Hide code cell source 
%load_ext watermark
# sys, file and nav packages:
import datetime as dt
import json
import functools
import time

# math packages:
import pandas as pd
import numpy as np
from scipy import stats
from statsmodels.distributions.empirical_distribution import ECDF

# charting:
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import ticker
from matplotlib import colors
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.gridspec import GridSpec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes

import seaborn as sns

import IPython
from PIL import Image as PILImage
from IPython.display import Markdown as md
from IPython.display import display
from myst_nb import glue

import time

start_date = '2020-03-01'
end_date ='2021-05-31'

a_qty = 20

a_fail_rate = .5

use_fail = False

unit_label = 'p/100m'

# survey data:
dfx= pd.read_csv('resources/checked_sdata_eos_2020_21.csv')


dfBeaches = pd.read_csv("resources/beaches_with_land_use_rates.csv")
dfCodes = pd.read_csv("resources/codes_with_group_names_2015.csv")

# set the index of the beach data to location slug
dfBeaches.set_index('slug', inplace=True)

# set the index of to codes
dfCodes.set_index("code", inplace=True)

# code description map
code_d_map = dfCodes.description

# code material map
code_m_map = dfCodes.material

def scaleTheColumn(x):
    
    xmin = x.min()
    xmax = x.max()
    xscaled = (x-xmin)/(xmax-xmin)
    
    return xscaled



# this defines the css rules for the note-book table displays
header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF;'}
even_rows = {"selector": 'tr:nth-child(even)', 'props': f'background-color: rgba(139, 69, 19, 0.08);'}
odd_rows = {'selector': 'tr:nth-child(odd)', 'props': 'background: #FFF;'}
table_font = {'selector': 'tr', 'props': 'font-size: 12px;'}
table_css_styles = [even_rows, odd_rows, table_font, header_row]

1. All Data and all locations: original maps#

In the original report the boundary of land use effects was limited to the topographical features within a radius of 1’500 m from the survey location. The purpose of this work is to quantify how the correlation between survey results and land use changes as we consider greater distances from the survey location.

All locations with land use data are considered (see notes)

Surveys from rivers and lakes are considered as a single group

The original work is reproduced here, using the map (cartographic) features available in 2019. For this example progressively more amounts of land were considered for each survey location. The radius from the survey location is increased by 500 m from the initial 1500 m up to 5 000 m. At 5 000 meters we skip a step and do one last test at 10k.

The results at each radius are compared by number of correlations.This is for informational purposes and serves as guide to the proceeding chapters where the updated maps from 2022 are used.

1.1. Notes#

excluded data

At the time the report was published we did not have access to the land use data around Walensee. The good news is that it is now available with the most recent download. However, for this example the data from Walensee is excluded. This includes all samples that were taken in the region represented in the map below.

_images/walensee_scaled.jpeg

Fig. 1.1 #

figure 1.1: Detail of the survey area for where there was no land use data.

objects of interest

The objects of interest are defined as those objects where 20 or more were collected at one or more surveys. In reference to the default data: that is the value of the quantity column:

Hide code cell source 
pdtype = pd.core.frame.DataFrame
pstype = pd.core.series.Series



new_label_keys = {
    1:"industrial",
    2:"buildings",
    3:"buildings",
    4:"buildings",
    5:"buildings",
    6:"transport",
    7:"transport",
    8:"transport",
    9:"buildings",
    10:"recreation",
    11:"agriculture",
    12:"agriculture",
    13:"agriculture",
    14:"agriculture",
    15:"agriculture",
    16:"agriculture",
    17:"woods",
    18:"agriculture",
    19:"woods",
    20:"woods",
    21:"woods",
    22:"woods",
    23:"water",
    24:"water",
    25:"unproductive",
    26:"unproductive",
    27:"unproductive"
}


def combineLandUseFeatures(buffer_data: pdtype = None, a_col: str = "AS18_27", new_col: str = "label",
                           new_label_keys: dict = new_label_keys) -> pdtype:
    """Assigns labels to landuse values according to <label_keys_new>. The new labels,
    when aggregated, create groups of landuse values that are similar. For exmaple,
    all the different types of buildings are labeled "buildings"
    
    Args:
        buffer_data: The land use values at a given radius
        a_col: The original column name that holds the labels for the land use values
        new_col: The new name of the column with the new labels
    
    Returns:
        The data frame with the new column and the new labels    
    """    
    
    buffer_data.rename(columns={"slug":"location", a_col:new_col}, inplace=True)
    buffer_data[new_col] = buffer_data[new_col].apply(lambda x : new_label_keys[x])
    
    return buffer_data

def adjustLandUse(buffer_data: pdtype = None, exclude_these: list = [], locations: list = []) -> pdtype:
    """The surface area of the water feature is removed from landuse calcluation. This
    can be bypassed. However, the study considers the surface area of the water as a fixed
    feature that exchanges with the different landuse features (which are not fixed).
    
    Args:
        buffer_data: The land use values at a given radius
        exclude_these: The labels to be excluded from the land use total
        locations: The locations of interest with buffer data
        
    Returns:
        The dataframe with the locations in locations and without the excluded labels      
    """
    data = buffer_data[~buffer_data.label.isin(exclude_these)]
    data = data[data.location.isin(locations)]
    
    return data

def addRoadLengthToBuffer(buffer_data: pdtype = None, location: str = None, 
                          road_lengths: pstype = None, scale: float = 1000.0):
    """Adds the length of road network to the % land use values.
    """
    
    road_length = road_lengths.loc[location]
    if scale != 1:
        road_length = round(road_length/scale, 1)
    
    buffer_data["roads"] = road_length
    
    return buffer_data

def addIntersectsToBuffer(buffer_data: pdtype = None, location: str = None, 
                          intersects: pstype = None, scale: float = 100.0):
    
    """Adds the number of river intersections to the buffer.
    
    The river intersections are the points where rivers join the body of water of interest
    """
    
    n_intersects = intersects.loc[location]
        
    buffer_data["intersects"] = n_intersects
    
    return buffer_data


def calculatePercentLandUse(buffer_data: pdtype = None, location: str = None, label: str = "label",
                           add_intersects_roads: bool = True,  road_lengths: pstype = None, intersects: pstype = None,) -> pd.Series:
    """Figures the % of total of each landuse feature for one location.
    
    Args:
        buffer_data: The land use values at a given radius
        location: The survey location of interest
    
    Returns:
        A pandas series of the % of total for each landuse feature in the index
    """
    
    try:
        # try to reitrieve the land use data for a location
        location_data = buffer_data[buffer_data.location == location][label].value_counts()
    except ValueError:
        print("The location data could not retrieved")
        raise
    
    # the sum of all land use features in the radius
    total = location_data.sum()
    # the amount of the area attributed to water (lakes and rivers)
    water = location_data.loc["water"]   
    # divide the land_use values by the total minus the amount attributed to water
    results = location_data/(total-water)
    # name the series
    results.name = location
    # the intersects and road-lengths are calculated seprately
    # if add_intersects and roads is true, attach them to the
    # the land use values
    if add_intersects_roads:
        results = addIntersectsToBuffer(buffer_data=results, location=location, intersects=intersects)
        results = addRoadLengthToBuffer(buffer_data=results, location=location, road_lengths=road_lengths)
        
    
    return results

class BufferData:
    a_col="AS18_27"
    new_col = "label"
    exclude_these = []
    label_keys = new_label_keys
    beach_data = dfBeaches
    
    
    
    def __init__(self, file_name: str = None, locations: list = []):
        self.buffer = pd.read_csv(file_name)
        self.buffer_data = combineLandUseFeatures(buffer_data=self.buffer, a_col=self.a_col, new_col=self.new_col)
        self.adjusted_buffer = adjustLandUse(buffer_data=self.buffer_data, exclude_these=self.exclude_these, locations=locations)
        self.pctLandUse = None
        self.locations = locations
        
    def percentLandUse(self):
        
        if isinstance(self.pctLandUse, pdtype):
            return self.pctLandUse
        
        if isinstance(self.adjusted_buffer, pdtype):
            results = []
            road_lengths = self.beach_data.streets
            intersects = self.beach_data.intersects
            for location in self.locations:
                result = calculatePercentLandUse(buffer_data=self.adjusted_buffer, location=location, road_lengths=road_lengths, intersects=intersects)
                results.append(result)
        else:
            raise TypeError
            
        self.pctLandUse = pd.concat(results, axis=1)
        
        return self.pctLandUse

def timer(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.perf_counter()
        value = func(*args, **kwargs)
        end_time = time.perf_counter()
        run_time = end_time - start_time
        print("Finished {} in {} secs".format(repr(func.__name__), round(run_time, 3)))
        return value

    return wrapper

def assignLanduseValue(sample: pstype=None, land_use: str=None) -> float:
    # returns the requested value of land use from the sample
    # if the requested landuse value is not present in the buffer
    # zero is returned
    try:
        result = sample.loc[land_use]
    except KeyError:
        result = 0
    
    return result

def cleanSurveyResults(data):
    # performs data cleaning operations on the
    # default data
    
    data['loc_date'] = list(zip(data.location, data["date"]))
    data['date'] = pd.to_datetime(data["date"])
    
