The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 145, in _generate_tables
dataset = json.load(f)
File "/usr/local/lib/python3.9/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries
out = read(*args, **kwargs)
File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
conversations
list | id
string |
|---|---|
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 0 1 0 1 0 2 0 0 0 0 0], and the recorded number of larcenies is [0 0 3 0 3 1 2 2 1 2 1 1]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Commercial, Education Facility, Religious Institution, Transportation Facility, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_0_1_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Water, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_1_2_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Transportation Facility, Education Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_2_3_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 1 0 0 3 0 0 1 1 0 0 1], and the recorded number of larcenies is [2 0 3 0 3 2 1 2 2 3 2 3]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Public Safety, Social Services, Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_3_4_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Transportation Facility, Education Facility, Water, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_4_5_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Religious Institution, Transportation Facility, Water, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_5_6_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Education Facility, Government Facility (non public safety), Health Services, Residential categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_6_7_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Education Facility, Public Safety, Social Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_7_8_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_8_9_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_9_10_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 1 0 0 1 0 0 0 1 0 0 0], and the recorded number of larcenies is [2 0 3 1 1 1 2 0 1 0 0 1]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Commercial, Transportation Facility, Water, Public Safety, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_10_11_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Commercial, Water, Public Safety, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_11_12_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Commercial, Religious Institution, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_12_13_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 0 0 0 0 1 0 0 0 1 0 0], and the recorded number of larcenies is [1 1 0 2 0 1 0 3 7 2 1 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_13_14_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_14_15_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Commercial, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_15_16_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Water, Social Services, Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_16_17_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Water, Social Services, Government Facility (non public safety), Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_17_18_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Commercial, Transportation Facility, Government Facility (non public safety), Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_18_19_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_19_20_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Transportation Facility, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_20_21_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 1 1 0 0 0 0], and the recorded number of larcenies is [1 1 0 0 1 2 2 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Transportation Facility, Religious Institution, Water, Social Services, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_21_22_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 0 0 1 0 0 1 0 1 0 0 0], and the recorded number of larcenies is [1 0 2 1 1 3 3 2 1 6 1 1]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Education Facility, Transportation Facility, Water, Public Safety, Social Services, Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_22_23_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Water, Public Safety, Social Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_23_24_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [2 0 1 1 2 2 1 1 0 2 0 0], and the recorded number of larcenies is [2 0 1 1 1 2 3 1 2 1 3 2]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Religious Institution, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_24_25_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Public Safety, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_25_26_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Education Facility, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_26_27_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 1 0 1 0 0 0 0], and the recorded number of larcenies is [1 0 2 0 2 0 1 0 0 1 1 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Government Facility (non public safety), Residential, Commercial categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_27_28_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 1 0 0 0 0 0 0], and the recorded number of larcenies is [3 1 2 1 5 2 3 0 3 3 2 4]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Water, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_28_29_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_29_30_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Water, Social Services, Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_30_31_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_31_32_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Commercial, Education Facility, Public Safety, Social Services, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_32_33_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_33_34_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Commercial, Religious Institution, Education Facility, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_34_35_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Government Facility (non public safety), Residential, Education Facility, Social Services categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_35_36_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Education Facility, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_36_37_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Water, Social Services, Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_37_38_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Religious Institution, Transportation Facility, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_38_39_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 2 2 2 0 0 0 2 0 1], and the recorded number of larcenies is [0 0 2 1 1 1 0 0 0 1 0 1]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Commercial, Transportation Facility, Water, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_39_40_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Water, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_40_41_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_41_42_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Transportation Facility, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_42_43_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 1 0 0 0 