Thunersee / Brienzersee
Contents
# -*- coding: utf-8 -*-
# This is a report using the data from IQAASL.
# IQAASL was a project funded by the Swiss Confederation
# It produces a summary of litter survey results for a defined region.
# These charts serve as the models for the development of plagespropres.ch
# The data is gathered by volunteers.
# Please remember all copyrights apply, please give credit when applicable
# The repo is maintained by the community effective January 01, 2022
# There is ample opportunity to contribute, learn and teach
# contact dev@hammerdirt.ch
# Dies ist ein Bericht, der die Daten von IQAASL verwendet.
# IQAASL war ein von der Schweizerischen Eidgenossenschaft finanziertes Projekt.
# Es erstellt eine Zusammenfassung der Ergebnisse der Littering-Umfrage für eine bestimmte Region.
# Diese Grafiken dienten als Vorlage für die Entwicklung von plagespropres.ch.
# Die Daten werden von Freiwilligen gesammelt.
# Bitte denken Sie daran, dass alle Copyrights gelten, bitte geben Sie den Namen an, wenn zutreffend.
# Das Repo wird ab dem 01. Januar 2022 von der Community gepflegt.
# Es gibt reichlich Gelegenheit, etwas beizutragen, zu lernen und zu lehren.
# Kontakt dev@hammerdirt.ch
# Il s'agit d'un rapport utilisant les données de IQAASL.
# IQAASL était un projet financé par la Confédération suisse.
# Il produit un résumé des résultats de l'enquête sur les déchets sauvages pour une région définie.
# Ces tableaux ont servi de modèles pour le développement de plagespropres.ch
# Les données sont recueillies par des bénévoles.
# N'oubliez pas que tous les droits d'auteur s'appliquent, veuillez indiquer le crédit lorsque cela est possible.
# Le dépôt est maintenu par la communauté à partir du 1er janvier 2022.
# Il y a de nombreuses possibilités de contribuer, d'apprendre et d'enseigner.
# contact dev@hammerdirt.ch
# sys, file and nav packages:
import datetime as dt
# math packages:
import pandas as pd
import numpy as np
from scipy import stats
from statsmodels.distributions.empirical_distribution import ECDF
# charting:
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import ticker
from matplotlib.colors import LinearSegmentedColormap
import seaborn as sns
# home brew utitilties
import resources.chart_kwargs as ck
import resources.sr_ut as sut
# images and display
from IPython.display import Markdown as md
# set some parameters:
start_date = "2020-03-01"
end_date ="2021-05-31"
start_end = [start_date, end_date]
a_fail_rate = 50
unit_label = "p/100m"
a_color = "saddlebrown"
# colors for gradients
cmap2 = ck.cmap2
colors_palette = ck.colors_palette
# set the maps
bassin_map = "resources/maps/thunersee_brienzersee_scaled.jpeg"
# top level aggregation
top = "All survey areas"
# define the feature level and components
this_feature = {'slug':'bt', 'name':"Thunersee / Brienzersee", 'level':'water_name_slug'}
this_level = 'city'
this_bassin = "aare"
bassin_label = "Aare survey area"
lakes_of_interest = [ 'thunersee','brienzersee']
# explanatory variables:
luse_exp = ["% buildings", "% recreation", "% agg", "% woods", "streets km", "intersects"]
# common aggregations
agg_pcs_quantity = {unit_label:"sum", "quantity":"sum"}
agg_pcs_median = {unit_label:"median", "quantity":"sum"}
# aggregation of dimensional data
agg_dims = {"total_w":"sum", "mac_plast_w":"sum", "area":"sum", "length":"sum"}
# columns needed
use_these_cols = ["loc_date" ,
"% to buildings",
"% to trans",
"% to recreation",
"% to agg",
"% to woods",
"population",
this_level,
"streets km",
"intersects",
"length",
"groupname",
"code"
]
# get your data:
dfBeaches = pd.read_csv("resources/beaches_with_land_use_rates.csv")
dfCodes = pd.read_csv("resources/codes_with_group_names_2015.csv")
dfDims = pd.read_csv("resources/corrected_dims.csv")
# set the index of the beach data to location slug
dfBeaches.set_index("slug", inplace=True)
# make a map to city names
city_map = dfBeaches.city
# map water_name_slug to water_name
wname_wname = dfBeaches[["water_name_slug","water_name"]].reset_index(drop=True).drop_duplicates().set_index("water_name_slug")
dfCodes.set_index("code", inplace=True)
codes_to_change = [
["G74", "description", "Insulation foams"],
["G940", "description", "Foamed EVA for crafts and sports"],
["G96", "description", "Sanitary-pads/tampons, applicators"],
["G178", "description", "Metal bottle caps and lids"],
["G82", "description", "Expanded foams 2.5cm - 50cm"],
["G81", "description", "Expanded foams .5cm - 2.5cm"],
["G117", "description", "Expanded foams < 5mm"],
["G75", "description", "Plastic/foamed polystyrene 0 - 2.5cm"],
["G76", "description", "Plastic/foamed polystyrene 2.5cm - 50cm"],
["G24", "description", "Plastic lid rings"],
["G33", "description", "Lids for togo drinks plastic"],
["G3", "description", "Plastic bags, carier bags"],
["G204", "description", "Bricks, pipes not plastic"],
["G904", "description", "Plastic fireworks"],
["G211", "description", "Swabs, bandaging, medical"],
]
for x in codes_to_change:
dfCodes = sut.shorten_the_value(x, dfCodes)
# the surveyor designated the object as aluminum instead of metal
dfCodes.loc["G708", "material"] = "Metal"
# make a map to the code descriptions
code_description_map = dfCodes.description
# make a map to the code materials
code_material_map = dfCodes.material
11. Thunersee / Brienzersee¶
Below: Map of survey locations March 2020 - May 2021. Marker diameter = the mean survey result in pieces of litter per 100 meters (p/100m).