    # get rid of microplastics
    mcr = data[data.groupname == "micro plastics (< 5mm)"].code.unique()
    
    # replace the bad code
    data.code = data.code.replace('G207', 'G208')
    data = data[~data.code.isin(mcr)]
    
    # walensee has no landuse values
    data = data[data.water_name_slug != 'walensee']   
    
    return data

class SurveyResults:
    """Creates a dataframe from a valid filename. Assigns the column names and defines a list of
    codes and locations that can be used in the CodeData class.
    """
    
    file_name = 'resources/checked_sdata_eos_2020_21.csv'
    columns_to_keep=[
        'loc_date',
        'location', 
        'river_bassin',
        'water_name_slug',
        'city',
        'w_t', 
        'intersects', 
        'code', 
        'pcs_m',
        'quantity'
    ]
        
    def __init__(self, data: str = file_name, clean_data: bool = True, columns: list = columns_to_keep, w_t: str = None):
        self.dfx = pd.read_csv(data)
        self.df_results = None
        self.locations = None
        self.valid_codes = None
        self.clean_data = clean_data
        self.columns = columns
        self.w_t = w_t
        
    def validCodes(self):
        # creates a list of unique code values for the data set    
        conditions = [
            isinstance(self.df_results, pdtype),
            "code" in self.df_results.columns
        ]

        if all(conditions):

            try:
                valid_codes = self.df_results.code.unique()
            except ValueError:
                print("There was an error retrieving the unique code names, self.df.code.unique() failed.")
                raise
            else:
                self.valid_codes = valid_codes
                
        
    def surveyResults(self):
        
        # if this method has been called already
        # return the result
        if self.df_results is not None:
            return self.df_results
        
        # for the default data self.clean data must be called        
        if self.clean_data is True:
            fd = cleanSurveyResults(self.dfx)
            
        # if the data is clean then if can be used directly
        else:
            fd = self.dfx
        
        # filter the data by the variable w_t
        if self.w_t is not None:
            fd = fd[fd.w_t == self.w_t]            
         
        # keep only the required columns
        if self.columns:
            fd = fd[self.columns]
        
        # assign the survey results to the class attribute
        self.df_results = fd
        
        # define the list of codes in this df
        self.validCodes()
        
        return self.df_results
    
    def surveyLocations(self):
        if self.locations is not None:
            return self.locations
        if self.df_results is not None:
            self.locations = self.dfResults.location.unique()
            return self.locations
        else:
            print("There is no survey data loaded")
            return None    


class CodeData:
    
    def __init__(self, data: pdtype = None, code: str = None, **kwargs):
                
        self.data = data
        self.code = code
        self.code_data = None       
    
    def makeCodeData(self)->pdtype:
        
        if isinstance(self.code_data, pdtype):
            return self.code_data        
        
        conditions = [
            isinstance(self.data, pdtype)            
        ]
        
        if all(conditions):
            self.code_data = self.data[self.data.code == self.code]
            return self.code_data    
    
class CodeResults:   
        
    def __init__(self, code_data: pdtype = None, buffer: pdtype = None, code: str = None, 
                 method: callable = stats.spearmanr, **kwargs):        
        
        self.code_data = code_data
        self.buffer = buffer
        self.code = code
        self.method = method
        self.y = None
        self.x = None
        super().__init__()
    
        
    def landuseValueForOneCondition(self, land_use: str = None, locations: list = None)-> (np.array, np.array):
        
        x = self.code_data.pcs_m.values      
        y = [self.buffer[x].loc[land_use] for x in self.code_data.location.values]
        self.x, self.y = x, np.array(y)
                
        return self.x, self.y
    
    def rhoForALAndUseCategory(self, x: np.ndarray = None, y: np.ndarray = None) -> (float, float):
        # returns the asymptotic results if ranking based method is used        
        c, p = self.method(x, y)
        return c, p
    
    def getRho(self, x: np.array = None)-> float:
        # assigns y from self        
        result = self.method(x, self.y)
                       
        return result.correlation            
        
    def exactPValueForRho(self)-> float:
        # perform a permutation test instead of relying on 
        # the asymptotic p-value. Only one of the two inputs 
        # needs to be shuffled.
        p = stats.permutation_test((self.x,) , self.getRho, permutation_type='pairings', n_resamples=1000)
        
        return p


def makeBufferObject(file_name: str = "resources/buffer_output/luse_1500.csv", buffer_locations: list = None) -> (pdtype, pdtype):
    # Makes a buffer object by calling the BufferData class
    # calls the percentLandUse method and fills Nan values
    # returns the buffer_data object and pct land use values
    
    buffer_data = BufferData(file_name=file_name, locations=buffer_locations)
    pct_vals = buffer_data.percentLandUse()
    pct_vals.fillna(0, inplace=True)
    
    return buffer_data, pct_vals

def asymptoticAndExactPvalues(data: pdtype = None, buffer: pdtype = None, code: 'str'=None, land_use: 'str'=None)-> dict:
    
    code_data = CodeData(data=data, code=code).makeCodeData()
    code_results = CodeResults(code_data=code_data, buffer=buffer)
    
    x, y = code_results.landuseValueForOneCondition(land_use=land_use)
    ci, pi = code_results.rhoForALAndUseCategory(x, y)
    px = code_results.exactPValueForRho()
    
    return {"code": code, "landuse": land_use, "a_symp": (round(pi, 3), ci), "exact": (round(px.pvalue, 3), px.statistic,)}

@timer
def rhoForOneBuffer(data: pdtype = None, buffer_file: str = "resources/buffer_output/luse_1500.csv", 
                    codes: list=None, land_use: list=None)->(list, pdtype, pdtype):
    
    buffer_locations = data.location.unique()
    new_buffer, buffer_vals = makeBufferObject(file_name=buffer_file, buffer_locations=buffer_locations)    
        
    rhovals_for_this_buffer = []
    for code in codes:
        for use in land_use:
            results = asymptoticAndExactPvalues(data=data, buffer=buffer_vals, code=code, land_use=use)
            rhovals_for_this_buffer.append(results)
    
    return rhovals_for_this_buffer, new_buffer, buffer_vals

def resultsDf(rhovals: pdtype = None, pvals: pdtype = None)-> pdtype:
    results_df = []
    for i, n in enumerate(pvals.index):
        arow_of_ps = pvals.iloc[i]
        p_fail = arow_of_ps[ arow_of_ps > 0.05]
        arow_of_rhos = rhovals.iloc[i]
        
        for label in p_fail.index:
            arow_of_rhos[label] = 0
        results_df.append(arow_of_rhos)
    
    return results_df

def rotateText(x):
    return 'writing-mode: vertical-lr; transform: rotate(-180deg);  padding:10px; margins:0; vertical-align: baseline;'

def nObjectsPerLandUse(uses,surveys):
    results = {}
    for a_use in uses.index:
        total = surveys[surveys.code.isin(uses.loc[a_use])].quantity.sum()
        results.update({a_use:total})
    
    return pd.DataFrame(index=results.keys(), data=results.values(), columns=["total"])

fdx = SurveyResults()
df = fdx.surveyResults()

location_no_luse = ["linth_route9brucke",
                "seez_spennwiesenbrucke",
                'limmat_dietikon_keiserp',
                "seez"]

city_no_luse = ["Walenstadt", "Weesen", "Glarus Nord", "Quarten"]

df = df[~df.location.isin(location_no_luse)]
df = df[~df.city.isin(city_no_luse)]

df[["location","city", "water_name_slug","loc_date", "code", "pcs_m", "quantity"]].head().style.set_table_styles(table_css_styles)
  location city water_name_slug loc_date code pcs_m quantity
0 maladaire La Tour-de-Peilz lac-leman ('maladaire', '2021-05-01') G191 0.010000 1
1 maladaire La Tour-de-Peilz lac-leman ('maladaire', '2021-05-01') G35 0.010000 1
2 maladaire La Tour-de-Peilz lac-leman ('maladaire', '2021-05-01') G21 0.010000 1
3 maladaire La Tour-de-Peilz lac-leman ('maladaire', '2021-05-01') G67 0.030000 2
4 maladaire La Tour-de-Peilz lac-leman ('maladaire', '2021-05-01') G23 0.010000 1

In the report the objects of interest were either in the top ten by quantity and/or found in at least 50% of the samples. Consider the research question when filtering the objects. A filter can be based on use as well as abundance or frequency.

percent attributed to buildings

Originally the % of land attributed to buildings was calculated by summing the area of each topographical representation of a building from the map layer. In this example the % attributed to buildings is calculated the same as forests, agriculture, unproductive, recreation and industrial. The number of 100 m squares attributed to citys was divided by the total number of 100 m squares in the buffer (minus the number of squares attributed to water).

1.2. The survey data#

The sampling period for the IQAASL project started in April 2020 and ended May 2021.

_images/all_survey_areas_summary.jpeg

Fig. 1.2 #

figure 1.2: All survey locations IQAASL.

Summary data of the surveys, not including locations in the Walensee area:

Hide code cell source 
## The survey results
locations = df.location.unique()
samples = df.loc_date.unique()
lakes = df[df.w_t == "l"].drop_duplicates("loc_date").w_t.value_counts().values[0]
river = df[df.w_t == "r"].drop_duplicates("loc_date").w_t.value_counts().values[0]
codes_identified = df[df.quantity > 0].code.unique()
codes_possible = df.code.unique()
total_id = df.quantity.sum()

data_summary = {
    "n locations": len(locations),
    "n samples": len(samples),
    "n lake samples": lakes,
    "n river samples": river,
    "n identified object types": len(codes_identified),
    "n possible object types": len(codes_possible),
    "total number of objects": total_id
}

pd.DataFrame(index = data_summary.keys(), data=data_summary.values()).style.set_table_styles(table_css_styles)
  0
n locations 128
n samples 349
n lake samples 300
n river samples 49
n identified object types 191
n possible object types 211
total number of objects 46832

1.3. Land use correlation#

1.3.1. Example: object = cigarette ends, landuse = buildings v/s agg#

When the survey results for cigarette ends are considered with respect to the percent of land attributed to buildings or agriculture the it appears that cigartette ends accumulate at different rates depending on the landuse. The plot below. demonstrates the relationship of the survey values under the two conditions.

_images/land_use_correlation_20_0.png

Fig. 1.3 #

figure 1.3: Left: survey totals cigarette ends with respect to % of land to buildings. = 0.39, p-value < .001. Right: survey totals cigarette ends with respect to % of land to aggriculture. = -0.31, p-value < .001.

