• Home
  • About

GIScience News Blog

News of Heidelberg University’s GIScience Research Group.

Feed on
Posts
Comments
« Humanitarian OSM Stats: How to monitor humanitarian mapping in the HOT Tasking Manager? - Part 3
Wanna give feedback about HeiGIT services? Survey Deadline extended to 05.03. »

An ohsome Railway Network Visualization and Analysis

Feb 24th, 2021 by Fabian Kowatsch

Welcome back to another #ohsome blog post written by our awesome student assistent Sarah! This time we will look at the completeness of railway network data of one specific city in OpenStreetMap, as well as its development. For this we looked at the city of Prague and its completeness of the operator tag. Furthermore, you’ll get to see the development of the railway network data of Prague in an animation (and can even learn how to make one yourself!). In case you haven’t read the last ohsome region of the month blogposts, you can find part 1 here & part 2 here.

Data:

As usual you will have to think of the boundaries you’re going to set in your analysis. For this you again have to get your hands on a spatial data set with the boundaries of Prague (e.g. from here) in the GeoJSON format. The dataset of interest in regard of our railway network analysis can be accessed by sending a request to the ohsome API.

Requests:

For the visualization of the evolution we decided to use the operator tag as indicator, so we can display the ratio of railway network with that information given, as well as the point in time where this information startet to get added and the point in time when it reached its maximum value. We created a snippet with the final cURL POST requests, as well as the parameter text files and further information here.

You will have to use two endpoints for getting the needed data. One is /elements/length/ratio for the part where you want to look at the ratio development over the years and the other one is /elementsFullHistory/geometry so you can access and visualize the whole evolution of railway network data (as given in the filter). With this data extraction request you’ll get all the changes to the railway network within your given timeframe, as well as the duration of validity of these changes, which comes in handy when working on the evolution animation.

Analytical Visualization:

endpoint: /elements/length/ratio

timestamp: 2009-01-01/2021-01-01/P1M

filter: type:way and railway in (rail,light_rail,subway,tram,narrow_gauge) and operator=*

Evolution Visualization:

endpoint: /elementsFullHistory/geometry

timestamp: 2009-01-01,2021-01-01

filter=type:way and railway in (rail,light_rail,subway,tram,narrow_gauge)

Here is the evolution of the railway network of the city of Prague:

As you can see there are two different colors in use. The blue lines symbolize the part of the railway network that does not carry any operator information and the yellow lines represent the part of the network that does have said information added. You might notice the slight “blinking” effect of some of the lines throughout the duration of the animation, which indicates that these lines got edited. For creating this visualization of the evolution you can use the QGIS native Temporal Controller. A short tutorial as well as an introduction to cosmetic options can be found in an additional snippet.

Data Exploration:

Below you can see the ratio development of the the operator tag in the City of Prague. The higher the value the better the covering of the railway network with this information, the highest possible value being 1 (so 100%):

Although the ratio values increase over the years they barely reach 25%. When looking at the datasets we got from our requests, the part of the railway network which actually bares the information of the operator tag seems rather „up-to-date“ as even the name change of the Správa železnic in January 2020 was implemented rather quickly after coming into effect. Yet some of the railway network does not bare the information of an operator, although they most likely belong with one of the two main operators that were named in the dataset, namely Správa železnic & Dopravní podnik hlavního města Prahy, e.g. parts of the metro network do not have the operator tag. The exact reason for that appears to be unclear.

There is a whole list given when looking at the source tag in the full-history dataset, with a lot of them appearing to be linked to the Czech Office for Surveying, Mapping and Cadastre (ČÚZK for short) who offers quite a bit of GIS data. Interestingly enough the operator count wasn’t really used until January 1st, 2012. Throughout the years the overall trend of the ratio values is positive with a few data jumps. Since October 1st of 2016 the ČÚZK has been modifying and updating the INSPIRE-dataset which also happened in connection to their participation of the European Location Framework (ELF) project. The availability of the data might be related for the better ratio values by the end of the given timeframe.