11.1. Sample locations¶
# this is the data before the expanded foams and fragmented plastics are aggregated to Gfrags and Gfoams
before_agg = pd.read_csv("resources/checked_before_agg_sdata_eos_2020_21.csv")
# this is the aggregated survey data that is being used
# a_data is all the data in the survey period
a_data = pd.read_csv(F"resources/checked_sdata_eos_2020_21.csv")
a_data["date"] = pd.to_datetime(a_data.date)
a_data.rename(columns={"% to agg":"% ag", "% to recreation": "% recreation", "% to woods":"% woods", "% to buildings":"% buildings"}, inplace=True)
luse_exp = ["% buildings", "% recreation", "% ag", "% woods", "streets km", "intersects"]
fd = sut.feature_data(a_data, this_feature["level"], these_features=lakes_of_interest)
# cumulative statistics for each code
code_totals = sut.the_aggregated_object_values(fd, agg=agg_pcs_median, description_map=code_description_map, material_map=code_material_map)
# daily survey totals
dt_all = fd.groupby(["loc_date","location",this_level, "date"], as_index=False).agg(agg_pcs_quantity )
# the materials table
fd_mat_totals = sut.the_ratio_object_to_total(code_totals)
# summary statistics, nsamples, nmunicipalities, names of citys, population
t = sut.make_table_values(fd, col_nunique=["location", "loc_date", "city"], col_sum=["quantity"], col_median=[])
# make a map to the population values for each survey location/city
fd_pop_map = dfBeaches.loc[fd.location.unique()][["city", "population"]].copy()
fd_pop_map.drop_duplicates(inplace=True)
# update t with the population data
t.update(sut.make_table_values(fd_pop_map, col_nunique=["city"], col_sum=["population"], col_median=[]))
# update t with the list of locations from fd
t.update({"locations":fd.location.unique()})
# join the strings into comma separated list
obj_string = "{:,}".format(t["quantity"])
surv_string = "{:,}".format(t["loc_date"])
pop_string = "{:,}".format(int(t["population"]))
# make strings
date_quantity_context = F"For the period between {start_date[:-3]} and {end_date[:-3]}, a total of {obj_string } objects were removed and identified over the course of {surv_string} surveys."
geo_context = F"The {this_feature['name']} results include {t['location']} different locations in {t['city']} different municipalities with a combined population of approximately {pop_string}."
munis_joined = ", ".join(sorted(fd_pop_map["city"]))
# put that all together:
lake_string = F"""
{date_quantity_context} {geo_context }
*{this_feature["name"]} municipalities:*\n\n>{munis_joined}
"""
md(lake_string)
For the period between 2020-03 and 2021-05, a total of 3,764 objects were removed and identified over the course of 36 surveys. The Thunersee / Brienzersee results include 10 different locations in 6 different municipalities with a combined population of approximately 69,120.
Thunersee / Brienzersee municipalities:
Beatenberg, Brienz (BE), Bönigen, Spiez, Thun, Unterseen
11.1.1. Cumulative totals by municipality¶
dims_parameters = dict(this_level=this_level,
locations=fd.location.unique(),
start_end=start_end,
city_map=city_map,
agg_dims=agg_dims)
dims_table = sut.gather_dimensional_data(dfDims, **dims_parameters)
# a map of total quantity for each component
q_map = fd.groupby(this_level).quantity.sum()
# assgin the quantity and sample numbers to the dims table
for name in dims_table.index:
dims_table.loc[name, "samples"] = fd[fd[this_level] == name].loc_date.nunique()
dims_table.loc[name, "quantity"] = q_map[name]
# make a total column for the top feature data, the sum of all the components
dims_table.loc[this_feature["name"]]= dims_table.sum(numeric_only=True, axis=0)
# change the column names to user friendly syle
dims_table.rename(columns=sut.update_dictionary(sut.dims_table_columns), inplace=True)
# format the numercial data
dims_table.sort_values(by=["items"], ascending=False, inplace=True)
# change to formatted strings
dims_table["plastic kg"] = dims_table["plastic kg"]/1000
dims_table[["m²", "meters", "samples", "items"]] = dims_table[["m²", "meters", "samples", "items"]].applymap(lambda x: "{:,}".format(int(x)))
dims_table[["plastic kg", "total kg"]] = dims_table[["plastic kg", "total kg"]].applymap(lambda x: "{:.2f}".format(x))
# figure caption
agg_caption = F"""
*__Below:__ The cumulative weights and measures for {this_feature["name"]} and municipalities*
"""
md(agg_caption)
Below: The cumulative weights and measures for Thunersee / Brienzersee and municipalities
# make table
data = dims_table.reset_index()
colLabels = data.columns
fig, ax = plt.subplots(figsize=(len(colLabels)*2,len(data)*.7))
sut.hide_spines_ticks_grids(ax)
table_one = sut.make_a_table(ax, data.values, colLabels=colLabels, colWidths=[.28, *[.12]*6], a_color="saddlebrown", )
table_one.get_celld()[(0,0)].get_text().set_text(" ")
plt.show()
plt.tight_layout()
plt.close()
11.1.2. Distribution of Survey results¶
# the feature surveys to chart
fd_dindex = dt_all.set_index("date")
# all the other surveys
ots = dict(level_to_exclude=this_feature["level"], components_to_exclude=fd[this_feature["level"]].unique())
dts_date = sut.the_other_surveys(a_data, **ots)
# group the outher surveys by date and total pcs_m
ots_params = dict(agg_this = {unit_label:"sum"}, these_columns = ["loc_date","date"])
dts_date = sut.group_these_columns(dts_date, **ots_params)
# get the monthly or quarterly results for the feature
resample_plot, rate = sut.quarterly_or_monthly_values(fd_dindex , this_feature["name"], vals=unit_label, quarterly=["ticino"])
# scale the chart as needed to accomodate for extreme values
y_lim = 98
y_limit = np.percentile(dts_date[unit_label], y_lim)
# label for the chart that alerts to the scale
not_included = F"Values greater than {round(y_limit, 1)}{unit_label} not shown."