0 0 0 1 0 1], and the recorded number of larcenies is [6 6 2 1 2 1 5 7 1 6 2 2]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Commercial, Education Facility, Water, Social Services, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_43_44_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Commercial, Education Facility, Religious Institution, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_44_45_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 0 1 0 0 1 0 0 1 0 0 0], and the recorded number of larcenies is [3 2 0 1 0 1 5 4 4 2 2 2]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Transportation Facility, Water, Social Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_45_46_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Commercial, Religious Institution, Transportation Facility, Water, Public Safety, Social Services, Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_46_47_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Religious Institution, Commercial, Social Services, Residential categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_47_48_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 1 1 0 0 0 0 1 0], and the recorded number of larcenies is [1 1 1 2 1 4 1 1 1 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Social Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_48_49_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 1 0 0 0 0 0 1 0 0 0 2], and the recorded number of larcenies is [0 1 1 0 1 0 0 0 1 1 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Commercial, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_49_50_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_50_51_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 1 0 0 1], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Commercial, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_51_52_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_52_53_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Commercial, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_53_54_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 1 0 0 0 0 0 1 0 0 2], and the recorded number of larcenies is [0 0 2 1 0 0 0 0 1 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Commercial, Education Facility, Transportation Facility, Religious Institution, Water, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_54_55_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_55_56_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Social Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_56_57_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_57_58_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Residential, Commercial, Education Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_58_59_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 1 0 0 0 1 1 0 0 0 1 0], and the recorded number of larcenies is [0 1 1 1 4 2 1 1 0 0 0 1]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Commercial, Water, Social Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_59_60_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 1 0 0 0 0 0 2 0 2], and the recorded number of larcenies is [4 2 0 3 5 1 6 2 2 3 5 4]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Transportation Facility, Religious Institution, Water, Public Safety, Social Services, Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_60_61_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_61_62_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 2 0 0 2 0 0 0 0 0], and the recorded number of larcenies is [0 1 1 0 3 1 0 0 0 2 3 1]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Education Facility, Religious Institution, Public Safety, Government Facility (non public safety), Health Services, Residential categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_62_63_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_63_64_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 1 0 0 1 0 0 0 0 0 1], and the recorded number of larcenies is [0 0 0 2 1 0 0 0 1 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Transportation Facility, Religious Institution, Water, Social Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_64_65_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Water, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_65_66_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 2 2 0 0 1 0 0 2 1 0], and the recorded number of larcenies is [3 1 1 2 4 1 2 3 2 3 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_66_67_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Commercial, Education Facility, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_67_68_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_68_69_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Commercial, Education Facility, Transportation Facility, Water, Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_69_70_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_70_71_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Transportation Facility, Religious Institution, Social Services, Government Facility (non public safety), Health Services, Residential categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_71_72_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_72_73_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Commercial, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_73_74_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Commercial, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_74_75_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Social Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_75_76_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Social Services, Government Facility (non public safety), Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_76_77_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a one-kilometer radius, covering Religious Institution, Education Facility, Transportation Facility, Social Services, Residential categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_77_78_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [1 0 0 0 2 0 1 0 0 1 0 0], and the recorded number of larcenies is [0 1 3 1 3 0 3 0 4 2 3 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_78_79_len_0 |
[
{
"from": "human",
"value": "Given the historical data for crime over 12 time steps in a specific region of New York City, the recorded number of burglaries is [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded number of larcenies is [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'January 28, 2017, 00:00, Saturday to February 8, 2017, 00:00, Wednesday, with data points recorded at 1-day intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a one-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Commercial, Transportation Facility, Water, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we aim to predict whether the two specific crimes will occur in this region within the next 12 time steps during the time period of 'February 9, 2017, 00:00, Thursday to February 20, 2017, 00:00, Monday, with data points recorded at 1-day intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical crime data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of burglaries and larcenies. Please conduct an analysis of the crime patterns in this region, considering the provided time and regional information, and then generate the prediction tokens for classification, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predicted tokens of crime in this region are <ST_PRE>."
