<jupyter_start><jupyter_text>**Data sets description** Below are the bicycle theft density graphs for each city. In Ottawa, a similar behavior is presented over the years, with an average value of 0.25 bicycles per $Km^2$. In Toronto, an increase is observed between 2014 and 2018, going from 4.6 to 6.11 bicycles stolen for $Km^2$. However, between 2018 and 2019 a slight decrease is observed, reaching a minimum value of 5.65 bicycles stolen for $Km^2$. The following chart shows the comparison of the percentage of stolen bicycles per year for $Km^2$. A stable behavior is observed between both cities during the years 2015 to 2018, and a slight decrease between 2018 and 2019 in both cities. However, the density of the percentage of bicycles stolen in the city of Toronto is generally higher than in the city of Ottawa by approximately 2 pp.<jupyter_code>fig = go.Figure()

fig.add_trace(go.Scatter(x=Density_Toronto['Occurrence_Year'],y=Density_Toronto['%Theft/sqKm'],mode='lines',name='Toronto city'))
fig.add_trace(go.Scatter(x=Density_Ottawa['Occurrence_Year'],y=Density_Ottawa['%Theft/sqKm'],mode='lines',name='Ottawa city'))

# Edit the layout
fig.update_layout(title='Percentage of bicycle theft per square kilometer',
                   xaxis_title='Year',
                   yaxis_title='%Theft/sqKm')
fig.show()
City=CityJoint_2014[CityJoint_2014['City']=='Ottawa']
fig=px.pie(data_frame=City,names='Status',title='Theft status')
fig.show()<jupyter_output><empty_output><jupyter_text>Day Analysis<jupyter_code>### Read the data for each year


CityJoint_2014 = pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint_2014.csv', header =0, index_col='Unnamed: 0')
CityJoint_2015 = pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint_2015.csv', header =0, index_col='Unnamed: 0')
CityJoint_2016 = pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint_2016.csv', header =0, index_col='Unnamed: 0')
CityJoint_2017 = pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint_2017.csv', header =0, index_col='Unnamed: 0')
CityJoint_2018 = pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint_2018.csv', header =0, index_col='Unnamed: 0')
CityJoint_2019 = pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint_2019.csv', header =0, index_col='Unnamed: 0')

CityJoint_2014.sample(5)
allJoint =pd.read_csv('/content/gdrive/My Drive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/CityJoint.csv', header =0, index_col='Unnamed: 0')
allJoint['Occurrence_Time'] = pd.to_datetime(allJoint['Occurrence_Time'])
allJoint['Hour'] = allJoint['Occurrence_Time'].dt.hour 

df_yd=allJoint[allJoint['Status']=='STOLEN'].groupby(['City','Occurrence_Month','Occurrence_Day']).size().reset_index().rename(columns={0:'SumThBikes'})

print(df_yd)
LandArea = allJoint['Area(sqKm)']
bikes = allJoint['BicycleT']
Population = allJoint['PopulationCT']

df_yd['BikesThDensity']=df_yd['SumThBikes']/bikes #Theft bikes density
df_yd['BikesDensity']= bikes / LandArea#Bikes density
df_yd['PopulationDensity'] =Population/LandArea#Population Density
df_yd['%Theft/sqKm'] = (df_yd['BikesThDensity']/df_yd['BikesDensity']) #Percentage of stoles bikes by sq Km
df_yd['%PopulationBike'] =(df_yd['BikesDensity']/df_yd['PopulationDensity']) #Percentage of bikes by sq Km
df_yd['SumThBikes'] = df_yd['SumThBikes']
df_hd=allJoint[allJoint['Status']=='STOLEN'].groupby(['City','Occurrence_Day', 'Hour']).size().reset_index().rename(columns={0:'SumThBikes'})

print(df_hd)
LandArea = allJoint['Area(sqKm)']
bikes = allJoint['BicycleT']
Population = allJoint['PopulationCT']

df_hd['BikesThDensity']=df_hd['SumThBikes']/bikes #Theft bikes density
df_hd['BikesDensity']= bikes / LandArea#Bikes density
df_hd['PopulationDensity'] =Population/LandArea#Population Density
df_hd['%Theft/sqKm'] = (df_hd['BikesThDensity']/df_hd['BikesDensity']) #Percentage of stoles bikes by sq Km
df_hd['%PopulationBike'] =(df_hd['BikesDensity']/df_hd['PopulationDensity']) #Percentage of bikes by sq Km
df_hd['SumThBikes'] = df_hd['SumThBikes']<jupyter_output>City  Occurrence_Day  Hour  SumThBikes
0      Ottawa               1     0          12
1      Ottawa               1     1           4
2      Ottawa               1     2           2
3      Ottawa               1     3           1
4      Ottawa               1     4           2
...       ...             ...   ...         ...
1431  Toronto              31    19          22
1432  Toronto              31    20          10
1433  Toronto              31    21          14
1434  Toronto              31    22          14
1435  Toronto              31    23          14

