crime_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#import the dataset\n",
    "df=pd.read_csv('crime.csv',encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incident_id                 int64\n",
      "offense_id                  int64\n",
      "offense_code                int64\n",
      "offense_code_extension      int64\n",
      "offense_type_id            object\n",
      "offense_category_id        object\n",
      "first_occurrence_date      object\n",
      "last_occurrence_date       object\n",
      "reported_date              object\n",
      "incident_address           object\n",
      "geo_x                     float64\n",
      "geo_y                     float64\n",
      "geo_lon                   float64\n",
      "geo_lat                   float64\n",
      "district_id                object\n",
      "precinct_id                 int64\n",
      "neighborhood_id            object\n",
      "is_crime                    int64\n",
      "is_traffic                  int64\n",
      "victim_count                int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#To get the datatypes of each column\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(386865, 20)\n"
     ]
    }
   ],
   "source": [
    "#To get the descriptive statistics of the dataset, so the data can be properly understood\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        incident_id    offense_id   offense_code  offense_code_extension  \\\n",
      "count  3.868650e+05  3.868650e+05  386865.000000           386865.000000   \n",
      "mean   6.179303e+09  6.179303e+15    2766.705473                0.263642   \n",
      "std    1.335478e+10  1.335478e+16    1231.562483                0.669589   \n",
      "min    2.019600e+04  2.019652e+10     902.000000                0.000000   \n",
      "25%    2.019359e+09  2.019359e+15    2304.000000                0.000000   \n",
      "50%    2.021482e+09  2.021482e+15    2399.000000                0.000000   \n",
      "75%    2.023468e+09  2.023468e+15    2999.000000                0.000000   \n",
      "max    2.020890e+12  2.020890e+18    7399.000000                6.000000   \n",
      "\n",
      "              geo_x         geo_y       geo_lon        geo_lat    precinct_id  \\\n",
      "count  3.713620e+05  3.713620e+05  3.710960e+05  371096.000000  386865.000000   \n",
      "mean   3.157516e+06  1.693963e+06 -1.049429e+02      39.730607     386.199894   \n",
      "std    3.851640e+05  9.558331e+04  1.078909e+00       0.409652     183.818122   \n",
      "min    1.000000e+00  1.110000e+02 -1.154641e+02       0.000000     111.000000   \n",
      "25%    3.140207e+06  1.683469e+06 -1.050014e+02      39.708748     222.000000   \n",
      "50%    3.146509e+06  1.694853e+06 -1.049790e+02      39.739995     324.000000   \n",
      "75%    3.165779e+06  1.702457e+06 -1.049110e+02      39.760928     523.000000   \n",
      "max    4.067477e+07  1.089045e+07  5.000000e-07      39.900961     999.000000   \n",
      "\n",
      "       is_crime  is_traffic   victim_count  \n",
      "count  386865.0    386865.0  386865.000000  \n",
      "mean        1.0         0.0       1.020304  \n",
      "std         0.0         0.0       0.234620  \n",
      "min         1.0         0.0       1.000000  \n",
      "25%         1.0         0.0       1.000000  \n",
      "50%         1.0         0.0       1.000000  \n",
      "75%         1.0         0.0       1.000000  \n",
      "max         1.0         0.0      19.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To convert columns to the appropriate datatypes\n",
    "df['first_occurrence_date']=pd.to_datetime(df['first_occurrence_date'])\n",
    "df['last_occurrence_date']=pd.to_datetime(df['last_occurrence_date'])\n",
    "df['reported_date']=pd.to_datetime(df['reported_date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incident_id                        int64\n",
      "offense_id                         int64\n",
      "offense_code                       int64\n",
      "offense_code_extension             int64\n",
      "offense_type_id                   object\n",
      "offense_category_id               object\n",
      "first_occurrence_date     datetime64[ns]\n",
      "last_occurrence_date      datetime64[ns]\n",
      "reported_date             datetime64[ns]\n",
      "incident_address                  object\n",
      "geo_x                            float64\n",
      "geo_y                            float64\n",
      "geo_lon                          float64\n",
      "geo_lat                          float64\n",
      "district_id                       object\n",
      "precinct_id                        int64\n",
      "neighborhood_id                   object\n",
      "is_crime                           int64\n",
      "is_traffic                         int64\n",
      "victim_count                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   incident_id        offense_id  offense_code  offense_code_extension  \\\n",
      "0    202268791   202268791299900          2999                       0   \n",
      "1   2021387586  2021387586299900          2999                       0   \n",
      "2   2020641486  2020641486299900          2999                       0   \n",
      "3   2018612468  2018612468299900          2999                       0   \n",
      "4   2020293614  2020293614299900          2999                       0   \n",
      "\n",
      "           offense_type_id offense_category_id first_occurrence_date  \\\n",
      "0  criminal-mischief-other     public-disorder   2022-02-10 02:50:00   \n",
      "1  criminal-mischief-other     public-disorder   2021-07-07 21:02:00   \n",
      "2  criminal-mischief-other     public-disorder   2020-10-29 01:30:00   \n",
      "3  criminal-mischief-other     public-disorder   2018-09-06 17:00:00   \n",
      "4  criminal-mischief-other     public-disorder   2020-05-08 05:00:00   \n",
      "\n",
      "  last_occurrence_date       reported_date     incident_address      geo_x  \\\n",
      "0                  NaT 2022-02-10 03:16:00   1107 N SANTA FE DR  3140929.0   \n",
      "1                  NaT 2021-07-08 00:55:00          815 16TH ST  3142470.0   \n",
      "2                  NaT 2020-10-29 04:31:00  4745 N FEDERAL BLVD  3133352.0   \n",
      "3  2018-09-06 23:00:00 2018-09-07 09:58:00    65 S FEDERAL BLVD  3133534.0   \n",
      "4  2020-05-08 18:30:00 2020-05-13 10:00:00   12295 E ALBROOK DR  3184065.0   \n",
      "\n",
      "       geo_y     geo_lon    geo_lat district_id  precinct_id neighborhood_id  \\\n",
      "0  1692612.0 -104.998910  39.733957           1          123    lincoln-park   \n",
      "1  1697098.0 -104.993342  39.746248           6          611             cbd   \n",
      "2  1710396.0 -105.025520  39.782888           1          111        berkeley   \n",
      "3  1685797.0 -105.025330  39.715357           4          411          barnum   \n",
      "4  1710782.0 -104.845074  39.783082           5          521       montbello   \n",
      "\n",
      "   is_crime  is_traffic  victim_count  \n",
      "0         1           0             1  \n",
      "1         1           0             1  \n",
      "2         1           0             1  \n",
      "3         1           0             1  \n",
      "4         1           0             1  \n"
     ]
    }
   ],
   "source": [
    "#To get the first 5 instances of the dataset\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#The dataset is to be cleaned. Start by checking and exterminating duplicates\n",
    "print(df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incident_id                    0\n",
      "offense_id                     0\n",
      "offense_code                   0\n",
      "offense_code_extension         0\n",
      "offense_type_id                0\n",
      "offense_category_id            0\n",
      "first_occurrence_date          0\n",
      "last_occurrence_date      175556\n",
      "reported_date                  0\n",
      "incident_address           15503\n",
      "geo_x                      15503\n",
      "geo_y                      15503\n",
      "geo_lon                    15769\n",
      "geo_lat                    15769\n",
      "district_id                   57\n",
      "precinct_id                    0\n",
      "neighborhood_id              689\n",
      "is_crime                       0\n",
      "is_traffic                     0\n",
      "victim_count                   0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#This dataset has no duplicates. The next step is to check the null values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(386131, 20)\n"
     ]
    }
   ],
   "source": [
    "#Get rid of the rows with the null values in the neighborhood_id column and the district_id column\n",
    "df=df.dropna(subset=['neighborhood_id','district_id'])\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this dataset, rows with no last_occurrence date are assumed to have happened only once\n",
    "#So, fill in the null last_occurrence_date values with the corresponding first_occurrence_date values\n",
    "df['last_occurrence_date'].fillna(df['first_occurrence_date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill null values in the Incident Address column with NA\n",
    "df['incident_address'].fillna(value='NA', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incident_id                   0\n",
      "offense_id                    0\n",
      "offense_code                  0\n",
      "offense_code_extension        0\n",
      "offense_type_id               0\n",
      "offense_category_id           0\n",
      "first_occurrence_date         0\n",
      "last_occurrence_date          0\n",
      "reported_date                 0\n",
      "incident_address              0\n",
      "geo_x                     15465\n",
      "geo_y                     15465\n",
      "geo_lon                   15465\n",
      "geo_lat                   15465\n",
      "district_id                   0\n",
      "precinct_id                   0\n",
      "neighborhood_id               0\n",
      "is_crime                      0\n",
      "is_traffic                    0\n",
      "victim_count                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['incident_id', 'offense_id', 'offense_code', 'offense_code_extension',\n",
