Update NC_Crime.py

NC_Crime.py

@@ -22,6 +22,8 @@ from typing import List
 import datasets
 import logging
 import pandas as pd
+from pyproj import Transformer
+
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -78,21 +80,236 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
  )
 
  
+ 
  def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
  # Use the raw GitHub link to download the CSV file
- downloaded_file_path = dl_manager.download_and_extract(
- "https://data.townofcary.org/api/explore/v2.1/catalog/datasets/cpd-incidents/exports/csv?lang=en&timezone=US%2FEastern&use_labels=true&delimiter=%2C"
- )
- unzipped_file_path = os.path.join(downloaded_file_path, "DCCR.csv")
- # Return a list of split generators
+ cary_path = dl_manager.download_and_extract("https://data.townofcary.org/api/explore/v2.1/catalog/datasets/cpd-incidents/exports/csv?lang=en&timezone=US%2FEastern&use_labels=true&delimiter=%2C")
+ durham_path = dl_manager.download_and_extract("https://www.arcgis.com/sharing/rest/content/items/7132216432df4957830593359b0c4030/data")
+ raleigh_path = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=19cZzyedCLUtQt9Ko4bcOixWIJHBn9CfI")
+ chapel_hill_path = dl_manager.download_and_extract("ttps://drive.google.com/uc?export=download&id=1SZi4e01TxwuDDb6k9EU_7i-qTP1Xq2sm")
+ # 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",
+ # Durham
+ # "https://www.arcgis.com/sharing/rest/content/items/7132216432df4957830593359b0c4030/data",
+ # Raleigh
+ # "https://drive.google.com/uc?export=download&id=19cZzyedCLUtQt9Ko4bcOixWIJHBn9CfI",
+ # Chapel Hill
+ # "https://drive.google.com/uc?export=download&id=1SZi4e01TxwuDDb6k9EU_7i-qTP1Xq2sm"
+
+ cary_df = self._preprocess_cary(cary_path)
+ durham_df = self._preprocess_durham(durham_path)
+ raleigh_df = self._preprocess_raleigh(raleigh_path)
+ chapel_hill_df = self._preprocess_chapel_hill(chapel_hill_path)
+ 
+ combined_df = pd.concat([cary_df, durham_df, raleigh_df, chapel_hill_df], ignore_index=True)
+ 
+ combined_file_path = os.path.join(dl_manager.download_dir, "combined_dataset.csv")
+ combined_df.to_csv(combined_file_path, index=False)
+
  return [
- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": unzipped_file_path})
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": combined_file_path})
  ]
 