# figure caption
chart_notes = F"""
*__Left:__ {this_feature['name']}, {start_date[:7]} through {end_date[:7]}, n={t["loc_date"]}. {not_included} __Right:__ {this_feature['name']} empirical cumulative distribution of survey results.*
"""
md(chart_notes )
Left: Thunersee / Brienzersee, 2020-03 through 2021-05, n=36. Values greater than 2375.0p/100m not shown. Right: Thunersee / Brienzersee empirical cumulative distribution of survey results.
# months locator, can be confusing
# https://matplotlib.org/stable/api/dates_api.html
# months = mdates.MonthLocator(interval=1)
months_fmt = mdates.DateFormatter("%b")
days = mdates.DayLocator(interval=7)
sns.set_style("whitegrid")
fig, axs = plt.subplots(1,2, figsize=(10,5))
# the survey totals by day
ax = axs[0]
# feature surveys
sns.scatterplot(data=dts_date, x=dts_date.index, y=unit_label, label=top, color="black", alpha=0.4, ax=ax)
# all other surveys
sns.scatterplot(data=fd_dindex, x=fd_dindex.index, y=unit_label, label=this_feature["name"], color="red", s=34, ec="white", ax=ax)
# monthly or quaterly plot
sns.lineplot(data=resample_plot, x=resample_plot.index, y=resample_plot, label=F"{this_feature['name']}: {rate} median", color="magenta", ax=ax)
ax.set_ylim(0,y_limit )
ax.set_ylabel(unit_label, **ck.xlab_k14)
ax.set_xlabel("")
ax.xaxis.set_minor_locator(days)
ax.xaxis.set_major_formatter(months_fmt)
ax.legend()
# the cumlative distributions:
axtwo = axs[1]
# the feature of interest
feature_ecd = ECDF(dt_all[unit_label].values)
sns.lineplot(x=feature_ecd.x, y=feature_ecd.y, color="darkblue", ax=axtwo, label=this_feature["name"])
# the other features
other_features = ECDF(dts_date[unit_label].values)
sns.lineplot(x=other_features.x, y=other_features.y, color="magenta", label=top, linewidth=1, ax=axtwo)
axtwo.set_xlabel(unit_label, **ck.xlab_k14)
axtwo.set_ylabel("Ratio of samples", **ck.xlab_k14)
plt.tight_layout()
plt.show()
11.1.3. Summary data and material types¶
Left: Thunersee / Brienzersee summary of survey totals. Right: Thunersee / Brienzersee material type and percent of total
# get the basic statistics from pd.describe
cs = dt_all[unit_label].describe().round(2)
# change the names
csx = sut.change_series_index_labels(cs, sut.create_summary_table_index(unit_label, lang="EN"))
combined_summary = sut.fmt_combined_summary(csx, nf=[])
fd_mat_totals = sut.fmt_pct_of_total(fd_mat_totals)
fd_mat_totals = sut.make_string_format(fd_mat_totals)
# applly new column names for printing
cols_to_use = {"material":"Material","quantity":"Quantity", "% of total":"% of total"}
fd_mat_t = fd_mat_totals[cols_to_use.keys()].values
# make tables
fig, axs = plt.subplots(1,2, figsize=(8,6))
# summary table
# names for the table columns
a_col = [this_feature["name"], "total"]
axone = axs[0]
sut.hide_spines_ticks_grids(axone)
table_two = sut.make_a_table(axone, combined_summary, colLabels=a_col, colWidths=[.5,.25,.25], bbox=[0,0,1,1], **{"loc":"lower center"})
table_two.get_celld()[(0,0)].get_text().set_text(" ")
# material table
axtwo = axs[1]
axtwo.set_xlabel(" ")
sut.hide_spines_ticks_grids(axtwo)
table_three = sut.make_a_table(axtwo, fd_mat_t, colLabels=list(cols_to_use.values()), colWidths=[.4, .3,.3], bbox=[0,0,1,1], **{"loc":"lower center"})
table_three.get_celld()[(0,0)].get_text().set_text(" ")
plt.tight_layout()
plt.subplots_adjust(wspace=0.2)
plt.show()
11.2. The most common objects¶
The most common objects are the ten most abundant by quantity AND/OR objects identified in at least 50% of all surveys.
# the top ten by quantity
most_abundant = code_totals.sort_values(by="quantity", ascending=False)[:10]
# the most common
most_common = code_totals[code_totals["fail rate"] >= a_fail_rate].sort_values(by="quantity", ascending=False)
# merge with most_common and drop duplicates
m_common = pd.concat([most_abundant, most_common]).drop_duplicates()
# get percent of total
m_common_percent_of_total = m_common.quantity.sum()/code_totals.quantity.sum()
# figure caption
rb_string = F"""
*__Below:__ {this_feature['name']} most common objects: fail rate >/= {a_fail_rate}% and/or top ten by quantity. Combined, the most abundant objects represent {int(m_common_percent_of_total*100)}% of all objects found.
