An open-source tool for calculating spatial indicators for healthy, sustainable cities worldwide using open or custom data.
Adds a new 'interpolate_stop_times' option for GTFS feeds to deal with the possible case of stop_times.txt missing departure times values. If that's the case, analysis of regular service frequencies cannot proceed accurately for that GTFS feed. As per the GTFS reference specification, missing departure or arrival times may be interpolated using the stop sequence start and end times within each trip id.
The new interpolate_stop_times option/function if set fills null arrival and departure values using a linear interpolation according to the provided stop sequence start and end times within each trip_id. This is an approximation based on the available information, and results may still differ from the actual service frequencies at these stops. However, in the absence of further information, a user may judge it as an adequate approximation (using the values implicit in the data itself), in which case it would allow analysis to proceed.
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.9...4.4.10
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.8...v4.4.9
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.7...v4.4.8
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.6...v4.4.7
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.5...v4.4.6
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.4...v4.4.5
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.3...v4.4.4
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.2...v4.4.3
This release has updated the region configuration template. This is now better documented, with more guidance, standalone (no need to edit datasets.yml), all options present but commented out if optional (and guidance provided on how to use them). There are also more informative errors now.
You can also initialise a new study region now withing Python using the ghsci module, and drop a database within Python too.
For example,
from subprocesses import ghsci
codename = 'your_study_region_codename'
# Initialise configuration file for your region
r = ghsci.Region(codename)
# Now, you need to source and download data, documenting metadata and file paths in the configuration file
# Once that is completed, you can proceed:
r.analysis()
r.generate()
# if you've analysed and generated results for other study regions, you can compare the main results
r.compare('another_previously_processed_codename')
# if for some reason you want to drop the database for your study region to start again:
r.drop()
# You will be asked if you really want to do this! It requires entering "ghscic" to confirm
# This doesn't remove any generated files or folders - you'll have to remove those yourself, if you want to
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.1...v4.4.2
Full Changelog: https://github.com/global-healthy-liveable-cities/global-indicators/compare/v4.4.0...v4.4.1