      "       'offense_type_id', 'offense_category_id', 'first_occurrence_date',\n",
      "       'last_occurrence_date', 'reported_date', 'incident_address', 'geo_x',\n",
      "       'geo_y', 'geo_lon', 'geo_lat', 'district_id', 'precinct_id',\n",
      "       'neighborhood_id', 'is_crime', 'is_traffic', 'victim_count'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "369199    1843.848611\n",
      "342357    1826.000000\n",
      "385107    1727.833333\n",
      "386695    1717.584028\n",
      "379935    1674.103472\n",
      "             ...     \n",
      "86429        0.000000\n",
      "86437        0.000000\n",
      "261945       0.000000\n",
      "18596       -0.034028\n",
      "199238      -0.325694\n",
      "Name: report_time_difference, Length: 386131, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Let us get the difference between first occurrence time and reported time and \n",
    "#This will help comb out errors in the first occurrence time column\n",
    "diff_in_report_time=df['reported_date']-df['first_occurrence_date']\n",
    "df['report_time_difference']=diff_in_report_time.dt.total_seconds()/(60*60*24) #To convert it into seconds and then calculate it into days format\n",
    "print(df['report_time_difference'].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        incident_id         offense_id  offense_code  offense_code_extension  \\\n",
      "18596    2020148442   2020148442299901          2999                       1   \n",
      "199238  20206011172  20206011172230500          2305                       0   \n",
      "\n",
      "                  offense_type_id       offense_category_id  \\\n",
      "18596   criminal-mischief-mtr-veh           public-disorder   \n",
      "199238   theft-items-from-vehicle  theft-from-motor-vehicle   \n",
      "\n",
      "       first_occurrence_date last_occurrence_date       reported_date  \\\n",
      "18596    2020-03-08 03:50:00  2020-03-08 03:50:00 2020-03-08 03:01:00   \n",
      "199238   2020-08-24 19:05:00  2020-08-24 21:05:00 2020-08-24 11:16:00   \n",
      "\n",
      "           incident_address  ...      geo_y     geo_lon    geo_lat  \\\n",
      "18596   2300 BLK E 12TH AVE  ...  1693164.0 -104.959446  39.735297   \n",
      "199238       3315 WALNUT ST  ...  1704385.0 -104.977720  39.766184   \n",
      "\n",
      "        district_id precinct_id  neighborhood_id is_crime  is_traffic  \\\n",
      "18596             2         213    congress-park        1           0   \n",
      "199238            2         211      five-points        1           0   \n",
      "\n",
      "        victim_count  report_time_difference  \n",
      "18596              1               -0.034028  \n",
      "199238             1               -0.325694  \n",
      "\n",
      "[2 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "#There are 2 instances where the crime was supposedly reported before it occurred\n",
    "print(df.loc[[18596,199238]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Row 18596 was reported 49 minutes before it happened, and row 199238 was reported over 7 hours before it happened\n",
    "#A solution would be to switch the values in both columns, as it can be assumed as an error in data collection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To create a function to switch the values in first occurrence date column and reported date column\n",
    "def switch(df, index):\n",
    "    column1 = 'first_occurrence_date'\n",
    "    column2 = 'reported_date'\n",
    "    temp = df.at[index, column1]\n",
    "    df.at[index, column1] = df.at[index, column2]\n",
    "    df.at[index, column2] = temp\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply the function to the dataset rows\n",
    "index=18596\n",
    "df=switch(df,index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        incident_id         offense_id  offense_code  offense_code_extension  \\\n",
      "18596    2020148442   2020148442299901          2999                       1   \n",
      "199238  20206011172  20206011172230500          2305                       0   \n",
      "\n",
      "                  offense_type_id       offense_category_id  \\\n",
      "18596   criminal-mischief-mtr-veh           public-disorder   \n",
      "199238   theft-items-from-vehicle  theft-from-motor-vehicle   \n",
      "\n",
      "       first_occurrence_date last_occurrence_date       reported_date  \\\n",
      "18596    2020-03-08 03:01:00  2020-03-08 03:50:00 2020-03-08 03:50:00   \n",
      "199238   2020-08-24 19:05:00  2020-08-24 21:05:00 2020-08-24 11:16:00   \n",
      "\n",
      "           incident_address  ...      geo_y     geo_lon    geo_lat  \\\n",
      "18596   2300 BLK E 12TH AVE  ...  1693164.0 -104.959446  39.735297   \n",
      "199238       3315 WALNUT ST  ...  1704385.0 -104.977720  39.766184   \n",
      "\n",
      "        district_id precinct_id  neighborhood_id is_crime  is_traffic  \\\n",
      "18596             2         213    congress-park        1           0   \n",
      "199238            2         211      five-points        1           0   \n",
      "\n",
      "        victim_count  report_time_difference  \n",
      "18596              1               -0.034028  \n",
      "199238             1               -0.325694  \n",
      "\n",
      "[2 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[[18596,199238]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incident_id               361580\n",
      "offense_id                386131\n",
      "offense_code                 141\n",
      "offense_code_extension         7\n",
      "offense_type_id              186\n",
      "offense_category_id           14\n",
      "first_occurrence_date     240675\n",
      "last_occurrence_date      263269\n",
      "reported_date             335334\n",
      "incident_address           90358\n",
      "geo_x                      45210\n",
      "geo_y                      43467\n",
      "geo_lon                    90773\n",
      "geo_lat                    90459\n",
      "district_id                    8\n",
      "precinct_id                   40\n",
      "neighborhood_id               78\n",
      "is_crime                       1\n",
      "is_traffic                     1\n",
      "victim_count                  18\n",
      "report_time_difference     32930\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#To check the amount of unique values in each column in the dataset\n",
    "print(df.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    386131\n",
      "Name: is_crime, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#The is_crime and is_traffic column have only one unique value. Use the valuecounts() to get a deeper understanding of the unique values\n",
    "print(df['is_crime'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    386131\n",
      "Name: is_traffic, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['is_traffic'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Every data entry has the same value in the is_traffic and is_crime column (1, which is yes)\n",
    "#So these columns can be dropped\n",
    "df.drop('is_crime',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('is_traffic',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['incident_id', 'offense_id', 'offense_code', 'offense_code_extension',\n",
      "       'offense_type_id', 'offense_category_id', 'first_occurrence_date',\n",
      "       'last_occurrence_date', 'reported_date', 'incident_address', 'geo_x',\n",
      "       'geo_y', 'geo_lon', 'geo_lat', 'district_id', 'precinct_id',\n",
      "       'neighborhood_id', 'is_traffic', 'victim_count',\n",
      "       'report_time_difference'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "five-points     23073\n",
      "central-park    17862\n",
      "capitol-hill    14655\n",
      "cbd             14651\n",
      "dia             14641\n",
      "                ...  \n",
      "skyland          1155\n",
      "rosedale         1099\n",
      "country-club      887\n",
      "indian-creek      718\n",
      "wellshire         381\n",
      "Name: neighborhood_id, Length: 78, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#The Exploratory Data Analysis (EDA) stage\n",
    "#To find the frequencies of crime in the neighbourhoods\n",
    "print(df['neighborhood_id'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['neighborhood_id'].value_counts().plot(kind='box')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Boxplot of Denver neighbourhood crime occurrences')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Five points is the neighbourhood with the highest crime frequency, and Wellshire is the neighbourhood with the lowest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theft-from-motor-vehicle        64567\n",
      "public-disorder                 56792\n",
      "larceny                         56147\n",
      "auto-theft                      55879\n",
      "all-other-crimes                46580\n",
      "burglary                        27637\n",
      "drug-alcohol                    21757\n",
      "other-crimes-against-persons    20416\n",
      "aggravated-assault              17181\n",
      "robbery                          6959\n",
      "white-collar-crime               6700\n",
      "sexual-assault                   4281\n",
      "arson                             825\n",
      "murder                            410\n",
      "Name: offense_category_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['offense_category_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Offense frequencies in Denver')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a bar chart with the data\n",
    "df['offense_category_id'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Offense')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Offense frequencies in Denver')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theft from Motor Vehicles is the most common crime in Denver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighborhood_id\n",
      "athmar-park        (athmar-park, theft-from-motor-vehicle)\n",
      "auraria                        (auraria, all-other-crimes)\n",