This relationship is considered for all the objects of interest. As the value of rho increases the points tend to accumulate to the right of the chart, it is the opposite as rho approaches -1.

1.3.2. Land use correlation @ 1500 m#

Hide code cell source 
no_luse_data = ["linth_route9brucke",
                "seez_spennwiesenbrucke",
                'limmat_dietikon_keiserp',
                "seez"]

df = df[~df.location.isin(no_luse_data)]

# the land use features we are interested in
land_use =["buildings",  "industrial", "roads", "recreation", "agriculture","woods", "intersects", "unproductive"]

# codes of interest
# we are interested in objects that were found in 1/2 surveys
fail = .5

# calculate the frequency that each code was found
codes_fail = df[df.quantity > 20].code.unique()

# identify the land_use data
buffer_file = "resources/buffer_output/luse_1500.csv"

# make the buffer and get the coeficients
rho_vals, new_buffer, buffer_vals = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

Finished 'rhoForOneBuffer' in 118.484 secs
Hide code cell source 
buffer_results = [{"code":x["code"], "use": x["landuse"], "exact_p": x["exact"][0], "p": x["a_symp"][0], "rho": x["exact"][1]} for x in rho_vals]
rho_at_buffer = pd.DataFrame(buffer_results)
    
pvals = rho_at_buffer.pivot_table(index="code", columns="use", values="exact_p", aggfunc='first')
rhovals = rho_at_buffer.pivot_table(index="code", columns="use", values="rho", aggfunc='first').round(3)

buffer_results = pd.DataFrame(resultsDf(rhovals, pvals))
buffer_results.columns.name = None
bfr = buffer_results.style.format(precision=2).set_table_styles(table_css_styles)
bfr = bfr.background_gradient(axis=None, vmin=buffer_results.min().min(), vmax=buffer_results.max().max(), cmap="coolwarm")
bfr = bfr.applymap_index(rotateText, axis=1)

glue("rho_at_1500", bfr, display=False)
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 -0.16 0.00 0.00 0.13 0.00 0.00 -0.11 0.00
G137 0.00 0.00 -0.12 0.00 0.00 0.00 0.00 0.00
G149 -0.11 0.00 0.00 0.00 0.00 0.00 -0.13 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.34 0.27 0.11 0.00 0.34 0.33 -0.23 -0.15
G178 -0.42 0.41 0.00 -0.21 0.24 0.27 -0.39 -0.17
G200 -0.23 0.21 -0.14 0.00 0.00 0.00 -0.13 0.00
G204 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G208 -0.10 0.10 0.00 0.00 0.00 0.00 -0.09 -0.09
G21 -0.13 0.00 0.00 0.11 0.13 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.12 0.00 0.00 0.00
G24 -0.12 0.13 0.00 0.00 0.13 0.00 -0.19 -0.13
G25 -0.13 0.12 0.00 0.11 0.12 0.00 0.00 -0.12
G27 -0.33 0.37 0.10 0.00 0.33 0.19 -0.34 -0.22
G3 -0.12 0.00 0.14 0.00 0.00 0.00 0.00 0.12
G30 -0.26 0.22 0.11 0.00 0.27 0.00 -0.17 -0.22
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 0.00 0.00 0.24 0.00 -0.11 0.00 0.00
G70 -0.12 0.00 0.00 0.14 0.00 0.00 -0.12 0.00
G73 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G74 -0.16 0.00 0.00 0.13 0.19 0.00 -0.17 0.00
G89 -0.13 0.00 0.00 0.14 0.00 0.00 0.00 0.00
G904 0.11 -0.16 -0.14 0.19 0.00 -0.17 0.18 0.21
G921 -0.12 0.00 0.00 0.00 -0.15 0.00 -0.23 0.00
G922 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00
G923 -0.17 0.16 0.00 0.00 0.12 0.15 -0.14 0.00
G928 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10
G941 0.00 0.00 0.00 0.11 0.14 0.00 0.17 0.00
G944 0.00 -0.14 0.00 0.14 0.00 0.00 0.00 0.00
G95 -0.15 0.00 0.00 0.00 0.18 0.00 -0.21 0.00
G96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G98 -0.16 0.00 0.15 -0.24 0.18 0.20 -0.12 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags 0.00 0.00 0.00 0.21 0.18 0.00 0.00 0.00

Fig. 1.4 #

Figure 1.4: The corelation coeficient of trash density to landuse features for All data. Coeffecients of 0 indicate that there is no statistical indication that the land use feature has an effect (positive or negative) on the trash density.

Hide code cell source 
def collectBufferResultsRhoAndPvals(rho_vals):
    buffer_results = [{"code":x["code"], "use": x["landuse"], "exact_p": x["exact"][0], "p": x["a_symp"][0], "rho": x["exact"][1]} for x in rho_vals]
    rho_at_buffer = pd.DataFrame(buffer_results)
    
    pvals = rho_at_buffer.pivot_table(index="code", columns="use", values="exact_p", aggfunc='first')
    rhovals = rho_at_buffer.pivot_table(index="code", columns="use", values="rho", aggfunc='first').round(3)    
    buffer_results = pd.DataFrame(resultsDf(rhovals, pvals))
    
    return rho_at_buffer, pvals, rhovals, buffer_results

def styleBufferResults(buffer_results):
    buffer_results.columns.name = None
    bfr = buffer_results.style.format(precision=2).set_table_styles(table_css_styles)
    bfr = bfr.background_gradient(axis=None, vmin=buffer_results.min().min(), vmax=buffer_results.max().max(), cmap="coolwarm")
    bfr = bfr.applymap_index(rotateText, axis=1)
    
    return bfr

1.3.3. Land use correlation @ 2 000 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_2000.csv"

# make the buffer and get the coeficients
rho_vals_2k, new_buffer_2k, buffer_vals_2k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_2k, pvals2k, rhovals2k, buffer_results2k = collectBufferResultsRhoAndPvals(rho_vals_2k)
styleBufferResults(buffer_results2k)

Finished 'rhoForOneBuffer' in 124.526 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 -0.12 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G149 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.32 0.26 0.13 0.00 0.33 0.33 -0.22 -0.14
G178 -0.40 0.39 0.00 -0.21 0.26 0.27 -0.40 -0.17
G200 -0.24 0.17 0.00 0.00 0.00 0.00 -0.11 0.00
G204 0.00 0.00 0.00 0.00 -0.11 0.00 -0.12 0.12
G208 -0.09 0.12 0.00 0.00 0.00 0.00 -0.09 -0.10
G21 -0.14 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.13 0.13 0.00 0.00 0.13 0.00 -0.22 -0.13
G25 0.00 0.00 0.00 0.11 0.11 0.00 0.00 0.00
G27 -0.33 0.35 0.15 0.00 0.34 0.19 -0.35 -0.18
G3 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.14
G30 -0.25 0.21 0.12 0.00 0.25 0.00 -0.16 -0.19
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.16 0.00 0.24 0.00 -0.11 0.00 0.12
G70 -0.11 0.00 0.00 0.14 0.00 0.00 -0.14 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00
G74 -0.21 0.00 0.00 0.13 0.17 0.00 -0.18 0.00
G89 -0.12 0.00 0.00 0.14 0.00 0.00 0.00 0.00
G904 0.11 -0.21 -0.18 0.19 0.00 -0.17 0.18 0.23
G921 0.00 0.00 0.00 0.00 0.00 0.00 -0.23 0.00
G922 -0.13 0.00 0.00 0.00 0.16 0.00 0.00 0.00
G923 -0.16 0.15 0.00 0.00 0.14 0.15 -0.12 0.00
G928 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 0.00 0.00 0.11 0.12 0.00 0.21 0.11
G944 0.00 -0.14 0.00 0.14 0.00 0.00 0.00 0.12
G95 -0.16 0.00 0.11 0.00 0.18 0.00 -0.23 0.00
G96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G98 -0.18 0.00 0.20 -0.24 0.20 0.20 0.00 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags -0.13 0.00 0.00 0.21 0.14 0.00 0.00 0.00

1.3.4. Land use correlation @ 2 500 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_2500.csv"

# make the buffer and get the coeficients
rho_vals_2_5k, new_buffer_2_5k, buffer_vals_2_5k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_25k, pvals25k, rhovals25k, buffer_results25k = collectBufferResultsRhoAndPvals(rho_vals_2_5k)
styleBufferResults(buffer_results25k)

Finished 'rhoForOneBuffer' in 124.391 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00
G149 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.30 0.26 0.13 -0.10 0.33 0.33 -0.22 -0.12
G178 -0.38 0.39 0.00 -0.21 0.26 0.27 -0.38 -0.19
G200 -0.24 0.13 0.00 0.00 0.00 0.00 0.00 0.00
G204 -0.10 0.00 0.00 0.00 -0.13 0.00 0.00 0.11
G208 -0.10 0.11 0.00 0.00 0.00 0.00 -0.09 -0.10
G21 -0.11 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.11 0.17 0.00 0.00 0.00 0.00 -0.22 -0.15
G25 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G27 -0.33 0.38 0.00 0.00 0.32 0.19 -0.37 -0.18
G3 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.13
G30 -0.22 0.23 0.00 0.11 0.24 0.00 -0.17 -0.17
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.14 0.00 0.24 0.00 -0.11 0.00 0.15
G70 0.00 0.00 0.00 0.14 0.00 0.00 -0.13 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 -0.13 0.00
G74 -0.23 0.12 0.00 0.13 0.12 0.00 -0.20 0.00
G89 -0.12 0.00 0.00 0.14 0.00 0.00 0.00 0.00
G904 0.11 -0.23 -0.21 0.19 -0.13 -0.17 0.17 0.23
G921 -0.12 0.00 0.00 0.00 -0.11 0.00 -0.22 0.00
G922 -0.12 0.00 0.00 0.00 0.12 0.00 0.00 0.00
G923 -0.17 0.14 0.00 0.00 0.14 0.15 -0.11 0.00
G928 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 0.00 0.00 0.11 0.12 0.00 0.20 0.18
G944 0.00 -0.11 0.00 0.14 0.00 0.00 0.00 0.12
G95 -0.13 0.12 0.00 0.00 0.14 0.00 -0.24 -0.11
G96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G98 -0.19 0.00 0.20 -0.24 0.18 0.20 -0.14 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags -0.11 0.00 0.00 0.21 0.00 0.00 0.00 0.00