 
+ def _preprocess_durham(self, file_path):
+ # Load the dataset
+ Durham = pd.read_excel(file_path)
+ 
+ # Clean the 'Weapon' column
+ Durham['Weapon'] = Durham['Weapon'].replace(['(blank)', 'Not Applicable/None', 'Unknown/Not Stated'], None)
+ 
+ # Define the category mapping
+ category_mapping = {
+ 'Theft': ['LARCENY - AUTOMOBILE PARTS OR ACCESSORIES', 'TOWED/ABANDONED VEHICLE', 'MOTOR VEHICLE THEFT', 'BURGLARY', 'LARCENY - FROM MOTOR VEHICLE', 'LARCENY - SHOPLIFTING', 'LOST PROPERTY', 'VANDALISM', 'LARCENY - ALL OTHER', 'LARCENY - FROM BUILDING', 'RECOVERED STOLEN PROPERTY (OTHER JURISDICTION)', 'LARCENY - POCKET-PICKING', 'LARCENY - FROM COIN-OPERATED DEVICE', 'LARCENY - PURSESNATCHING'],
+ 'Fraud': ['FRAUD-IDENTITY THEFT', 'EMBEZZLEMENT', 'COUNTERFEITING/FORGERY', 'FRAUD - CONFIDENCE GAMES/TRICKERY', 'FRAUD - CREDIT CARD/ATM', 'FRAUD - UNAUTHORIZED USE OF CONVEYANCE', 'FRAUD - FALSE PRETENSE', 'FRAUD - IMPERSONATION', 'FRAUD - WIRE/COMPUTER/OTHER ELECTRONIC', 'FRAUD - WORTHLESS CHECKS', 'FRAUD-FAIL TO RETURN RENTAL VEHICLE', 'FRAUD-HACKING/COMPUTER INVASION', 'FRAUD-WELFARE FRAUD'],
+ 'Assault': ['SIMPLE ASSAULT', 'AGGRAVATED ASSAULT'],
+ 'Drugs': ['DRUG/NARCOTIC VIOLATIONS', 'DRUG EQUIPMENT/PARAPHERNALIA'],
+ 'Sexual Offenses': ['SEX OFFENSE - FORCIBLE RAPE', 'SEX OFFENSE - SEXUAL ASSAULT WITH AN OBJECT', 'SEX OFFENSE - FONDLING', 'SEX OFFENSE - INDECENT EXPOSURE', 'SEX OFFENSE - FORCIBLE SODOMY', 'SEX OFFENSE - STATUTORY RAPE', 'SEX OFFENSE - PEEPING TOM', 'SEX OFFENSE - INCEST'],
+ 'Homicide': ['HOMICIDE-MURDER/NON-NEGLIGENT MANSLAUGHTER', 'JUSTIFIABLE HOMICIDE', 'HOMICIDE - NEGLIGENT MANSLAUGHTER'],
+ 'Arson': ['ARSON'],
+ 'Kidnapping': ['KIDNAPPING/ABDUCTION'],
+ 'Weapons Violations': ['WEAPON VIOLATIONS'],
+ 'Traffic Violations': ['ALL TRAFFIC (EXCEPT DWI)'],
+ 'Disorderly Conduct': ['DISORDERLY CONDUCT', 'DISORDERLY CONDUCT-DRUNK AND DISRUPTIVE', 'DISORDERLY CONDUCT-FIGHTING (AFFRAY)', 'DISORDERLY CONDUCT-UNLAWFUL ASSEMBLY'],
+ 'Gambling': ['GAMBLING - OPERATING/PROMOTING/ASSISTING', 'GAMBLING - BETTING/WAGERING'],
+ 'Animal-related Offenses': ['ANIMAL CRUELTY'],
+ 'Prostitution-related Offenses': ['PROSTITUTION', 'PROSTITUTION - ASSISTING/PROMOTING', 'PROSTITUTION - PURCHASING']
+}
+
+ # Function to categorize crime
+ def categorize_crime(crime):
+ for category, crimes in category_mapping.items():
+ if crime in crimes:
+ return category
+ return 'Miscellaneous'
+ 
+ # Coordinate transformation function
+ def convert_coordinates(x, y):
+ transformer = Transformer.from_crs("epsg:2264", "epsg:4326", always_xy=True)
+ lon, lat = transformer.transform(x, y)
+ return pd.Series([lat, lon])
+ 
+ # Create a new DataFrame with simplified crime categories
+ Durham_new = pd.DataFrame({
+ # Your DataFrame creation code
+ })
+
+ # Convert coordinates and round/fill missing values
+ Durham_new[['latitude', 'longitude']] = Durham.apply(lambda row: convert_coordinates(row['X'], row['Y']), axis=1).round(5).fillna(0)
+ 
+ # Filter records and handle missing values
+ Durham_new = Durham_new[Durham_new['year'] >= 2015].fillna("No Data")
+
+ return Durham_new
+
+ def _preprocess_chapel_hill(self, file_path):
+ # Load the dataset
+ Chapel = pd.read_csv(file_path, low_memory=False)
+
+ # Replace specified values with None
+ replace_values = {'<Null>': None, 'NONE': None}
+ Chapel['Weapon_Description'] = Chapel['Weapon_Description'].replace(replace_values)
+
+ # Define the category mapping
+ category_mapping = {
