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This software was used for the article Spuhler et al.(2018) and the related data has been published under ERIC: https://doi.org/10.25678/0001PH.

If you use this software for scientific work, please cite: Spuhler, D., Scheidegger, A., & Maurer, M. (2018). Generation of sanitation system options for urban planning considering novel technologies. Water Research. https://doi.org/10.1016/j.watres.2018.08.021

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TechAppA – Technology Appropriateness Assessment

TechAppA is a model written in R for the quantification of the appropriateness of technologies (techs) in a given context (case) considering uncertainties. It has been developed to assess the appropriateness of sanitation technologies in developing urban areas using a number of appropriateness criteria. The development was part of the GRASP project (Generation and Assessment of Sanitation options for urban Planning) by Dorothee Spuhler at Eawag: the Swiss Federal Institute of Aquatic Science and Technology. The input of the model is a techdata.csv file and a casedata.csv file that contain for each criteria uncertainty function of how the tech performs regarding a given condition (e.g. temperature) and a condition given by the case (e.g. temperature distribution over the year). By matching the tech criteria with the case criteria, a tech criteria appropriateness score is generated. The aggregation of all scores results in a technology appropriateness score (TAS) for each tech in a given case.

Installation

1. Install R and R-Studio or any other editor.

2. Install required packages (type in the R command line)

install.packages(c("triangle", "trapezoid", "rlist",
                   "gridExtra", "ColorPalette", "ggplot2", "reshape2"))

Usage

This is a minimal example of how you can run model:

### Set working directory
# Use setwd("/YourWorkingDirectory/TechAppA/")

### Load required library packages
library(triangle)  # package for triangular distribution
library(trapezoid) # package for trapezoidial distribution
library(rlist)     # package to manupulate/filter app list
library(gridExtra)
library(ColorPalette)
library(ggplot2)
library(reshape2)

### Load required functions (all part of the Appropriateness folder)
source("R/build.list.R")   # This function reads input data. The file format is csv. The model requires one techdata.csv and one casedata.csv.
# See below for more details on the format of the input data files.
# build.list(filename,n.info.row)
source("R/appfunctions.R") # contains functions that can be used to compute attribute values
  # prange(x, lower=-Inf, upper=Inf)
  # drange(x, lower=-Inf, upper=Inf)
  # rrange(x, lower=-Inf, upper=Inf)
  # ptrapez(x, a, b=(d-a)/2+a, c=b, d)
  # dtrapez(x, a, b=(d-a)/2+a, c=b, d)
  # rtrapez(x, a, b=(d-a)/2+a, c=b, d)
  # pcat(x, probs)
  # dcat(x, probs), probs is the vector of categories and respective probabilities. E.g. c(no=0.4,yes=0.6) !NB. the sum of probs has to be =1
  # rcat(x, probs)
source("R/mc.integrate.R") # This functions computes a monte carlo integration of two continous functions
  # mc.integrate(case.app.fun, tech.app.fun, n.sample=10000)
source("R/compute.techappscore.R") # Returns app.profile and app.score (aggregated profile)
  # compute.techappscore(tech, case,lshowplot=FALSE)
  # plots provide a graphical representaiton of the two functions and the overall
source("R/compute.techapplist.R") # Returns a list of app.profiles & app.score for all the techs and caes of a techlist and caseplist
  # compute.techapplist(techlist, caselist, listsep=" ", filename="")
source("R/techapplist.write.R") # writes applist either to screen or to a file if listsep and filename are provided
 # function(applist, listsep=" ", filename="")

source("R/techapplist.frame.R")


## =======================================================
## EXAMPLES
## =======================================================

### Read data input files using build.list to generate a caselist and techlist
caselist_demo <- build.list("demo/casedata_demo2.csv")
techlist_demo <- build.list("demo/techdata_demo2.csv")
# alternatively use:
# caselist_demo <- build.list("casedata_demo.csv")
# techlist_demo <- build.list("techdata_demo.csv")

