{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "711a0e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "from io import BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6abd0a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "from io import BytesIO\n",
    "\n",
    "# Function to handle ZIP files containing CSVs\n",
    "def download_and_read_zip_csv(url):\n",
    "    with requests.get(url) as response:\n",
    "        response.raise_for_status() \n",
    "        with zipfile.ZipFile(BytesIO(response.content)) as zip_file:\n",
    "            data_file_name = zip_file.namelist()[0]  \n",
    "            with zip_file.open(data_file_name) as df:\n",
    "                data = pd.read_csv(df, low_memory=False)\n",
    "    return data\n",
    "\n",
    "# Function to download and read an XLSX file\n",
    "def download_and_read_xlsx(url):\n",
    "    with requests.get(url) as response:\n",
    "        response.raise_for_status()\n",
    "        data = pd.read_excel(BytesIO(response.content))\n",
    "    return data\n",
    "\n",
    "# URLs\n",
    "url_chapel = \"https://huggingface.co/datasets/zwn22/NC_Crime/resolve/main/Chapel_Hill.csv.zip\"\n",
    "url_raleigh = \"https://huggingface.co/datasets/zwn22/NC_Crime/resolve/main/Raleigh.csv.zip\"\n",
    "url_cary = \"https://data.townofcary.org/api/explore/v2.1/catalog/datasets/cpd-incidents/exports/csv?lang=en&timezone=US%2FEastern&use_labels=true&delimiter=%2C\"\n",
    "url_durham = \"https://www.arcgis.com/sharing/rest/content/items/7132216432df4957830593359b0c4030/data\"\n",
    "\n",
    "Chapel = download_and_read_zip_csv(url_chapel)\n",
    "Raleigh = download_and_read_zip_csv(url_raleigh)\n",
    "Cary = pd.read_csv(url_cary, low_memory=False) \n",
    "Durham = download_and_read_xlsx(url_durham)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c7195730",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pyproj import Transformer\n",
    "\n",
    "def process_crime_data(filename, city_name):\n",
    "    pd.options.mode.chained_assignment = None  \n",
    "\n",
    "    def categorize_crime(crime):\n",
    "        for category, crimes in crime_mapping.items():\n",
    "            if crime in crimes:\n",
    "                return category\n",
    "        return 'Miscellaneous'\n",
    "    \n",
    "    def convert_coordinates(x, y):\n",
    "            transformer = Transformer.from_crs(\"epsg:2264\", \"epsg:4326\", always_xy=True)\n",
    "            lon, lat = transformer.transform(x, y)\n",
    "            return pd.Series([lat, lon])\n",
    "        \n",
    "    crime_mapping = {\n",
    "        'Theft': [\n",
    "            'BURGLARY', 'MOTOR VEHICLE THEFT', 'LARCENY',\n",
    "            'LARCENY - AUTOMOBILE PARTS OR ACCESSORIES', 'TOWED/ABANDONED VEHICLE',\n",
    "            'LARCENY - FROM MOTOR VEHICLE', 'LARCENY - SHOPLIFTING', 'LOST PROPERTY',\n",
    "            'VANDALISM', 'LARCENY - ALL OTHER', 'LARCENY - FROM BUILDING',\n",
    "            'RECOVERED STOLEN PROPERTY (OTHER JURISDICTION)', 'LARCENY - POCKET-PICKING',\n",