      "baker                                     (baker, larceny)\n",
      "barnum                          (barnum, all-other-crimes)\n",
      "barnum-west                (barnum-west, all-other-crimes)\n",
      "                                   ...                    \n",
      "west-colfax                 (west-colfax, public-disorder)\n",
      "west-highland    (west-highland, theft-from-motor-vehicle)\n",
      "westwood                      (westwood, all-other-crimes)\n",
      "whittier                       (whittier, public-disorder)\n",
      "windsor                              (windsor, auto-theft)\n",
      "Name: offense_category_id, Length: 78, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#To find the most common crime per neighbourhood\n",
    "most_common_crime=df.groupby('neighborhood_id')['offense_category_id'].value_counts().groupby('neighborhood_id').idxmax()\n",
    "print(most_common_crime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17    22428\n",
      "18    21870\n",
      "0     21740\n",
      "16    21662\n",
      "12    21476\n",
      "22    21383\n",
      "20    21107\n",
      "21    19850\n",
      "15    19483\n",
      "19    19472\n",
      "23    17184\n",
      "14    17012\n",
      "8     15915\n",
      "13    15697\n",
      "10    13929\n",
      "11    13748\n",
      "1     13728\n",
      "9     13024\n",
      "2     11627\n",
      "7     10916\n",
      "3      9318\n",
      "6      8692\n",
      "4      7781\n",
      "5      7089\n",
      "Name: first_occ_hour, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#To create a new column that shows the hour of the first occurrence of the crime\n",
    "df['first_occ_hour']=df['first_occurrence_date'].dt.hour\n",
    "print(df['first_occ_hour'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Crime frequencies of Denver neighbourhoods')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y5s6di7lz59Ybi7+/Pz744AM8+uijaNeuHTp06IAhQ4Zg4sSJ6NSp0z0f499//0X37t1rXR8eHs4f79ChQ4PiNkZxcTGAO38kNPT9ZcxrztUHbdy4EQMHDgQAbNy4EZ07d0a7du0ANOw15jT1M8f9wudi5RLdSZMm1XsfhYWFBokCh0t07j4j1c3Nrc7xxsTDadOmTa2Ennvdrl+/Dh8fH7O9f/z9/Q0SzYbEz70mdX0HhIeHY9euXSgpKUFRURFKS0vrHafT6ZCWlobIyMh6n+fdt+3bty/GjBmD999/H8uWLUO/fv0wcuRIPPnkk3TGYz0oaSKNolQq4efnZ1CcaoyGnmVW11/I9f3VzBjj/63T6dCxY0csXbq0zrGBgYENiqOmup6DTqfDoEGD8Oabb9Z5G+7LuyG4Yua7abXaWo/t5eWFH3/8sc77ufsXuzGvX2Nwswuvv/56rZk4DvfLsk+fPrh69Sp+++037N69G1999RWWLVuGNWvWGMwuWopz585BIpHwCUhD31/GvOZyuRwjR47Eli1bsHr1amRnZ+PQoUP4v//7P35MQ15jjik+czVj5WJYvHgxOnfuXOfYu/8oaApzvV/vpb7Z1Ls/e5x7vcZCxG8skUiEn3/+GUeOHMEff/yBXbt24bnnnsOSJUtw5MgRk/4cbQUlTaTRhg8fjrVr1yIhIQExMTFCh2MgNDQUp0+fxsCBAxt95lRDH6+4uBixsbH3HBccHIw9e/aguLjY4AspJSWl1lhXV1eD5RvO3csSoaGh+Ouvv9CzZ0+TtT4IDQ1tcELMLRnY2dnd93UA9LMLzz77LJ599lkUFxejT58+mD9//j2TpuDg4Dpfq4sXL/LHTe3GjRs4cOAAYmJi+Jkmc72/xo4di2+//RZ79uxBcnIyGGP80hzQ8NfYHLhlRqVS2eAYuJ/PlStXDGbA8vLyas0cNRQ3C1fz53Hp0iUA4M82Nfb9w80GFRQUGIy7+7NnCtxj1heXh4cHHB0doVAo4ODgUO84sVjMJ+vBwcF1Ln3XdVsA6NGjB3r06IGPPvoI69evx4QJE7BhwwaL/ANGaFTTRBrtzTffhKOjI55//nlkZ2fXOn716lUsX75cgMiAJ554Aunp6fjyyy9rHSsrK0NJSYnJHy8hIQG7du2qdaygoABVVVUAgIcffhhVVVX4/PPP+eNarRYrVqyodbvQ0FBcvHjR4PTq06dP1zqt+4knnoBWq8WCBQtq3UdVVVWtL35jjBkzBqdPn8aWLVtqHavvL2QvLy/069cPX3zxBTIzM2sdr/k8ai6lAvqZiTZt2ty3HcTDDz+MY8eOISEhgb+upKQEa9euRatWrRpUL2eM/Px8jB8/HlqtFu+88w5/vbneX7GxsXBzc8PGjRuxceNGdOvWzSC5aMhrbC7R0dEIDQ3Fp59+yi9bGhvDwIEDIZVKDd7/ALBy5comx5WRkWHwflWr1fjuu+/QuXNn+Pj4ADD+/cMlhlwtIqD/nJpjZwBfX1907twZ3377rcFn9dy5c9i9ezffG0wikWDw4MH47bffDFohZGdnY/369ejVqxdfNvHwww/jyJEjOHbsGD8uNze31mz07du3a32eudnD+30WWyqaaSKNFhoaivXr12Ps2LEIDw836Ah++PBhbN68uc6+K81h4sSJ2LRpE1566SXs27cPPXv2hFarxcWLF7Fp0ya+z5KpvPHGG/j9998xfPhwPPPMM4iOjkZJSQnOnj2Ln3/+GdevX4eHhwdGjBiBnj174q233sL169f5XjKFhYW17vO5557D0qVLERcXh8mTJyMnJwdr1qxBZGQkX/AJ6OsSXnzxRSxcuBBJSUkYPHgw7OzscPnyZWzevBnLly/HY4891uDn8/PPP+Pxxx/Hc889h+joaOTn5+P333/HmjVrEBUVVeftVq1ahV69eqFjx46YMmUKWrdujezsbCQkJODmzZs4ffo0AH0xer9+/RAdHQ03NzecOHECP//8M6ZNm3bPuN566y389NNPGDp0KP7zn//Azc0N3377LVJTU/HLL79ALG7834GXLl3CDz/8AMYY1Go1Tp8+jc2bN6O4uBhLly412IbFXO8vOzs7jB49Ghs2bEBJSUmd+5YZ+xqbi1gsxldffYWhQ4ciMjISzz77LPz9/ZGeno59+/ZBqVTijz/+qPO23t7eePXVV7FkyRI88sgjGDJkCE6fPo0///wTHh4eTZq1a9euHSZPnozjx4/D29sb//vf/5CdnY1vvvmGH2Ps+ycyMhI9evTAnDlzkJ+fDzc3N2zYsIH/48fUFi9ejKFDhyImJgaTJ09GWVkZVqxYAZVKZdC/7sMPP0R8fDx69eqFV155BVKpFF988QUqKiqwaNEiftybb77Jbx306quvwtHREWvXrkVwcDDOnDnDj+M63o8aNQqhoaEoKirCl19+CaVSafWNXM1GgDP2iI25dOkSmzJlCmvVqhWTyWTM2dmZ9ezZk61YscLgtGjc47Ru1NNy4O5T/idNmsQcHR1r3b5v374GvWwYY6yyspJ98sknLDIyksnlcubq6sqio6PZ+++/z/fbqc+9Wg7Ud6p8UVERmzNnDmvTpg2TyWTMw8ODPfTQQ+zTTz816MmSl5fHJk6cyJRKJVOpVGzixIns1KlTtU7fZ4yxH374gbVu3ZrJZDLWuXNntmvXrjr7NDHG2Nq1a1l0dDSzt7dnzs7OrGPHjuzNN99kGRkZ942/rvYGeXl5bNq0aczf35/JZDIWEBDAJk2axPcWqqvlAGP6PklPP/008/HxYXZ2dszf358NHz6c/fzzz/yYDz/8kHXr1o25uLgwe3t7FhYWxj766COD16k+V69eZY899hhzcXFhCoWCdevWjW3btq3WuHu93+oay13EYjFzcXFhXbp0Ya+++mqdLTIYM/79VV8cwcHBBqfYc+Lj4xkAJhKJWFpaWr2vwf1eY+60eWNPJ+dOp9+8ebPB9fX9nE+dOsVGjx7N3N3dmVwuZ8HBweyJJ55ge/bsqRVDzVP3q6qq2Ny5c5mPjw+zt7dnAwYMYMnJyczd3Z299NJL942/rtP+uff1rl27WKdOnZhcLmdhYWG1ngv32hnz/rl69SqLjY1lcrmc72vG/Wzubjlw93dPzddt8eLFtY7d/X3HGGN//fUX69mzJ7O3t2dKpZKNGDGCXbhwodZtT548yeLi4piTkxNzcHBg/fv3Z4cPH6417syZM6xv375MoVAwf39/tmDBAvb1118b/DxOnjzJxo8fz4KCgphcLmdeXl5s+PDh7MSJE7Xuj+iJGLOAajRCWrjr168jJCQE33zzjWCzc4QIpaCgAK6urvjwww8NlkEJsTRU00QIIaTZlJWV1brus88+A4A6tw0ixJJQTRMhhJBms3HjRqxbtw4PP/wwnJyc8M8//+Cnn37C4MGD0bNnT6HDI+SeKGkihBDSbDp16gSpVIpFixZBrVbzxeEffvih0KERcl9U00QIIYQQYgSqaSKEEEIIMQIlTYQQQgghRqCaJhPR6XTIyMiAs7Nzs2zbQQghhJCmY4yhqKgIfn5+922QS0mTiWRkZDRpE1hCCCGECCctLQ0BAQH3HENJk4lwG3mmpaXx+/8QQgghxLKp1WoEBgbyv8fvhZImE+GW5JRKJSVNhBBCiJUxprSGCsEJIYQQQoxASRMhhBBCiBEoaSKEEEIIMQIlTYQQQgghRqCkiRBCCCHECJQ0EUIIIYQYgZImQgghhBAjUNJECCGEEGIESpoIIYQQQoxASRMhhBBCiBEoaSKEEEIIMQIlTYQQQgghRqCkiRBCiE2p0upQrtEKHQaxQZQ0EUIIsRlXc4sxeNlBRC+Ix9f/pKJKqxM6JGJDKGkihBBiExKu5mH06sO4dqsEJZVaLNh2ASNXH8KZmwVCh0ZsBCVNhBBCrN7PiTfx9P+OorBMgy5BLpg3IgJKhRTn0tUYueoQ5v9+HkXlGqHDJFaOkiZCCCFWS6dj+HRXCl7ffBoaLcPwTr74aUoPPNszBHte64dHO/tBx4B1h69j0NKD2HkuE4wxocMmVkrE6N1jEmq1GiqVCoWFhVAqlUKHQwghNq9co8Xrm09j25lMAMDU/qF4bVB7iMUig3EHL+Vi7m/n8G9eKQAgNtwL7z/aAf4u9s0eM7E8Dfn9TUmTiVDSRAghzSevuAJTvjuBkzcKIBWL8H+jO+KJBwPrHV+u0WLl3iv44uBVaLQMDjIJZsa2w7M9W0EqoUWXlqwhv78FfacsXLgQXbt2hbOzM7y8vDBy5EikpKTwx/Pz8zF9+nS0b98e9vb2CAoKwn/+8x8UFhYa3M+NGzcwbNgwODg4wMvLC2+88QaqqqoMxuzfvx8PPPAA5HI52rRpg3Xr1tWKZ9WqVWjVqhUUCgW6d++OY8eOmeV5E0IIabwrOUUYufoQTt4ogFIhxXeTu90zYQIAhZ0Er8e1x47/9EbXVq4ordTiox3JeGTlISSlFTRP4MTqCZo0HThwAFOnTsWRI0cQHx8PjUaDwYMHo6SkBACQkZGBjIwMfPrppzh37hzWrVuHnTt3YvLkyfx9aLVaDBs2DJWVlTh8+DC+/fZbrFu3Du+99x4/JjU1FcOGDUP//v2RlJSEGTNm4Pnnn8euXbv4MRs3bsSsWbMwb948nDx5ElFRUYiLi0NOTk7zvSCEEELu6fCVWxi1+jDS8ssQ5OaAX1/piYdCPYy+fVtvZ2x8IQafjOkIlb0dLmSqMWr1Ibz32zmoqVCc3IdFLc/l5ubCy8sLBw4cQJ8+feocs3nzZjz11FMoKSmBVCrFn3/+ieHDhyMjIwPe3t4AgDVr1mD27NnIzc2FTCbD7NmzsX37dpw7d46/n3HjxqGgoAA7d+4EAHTv3h1du3bFypUrAQA6nQ6BgYGYPn063nrrrfvGTstzhBBiXpuOp+HtLWdRpWOIDnbF2onRcHeSN/r+bhVX4P+2J+PXU+kAAC9nOeY/EomhHXwgEonuc2tiK6xmee5u3LKbm5vbPccolUpIpVIAQEJCAjp27MgnTAAQFxcHtVqN8+fP82NiY2MN7icuLg4JCQkAgMrKSiQmJhqMEYvFiI2N5cfcraKiAmq12uBCCCHE9HQ6hkU7L+LNX86gSscwIsoPPz7fvUkJEwB4OMmxdGxnrH++O0I8HJFTVIFXfjyJ59YdR1p+qYmiJ7bEYpImnU6HGTNmoGfPnujQoUOdY27duoUFCxbghRde4K/LysoySJgA8P/Pysq65xi1Wo2ysjLcunULWq22zjHcfdxt4cKFUKlU/CUw8N7r6YQQQhquXKPF9J9OYfX+qwCA6QPaYPnYzlDYSUz2GA+18cCfr/bGfwa2hUwixr6UXAxadgBrDlyFhjqKkxosJmmaOnUqzp07hw0bNtR5XK1WY9iwYYiIiMD8+fObN7g6zJkzB4WFhfwlLS1N6JAIIcSm3CquwPgvj2D72UzYSUT49PEovDa4dksBU1DYSTBrUDvseLU3uoe4oVyjw8d/XsSIFf8g8d/bJn88Yp0sImmaNm0atm3bhn379iEgIKDW8aKiIgwZMgTOzs7YsmUL7Ozs+GM+Pj7Izs42GM/938fH555jlEol7O3t4eHhAYlEUucY7j7uJpfLoVQqDS6EEEJM43J2EUauOoRTNwqgsrfD95O747Ho2r8fTK2NlxM2vNADnz4eBVcHO1zMKsJjaw5j3aFUsz82sXyCJk2MMUybNg1btmzB3r17ERISUmuMWq3G4MGDIZPJ8Pvvv0OhUBgcj4mJwdmzZw3OcouPj4dSqURERAQ/Zs+ePQa3i4+PR0xMDABAJpMhOjraYIxOp8OePXv4MYQQQprHP5dvYfTnh3HzdhmC3R3w6ysPoUdr92Z7fJFIhMeiA7DntX4Y3cUfjAGf7bmMiipts8VALJOgSdPUqVPxww8/YP369XB2dkZWVhaysrJQVlYG4E7CVFJSgq+//hpqtZofo9Xq37yDBw9GREQEJk6ciNOnT2PXrl149913MXXqVMjl+iLBl156CdeuXcObb76JixcvYvXq1di0aRNmzpzJxzJr1ix8+eWX+Pbbb5GcnIyXX34ZJSUlePbZZ5v/hblLRZUWuUUVQodBCCFmt+HYDTzzzTEUlVehaytXbHmlJ0I9nQSJxc1RhsWPR8FHqUBBqQZ7k6kFTYvHBASgzss333zDGGNs37599Y5JTU3l7+f69ets6NChzN7ennl4eLDXXnuNaTQag8fat28f69y5M5PJZKx169b8Y9S0YsUKFhQUxGQyGevWrRs7cuSI0c+lsLCQAWCFhYWNeSnq9felXBY8exuLW3bApPdLCCGWRKvVsYU7klnw7G0sePY29p+fTrJyTZXQYTHGGPv4T31ck9cdEzoUYgYN+f1tUX2arJm5+jSlZBUh7rODcHGwQ9J7g012v4QQYinKNVrM2pSEHWf1Zyu/OrAtZsS2tZheSVdyihG79AAkYhGOzBkIT+emtToglsVq+zSR2nxd9DVcBaUalFXSejohxPYs++sSdpzNgp1EhKVPRGHmoHYWkzAB+uLwzoEu0OoYfktKFzocIiBKmiycs1wKR5m+H0lmYZnA0RBCiGmVa7TYcEzfsmXpE50x+gHznyHXGGOqz9z7OfEmaIGm5aKkycKJRCL4utgDADILywWOhhBCTGvbmUwUlmng72KPhzv6Ch1OvR7p5AeZRIyLWUU4n0E7QLRUlDRZAV+Vfokuo4BmmgixZPkllUinz2mD/HDkXwDAk92DIDFD00pTUTnYYVCkfteIX07eFDgaIhRKmqwAlzRl0UwTIRZtzOeHMWjpARSWaoQOxSqcSy9EUloB7CQiPPGg5W9F9Vj10uFvSRmorKLtVVoiSpqsgK9KvzyXQUkTIRarqFyD1FslKK3UIjmLlm+M8eNR/SzTkA6+VnFGWu+2HvB0liO/pBL7U6hn0/0UV1TZXI9BSpqswJ2ZJpr2J8RS1aw5vJZbImAk1kFdrsHWUxkAgKe6BwkcjXGkEjFGdfEHoC8IJ/Ur12gxctUh9Fu8z6YSJ0qarAAVghNi+WrWMl3NLRYwEuuw5WQ6yjRatPN2QrcQN6HDMdqY6iW6vRdzkFdsO8mAqX158Bqu5BSjpFKLo6l5QodjMpQ0WQEqBCfE8mVQ0mQ0xhi+ry4An9A92KJ6Mt1Pex9ndPRXoUrH8PvpDKHDsUgZBWVYvf8q//+kGwXCBWNilDRZAS5pUpdXoaSiSuBoCCF1ySyg5TljHU3Nx5WcYtjbSTDqAX+hw2mwx6p7NtFZdHVb+OdFlGm0sLfT9xg8fbNA2IBMiJImK+CssIOzXAqAlugIsVQ1Z5rSbpeiXEMd/OvDtRkY2cUPSoWdwNE03CNRfrCTiHAuXY3kTCr6r+lYaj7+OJ0BkQj45LFOAICz6YXQaG3jbENKmqyED7UdIMSi1axpYgy4nkezTXXJLarArvP6Peae6hEscDSN4+oow8Cw6p5NAheEF5ZqUFppGSsQWh3D/N/PAwDGdQ3C8I6+cFZIUa7R4VJ2kcDRmQYlTVaCKwbPoDPoCLFI3CywTKL/WqUlurptOpEGjZahS5ALIv1UQofTaNy2KluTMgSbRckoKEOfxfswdPnfKCwTvjfYhuM3cCFTDWeFFK8PbgexWITOgS4AgKS0AkFjMxVKmqyEr1I/01SzboIQYhl0OsbvDRkd7AoAuJpDxeB30+oY1h+9AQB4qrt1zjJx+rX3hLujDLeKK3DwUq4gMSyNv4TCMg3+zSvFO1vOCronXmGpBp/uSgEAzBrUDu5O+r5bXNJ0mpIm0px8XaqX59Q000SIpblVXAGNlkEsAh4KdQcAXLtFM01325+Sg/SCMrg42GFYJ8vdZ84YdhIxHu2sL2IXoiD8Qoaaf1yJWIRtZzKx5VR6s8fBWfbXJdwu1aCtl5PBsmtUgAsAmmkizcyP6wpOM02EWByuW7+3UoF2Ps4AqO1AXbg2A49HB0BRfWaVNePOovvrQg4KSiub9bE/3nkRjAHDOvliZmxbAMB7v53HjbzSZo0DAFKyivif7bwRkbCT3Ektoqpnmi7nFKOoXPglxKaipMlKUCE4IZaLO3POz8UeoZ6OAPTLc0Iul1iatPxSHKhexnrSypfmOBF+SkT4KlGp1eGPZuzZ9PflXBy8lAs7iQhvxrXHy/3aoFsrNxRXVGHGxlOoasYaK8YY3v/jPLQ6hrhIb/Rq62Fw3NNZDn8XezCmP4vO2lHSZCX8qpfnqBCcEMvDJU2+KgWC3BwhEYtQUqlFjg1tH9FUPx69Acb0+7eFeDgKHY7JcAXhzbWtik7HsHDHRQD6sw+D3fXvt6Vjo+CskOLkjQKs2HulWWIBgF3ns3D4ah5kUjHeHRZR55jOQS4AbGOJjpImK+FTvTxXVF6FYmpwSYhF4ZbN/V3sIZOKEeTmAICKwTkVVVpsOpEGQN8B3JY82tkPUrEIp28W4nIznFb/2+l0/RlqcimmD2jLXx/g