[1436 rows x 4 columns]<jupyter_text>Below we can observe a comparison of bicycle theft rates by city on an day basis. We did some aggrupation to the data in order to obtain a graphical visualization.<jupyter_code>#We do agrupations per year with the columns that we consider are import for extract the insights of the data

Stolen_bikes_df_2014 = CityJoint_2014[CityJoint_2014['Status']=='STOLEN'].groupby(['City','Occurrence_Day','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_2015 = CityJoint_2015[CityJoint_2015['Status']=='STOLEN'].groupby(['City','Occurrence_Day','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_2016 = CityJoint_2016[CityJoint_2016['Status']=='STOLEN'].groupby(['City','Occurrence_Day','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_2017 = CityJoint_2017[CityJoint_2017['Status']=='STOLEN'].groupby(['City','Occurrence_Day','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_2018 = CityJoint_2018[CityJoint_2018['Status']=='STOLEN'].groupby(['City','Occurrence_Day','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_2019 = CityJoint_2019[CityJoint_2019['Status']=='STOLEN'].groupby(['City','Occurrence_Day','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})

def Create_DensityFrameDay(yearframe):

  #Extraction of just stolen bikes status

  #Year_aux=_Joint[_Joint['Status']=='STOLEN'].groupby(['Occurrence_Year','GeoUID']).size().reset_index().rename(columns={0:'Count'}) 
  #Aux_Geo_ot = pd.merge(Year_aux,_pop[['GeoUID','PopulationCT']], on='GeoUID', how='left')
  Theft_year_day=yearframe.groupby(['Occurrence_Day']).sum().reset_index().rename(columns={'Count':'SumThBikes'}) #Sum total per year dataframe 
  LandArea = yearframe['Area(sqKm)'].sum()
  bikes = yearframe['BicycleT'].sum()
  Population = yearframe['PopulationCT'].sum()
  

  #####NEW VARIABLES OF DENSITY ANALYSIS #########
  #Theft_year_day['BicycleT']=bikes
  Theft_year_day['BikesThDensity']=Theft_year_day['SumThBikes']/LandArea #Theft bikes density
  Theft_year_day['BikesDensity']=bikes/ LandArea#Bikes density
  Theft_year_day['PopulationDensity'] = Population/LandArea #Population Density
  #Theft_year_day['LandArea']=LandArea_
  Theft_year_day['%Theft/sqKm'] = (Theft_year_day['BikesThDensity']/Theft_year_day['BikesDensity'])*100 #Percentage of stoles bikes by sq Km
  Theft_year_day['%PopulationBike'] =(Theft_year_day['BikesDensity']/Theft_year_day['PopulationDensity'])*100 #Percentage of bikes by sq Km
  
  return Theft_year_day
DensityStolenBikes2014=Create_DensityFrameDay(Stolen_bikes_df_2014)
DensityStolenBikes2015=Create_DensityFrameDay(Stolen_bikes_df_2015)
DensityStolenBikes2016=Create_DensityFrameDay(Stolen_bikes_df_2016)
DensityStolenBikes2017=Create_DensityFrameDay(Stolen_bikes_df_2017)
DensityStolenBikes2018=Create_DensityFrameDay(Stolen_bikes_df_2018)
DensityStolenBikes2019=Create_DensityFrameDay(Stolen_bikes_df_2019)