+ 'Theft': ['THEFT/LARCENY', 'LARCENY FROM AU', 'LARCENY FROM PE', 'LARCENY OF OTHE', 'LARCENY FROM BU', 'LARCENY OF BIKE', 'LARCENY FROM RE', 'LARCENY OF AUTO'],
+ 'Assault': ['ASSAULT/SEXUAL', 'ASSAULT', 'STAB GUNSHOT PE', 'ACTIVE ASSAILAN'],
+ 'Burglary': ['BURGLARY', 'BURGLARY ATTEMP', 'STRUCTURE COLLAPSE', 'ROBBERY/CARJACK'],
+ 'Drugs': ['DRUGS'],
+ 'Traffic Violations': ['TRAFFIC STOP', 'TRAFFIC/TRANSPO', 'TRAFFIC VIOLATI', 'MVC', 'MVC W INJURY', 'MVC W INJURY AB', 'MVC W INJURY DE', 'MVC ENTRAPMENT'],
+ 'Disorderly Conduct': ['DISTURBANCE/NUI', 'DOMESTIC DISTUR', 'DISPUTE', 'DISTURBANCE', 'LOST PROPERTY', 'TRESPASSING/UNW', 'REFUSAL TO LEAV', 'SUSPICIOUS COND', 'STRUCTURE FIRE'],
+ 'Fraud': ['FRAUD OR DECEPT'],
+ 'Sexual Offenses': ['SEXUAL OFFENSE'],
+ 'Homicide': ['SUICIDE ATTEMPT', 'ABUSE/ABANDOMEN', 'DECEASED PERSON'],
+ 'Weapons Violations': ['WEAPON/FIREARMS'],
+ 'Animal-related Offenses': ['ANIMAL BITE', 'ANIMAL', 'ANIMAL CALL'],
+ 'Missing Person': ['MISSING PERSON'],
+ 'Public Service': ['PUBLIC SERVICE', 'PUBLICE SERVICE'],
+ 'Miscellaneous': ['<Null>', 'SUSPICIOUS/WANT', 'MISC OFFICER IN', 'INDECENCY/LEWDN', 'PUBLIC SERVICE', 'TRESPASSING', 'UNKNOWN PROBLEM', 'LOUD NOISE', 'ESCORT', 'ABDUCTION/CUSTO', 'THREATS', 'BURGLAR ALARM', 'DOMESTIC', 'PROPERTY FOUND', 'FIREWORKS', 'MISSING/RUNAWAY', 'MENTAL DISORDER', 'CHECK WELL BEIN', 'PSYCHIATRIC', 'OPEN DOOR', 'ABANDONED AUTO', 'HARASSMENT THRE', 'JUVENILE RELATE', 'ASSIST MOTORIST', 'HAZARDOUS DRIVI', 'MVC', 'GAS LEAK FIRE', 'ASSIST OTHER AG', 'DOMESTIC ASSIST', 'SUSPICIOUS VEHI', 'UNKNOWN LE', 'ALARMS', '911 HANGUP', 'BOMB/CBRN/PRODU', 'STATIONARY PATR', 'LITTERING', 'HOUSE CHECK', 'CARDIAC', 'CLOSE PATROL', 'BOMB FOUND/SUSP', 'INFO FOR ALL UN', 'UNCONCIOUS OR F', 'LIFTING ASSISTA', 'ATTEMPT TO LOCA', 'SICK PERSON', 'HEAT OR COLD EX', 'CONFINED SPACE', 'TRAUMATIC INJUR', 'DROWNING', 'CITY ORDINANCE']
+}
+
+ # Function to categorize crime
+ def categorize_crime(crime):
+ for category, crimes in category_mapping.items():
+ if crime in crimes:
+ return category
+ return 'Miscellaneous'
+
+ # Create a new DataFrame with simplified crime categories
+ Chapel_new = pd.DataFrame({
+ "year": pd.to_datetime(Chapel['Date_of_Occurrence']).dt.year,
+ "city": "Chapel Hill",
+ "crime_major_category": Chapel['Reported_As'].apply(categorize_crime),
+ "crime_detail": Chapel['Offense'].str.title(),
+ "latitude": Chapel['X'].round(5).fillna(0),
+ "longitude": Chapel['Y'].round(5).fillna(0),
+ "occurance_time": pd.to_datetime(Chapel['Date_of_Occurrence'].str.replace(r'\+\d{2}$', '', regex=True)).dt.strftime('%Y/%m/%d %H:%M:%S'),
+ "clear_status": None,
+ "incident_address": Chapel['Street'].str.replace("@", " "),
+ "notes": Chapel['Weapon_Description'].apply(lambda x: f"Weapon: {x}" if pd.notnull(x) else "Weapon: None").str.title()
+ }).fillna("No Data")
+
+ # Correct the latitude and longitude if necessary
+ Chapel_new.loc[(Chapel_new['latitude'].between(-80, -70)) & (Chapel_new['longitude'].between(30, 40)), ['latitude', 'longitude']] = Chapel_new.loc[(Chapel_new['latitude'].between(-80, -70)) & (Chapel_new['longitude'].between(30, 40)), ['longitude', 'latitude']].values
+
+ # Ensure latitude and longitude are in the expected range
+ Chapel_new = Chapel_new.loc[(Chapel_new['latitude'].between(30, 40)) & (Chapel_new['longitude'].between(-80, -70))]
+
+ # Filter for years 2015 and onwards
+ Chapel_new = Chapel_new[Chapel_new['year'] >= 2015]
+
+ return Chapel_new
+
+ def _preprocess_cary(self, file_path):
+ # Load the dataset
+ df = pd.read_csv(file_path, low_memory=False).dropna(subset=['Year'])
