60), S3 [0.60, 0.70), S2 [0.70, 0.90),
# and S1 [0.90, 1]. It should be emphasized that the
# `interval` argument cannot be set to `unbias` as in
# the case of the `interval` argument of the `suit`
# function. This follows from the fact that the
# `overall_suit` function does not use a membership
# function for computing the score, but an aggregation function.
# Other examples
library(ALUES)
out <- suit("ricebr", terrain=MarinduqueLT,
water=MarinduqueWater, temp=MarinduqueTemp, sow_month=1)
#> Warning: maximum is set to 16 for factor CECc since all parameter intervals are equal.
lapply(out[["terrain"]], function(x) head(x))
#> [[1]]
#> [1] "Error: No factor(s) to be evaluated, since none matches with the crop requirements. If water or temp characteristics was specified then maybe you forgot to specify the sow_month argument, read doc for suit."
#>
lapply(out[["water"]], function(x) head(x))
#> $`Factors Evaluated`
#> [1] "Jan" "Feb" "Mar" "Apr"
#>
#> $`Suitability Score`
#> Jan Feb Mar Apr
#> 1 0.3051852 0.1925926 0.1629630 0.3542857
#> 2 0.3081481 0.1925926 0.1629630 0.3485714
#> 3 0.3081481 0.1955556 0.1659259 0.3485714
#> 4 0.3111111 0.1925926 0.1688889 0.3371429
#> 5 0.3111111 0.1925926 0.1718519 0.3371429
#> 6 0.3140741 0.1955556 0.1748148 0.3257143
#>
#> $`Suitability Class`
#> Jan Feb Mar Apr
#> 1 S3 N N S3
#> 2 S3 N N S3
#> 3 S3 N N S3
#> 4 S3 N N S3
#> 5 S3 N N S3
#> 6 S3 N N S3
#>
#> $`Factors' Minimum Values`
#> Jan Feb Mar Apr
#> 0 0 0 0
#>
#> $`Factors' Maximum Values`
#> Jan Feb Mar Apr
#> 880.0 880.0 880.0 714.2
#>
#> $`Factors' Weights`
#> [1] NA NA NA NA
#>
#> $`Crop Evaluated`
#> [1] "RICEBRWater"
#>
# Soil Overall Suitability
head(overall_suit(out[["soil"]]))
#> Score Class
#> 1 0 N
#> 2 0 N
#> 3 0 N
#> 4 0 N
#> 5 0 N
#> 6 0 N
head(overall_suit(out[["soil"]], "average"))
#> Score Class
#> 1 0.5114379 S2
#> 2 0.5245098 S2
#> 3 0.5179739 S2
#> 4 0.5179739 S2
#> 5 0.5049020 S2
#> 6 0.5322712 S2
head(overall_suit(out[["soil"]], "maximum"))
#> Score Class
#> 1 0.7843137 S1
#> 2 0.8235294 S1
#> 3 0.8039216 S1
#> 4 0.8039216 S1
#> 5 0.7647059 S1
#> 6 0.8125000 S1
head(overall_suit(out[["soil"]], "average", c(0, 0.3, 0.35, 0.6, 1.0)))
#> Score Class
#> 1 0.5114379 S2
#> 2 0.5245098 S2
#> 3 0.5179739 S2
#> 4 0.5179739 S2
#> 5 0.5049020 S2
#> 6 0.5322712 S2
# Water Overall Suitability
head(overall_suit(out[["water"]], "average"))
#> Score Class
#> 1 0.2537566 S3
#> 2 0.2530688 S3
#> 3 0.2545503 S3
#> 4 0.2524339 S3
#> 5 0.2531746 S3
#> 6 0.2525397 S3
head(overall_suit(out[["water"]], "maximum"))
#> Score Class
#> 1 0.3542857 S3
#> 2 0.3485714 S3
#> 3 0.3485714 S3
#> 4 0.3371429 S3
#> 5 0.3371429 S3
#> 6 0.3257143 S3
head(overall_suit(out[["water"]], "average", c(0, 0.3, 0.35, 0.6, 1.0)))
#> Score Class
#> 1 0.2537566 N
#> 2 0.2530688 N
#> 3 0.2545503 N
#> 4 0.2524339 N
#> 5 0.2531746 N
#> 6 0.2525397 N
# Temperature Overall Suitability
head(overall_suit(out[["temp"]], "average"))
#> Warning: No overall suitability computed since there is only one factor.
#> Score Class
#> 1 0.8366667 S1
#> 2 0.8333333 S1
#> 3 0.8400000 S1
#> 4 0.8366667 S1
#> 5 0.8366667 S1
#> 6 0.8500000 S1
head(overall_suit(out[["temp"]], "maximum"))
#> Warning: No overall suitability computed since there is only one factor.
#> Score Class
#> 1 0.8366667 S1
#> 2 0.8333333 S1
#> 3 0.8400000 S1
#> 4 0.8366667 S1
#> 5 0.8366667 S1
#> 6 0.8500000 S1
head(overall_suit(out[["temp"]], "average", c(0, 0.3, 0.35, 0.6, 1.0)))
#> Warning: No overall suitability computed since there is only one factor.
#> Score Class
#> 1 0.8366667 S1
#> 2 0.8333333 S1
#> 3 0.8400000 S1
#> 4 0.8366667 S1
#> 5 0.8366667 S1
#> 6 0.8500000 S1