DensityStolenBikes2018.columns
import plotly.express as px
def bargraph(Density,x_data,y_data,colord, title):
  fig = px.bar(Density, x=x_data, y=y_data,color=colord, title=title)
  fig.show()
allJoint.columns<jupyter_output><empty_output><jupyter_text>Analysis by Day and HourThis section seeks to analyze the information by day and hour, in order to determine some behavior in the data, in each city.<jupyter_code>### Function of Bar Graph

import plotly.express as px
def bargraph(Density,x_data,y_data,colord, title):
  fig = px.bar(Density, x=x_data, y=y_data,color=colord, title=title)
  fig.show()
allJoint['Occurrence_Time'] = pd.to_datetime(allJoint['Occurrence_Time']) # Time format to the incident column
allJoint['Hour'] = allJoint['Occurrence_Time'].dt.hour # Extract the hour and add to the dataframe

df_yd=allJoint[allJoint['Status']=='STOLEN'].groupby(['City','Occurrence_Month','Occurrence_Day']).size().reset_index().rename(columns={0:'SumThBikes'}) #Aggrupate data by vity month and day

print(df_yd)
LandArea = allJoint['Area(sqKm)']
bikes = allJoint['BicycleT']
Population = allJoint['PopulationCT']

df_yd['BikesThDensity']=df_yd['SumThBikes']/bikes #Theft bikes density
df_yd['BikesDensity']= bikes / LandArea#Bikes density
df_yd['PopulationDensity'] =Population/LandArea#Population Density
df_yd['%Theft/sqKm'] = (df_yd['BikesThDensity']/df_yd['BikesDensity']) #Percentage of stoles bikes by sq Km
df_yd['%PopulationBike'] =(df_yd['BikesDensity']/df_yd['PopulationDensity']) #Percentage of bikes by sq Km
df_yd['SumThBikes'] = df_yd['SumThBikes']
df_hd=allJoint[allJoint['Status']=='STOLEN'].groupby(['City','Occurrence_Day', 'Hour']).size().reset_index().rename(columns={0:'SumThBikes'}) #Aggrupate data by city, Day and hour

print(df_hd)
LandArea = allJoint['Area(sqKm)']
bikes = allJoint['BicycleT']
Population = allJoint['PopulationCT']

df_hd['BikesThDensity']=df_hd['SumThBikes']/bikes #Theft bikes density
df_hd['BikesDensity']= bikes / LandArea#Bikes density
df_hd['PopulationDensity'] =Population/LandArea#Population Density
df_hd['%Theft/sqKm'] = (df_hd['BikesThDensity']/df_hd['BikesDensity']) #Percentage of stoles bikes by sq Km
df_hd['%PopulationBike'] =(df_hd['BikesDensity']/df_hd['PopulationDensity']) #Percentage of bikes by sq Km
df_hd['SumThBikes'] = df_hd['SumThBikes']


bargraph(df_yd[df_yd['City']== 'Toronto'],'Occurrence_Month','Occurrence_Day','%Theft/sqKm','Bike day theft density for Toronto')
# fig = px.histogram(df_yd, x="Occurrence_Day", color="City")
# fig.show()<jupyter_output><empty_output><jupyter_text>Above we can see a bar graph, along with a heat map that represents the grouping of all the months of the year and each of these is represented along with the days that make it up, the metric used is the percentage of theft per square kilometer, for the city of Toronto you can see that the months in which this metric is higher is October, November and December, you can see that for October when the rate of theft is higher is within the 17-29 of the respective month, also you can see that for November is for the 12-20.<jupyter_code>bargraph(df_yd[df_yd['City']== 'Ottawa'],'Occurrence_Month','Occurrence_Day','%Theft/sqKm','Bike day-month theft density per sqkm for Ottawa')<jupyter_output><empty_output><jupyter_text>Above we can see a bar graph as above, the metric used is the percentage of theft per square kilometer, for the city of Ottawa you can see that the months in which this metric is higher is June, July, August and September, you see that for June when the rate of theft is higher is within the day 10-27 of the respective month, also you can see that for July, August and September is maintained a high value throughout the month.<jupyter_code>bargraph(df_hd[df_hd['City']== 'Toronto'],'Occurrence_Day','Hour','%Theft/sqKm','Bike day-hour theft density for Toronto')<jupyter_output><empty_output><jupyter_text>In the previous graph you can see a grouping by days of the month, separated by the 24 hours of the day in order to observe at what time of day are presented May thefts using the ratio of thefts per square kilometer (%theft/sqKM), you can see that where there are more thefts is the day 18-30 of the average days of all months, and when the metric is higher during these days is usually during the 15 h - 21h, this is for Toronto.<jupyter_code>bargraph(df_hd[df_hd['City']== 'Ottawa'],'Occurrence_Day','Hour','%Theft/sqKm','Bike day-hour theft density for Ottawa')<jupyter_output><empty_output><jupyter_text>This graph represents the same as above using the ratio of thefts per square kilometer (%theft/sqKM), you can see that where there are more thefts is the day 28-30 of the average days of all months, and when the metric is higher during these days is throughout the course of the day, for the city of Ottawa.<jupyter_code>allJoint['BikesDensity']= bikes / LandArea#Bikes density
df_hd=allJoint[allJoint['Status']=='STOLEN'].groupby(['City','Neighbourhood', 'Hour']).size().reset_index().rename(columns={0:'SumThBikes'}) #Aggrupation by City, Location Type and Hour 
#df_hd['BikesDensity']= bikes / LandArea#Bikes density
print(df_hd)

