Package: ptac
PtACPtAC is a Python package to automatically compute walking
accessibilities from residential areas to public transport stops for the Sustainable Development Goal 11.2
defined by the United Nations. The goal aims to measure and monitor the proportion
of the population in a city that has convenient access to public transport
(seehttps://sdgs.un.org/goals/goal11). With this library users can download and processOpenStreetMap(OSM)
street networks and population information worldwide. Based on this it is possible to calculate accessibilities
from population points to public transit stops based on minimum street network distance.In order to calculate SDG 11.2.1 indicator the necessary input sources are
population information, public transit stops and city networks.
Worldwide population information can be downloaded via WMS
fromWorld Settlement Footprint(WSF) and converted
to points. Public transit stops can be obtained fromOpenStreetMap (OSM)orGeneral Transit Feed Specification (GTFS)feeds (have a look at the examples if you want to know how this
works exactly). The street network can be downloaded and prepared for routing automatically within the library.Installation and UsagePlease see theuser guidefor information about installation and usage.ExamplesTo get started with PtAC, read the user reference and see sample code and input data inexamples repository.FeaturesPtAC is built on top of osmnx, geopandas, networkx and
usesUrMoACfor accessibility computation.Download and prepare road networks from OpenStreetMap for accessibility calculationCalculate accessibilities from origins to the next destinationGenerate a population point dataset from population raster datasetCalculate Sustainable Development Goal 11.2 based on starting points with population informationAuthorsSimon NielandSerra YosmaogluContributorsMirko GoletzDaniel KrajzewiczAndreas RadkeSupportIf you have a usage question, please contact us via email (simon.nieland@dlr.de,serra.yosmaoglu@dlr.de).License InformationPtAC is licensed under the Eclipse Public License 2.0. See theLICENSE.mdfile for more information.DisclaimerThis is a test version only and must not be given to any third party.We have chosen some links to external pages as we think they contain useful information.
However, we are not responsible for the contents of the pages we link to.The software is provided "AS IS".We tested the software, and it worked as expected. Nonetheless, we cannot guarantee it will work as you expect.ReferencesBoeing, G. (2017). OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks.
Computers, Environment and Urban Systems 65, 126-139. doi:10.1016/j.compenvurbsys.2017.05.004Krajzewicz, D., Heinrichs, D. & Cyganski, R. (2017). Intermodal Contour Accessibility Measures Computation Using the 'UrMo Accessibility Computer'.
International Journal On Advances in Systems and Measurements, 10 (3&4), Seiten 111-123. IARIA.Palacios-Lopez, D., Bachofer, F., Esch, T., Heldens, W., Hirner, A., Marconcini, M., ... & Reinartz, P. (2019).
New perspectives for mapping global population distribution using world settlement footprint products. Sustainability, 11(21), 6056.Palacios-Lopez, D., Bachofer, F., Esch, T., Marconcini, M., MacManus, K., Sorichetta, A., ... & Reinartz, P. (2021).
High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019
Imperviousness Layer for the African Continent. Remote Sensing, 13(6), 1142.Marconcini, M., Metz-Marconcini, A., Üreyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., ... & Strano, E. (2020).
Outlining where humans live, the World Settlement Footprint 2015. Scientific Data, 7(1), 1-14.