    "            'LARCENY - FROM COIN-OPERATED DEVICE', 'LARCENY - PURSESNATCHING',\n",
    "            'LARCENY FROM MV', 'MV THEFT', 'STOLEN PROPERTY',\n",
    "            'THEFT/LARCENY', 'LARCENY FROM AU', 'LARCENY FROM PE', 'LARCENY OF OTHE',\n",
    "            'LARCENY FROM BU', 'LARCENY OF BIKE', 'LARCENY FROM RE', 'LARCENY OF AUTO'\n",
    "        ],\n",
    "        'Fraud': [\n",
    "            'FRAUD-IDENTITY THEFT', 'EMBEZZLEMENT', 'COUNTERFEITING/FORGERY',\n",
    "            'FRAUD - CONFIDENCE GAMES/TRICKERY', 'FRAUD - CREDIT CARD/ATM',\n",
    "            'FRAUD - UNAUTHORIZED USE OF CONVEYANCE', 'FRAUD - FALSE PRETENSE',\n",
    "            'FRAUD - IMPERSONATION', 'FRAUD - WIRE/COMPUTER/OTHER ELECTRONIC',\n",
    "            'FRAUD - WORTHLESS CHECKS', 'FRAUD-FAIL TO RETURN RENTAL VEHICLE',\n",
    "            'FRAUD-HACKING/COMPUTER INVASION', 'FRAUD-WELFARE FRAUD', 'FRAUD', 'BRIBERY',\n",
    "            'FRAUD OR DECEPT'\n",
    "        ],\n",
    "        'Assault': [\n",
    "            'SIMPLE ASSAULT', 'AGGRAVATED ASSAULT', 'ASSAULT', 'ASSAULT/SEXUAL',\n",
    "            'STAB GUNSHOT PE', 'ACTIVE ASSAILAN'\n",
    "        ],\n",
    "        'Drugs': [\n",
    "            'DRUG/NARCOTIC VIOLATIONS', 'DRUG EQUIPMENT/PARAPHERNALIA', 'DRUGS',\n",
    "            'DRUG VIOLATIONS'\n",
    "        ],\n",
    "        'Sexual Offenses': [\n",
    "            'SEX OFFENSE - FORCIBLE RAPE', 'SEX OFFENSE - SEXUAL ASSAULT WITH AN OBJECT',\n",
    "            'SEX OFFENSE - FONDLING', 'SEX OFFENSE - INDECENT EXPOSURE', 'SEX OFFENSE - FORCIBLE SODOMY',\n",
    "            'SEX OFFENSE - STATUTORY RAPE', 'SEX OFFENSE - PEEPING TOM', 'SEX OFFENSE - INCEST',\n",
    "            'SEX OFFENSES', 'SEXUAL OFFENSE'\n",
    "        ],\n",
    "        'Homicide': [\n",
    "            'HOMICIDE-MURDER/NON-NEGLIGENT MANSLAUGHTER', 'JUSTIFIABLE HOMICIDE',\n",
    "            'HOMICIDE - NEGLIGENT MANSLAUGHTER', 'MURDER', 'SUICIDE ATTEMPT', 'ABUSE/ABANDOMEN',\n",
    "            'DECEASED PERSON'\n",
    "        ],\n",
    "        'Arson': ['ARSON'],\n",
    "        'Kidnapping': ['KIDNAPPING/ABDUCTION', 'KIDNAPPING'],\n",
    "        'Weapons Violations': ['WEAPON VIOLATIONS', 'WEAPONS VIOLATION', 'WEAPON/FIREARMS'],\n",
    "        'Traffic Violations': [\n",
    "            'ALL TRAFFIC (EXCEPT DWI)', 'TRAFFIC', 'UNAUTHORIZED MOTOR VEHICLE USE',\n",
    "            'TRAFFIC VIOLATIONS', 'LIQUOR LAW VIOLATIONS', 'TRAFFIC STOP', 'TRAFFIC/TRANSPO',\n",
    "            'TRAFFIC VIOLATI', 'MVC', 'MVC W INJURY', 'MVC W INJURY AB', 'MVC W INJURY DE',\n",
    "            'MVC ENTRAPMENT'\n",
    "        ],\n",
    "        'Disorderly Conduct': [\n",
    "            'DISORDERLY CONDUCT', 'DISORDERLY CONDUCT-DRUNK AND DISRUPTIVE',\n",
    "            'DISORDERLY CONDUCT-FIGHTING (AFFRAY)', 'DISORDERLY CONDUCT-UNLAWFUL ASSEMBLY',\n",
    "            'DISTURBANCE/NUI', 'DOMESTIC DISTUR', 'DISPUTE', 'DISTURBANCE', 'LOST PROPERTY',\n",