}
] | train_NYCmulti_region_79_80_len_0 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [18 15 8 21 18 22 17 21 31 18 31 31], and the recorded taxi outflows are [2 0 0 0 3 2 0 0 0 2 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: No description is available for this region. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_0_1_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a three-kilometer radius, covering Transportation Facility, Public Safety, Social Services, Health Services, Residential categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_1_2_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 0 0 0 1 0 0 0], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Education Facility, Transportation Facility, Social Services, Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_2_3_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [22 21 18 19 21 39 31 36 30 36 36 31], and the recorded taxi outflows are [24 18 13 12 14 18 17 11 12 18 19 20]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a three-kilometer radius, covering Education Facility, Commercial, Transportation Facility, Religious Institution, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_3_4_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 0 0 0 0 0 0 0], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Staten Island borough district and encompasses various POIs within a three-kilometer radius, covering Education Facility, Residential, Religious Institution categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_4_5_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 1 0 0 0 1 0 0], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Staten Island borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_5_6_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [16 18 23 26 19 17 29 20 15 23 25 27], and the recorded taxi outflows are [18 17 15 18 9 11 13 13 6 10 11 8]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Social Services, Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_6_7_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 0 0 0 0 0 0 1], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 1 0 1]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Government Facility (non public safety), Residential, Religious Institution categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_7_8_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 1 0 1 1 0 0 0], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Religious Institution, Transportation Facility, Water, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_8_9_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 1 2 0 1 2 1 0 1 2 2 3], and the recorded taxi outflows are [0 1 3 1 0 1 1 0 1 0 3 1]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Water, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_9_10_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 1 0 0 0 0 0 0 0], and the recorded taxi outflows are [1 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Water, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_10_11_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [27 14 15 13 15 12 12 10 12 6 7 8], and the recorded taxi outflows are [0 0 6 0 8 3 8 16 18 10 12 11]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Transportation Facility, Education Facility, Social Services, Health Services, Residential categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_11_12_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [50 71 104 87 88 97 93 81 90 110 110 104], and the recorded taxi outflows are [41 68 70 77 99 121 103 97 99 87 109 89]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Manhattan borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Transportation Facility, Religious Institution, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_12_13_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [1 2 1 4 1 3 1 4 1 1 0 0], and the recorded taxi outflows are [0 0 0 0 0 1 1 0 1 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Religious Institution, Education Facility, Transportation Facility, Commercial, Water, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_13_14_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 1 0 0 0 1 0 0 2], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Commercial, Water, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_14_15_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 1 1 0 1 0 0 0 0 1], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_15_16_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [3 6 5 1 5 7 2 8 8 5 2 5], and the recorded taxi outflows are [2 1 1 2 1 2 1 1 2 0 0 2]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Brooklyn borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Commercial, Religious Institution, Transportation Facility, Water, Public Safety, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_16_17_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 1 0 0 0 1 0 2 0 0 2 0], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 0 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Transportation Facility, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_17_18_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 0 0 0 0 0 0 1 1 1], and the recorded taxi outflows are [0 0 0 0 0 0 0 0 1 0 0 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Queens borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Education Facility, Religious Institution, Public Safety, Social Services, Government Facility (non public safety), Health Services, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_18_19_len_1 |
[
{
"from": "human",
"value": "Given the historical data for taxi flow over 12 time steps in a specific region of New York City, the recorded taxi inflows are [0 0 0 1 1 0 3 0 1 0 1 0], and the recorded taxi outflows are [0 0 0 0 0 0 1 1 0 0 1 0]. The recording time of the historical data is 'February 19, 2017, 09:30, Sunday to February 19, 2017, 15:00, Sunday, with data points recorded at 30-minute intervals'. Here is the region information: This region is located within the Bronx borough district and encompasses various POIs within a three-kilometer radius, covering Recreational Facility, Religious Institution, Commercial, Transportation Facility, Education Facility, Social Services, Government Facility (non public safety), Health Services, Miscellaneous, Residential, Cultural Facility categories. Now we want to predict the taxi inflow and outflow for the next 12 time steps during the time period of 'February 19, 2017, 15:30, Sunday to February 19, 2017, 21:00, Sunday, with data points recorded at 30-minute intervals'. To improve prediction accuracy, a spatio-temporal model is utilized to encode the historical taxi data as tokens <ST_HIS>, where the first and the second tokens correspond to the representations of taxi inflow and outflow. Please conduct an analysis of the traffic patterns in this region, taking into account the provided time and regional information, and then generate the predictive tokens for regression, in the form \"<ST_PRE>\"."
},
{
"from": "gpt",
"value": "Based on the given information, the predictive tokens of taxi inflow and outflow in this region are <ST_PRE>."
}
] | train_NYCmulti_region_19_20_len_1 |
End of preview.