+
+ # Define the crime categorization function
+ def categorize_crime(crime):
+ crime_mapping = {
+ 'Theft': ['BURGLARY', 'MOTOR VEHICLE THEFT', 'LARCENY'],
+ 'Arson': ['ARSON'],
+ 'Assault': ['AGGRAVATED ASSAULT'],
+ 'Homicide': ['MURDER'],
+ 'Robbery': ['ROBBERY']
+ }
+ for category, crimes in crime_mapping.items():
+ if crime in crimes:
+ return category
+ return 'Miscellaneous'
+
+ # Apply the crime categorization function and preprocess the dataset
+ processed_df = pd.DataFrame({
+ "year": df["Year"].astype(int),
+ "city": "Cary",
+ "crime_major_category": df['Crime Category'].apply(categorize_crime).str.title(),
+ "crime_detail": df['Crime Type'].str.title(),
+ "latitude": df['Lat'].fillna(0).round(5).fillna(0),
+ "longitude": df['Lon'].fillna(0).round(5).fillna(0),
+ "occurance_time": pd.to_datetime(df['Begin Date Of Occurrence'] + ' ' + df['Begin Time Of Occurrence']).dt.strftime('%Y/%m/%d %H:%M:%S'),
+ "clear_status": None,
+ "incident_address": df['Geo Code'],
+ "notes": 'District: '+ df['District'].str.title() + ' Violent Property: ' + df['Violent Property'].str.title()
+ }).fillna("No Data")
+
+ # Filter the dataset for records from 2015 onwards
+ processed_df = processed_df[processed_df['year'] >= 2015]
+ 
+ return processed_df
+
+ def _preprocess_raleigh(self, file_path):
+ # Load the dataset
+ Raleigh = pd.read_csv(file_path, low_memory=False)
+
+ # Define category mapping
+ category_mapping = {
+ 'Miscellaneous': ['MISCELLANEOUS', 'ALL OTHER OFFENSES'],
+ 'Sexual Offenses': ['SEX OFFENSES'],
+ 'Assault': ['ASSAULT', 'SIMPLE ASSAULT'],
+ 'Juvenile': ['JUVENILE'],
+ 'Traffic Violations': ['TRAFFIC', 'UNAUTHORIZED MOTOR VEHICLE USE', 'TRAFFIC VIOLATIONS', 'LIQUOR LAW VIOLATIONS'],
+ 'Fraud': ['FRAUD', 'EMBEZZLEMENT', 'BRIBERY'],
+ 'Vandalism': ['VANDALISM'],
+ 'Theft': ['LARCENY FROM MV', 'LARCENY', 'MV THEFT', 'STOLEN PROPERTY'],
+ 'Burglary': ['BURGLARY/COMMERCIAL', 'BURGLARY/RESIDENTIAL'],
+ 'Disorderly Conduct': ['DISORDERLY CONDUCT'],
+ 'Weapons Violations': ['WEAPONS VIOLATION'],
+ 'Drugs': ['DRUGS', 'DRUG VIOLATIONS'],
+ 'Arson': ['ARSON'],
+ 'Robbery': ['ROBBERY'],
+ 'Kidnapping': ['KIDNAPPING'],
+ 'Extortion': ['EXTORTION'],
+ 'Human Trafficking': ['HUMAN TRAFFICKING'],
+ 'Murder': ['MURDER'],
+ 'Prostitution-related Offenses': ['PROSTITUTION'],
+ 'Gambling': ['GAMBLING'],
+ }
+
+ # Function to categorize crime based on the mapping dictionary
+ def categorize_crime(crime):
+ for category, crimes in category_mapping.items():
+ if crime in crimes:
+ return category
+ return 'Miscellaneous'
 
+ # Create a new DataFrame with simplified crime categories
+ Raleigh_new = pd.DataFrame({
+ "year": Raleigh['reported_year'],
+ "city": "Raleigh",
+ "crime_major_category": Raleigh['crime_category'].apply(categorize_crime),
+ "crime_detail": Raleigh['crime_description'],
+ "latitude": Raleigh['latitude'].round(5).fillna(0),
+ "longitude": Raleigh['longitude'].round(5).fillna(0),
+ "occurance_time": pd.to_datetime(Raleigh['reported_date'].str.replace(r'\+\d{2}$', '', regex=True), errors='coerce').dt.strftime('%Y/%m/%d %H:%M:%S'),
+ "clear_status": None,
+ "incident_address": Raleigh['reported_block_address'] + ', ' + Raleigh['district'] + ', Raleigh',
+ "notes": 'District: '+ Raleigh['district'].str.title()
+ }).fillna("No Data")
 
+ # Filter the dataset for records from 2015 onwards
+ Raleigh_new = Raleigh_new[Raleigh_new['year'] >= 2015]
 
+ return Raleigh_new
 
  def _generate_examples(self, filepath):
  # Read the CSV file