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{
"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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