6oCPRnUEAKzYexmJ/+abPZZyjRYLtiUDAF7s0xqB1e/7u3WurmuyhWJwSpqshJNcCmeFvsElbdxLiGWpuTwH4M4SHdU1AQB2nstCfkklfJQKxIZ7CR2OSXk4ydGvvf45/WzmgvByjRaf7roEAHi5fyjcHGUGxx+J8sPoLv7QMeDVDUlmryFae/Aa0gvK4KtS4OV+ofWOo5kmIggqBifEMnHtBrgtj0I9nQAAV6lXE4A7HcDHdwuCVGJ7v3a4gvAtJ9PNWk/0XcJ1Pkl5rmdInWPefzQSgW72uHm7DO/9dt5ssej3l9MvA855OBwOMmm9Yzv4qSARi5CtrrD6ulzbe/faMCoGJ8QypVf/IcPNNLWmmSbexSw1jl+/DYlYhHHdAoUOxywGhHnB1cEOOUUV+OfKLbM8RkFpJVZW1yrNGtSu3rMPnRV2+GxsF0jEImw5lY7fkszThuD/diSjXKNDt1ZuGHGf9hH2Mgnae+vPKk1Ku22WeJoLJU1WhIrBCbE8FVVa3CrWF3z788tz+pkm6gp+Z5ZpcIQ3vKub9NoamfROzyZzFYSv3HsF6vIqhPk4Y/QDAfccGx3siukD2gAA3t1yDmn5pm1DcPRaHradyYRYBMx7JAIikei+t+GW6E5Z+RIdJU1WxEep/0KmmSZCLAf3eVTYieHioN98lkua0gvKUFbZcjfuLa6owpaT+pkOa91nzlhjqhOZ3ReyTb6lSVp+Kb5L0Cefbw0Ng0R8/yRlWv82eCDIBUUVVZi1Kclky4ZaHcP8Py4A0C+3GrsVjq0Ug1PSZEV8+ZkmSpoIsRTpNYrAub+4XR1lcK1OoK7darlLdFtPpaOkUovWHo58p3Rb1cFfifbezqis0mHbGdP2bPp0dwoqtTr0auOBvu08jbqNVCLG8nFd4CSX4vj12/h8/1WTxPLTsRtIzlRDqZDitcHtjb4dN9N09mYhtDrr7V9GSZMV4QrBMwtoeY4QS8GdmMF9PjktvRicMcYvzT3ZPcioJRxrJhKJMCa6elsVEy7Rnb1ZiN+S9EnYW0PDGvQ6Bro5YMHISADAZ3su49SNptUTFZRW4tPdd/aXu/vsvXsJ9XSCo0yCkkotrlhxKw5KmqwIFYITYnky+Zkmw3qdO3VN1vsLoilO3riNi1lFUNiJ8Xi0bRaA321kZ39IxCKcvFFgkpMAGGP4vx36Pkijuvijg79xS2F3x/RIlB+0OoYZG5Oa1OdvWfwlFJRq0M7bqcHLrRKxCJ34feistxickiYrwp3OXFRRZRN7+BBiC7gTM7gz5zh3zqBrmTNNPxy5AQAY0ckPquqlSlvnpVSgT/U2Ir+aoGfT/ku5SLiWB5lEjNcGt2vUfYhEIiwY2QH+Lvb4N68U839vXBuCi1lq/HBU/zOdPyKyUa0juH3oktKsdzsVSpqsiKNcCiXf4JJmmwixBOn3W56z4qWIxsovqcT2M5kAbL8A/G6PVc+q/XoyvUm1O1odw8fV26U807MVAlzr7rZtDJW9HZaN7QyxSH92X0NrrhhjeP/3C9DqGIZ28MFDbTzuf6M6dOaTpoJG3d4SUNJkZbi/ZqkYnBDLkFlQ90xTqJc+aUq9VQKdFRe+NsamE2mo1OrQ0V/Fzy60FAPDvaCyt0NmYTkSruY1+n5+SbyJlOwiqOztMLVfmybH1S3EDVP76+/n7V/P8l3sjbHzXBYSruVBLhXj7YfDGx0DlzSlZKlRWmmd24FR0mRluCU6KgYnRHiMsRpbqBjWNAW62sNOIkKZRotMdcv5I0enY1hfvYzzVI8ggaNpfgo7CUZE6Zs9/pyY1qj7KKvUYkm8vuB6+oA2Jlve/M/AtogKdIG6vAozNyYZNRNWrtHiw+3V+8v1Da13fzlj+KgU8FEqoGPAuXR1o+9HSJQ0WRlu495MmmkiRHDqsiqUVPdh8r1reU4qESPYXV/X1JKKwQ9ezsWN/FI4K6QYEeUndDiC4Jbodp7PalT96f8OpSJbXYEAV3tMjDHd8qadRIzlYzvDUSbB0dR8fHHw/m0Ivjig31/OT6XAy33r31/OWHeW6KyzGJySJivjx800UVdwQgTHFYG7OcpgL6u9rQW/cW8LqmviCsDHPBBwz/3IbFlUgAqhno4o1+iw42xmg26bV1zB91R6I6495NK6t0tprFYejpj/iL4NwdLdl3DmZkG9Y9MLyvD5Af3WLW8PC6/zPd5Q3HLtaSstBhc0aVq4cCG6du0KZ2dneHl5YeTIkUhJSTEYU15ejqlTp8Ld3R1OTk4YM2YMsrOzDcbcuHEDw4YNg4ODA7y8vPDGG2+gqspwvXT//v144IEHIJfL0aZNG6xbt65WPKtWrUKrVq2gUCjQvXt3HDt2zOTPual8+KSJZpoIEVp9S3Oc1i2sV1N6QRn2XtR/P7e0AvCaRCIRP9vU0G1VVuy9guKKKnTwV2JEJ/PM1D0WHYBhHX1RpWN4dUNSvfVF3P5y3UPcMKzjvfeXM5a1F4MLmjQdOHAAU6dOxZEjRxAfHw+NRoPBgwejpOTOF8zMmTPxxx9/YPPmzThw4AAyMjIwevRo/rhWq8WwYcNQWVmJw4cP49tvv8W6devw3nvv8WNSU1MxbNgw9O/fH0lJSZgxYwaef/557Nq1ix+zceNGzJo1C/PmzcPJkycRFRWFuLg45OTkNM+LYSSu2JSSJkKExyVNdy/NcfheTS2kK/iGYzegY0BMa3e0qS6Eb6lGdfGHWAQcv34b128ZlzRfv1XCNwR9e2g4xEZsl9IYIpEI/zeqI3xVCqTeKsEH1dui1JRwNQ/buf3lRkSarDlpxwAVRCJ9gp1TZH2/xwRNmnbu3IlnnnkGkZGRiIqKwrp163Djxg0kJiYCAAoLC/H1119j6dKlGDBgAKKjo/HNN9/g8OHDOHLkCABg9+7duHDhAn744Qd07twZQ4cOxYIFC7Bq1SpUVlYCANasWYOQkBAsWbIE4eHhmDZtGh577DEsW7aMj2Xp0qWYMmUKnn32WURERGDNmjVwcHDA//73v+Z/Ye6hZiE4Yy3rjBxCLA13Fqu/S91JE9+rKcf2Z5oqq3TYcFxf+NySZ5k4PioFerXVb3libM+mxbtSUKVj6Nfes9Gn9RtL5WCHpU90hkgEbDiehp3n7iwjVml1eP8PfT+nJ7sHIcJPabLHdZJL0c7LGYB1LtFZVE1TYaH+BXRzcwMAJCYmQqPRIDY2lh8TFhaGoKAgJCQkAAASEhLQsWNHeHt782Pi4uKgVqtx/vx5fkzN++DGcPdRWVmJxMREgzFisRixsbH8mLtVVFRArVYbXJoD9xdtSaUWRU3o7EoIabr7Lc+FeuhnW7LU5U3qxGwNdl/IQm5RBTyd5Rgc6X3/G7QAYx6o3lblZPp9206cvHEb28/qZ3beGhrWHOEhJtQdL1UXd7/161m+/99Px9NwMUvf7uC1QcbvL2esqEB9Z3NrLAa3mKRJp9NhxowZ6NmzJzp06AAAyMrKgkwmg4uLi8FYb29vZGVl8WNqJkzcce7Yvcao1WqUlZXh1q1b0Gq1dY7h7uNuCxcuhEql4i+Bgc2zTYC9TMLvpJ5ZYH1Tm4TYkvstz6kc7ODhJAcApNp4XRO3rDSuayDsGtEt2hbFRfrAWS5FekEZjqTW37OJMYaF1dulPBYdgDAf083s3M/M2Hbo6K9CQakGr21OQn5JJZZU7y/32uB2cG3A/nLG6hzoCoBmmppk6tSpOHfuHDZs2CB0KEaZM2cOCgsL+UtaWuP6cTSGj5LOoCPEEvCb9dazPAfU3E7FduuaruQU4ci1fIhFwPhuLa83U30UdhIMr+7Z9Etier3j4i9k4/j121DYiTFzUOO2S2ksmVSMz8Z1hr2dBIeu5GH06kMoKNUgzMcZT5rpZ8nNNJ1OK7C6xq8WkTRNmzYN27Ztw759+xAQEMBf7+Pjg8rKShQUFBiMz87Oho+PDz/m7rPpuP/fb4xSqYS9vT08PDwgkUjqHMPdx93kcjmUSqXBpblQMTghwtPqGLLU965pAmpsp2LDSRPXZmBAmPc9E8iW6LFo/e+0P89loqSOJdoqrQ4f79RvlzK5V0i9s5bmFOrphHkjIgAA1/NKAeiLvxuzv5wx2ns7w95OgqKKKlwzskjeUgiaNDHGMG3aNGzZsgV79+5FSEiIwfHo6GjY2dlhz549/HUpKSm4ceMGYmJiAAAxMTE4e/aswVlu8fHxUCqViIiI4MfUvA9uDHcfMpkM0dHRBmN0Oh327NnDj7Ek1BWcEOHlFlVAq2OQikXwdJbXO47r1XTNRpfnSiur8Et1obMpGzHaigeCXBHi4YjSSi3+PFe73GPjiTRcyy2Bm6MML5qgeWRjje0aiLjqWrRhHX0RE+putseSSsTo6M/VNRWY7XHMQdCkaerUqfjhhx+wfv16ODs7IysrC1lZWSgr0ycDKpUKkydPxqxZs7Bv3z4kJibi2WefRUxMDHr06AEAGDx4MCIiIjBx4kScPn0au3btwrvvvoupU6dCLtd/kb300ku4du0a3nzzTVy8eBGrV6/Gpk2bMHPmTD6WWbNm4csvv8S3336L5ORkvPzyyygpKcGzzz7b/C/MffhSryZCBJde/UeLt1IByT1ODbf1maY/TmegqLwKwe4O6G3mM76skUgk4gvC795WpaSiCsviLwMA/jOgDZQK02yX0hgikQifje2CZWOjsOixTmZ/vJpLdNZE0Hatn3/+OQCgX79+Btd/8803eOaZZwAAy5Ytg1gsxpgxY1BRUYG4uDisXr2aHyuRSLBt2za8/PLLiImJgaOjIyZNmoQPPviAHxMSEoLt27dj5syZWL58OQICAvDVV18hLi6OHzN27Fjk5ubivffeQ1ZWFjp37oydO3fWKg63BL60lQohgrvfmXOcO72aSqDVsXsmWNaGMYbvqwvAn+wWZLa+QtZu1AMBWBJ/CUeu5SMtv5Tfv+3Lv6/hVnEFgt0d8GR34Wfp7GUSjOoScP+BJqAvBk+1upkmQZMmY/oMKRQKrFq1CqtWrap3THBwMHbs2HHP++nXrx9OnTp1zzHTpk3DtGnT7huT0HxpKxVCBMd9/u5Xw+Pvag+ZVIzKKh0yCsqatOGppTl9sxDn0tWQScV4/MHmOYPYGvm72OOhUHccupKHX0+m49XYtsgpKsfag9cAAG/GhUEmtYgS42bDzTQlZ6pRrtFCYWfa7WLMpWX9lGyEb41CcGpwSYgwjDlzDgAkYhFCqjfuvWJjS3Rcm4FhHX3hZoZT020JVxD+y8mbYIzhs78uo7RSi86BLni4Y90nHNkyfxd7eDjJUaVjOJ/RPH0OTYGSJivEzTSVVmqhLrPthnmEWCquponbRPteQr1srxi8oLQSf5zOAAA81YPaDNxPXKQPHGUS3MgvxU/H0rCxunv62w+Hm2yLEmsiEonQOdD6isEpabJCCjsJXLkGl2paoiNECMYuzwG2WQz+c+JNVFTpEObjjAeCXIUOx+I5yKQY1knfs+ndrWeh1TEMivBGtxA3gSMTDrd5rzUVg1PSZKX4YnDqCk6IIIxdngNq7kFnG0kTYwzrj+p7Mz3VI7hFzpQ0xpgH9Et0OqZftp09pHm2S7FUUdVJE800EbOjtgOECKesUov8Ev2G4H5GNCOseQadLTiWmo9rt0rgKJNgZBd/ocOxGl1buSGo+kSAsV0D0cbLSeCIhNUpwAUAcCO/lP88WTpKmqyUrwudQUeIULjPnaNMAqX9/U9CDvHQzzTlFlWgsExj1tiaw5ZT+i1BHu7oCye5oCdhWxWxWISPR3fEk92D8Gac6TfCtTYqezu++au1LNFR0mSluOW5DFqeI6TZ1VyaM2ZpyllhB2+lvtnuNSuvayrXaLH9bCYAYNQDNMvUUA+18cD/jeoIFwc62xC4s0R3ipImYk7c8lwWFYIT0uy4xpa+DdhnjV+is/Iz6PZezEFReRV8VQr0CDHfVhukZehiZcXglDRZKSoEJ0Q4GdXLc/736QZeE18MbuUzTb+e1C/NPdrZnzqAkybjZppO3yywir6DlDRZqZqF4NbwRiPElvBbqDRgR3pbaDuQX1KJ/Sn6zdFH09IcMYEwHyVkUjEKSjX4N69U6HDui5ImK+VTnTSVabQ2UVhKiDXhappa2vLc9jMZqNIxRPop0c7bWehwiA2QScWI9FMCsI7WA5Q0WSmFnQTu1dsWUDE4Ic0ro9C4zXpr4pbnrueVoEqrM0tc5vZr9Vlzo6jNADGhzlbUr4mSJivmQ8XghDQ7xhi/POffgJkmP5U9FHZiaLQMabet7zObeqsEp24UQCwCHonyEzocYkMoaSLNgtoOENL8bpdqUK7RzxT5GLHvHEcsFqG1B7dEZ311TVurZ5l6tfWEl9L4503I/XBJ04UMNSqqtMIGcx+UNFkxvu0AdQUnpNlws0weTnLIpZIG3dZaz6BjjGFrkj5pGk1Lc8TEgtwc4Opgh0qtDhczi4QO554oabJiXFfwDOoKTkiz4c+ca0A9E8dai8FP3riNf/NK4SCTYHCkt9DhEBsjEomsZh86SpqsmB/1aiKk2XH7PTak3QAn1Ms62w5w26YMifSBg4y2TSGm19lKmlxS0mTF7hSCU9JESHO5M9PU8KSptQe3PGc9M02VVTpsO0PbphDzopkmYnZ+fCF4GTW4JKSZpDdheY6racovqcRtK9nVfV9KDgpKNfByluOhUA+hwyE2qnOACwDg2q0SFJZabu9BSpqsmLdKvwFoRZUOBRb8JiPElvDLc42YaXKQSfk2BdduWccS3RZ+2xQ/SGjbFGImro4yBLs7ANBvqWKpKGmyYnKpBB5O1Q0uqRickGbRlOU5oMYZdDmWv0RXWKrB3ov6bVNGdQkQOBpi66yhXxMlTVaONu4lpPlUaXXIVnOF4I3rVcTvQWcFM03bz2aiUqtDmI8zIqq3uiDEXKKql+gsuRickiYrxxWDZ1IxOCFml11UAR0D7CQieDjJG3Uf1jTTtOXUTQC0bQppHp2DXADoZ5ostU6XkiYrx/21m1lAy3OEmBu3NOersoe4kfU9d3o1WfZMU1p+KY5fvw2RCHikM22bQswvwlcJO4kIeSWVuGmhWw1R0mTlfKqX56grOCHmdydpavw2IlzSdCO/FBoL3riX2zbloVB3vgyAEHNS2EkQ7qtfBrbUuiZKmqycH3UFJ6TZcPs8NmSj3rt5K+VwlElQpWP4N6/UVKGZFGOMb2hJBeCkOVl6MTglTVaOLwSnmSZCzK6pZ84B+i0jWntadmfw0zcLce1WCRR2Ygzp4CN0OKQFsfRicEqarBy3TJBZWG6xhXOE2Ap+ea4RjS1rCq0uBrfUPei4pbnBET5wktO2KaT5cMXgZ9MLLXL5mpImK+et1H95V1bpkG8lHYYJsVYZTWhsWZMlzzRptDr8cToDAG2bQppfiLsjnBVSVFTpkJJVJHQ4tVDSZOVkUjF/6jMt0RFiXtxMU1NqmoAavZosMGk6eCkXeSWV8HCSoXcb2jaFNC+xWGTRdU2UNNkArhickiZCzKekogqFZfrtippy9hwAhHrdWZ6ztGV1rgB8RJQfpBL6FUGaH5c0WWJdE30ibMCduiY6g44Qc+E+X84KKZwVdk26r1bujhCJgMIyDfIsaFldXa5B/IVsAMBoOmuOCIQrBqeZJmIWdAYdIeaXXsBtn9L0nkUKOwkCXKs37rWgYvCdZ7NQUaVDGy8ndPCnbVOIMKKqZ5qu5BajqNyyNqMXNGk6ePAgRowYAT8/P4hEImzdutXgeHFxMaZNm4aAgADY29sjIiICa9asMRhTXl6OqVOnwt3dHU5OThgzZgyys7MNxty4cQPDhg2Dg4MDvLy88MYbb6CqqspgzP79+/HAAw9ALpejTZs2WLdunTmesln4UldwQszuTruBpi3NcSyxrunXGtumiESN63hOSFN5Osvh72IPxoCzNwuFDseAoElTSUkJoqKisGrVqjqPz5o1Czt37sQPP/yA5ORkzJgxA9OmTcPvv//Oj5k5cyb++OMPbN68GQcOHEBGRgZGjx7NH9dqtRg2bBgqKytx+PBhfPvtt1i3bh3ee+89fkxqaiqGDRuG/v37IykpCTNmzMDzzz+PXbt2me/Jm5CPimqaCDG3TBP0aKqptUd10pRjGUlTekEZjlzLBwA8StumEIFxrQdOWdgSnaANOIYOHYqhQ4fWe/zw4cOYNGkS+vXrBwB44YUX8MUXX+DYsWN45JFHUFhYiK+//hrr16/HgAEDAADffPMNwsPDceTIEfTo0QO7d+/GhQsX8Ndff8Hb2xudO3fGggULMHv2bMyfPx8ymQxr1qxBSEgIlixZAgAIDw/HP//8g2XLliEuLs7sr0NTcV/ilDQRYj788pyJkia+GPyWZSzP/ZakLwDvHuKGAFcHgaMhLV3nABdsP5NpccXgFl3T9NBDD+H3339Heno6GGPYt28fLl26hMGDBwMAEhMTodFoEBsby98mLCwMQUFBSEhIAAAkJCSgY8eO8Pb25sfExcVBrVbj/Pnz/Jia98GN4e6jLhUVFVCr1QYXoXDLc1mF5dDpLOtMHEJshamX5/iZJgtYnmOMYctJfdI0mnozEQvAzTQlpRVY1BmmFp00rVixAhEREQgICIBMJsOQIUOwatUq9OnTBwCQlZUFmUwGFxcXg9t5e3sjKyuLH1MzYeKOc8fuNUatVqOsrO46oYULF0KlUvGXwMDAJj/fxvJWKiASAZVaHfJLLedMHEJsCXf2nCkKwYE7M01p+aWoqNKa5D4b63yGGpdziiGTijGkg6+gsRACAB38VJCIRcgpqkCW2nJWUSw+aTpy5Ah+//13JCYmYsmSJZg6dSr++usvoUPDnDlzUFhYyF/S0tIEi8VOIoYn1+CywHLeXITYCsaYybqBczyd5HBWSKFjEHzjXq4306Bwb6jsm9ZOgRBTsJdJ0N7bGQCQdKNA2GBqsNikqaysDG+//TaWLl2KESNGoFOnTpg2bRrGjh2LTz/9FADg4+ODyspKFBQUGNw2OzsbPj4+/Ji7z6bj/n+/MUqlEvb2dX9ByuVyKJVKg4uQqFcTIeaTV1KJyiodRKI7Wxc1lcHGvQIWg1dpdfgtqXrblC60NEcsR80lOkthsUmTRqOBRqOBWGwYokQigU6n38QvOjoadnZ22LNnD388JSUFN27cQExMDAAgJiYGZ8+eRU5ODj8mPj4eSqUSERER/Jia98GN4e7DGlCvJkLMh6tn8nKWQyY13dcmt3GvkHVN/1y5hVvFFXB1sEPf9p6CxUHI3TpbYJNLQc+eKy4uxpUrV/j/p6amIikpCW5ubggKCkLfvn3xxhtvwN7eHsHBwThw4AC+++47LF26FACgUqkwefJkzJo1C25ublAqlZg+fTpiYmLQo0cPAMDgwYMRERGBiRMnYtGiRcjKysK7776LqVOnQi7XL2m99NJLWLlyJd58800899xz2Lt3LzZt2oTt27c3/4vSSL60lQohZsMlTb4mqmficL2ahGxwubXGtil2tG0KsSDcTNPZ9EJodQwSsfC9wwRNmk6cOIH+/fvz/581axYAYNKkSVi3bh02bNiAOXPmYMKECcjPz0dwcDA++ugjvPTSS/xtli1bBrFYjDFjxqCiogJxcXFYvXo1f1wikWDbtm14+eWXERMTA0dHR0yaNAkffPABPyYkJATbt2/HzJkzsXz5cgQEBOCrr76yinYDHFqeI8R8MqprBZu6Ue/dhJ5pKqmowq7z+tIEWpojlibU0wmOMglKKrW4nFOEMB/hu9QLmjT169fvnqcS+vj44JtvvrnnfSgUCqxatareBpkAEBwcjB07dtw3llOnTt07YAvGL89RITghJmfqdgOcO13B9Rv3NncX7p3nslCm0SLEw5HfJJUQSyERi9ApwAUJ1/KQdKPAIpImmou1EfxMk5pmmggxtYxC8yzPBbk7QCIWobiiCrlFFSa9b2NwZ83RtinEUnH70J2+WSBoHBxKmmyEb/WyATW4JMT0MkzcDZwjl0oQWL1x75VmXqLLVpfj0NVbAICRnWlpjlgmbgb0lIW0HaCkyUZ4OcshFgEaLUNeCTW4JMSUuOU5U9c0AcIVg/+WlA7GgAeDXRHkTtumEMvEJU2XsotQUlElbDCgpMlm2EnE8HSubnBJxeCEmExllQ65xfqlM18T1zQBQKiXMNup/Fq9bcoo2jaFWDAflQI+SgV0DDiXXih0OJQ02RKu3iKDisEJMZlsdTkYA2RSMdwdZSa//9Ye3Bl0zTfTlJypxsWsIsgkYgzrSNumEMvGzTZZQr8mSppsyJ2Ne2mmiRBTSefOnFMpzFIszc00XWvGmSauN1P/ME+4OJg+ESTElCypGJySJhtCXcEJMb077QZMX88E3KlpSi8oQ1ml+Tfu1eoYtiZxZ80FmP3xCGkqfqbJAorBKWmyIX7UFZwQk8s08Ua9d3NzlMHFwQ6MAam3zL9El3A1D9nqCqjs7dA/jLZNIZavY4AKIhGQUViOHLWwv98oabIhPtQVnBCTq7k8Zy78GXS3zL9Ex/VmGtbJF3KpxOyPR0hTOcmlaOflDED4uiZKmmwIFYITYnrmXp4DahSD55h3pqmsUoud5zIBAKNp2xRiRaICVQAoaSImxBWCZ6upwSUhppJppsaWNTVX24HdF7JQUqlFkJsDooNdzfpYhJhS50D9+1XoYnBKmmwI1+CySsdwq7j5t2QgxBaZa9+5mppreY5bmhtJ26YQKxMVqIKHkxxezub7HBpD0A17iWlJJWJ4KxXILCxHZmE5vJTCvrkIsXbqcg2KqrsQm3rfuZpae95ZntPpGMRi0yc0uUUV+PuyftuUUbQ0R6xMhK8Sx98ZKHiyTzNNNoaKwQkxHW5pzsXBDo5y8/2NGeTmAKlYhDKNFllmOjvo99MZ0OoYOge6IKS6hooQayESiQRPmABKmmyOHxWDE2Iy3NKcOWeZAP02SMHV+7+ZYw+6onIN/vdPKgBgNG2bQkijUdJkY7iZJnP9tUpIS5JRyG3Ua/6l7tae5isGX7DtAtILyhDk5oAxD1BDS0Iai5ImG8OdQcf9hUwIabzmaDfA4YvBTZw07UnOxqYTNyESAZ8+HmXWZUZCbB0lTTaG+3LPoq7ghDQZt8xt7uU5AAj1NP3GvbdLKvHWr2cBAM/3CkG3EDeT3TchLRElTTbmTiE4JU2ENFVztBvgmGN5bu5v55BbVIE2Xk54bXB7k90vIS0VJU02hisEz1KXQ0sNLglpkjs1Tc0305RZWI6S6jYHTfHH6QxsO5MJiViEpU9EQWFHW6YQ0lSUNNkYT2c5JGIRtNTgkpAm0ekYv8zt2wxJk4uDDB5OMgBN37g3p6gcc387BwCY2r8NOgW4NDU8QggoabI5ErEI3s5yAFQMTkhT3CqugEbLIBaB/0yZW2uPpi/RMcYw55ezKCjVINJPiWn925gqPEJaPEqabJAvFYMT0mTp1X90eCsVkEqa56sy1KvpxeCbE29iz8UcyCRiLHkiCjIpfc0TYir0abJBXDF4BiVNhDRaRjNs1Hu3ps403bxdig/+uAAAmDmoHcJ8lCaLjRBCSZNN8uPOoKPlOUIajduKqDmTJn6mKafhSZNOx/Dmz2dQXFGFB4Jc8EKf1qYOj5AWj5ImG+RTfQZdJnUFJ6TRuOU57o+Q5sA1uEy9pd+4tyF+OPovDl/Ng8JOjCVPdIbEDJv+EtLSUdJkg2imiZCma85u4JwAVwfIJGJUVOn4pM0YqbdK8H87kgEAc4aG04a8hJgJJU02iArBCWk6rkFscyZNErEIrTz0G/caW9ek1TG8tikJ5RodHgp1x8QeweYMkZAWjZImG8TtP5ddVEENLglpJG6mybcZl+eAmnvQGXcG3Zd/X8PJGwVwkkux6LFOENOyHCFmQ0mTDfJwkkNa3eAyp4hmmwhpqHKNFreKKwE0Tzfwmlrze9Ddf6YpJasIS3dfAgC8NyICAa4OZo2NkJaOkiYbJBGL4K2kPegIaSxuadveTgIXB7tmfexQI/egq6zSYdamJFRqdRgY5oXHowOaIzxCWjRKmmyUL18MTkkTIQ3FL825KCASNe9yl7HLcyv3XcH5DDVcHOywcHTHZo+TkJZI0KTp4MGDGDFiBPz8/CASibB169ZaY5KTk/HII49ApVLB0dERXbt2xY0bN/jj5eXlmDp1Ktzd3eHk5IQxY8YgOzvb4D5u3LiBYcOGwcHBAV5eXnjjjTdQVWW4Ieb+/fvxwAMPQC6Xo02bNli3bp05nnKz4YrBuV4zhBDjcWeuNffSHHBneS6nqAJF5Zo6x5y5WYBV+64AABY82gFeyuatuyKkpRI0aSopKUFUVBRWrVpV5/GrV6+iV69eCAsLw/79+3HmzBnMnTsXCsWdL4iZM2fijz/+wObNm3HgwAFkZGRg9OjR/HGtVothw4ahsrIShw8fxrfffot169bhvffe48ekpqZi2LBh6N+/P5KSkjBjxgw8//zz2LVrl/mevJnxM020PEdIg/FnzqmaP2lyVtjBq3qvu7pmm8o1WszadBpaHcPwTr4YEeXX3CES0mJJhXzwoUOHYujQofUef+edd/Dwww9j0aJF/HWhoaH8vwsLC/H1119j/fr1GDBgAADgm2++QXh4OI4cOYIePXpg9+7duHDhAv766y94e3ujc+fOWLBgAWbPno358+dDJpNhzZo1CAkJwZIlSwAA4eHh+Oeff7Bs2TLExcWZ6dmb152kiWaaCGmomstzQgj1dEJOUQWu5hYjKtDF4NiS3Sm4klMMDyc5FjzaQZD4CGmpLLamSafTYfv27WjXrh3i4uLg5eWF7t27GyzhJSYmQqPRIDY2lr8uLCwMQUFBSEhIAAAkJCSgY8eO8Pb25sfExcVBrVbj/Pnz/Jia98GN4e7DGtFMEyGNly5AY8ua6juD7lhqPr76JxUA8PHojnB1lDV7bIS0ZBabNOXk5KC4uBgff/wxhgwZgt27d2PUqFEYPXo0Dhw4AADIysqCTCaDi4uLwW29vb2RlZXFj6mZMHHHuWP3GqNWq1FWVvdMTUVFBdRqtcHFkvhyW6lQITghDcb9sSFETRNQdzF4SUUVXt98GowBj0cHIDbCu76bE0LMxGKTJp1OBwB49NFHMXPmTHTu3BlvvfUWhg8fjjVr1ggcHbBw4UKoVCr+EhgYKHRIBrhlhZyiclRpdQJHQ6yJTsew/UwmbhVXCB2KIBhjgjW25IR61W478H87knEjvxT+LvZ4b0SEIHER0tJZbNLk4eEBqVSKiAjDL4fw8HD+7DkfHx9UVlaioKDAYEx2djZ8fHz4MXefTcf9/35jlEol7O3r/ktzzpw5KCws5C9paWmNe6Jm4uEoh51EBB3Tn4VDiLG2n83E1PUnMfvnM0KHIgh1WRVKK7UABFyeq9477vqtUmh1DAcu5eLHo/rvvUWPdYKzonl7RxFC9Cw2aZLJZOjatStSUlIMrr906RKCg/V7K0VHR8POzg579uzhj6ekpODGjRuIiYkBAMTExODs2bPIycnhx8THx0OpVPIJWUxMjMF9cGO4+6iLXC6HUqk0uFgSsUGDSyoGJ8Y7eeM2AODg5Vyo6znl3ZZx9UzujjIo7CSCxODvYg+5VIxKrQ4XMtR8AjspJhg923gIEhMhROCz54qLi3HlyhX+/6mpqUhKSoKbmxuCgoLwxhtvYOzYsejTpw/69++PnTt34o8//sD+/fsBACqVCpMnT8asWbPg5uYGpVKJ6dOnIyYmBj169AAADB48GBEREZg4cSIWLVqErKwsvPvuu5g6dSrkcv1pvS+99BJWrlyJN998E8899xz27t2LTZs2Yfv27c3+mpiSr0qBm7fLqBicNMjFzCIAgEbLsD8lF4+0sFPahT5zDtD/0RPi4YiLWUWY/tNJZKnL0crdAbOHhgkWEyFE4JmmEydOoEuXLujSpQsAYNasWejSpQvfQ2nUqFFYs2YNFi1ahI4dO+Krr77CL7/8gl69evH3sWzZMgwfPhxjxoxBnz594OPjg19//ZU/LpFIsG3bNkgkEsTExOCpp57C008/jQ8++IAfExISgu3btyM+Ph5RUVFYsmQJvvrqK6ttN8ChYnDSUIwxXMy6c1JD/IXse4y2TdzMrBA9mmri6pqu55VCLAKWPBEFB5mgf+cS0uIJ+gns168fGGP3HPPcc8/hueeeq/e4QqHAqlWr6m2QCQDBwcHYsWPHfWM5derUvQO2MtxfyjTTRIyVW1SB26V3luT2X8xBZZUOMqnFruSbXHr1HxlC1TNxuDPoAOCFPqGIDnYTMBpCCGDBNU2k6Xyppok0UHKWfmmutYcjPJzkKKqowtHUPIGjal4ZfI8mYbcmiQpQAQDaeztj5qC2gsZCCNGjpMmGcfvPZdBMEzHSxUz90ly4nxKDIrwAALvPt6wlOn55TuCZpgFhXvh60oPY+GIPyKXCFKQTQgxR0mTDuB4zWTTTRIx0sXqmKdzHGYOqmyf+lZx932V0W5JRvTznK3BNk0gkwsBwb7g4UNdvQixFo5Km1q1bIy+v9pR9QUEBWrdu3eSgiGlwX/o5RRXQUINLYoTk6pmm9j5KPBTqAQeZBJmF5TiXblkd781Fq2PIUgvbDZwQYrkalTRdv34dWq221vUVFRVIT09vclDENNwdZZBJxGDU4JIYQaPV8R2ow3ycobCToG87TwBA/IUsIUNrNjlF5dDqGKRiETyd5UKHQwixMA06e+7333/n/71r1y6oVCr+/1qtFnv27EGrVq1MFhxpGrFYBG+VHGn5ZcgsKKO/nMk9XcstgUbL4CSXIsBV/14ZFOGNP89lYfeFbMwa3F7gCM2PKwL3ViogEYsEjoYQYmkalDSNHDkSgH6tfdKkSQbH7Ozs0KpVKyxZssRkwZGm81XZIy2/jIrByX1x/ZnCfJwhEukThgFhXpCIRbiYVYS0/FIEujkIGaLZce0G6A8MQkhdGrQ8p9PpoNPpEBQUhJycHP7/Op0OFRUVSElJwfDhw80VK2kEKgYnxkqu7gQe5uvMX+fiIEPXVq4AgN0toNFlpoW0GyCEWKZG1TSlpqbCw4P2P7IGXDF4BnUFJ/dxZ6bJcB/FwRH6ja1bQl3TnS1UaKaJEFJbozuC79mzB3v27OFnnGr63//+1+TAiGlwfzFn0fIcuQ9uz7kwH2eD6wdFeOODbRdwLDUft0sq4epou6fAW0o3cEKIZWrUTNP777+PwYMHY8+ePbh16xZu375tcCGWw4e6ghMjFJRW8qfat7sraQp0c0CYjzN0DNh7MUeI8JoN9znxp+U5QkgdGjXTtGbNGqxbtw4TJ040dTzExPyoKzgxAtfUMsDVHkqFXa3jgyO8cTGrCPEXsjEmOqC5w2s2/PKcwI0tCSGWqVEzTZWVlXjooYdMHQsxA5/qQvBbxRWorKIGl6Ru3PYpd9czcQZH6uuaDl7ORbmmdo82W1BWqeU3K6blOUJIXRqVND3//PNYv369qWMhZuDuKINMqm9wma2m2SZSN377FF/nOo9H+inhp1KgtFKLQ1duNWdozSajemnOSS6FUtHock9CiA1r1DdDeXk51q5di7/++gudOnWCnZ3hdP7SpUtNEhxpOpFIBF+VAv/mlSJLXW7zfXZI4yRncUXgdc80iUQixEZ447uEfxF/IRsDw72bM7xmcWdpTsH3qSKEkJoalTSdOXMGnTt3BgCcO3fO4Bh92VgeH6U+aeJ+KRBSk07HcKk6aWrvU/dME6A/i+67hH/xV3IOdDoGsY11zM6kM+cIIffRqKRp3759po6DmBH3SyCTisFJHW7kl6JMo4VcKkYr9/pnIruHuMNZIcWt4gqcSitAdLBrM0ZpfunU2JIQch+Nqmki1sVHRb2aSP24ppbtvJ0hldT/lSCTitG/vRcAYLcNNrrkZmL96Mw5Qkg9GjXT1L9//3suw+3du7fRARHT86tOmmh5jtQluZ6mlnUZFOGN309nIP5CNuYMDTd3aM2Km4ml5TlCSH0alTRx9UwcjUaDpKQknDt3rtZGvkR4XM+ZLDp7jtSB3z7Ft+4i8Jr6tfeEnUSEa7kluJpbjFBPJ3OH12zubKFCy3OEkLo1KmlatmxZndfPnz8fxcXFTQqImJ4PP9NESROpjW83YMRMk7PCDjGhHjh4KRfxF7IR2tc2kibGGF/T5E8zTYSQepi0pumpp56ifecsELfcQA0uyd1KKqrwb14pgHufOVfToAh9u4Hd522nrul2qQYV1Z8N7o8MQgi5m0mTpoSEBCgU9IVjaVwd7CCX6n/U1OCS1HQpWz/L5Oksh7uT3KjbDKru0XQqrQC5RRVmi605cUtzHk5yyKUSgaMhhFiqRi3PjR492uD/jDFkZmbixIkTmDt3rkkCI6bDNbi8Xt2riRpcEs7FLOOLwDk+KgU6Bahw5mYh9iRnY1y3IHOF12zuLM3RH32EkPo1aqZJpVIZXNzc3NCvXz/s2LED8+bNM3WMxASoGJzUhdtzLtyIIvCaBlcv0cVfyDZ5TELI5Hs0UT0TIaR+jZpp+uabb0wdBzEzXyoGJ3VIbsRMEwAMivDBp7sv4e8rt1BSUQVHuXXv1ZZR3W7Al3o0EULuoUnfdImJiUhOTgYAREZGokuXLiYJipgedxp1ViH1aiJ6jDF+pqm+Pefq087bCUFuDriRX4q/L+diSAdfc4TYbKgbOCHEGI1KmnJycjBu3Djs378fLi4uAICCggL0798fGzZsgKenpyljJCbgU/0XdAZ1BSfVMgvLoS6vgkQsQqiXY4NuKxKJMCjCG1//k4rdF7KtPmnKpHYDhBAjNKqmafr06SgqKsL58+eRn5+P/Px8nDt3Dmq1Gv/5z39MHSMxAa4reCbNNJFqKdVLc6Gejo06Y4yra9p7MQdVWutuZcEtW/tS0kQIuYdGzTTt3LkTf/31F8LD72yjEBERgVWrVmHw4MEmC46YDl8ITjNNpFpyVuOW5jjRwa5wdbDD7VINjl+/jZhQd1OG12w0Wh2yi7gtVGh5jhBSv0bNNOl0OtjZ2dW63s7ODjqddf/Faau4QvBbxZWoqNIKHA2xBBe5Ped8G1YEzpFKxBgQZv1n0WWry8EYIJOI4eFoXK8qQkjL1KikacCAAXj11VeRkZHBX5eeno6ZM2di4MCBJguOmI6Lgx0UdtUNLgttoyEhaRpuz7nwRs40AXe6g8cnZ4ExZpK4mhu3NOejUkAsrn8jckIIaVTStHLlSqjVarRq1QqhoaEIDQ1FSEgI1Go1VqxYYeoYiQnoG1xyxeBU19TSVVRpcTW3BEDjZ5oAoE87D8ilYqTllyGluru4tcmgM+cIIUZqVNIUGBiIkydPYvv27ZgxYwZmzJiBHTt24OTJkwgICDD6fg4ePIgRI0bAz88PIpEIW7durXfsSy+9BJFIhM8++8zg+vz8fEyYMAFKpRIuLi6YPHlyrU2Dz5w5g969e0OhUCAwMBCLFi2qdf+bN29GWFgYFAoFOnbsiB07dhj9PKyFLxWDk2pXcoqh1TEoFVL4KBufLDjIpOjd1gMAsPu8dS7RcX9E+FGPJkLIfTQoadq7dy8iIiKgVqv1pxwPGoTp06dj+vTp6Nq1KyIjI/H3338bfX8lJSWIiorCqlWr7jluy5YtOHLkCPz8/GodmzBhAs6fP4/4+Hhs27YNBw8exAsvvMAfV6vVGDx4MIKDg5GYmIjFixdj/vz5WLt2LT/m8OHDGD9+PCZPnoxTp05h5MiRGDlyJM6dO2f0c7EG3ExTJhWDt3jcmXNhvkqIRE1bkhpk5d3BM6gbOCHESA1Kmj777DNMmTIFSmXtGgiVSoUXX3wRS5cuNfr+hg4dig8//BCjRo2qd0x6ejqmT5+OH3/8sVbxeXJyMnbu3ImvvvoK3bt3R69evbBixQps2LCBr7f68ccfUVlZif/973+IjIzEuHHj8J///McgzuXLl2PIkCF44403EB4ejgULFuCBBx7AypUrjX4u1oCfaaKu4C0et+dceAM7gddlQJg3RCLgbHqhVc5icjVNlDQRQu6nQUnT6dOnMWTIkHqPDx48GImJiU0OiqPT6TBx4kS88cYbiIyMrHU8ISEBLi4uePDBB/nrYmNjIRaLcfToUX5Mnz59IJPJ+DFxcXFISUnB7du3+TGxsbEG9x0XF4eEhASTPRdLwHUFp5kmksx1Am/gnnN18XSW44EgVwDAX1Y428TNNPlSTRMh5D4alDRlZ2fX2WqAI5VKkZub2+SgOJ988gmkUmm9DTOzsrLg5eVVKwY3NzdkZWXxY7y9vQ3GcP+/3xjueF0qKiqgVqsNLpaOapoI52Ij95yrD9focrcVJ03UDZwQcj8Nam7p7++Pc+fOoU2bNnUeP3PmDHx9TbOdQmJiIpYvX46TJ082uebCHBYuXIj3339f6DAahGqaCADcKq5AblEFRCKgnbdpkqZBEd5Y+OdFHLmWB3W5BkpF/X9cNcWNvFK89EMiyjVadAxQoaO/Cp0CXBDpp2zUpsHFFVVQl1cBuPNHBSGE1KdB3zIPP/ww5s6diyFDhkChMPyCKSsrw7x58zB8+HCTBPb3338jJycHQUFB/HVarRavvfYaPvvsM1y/fh0+Pj7IyckxuF1VVRXy8/Ph4+MDAPDx8UF2tuFfv9z/7zeGO16XOXPmYNasWfz/1Wo1AgMDG/FMmw93dlB+SSXKNVoo7Bq+dQaxflwReJCbQ6MSjbq09nRCqKcjruaWYH9KLh6Jqn3SRlOl5Zdi/JdH+M11r90qwW9J+tpFkQgI9XRCJ38VOgao0ClAhQhfFexl936Pc3vOOSukcDZTokcIsR0N+sZ899138euvv6Jdu3aYNm0a2rdvDwC4ePEiVq1aBa1Wi3feecckgU2cOLHOOqOJEyfi2WefBQDExMSgoKAAiYmJiI6OBqA/w0+n06F79+78mHfeeQcajYZfWoyPj0f79u3h6urKj9mzZw9mzJjBP1Z8fDxiYmLqjU8ul0Mut67uwUp7KeztJCjTaJFVWI5WHg3bpJXYBlMvzXEGRfjg6oGriL+QbfKk6ebtUoxbq0+YWns4YvbQMFzKKsKZ9EKcvVmILHU5ruQU40pOMX49lQ4AEIuAtl7OfBLV0V+FcF+lwR8L6bQ0RwhpgAYlTd7e3jh8+DBefvllzJkzh+8ALBKJEBcXh1WrVtWqDbqX4uJiXLlyhf9/amoqkpKS4ObmhqCgILi7G+5lZWdnBx8fHz5ZCw8Px5AhQzBlyhSsWbMGGo0G06ZNw7hx4/j2BE8++STef/99TJ48GbNnz8a5c+ewfPlyLFu2jL/fV199FX379sWSJUswbNgwbNiwASdOnDBoS2ALRCIRfF0UuJZbgkxKmlqsi5lN23OuPoMjvbHmwFXsv5iDyiodZNJGtYGrJb2gjJ9hauXugPVTesBHpUBc5J2Z4JyicpxLL8SZm/ok6kx6IXKLKpCSXYSU7CL8nHgTACAVi9DW25mfkUq7XQqAzpwjhBinwXPzwcHB2LFjB27fvo0rV66AMYa2bdvyszYNceLECfTv35//P7fcNWnSJKxbt86o+/jxxx8xbdo0DBw4EGKxGGPGjMF///tf/rhKpcLu3bsxdepUREdHw8PDA++9955BL6eHHnoI69evx7vvvou3334bbdu2xdatW9GhQ4cGPydL56vikiYqBm+p+HYDTegEXpfOAS7wdJYjt6gCR67loU87zybfZ2ZhGcavPYK0/DIEuzvgpxf0CdPdvJwVGBCm4PfCA/R7yumTqAKcrU6o8koqkZypRnKmGhtPpPFjqZ6JEGIMEbPWDaMsjFqthkqlQmFhYZ19rCzF7J/PYOOJNDzzUCvMf6R2Gwdi26q0OkTO24WKKh32v97P5LONc349g5+OpWFij2AsGNm0PzqyCssxbm0CrueVIsjNARte6NGkGSHGGDIL9YnUufTC6qW9AqjLq/D5hAcwOLL+GkZCiO1qyO9v01SBEqsxtKMPNp5Iwy+JN/FGXHuTFQIT63A9rxQVVTrY20kQ5OZg8vsfFOGNn46lIf5CNj54NLLRZ75mq8vx5JdHcD2vFAGu9vipiQkToF+e9nOxh5+LPYZ00CdIjDFUVOnopAhCiFFMU3RArEaftp4I8XBEUUUVfj15U+hwSDO7mKWvZ2rn4wyx2PStPB4K9YCDTIIsdTnOphc26j5y1OUY/+URXLtVAn8Xe/w0pYfZCrVFIhElTIQQo1HS1MKIxSI8HRMMAPg24V/Q6mzLkmLC7VPqorCToG91LVNj9qLLLarQJ0y5JfBTKbDhhR4INMOMGCGENAYlTS3QY9EBcJRJcCWnGIeu5AkdDmlGyZnmaTdQU2M38L1VXIEnvzyCq7kl8FUp8BMlTIQQC0NJUwvkrLDDmOgAAMC6w9eFDYY0K255zhR7ztVnQJgXJGIRLmYV4UZeqVG3ySuuwIQvj+JyTjG8lXL8NKUHgt2pJQYhxLJQ0tRCPR3TCgCw52I20vKN+8VGrJu6XIObt/WtJsw50+TiIEO3Vm4AgN0X6t+/kZNfUokJXx1FSnYRvJzl2PBCDPUQI4RYJEqaWqg2Xk7o3dYDjAHfH/lX6HBIM7hUXc/kq1LAxUFm1scydomuoLQST311FBeziuDpLMdPL/RACCVMhBALRUlTC/bMQ60AABuPp6GsUitsMMTskquTpvZmnGXicEnT8ev5uF1SWeeYglL9DNOFTDU8nOT4aUp3hHo6mT02QghpLEqaWrB+7b0Q5OaAwjINtialCx0OMbOULPNsn1KXQDcHhPk4Q8eAvRdzah0vLNVg4tfHcD5DDXdHGX6a0h1tvMyfzBFCSFNQ0tSCSWq2Hzh8ndoP2LiLmebZPqU+XIftu+uaCss0ePp/R3E2vRBujjKsn9IDbb0pYSKEWD5Kmlq4xx8MhL2dBBezinA0NV/ocIiZMMb4PeeaY6YJAAZXL9EdvHQL5Rr98m9RuQaT/ncMp28WwtXBDj8+371ZlgsJIcQUKGlq4VT2dhj1gD8A/WwTsU03b5ehuKIKdhIRWns2T6F1pJ8SfioFyjRaHLpyC8UVVZj0v2NISiuAi4Mdfny+B8LN2PqAEEJMjZImgknV7Qd2X8hGRkGZsMEQs+Bmmdp4OcNO0jwfe5FIhNjq2aatSRl45n/HcPJGAVT2dvhhcndE+FHCRAixLpQ0EbT3cUZMa3dodQw/UPsBm3QxU18Ebq7tU+ozOEJf1/TH6Qyc+Pc2lAopfpjcHR38Vc0aByGEmAIlTQQAMKm6/cBPx27w9SfEdlzMbr52AzV1b+0GZ4UUAOCskOL7yd3RMYASJkKIdaKkiQAAYsO94O9ij9ulGvx+OkPocIiJcTNN5tw+pS52EjFe6huK1p6O+O65bogKdGnWxyeEEFOipIkAAKQSMZ7qQe0HbFG5RovUWyUAmn95DgCm9m+Dva/1Q5cg12Z/bEIIMSVKmghvXNdAyKVinM9QI/Hf20KHQ0zkcnYxdAxwc5TB01kudDiEEGK1KGkiPFdHGR7t7AcAWEftB2xGMt8J3BkikUjgaAghxHpR0kQMcAXhO89lIVtdLmwwxCS4TuDN1dSSEEJsFSVNxECknwpdW7miSsfwI7UfsAkp2XdmmgghhDQeJU2kFm62af2xG6ioovYD1owxhmRupqmZ9pwjhBBbRUkTqSUu0gc+SgVuFVdix9lMocMhTZBbXIH8kkqIRUBbL0qaCCGkKShpIrXYScSY0D0IALDuMC3RWTOunqmVhyPsZRKBoyGEEOtGSROp0/juQZBJxDidVoCktAKhwyGNdDGL2z6FisAJIaSpKGkidfJwkmN4J18A+maXxDrdOXOOluYIIaSpKGki9eIKwredyUBuUYWwwZBGuZglzJ5zhBBiiyhpIvWKCnRB50AXaLQMPx27IXQ4pIE0Wh2u5BQDAMKbec85QgixRZQ0kXt6tmcrAMCPR/+FRqsTNhjSIKm3SlCp1cFJLoW/i73Q4RBCiNWjpInc09AOvvB0liNbXYGd57KEDoc0QHKmvgi8vY8zxGLaPoUQQpqKkiZyTzKpGE9207cfoIJw68LVM1EROCGEmAYlTeS+JnQPglQswol/b+NceqHQ4RAjXayeaQqjeiZCCDEJSprIfXkpFXi4I7UfsDYpNNNECCEmJWjSdPDgQYwYMQJ+fn4QiUTYunUrf0yj0WD27Nno2LEjHB0d4efnh6effhoZGRkG95Gfn48JEyZAqVTCxcUFkydPRnFxscGYM2fOoHfv3lAoFAgMDMSiRYtqxbJ582aEhYVBoVCgY8eO2LFjh1mes7Xi2g/8djoD+SWVwgZD7quwVIOMwnIA1G6AEEJMRdCkqaSkBFFRUVi1alWtY6WlpTh58iTmzp2LkydP4tdff0VKSgoeeeQRg3ETJkzA+fPnER8fj23btuHgwYN44YUX+ONqtRqDBw9GcHAwEhMTsXjxYsyfPx9r167lxxw+fBjjx4/H5MmTcerUKYwcORIjR47EuXPnzPfkrcwDQS7o6K9CZZUOG45T+wFLx3UC93exh1JhJ3A0hBBiI5iFAMC2bNlyzzHHjh1jANi///7LGGPswoULDAA7fvw4P+bPP/9kIpGIpaenM8YYW716NXN1dWUVFRX8mNmzZ7P27dvz/3/iiSfYsGHDDB6re/fu7MUXXzQ6/sLCQgaAFRYWGn0ba7P5RBoLnr2NxfzfX0xTpRU6HHIP6w6lsuDZ29jkdceEDoUQQixaQ35/W1VNU2FhIUQiEVxcXAAACQkJcHFxwYMPPsiPiY2NhVgsxtGjR/kxffr0gUwm48fExcUhJSUFt2/f5sfExsYaPFZcXBwSEhLqjaWiogJqtdrgYuuGd/KFm6MMGYXl+Cs5W+hwyD1wM01htOccIYSYjNUkTeXl5Zg9ezbGjx8PpVL/iyArKwteXl4G46RSKdzc3JCVlcWP8fb2NhjD/f9+Y7jjdVm4cCFUKhV/CQwMbNoTtAIKOwnGd9M/z28OXRc2GHJPydyec75Uz0QIIaZiFUmTRqPBE088AcYYPv/8c6HDAQDMmTMHhYWF/CUtLU3okJrFUz2CIRGLcDQ1n2+eSCyLTsdwKZvOnCOEEFOz+KSJS5j+/fdfxMfH87NMAODj44OcnByD8VVVVcjPz4ePjw8/JjvbcCmJ+//9xnDH6yKXy6FUKg0uLYGvyh5xkfpZue8SrgsbDKlT2u1SlFZqIZOK0crdUehwCCHEZlh00sQlTJcvX8Zff/0Fd3d3g+MxMTEoKChAYmIif93evXuh0+nQvXt3fszBgweh0Wj4MfHx8Wjfvj1cXV35MXv27DG47/j4eMTExJjrqVm1STGtAABbTqWjoJTaD1gabmmunbcTpBKL/ogTQohVEfQbtbi4GElJSUhKSgIApKamIikpCTdu3IBGo8Fjjz2GEydO4Mcff4RWq0VWVhaysrJQWan/RR0eHo4hQ4ZgypQpOHbsGA4dOoRp06Zh3Lhx8PPzAwA8+eSTkMlkmDx5Ms6fP4+NGzdi+fLlmDVrFh/Hq6++ip07d2LJkiW4ePEi5s+fjxMnTmDatGnN/ppYg24hbgjzcUa5RodNJ1rGsqQ1oSJwQggxE/OfzFe/ffv2MQC1LpMmTWKpqal1HgPA9u3bx99HXl4eGz9+PHNycmJKpZI9++yzrKioyOBxTp8+zXr16sXkcjnz9/dnH3/8ca1YNm3axNq1a8dkMhmLjIxk27dvb9BzaQktB2r66ei/LHj2Ntbz4z2sSqsTOhxSw4vfnWDBs7exLw9eFToUQgixeA35/S1ijDFBsjUbo1aroVKpUFhY2CLqm8oqteixcA8KyzT48ukHMSjC+/43Is2i3+J9uJ5Xih+f746ebTyEDocQQixaQ35/U8EDaRR7mQTjuurbD9B+dJajtLIK/+aXAqDtUwghxNQoaSKN9lSPYIhFwD9XbiH1VonQ4RAAl7KLwRjg4SSHh5Nc6HAIIcSmUNJEGi3QzQF923kCABWEW4iL1b2zwqmpJSGEmBwlTaRJxlYv0f2ceBNVWp3A0ZCLWdTUkhBCzIWSJtIkA8K84e4oQ25RBfal5AodTovHdWmndgOEEGJ6lDSRJpFJxRj9gD8AYONxWqITEmPszkwTLc8RQojJUdJEmoxbotuXkoMcdbnA0bRc2eoKFJZpIBGL0MbLSehwCCHE5lDSRJqsjZczooNdodUx/HIyXehwWqzk6k7grT0cIZdKBI6GEEJsDyVNxCTGPqifbdp0Ig3UL1UYFzO5pTmqZyKEEHOgpImYxLBOvnCUSZB6qwTHUvOFDqdFurPnHNUzEUKIOVDSREzCUS7FiCj9JskbqWeTILiZJurRRAgh5kFJEzGZJ6oLwneczYS6XCNwNC1LZZUOV3OLAVC7AUIIMRdKmojJdAl0QVsvJ5RrdPg9KUPocFqUq7nFqNIxOCuk8FUphA6HEEJsEiVNxGREIhHffoC2VWleXD1TuI8SIpFI4GgIIcQ2UdJETGpUF3/YSUQ4c7OQ705NzO/OmXNUz0QIIeZCSRMxKXcnOQZFeAOgDuHNKZnfc47qmQghxFwoaSIm90R1z6Ytp9JRrtEKHI3tY4zd2XOOZpoIIcRsKGkiJte7rSf8VAoUlmmw+0K20OHYvBP/3kZuUQXs7STUo4kQQsyIkiZichKxCI9xHcJpic7s1h+9AQAYEeULB5lU4GgIIcR2UdJEzOLx6ACIRMA/V24hLb9U6HBs1u2SSmw/mwkAeLJ7sMDREEKIbaOkiZhFoJsDeoZ6AAA2J94UOBrb9cvJm6is0iHCV4moAJXQ4RBCiE2jpImYDdchfPOJNGh1tImvqTHGsP6Yfmnuye5B1J+JEELMjJImYjaDI7zh4mCHzMJy/H05V+hwbM7R1Hxcyy2Bg0yCRzv7CR0OIYTYPEqaiNko7CQY2dkfAHUINweuAPzRzn5wVtgJHA0hhNg+SpqIWXHbqsRfyEZecYXA0diO/JJK7DyXBQB4shsVgBNCSHOgpImYVbivEp0CVNBoGbacShc6HJvxc2IaKrU6dPRXoSMVgBNCSLOgpImYHTfbtPF4GhijgvCmYozhp2P65c4nuwcJHA0hhLQclDQRsxsR5QeFnRiXc4pxKq1A6HCsXsLVPKTeKoGTXIpHoqgAnBBCmgslTcTslAo7PNzRFwB1CDeFH4/dKQB3lFMHcEIIaS6UNJFmMbZ6W5U/TmegpKJK4Gis163iCuw+X10ATktzhBDSrChpIs2iW4gbQjwcUVKpxfYzmUKHY7U2n7gJjZYhKtAFkX5UAE4IIc2JkibSLEQiEZ6onm3aSD2bGkWnY/ipemluQjeaZSKEkOZGSRNpNmOi/SERi5D4721cySkSOhyrc+jqLdzIL4WzXIrhUb5Ch0MIIS2OoEnTwYMHMWLECPj5+UEkEmHr1q0GxxljeO+99+Dr6wt7e3vExsbi8uXLBmPy8/MxYcIEKJVKuLi4YPLkySguLjYYc+bMGfTu3RsKhQKBgYFYtGhRrVg2b96MsLAwKBQKdOzYETt27DD5823pvJwV6N/eCwCw6QRt4ttQXAfwUQ/4w0FGBeCEENLcBE2aSkpKEBUVhVWrVtV5fNGiRfjvf/+LNWvW4OjRo3B0dERcXBzKy8v5MRMmTMD58+cRHx+Pbdu24eDBg3jhhRf442q1GoMHD0ZwcDASExOxePFizJ8/H2vXruXHHD58GOPHj8fkyZNx6tQpjBw5EiNHjsS5c+fM9+RbKK5n0y+JN1FZpRM4GuuRU1SO+AvZAKgAnBBCBMMsBAC2ZcsW/v86nY75+PiwxYsX89cVFBQwuVzOfvrpJ8YYYxcuXGAA2PHjx/kxf/75JxOJRCw9PZ0xxtjq1auZq6srq6io4MfMnj2btW/fnv//E088wYYNG2YQT/fu3dmLL75odPyFhYUMACssLDT6Ni2RpkrLun4Yz4Jnb2N/ns0QOhyrsXLvZRY8exsbteofoUMhhBCb0pDf3xZb05SamoqsrCzExsby16lUKnTv3h0JCQkAgISEBLi4uODBBx/kx8TGxkIsFuPo0aP8mD59+kAmk/Fj4uLikJKSgtu3b/Njaj4ON4Z7nLpUVFRArVYbXMj9SSVijIkOAKDvEE7ur2YB+JPdaZ85QggRisUmTVlZ+l403t7eBtd7e3vzx7KysuDl5WVwXCqVws3NzWBMXfdR8zHqG8Mdr8vChQuhUqn4S2BgYEOfYovFnUV34FIuMgvLBI7G8h28nIubt8ugVEgxvBMVgBNCiFAsNmmydHPmzEFhYSF/SUujWRNjhXg4oluIG3QM+JkKwu+LKwAf/UAAFHYSgaMhhJCWy2KTJh8fHwBAdna2wfXZ2dn8MR8fH+Tk5Bgcr6qqQn5+vsGYuu6j5mPUN4Y7Xhe5XA6lUmlwIcYbV10QvikxDTodbeJbn2x1OfZc1L/HJ1ABOCGECMpik6aQkBD4+Phgz549/HVqtRpHjx5FTEwMACAmJgYFBQVITEzkx+zduxc6nQ7du3fnxxw8eBAajYYfEx8fj/bt28PV1ZUfU/NxuDHc4xDTG9rBF85yKdLyy3DkWp7Q4VisjcfToNUxdG3lirbezkKHQwghLZqgSVNxcTGSkpKQlJQEQF/8nZSUhBs3bkAkEmHGjBn48MMP8fvvv+Ps2bN4+umn4efnh5EjRwIAwsPDMWTIEEyZMgXHjh3DoUOHMG3aNIwbNw5+fvrd35988knIZDJMnjwZ58+fx8aNG7F8+XLMmjWLj+PVV1/Fzp07sWTJEly8eBHz58/HiRMnMG3atOZ+SVoMe5kEj3TW/4yoQ3jdtDqGDXwBOM0yEUKI4JrhbL567du3jwGodZk0aRJjTN92YO7cuczb25vJ5XI2cOBAlpKSYnAfeXl5bPz48czJyYkplUr27LPPsqKiIoMxp0+fZr169WJyuZz5+/uzjz/+uFYsmzZtYu3atWMymYxFRkay7du3N+i5UMuBhjuddpsFz97G2r6zgxWUVAodjsXZk5zFgmdvY1Hv72JllVVCh0MIITapIb+/RYwxKigxAbVaDZVKhcLCQqpvMhJjDA//9x8kZ6rx/iORmPRQK6FDsijPf3scfyXn4PleIXh3eITQ4RBCiE1qyO9vi61pIrZPJBJh7IPUs6kuGQVl2FtdAD6eluYIIcQiUNJEBDWyiz9kUjEuZKpxLr1Q6HAsxsbjadAxoEdrN4R6OgkdDiGEEFDSRATm4iBDXKS+tQPNNulVaXX8a0EdwAkhxHJQ0kQEx/Vs2pqUjnKNVuBohLcvJRdZ6nK4OcoQF+l9/xsQQghpFpQ0EcHFtHZHoJs9isqr8Oe5TKHDEdz6o/8CAB6PDoBcSh3ACSHEUlDSRAQnFovweLR+tqmlL9HdvF2K/ZdyAQDju1EBOCGEWBJKmohFeCw6ACIRcORaPq7fKhE6HMFsPJ4GxoCebdzRysNR6HAIIYTUQEkTsQh+Lvbo284TAPDT8RsCRyMMTc0C8G5UAE4IIZaGkiZiMSZUnyn27eHrSC8oEzia5rcnOQc5RRXwcJJhUAQVgBNCiKWhpIlYjNhwL3QPcUO5Rof/25EsdDjNbn31PnOPPxgImZQ+moQQYmnom5lYDJFIhHkjIiEWAdvPZOLItTyhQ2o2afml+PtydQF4VyoAJ4QQS0RJE7EoEX5K/qyx9/+4AK2uZWyN+NOxG2AM6N3WA0HuDkKHQwghpA6UNBGL89rg9lAqpEjOVOOnY7ZfFK7R6rDpxE0AwATaZ44QQiwWJU3E4rg5yjBrUDsAwJLdKSgs1QgckXnFX8jGreIKeDrLMTCcCsAJIcRSUdJELNJTPYLRztsJt0s1WPbXJaHDMav1R/WzaWMfDISdhD6ShBBiqegbmlgkqUSMeSMiAQDfH/kXKVlFAkdkHtdvleCfK7cgEgHjugUKHQ4hhJB7oKSJWKyebTwwJNIHWh3DB9vOgzHbKwrnGnn2beeJAFcqACeEEEtGSROxaO8MC4dMKsahK3nYdT5b6HBMqrJKh5+rC8CfpH3mCCHE4lHSRCxaoJsDXuzTGgDw0Y4LKNdoBY7IdHadz0JeSSV8lAoMCPMSOhxCCCH3QUkTsXgv9wuFr0qBtPwyfPX3NaHDMRmuAPyJroGQUgE4IYRYPPqmJhbPQSbFW0PDAACr9l1FZqH170t3LbcYCdfyIBYB47pSATghhFgDSpqIVXgkyg9dW7miTKPFwh0XhQ6nydYdvg4A6N/eC34u9sIGQwghxCiUNBGrwO1LJxIBv5/OwPHr+UKH1GjXcov5pbnJvUMEjoYQQoixKGkiVqODv4pfypr/+3mr3Zdu4Z8XUaVjGBjmhYdCPYQOhxBCiJEoaSJW5fXB7eGskOJ8hhqbTqQJHU6DHbmWh/gL2ZCIRZjzcLjQ4RBCCGkASpqIVXF3kmNGrH5fusW7UlBYZj370ul0DB9uvwBA35epjZeTwBERQghpCEqaiNV5OiYYbbyckF9SieV/XRY6HKNtTUrHuXQ1nOVSzIhtK3Q4hBBCGoiSJmJ17CRivDc8AgDwXcJ1XMmx/H3pyiq1WLwrBQDwSv82cHeSCxwRIYSQhqKkiVilPu08ERvujSodw/t/XLD4fem+/ucaMgvL4e9ij2d7thI6HEIIIY1ASROxWnOHh0MmEePvy7fwV3KO0OHUK6eoHJ/vvwoAeHNIeyjsJAJHRAghpDEoaSJWK9jdEc9X9zn6cPsFVFRZ5r50y+Ivo6RSi6hAFzwS5Sd0OIQQQhqJkiZi1ab2bwNvpRz/5pXi639ShQ6nlkvZRdh4XN/I8t1h4RCJRAJHRAghpLEoaSJWzVF+Z1+6lXuvIFtdLnBEhv5vRzJ0DBgS6YOurdyEDocQQkgTWHTSpNVqMXfuXISEhMDe3h6hoaFYsGCBQdEvYwzvvfcefH19YW9vj9jYWFy+bHgaen5+PiZMmAClUgkXFxdMnjwZxcXFBmPOnDmD3r17Q6FQIDAwEIsWLWqW50iabmRnfzwQ5ILSSi0+/tNy9qX7+3Iu9qfkwk4i4hM7Qggh1suik6ZPPvkEn3/+OVauXInk5GR88sknWLRoEVasWMGPWbRoEf773/9izZo1OHr0KBwdHREXF4fy8jszDhMmTMD58+cRHx+Pbdu24eDBg3jhhRf442q1GoMHD0ZwcDASExOxePFizJ8/H2vXrm3W50saRyQSYf4j+n3ptpxKR+K/t4UOCVodw0fbkwEAE3u0QisPR4EjIoQQ0lQiZsHnag8fPhze3t74+uuv+evGjBkDe3t7/PDDD2CMwc/PD6+99hpef/11AEBhYSG8vb2xbt06jBs3DsnJyYiIiMDx48fx4IMPAgB27tyJhx9+GDdv3oSfnx8+//xzvPPOO8jKyoJMJgMAvPXWW9i6dSsuXjRu5kKtVkOlUqGwsBBKpdLErwQxxps/n8amEzfRKUCFra/0hFgsXP3QxuM3MPuXs1DZ2+HAG/3g4iATLBZCCCH1a8jvb4ueaXrooYewZ88eXLp0CQBw+vRp/PPPPxg6dCgAIDU1FVlZWYiNjeVvo1Kp0L17dyQkJAAAEhIS4OLiwidMABAbGwuxWIyjR4/yY/r06cMnTAAQFxeHlJQU3L5d96xFRUUF1Gq1wYUI6424MDjLpThzsxA/J94ULI6Siip8ulv/np0+oA0lTIQQYiMsOml66623MG7cOISFhcHOzg5dunTBjBkzMGHCBABAVlYWAMDb29vgdt7e3vyxrKwseHl5GRyXSqVwc3MzGFPXfdR8jLstXLgQKpWKvwQGBjbx2ZKm8nSW4z8D9duTLNp1EepyYfal++LgNeQWVSDY3QFPx7QSJAZCCCGmZ9FJ06ZNm/Djjz9i/fr1OHnyJL799lt8+umn+Pbbb4UODXPmzEFhYSF/SUtLEzokAmDSQ63Q2tMRt4orsWJP8+9Ll1VYjrUH9Y0s3xoSBpnUoj9ihBBCGsCiv9HfeOMNfrapY8eOmDhxImbOnImFCxcCAHx8fAAA2dnZBrfLzs7mj/n4+CAnx7BbdFVVFfLz8w3G1HUfNR/jbnK5HEql0uBChCeTijG3el+6bw5dx9Xc4vvcwrQ+3Z2Cco0OXVu5YkiHut87hBBCrJNFJ02lpaUQiw1DlEgk0Ol0AICQkBD4+Phgz549/HG1Wo2jR48iJiYGABATE4OCggIkJibyY/bu3QudTofu3bvzYw4ePAiN5s5yTnx8PNq3bw9XV1ezPT9iHv3be2FAmBeqdAwLtl1otsc9n1GIX07qa6neGRZBjSwJIcTGWHTSNGLECHz00UfYvn07rl+/ji1btmDp0qUYNWoUAP2p5jNmzMCHH36I33//HWfPnsXTTz8NPz8/jBw5EgAQHh6OIUOGYMqUKTh27BgOHTqEadOmYdy4cfDz029p8eSTT0Imk2Hy5Mk4f/48Nm7ciOXLl2PWrFlCPXXSRHOHR8BOIsL+lFy8u/Ws2bdYYUzfYoAx4JEoP3QOdDHr4xFCCGl+Ft1yoKioCHPnzsWWLVuQk5MDPz8/jB8/Hu+99x5/phtjDPPmzcPatWtRUFCAXr16YfXq1WjXrh1/P/n5+Zg2bRr++OMPiMVijBkzBv/973/h5OTEjzlz5gymTp2K48ePw8PDA9OnT8fs2bONjpVaDlie//2Tig+qZ5qiAl2wesID8HexN8tj7UnOxuRvT0AmFWPPrL4IdHMwy+MQQggxrYb8/rbopMmaUNJkmfZezMbMjadRWKaBq4Mdlo/rgj7tPE36GBqtDkM+O4iruSV4sW9rzBkabtL7J4QQYj4206eJkKYaEOaNbdN7oYO/ErdLNZj0zTH8d89l6HSm+1thw7EbuJpbAjdHGab2b2Oy+yWEEGJZKGkiNi/QzQE/v/QQxncLAmPA0vhLeO7b4ygorWzyfavLNVj2l761wYzYtlAq7Jp8n4QQQiwTJU2kRVDYSbBwdEcsfqwT5FIx9qfkYth//8HZm4VNut/V+64iv6QSrT0dMb5bkImiJYQQYokoaSItyuMPBuLXVx5CsLsD0gvKMObzw/jp2A00prQvLb8U/zuUCgB4e2g47CT0cSKEEFtG3/KkxYn0U+H3ab0QG+6NSq0Oc349izd+PoOyyoa1Jfh0dwoqq3SIae2OgeFe978BIYQQq0ZJE2mRVPZ2WDsxGrOHhEEsAn5OvInRnx/G9VslRt0+Ka0AvyVlQCQC3hkWTo0sCSGkBaCkibRYYrEIL/cLxQ/Pd4eHkwzJmWqMWPkPdp+ve5Nmjr