### Usinge compute.techapp.R to compute app.proiles for a pair of case and tech (caselist$case, techlist$tech)
applist_demo <- list()
app.item.tmp <- compute.techappscore(caselist_demo$Katarniya, techlist_demo$uddt,lshowplot = TRUE)
applist_demo <- append(applist_demo,list(app.item.tmp))
app.item.tmp <- compute.techappscore(caselist_demo$Katarniya, techlist_demo$dry.toilet,lshowplot = TRUE)
applist_demo <- append(applist_demo,list(app.item.tmp))
app.item.tmp <- compute.techappscore(caselist_demo$Katarniya, techlist_demo$application.compost,lshowplot = TRUE)
applist_demo <- append(applist_demo,list(app.item.tmp))
# Write the list to the screen
techapplist.write(applist_demo)
# Write to file
techapplist.write(applist_demo, listsep=";", filename="app_list_demo.csv") #giving a list separation charachter and a filename creates a csv file with the results


### Using compute.techapplistto compute the entire appropriateness profiles for a list of cases and a list of technologies
applist_demo <- compute.techapplist(caselist_demo,techlist_demo, lshowplot = F, lpdfplot = F)
# use lshowplot=F and lpdfplot to display the match of the case attribute function and the tech attribute functions
#write the list to the screen
techapplist.write(applist_demo)

### Convert to dataframe and write to csv
appframe_demo <- techapplist.frame(applist_demo,techlist_demo, caselist_demo)
View(appframe_demo)
write.table(appframe_demo, file = "appframe_demo.csv", sep = ";",row.names=F)


## =======================================================
## EXAMPLES PLOTS
## =======================================================

### Define order of functional groups
appframe_demo$functional.group <- factor(appframe_demo$functional.group, levels=c('U','Uadd','S','C','T','D'))
appframe_demo_long <- melt(appframe_demo, id=c("case", "tech", "functional.group"))

### Plot a histogramm per tech of all scores
ggplot(appframe_demo_long, aes(x=variable, y=value, fill=functional.group)) +
  geom_boxplot() +
  facet_wrap( ~ functional.group) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

# -- plot histogram of tech app scores per functional group
ggplot(appframe_demo, aes(x=techapp.score, fill=functional.group)) + geom_histogram(show.legend=F) + facet_wrap( ~ functional.group)
# -- plot histogram of all tech app scores (coloured per functional group)
ggplot(appframe_demo, aes(x=techapp.score, fill=functional.group, order=functional.group)) + geom_histogram()
# -- boxplot of scores per functional group
ggplot(appframe_demo, aes(y=techapp.score, x=functional.group, fill=functional.group)) +  geom_boxplot()

Guidelines for the preparation of input files

Each data files contains a list of items (either techs or cases in the columns) Each items has a few information attributes (info.rows), build.list automatically detects the number of info rows, so you do not need to provide this. This is followed by a list of appropriateness attributes (attr1,...., attrn). Info rows are used to provide comments about the case or the technology. Moreover there is a line for the functional groups, two lines for the in- and out-products, and one line for the technology appropriateness score. These informations are used to generate entire sanitaiton systems from the basic technology moduls

Predefined functional groups are:

User interface (U), Collection and Storage (S), Conveyance (C), (Semi-)centralized Treatement (T), Reuse and/or Disposal (D) (see also http://ecompendium.sswm.info)

Pre-defined poducts:

urine, faeces, excreta, blackwater, greywater, stormwater, storedurine, driedfaeces, pit humus, compost, sludge, effluent, stabilizedsludge, secondaryeffluent, biogas

Appropriateness attributes are defined by three rows:

1. Name of the attributes: e.g. bod, water, temp, omskil, etc.
2. Name of attribute appropriateness function describing the technology/case requirement/capactiy
3. Parameters required for this function Each distinct attriute is described by a pair of functions, one for the case and one for the tech. !!! A pair has always to consits of one probability distribution function ('d...') and a conditional probability ('p…'). The distribution functions are used to describe the probability that an attribute takes a certain value (e.g. temperature) and conditional functions are used to describe the performance for a given attribute a certain condition (e.g. the performance of a technology given a certain temperaturegiven)

Recommended attribute functions are:

• p or drange(x, lower=-Inf, upper=Inf)
• p or dtrapez(x, a, b, c, d)
• dtriangle(x, a, b, c)
• dunif(x, min, max)
• dcat(x, probs) # probs is the vector of categories and respective probabilities. E.g. c(no=0.4,yes=0.6) Other functions that might work are: dnorm, dlnorm, dbeta, dweibull, dgamma, dlogis, etc.