NC_Crime.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
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
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {NC Crime Dataset},
author={huggingface, Inc.
},
year={2024}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The dataset, compiled from public police incident reports across various cities in North Carolina, covers a period from the early 2000s through to 2024. It is intended to facilitate the study of crime trends and patterns.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = ""
_URLS = ""

class NCCrimeDataset(datasets.GeneratorBasedBuilder):
    """Dataset for North Carolina Crime Incidents."""
    _URLS = _URLS
    
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
            {
                "year": datasets.Value("int64"),
                "city": datasets.Value("string"),
                "crime_major_category": datasets.Value("string"),
                "crime_detail": datasets.Value("string"),
                "latitude": datasets.Value("float64"),
                "longitude": datasets.Value("float64"),
                "occurance_time": datasets.Value("string"),
                "clear_status": datasets.Value("string"),
                "incident_address": datasets.Value("string"),
                "notes": datasets.Value("string"),
            }),
            citation=_CITATION,
        )

    
    
    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")
        # 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",
        # 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_df = pd.concat([cary_df, durham_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": 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_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 _generate_examples(self, filepath):
        # Read the CSV file
        df = pd.read_csv(filepath)  
        # Iterate over the rows and yield examples
        for i, row in df.iterrows():
            yield i, {
                "year": int(row["year"]),
                "city": row["city"],
                "crime_major_category": row["crime_major_category"],
                "crime_detail": row["crime_detail"],
                "latitude": float(row["latitude"]),
                "longitude": float(row["longitude"]),
                "occurance_time": row["occurance_time"],
                "clear_status": row["clear_status"],
                "incident_address": row["incident_address"],
                "notes": row["notes"],
        }