Note : {unit_label} = median survey value.*
"""
md(rb_string)
Below: Thunersee / Brienzersee most common objects: fail rate >/= 50% and/or top ten by quantity. Combined, the most abundant objects represent 72% of all objects found. Note : p/100m = median survey value.
# format values for table
m_common["item"] = m_common.index.map(lambda x: code_description_map.loc[x])
m_common["% of total"] = m_common["% of total"].map(lambda x: F"{x}%")
m_common["quantity"] = m_common.quantity.map(lambda x: "{:,}".format(x))
m_common["fail rate"] = m_common["fail rate"].map(lambda x: F"{x}%")
m_common[unit_label] = m_common[unit_label].map(lambda x: F"{round(x,1)}")
# final table data
cols_to_use = {"item":"Item","quantity":"Quantity", "% of total":"% of total", "fail rate":"Fail rate", unit_label:unit_label}
all_survey_areas = m_common[cols_to_use.keys()].values
colWidths=[.52, .12, .12, .12, .12]
bbox=[0,0,1,1],
kwargs = {"loc":"lower center"}
colLabels=list(cols_to_use.values())
figsize=(len(colLabels)*2,len(all_survey_areas)*.7)
fig, axs = plt.subplots(figsize=figsize)
sut.hide_spines_ticks_grids(axs)
table_four = sut.make_a_table(axs, all_survey_areas, colLabels=colLabels, colWidths=colWidths, a_color="saddlebrown")
table_four.get_celld()[(0,0)].get_text().set_text(" ")
plt.tight_layout()
plt.show()
11.2.1. Most common objects results by municipality¶
Below: Thunersee / Brienzersee most common objects: median p/100m
# aggregated survey totals for the most common codes for all the water features
m_common_st = fd[fd.code.isin(m_common.index)].groupby([this_level, "loc_date","code"], as_index=False).agg(agg_pcs_quantity)
m_common_ft = m_common_st.groupby([this_level, "code"], as_index=False)[unit_label].median()
# map the desctiption to the code
m_common_ft["item"] = m_common_ft.code.map(lambda x: code_description_map.loc[x])
# pivot that
m_c_p = m_common_ft[["item", this_level, unit_label]].pivot(columns=this_level, index="item")
# quash the hierarchal column index
m_c_p.columns = m_c_p.columns.get_level_values(1)
## the aggregated totals for the feature data
c = sut.aggregate_to_group_name(fd[fd.code.isin(m_common.index)], column="code", name=this_feature["name"], val="med")
m_c_p[this_feature["name"]]= sut.change_series_index_labels(c, {x:code_description_map.loc[x] for x in c.index})
# the aggregated totals of the survey area
c = sut.aggregate_to_group_name(a_data[(a_data.river_bassin == this_bassin)&(a_data.code.isin(m_common.index))], column="code", name=top, val="med")
m_c_p[bassin_label] = sut.change_series_index_labels(c, {x:code_description_map.loc[x] for x in c.index})
# the aggregated totals of all the data
c = sut.aggregate_to_group_name(a_data[(a_data.code.isin(m_common.index))], column="code", name=top, val="med")
m_c_p[top] = sut.change_series_index_labels(c, {x:code_description_map.loc[x] for x in c.index})
# chart that
fig, ax = plt.subplots(figsize=(len(m_c_p.columns)*.9,len(m_c_p)*.9))
axone = ax
sns.heatmap(m_c_p, ax=axone, cmap=cmap2, annot=True, annot_kws={"fontsize":12}, fmt=".1f", square=True, cbar=False, linewidth=.1, linecolor="white")
axone.set_xlabel("")
axone.set_ylabel("")
axone.tick_params(labelsize=14, which="both", axis="x")
axone.tick_params(labelsize=12, which="both", axis="y")
plt.setp(axone.get_xticklabels(), rotation=90)
plt.show()
plt.close()
11.2.2. Most common objects monthly average¶
# collect the survey results of the most common objects
m_common_m = fd[(fd.code.isin(m_common.index))].groupby(["loc_date", "date", "code", "groupname"], as_index=False).agg(agg_pcs_quantity)
m_common_m.set_index("date", inplace=True)
# set the order of the chart, group the codes by groupname columns
an_order = m_common_m.groupby(["code", "groupname"], as_index=False).quantity.sum().sort_values(by="groupname")["code"].values
# a manager dict for the monthly results of each code
mgr = {}
# get the monhtly results for each code:
for a_group in an_order:
# resample by month
a_plot = m_common_m[(m_common_m.code==a_group)][unit_label].resample("M").mean().fillna(0)
this_group = {a_group:a_plot}
mgr.update(this_group)
monthly_mc = F"""
*__Below:__ {this_feature['name']}, monthly average survey result {unit_label}. Detail of the most common objects*
"""
md(monthly_mc)
Below: Thunersee / Brienzersee, monthly average survey result p/100m. Detail of the most common objects
# convenience function to lable x axis
def new_month(x):
if x <= 11:
this_month = x
else:
this_month=x-12
return this_month
months={
0:'Jan',
1:'Feb',
2:'Mar',
3:'Apr',
4:'May',
5:'Jun',
6:'Jul',
7:'Aug',
8:'Sep',
9:'Oct',
10:'Nov',
11:'Dec'
}
fig, ax = plt.subplots(figsize=(9,8))
# define a bottom
bottom = [0]*len(mgr["G27"])