    "            'TRESPASSING/UNW', 'REFUSAL TO LEAV', 'SUSPICIOUS COND', 'STRUCTURE FIRE'\n",
    "        ],\n",
    "        'Gambling': [\n",
    "            'GAMBLING - OPERATING/PROMOTING/ASSISTING', 'GAMBLING - BETTING/WAGERING', 'GAMBLING'\n",
    "        ],\n",
    "        'Animal-related Offenses': ['ANIMAL CRUELTY', 'ANIMAL BITE', 'ANIMAL', 'ANIMAL CALL'],\n",
    "        'Prostitution-related Offenses': [\n",
    "            'PROSTITUTION', 'PROSTITUTION - ASSISTING/PROMOTING', 'PROSTITUTION - PURCHASING'\n",
    "        ],\n",
    "        'Miscellaneous': [\n",
    "            'MISCELLANEOUS', 'ALL OTHER OFFENSES', '<Null>', 'SUSPICIOUS/WANT', 'MISC OFFICER IN',\n",
    "            'INDECENCY/LEWDN', 'PUBLIC SERVICE', 'TRESPASSING', 'UNKNOWN PROBLEM', 'LOUD NOISE',\n",
    "            'ESCORT', 'ABDUCTION/CUSTO', 'THREATS', 'BURGLAR ALARM', 'DOMESTIC', 'PROPERTY FOUND',\n",
    "            'FIREWORKS', 'MISSING/RUNAWAY', 'MENTAL DISORDER', 'CHECK WELL BEIN', 'PSYCHIATRIC',\n",
    "            'OPEN DOOR', 'ABANDONED AUTO', 'HARASSMENT THRE', 'JUVENILE RELATE', 'ASSIST MOTORIST',\n",
    "            'HAZARDOUS DRIVI', 'GAS LEAK FIRE', 'ASSIST OTHER AG', 'DOMESTIC ASSIST', 'SUSPICIOUS VEHI',\n",
    "            'UNKNOWN LE', 'ALARMS', '911 HANGUP', 'BOMB/CBRN/PRODU', 'STATIONARY PATR', 'LITTERING',\n",
    "            'HOUSE CHECK', 'CARDIAC', 'CLOSE PATROL', 'BOMB FOUND/SUSP', 'INFO FOR ALL UN', 'UNCONCIOUS OR F',\n",
    "            'LIFTING ASSISTA', 'ATTEMPT TO LOCA', 'SICK PERSON', 'HEAT OR COLD EX', 'CONFINED SPACE',\n",
    "            'TRAUMATIC INJUR', 'DROWNING', 'CITY ORDINANCE', 'JUVENILE', 'MISSING PERSON',\n",
    "            'PUBLIC SERVICE', 'PUBLICE SERVICE'\n",
    "        ],\n",
    "        'Robbery': ['ROBBERY'],\n",
    "        'Extortion': ['EXTORTION'],\n",
    "        'Human Trafficking': ['HUMAN TRAFFICKING']\n",
    "    }\n",
    "    \n",
    "    crime_severity_mapping = {\n",
    "        'Miscellaneous': 'Minor',\n",
    "        'Disorderly Conduct': 'Minor',\n",
    "        'Traffic Violations': 'Minor',\n",
    "        'Animal-related Offenses': 'Minor',\n",
    "        'Prostitution-related Offenses': 'Minor',\n",
    "        'Gambling': 'Minor',\n",
    "        'Public Service': 'Minor',\n",
    "        'Juvenile': 'Minor',\n",
    "        'Fraud': 'Moderate',\n",
    "        'Theft': 'Moderate',\n",
    "        'Drugs': 'Moderate',\n",
    "        'Assault': 'Moderate',\n",
    "        'Sexual Offenses': 'Moderate',\n",
    "        'Weapons Violations': 'Moderate',\n",
    "        'Vandalism': 'Moderate',\n",
    "        'Burglary': 'Moderate',\n",
    "        'Robbery': 'Moderate',\n",
    "        'Kidnapping': 'Severe',\n",
    "        'Homicide': 'Severe',\n",
    "        'Arson': 'Severe',\n",
    "        'Extortion': 'Severe',\n",
    "        'Human Trafficking': 'Severe',\n",
    "        'Murder': 'Severe'\n",
    "    }\n",
    "\n",
    "    df = pd.DataFrame()  # Initialize an empty DataFrame for generic use\n",
    "    \n",
    "    \n",