1.3.5. Land use correlation @ 3 000 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_3000.csv"

# make the buffer and get the coeficients
rho_vals_3k, new_buffer_3k, buffer_vals_3k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_3k, pvals3k, rhovals3k, buffer_results3k = collectBufferResultsRhoAndPvals(rho_vals_3k)
styleBufferResults(buffer_results3k)

Finished 'rhoForOneBuffer' in 126.563 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 -0.12 0.00 0.00 0.00 0.00 0.00
G149 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.28 0.24 0.17 0.00 0.32 0.33 -0.28 0.00
G178 -0.38 0.39 0.00 -0.21 0.27 0.27 -0.36 -0.17
G200 -0.23 0.12 0.00 0.00 0.00 0.00 0.00 0.00
G204 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G208 -0.10 0.12 0.00 0.00 0.00 0.00 -0.08 -0.10
G21 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.12 0.18 0.00 0.00 0.00 0.00 -0.20 -0.16
G25 0.00 0.11 0.00 0.11 0.00 0.00 0.00 0.00
G27 -0.32 0.39 0.11 0.00 0.32 0.19 -0.38 -0.15
G3 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G30 -0.21 0.22 0.00 0.00 0.19 0.00 -0.17 -0.14
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.14 0.00 0.24 -0.11 -0.11 0.00 0.17
G70 0.00 0.00 0.00 0.14 -0.11 0.00 0.00 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00
G74 -0.24 0.13 0.00 0.13 0.00 0.00 -0.17 0.00
G89 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00
G904 0.12 -0.23 -0.20 0.19 -0.16 -0.17 0.19 0.19
G921 -0.12 0.12 0.00 0.00 0.00 0.00 -0.15 0.00
G922 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G923 -0.16 0.14 0.00 0.00 0.14 0.15 -0.10 0.00
G928 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 -0.11 0.00 0.11 0.00 0.00 0.17 0.20
G944 0.00 -0.11 -0.10 0.14 0.00 0.00 0.00 0.12
G95 -0.13 0.12 0.00 0.00 0.11 0.00 -0.24 -0.13
G96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G98 -0.19 0.00 0.17 -0.24 0.19 0.20 -0.20 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags 0.00 0.00 0.00 0.21 0.00 0.00 -0.11 0.00

1.3.6. Land use correlation @ 3 500 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_3500.csv"

# make the buffer and get the coeficients
rho_vals_3_5k, new_buffer_3_5k, buffer_vals_3_5k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_35k, pvals35k, rhovals35k, buffer_results35k = collectBufferResultsRhoAndPvals(rho_vals_3_5k)
styleBufferResults(buffer_results35k)

Finished 'rhoForOneBuffer' in 127.741 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 -0.12 0.00 0.00 0.00 0.00 0.00
G149 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.23 0.24 0.18 0.00 0.34 0.33 -0.29 0.00
G178 -0.36 0.37 0.00 -0.21 0.32 0.27 -0.36 -0.15
G200 -0.22 0.12 0.00 0.00 0.11 0.00 0.00 0.00
G204 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G208 -0.10 0.13 0.00 0.00 0.00 0.00 -0.09 -0.10
G21 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.14 0.16 0.00 0.00 0.11 0.00 -0.21 -0.13
G25 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G27 -0.30 0.37 0.14 0.00 0.35 0.19 -0.38 -0.13
G3 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G30 -0.20 0.20 0.12 0.11 0.18 0.00 -0.17 -0.12
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.17 0.00 0.24 -0.16 -0.11 0.00 0.18
G70 0.00 0.00 0.00 0.14 -0.10 0.00 0.00 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G74 -0.25 0.00 0.00 0.13 0.00 0.00 -0.16 0.00
G89 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.10
G904 0.12 -0.23 -0.18 0.19 -0.14 -0.17 0.18 0.16
G921 -0.13 0.12 0.00 0.00 0.00 0.00 -0.13 0.00
G922 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G923 -0.15 0.13 0.00 0.00 0.14 0.15 -0.12 0.00
G928 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 -0.11 0.00 0.11 0.00 0.00 0.15 0.21
G944 0.00 -0.12 -0.10 0.14 0.00 0.00 0.00 0.12
G95 -0.11 0.00 0.00 0.00 0.12 0.00 -0.24 -0.11
G96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G98 -0.17 0.00 0.16 -0.24 0.19 0.20 -0.22 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags 0.00 0.00 0.00 0.21 0.00 0.00 -0.11 0.00

1.3.7. Land use correlation @ 4 000 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_4000.csv"

# make the buffer and get the coeficients
rho_vals_4k, new_buffer_4k, buffer_vals_4k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_4k, pvals4k, rhovals4k, buffer_results4k = collectBufferResultsRhoAndPvals(rho_vals_4k)
styleBufferResults(buffer_results4k)

Finished 'rhoForOneBuffer' in 132.253 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G149 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.23 0.23 0.17 -0.10 0.32 0.33 -0.30 0.00
G178 -0.35 0.38 0.00 -0.21 0.32 0.27 -0.36 -0.15
G200 -0.20 0.12 0.00 0.00 0.00 0.00 0.00 0.00
G204 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G208 -0.09 0.12 0.00 0.00 0.00 0.00 -0.09 -0.08
G21 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.13 0.15 0.00 0.00 0.12 0.00 -0.20 -0.12
G25 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G27 -0.28 0.36 0.14 0.00 0.35 0.19 -0.37 -0.12
G3 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.11
G30 -0.18 0.18 0.00 0.00 0.16 0.00 -0.16 -0.11
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.20 0.00 0.24 -0.17 -0.11 0.12 0.19
G70 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G74 -0.24 0.00 0.00 0.13 0.11 0.00 -0.12 0.00
G89 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.10
G904 0.12 -0.22 -0.20 0.19 -0.15 -0.17 0.16 0.14
G921 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G922 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G923 -0.14 0.12 0.00 0.00 0.14 0.15 -0.13 0.00
G928 -0.10 0.00 0.00 0.00 0.11 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 -0.12 0.00 0.11 0.00 0.00 0.14 0.20
G944 0.00 -0.12 -0.12 0.14 0.00 0.00 0.00 0.12
G95 0.00 0.00 0.00 0.00 0.12 0.00 -0.23 -0.11
G96 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00
G98 -0.16 0.00 0.16 -0.24 0.19 0.20 -0.23 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags 0.00 0.00 0.00 0.21 0.00 0.00 -0.11 0.00

1.3.8. Land use correlation @ 4 500 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_4500.csv"

# make the buffer and get the coeficients
rho_vals_4_5k, new_buffer_4_5k, buffer_vals_4_5k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_45k, pvals45k, rhovals45k, buffer_results45k = collectBufferResultsRhoAndPvals(rho_vals_4_5k)
styleBufferResults(buffer_results45k)

Finished 'rhoForOneBuffer' in 134.984 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G149 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.22 0.23 0.18 0.00 0.31 0.33 -0.31 0.00
G178 -0.34 0.39 0.00 -0.21 0.34 0.27 -0.34 -0.14
G200 -0.18 0.12 0.00 0.00 0.11 0.00 0.00 0.00
G204 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11
G208 -0.09 0.13 0.00 0.00 0.00 0.00 -0.08 -0.07
G21 -0.10 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.12 0.14 0.00 0.00 0.11 0.00 -0.20 -0.11
G25 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G27 -0.28 0.36 0.15 0.00 0.36 0.19 -0.36 -0.11
G3 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.11
G30 -0.16 0.17 0.00 0.00 0.16 0.00 -0.17 0.00
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.20 0.00 0.24 -0.17 -0.11 0.00 0.20
G70 0.00 0.00 -0.11 0.14 0.00 0.00 0.00 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G74 -0.24 0.00 0.00 0.13 0.11 0.00 -0.12 0.00
G89 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00
G904 0.12 -0.24 -0.22 0.19 -0.17 -0.17 0.14 0.12
G921 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00
G922 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G923 -0.13 0.11 0.00 0.00 0.12 0.15 -0.13 0.00
G928 -0.11 0.00 0.00 0.00 0.11 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 -0.14 0.00 0.11 0.00 0.00 0.12 0.20
G944 0.00 -0.12 -0.12 0.14 0.00 0.00 0.00 0.13
G95 0.00 0.00 0.00 0.00 0.00 0.00 -0.24 0.00
G96 0.00 0.00 0.00 0.00 0.00 0.00 -0.13 0.00
G98 -0.16 0.00 0.19 -0.24 0.20 0.20 -0.26 0.00
Gfoam 0.00 0.00 0.00 0.25 0.00 -0.19 0.00 0.00
Gfrags 0.00 0.00 0.00 0.21 0.00 0.00 -0.13 0.00

1.3.9. Land use correlation @ 5 000 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_5k.csv"

# make the buffer and get the coeficients
rho_vals_5k, new_buffer_5k, buffer_vals_5k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_5k, pvals5k, rhovals5k, buffer_results5k = collectBufferResultsRhoAndPvals(rho_vals_5k)
styleBufferResults(buffer_results5k)