6Rpb7/0+guAejgr2qOcAkhhAiMkibS4j0U6oFt03sjOtgVReVVeOH7RHyy8yKqtLo6x+86n4Xj129DYSfGG3HtmzlaQgghQqGkiRAAPioFNrzQA8/1DAEAfL7/KiZ+fQy5RRUG4yqrdPj4z4sAgBd6t4aPStHssRJCCBEGJU2EVLOTiPHeiAisfLILHGUSJFzLw/AVf+PE9Xx+zPdH/sX1vFJ4OMnxQt9QAaMlhBDS3ChpIuQuwzv54bdpPdHGywnZ6gqMW3sEX/+TioLSSvx3j76R5WuD28FJLhU4UkIIIc2J9p4zEdp7zvaUVFThrV/P4o/TGQAAP5UCGYXlaO/tjB2v9oZETGfMEUKItaO95wgxAUe5FP8d1xnzR0RAKhYho7AcAPD2sHBKmAghpAWi9QVC7kEkEuGZniHoGOCCeb+fQ+dAF/Rt5yl0WIQQQgRASRMhRogOdsW26b2FDoMQQoiAaHmOEEIIIcQIlDQRQgghhBiBkiZCCCGEECNQ0kQIIYQQYgRKmgghhBBCjEBJEyGEEEKIEShpIoQQQggxAiVNhBBCCCFGoKSJEEIIIcQIlDQRQgghhBiBkiZCCCGEECNQ0kQIIYQQYgRKmgghhBBCjEBJEyGEEEKIEaRCB2ArGGMAALVaLXAkhBBCCDEW93ub+z1+L5Q0mUhRUREAIDAwUOBICCGEENJQRUVFUKlU9xwjYsakVuS+dDodMjIy4OzsDJFIZNL7VqvVCAwMRFpaGpRKpUnvmxiPfg6WgX4OloF+DpaBfg5NxxhDUVER/Pz8IBbfu2qJZppMRCwWIyAgwKyPoVQq6UNhAejnYBno52AZ6OdgGejn0DT3m2HiUCE4IYQQQogRKGkihBBCCDECJU1WQC6XY968eZDL5UKH0qLRz8Ey0M/BMtDPwTLQz6F5USE4IYQQQogRaKaJEEIIIcQIlDQRQgghhBiBkiZCCCGEECNQ0kQIIYQQYgRKmizcqlWr0KpVKygUCnTv3h3Hjh0TOqQWZ/78+RCJRAaXsLAwocOyeQcPHsSIESPg5+cHkUiErVu3GhxnjOG9996Dr68v7O3tERsbi8uXLwsTrA2738/hmWeeqfX5GDJkiDDB2qiFCxeia9eucHZ2hpeXF0aOHImUlBSDMeXl5Zg6dSrc3d3h5OSEMWPGIDs7W6CIbRclTRZs48aNmDVrFubNm4eTJ08iKioKcXFxyMnJETq0FicyMhKZmZn85Z9//hE6JJtXUlKCqKgorFq1qs7jixYtwn//+1+sWbMGR48ehaOjI+Li4lBeXt7Mkdq2+/0cAGDIkCEGn4+ffvqpGSO0fQcOHMDUqVNx5MgRxMfHQ6PRYPDgwSgpKeHHzJw5E3/88Qc2b96MAwcOICMjA6NHjxYwahvFiMXq1q0bmzp1Kv9/rVbL/Pz82MKFCwWMquWZN28ei4qKEjqMFg0A27JlC/9/nU7HfHx82OLFi/nrCgoKmFwuZz/99JMAEbYMd/8cGGNs0qRJ7NFHHxUknpYqJyeHAWAHDhxgjOnf+3Z2dmzz5s38mOTkZAaAJSQkCBWmTaKZJgtVWVmJxMRExMbG8teJxWLExsYiISFBwMhapsuXL8PPzw+tW7fGhAkTcOPGDaFDatFSU1ORlZVl8PlQqVTo3r07fT4EsH//fnh5eaF9+/Z4+eWXkZeXJ3RINq2wsBAA4ObmBgBITEyERqMx+DyEhYUhKCiIPg8mRkmThbp16xa0Wi28vb0Nrvf29kZWVpZAUbVM3bt3x7p167Bz5058/vnnSE1NRe/evVFUVCR0aC0W9xmgz4fwhgwZgu+++w579uzBJ598ggMHDmDo0KHQarVCh2aTdDodZsyYgZ49e6JDhw4A9J8HmUwGFxcXg7H0eTA9qdABEGLphg4dyv+7U6dO6N69O4KDg7Fp0yZMnjxZwMgIEd64ceP4f3fs2BGdOnVCaGgo9u/fj4EDBwoYmW2aOnUqzp07R3WVAqGZJgvl4eEBiURS6+yH7Oxs+Pj4CBQVAQAXFxe0a9cOV65cETqUFov7DNDnw/K0bt0aHh4e9Pkwg2nTpmHbtm3Yt28fAgIC+Ot9fHxQWVmJgoICg/H0eTA9SposlEwmQ3R0NPbs2cNfp9PpsGfPHsTExAgYGSkuLsbVq1fh6+srdCgtVkhICHx8fAw+H2q1GkePHqXPh8Bu3ryJvLw8+nyYEGMM06ZNw5YtW7B3716EhIQYHI+OjoadnZ3B5yElJQU3btygz4OJ0fKcBZs1axYmTZqEBx98EN26dcNnn32GkpISPPvss0KH1qK8/vrrGDFiBIKDg5GRkYF58+ZBIpFg/PjxQodm04qLiw1mK1JTU5GUlAQ3NzcEBQVhxowZ+PDDD9G2bVuEhIRg7ty58PPzw8iRI4UL2gbd6+fg5uaG999/H2PGjIGPjw+uXr2KN998E23atEFcXJyAUduWqVOnYv369fjtt9/g7OzM1ympVCrY29tDpVJh8uTJmDVrFtzc3KBUKjF9+nTExMSgR48eAkdvY4Q+fY/c24oVK1hQUBCTyWSsW7du7MiRI0KH1OKMHTuW+fr6MplMxvz9/dnYsWPZlStXhA7L5u3bt48BqHWZNGkSY0zfdmDu3LnM29ubyeVyNnDgQJaSkiJs0DboXj+H0tJSNnjwYObp6cns7OxYcHAwmzJlCsvKyhI6bJtS1+sPgH3zzTf8mLKyMvbKK68wV1dX5uDgwEaNGsUyMzOFC9pGiRhjrPlTNUIIIYQQ60I1TYQQQgghRqCkiRBCCCHECJQ0EUIIIYQYgZImQgghhBAjUNJECCGEEGIESpoIIYQQQoxASRMhhBBCiBEoaSKEEEIIMQIlTYSQFuOZZ56pc5uV/fv3QyQS1drwlBBCaqKkiRBCmoFGoxE6BEJIE1HSRAghd/nll18QGRkJuVyOVq1aYcmSJQbHRSIRtm7danCdi4sL1q1bBwC4fv06RCIRNm7ciL59+0KhUODHH39spugJIeYiFToAQgixJImJiXjiiScwf/58jB07FocPH8Yrr7wCd3d3PPPMMw26r7feegtLlixBly5doFAozBMwIaTZUNJECGlRtm3bBicnJ4PrtFot/++lS5di4MCBmDt3LgCgXbt2uHDhAhYvXtzgpGnGjBkYPXp0k2MmhFgGWp4jhLQo/fv3R1JSksHlq6++4o8nJyejZ8+eBrfp2bMnLl++bJBcGePBBx80ScyEEMtAM02EkBbF0dERbdq0Mbju5s2bDboPkUgExpjBdXUVejs6OjY8QEKIxaKZJkIIqSE8PByHDh0yuO7QoUNo164dJBIJAMDT0xOZmZn88cuXL6O0tLRZ4ySEND+aaSKEkBpee+01dO3aFQsWLMDYsWORkJCAlStXYvXq1fyYAQMGYOXKlYiJiYFWq8Xs2bNhZ2cnYNSEkOZAM02EEFLDAw88gE2bNmHDhg3o0KED3nvvPXzwwQcGReBLlixBYGAgevfujSeffBKvv/46HBwchAuaENIsROzuhXlCCCGEEFILzTQRQgghhBiBkiZCCCGEECNQ0kQIIYQQYgRKmgghhBBCjEBJEyGEEEKIEShpIoQQQggxAiVNhBBCCCFGoKSJEEIIIcQIlDQRQgghhBiBkiZCCCGEECNQ0kQIIYQQYgRKmgghhBBCjPD/kVkDcBeHzV0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a line chart with the data\n",
    "df['first_occ_hour'].value_counts().sort_index().plot(kind='line')\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Crime frequencies of Denver neighbourhoods')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auto-theft                      1374\n",
      "theft-from-motor-vehicle        1293\n",
      "public-disorder                  987\n",
      "burglary                         936\n",
      "larceny                          826\n",
      "all-other-crimes                 694\n",
      "aggravated-assault               331\n",
      "other-crimes-against-persons     258\n",
      "robbery                          140\n",
      "drug-alcohol                     126\n",
      "sexual-assault                    62\n",
      "white-collar-crime                29\n",
      "arson                             28\n",
      "murder                             5\n",
      "Name: offense_category_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#To find the crime frequencies in 5pm\n",
    "fivepm=df[df['first_occ_hour']==5]\n",
    "print(fivepm['offense_category_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighborhood_id\n",
      "athmar-park        (athmar-park, 20)\n",
      "auraria                (auraria, 12)\n",
      "baker                    (baker, 17)\n",
      "barnum                  (barnum, 22)\n",
      "barnum-west        (barnum-west, 22)\n",
      "                        ...         \n",
      "west-colfax        (west-colfax, 12)\n",
      "west-highland    (west-highland, 20)\n",
      "westwood              (westwood, 22)\n",
      "whittier              (whittier, 17)\n",
      "windsor                (windsor, 12)\n",
      "Name: first_occ_hour, Length: 78, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#One can choose to be more specific and check the most common crime hour per neighbourhoood\n",
    "most_time=df.groupby('neighborhood_id')['first_occ_hour'].value_counts().groupby('neighborhood_id').idxmax()\n",
    "print(most_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighborhood_id\n",
      "athmar-park        (athmar-park, theft-from-motor-vehicle)\n",
      "auraria                        (auraria, all-other-crimes)\n",
      "baker                                     (baker, larceny)\n",
      "barnum                          (barnum, all-other-crimes)\n",
      "barnum-west                (barnum-west, all-other-crimes)\n",
      "                                   ...                    \n",
      "west-colfax                 (west-colfax, public-disorder)\n",
      "west-highland    (west-highland, theft-from-motor-vehicle)\n",
      "westwood                      (westwood, all-other-crimes)\n",
      "whittier                       (whittier, public-disorder)\n",
      "windsor                              (windsor, auto-theft)\n",
      "Name: offense_category_id, Length: 78, dtype: object\n"
     ]
    }
   ],
   "source": [
    "most_common_crime=df.groupby('neighborhood_id')['offense_category_id'].value_counts().groupby('neighborhood_id').idxmax()\n",
    "print(most_common_crime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offense_category_id\n",
      "sexual-assault                  47.961713\n",
      "white-collar-crime              26.077710\n",
      "larceny                          6.775805\n",
      "theft-from-motor-vehicle         6.032673\n",
      "burglary                         5.847324\n",
      "auto-theft                       3.814023\n",
      "public-disorder                  3.480932\n",
      "all-other-crimes                 2.835307\n",
      "other-crimes-against-persons     2.349495\n",
      "aggravated-assault               1.919446\n",
      "arson                            1.450855\n",
      "robbery                          1.074017\n",
      "drug-alcohol                     0.510000\n",
      "murder                           0.321936\n",
      "Name: report_time_difference, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#To find the crime with the highest average difference between reported time and first occurrence time\n",
    "print(df.groupby('offense_category_id')['report_time_difference'].mean().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sexual assault is the crime with the most delayed reporting time with an average of 48 days.The 2nd highest is white collar crime with an average of 26 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7     37049\n",
      "8     36713\n",
      "5     35449\n",
      "6     34953\n",
      "9     34922\n",
      "1     32814\n",
      "4     32142\n",
      "3     31555\n",
      "2     28803\n",
      "10    28660\n",
      "12    26894\n",
      "11    26177\n",
      "Name: crime_month, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#To check most common month, extract the months from the datetime, and compare their frequencies \n",
    "df['crime_month']=df['first_occurrence_date'].dt.month\n",
    "print(df['crime_month'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array(['07-01', '07-02', '07-03', '07-04', '07-05', '07-06', '07-07',\n",
      "       '07-08', '07-09', '07-10', '07-11', '07-12', '07-13', '07-14',\n",
      "       '07-15', '07-16', '07-17', '07-18', '07-19', '07-20', '07-21',\n",
      "       '07-22', '07-23', '07-24', '07-25', '07-26', '07-27', '07-28',\n",
      "       '07-29', '07-30', '07-31'], dtype='<U5'), array([1413, 1161, 1189, 1236, 1197, 1142, 1162, 1251, 1159, 1219, 1176,\n",
      "       1221, 1146, 1189, 1175, 1182, 1120, 1166, 1220, 1214, 1196, 1242,\n",
      "       1213, 1214, 1201, 1201, 1185, 1158, 1178, 1159, 1164], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "#July has the highest crime occurrences of all the months. I had a suspicion that the 4th of July was a major cause of this, because the celebrations might lead into an increase in disorder\n",
    "# The days were extracted from all July crime occurrences and counted to check if the times around the 4th of July had a crime increase\n",
    "df['month_day']=df['first_occurrence_date'].dt.strftime('%m-%d')\n",
    "month_day=list(df['month_day'])\n",
    "july=[date for date in month_day if date.startswith('07')]\n",
    "print(np.unique(july,return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#There was a slight increase on July 4th\n",
    "#The 1st of July had the most crime occurrencies. Two other months were checked for their most frequent day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array(['08-01', '08-02', '08-03', '08-04', '08-05', '08-06', '08-07',\n",