    "    \n",
    "    if city_name == 'Durham':\n",
    "        df = pd.read_excel(filename)\n",
    "        df['Weapon'] = df['Weapon'].replace(['(blank)', 'Not Applicable/None', 'Unknown/Not Stated'], None)        \n",
    "        df['crime_major_category'] = df['Offense'].apply(categorize_crime)\n",
    "        \n",
    "        # Apply coordinate conversion and categorization\n",
    "        coordinates = df.apply(lambda row: convert_coordinates(row['X'], row['Y']), axis=1)\n",
    "        df['latitude'], df['longitude'] = coordinates[0], coordinates[1]\n",
    "\n",
    "        new_df = pd.DataFrame({\n",
    "            \"year\": pd.to_datetime(df['Report Date']).dt.year,\n",
    "            \"city\": \"Durham\",\n",
    "            \"crime_major_category\": df['crime_major_category'],\n",
    "            \"crime_detail\": df['Offense'].str.title(),\n",
    "            \"latitude\": df['latitude'],\n",
    "            \"longitude\": df['longitude'],\n",
    "            \"occurance_time\": pd.to_datetime(df['Report Date'].astype(str) + ' ' + df['Report Time'], errors='coerce').dt.strftime('%Y/%m/%d %H:%M:%S'),\n",
    "            \"clear_status\": df['Status'],\n",
    "            \"incident_address\": df['Address'],\n",
    "            \"notes\": df['Weapon'].apply(lambda x: f\"Weapon: {x}\" if pd.notnull(x) else \"No Data\")\n",
    "        }).fillna(\"No Data\")\n",
    "\n",
    "        \n",
    "    elif city_name == 'Raleigh':\n",
    "        df = pd.read_csv(filename, low_memory=False)\n",
    "        new_df = pd.DataFrame({\n",
    "            \"year\": df['reported_year'],\n",
    "            \"city\": \"Raleigh\",\n",
    "            \"crime_major_category\": df['crime_category'].apply(categorize_crime),\n",
    "            \"crime_detail\": df['crime_description'],\n",
    "            \"latitude\": df['latitude'].round(5).fillna(0),\n",
    "            \"longitude\": df['longitude'].round(5).fillna(0),\n",
    "            \"occurance_time\": pd.to_datetime(df['reported_date'].str.replace(r'\\+\\d{2}$', '', regex=True), errors='coerce').dt.strftime('%Y/%m/%d %H:%M:%S'),\n",
    "            \"clear_status\": None,\n",
    "            \"incident_address\": df['reported_block_address'] + ', ' + df['district'] + ', Raleigh',\n",
    "            \"notes\": 'District: '+ df['district'].str.title()\n",
    "        }).fillna(\"No Data\")\n",
    "        \n",
    "    elif city_name == 'Cary':\n",
    "        df = pd.read_csv(filename, low_memory=False).dropna(subset=['Year'])\n",
    "        new_df = pd.DataFrame({\n",
    "            \"year\": df[\"Year\"].astype(int),\n",
    "            \"city\": \"Cary\",\n",
    "            \"crime_major_category\": df['Crime Category'].apply(categorize_crime).str.title(),\n",
    "            \"crime_detail\": df['Crime Type'].str.title(),\n",
    "            \"latitude\": df['Lat'].fillna(0).round(5).fillna(0),\n",
    "            \"longitude\": df['Lon'].fillna(0).round(5).fillna(0),\n",
