Update NC_Crime.py

NC_Crime.py

@@ -84,8 +84,8 @@ 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
         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")
-        # chapel_hill_path = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=19cZzyedCLUtQt9Ko4bcOixWIJHBn9CfI")
+        # durham_path = dl_manager.download_and_extract("https://www.arcgis.com/sharing/rest/content/items/7132216432df4957830593359b0c4030/data")
+        chapel_hill_path = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=19cZzyedCLUtQt9Ko4bcOixWIJHBn9CfI")
         # raleigh_path = dl_manager.download_and_extract("https://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",
@@ -97,13 +97,13 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
         # "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)
+        # durham_df = self._preprocess_durham(durham_path)
         # raleigh_df = self._preprocess_raleigh(raleigh_path)
-        # chapel_hill_df = self._preprocess_chapel_hill(chapel_hill_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_df = pd.concat([cary_df, durham_df], ignore_index=True)
+        combined_df = pd.concat([cary_df, chapel_hill_df], ignore_index=True)
 
         
         combined_file_path = os.path.join(dl_manager.download_dir, "combined_dataset.csv")
@@ -114,56 +114,62 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
         ]
 
 
-    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)
-        
+    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': ['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']
+        '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
+         # 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
-        })
+        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")
 
-        # 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")
+        # 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 Durham_new
+        return Chapel_new
 
     def _preprocess_cary(self, file_path):
         # Load the dataset