# the monhtly survey average for all objects and locations
monthly_fd = fd.groupby(["loc_date", "date"], as_index=False).agg(agg_pcs_quantity)
monthly_fd.set_index("date", inplace=True)
m_fd = monthly_fd[unit_label].resample("M").mean().fillna(0)
# define the xaxis
this_x = [i for i,x in enumerate(m_fd.index)]
# plot the monthly total survey average
ax.bar(this_x, m_fd.to_numpy(), color=a_color, alpha=0.2, linewidth=1, edgecolor="teal", width=1, label="Monthly survey average")
# plot the monthly survey average of the most common objects
for i, a_group in enumerate(an_order):
# define the axis
this_x = [i for i,x in enumerate(mgr[a_group].index)]
# collect the month
this_month = [x.month for i,x in enumerate(mgr[a_group].index)]
# if i == 0 laydown the first bars
if i == 0:
ax.bar(this_x, mgr[a_group].to_numpy(), label=a_group, color=colors_palette[a_group], linewidth=1, alpha=0.6 )
# else use the previous results to define the bottom
else:
bottom += mgr[an_order[i-1]].to_numpy()
ax.bar(this_x, mgr[a_group].to_numpy(), bottom=bottom, label=a_group, color=colors_palette[a_group], linewidth=1, alpha=0.8)
# collect the handles and labels from the legend
handles, labels = ax.get_legend_handles_labels()
# set the location of the x ticks
ax.xaxis.set_major_locator(ticker.FixedLocator([i for i in np.arange(len(this_x))]))
#label the xticks by month
axisticks = ax.get_xticks()
labelsx = [months[new_month(x-1)] for x in this_month]
plt.xticks(ticks=axisticks, labels=labelsx)
ax.set_ylabel(unit_label, **ck.xlab_k14)
# make the legend
# swap out codes for descriptions
new_labels = [code_description_map.loc[x] for x in labels[1:]]
new_labels = new_labels[::-1]
# insert a label for the monthly average
new_labels.insert(0,"Monthly average")
handles = [handles[0], *handles[1:][::-1]]
plt.legend(handles=handles, labels=new_labels, bbox_to_anchor=(.5, -.05), loc="upper center", ncol=2, fontsize=14)
plt.tight_layout()
plt.show()
11.3. Utility of the objects found¶
The utility type is based on the utilization of the object prior to it being discarded or object description if the original use is undetermined. Identified objects are classified into one of 260 predefined categories. The categories are grouped according to utilization or item description.
wastewater: items released from water treatment plants includes items likely toilet flushed
micro plastics (< 5mm): fragmented plastics and pre-production plastic resins
infrastructure: items related to construction and maintenance of buildings, roads and water/power supplies
food and drink: all materials related to consuming food and drink
agriculture: primarily industrial sheeting i.e., mulch and row covers, greenhouses, soil fumigation, bale wraps. Includes hard plastics for agricultural fencing, flowerpots etc.
tobacco: primarily cigarette filters, includes all smoking related material
recreation: objects related to sports and leisure i.e., fishing, hunting, hiking etc.
packaging non food and drink: packaging material not identified as food, drink nor tobacco related
plastic fragments: plastic pieces of undetermined origin or use
personal items: accessories, hygiene and clothing related
See the annex for the complete list of objects identified, includes descriptions and group classification. The section Code groups describes each code group in detail and provides a comprehensive list of all objects in a group.
Below: Thunersee / Brienzersee utility of objects found % of total by municipality. Fragmented objects with no clear identification remain classified by size:
# code groups resluts aggregated by survey
groups = ["loc_date","groupname"]
cg_t = fd.groupby([this_level,*groups], as_index=False).agg(agg_pcs_quantity)
# the total per water feature
cg_tq = cg_t.groupby(this_level).quantity.sum()
# get the fail rates for each group per survey
cg_t["fail"]=False
cg_t["fail"] = cg_t.quantity.where(lambda x: x == 0, True)
# aggregate all that for each municipality
agg_this = {unit_label:"median", "quantity":"sum", "fail":"sum", "loc_date":"nunique"}
cg_t = cg_t.groupby([this_level, "groupname"], as_index=False).agg(agg_this)
# assign survey area total to each record
for a_feature in cg_tq.index:
cg_t.loc[cg_t[this_level] == a_feature, "f_total"] = cg_tq.loc[a_feature]
# get the percent of total for each group for each survey area
cg_t["pt"] = (cg_t.quantity/cg_t.f_total).round(2)
# pivot that
data_table = cg_t.pivot(columns=this_level, index="groupname", values="pt")
# aggregated values for the lake
data_table[this_feature["name"]]= sut.aggregate_to_group_name(fd, unit_label=unit_label, column="groupname", name=bassin_label, val="pt")