bargraph(df_hd[df_hd['City']== 'Ottawa'],'Neighbourhood','Hour','SumThBikes', 'Bike day-hour location type theft density for Ottawa')<jupyter_output>City            Neighbourhood  Hour  SumThBikes
0      Ottawa      Bayshore - Belltown     6           1
1      Ottawa      Bayshore - Belltown     8           2
2      Ottawa      Bayshore - Belltown    10           3
3      Ottawa      Bayshore - Belltown    11           3
4      Ottawa      Bayshore - Belltown    12           3
...       ...                      ...   ...         ...
3649  Toronto  Yorkdale-Glen Park (31)    19           4
3650  Toronto  Yorkdale-Glen Park (31)    20           2
3651  Toronto  Yorkdale-Glen Park (31)    21           5
3652  Toronto  Yorkdale-Glen Park (31)    22           3
3653  Toronto  Yorkdale-Glen Park (31)    23           1

[3654 rows x 4 columns]<jupyter_text>You can see the grouping of neighborhoods with respect to the hours of the day to observe the number of robberies throughout the day, in Ottawa you can see that the neighborhoods where most thefts occur is centretown and sandy_hill from 8h - 21h.<jupyter_code>bargraph(df_hd[df_hd['City']== 'Toronto'],'Neighbourhood','Hour','SumThBikes', 'Bike day-hour location type theft density for Toronto')<jupyter_output><empty_output><jupyter_text>You can see the grouping of neighborhoods with respect to the hours of the day to observe the number of robberies throughout the day, in toronto you can see that the neighborhoods where most thefts occur is Bay Street Corrido and Waterfront Communities from 8h - 21h. During most of the day.  Hour Analysis<jupyter_code>Stolen_bikes_df_H2014 = CityJoint_2014[CityJoint_2014['Status']=='STOLEN'].groupby(['Occurrence_Time','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_H2015 = CityJoint_2015[CityJoint_2015['Status']=='STOLEN'].groupby(['Occurrence_Time','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_H2016 = CityJoint_2016[CityJoint_2016['Status']=='STOLEN'].groupby(['Occurrence_Time','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_H2017 = CityJoint_2017[CityJoint_2017['Status']=='STOLEN'].groupby(['Occurrence_Time','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_H2018 = CityJoint_2018[CityJoint_2018['Status']=='STOLEN'].groupby(['Occurrence_Time','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})
Stolen_bikes_df_H2019 = CityJoint_2019[CityJoint_2019['Status']=='STOLEN'].groupby(['Occurrence_Time','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'Count'})

def Create_DensityFrameHour(yearframe):

  #Extraction of just stolen bikes status

  yearframe['Occurrence_Time'] = pd.to_datetime(yearframe['Occurrence_Time'])
  yearframe['Hour'] = yearframe['Occurrence_Time'].dt.hour 
  Theft_year_hour=yearframe.groupby(['Hour']).sum().reset_index().rename(columns={'Count':'SumThBikes'}) #Sum total per year dataframe 
  LandArea = yearframe['Area(sqKm)'].sum()
  bikes = yearframe['BicycleT'].sum()
  Population = yearframe['PopulationCT'].sum()

  #####NEW VARIABLES OF DENSITY ANALYSIS #########
  Theft_year_hour['BikesThDensity']=Theft_year_hour['SumThBikes']/LandArea #Theft bikes density
  Theft_year_hour['BikesDensity']=bikes/ LandArea#Bikes density
  Theft_year_hour['PopulationDensity'] = Population/LandArea #Population Density
  Theft_year_hour['%Theft/sqKm'] = (Theft_year_hour['BikesThDensity']/Theft_year_hour['BikesDensity'])*100 #Percentage of stoles bikes by sq Km
  Theft_year_hour['%PopulationBike'] =(Theft_year_hour['BikesDensity']/Theft_year_hour['PopulationDensity'])*100 #Percentage of bikes by sq Km
  
