pystats19 0.1.0

Python package for working with open road traffic casualty data from Great Britain

🚸 py-stats19

Authors:

Xiaowei Gao [📩 Email: [e-mail]] (SpacetimeLab, UCL, UK)

Jinshuai Ma [📩 Email: [e-mail]] (LSE Data Science Institute, UK)

Supervisors:

Dr. James Haworth, Associate Professor in Spatio-temporal Analytics, SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, UCL

Prof. Tao Cheng, Professor in GeoInformatics, SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, UCL

🚸 py-stats19 is a Python package developed to support digital twin applications for spatio-temporal urban crash analysis. Inspired by the R stats19 package package, this package provides a more efficient tool to download and format the Road Safety Data from the official Road Safety Database published by the Department for Transport, UK, since 1979. Additionally, py-stats19 enhances the data by incorporating extra temporal information and geometric details.

The whole data set contains three tables: casualty, collision, and vehicle. The data set is updated annually and contains detailed information about road traffic accidents in Great Britain.

🧰 The current py-stats19 package is under development and testing stages. It is available as a beta version for early access.

Installation

Download the latest release, e.g. pystats19-0.0.1-py3-none-any.whl.

$ pip install pystats19-0.0.1-py3-none-any.whl

list_files()

list_files() can list all available stats19 dataset files, which can be simply filtered by passing year and table arguments.

Here, you could specify the table name as casualty, collision, or vehicle. Those files could be merged by the accident_index key.

from pystats19.read import list_files

# List all files contain year 2021 data
list_files(year=2021) 
# ['dft-road-casualty-statistics-casualty-1979-latest-published-year.csv',
#  'dft-road-casualty-statistics-casualty-2021.csv',
#  'dft-road-casualty-statistics-casualty-last-5-years.csv',
#  'dft-road-casualty-statistics-collision-1979-latest-published-year.csv',
#  'dft-road-casualty-statistics-collision-2021.csv',
#  'dft-road-casualty-statistics-collision-last-5-years.csv',
#  'dft-road-casualty-statistics-vehicle-1979-latest-published-year.csv',
#  'dft-road-casualty-statistics-vehicle-2021.csv',
#  'dft-road-casualty-statistics-vehicle-e-scooter-2020-Latest-Published-Year.csv',
#  'dft-road-casualty-statistics-vehicle-last-5-years.csv']

# List all files contain year 2021 and table vehicle data
list_files(year=2021, table="vehicle")
# ['dft-road-casualty-statistics-vehicle-1979-latest-published-year.csv',
#  'dft-road-casualty-statistics-vehicle-2021.csv',
#  'dft-road-casualty-statistics-vehicle-last-5-years.csv']

pull_file()

pull() requires filename parameter, downloading the data file. filename should be obtained using list_files().

Optionally, data_dir can specify the location where the file will be stored.

from pystats19.source import pull_file

pull_file('dft-road-casualty-statistics-vehicle-2019.csv', data_dir="./data")

Data directory

Data directory can also be configured globally by setting an environment variable PYSTATS19_DOWNLOAD_DIRECTORY

$ export PYSTATS19_DOWNLOAD_DIRECTORY=~/my_pystats19_data

load()

load() loads the data file as a pandas.DataFrame or geopandas.GeoDataFrame. Set auto_download=True to automatically download the file if not exists.

Optionally,

set convert_code_to_label=True to convert categorical data codes to text labels.

set add_temporal_info=True to format datetime and time and add additional time information.

set add_geo_info=True to add geo information. This will return a geopandas.GeoDataFrame.

from pystats19.read import load

load(
    'dft-road-casualty-statistics-collision-2021.csv',
    auto_download=True,
    convert_code_to_label=True,
    add_temporal_info=True,
    add_geo_info=True
)

# Removed 17 records due to missing Latitude or Longitude.
# 
#        accident_index  ...                       geometry
# 0       2021010287148  ...   POINT (521509.659 193079.41)
# 1       2021010287149  ...  POINT (535380.824 180783.228)
# 2       2021010287151  ...  POINT (529702.828 170398.085)
# 3       2021010287155  ...  POINT (525313.658 178385.183)
# 4       2021010287157  ...  POINT (512145.497 171526.072)
# ...               ...  ...                            ...
# 101082  2021991196247  ...  POINT (325545.894 674547.399)
# 101083  2021991196607  ...  POINT (271195.339 558271.954)
# 101084  2021991197944  ...   POINT (357296.909 860766.24)
# 101085  2021991200639  ...  POINT (326935.908 675924.391)
# 101086  2021991201030  ...  POINT (270574.351 556367.939)
# [101070 rows x 39 columns]