# repeat for the survey area
data_table[bassin_label] = sut.aggregate_to_group_name(a_data[a_data.river_bassin == this_bassin], unit_label=unit_label, column="groupname", name=bassin_label, val="pt")
# repeat for all the data
data_table[top] = sut.aggregate_to_group_name(a_data, unit_label=unit_label, column="groupname", name=top, val="pt")
data = data_table
fig, ax = plt.subplots(figsize=(11,10))
axone = ax
sns.heatmap(data , ax=axone, cmap=cmap2, annot=True, annot_kws={"fontsize":12}, cbar=False, fmt=".0%", linewidth=.1, square=True, linecolor="white")
axone.set_ylabel("")
axone.set_xlabel("")
axone.tick_params(labelsize=14, which="both", axis="both", labeltop=False, labelbottom=True)
plt.setp(axone.get_xticklabels(), rotation=90, fontsize=14)
plt.setp(axone.get_yticklabels(), rotation=0, fontsize=14)
plt.show()
cg_medpcm = F"""
<br></br>
*__Below:__ {this_feature['name']} utility of objects found median {unit_label}. Fragmented objects with no clear identification remain classified by size:*
"""
md(cg_medpcm)
Below: Thunersee / Brienzersee utility of objects found median p/100m. Fragmented objects with no clear identification remain classified by size:
# median p/50m of all the water features
data_table = cg_t.pivot(columns=this_level, index="groupname", values=unit_label)
# aggregated values for the lake
data_table[this_feature["name"]]= sut.aggregate_to_group_name(fd, unit_label=unit_label, column="groupname", name=bassin_label, val="med")
# the survey area columns
data_table[bassin_label] = sut.aggregate_to_group_name(a_data[a_data.river_bassin == this_bassin], unit_label=unit_label, column="groupname", name=bassin_label, val="med")
# column for all the surveys
data_table[top] = sut.aggregate_to_group_name(a_data, unit_label=unit_label, column="groupname", name=top, val="med")
# merge with data_table
data = data_table
fig, ax = plt.subplots(figsize=(11,10))
axone = ax
sns.heatmap(data , ax=axone, cmap=cmap2, annot=True, annot_kws={"fontsize":12}, fmt="g", cbar=False, linewidth=.1, square=True, linecolor="white")
axone.set_xlabel("")
axone.set_ylabel("")
axone.tick_params(labelsize=14, which="both", axis="both", labeltop=False, labelbottom=True)
plt.setp(axone.get_xticklabels(), rotation=90, fontsize=14)
plt.setp(axone.get_yticklabels(), rotation=0, fontsize=14)
plt.show()
11.4. Annex¶
11.4.1. Fragmented foams and plastics by size¶
The table below contains the “Gfoam” and “Gfrags” components grouped for analysis. Objects labeled expanded foams are grouped as Gfoam and includes all expanded polystyrene foamed plastics > 0.5 cm. Plastic pieces and objects made of combined plastic and foamed plastic materials > 0.5 cm. are grouped for analysis as Gfrags.
Below: Thunersee / Brienzersee fragmented foams and plastics by size group.
# collect the data before aggregating foams for all locations in the survey area
# the codes for the foams
some_foams = ["G81", "G82", "G83", "G74"]
# the codes for the fragmented plastics
some_frag_plas = list(before_agg[before_agg.groupname == "plastic pieces"].code.unique())
# aggregate all the codes by loc_date and get the total quantity and the median pcs/m
fd_frags_foams = before_agg[(before_agg.code.isin([*some_frag_plas, *some_foams]))&(before_agg.location.isin(t["locations"]))].groupby(["loc_date","code"], as_index=False).agg(agg_pcs_quantity)
fd_frags_foams = fd_frags_foams.groupby("code").agg({unit_label:"median", "quantity":"sum"})
# add code description and format for printing
fd_frags_foams["item"] = fd_frags_foams.index.map(lambda x: code_description_map.loc[x])
fd_frags_foams["% of total"] = (fd_frags_foams.quantity/fd.quantity.sum()*100).round(2)
fd_frags_foams["% of total"] = fd_frags_foams["% of total"].map(lambda x: F"{x}%")
fd_frags_foams["quantity"] = fd_frags_foams["quantity"].map(lambda x: F"{x:,}")
# table data
data = fd_frags_foams[["item", unit_label, "quantity", "% of total"]]
fig, axs = plt.subplots(figsize=(11,len(data)*.7))
sut.hide_spines_ticks_grids(axs)
a_table = sut.make_a_table(axs, data.values, colLabels=data.columns, colWidths=[.6, .13, .13, .13])
a_table.get_celld()[(0,0)].get_text().set_text(" ")
plt.show()
plt.tight_layout()
plt.close()
11.4.2. The survey locations¶
# display the survey locations
disp_columns = ["latitude", "longitude", "city"]
disp_beaches = dfBeaches.loc[t["locations"]][disp_columns]
disp_beaches.reset_index(inplace=True)
disp_beaches.rename(columns={"slug":"location"}, inplace=True)
disp_beaches.set_index("location", inplace=True, drop=True)
disp_beaches
latitude | longitude | city | |
---|---|---|---|
location | |||
weissenau-neuhaus | 46.676583 | 7.817528 | Unterseen |
thun-strandbad | 46.739939 | 7.633520 | Thun |