Finished 'rhoForOneBuffer' in 138.999 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00
G137 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G149 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 -0.20 0.23 0.18 0.00 0.30 0.33 -0.33 0.00
G178 -0.31 0.39 0.11 -0.21 0.33 0.27 -0.34 -0.14
G200 -0.16 0.12 0.00 0.00 0.12 0.00 0.00 0.00
G204 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11
G208 -0.08 0.13 0.00 0.00 0.08 0.00 -0.09 0.00
G21 -0.10 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 -0.11 0.14 0.00 0.00 0.11 0.00 -0.20 -0.10
G25 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G27 -0.26 0.35 0.17 0.00 0.34 0.19 -0.35 0.00
G3 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G30 -0.15 0.16 0.00 0.00 0.15 0.00 -0.18 -0.10
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.22 -0.12 0.24 -0.17 -0.11 0.00 0.19
G70 0.00 0.00 -0.13 0.14 0.00 0.00 0.00 0.00
G73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G74 -0.24 0.00 0.00 0.13 0.00 0.00 -0.11 0.00
G89 0.00 0.00 -0.12 0.14 0.00 0.00 0.00 0.00
G904 0.13 -0.25 -0.23 0.19 -0.17 -0.17 0.12 0.00
G921 0.00 0.13 0.00 0.00 0.00 0.00 -0.10 0.00
G922 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G923 -0.11 0.11 0.00 0.00 0.13 0.15 -0.12 0.00
G928 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 -0.16 0.00 0.11 0.00 0.00 0.12 0.21
G944 0.00 -0.13 -0.12 0.14 0.00 0.00 0.11 0.13
G95 0.00 0.00 0.00 0.00 0.00 0.00 -0.25 0.00
G96 0.00 0.00 0.00 0.00 0.00 0.00 -0.13 0.00
G98 -0.12 0.00 0.18 -0.24 0.19 0.20 -0.24 0.00
Gfoam 0.00 0.00 -0.11 0.25 0.00 -0.19 0.00 0.00
Gfrags 0.00 0.00 0.00 0.21 0.00 0.00 -0.14 0.00

1.3.10. Land use correlation @ 10 000 m#

Hide code cell source 
# identify the land_use data
buffer_file = "resources/buffer_output/luse_10k.csv"

# make the buffer and get the coeficients
rho_vals_10k, new_buffer_10k, buffer_vals_10k = rhoForOneBuffer(data=df, buffer_file=buffer_file , codes=codes_fail, land_use=land_use)

rho_at_10k, pvals10k, rhovals10k, buffer_results10k = collectBufferResultsRhoAndPvals(rho_vals_10k)
styleBufferResults(buffer_results10k)

Finished 'rhoForOneBuffer' in 177.922 secs
  agriculture buildings industrial intersects recreation roads unproductive woods
G10 0.00 0.00 -0.11 0.13 0.00 0.00 0.00 0.20
G137 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G149 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G155 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G177 0.00 0.18 0.12 0.00 0.15 0.33 -0.23 0.00
G178 -0.16 0.33 0.23 -0.21 0.30 0.27 0.00 -0.14
G200 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G204 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12
G208 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00
G21 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00
G22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13
G23 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00
G24 0.00 0.18 0.00 0.00 0.13 0.00 0.00 0.00
G25 0.00 0.17 0.11 0.00 0.14 0.00 0.00 0.00
G27 0.00 0.24 0.14 0.00 0.15 0.19 0.00 0.00
G3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G30 0.00 0.19 0.00 0.11 0.12 0.00 0.00 0.00
G38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G67 0.00 -0.18 -0.20 0.24 -0.21 -0.11 0.13 0.23
G70 -0.16 0.00 -0.14 0.14 -0.10 0.00 0.11 0.18
G73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G74 -0.15 0.00 0.00 0.13 0.00 0.00 0.00 0.15
G89 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.16
G904 0.00 0.00 0.00 0.19 0.00 -0.17 0.00 0.00
G921 -0.12 0.00 -0.12 0.00 0.00 0.00 0.16 0.16
G922 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G923 0.00 0.00 0.00 0.00 0.00 0.15 0.00 0.00
G928 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G940 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
G941 0.00 -0.10 0.00 0.11 0.00 0.00 0.00 0.11
G944 -0.11 -0.15 -0.15 0.14 -0.15 0.00 0.15 0.11
G95 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00
G96 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00
G98 0.12 0.00 0.00 -0.24 0.00 0.20 -0.13 0.00
Gfoam 0.00 -0.11 -0.18 0.25 -0.18 -0.19 0.00 0.18
Gfrags 0.00 0.00 0.00 0.21 0.00 0.00 -0.13 0.00

1.3.11. Covariance of explanatory variables @ 1500 m#

Hide code cell source 
buffer_percents = buffer_vals.T
buffer_percents = buffer_percents[land_use]
rho = buffer_percents.corr(method='spearman')
pval = buffer_percents.corr(method=lambda x, y: stats.spearmanr(x, y)[1])
pless = pd.DataFrame(resultsDf(rho, pval))

pless.columns.name = None
bfr = pless.style.format(precision=2).set_table_styles(table_css_styles)
bfr = bfr.background_gradient(axis=None, vmin=rho.min().min(), vmax=rho.max().max(), cmap="coolwarm")
bfr = bfr.applymap_index(rotateText, axis=1)
bfr
  buildings industrial roads recreation agriculture woods intersects unproductive
buildings 0.00 0.20 0.48 0.69 -0.67 -0.65 0.00 -0.61
industrial 0.20 0.00 0.38 0.31 0.00 -0.26 -0.21 0.00
roads 0.48 0.38 0.00 0.44 -0.37 -0.20 -0.68 -0.33
recreation 0.69 0.31 0.44 0.00 -0.47 -0.55 0.00 -0.34
agriculture -0.67 0.00 -0.37 -0.47 0.00 0.00 0.00 0.40
woods -0.65 -0.26 -0.20 -0.55 0.00 0.00 0.00 0.36
intersects 0.00 -0.21 -0.68 0.00 0.00 0.00 0.00 0.21
unproductive -0.61 0.00 -0.33 -0.34 0.40 0.36 0.21 0.00

1.3.12. Land use characteristics @ 1500 m#

Hide code cell source 
fdx = SurveyResults()
df = fdx.surveyResults()
location_no_luse = ["linth_route9brucke",
                "seez_spennwiesenbrucke",
                'limmat_dietikon_keiserp',
                "seez"]

city_no_luse = ["Walenstadt", "Weesen", "Glarus Nord", "Quarten"]

df = df[~df.location.isin(no_luse_data)]
df = df[~df.city.isin(city_no_luse)]

dfdt = df.groupby(["loc_date", "location"], as_index=False).pcs_m.sum()

def applyBufferDataToSurveys(data, buffer_data):
    
    locations = data.location.unique()
    
    
    for location in locations:
        if location in buffer_data.columns:
            landuse = buffer_data[location]
            data.loc[data.location == location, landuse.index] = landuse.values
        else:
            print(location)
            pass
    return data

daily_totals_landuse = applyBufferDataToSurveys(dfdt, buffer_vals)



pretty_names = {"buildings":"Built-up environment [%]",
                'industrial':'Industries [%]',
                'recreation': 'Recreational [%]',
                'agriculture':'Agriculture [%]',
                'woods':'Forests [%]',               
                'unproductive':'Unproductive land [%]',
                'roads':'Road network \nlength [km]',
                'intersects': 'Rivers/canals [#]',
               }

# method to get the ranked correlation of pcs_m to each explanatory variable
def make_plot_with_spearmans(data, ax, n):
    sns.scatterplot(data=data, x=n, y=unit_label, ax=ax, color='black', s=30, edgecolor='white', alpha=0.6)
    corr, a_p = stats.spearmanr(data[n], data[unit_label])
    return ax, corr, a_p

sns.set_style("whitegrid")
fig, axs = plt.subplots(1,4, figsize=(10,3.2), sharey=True)

data = daily_totals_landuse
loi = ['buildings', 'industrial','recreation','agriculture','woods','unproductive', 'intersects', 'roads']
for i, n in enumerate(loi[:4]):
    ax=axs[i]
    
    # the ECDF of the land use variable
    the_data = ECDF(data[n].values)
    sns.lineplot(x=the_data.x, y= (the_data.y), ax=ax, color='dodgerblue', label="% of surface area" )
    
    # get the median % of land use for each variable under consideration from the data
    the_median = data[n].median()
    
    # plot the median and drop horzontal and vertical lines
    ax.scatter([the_median], .5, color='red',s=50, linewidth=2, zorder=100, label="the median")
    ax.vlines(x=the_median, ymin=0, ymax=.5, color='red', linewidth=2)
    ax.hlines(xmax=the_median, xmin=0, y=0.5, color='red', linewidth=2)
    
    #remove the legend from ax   
    ax.get_legend().remove()
    
    if i == 0:
        ax.set_ylabel("Share of \nsurveys [%]", labelpad = 15)
    else:
        pass
    
    # add the median value from all locations to the ax title
    ax.set_title(F"median: {(round(the_median, 2))}",fontsize=12, loc='left')
    ax.set_xlabel(pretty_names[n], fontsize=14, labelpad=15)

plt.tight_layout()

plt.show()
_images/8cd2760be072e92499de8a037abb15539706f1a1c84f7760eff72ac13c734d84.png
_images/ba6f5b1bd57857f646db40df08611bc4c34b3587e6bc22439f3efefa33490386.png

1.3.13. Total correlations, total positive correlations, total negative corrrelations for each buffer radius#

Hide code cell source 
def countTheNumberOfCorrelationsPerBuffer(pvals: pdtype = None, rhovals: pdtype = None) -> (pdtype, pstype):
    
    # the number of times p <= 0.05
    number_p_less_than = (pvals <= 0.05).sum()
    number_p_less_than.name = "correlated"
    
