Datasets:

xx103
/

NYC_Motor_Vehicle_Collisions_and_Weather_Dataset

	import json
	import os
	import tempfile
	import datasets
	import boto3
	from botocore.config import Config
	from botocore import UNSIGNED
	import s3fs
	from datasets import load_dataset


	_CITATION = """\
	@misc{ny_motor_vehicle_collisions_weather_dataset,
	title={New York City Motor Vehicle Collisions and Weather Dataset},
	author={Xingzhi Xie},
	year={2024},
	url={https://huggingface.co/datasets/xx103/NYC_Motor_Vehicle_Collisions_and_Weather_Dataset},
	}
	"""

	_DESCRIPTION = """\
	This dataset contains information about motor vehicle collisions in New York City, along with associated weather conditions.
	Each record includes details about the crash date, location, collision ID, crash time period, contributing factors, vehicle types,
	the number of injuries and deaths, street name, weather description, and temperature metrics.
	"""

	_HOMEPAGE = "https://huggingface.co/datasets/xx103/NYC_Motor_Vehicle_Collisions_and_Weather_Dataset"

	_LICENSE = ""

	# The dataset is stored in AWS S3 bucket
	_BUCKET_NAME = 'sta663data1'
	_FILE_KEY = 'NYC_Motor_Vehicle_Collisions_and_Weather_Dataset.json'
	_LOCAL_FILE_NAME = 'NYC_Motor_Vehicle_Collisions_and_Weather_Dataset.json'


	class NYCMotorVehicleCollisionsWeatherDataset(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({
	"crash_date": datasets.Value("string"),
	"borough": datasets.Value("string"),
	"zip_code": datasets.Value("string"),
	"latitude": datasets.Value("float"),
	"longitude": datasets.Value("float"),
	"collision_id": datasets.Value("int32"),
	"crash_time_period": datasets.Value("string"),
	"contributing_factor_vehicles": datasets.Sequence(datasets.Value("string")),
	"vehicle_types": datasets.Sequence(datasets.Value("string")),
	"number_of_injuries": datasets.Value("int32"),
	"number_of_deaths": datasets.Value("int32"),
	"street_name": datasets.Value("string"),
	"street_type": datasets.Value("string"),
	"weather_description": datasets.Value("string"),
	"precipitation": datasets.Value("float"),
	"precipitation_type": datasets.Value("string"),
	"temp_max": datasets.Value("float"),
	"temp_min": datasets.Value("float"),
	}),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
	s3.download_file(_BUCKET_NAME, _FILE_KEY, _LOCAL_FILE_NAME)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": _LOCAL_FILE_NAME},
	),
	]



	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	for key, line in enumerate(f):
	row = json.loads(line)
	yield key, {
	"crash_date": row.get("CRASH DATE", ""),
	"borough": row.get("BOROUGH", ""),
	"zip_code": row.get("ZIP CODE", ""),
	"latitude": row.get("LATITUDE", None),
	"longitude": row.get("LONGITUDE", None),
	"collision_id": row.get("COLLISION_ID", None),
	"crash_time_period": row.get("CRASH TIME PERIOD", ""),
	"contributing_factor_vehicles": row.get("CONTRIBUTING FACTOR VEHICLES", "").split(", ") if row.get("CONTRIBUTING FACTOR VEHICLES") else [],
	"vehicle_types": row.get("VEHICLE TYPES", "").split(", ") if row.get("VEHICLE TYPES") else [],
	"number_of_injuries": row.get("NUMBER OF INJURIES", None),
	"number_of_deaths": row.get("NUMBER OF DEATHS", None),
	"street_name": row.get("STREET NAME", ""),
	"street_type": row.get("STREET TYPE", ""),
	"weather_description": row.get("WEATHER DESCRIPTION", ""),
	"precipitation": row.get("PRECIPITATION", None),
	"precipitation_type": row.get("PRECIPITATION TYPE", ""),
	"temp_max": row.get("TEMPMAX", None),
	"temp_min": row.get("TEMPMIN", None),
	}