oben-am-see | 46.744451 | 8.049921 | Brienz (BE) |
thunersee_spiez_meierd_1 | 46.704437 | 7.657882 | Spiez |
delta-park | 46.720078 | 7.635304 | Spiez |
sundbach-strand | 46.684386 | 7.794768 | Beatenberg |
wycheley | 46.740370 | 8.048640 | Brienz (BE) |
camping-gwatt-strand | 46.727140 | 7.629620 | Thun |
augustmutzenbergstrandweg | 46.686640 | 7.689760 | Spiez |
hafeli | 46.690283 | 7.898592 | Bönigen |
11.4.3. Inventory of items¶
pd.set_option("display.max_rows", None)
complete_inventory = code_totals[code_totals.quantity>0][["item", "groupname", "quantity","% of total","fail rate"]]
complete_inventory.sort_values(by="quantity", ascending=False)
item | groupname | quantity | % of total | fail rate | |
---|---|---|---|---|---|
code | |||||
G27 | Cigarette filters | tobacco | 748 | 19.87 | 88 |
Gfrags | Fragmented plastics | plastic pieces | 515 | 13.68 | 91 |
G67 | Industrial sheeting | agriculture | 352 | 9.35 | 83 |
Gfoam | Expanded polystyrene | infrastructure | 281 | 7.47 | 83 |
G30 | Food wrappers; candy, snacks | food and drink | 228 | 6.06 | 77 |
G74 | Insulation foams | infrastructure | 158 | 4.20 | 66 |
G200 | Glass drink bottles, pieces | food and drink | 141 | 3.75 | 72 |
G112 | Industrial pellets (nurdles) | micro plastics (< 5mm) | 115 | 3.06 | 11 |
G941 | Packaging films nonfood or unknown | packaging non food | 83 | 2.21 | 44 |
G89 | Plastic construction waste | infrastructure | 65 | 1.73 | 50 |
G904 | Plastic fireworks | recreation | 39 | 1.04 | 38 |
G21 | Drink lids | food and drink | 38 | 1.01 | 38 |
G117 | Expanded foams < 5mm | micro plastics (< 5mm) | 36 | 0.96 | 33 |
G156 | Paper fragments | packaging non food | 35 | 0.93 | 22 |
G95 | Cotton bud/swab sticks | waste water | 35 | 0.93 | 52 |
G106 | Plastic fragments angular <5mm | micro plastics (< 5mm) | 34 | 0.90 | 25 |
G944 | Pellet mass from injection molding | unclassified | 34 | 0.90 | 8 |
G23 | Lids unidentified | packaging non food | 30 | 0.80 | 30 |
G24 | Plastic lid rings | food and drink | 28 | 0.74 | 36 |
G213 | Paraffin wax | recreation | 27 | 0.72 | 33 |
G177 | Foil wrappers, aluminum foil | food and drink | 26 | 0.69 | 36 |
G936 | Sheeting ag. greenhouse film | agriculture | 26 | 0.69 | 22 |
G50 | String < 1cm | recreation | 25 | 0.66 | 33 |
G922 | Labels, bar codes | packaging non food | 24 | 0.64 | 33 |
G153 | Cups, food containers, wrappers (paper) | food and drink | 21 | 0.56 | 11 |
G186 | Industrial scrap | infrastructure | 20 | 0.53 | 25 |
G25 | Tobacco; plastic packaging, containers | tobacco | 19 | 0.50 | 27 |
G201 | Jars, includes pieces | food and drink | 18 | 0.48 | 16 |
G10 | Food containers single use foamed or plastic | food and drink | 18 | 0.48 | 27 |
G211 | Swabs, bandaging, medical | personal items | 17 | 0.45 | 27 |
G35 | Straws and stirrers | food and drink | 17 | 0.45 | 30 |
G32 | Toys and party favors | recreation | 16 | 0.43 | 25 |
G87 | Tape, masking/duct/packing | infrastructure | 16 | 0.43 | 19 |
G908 | Tape; electrical, insulating | infrastructure | 16 | 0.43 | 22 |
G91 | Biomass holder | waste water | 16 | 0.43 | 27 |
G31 | Lollypop sticks | food and drink | 16 | 0.43 | 33 |
G943 | Fencing agriculture, plastic | agriculture | 14 | 0.37 | 8 |
G208 | Glass or ceramic fragments > 2.5 cm | unclassified | 14 | 0.37 | 11 |
G204 | Bricks, pipes not plastic | infrastructure | 14 | 0.37 | 16 |
G178 | Metal bottle caps and lids | food and drink | 14 | 0.37 | 25 |
G66 | Straps/bands; hard, plastic package fastener | infrastructure | 14 | 0.37 | 33 |
G927 | String trimmer line, used to cut grass, weeds,... | infrastructure | 13 | 0.35 | 16 |
G70 | Shotgun cartridges | recreation | 13 | 0.35 | 22 |
G210 | Other glass/ceramic | unclassified | 12 | 0.32 | 5 |
G90 | Plastic flower pots | agriculture | 12 | 0.32 | 19 |
G942 | Plastic shavings from lathes, CNC machining | unclassified | 11 | 0.29 | 11 |
G3 | Plastic bags, carier bags | packaging non food | 11 | 0.29 | 19 |
G131 | Rubber bands | personal items | 10 | 0.27 | 19 |
G923 | Tissue, toilet paper, napkins, paper towels | personal items | 10 | 0.27 | 19 |
G149 | Paper packaging | packaging non food | 10 | 0.27 | 11 |
G203 | Tableware ceramic or glass, cups, plates, pieces | food and drink | 10 | 0.27 | 11 |
G98 | Diapers - wipes | waste water | 10 | 0.27 | 19 |
G152 | Cigarette boxes, tobacco related paper/cardboard | tobacco | 10 | 0.27 | 16 |
G905 | Hair clip, hair ties, personal accessories pl... | personal items | 9 | 0.24 | 16 |
G65 | Buckets | agriculture | 8 | 0.21 | 8 |
G100 | Medical; containers/tubes/ packaging | waste water | 8 | 0.21 | 16 |
G22 | Lids for chemicals, detergents (non-food) | infrastructure | 8 | 0.21 | 8 |
