isaacarroyov

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

#GENERAL PLOT SETTINGS

#style sheet
plt.style.use('seaborn-paper')

isaacarroyov

#select the relevant variables
variables = np.array( [ 'HOMICIDES', 'CAR_THEFT', 
                       'EXTORTION', 'STREET_TRANSPORT_THEFT' ] )

#Here we standardize a dataset along any axis. 
#Center to the mean and component wise scale to unit variance.
from sklearn import preprocessing
df_standardized = preprocessing.scale( df[variables] )
df_standardized = pd.DataFrame( df_standardized )

isaacarroyov

from sklearn.metrics import silhouette_samples, silhouette_score

#create a list of K-Means models. Each element is a KMeans model with a specific number of clusters
kmeans2_sil= [KMeans(n_clusters=i, random_state=11, init='k-means++').fit(df_standardized) for i in range(1,15)]

#calculamos el silhouette score a cada modelo (de diferente número de clusters)
sil_values = [ silhouette_score(df_standardized, model.labels_, random_state=11)
                    for model in kmeans2_sil[1:]
                   ]
#save the silhouette coefficient in an array

isaacarroyov

from sklearn.cluster import KMeans

#create a list for the wcss parameter
wcss = []
#test with 14 clusters
for i in range(1, 15):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 11)
    kmeans.fit(df_standardized)
    wcss.append(kmeans.inertia_)

isaacarroyov

print('Hola a todos')

isaacarroyov

def degres2decimal(a_degree, a_minute, a_seconds):
    """
    This functions has as input numpy-arrays of degrees, minutes and seconds and 
    returns a decimal degree array
    """
    degree = a_degree.astype(int)
    minute = a_minute.astype(int)
    seconds = a_seconds.astype(int)
    return degree + minute/60 + seconds/3600

isaacarroyov

#assign colours to duration days
def colour_duration(string):
    if string == '1 Día':
        return '#003049'
    elif string == '2 a 3 Días':
        return '#FCBF49'
    elif string == '4 a 7 Días':
        return '#F77F00'
    else:
        return '#D62828'

isaacarroyov

style_function = lambda feature: {
                                    'fill_color': COLOUR_FUNCTION(feature),
                                    'fillOpacity': 0.95, 
                                    'color': 'black' #contour colour,
                                    'weight': 1 #tickness of the colour
                                 }

isaacarroyov

def get_color(feature):
    value = map_dict.get(feature['id'])
    if value is None:
        return '#8c8c8c' # MISSING -> gray
    elif value ==1 :
        return '#DAF7A6'
    elif value ==2 :
        return '#FFC300'
    elif value ==3 :
        return '#FF5733'

isaacarroyov

import pandas as pd
import numpy as np
import folium
from folium.plugins import HeatMap

loc = df[ ['Latitude', 'Longitude'] ].to_numpy()
map_heatmap = folium.Map( location= (df['Latitude'].mean(), df['Longitude'].mean()), zoom_start= 5 )

HeatMap( 
    data = loc,