  return Theft_year_hour
DensityStolenBikesHour2014=Create_DensityFrameHour(Stolen_bikes_df_H2014)
DensityStolenBikesHour2015=Create_DensityFrameHour(Stolen_bikes_df_H2015)
DensityStolenBikesHour2016=Create_DensityFrameHour(Stolen_bikes_df_H2016)
DensityStolenBikesHour2017=Create_DensityFrameHour(Stolen_bikes_df_H2017)
DensityStolenBikesHour2018=Create_DensityFrameHour(Stolen_bikes_df_H2018)
DensityStolenBikesHour2019=Create_DensityFrameHour(Stolen_bikes_df_H2019)

DensityStolenBikesHour2017.sample(2)
CityJoint_2014.columns
def Create_DensityFrameJohan(yearframe):

  #Extraction of just stolen bikes status

  Year_aux = yearframe.copy(deep=True)
  Year_aux['Occurrence_Time'] = pd.to_datetime(yearframe['Occurrence_Time'])
  Year_aux['Hour'] = yearframe['Occurrence_Time'].dt.hour 
  #Year_aux=yearframe[yearframe['Status'] == 'STOLEN'].groupby(['City','GeoUID','Hour','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'SumThBikes'})
  Year_aux=yearframe.groupby(['City','GeoUID','Hour','PopulationCT', 'BicycleT', 'Area(sqKm)']).size().reset_index().rename(columns={0:'SumThBikes'})

  #Year_aux=yearframe.groupby(['Hour']).reset_index().rename(columns={'Count':'SumThBikes'}) #Sum total per year dataframe 
  LandArea = Year_aux['Area(sqKm)']
  bikes = Year_aux['BicycleT']
  Population = Year_aux['PopulationCT']
  

  #####NEW VARIABLES OF DENSITY ANALYSIS #########
  #Theft_year_day['BicycleT']=bikes
  Year_aux['BikesThDensity']=Year_aux['SumThBikes']/Year_aux['Area(sqKm)'] #Theft bikes density
  Year_aux['BikesDensity']= Year_aux['BicycleT']/ Year_aux['Area(sqKm)']#Bikes density
  Year_aux['PopulationDensity'] =Year_aux['PopulationCT']/Year_aux['Area(sqKm)']#Population Density
  Year_aux['%Theft/sqKm'] = (Year_aux['BikesThDensity']/Year_aux['BikesDensity']) #Percentage of stoles bikes by sq Km
  Year_aux['%PopulationBike'] =(Year_aux['BikesDensity']/Year_aux['PopulationDensity']) #Percentage of bikes by sq Km
  
  return Year_aux

Stolen_bikes_johan_2014 = Create_DensityFrameJohan(CityJoint_2014)
Stolen_bikes_johan_2015 = Create_DensityFrameJohan(CityJoint_2015)
Stolen_bikes_johan_2016 = Create_DensityFrameJohan(CityJoint_2016)
Stolen_bikes_johan_2017 = Create_DensityFrameJohan(CityJoint_2017)
Stolen_bikes_johan_2018 = Create_DensityFrameJohan(CityJoint_2018)
Stolen_bikes_johan_2019 = Create_DensityFrameJohan(CityJoint_2019)

display(Stolen_bikes_johan_2014.shape)
display(Stolen_bikes_johan_2015.shape)
display(Stolen_bikes_johan_2016.shape)
Stolen_bikes_johan_2017.shape
Stolen_bikes_johan_2018.shape
#### Export the .csv for the interactive map

Stolen_bikes_johan_2014.to_csv('/content/gdrive/MyDrive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/Stolen_bikes_johan_2014.csv')
Stolen_bikes_johan_2015.to_csv('/content/gdrive/MyDrive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/Stolen_bikes_johan_2015.csv')
Stolen_bikes_johan_2016.to_csv('/content/gdrive/MyDrive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/Stolen_bikes_johan_2016.csv')
Stolen_bikes_johan_2017.to_csv('/content/gdrive/MyDrive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/Stolen_bikes_johan_2017.csv')
Stolen_bikes_johan_2018.to_csv('/content/gdrive/MyDrive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/Stolen_bikes_johan_2018.csv')
Stolen_bikes_johan_2019.to_csv('/content/gdrive/MyDrive/IMT Atlantique/Data Science/Project Bycicle/bikes-theft-model/Data/Stolen_bikes_johan_2019.csv')<jupyter_output><empty_output>
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