    "            \"occurance_time\": pd.to_datetime(df['Begin Date Of Occurrence'] + ' ' + df['Begin Time Of Occurrence']).dt.strftime('%Y/%m/%d %H:%M:%S'),\n",
    "            \"clear_status\": None,\n",
    "            \"incident_address\": df['Geo Code'],\n",
    "            \"notes\": 'District: '+ df['District'].str.title() + ' Violent Property: ' + df['Violent Property'].str.title()\n",
    "        }).fillna(\"No Data\")\n",
    "        \n",
    "    elif city_name == 'Chapel Hill':\n",
    "        df = pd.read_csv(filename, low_memory=False)\n",
    "        replace_values = {'<Null>': None, 'NONE': None}\n",
    "        df['Weapon_Description'] = df['Weapon_Description'].replace(replace_values)\n",
    "        new_df = pd.DataFrame({\n",
    "            \"year\": pd.to_datetime(df['Date_of_Occurrence']).dt.year,\n",
    "            \"city\": \"Chapel Hill\",\n",
    "            \"crime_major_category\": df['Reported_As'].apply(categorize_crime),\n",
    "            \"crime_detail\": df['Offense'].str.title(),\n",
    "            \"latitude\": df['X'].round(5).fillna(0),\n",
    "            \"longitude\": df['Y'].round(5).fillna(0),\n",
    "            \"occurance_time\": pd.to_datetime(df['Date_of_Occurrence'].str.replace(r'\\+\\d{2}$', '', regex=True)).dt.strftime('%Y/%m/%d %H:%M:%S'),\n",
    "            \"clear_status\": None,\n",
    "            \"incident_address\": df['Street'].str.replace(\"@\", \" \"),\n",
    "            \"notes\": df['Weapon_Description'].apply(lambda x: f\"Weapon: {x}\" if pd.notnull(x) else \"Weapon: None\").str.title()\n",
    "        }).fillna(\"No Data\")\n",
    "        indices_to_switch = new_df.loc[(new_df['latitude'].between(-82, -75)) & (new_df['longitude'].between(35, 40))].index\n",
    "        for idx in indices_to_switch:\n",
    "            new_df.at[idx, 'latitude'], new_df.at[idx, 'longitude'] = new_df.at[idx, 'longitude'], new_df.at[idx, 'latitude']\n",
    "\n",
    "    \n",
    "    new_df = new_df[new_df['year'] >= 2015]\n",
    "    new_df = new_df.loc[(new_df['latitude'].between(35, 40)) & (new_df['longitude'].between(-82, -75))]\n",
    "    new_df['crime_severity'] = new_df['crime_major_category'].map(crime_severity_mapping)\n",
    "    return new_df\n",
    "\n",
    "# Example usage\n",
    "Cary_new = process_crime_data(\"Cary.csv\", \"Cary\")\n",
    "Chapel_new = process_crime_data(\"Chapel_hill.csv\", \"Chapel Hill\")\n",
    "Durham_new = process_crime_data(\"Durham.xlsx\", \"Durham\")\n",
    "Raleigh_new = process_crime_data(\"Raleigh.csv\", \"Raleigh\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cfd5d140",
   "metadata": {},
   "outputs": [],
   "source": [
    "NC_v1 = pd.concat([Durham_new, Chapel_new, Cary_new, Raleigh_new], ignore_index=True)\n",
    "NC_v1.to_csv('NC_v1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8186c46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(585886, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NC_v1 = pd.read_csv(\"NC_v1.csv\")\n",
    "NC_v1.shape"
   ]
  }
 ],
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