      "       '08-08', '08-09', '08-10', '08-11', '08-12', '08-13', '08-14',\n",
      "       '08-15', '08-16', '08-17', '08-18', '08-19', '08-20', '08-21',\n",
      "       '08-22', '08-23', '08-24', '08-25', '08-26', '08-27', '08-28',\n",
      "       '08-29', '08-30', '08-31'], dtype='<U5'), array([1332, 1133, 1054, 1210, 1170, 1159, 1147, 1234, 1187, 1217, 1163,\n",
      "       1136, 1128, 1217, 1218, 1254, 1207, 1194, 1213, 1250, 1161, 1165,\n",
      "       1096, 1222, 1129, 1191, 1199, 1187, 1147, 1189, 1204], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "aug=[date for date in month_day if date.startswith('08')]\n",
    "print(np.unique(aug,return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array(['03-01', '03-02', '03-03', '03-04', '03-05', '03-06', '03-07',\n",
      "       '03-08', '03-09', '03-10', '03-11', '03-12', '03-13', '03-14',\n",
      "       '03-15', '03-16', '03-17', '03-18', '03-19', '03-20', '03-21',\n",
      "       '03-22', '03-23', '03-24', '03-25', '03-26', '03-27', '03-28',\n",
      "       '03-29', '03-30', '03-31'], dtype='<U5'), array([1239, 1094, 1030,  989, 1004, 1028, 1021,  966, 1025, 1078, 1122,\n",
      "       1004,  936,  831,  956, 1042,  998, 1006,  976, 1000, 1034, 1030,\n",
      "       1053,  984, 1038, 1090,  992, 1004,  961, 1029,  995], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "#August 1st was also the day with the highest crime occurrences.\n",
    "march=[date for date in month_day if date.startswith('03')]\n",
    "print(np.unique(march,return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     15275\n",
      "15    12945\n",
      "20    12903\n",
      "5     12841\n",
      "16    12833\n",
      "10    12825\n",
      "2     12809\n",
      "23    12753\n",
      "3     12714\n",
      "6     12694\n",
      "8     12666\n",
      "4     12643\n",
      "17    12637\n",
      "11    12588\n",
      "21    12577\n",
      "7     12554\n",
      "26    12535\n",
      "18    12531\n",
      "14    12526\n",
      "28    12518\n",
      "12    12515\n",
      "9     12495\n",
      "24    12456\n",
      "19    12455\n",
      "22    12450\n",
      "27    12421\n",
      "13    12316\n",
      "25    12211\n",
      "30    11504\n",
      "29    11461\n",
      "31     7480\n",
      "Name: day, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#So was march.\n",
    "#The frequency of each day was checked\n",
    "df['day']=df['first_occurrence_date'].dt.day\n",
    "print(df['day'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The 1st of the month has the highest crime occurrences. Interestingly, the 3 lowest days of occurrences are the 29th, 30th, and 31st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theft-from-motor-vehicle        2775\n",
      "public-disorder                 2359\n",
      "larceny                         2183\n",
      "auto-theft                      1907\n",
      "all-other-crimes                1701\n",
      "burglary                        1145\n",
      "other-crimes-against-persons     763\n",
      "drug-alcohol                     687\n",
      "aggravated-assault               632\n",
      "sexual-assault                   418\n",
      "white-collar-crime               410\n",
      "robbery                          244\n",
      "arson                             35\n",
      "murder                            16\n",
      "Name: offense_category_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "subset=df[df['day']==1]\n",
    "print(subset['offense_category_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theft-from-motor-vehicle         18.166939\n",
      "public-disorder                  33.610475\n",
      "larceny                          47.901800\n",
      "auto-theft                       60.386252\n",
      "all-other-crimes                 71.522095\n",
      "burglary                         79.018003\n",
      "other-crimes-against-persons     84.013093\n",
      "drug-alcohol                     88.510638\n",
      "aggravated-assault               92.648118\n",
      "sexual-assault                   95.384615\n",
      "white-collar-crime               98.068740\n",
      "robbery                          99.666121\n",
      "arson                            99.895254\n",
      "murder                          100.000000\n",
      "Name: offense_category_id, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Using Pareto analysis to extract the 'vital few'\n",
    "column= subset['offense_category_id']\n",
    "total_count = len(subset)\n",
    "subset_sorted = column.value_counts().sort_values(ascending=False)\n",
    "subset_sorted_cumsum = subset_sorted.cumsum() / total_count * 100\n",
    "print(subset_sorted_cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a pareto chart\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot the bars\n",
    "ax1.bar(subset_sorted.index, subset_sorted, color='b', alpha=0.7,align='center')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Create a secondary y-axis for cumulative percentage\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(subset_sorted.index, subset_sorted_cumsum, color='r', marker='o',linestyle='-', linewidth=2)\n",
    "\n",
    "ax1.set_xlabel('Offense')\n",
    "ax1.set_ylabel('Frequency', color='b')\n",
    "ax2.set_ylabel('Cumulative Percentage', color='r')\n",
    "\n",
    "plt.title('Pareto chart for crimes on the 1st')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighborhood_id\n",
      "wellshire         383\n",
      "indian-creek      723\n",
      "country-club      891\n",
      "rosedale         1116\n",
      "skyland          1181\n",
      "                ...  \n",
      "dia             14743\n",
      "capitol-hill    14862\n",
      "cbd             14882\n",
      "central-park    18068\n",
      "five-points     23519\n",
      "Name: victim_count, Length: 78, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "victim_count=df.groupby('neighborhood_id')['victim_count'].sum().sort_values()\n",
    "print(victim_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offense_category_id\n",
      "arson                           1.000000\n",
      "auto-theft                      1.000000\n",
      "burglary                        1.000000\n",
      "drug-alcohol                    1.000000\n",
      "larceny                         1.000000\n",
      "robbery                         1.000000\n",
      "theft-from-motor-vehicle        1.000000\n",
      "white-collar-crime              1.000000\n",
      "all-other-crimes                1.004897\n",
      "public-disorder                 1.012834\n",
      "sexual-assault                  1.022341\n",
      "murder                          1.080097\n",
      "other-crimes-against-persons    1.085961\n",
      "aggravated-assault              1.290827\n",
      "Name: victim_count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "victim_per_crime=df.groupby('offense_category_id')['victim_count'].mean().sort_values()\n",
    "print(victim_per_crime)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theft-from-motor-vehicle        64640\n",
      "public-disorder                 56881\n",
      "larceny                         56243\n",
      "auto-theft                      55953\n",
      "all-other-crimes                46768\n",
      "burglary                        27661\n",
      "drug-alcohol                    21804\n",
      "other-crimes-against-persons    20486\n",
      "aggravated-assault              17213\n",
      "robbery                          6972\n",
      "white-collar-crime               6706\n",
      "sexual-assault                   4297\n",
      "arson                             829\n",
      "murder                            412\n",
      "Name: offense_category_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['offense_category_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offense_category_id\n",
      "aggravated-assault                        (22, aggravated-assault)\n",
      "all-other-crimes                            (16, all-other-crimes)\n",
      "arson                                                   (0, arson)\n",
      "auto-theft                                        (22, auto-theft)\n",
      "burglary                                             (0, burglary)\n",
      "drug-alcohol                                    (16, drug-alcohol)\n",
      "larceny                                              (12, larceny)\n",
      "murder                                                 (0, murder)\n",
      "other-crimes-against-persons    (20, other-crimes-against-persons)\n",
      "public-disorder                               (0, public-disorder)\n",
      "robbery                                              (21, robbery)\n",
      "sexual-assault                                 (0, sexual-assault)\n",
      "theft-from-motor-vehicle             (0, theft-from-motor-vehicle)\n",
      "white-collar-crime                        (12, white-collar-crime)\n",
      "Name: offense_category_id, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#To find the most common time for the crimes in Denver\n",
    "df['dfhr']=df['first_occurrence_date'].dt.hour\n",
    "hrtm=df.groupby('dfhr')['offense_category_id'].value_counts().groupby('offense_category_id').idxmax()\n",
    "print(hrtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "public-disorder                 3782\n",
      "theft-from-motor-vehicle        3716\n",
      "larceny                         3289\n",
      "all-other-crimes                3042\n",
      "auto-theft                      2286\n",
      "other-crimes-against-persons    1626\n",
      "drug-alcohol                    1585\n",
      "burglary                        1425\n",
      "aggravated-assault              1376\n",
      "robbery                          406\n",
      "sexual-assault                   272\n",
      "white-collar-crime               188\n",
      "arson                             56\n",
      "murder                            24\n",
      "Name: offense_category_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "pri=df[df['neighborhood_id']=='five-points']\n",
    "print(pri['offense_category_id'].value_counts())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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