Below you can see the output dataset of the full-history extraction with the Správa železnic operator data highlighted in magenta and the Dopravní podnik hlavního města Prahy operator data highlighted in yellow. The rest of the the railway network remains without an operator tag:

Interestingly enough most of the Metro Network (yellow highlighted lines) appears to be tagged with the operator information when looking at the picture. So at least the subway of Prague appears to have that tag added to it through the years. The “operator-less” part of the railway network however appears to be most of the cities tram network and only some parts of the railway=rail are tagged with operator information (highlighted in magenta).

Even though the ratio values itself are quite low, there is a lot of overall railway data given, especially at the beginning of the timeframe. When looking at the sources, it appears like there has been the opportunity to import data from e.g. orthophotos and datasets given by the Ústav pro hospodářskou úpravu lesů Brandýs nad Labem (ÚHÚL for short), so the Czech Forest Management Institute, or the ČÚZK. Furthermore, the source given for quite some data was Bing. So these input opportunities appear to be the reason why there is quite a lot data given from the start, but when taking the operator tag as our indicator of completeness into consideration, a great part of it appears to be incomplete for some reason. Note: the source=uhul:ortofoto is not being used anymore (since ~Summer 2015) but still had an impact on the dataset in the beginning of the timeframe looked at.

Conclusion:

At last, our region could ideally teach you how to animate a map yourself and has shown you an approach to a completeness analysis with a certain tag. Although the overall ratio values of the city of Prague are still quite small, the local mapping community appears to be rather motivated and active, so one can assume that there is a good chance for an operator tagged future for Prague.

Thank you for reading this months blogpost and stay tuned for there is more to come! As always, you can reach out to us via our email address ohsome(at)heigit(dot)org.

Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more easily accessible for various kinds of OSM data analytics tasks, such as data quality analysis, on a regional, country-wide, or global scale. The ohsome API is one of its components, providing free and easy access to some of the functionalities of the ohsome platform via HTTP requests. Some intro can be found here:

  • ohsome general idea
  • ohsome general architecture
  • how to become ohsome blog series
  • how spatial joins queries work in the OpenStreetMap History Database OSHDB

Tags: become-ohsome, heigit, intrinsic quality analysis, ohsome, ohsome example, OSM, OSM History Analytics, visualization

Posted in Services, Software

Comments are closed.

  • About

    GIScience News Blog
    News of Heidelberg University’s GIScience Research Group.
    There are 1,675 Posts and 0 Comments so far.

  • Meta

    • Log in
    • Entries RSS
    • Comments RSS
    • WordPress.org
  • Recent Posts

    • Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019
    • New paper on the automatic characterization of surface activities from 4D point clouds
    • OSHDB Version 1.0 Has Arrived
    • Job Opening for Postdoc / Senior Researcher on OpenStreetMap Road Quality Analysis
    • Geography Awareness Week 14.-19.11.2022
  • Tags

    3D 3DGEO Big Spatial Data CAP4Access Citizen Science Conference crisis mapping Crowdsourcing data quality deep learning disaster DisasterMapping GeoNet.MRN GIScience heigit HOT humanitarian humanitarian mapping Humanitarian OpenStreetMap team intrinsic quality analysis landuse laser scanning Lidar machine-learning Mapathon MapSwipe MissingMaps Missing Maps ohsome ohsome example Open data openrouteservice OpenStreetMap OSM OSM History Analytics Public Health Quality quality analysis remote sensing routing social media spatial analysis Teaching VGI Workshop
  • Archives

    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
    • October 2014
    • September 2014
    • August 2014
    • July 2014
    • June 2014
    • May 2014
    • April 2014
    • March 2014
    • February 2014
    • January 2014
    • December 2013
    • November 2013
    • October 2013
    • September 2013
    • August 2013
    • July 2013
    • June 2013
    • May 2013
    • April 2013
  •  

    February 2021
    M T W T F S S
    « Jan   Mar »
    1234567
    891011121314
    15161718192021
    22232425262728
  • Recent Comments

    GIScience News Blog CC by-nc-sa Some Rights Reserved.

    Free WordPress Themes | Fresh WordPress Themes