    # the number of postive correlations
    number_pos = (rhovals > 0).sum()
    number_pos.name = "positive"
    
    # the number of negative correlations
    number_neg = (rhovals < 0).sum()
    number_neg.name = "negative"

    ncorrelated = pd.DataFrame([number_p_less_than, number_pos, number_neg])
    ncorrelated["total"] = ncorrelated.sum(axis=1)
    totals = ncorrelated.total
    
    
    return ncorrelated, totals

ncorrelated, total = countTheNumberOfCorrelationsPerBuffer(pvals, rhovals)

pairs = [
    (pvals, rhovals, "1.5k"),
    (pvals2k, rhovals2k, "2k"),
    (pvals25k, rhovals25k, "2.5k"),
    (pvals3k, rhovals3k, "3k"),
    (pvals35k, rhovals35k, "3.5k"),
    (pvals4k, rhovals4k, "4k"),
    (pvals45k, rhovals45k, "4.5k"),
    (pvals5k, rhovals5k, "5k"),
    (pvals10k, rhovals10k, "10k")
]

rho_vals_all = [
    rho_at_buffer,
    rho_at_2k,
    rho_at_25k,
    rho_at_3k,
    rho_at_35k,
    rho_at_4k,
    rho_at_45k,
    rho_at_5k,
    rho_at_10k
]

buffers = [
    new_buffer,
    new_buffer_2k,
    new_buffer_2_5k,
    new_buffer_3k,
    new_buffer_3_5k,
    new_buffer_4k,
    new_buffer_4_5k,
    new_buffer_5k,
    new_buffer_10k
]    

rhos_and_ps = []
for pair in pairs:
    _, total = countTheNumberOfCorrelationsPerBuffer(pair[0], pair[1])
    total.name = pair[2]
    rhos_and_ps.append(total)

correlation_results = pd.DataFrame(rhos_and_ps)

res = correlation_results.style.set_table_styles(table_css_styles)
res
  correlated positive negative
1.5k 106 53 53
2k 102 53 49
2.5k 103 50 53
3k 97 48 49
3.5k 98 50 48
4k 95 49 46
4.5k 93 49 44
5k 95 49 46
10k 81 52 29

1.3.14. Changes of land use profile for different buffer zones#

As the radius of the buffer zone changes the land use mix changes. Defining the radius of the buffer zone is determined by the scale at which the reporting is being done. For the report to Switzerland the target administrative level was the municipality. Therefore a radius of 1500m was appropriate, given the geographic size of a municipality in Switzerland.

Hide code cell source 
def combineAdjustedBuffers(buffers, pairs):
    
    combined_buffers = []
    for i,n in enumerate(pairs):
        a_buffer=buffers[i].adjusted_buffer
        a_buffer["radius"] = n[2]
        combined_buffers.append(a_buffer[["label", "radius"]])
        
    return pd.concat(combined_buffers, axis=0)
combined_buffers = combineAdjustedBuffers(buffers, pairs)

total_radius = combined_buffers.groupby("radius", as_index=False).label.value_counts()

total_radius = total_radius[total_radius.label != "water"]
total_radius = total_radius.pivot(index="radius", columns="label")
total_radius.columns = total_radius.columns.droplevel()
total_radius = total_radius.div(total_radius.sum(axis=1), axis=0).T
total_radius = total_radius[[total_radius.columns[0], *total_radius.columns[2:], total_radius.columns[1]]]
total_radius = total_radius.mul(100).astype(int)
total_radius.columns.name = None
total_radius.index.name = None
total_radius.style.set_table_styles(table_css_styles)
  1.5k 2.5k 2k 3.5k 3k 4.5k 4k 5k 10k
agriculture 24 26 25 29 28 31 30 32 40
buildings 28 25 26 22 23 19 20 18 8
industrial 3 2 2 2 2 2 2 2 1
recreation 5 4 5 3 4 3 3 2 1
transport 12 10 11 9 10 8 8 8 4
unproductive 2 1 1 1 1 2 1 2 5
woods 24 28 26 30 29 32 31 33 38

Below: Chart of different land use profiles at different buffer radiuses

Hide code cell source 
data =total_radius.values
labels = total_radius.index
colors = total_radius.index
xlabels = [str(x) for x in total_radius.columns]

colors = ['bisque','lightcoral','k','orchid','lightgrey','saddlebrown', 'forestgreen']

bottom = [0]*(len(total_radius.columns))

width = 0.8      # the width of the bars: can also be len(x) sequence

fig, ax = plt.subplots(figsize=(8,5))

for i,group in enumerate(data):
    ax.bar(xlabels, group, width, bottom=bottom, label=labels[i], color = colors[i])
    bottom += group


ax.set_ylabel('Land-use profile [%]', fontsize=16)

ax.set_xlabel("Buffer zone radius [m]", labelpad =15, fontsize=16)
ax.set_facecolor('white')

ax.spines['top'].set_color('0.5')
ax.spines['right'].set_color('0.5')
ax.spines['bottom'].set_color('0.5')
ax.spines['left'].set_color('0.5')
ax.set_ylim(0,100)

ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.tick_params(labelcolor='k', labelsize=14, width=1)

ax.legend(bbox_to_anchor=(1,1), facecolor = 'white', fontsize=14)

plt.show()
_images/76fdc538992ae05000b19aff9e893dd7847e152baa543efe38d95ef1c012a833.png

1.3.15. Sum of the total number of objects with a correlation (positive or negative) collected under the different land use categories#

Hide code cell source 
def numberOfObjectsCorrelatedPerAttribute(rho_at_buffer: pdtype = None, df: pdtype = None) -> pdtype:
    
    # select all the codes that have a p-value less than 0.05
    p_less_than = rho_at_buffer[rho_at_buffer.exact_p <= 0.05]

    # group the codes by land use attribute
    p_less_than_use_codes = p_less_than.groupby("use").code.unique()

    # sume the number of objects with a correlation (positive or negative) for each land use attribute
    results = nObjectsPerLandUse(p_less_than_use_codes,df)
    
    return results

numberOfObjectsCorrelatedPerAttribute(rho_at_buffer, df)
total
agriculture 22736
buildings 16004
industrial 14762
intersects 21301
recreation 26231
roads 17108
unproductive 20865
woods 14104

1.3.16. The % total of materials of the objects of interest with respect to the total number of objects collected.#

Hide code cell source 
def thePercentTotalOfMaterials(codes, df, materialmap):
    
    total = df.quantity.sum()
    buffer_codes = df[df.code.isin(codes)].copy()
    buffer_codes["material"] = buffer_codes.code.apply(lambda x: materialmap[x])
    
    material_df = pd.DataFrame(buffer_codes.groupby("material").quantity.sum()/total).round(3)
    
    material_df["quantity"] = material_df.quantity.apply(lambda x: f'{int((x*100))}%')
    
    return material_df

thePercentTotalOfMaterials(codes_fail, df, code_m_map)
quantity
material
Cloth 0%
Glass 5%
Metal 2%
Paper 1%
Plastic 75%

1.3.17. The % total of the objects of interest with respect to all objects collected.#

Hide code cell source 
def thePercentTotalOfTheTopXObjects(df, codes):
    index = [f'ratio of the items of interest over all items:',f'number of items of interest:']
    item_totals = df.groupby("code").quantity.sum().sort_values(ascending=False)
    top_x = item_totals.loc[codes].sum()
    total = df.quantity.sum()
    data = [f"{(round((top_x /total)*100))}%","{:,}".format(round(top_x))]
    return pd.DataFrame(data=data, index=index, columns=["value"])

thePercentTotalOfTheTopXObjects(df,codes_fail)
value
ratio of the items of interest over all items: 84%
number of items of interest: 39,442

1.3.18. The cumulative totals of the objects of interest, grouped by economic source#

The object of interest were grouped according to economic source by considering the possible uses for each object, as defined in the object description and sources columns of the default codes data:

  1. tobaco: “Tobacco”, “Smoking related”

  2. industry: ‘Industry’,’Construction’, ‘Industrial’, ‘Manufacturing’

  3. sanitary: “Sanitary”, “Personal hygiene”, “Water treatment”

  4. packaging: ‘Packaging (non-food)’,’Packaging films nonfood or unknown’, ‘Paper packaging’

  5. food: ‘Food and drinks’,’Foil wrappers, aluminum foil’, ‘Food and drinks’, ‘Food and drink’

  6. fragments: ‘Plastic fragments and pieces’, ‘Plastic fragments angular <5mm’, ‘Styrofoam < 5mm’, ‘Plastic fragments rounded <5mm’, ‘Foamed plastic <5mm’, ‘Fragmented plastics’

Hide code cell source 
def check_condition(x, conditions, i):
    
    if list(set(x)&set(conditions[i])):
        data = conditions[i][0]        
    elif i == 0 and not list(set(x)&set(conditions[i])):
        data = "Others"    
    else:
        data = check_condition(x, conditions, i-1)
    return data