G48 | Rope, synthetic | recreation | 8 | 0.21 | 13 |
G33 | Lids for togo drinks plastic | food and drink | 8 | 0.21 | 16 |
G928 | Ribbons and bows | personal items | 8 | 0.21 | 8 |
G159 | Corks | food and drink | 7 | 0.19 | 16 |
G175 | Cans, beverage | food and drink | 7 | 0.19 | 13 |
G73 | Foamed items & pieces (non packaging/insulatio... | recreation | 6 | 0.16 | 13 |
G198 | Other metal pieces < 50cm | infrastructure | 6 | 0.16 | 16 |
G931 | Tape-caution for barrier, police, construction... | infrastructure | 5 | 0.13 | 5 |
G125 | Balloons and balloon sticks | recreation | 5 | 0.13 | 11 |
G170 | Wood (processed) | agriculture | 5 | 0.13 | 13 |
G34 | Cutlery, plates and trays | food and drink | 5 | 0.13 | 13 |
G191 | Wire and mesh | agriculture | 5 | 0.13 | 11 |
G940 | Foamed EVA for crafts and sports | recreation | 5 | 0.13 | 5 |
G155 | Fireworks paper tubes and fragments | recreation | 5 | 0.13 | 13 |
G4 | Small plastic bags; freezer, zip-lock etc. | packaging non food | 5 | 0.13 | 11 |
G148 | Cardboard (boxes and fragments) | packaging non food | 5 | 0.13 | 8 |
G917 | Terracotta balls | unclassified | 5 | 0.13 | 11 |
G925 | Packets: desiccant/ moisture absorbers, plasti... | packaging non food | 4 | 0.11 | 5 |
G59 | Fishing line monofilament (angling) | recreation | 4 | 0.11 | 8 |
G194 | Cables, metal wire(s) often inside rubber or p... | infrastructure | 4 | 0.11 | 8 |
G96 | Sanitary-pads/tampons, applicators | waste water | 4 | 0.11 | 11 |
G176 | Cans, food | food and drink | 3 | 0.08 | 5 |
G913 | Pacifier | personal items | 3 | 0.08 | 8 |
G933 | Bags, cases for accessories; glasses, electron... | personal items | 3 | 0.08 | 8 |
G37 | Mesh bags | personal items | 3 | 0.08 | 8 |
G6 | Bottles and containers, plastic non food/drink | packaging non food | 3 | 0.08 | 2 |
G914 | Paperclips, clothespins, plastic utility items | personal items | 3 | 0.08 | 8 |
G921 | Ceramic tile and pieces | infrastructure | 3 | 0.08 | 8 |
G134 | Other rubber | unclassified | 3 | 0.08 | 5 |
G158 | Other paper items | packaging non food | 3 | 0.08 | 8 |
G146 | Paper, cardboard | packaging non food | 3 | 0.08 | 2 |
G165 | Ice cream sticks, toothpicks, chopsticks | food and drink | 3 | 0.08 | 8 |
G103 | Plastic fragments rounded <5mm | micro plastics (< 5mm) | 2 | 0.05 | 5 |
G918 | Safety pins, paper clips, small metal utility ... | personal items | 2 | 0.05 | 5 |
G101 | Dog feces bag | personal items | 2 | 0.05 | 5 |
G124 | Other plastic or foam products | unclassified | 2 | 0.05 | 5 |
G133 | Condoms incl. packaging | waste water | 2 | 0.05 | 5 |
G2 | Bags | packaging non food | 2 | 0.05 | 2 |
G174 | Aerosol spray cans | infrastructure | 2 | 0.05 | 5 |
G49 | Rope > 1cm | recreation | 2 | 0.05 | 5 |
G26 | Cigarette lighters | tobacco | 2 | 0.05 | 5 |
G901 | Mask medical, synthetic | personal items | 1 | 0.03 | 2 |
G938 | Toothpicks, dental floss plastic | food and drink | 1 | 0.03 | 2 |
G939 | Flowers, plants plastic | personal items | 1 | 0.03 | 2 |
G20 | Caps and lids | packaging non food | 1 | 0.03 | 2 |
G111 | Spheruloid pellets < 5mm | micro plastics (< 5mm) | 1 | 0.03 | 2 |
G11 | Cosmetics for the beach, e.g. sunblock | recreation | 1 | 0.03 | 2 |
G197 | Other metal | infrastructure | 1 | 0.03 | 2 |
G202 | Light bulbs | unclassified | 1 | 0.03 | 2 |
G190 | Paint cans | infrastructure | 1 | 0.03 | 2 |
G182 | Fishing; hooks, weights, lures, sinkers etc. | recreation | 1 | 0.03 | 2 |
G97 | Toilet fresheners | waste water | 1 | 0.03 | 2 |
G161 | Processed timber | agriculture | 1 | 0.03 | 2 |
G114 | Films <5mm | micro plastics (< 5mm) | 1 | 0.03 | 2 |
G930 | Foam earplugs | personal items | 1 | 0.03 | 2 |
G214 | Oil/tar | infrastructure | 1 | 0.03 | 2 |
G129 | Inner tubes and rubber sheets | unclassified | 1 | 0.03 | 2 |
G93 | Cable ties; steggel, zip, zap straps | infrastructure | 1 | 0.03 | 2 |
G138 | Shoes and sandals | personal items | 1 | 0.03 | 2 |
G926 | Chewing gum, often contains plastics | food and drink | 1 | 0.03 | 2 |
G135 | Clothes, footware, headware, gloves | personal items | 1 | 0.03 | 2 |
G29 | Combs, brushes and sunglasses | personal items | 1 | 0.03 | 2 |
G12 | Cosmetics, non-beach use personal care containers | personal items | 1 | 0.03 | 2 |
G36 | Bags/sacks heavy duty plastic for 25 Kg or mor... | agriculture | 1 | 0.03 | 2 |
G919 | Nails, screws, bolts etc. | infrastructure | 1 | 0.03 | 2 |
G43 | Tags fishing or industry (security tags, seals) | recreation | 1 | 0.03 | 2 |
G167 | Matches or fireworks | recreation | 1 | 0.03 | 2 |
G64 | Fenders | unclassified | 1 | 0.03 | 2 |
G71 | Shoes sandals | personal items | 1 | 0.03 | 2 |
G28 | Pens, lids, mechanical pencils etc. | personal items | 1 | 0.03 | 2 |