In fozy81/policy:

Policy Statement

Reduce street litter by r reduction_target <- 75``r reduction_target%.

Administrative area r admin_area <- params$admin_area``r admin_area_html <- gsub(pattern=" ","%20",admin_area)

# create map of admin area
library(osmar)
library(RCurl)
library(jsonlite)
# query to openstreetmap 'overpass' api

#url <-  'http://nominatim.openstreetmap.org/search/North%20Lanarkshire?format=json'

#query_response <- getURL(url)
#dq <- fromJSON(query_response)

url <- paste('http://overpass-api.de/api/interpreter?data=%3C!--%0AThis%20query%20looks%20for%20nodes%2C%20ways%20or%20relations%20%0Awith%20the%20given%20key%2Fvalue%20combination.%0AChoose%20your%20region%20and%20hit%20the%20Run%20button%20above!%0A--%3E%0A%0A%3Cosm-script%20output%3D%22xml%22%3E%0A%20%20%0A%20%20%3Cquery%20type%3D%22relation%22%3E%0A%20%20%20%20%3Chas-kv%20k%3D%22name%22%20v%3D%22',admin_area_html,'%22%2F%3E%0A%20%20%3C%2Fquery%3E%0A%20%20%0A%20%20%3Cprint%20mode%3D%22body%22%2F%3E%0A%20%20%3Crecurse%20type%3D%22down%22%2F%3E%0A%20%20%3Cprint%20mode%3D%22meta%22%2F%3E%0A%3C%2Fosm-script%3E%0A&target=xml',sep="")

# url <- "https://www.openstreetmap.org/relation/1920584"
 #url <-  "http://nominatim.openstreetmap.org/search?q=North%20Lanarkshire&format=json&polygon=1"
map_data <- getURL(url)
write(map_data,file="map_data.osm")
# use osmar library to load data and plot map
osm_map <-  get_osm(complete_file(), source = osmsource_file("map_data.osm"))
plot(osm_map)

Litter

Meaning of litter

(a) Rubbish such as paper, tins, and bottles left lying in an open or public place.

library(pander)
litter_cat <- data.frame("category"= c(1,2,3,4,5),
                         "name"= c("Dangerous",
                                   "Harmful",
                                   "Severe",
                                   "Nuisance",
                                   "Minor"),
                   "description"=c("Harmful litter which threatens human life",
                                    "Harmful litter which could severely injury human health",
                                    "Litter posing moderate risk to human health",
                                    "Litter posing minor risk to human health",
                                    "Litter posing minimal or no risk to human health"),
                                    reduction_target)

#knitr::kable(litter_cat, align='c')
set.caption("This table defines the categories of social impact of litter")
pandoc.table(litter_cat)

Public highways

Publicly accessible streets excluding gated or no access highways. r exclude_access <- c('access=no|private|destination') The street area covers the entire width of the thorough fare up until meeting with private or fenced property. Including the road verge or sidewalk.

# create map of admin area
library(osmar)
library(RCurl)
# query to openstreetmap 'overpass' api
url <- readLines("road_query")
# admin_area_html <- "Falkirk%20Council"
url <- gsub(pattern = "Falkirk%20Council", url, replacement = admin_area_html)


road_data <- getURL(url)
write(road_data,file="road_data.osm")
# use osmar library to load data and plot map
road_map <-  get_osm(complete_file(), source = osmsource_file("road_data.osm"))
plot(road_map)

Reduction

The reduction target of r reduction_target% is based on a snapshot of highways within the admin area of r admin_area
taken at the start of a four year programme. The snapshot survey will be recorded again each year at approximately the same time of year.

The aim is to meet the reduction of r reduction_target% over the course of r target_time <- 4``r target_time years based on the original base line. The reduction target applies equally over all r length(litter_cat) categories of litter.

Snapshot survey

The snapshot will select r sub_sample <- 5``r sub_sample% of the r highway_network <- paste('highway in (\'primary\',\'secondary\',\'tertiary\',\'residential\',\'unclassified\') and access not = \'no\', \'private\', \'destination\'',sep="") highway network as defined in OpenStreetMap as r highway_network

Each 2km section of randomly selected highway will be selected. Each section split into 100m sub-sections. The survey will assess each 100m section, taking photos and identifying the amount of each category of litter found.

litter_questions <- data.frame(
'question'=c('Unique id of section',
             'Date',
             'Sampler ID',
             litter_cat[1,2],
             'Amount'),
'answers'=c('1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20',
            'Date',
            'Text',
            litter_cat[1,2],
            'huge amount of litter,large amount of litter,moderate amount of litter,minor amount of litter,no litter'
            ))

#knitr::kable(litter_questions)

set.caption("Snapshot litter survey questions")
#knitr::kable(social_questions, align = 'c')
pandoc.table(litter_questions)

r percent_road <- 5 r percent_road% of highway network 1km sections randomly selected highlighted in black

library(osmar)
library(RCurl)
library(sp)
library(rgeos)
library(spatialEco)
library(rgdal)
road_line <- as_sp(road_map, "lines")
road_line_merge <-  gLineMerge(road_line)

road_line_merge <- spTransform(road_line_merge,CRS("+proj=utm"))
road_line <- spTransform(road_line,CRS("+proj=utm"))

road_length <- gLength(road_line_merge) / 1000
num_surveys <- round(road_length * as.numeric(paste("0.0",percent_road,sep="")),digits=0)
#sample.grid <- makegrid(road_line_merge, cellsize=1000,
#   pretty = TRUE)
roads <- spsample(road_line_merge , n = 2000, "stratified")

#plot(sample.grid)
plot(road_line)
plot(sample(roads,num_surveys), pch =15,cex= 1.5, col="black" ,add=T,box=T,title="2km points")

Social survey

The a survey of 500 people will record social attitudes to litter via webiste questionaire

The questions will be:

social_questions <- data.frame(
'question'=c('How old are you?',
             'Which postal area to you live in?',
             'Do you drop litter?',
             'In the last week have you dropped litter?',
             'Does litter concern you?',
             'How much litter have you seen in the last week?',
             'Would you be interested to participant in litter picking?',
             'How many times per year who you participant?'),
'answers'=c('< 16,16-20,20-30,30-45,45-65,>65',
            'text',
            'yes,no',
            'yes,no',
            'very high concern,high concern,moderate concern,low concern,not concerned',
            'huge amount of litter,large amount of litter,moderate amount of litter,minor amount of litter,no litter',
            'yes,no',
            'numeric'))

library(pander)
#knitr::kable(social_questions, align = 'c')
set.caption("Social attitude questionaire")
pandoc.table(social_questions, split.cells = c(30,30,30,30,30,30,30,30),style="multiline")

Policy tools

Fines

fine_cat <- data.frame('fine_categories'=c(100,200,400,800,1600))
categories <- cbind(litter_cat,fine_cat)
set.caption("Increased Litter Fine Categories")
pandoc.table(categories,split.cells = 30,style="multiline")

Street cleaning budget

Annual Budget
Employees

Number of public rubbish bins

Budget for education

Buget for re-education

Community litter action

Private sector action

Example data:

library()
example_results <- data.frame("year"=c(seq(2015,2019,1)),"percentage_drop"=c(0,10,20,30,75))
save(example_results,file="example_results.rdata")
pandoc.table(example_results)


get_result <- function(year){

 result <- example_results
  result <- result$percentage_drop[result$year == year]
  return(result)
}

save(get_result,file="get_result.R")

#policy::get_result(2016)