# define the broad categories:
tobaco = ["Tobacco", "Smoking related"]
industry = ['Industry','Construction', 'Industrial', 'Manufacturing']
sanitary = ["Sanitary", "Personal hygiene", "Water treatment"]
packaging = ['Packaging (non-food)','Packaging films nonfood or unknown', 'Paper packaging']
food = ['Food and drinks','Foil wrappers, aluminum foil', 'Food and drinks', 'Food and drink']
fragments = ['Plastic fragments and pieces',
             'Plastic fragments angular <5mm',
             'Styrofoam < 5mm', 
             'Plastic fragments rounded <5mm',
             'Foamed  plastic <5mm',
             'Fragmented plastics',
            ]

conditions = [tobaco, industry, sanitary, packaging, food, fragments]


dT20 = df[df.code.isin(codes_fail)].groupby("code", as_index=False).quantity.sum().sort_values("quantity", ascending=False)
dT20["description"] = dT20.code.map(lambda x: code_d_map[x])
dT20.set_index("code", drop=True, inplace=True)

    
for each_code in dT20.index:
    srcs = dfCodes.loc[each_code][["source", "source_two", "source_three", "description"]]
    a = check_condition(srcs.values, conditions, len(conditions)-1)
    dT20.loc[each_code, "Type"] = a
    
total_type = dT20.groupby(["Type"], as_index=False).quantity.sum()
total_type["Proportion [%]"] = ((total_type.quantity/df.quantity.sum())*100).round(1)
total_type.style.format(precision=1).set_table_styles(table_css_styles)
  Type quantity Proportion [%]
0 Food and drinks 7652 16.3
1 Industry 10060 21.5
2 Others 3001 6.4
3 Packaging (non-food) 971 2.1
4 Plastic fragments and pieces 6943 14.8
5 Sanitary 2381 5.1
6 Tobacco 8434 18.0
Hide code cell source 
fig, ax = plt.subplots(figsize=(7,9))
colors = {'Industry': 'firebrick', 'Tobacco': 'darkslategrey', 'Food and drinks': 'navy', 'Plastic fragments and pieces':'lightgrey',
         'Others':'linen','Sanitary':'plum','Packaging (non-food)':'saddlebrown'}

width = 0.6

labels = list(colors.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors[label]) for label in labels]

ax.barh(dT20.description, dT20.quantity, color=[colors[i] for i in dT20.Type], edgecolor='darkgrey')
ax.invert_yaxis()  
ax.set_ylabel('')
xticks = [0,1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000]
ax.set_xticks(xticks)
ax.set_xticklabels([str(x) for x in xticks])


ax.set_xlabel('Total item count', fontsize=16, labelpad =15)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.tick_params(labelcolor='k', labelsize=10, width=1)
ax.yaxis.grid(color='lightgray')
ax.xaxis.grid(color='lightgray')
ax.set_facecolor('white')

plt.legend(handles, labels, fontsize=13,facecolor='white', loc="lower right")
for ha in ax.legend_.legend_handles:
    ha.set_edgecolor("darkgrey")

plt.grid(True)
ax.spines['top'].set_color('0.5')
ax.spines['right'].set_color('0.5')
ax.spines['bottom'].set_color('0.5')
ax.spines['left'].set_color('0.5')

# plt.savefig('C:/Users/schre086/figures/land_use_ch/top_20items.png', bbox_inches='tight')

plt.show()
_images/842d36fab2f936a969b7491615631f0079d2e43e19d74331d077bdbba4492f69.png

1.3.18.1. Litter items of local origin#

Items that have four or more positive associations with a land use category.

Hide code cell source 
correlated = rho_at_buffer[(rho_at_buffer.exact_p <= 0.05) & (rho_at_buffer.rho > 0)]
local = correlated.code.value_counts()
local = local[local >= 4]
local.name = "correlations"
quantity = df[df.code.isin(local.index)].groupby("code").quantity.sum()
items_of_local_origin = pd.DataFrame([local, quantity]).T
items_of_local_origin
correlations quantity
code
G27 4 7821
G904 4 279
G177 4 482

1.3.18.2. Ubiquitous items#

Items that have three or less positive associations with a land use category and are positively associated with river or stream intersections.

Hide code cell source 
correlated = rho_at_buffer[(rho_at_buffer.exact_p <= 0.05) & (rho_at_buffer.rho > 0)]
pos_intersects = rho_at_buffer[(rho_at_buffer.use == "intersects") & (rho_at_buffer.exact_p <= 0.05)]
correlated = correlated[correlated.code.isin(pos_intersects.code.unique())]
local = correlated.code.value_counts()
local = local[local <= 3]
local.name = "correlations"
quantity = df[df.code.isin(local.index)].groupby("code").quantity.sum()
items_of_local_origin = pd.DataFrame([local, quantity]).T
items_of_local_origin
correlations quantity
code
G98 3 581
G941 3 857
G178 3 668
G25 3 613
G21 2 593
G23 2 405
Gfrags 2 6943
G74 2 1546
G67 1 2221
G10 1 422
G89 1 916
G70 1 390
G944 1 34
Gfoam 1 4833

1.3.18.3. Other items#

Items that have three or less positive associations with a land use category and are NOT positively associated with river or stream intersections.

Hide code cell source 
correlated = rho_at_buffer[(rho_at_buffer.exact_p <= 0.05) & (rho_at_buffer.rho > 0)]
pos_intersects = rho_at_buffer[(rho_at_buffer.use == "intersects") & (rho_at_buffer.exact_p > 0.05)]
correlated = correlated[correlated.code.isin(pos_intersects.code.unique())]
local = correlated.code.value_counts()
local = local[local <= 3]
local.name = "correlations"
quantity = df[df.code.isin(local.index)].groupby("code").quantity.sum()
items_of_local_origin = pd.DataFrame([local, quantity]).T
items_of_local_origin
correlations quantity
code
G30 3 3159
G923 3 223
G3 2 277
G24 2 509
G200 1 2051
G95 1 1369
G208 1 165
G940 1 131
G922 1 233

1.4. Annex#

Land-use attributes of the survey locations.

Hide code cell source 
results = buffer_vals.T.round(3)
display_columns = ["buildings",  "industrial", "roads", "recreation", "agriculture","woods", "intersects", "unproductive"]

res = results[display_columns].style.format(precision=3).set_table_styles(table_css_styles)

