In y-yasutomo/maliaver0:

Authors:

Tomoki Yasuhara

Hiroaki Murata

Toshihide Kitakado

library(knitr)
library(rmarkdown)
options(max.print="90")
opts_chunk$set(echo =TRUE,
               eval=TRUE,
               cache=!TRUE,
               prompt=FALSE,
               tidy=FALSE,
               comment=NA,
               message=FALSE,
               warning=FALSE,
               results= 'markup',
               fig.margin=TRUE, 
               fig.height=5, 
               fig.width=8
)

1.Introduction

We'd like to demonstrate how marine litter analyses are conducted.

In the analysis, ---expl

2. Prerequisite

2.1 Installation of packages

If the follwing pacakges have not been installed, please execute next commented lines first.

#install.packages("dev.tools")
#install.packages("dplyr")
library(devtools)
library(dplyr) #data handling

#install_github("y-yasutomo/maliaver0")
#calling package in order to analyze marine debris data
library(malia0)

3. See Data and set up of estimation

3.1 Data visualization

Survey track line

#Designate voyage name
Vname<-"O18y1"
survey.plot(read.csv(paste(Vname,".effort.csv",sep="")))

3.2 Set up of estimation

Simple estimation using MALIA function

Reading data

Sight.Data<-read.csv(paste(Vname,".debris.csv",sep=""))
Effort.Data<-read.csv(paste(Vname,".effort.csv",sep=""))

3.3 Estimation

We shall estimate density of 'EPS' by 'MALIA' function

tmp.Data<-Sight.Data %>%filter(type == "EPS")
res<-MALIA(tmp.Data,Effort.Data,key="hn",td=200)

Densities in each leg

leg.D.plot(res$leg.D.obs$leg.result,xl=c(120,160),yl=c(20,50),save=F,Type="EPS")

3.4 Using full data

SDAM function can conduct whole analysis

i.e) estimate detection function, calculate densities in each debris type, covariate and detection function

As it takes some time to finish all trials, we use already created results for mapping, model selection and making table.

#res<-SDAM(Voyage.name=Vname,COVARIATE = c("conv", "occo","weather", "size"), key.list = c("hn","hr", "hhn", "hhr"),td=200,cp=10)
#Reading result object
res<-readRDS(paste(Vname,".result.obj",sep=""))

4. Result handling

4.1 Drawing maps

Maps with trackline and estimated density

#let's see type 'EPS'
leg.D.res<-res$hhr$weather$EPS$leg.D.obs$leg.result
leg.D.plot(leg.D.res,save=F,Type='EPS')

Density of "EPS" by grid

 grid.D.res<-res$hhr$weather$EPS$grid.D.res
 grid.D.plot(grid.D.res,save=F,Type='EPS')

4.2 Model selection

model summary

 aic.mat<-aic.summary(res)
 aic.mat$EPS

model.extract function can extract best models in each debris type easily from SDAM result

 best.model<-model.extract(aic.mat,res) 
 best.model$best.mat

4.3 Creating table

Summary of densities by leg

 leg.table<-leg.D.table(best.model$best.list)
 head(leg.table)
 #write.xlsx(leg.table,file=paste(table.pass,"leg.D.table.xlsx",sep=""),row.names=F)

Summary of densities by grid

 grid.table<-grid.D.table(best.model$best.list)
 head(grid.table)
 #write.xlsx(grid.table,file=paste(table.pass,"grid.D.table.xlsx",sep=""),row.names=F)

Summary of densities by area

 area.table<-area.D.table(best.model$best.list)
 head(area.table)
 #write.xlsx(area.table,file=paste(table.pass,"area.D.table.xlsx",sep=""),row.names=F)

5. Ar and Nt products

5.1. Classify each debris into categories

ls. function can sum up artificial and natural products

 Ar<-c("FGN","FGF","FGO","EPS","PBA","PBO","FP","PC","G","M","W","UO")
 Nt<-c("SW","DW","NO")
 An.table<-ls.(leg.table,Voyage.name,Ar,Nt)
 head(An.table)

Plot of Artificial product

 Artificial.leg<-An.table %>%  select(Leg.No.,Leg.Length,Lat.Start,Lon.Start,Lat.End,Lon.End,Artificial_Density) %>% rename(Density=Artificial_Density)
 leg.D.plot(Artificial.leg,save=F)
 Artificial.grid<-grid.D(Artificial.leg)
 grid.D.plot(Artificial.grid,save = F)

Plot of Natural product

Natural.leg<-An.table %>%  select(Leg.No.,Leg.Length,Lat.Start,Lon.Start,Lat.End,Lon.End,Natural_Density) %>% rename(Density=Natural_Density)
 leg.D.plot(Natural.leg,save=F)
 Natural.grid<-grid.D(Natural.leg)
 grid.D.plot(Natural.grid,save = F)

5.2. Combining results

Combining results across multiple surveys. In this procedure, it is necessary result objects has to already be created.

Survey legs

 vname<-c("S18y4","O18y1","K18y1")
 tmp<-data.frame()
for(i in 1:length(vname)){
  Effort.Data<-read.csv(paste(vname[i],".effort.csv",sep=""))
  Effort.Data$voyage_name<-vname[i]
  tmp<-rbind(tmp,Effort.Data)
}
 survey.plot(tmp,multi=T)

Combine leg D results

In the leg.D.comb function, model selection and extraction of the best model are conducted.

 tmp<-leg.D.comb(vname) 
#let's see type "DW"
 tmp2<-grid.D(tmp$DW)
 grid.D.plot(tmp2,save = F)
#area D
 tmp3<-area.D(tmp$DW)
 tmp3$area.density

Artificial and Natural products

Artificial product

 tmp<-vanc(vname)
 leg.D.plot(tmp$Ar.leg.D,save=F)
 Artificial.grid<-grid.D(tmp$Ar.leg.D)
 grid.D.plot(Artificial.grid,save = F)

Natural product

 leg.D.plot(tmp$Nt.leg.D,save=F)
 Natural.grid<-grid.D(tmp$Nt.leg.D)
 grid.D.plot(Natural.grid,save = F)

y-yasutomo/maliaver0 documentation built on July 5, 2019, 6:22 a.m.