glue("all_luse", res, display=False)
label buildings industrial roads recreation agriculture woods intersects unproductive
maladaire 0.509 0.015 75.300 0.044 0.238 0.032 3.000 0.000
preverenges 0.294 0.084 67.100 0.065 0.305 0.078 2.000 0.003
caprino 0.046 0.000 14.900 0.003 0.020 0.904 14.000 0.013
foce-del-cassarate 0.631 0.005 86.200 0.081 0.005 0.049 5.000 0.000
lido 0.632 0.006 83.300 0.084 0.006 0.048 5.000 0.000
lugano-centro 0.636 0.017 105.700 0.059 0.025 0.042 5.000 0.000
spiaggia-parco-ciani 0.629 0.005 87.600 0.080 0.005 0.051 5.000 0.000
via-brunari-spiaggia 0.283 0.021 116.400 0.059 0.086 0.410 0.000 0.009
golene-gudo 0.078 0.001 74.500 0.006 0.558 0.315 0.000 0.018
isole-di-brissago-porto 0.365 0.000 13.200 0.054 0.014 0.473 9.000 0.000
magadino-lido 0.100 0.005 41.300 0.005 0.128 0.585 23.000 0.123
rivapiana 0.580 0.018 87.700 0.093 0.039 0.137 7.000 0.000
via-mirasole-spiaggia 0.312 0.022 121.700 0.065 0.084 0.378 0.000 0.007
aare-port 0.233 0.063 120.300 0.063 0.185 0.379 0.000 0.001
schutzenmatte 0.265 0.029 117.500 0.023 0.263 0.336 0.000 0.003
zugerseecholler_cham_blarerm 0.250 0.121 64.600 0.057 0.325 0.020 8.000 0.040
la-petite-plage 0.335 0.040 72.100 0.123 0.144 0.109 5.000 0.079
vidy-ruines 0.312 0.066 99.600 0.309 0.066 0.018 2.000 0.000
baby-plage-geneva 0.540 0.007 136.100 0.161 0.045 0.022 2.000 0.000
grand-clos 0.164 0.012 39.700 0.044 0.070 0.599 8.000 0.020
weissenau-neuhaus 0.027 0.015 46.200 0.189 0.311 0.337 13.000 0.077
evole-plage 0.396 0.023 113.900 0.085 0.025 0.238 1.000 0.003
oberi-chlihochstetten 0.124 0.031 75.200 0.009 0.505 0.229 0.000 0.037
plage-de-serriere 0.408 0.053 103.500 0.056 0.028 0.233 2.000 0.000
vierwaldstattersee_weggis_schoberls_1 0.145 0.012 44.300 0.005 0.672 0.123 5.000 0.000
vierwaldstattersee_weggis_schoberls_2 0.140 0.013 43.700 0.005 0.679 0.122 5.000 0.000
vierwaldstattersee_weggis_schoberls_3 0.207 0.014 34.700 0.014 0.571 0.136 4.000 0.000
mullermatte 0.258 0.022 109.600 0.069 0.084 0.435 3.000 0.007
limmat_unterengstringen_oggierbuhrer 0.310 0.092 125.800 0.035 0.306 0.106 0.000 0.003
limmat_zuerich_wagnerc 0.385 0.079 144.500 0.130 0.130 0.120 0.000 0.000
limmat_zurich_mortensena_meiera 0.442 0.051 153.200 0.149 0.088 0.079 0.000 0.000
quai-maria-belgia 0.582 0.028 72.600 0.060 0.043 0.021 4.000 0.000
sihl_zuerich_eggerskoehlingera 0.601 0.015 173.900 0.066 0.001 0.015 0.000 0.000
bielersee_vinelz_fankhausers 0.155 0.008 58.500 0.034 0.568 0.134 5.000 0.063
erlach-camping-strand 0.136 0.011 38.800 0.057 0.430 0.186 7.000 0.133
anarchy-beach 0.665 0.009 48.500 0.052 0.061 0.004 3.000 0.000
rastplatz-stampf 0.284 0.060 40.200 0.108 0.297 0.004 10.000 0.121
zuerichsee_richterswil_benkoem_2 0.389 0.017 61.800 0.040 0.248 0.164 3.000 0.000
sentiero-giro-del-golf-spiaggia 0.311 0.000 26.700 0.286 0.252 0.063 3.000 0.029
vira-gambarogno 0.174 0.003 41.500 0.003 0.066 0.660 18.000 0.006
canale-saleggi 0.304 0.021 120.200 0.061 0.089 0.383 0.000 0.009
gummligrabbe 0.046 0.002 59.000 0.002 0.522 0.354 0.000 0.054
mannewil 0.039 0.002 68.300 0.000 0.461 0.461 0.000 0.017
lavey-les-bains-2 0.208 0.033 93.000 0.019 0.186 0.397 0.000 0.035
schusspark-strand 0.384 0.085 140.800 0.069 0.057 0.228 0.000 0.001
pfafikon-bad 0.273 0.086 62.600 0.024 0.252 0.099 6.000 0.110
zurichsee_kusnachterhorn_thirkell-whitej 0.651 0.010 57.800 0.042 0.072 0.098 3.000 0.003
plage-de-cheyres 0.143 0.003 36.300 0.030 0.309 0.342 4.000 0.127
leuk-mattenstrasse 0.112 0.002 87.200 0.014 0.189 0.514 0.000 0.106
thun-strandbad 0.627 0.055 97.200 0.089 0.055 0.026 4.000 0.002
zurichsee_wollishofen_langendorfm 0.441 0.028 94.600 0.195 0.051 0.036 1.000 0.013
pont-sous-terre 0.477 0.071 212.000 0.117 0.012 0.047 0.000 0.002
oben-am-see 0.117 0.030 58.300 0.024 0.324 0.421 12.000 0.016
thunersee_spiez_meierd_1 0.142 0.025 45.800 0.009 0.481 0.245 3.000 0.009
cully-plage 0.087 0.002 62.800 0.005 0.638 0.135 11.000 0.002
preverenges-le-sout 0.318 0.066 55.000 0.079 0.325 0.103 1.000 0.003
la-pecherie 0.091 0.023 41.400 0.005 0.665 0.146 3.000 0.005
villa-barton 0.467 0.006 85.100 0.230 0.021 0.015 0.000 0.000
zurcher-strand 0.561 0.009 156.600 0.129 0.005 0.009 3.000 0.000
oyonne 0.631 0.016 97.300 0.045 0.061 0.024 2.000 0.000
lavey-la-source 0.091 0.029 83.900 0.010 0.136 0.620 0.000 0.019
lavey-les-bains 0.178 0.032 89.600 0.016 0.182 0.445 0.000 0.032
spackmatt 0.229 0.022 96.400 0.040 0.342 0.289 0.000 0.001
luscherz-plage 0.079 0.000 37.300 0.009 0.269 0.601 6.000 0.015
strandboden-biel 0.346 0.025 124.400 0.079 0.048 0.341 3.000 0.007
jona-river-rastplatz 0.304 0.062 45.600 0.104 0.277 0.004 11.000 0.127
reuss_hunenberg_eberhardy 0.059 0.005 66.000 0.002 0.668 0.042 0.000 0.186
reuss_ottenbach_schoenenbergerl 0.114 0.011 73.400 0.005 0.650 0.082 0.000 0.094
zuerichsee_staefa_hennm 0.242 0.011 51.400 0.006 0.489 0.163 4.000 0.017
signalpain 0.411 0.068 83.900 0.124 0.036 0.099 7.000 0.056
baby-plage-ii-geneve 0.549 0.007 132.200 0.162 0.053 0.023 2.000 0.000
pointe-dareuse 0.143 0.056 32.300 0.012 0.563 0.163 8.000 0.004
les-glariers 0.078 0.014 107.000 0.009 0.546 0.207 0.000 0.036
nidau-strand 0.383 0.040 119.600 0.099 0.099 0.200 3.000 0.008
tiger-duck-beach 0.357 0.035 105.300 0.239 0.130 0.063 1.000 0.002
zuerichsee_waedenswil_colomboc_1 0.346 0.014 68.000 0.046 0.320 0.157 3.000 0.000
camp-des-peches 0.168 0.024 74.200 0.053 0.513 0.141 8.000 0.004
delta-park 0.213 0.074 56.700 0.016 0.279 0.240 5.000 0.049
le-pierrier 0.539 0.027 62.700 0.072 0.154 0.031 4.000 0.000
pecos-plage 0.155 0.030 59.800 0.045 0.565 0.096 6.000 0.011
beich 0.021 0.003 67.100 0.003 0.692 0.215 0.000 0.033
aare_suhrespitz_badert 0.298 0.067 132.800 0.038 0.259 0.227 0.000 0.006
tschilljus 0.086 0.014 83.400 0.027 0.356 0.420 0.000 0.037
sundbach-strand 0.076 0.000 40.400 0.021 0.217 0.645 9.000 0.003
wycheley 0.074 0.027 50.800 0.017 0.291 0.517 12.000 0.016
aare_koniz_hoppej 0.466 0.010 157.200 0.088 0.133 0.151 0.000 0.003
camping-gwatt-strand 0.297 0.088 66.500 0.069 0.230 0.145 6.000 0.044
bern-tiergarten 0.451 0.010 152.300 0.086 0.131 0.179 0.000 0.004
boiron 0.183 0.079 35.600 0.029 0.412 0.167 1.000 0.000
rocky-plage 0.533 0.007 126.900 0.167 0.057 0.024 2.000 0.000
lacleman_gland_lecoanets 0.123 0.005 42.800 0.123 0.378 0.332 0.000 0.000
route-13 0.467 0.037 131.800 0.119 0.112 0.107 1.000 0.003
schmerikon-bahnhof-strand 0.119 0.056 76.000 0.022 0.381 0.241 12.000 0.101
aabach 0.121 0.050 65.300 0.026 0.255 0.384 16.000 0.119
la-thiele_le-mujon_confluence 0.318 0.050 116.200 0.060 0.405 0.012 0.000 0.001
nouvelle-plage 0.280 0.050 52.600 0.073 0.335 0.090 4.000 0.090
baye-de-montreux-g 0.476 0.000 70.700 0.052 0.111 0.129 3.000 0.000
les-vieux-ronquoz 0.241 0.133 138.000 0.036 0.188 0.187 0.000 0.013
sihl_zuerich_eichenbergerd 0.237 0.007 124.300 0.068 0.130 0.420 0.000 0.014
versoix 0.592 0.022 66.800 0.049 0.140 0.063 5.000 0.003
augustmutzenbergstrandweg 0.361 0.010 78.700 0.032 0.271 0.195 0.000 0.005
parc-des-pierrettes 0.468 0.008 84.400 0.177 0.124 0.056 1.000 0.000
plage-de-st-sulpice 0.494 0.014 73.000 0.163 0.157 0.057 1.000 0.000
aare_bern_gerberm 0.277 0.025 112.300 0.021 0.375 0.204 0.000 0.001
brugg-be-buren-bsg-standort 0.196 0.048 108.800 0.045 0.346 0.292 0.000 0.001
kusnachter-dorfbach-tobelweg-1-4 0.504 0.007 96.500 0.033 0.139 0.219 3.000 0.004
ruisseau-de-la-croix-plage 0.118 0.003 32.200 0.032 0.339 0.385 5.000 0.078
ligerz-strand 0.118 0.000 50.800 0.018 0.366 0.416 3.000 0.029
hafeli 0.190 0.055 70.300 0.021 0.352 0.286 6.000 0.005
aare-solothurn-lido-strand 0.133 0.024 86.300 0.027 0.400 0.289 0.000 0.002
bern-fahrstrasse 0.253 0.030 135.600 0.047 0.192 0.362 0.000 0.003
gals-reserve 0.113 0.009 58.500 0.055 0.443 0.300 7.000 0.028
ascona-traghetto-spiaggia 0.447 0.024 79.400 0.116 0.049 0.218 2.000 0.004
pacha-mama-gerra 0.138 0.000 28.400 0.000 0.003 0.807 17.000 0.003
gerra-gambarogno 0.132 0.000 32.600 0.000 0.011 0.807 17.000 0.006
san-nazzaro-gambarogno 0.242 0.003 45.700 0.003 0.026 0.667 13.000 0.003
hauterive-petite-plage 0.279 0.048 85.200 0.099 0.189 0.243 2.000 0.013
aare-limmatspitz 0.169 0.055 111.900 0.013 0.316 0.323 0.000 0.003
aare_brugg_buchie 0.222 0.067 120.300 0.033 0.240 0.302 0.000 0.008
bain-des-dames 0.644 0.013 65.400 0.046 0.092 0.013 2.000 0.000
luscherz-two 0.069 0.000 39.700 0.008 0.271 0.613 6.000 0.014
impromptu_cudrefin 0.113 0.005 36.100 0.038 0.523 0.190 5.000 0.100
vira-spiaggia-left-of-river 0.167 0.003 42.300 0.003 0.067 0.669 18.000 0.008
tolochenaz 0.241 0.065 63.900 0.078 0.346 0.108 2.000 0.000
limmat_zuerich_suterdglauserp 0.409 0.068 145.700 0.087 0.052 0.130 0.000 0.000
aare_post 0.223 0.062 118.700 0.062 0.194 0.386 0.000 0.001
aare_bern_scheurerk 0.323 0.081 167.200 0.065 0.072 0.256 0.000 0.002
strandbeiz 0.297 0.064 45.200 0.112 0.273 0.004 9.000 0.104

Fig. 1.5 #

figure 1.5: The land use attributes of the survey locations @ 1 500 m radius

This script updated 09/08/2023 in Biel, CH

❤️ what you do everyday

analyst at hammerdirt

Git repo: https://github.com/hammerdirt-analyst/landuse.git

Git branch: main

json      : 2.0.9
seaborn   : 0.12.2
matplotlib: 3.7.1
pandas    : 2.0.0
numpy     : 1.24.2
PIL       : 9.5.0
scipy     : 1.10.1
IPython   : 8.12.0