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Last week, our colleagues Franz-Benjamin Mocnik and René Westerholt participated in a summer school of the DFG priority programme on “VGI”. GIScience Heidelberg is involved in that programme by two projects, one of which is dealing with data quality issues and another one that investigates the assessment of spatial assotiations in social media data. The summer school included lectures and hands-on sessions. Franz-Benjamin Mocnik contributed a talk about OSMvis, a platform offering innovative visualization tools for OpenStreetMap data (cmp. 1). In addition, a collaboration with the Institute of Cartography of TU Dresden on the investigation of food habits by using different social media platforms was negotiated by René Westerholt.

(1) Mocnik, F.-B., Zipf, A., Raifer, M. (2017): The OpenStreetMap folksonomy and its evolution. Geo-spatial Information Science. DOI: 10.1080/10095020.2017.1368193.

We are currently guest-editing a special issue on “urban geoinformatics” in the Taylor & Francis journal “Geo-Spatial Information Science”. On this occasion, we’d like to encourage you to submit your crowdsourcing-related work emphasizing urban issues:

Title: Crowdsourcing for Urban Geoinformatics

Deadline: 30 October 2017

Guest editors:

Hongchao Fan, Wuhan University, (hongchao.fan@whu.edu.cn)
João Porto de Albuquerque, University of Warwick, (J.Porto@warwick.ac.uk)
René Westerholt, Heidelberg University, (westerholt@uni-heidelberg.de)
Alexander Zipf, Heidelberg University, (zipf@uni-heidelberg.de)
Bernd Resch, University of Salzburg, (bernd.resch@sbg.ac.at)


Modern mobile devices are pervasively equipped with embedded sensors and cameras, and allow the positioning of media contents in geographic space. In combination with Web 2.0 technologies, this has led to the crowdsourcing approach, which in turn has become an important data acquisition paradigm. It is now possible to collect large amounts of geospatial data in a timely fashion and at low costs, especially in urban areas that feature vast numbers of contributors. Crowdsourcing thus opens up new possibilities for the disclosure of social processes, and for the coping of a range of societal and environmental issues related to urban conurbations. For instance, crowdsourcing allows the integration of user-generated information into urban planning and management workflows—and is thus a crucial step towards the design of smarter and more sustainable cities. However, at the same time, crowdsourcing brings up new issues to the research agendas: a) huge amounts of data need to be processed, b) more thorough interdisciplinary collaboration is needed, and c) a general lack of theorizing on crowdsourced geodata and underlying related processes. Further, researchers as well as practitioners are often sceptical about the suitability of crowdsourced data. This is mostly being caused by potential quality issues, heterogeneous data characteristics, lacks of user credibility, semantic ambiguities, and potential positional inaccuracies, among others.

Call for Papers:

In order to address the outlined gaps, we call for the submission of papers on the analysis and application of crowdsourced geographic data, with a specific emphasis on urban research and issues. We welcome contributions on the following topics:

  • Reviews of the state-of-the-art in using crowdsourced geographic information in urban research, planning, and management.
  • Applications and empirical case studies that investigate urban issues by using crowdsourced geographic data (e.g., OSM, social media data)
  • Supplementation of involuntary/authoritative urban datasets with crowdsourced geographic data.
  • Extraction of 3D information from crowdsourced geographic data (e.g., from Kinect data, OSM data).
  • The analysis of human behaviours for emergency (disaster) management, tourism, or urban planning and management.
  • Quality assessment for crowdsourcing geographic data.
  • (Further topics are welcome if they fit the overall theme.)

Submission Guidelines:

Manuscripts should be submitted online: here.

Submitted articles should not have been published previously, or be under consideration for publication elsewhere (except of conference proceedings papers). All manuscripts are subject to a rigorous peer-review process based on scientific standards. Accepted manuscripts will be published open access in GSIS.

Please note: All article publishing charges (APC) will be generously covered by Wuhan University. You can enjoy the benefits of publishing open access at no cost. Authors are recommended to prepare their manuscript by following the official T&F full instructions for authors.

The second noteworthy news this week is that we have finally introduced the much-awaited time-distance matrix service with which you can finally compute one-to-many, many-to-one or many-to-many routes for any mode of transport provided by OpenRouteService. This service basically provides a terribly fast computation of time and distance between a set of input locations. Let’s assume you have three locations Frankfurt, Munich and Berlin and want to know the times and distances between these pairs. The matrix endpoints returns this information in a resulting structured matrix table, so something very similar to this:

From To Time (seconds) Distance (kilometers)
Frankfurt Berlin 227544 650
Frankfurt Munich 627324 400
Munich Berlin 315123 500

One typical application using this matrix can be found in logistics. Vehicle fleets of these kind of companies may have several locations that need to be visited, preferably in the fastest order. With help of the matrix service one is now able to figure out exactly this best order - also commonly referred to as the Travelling Salesman Problem.

Time-distance Matrix API

Time-distance Matrix

If you are eager to play around with this new feature please sign up for a key at go.openrouteservice.org.

Less restrictions; added SAC
In the past you might have noticed that by using dynamic options - for instance avoidables or vehicle characteristics - your routes were restricted to a much smaller distances than without using any kind of profile parameters. The latest version of the API resolves this issue and lets you compute routes up to 6.000 km for both car and heavy vehicle profiles with any arbitrary option added. We also have added a new extra information option for the outdoor profiles (bicycle and pedestrian) which help you learn about the difficulty level of specific segments. To this end, we utilize an official standard which is defined by the so-called SAC-scale. For further information, please read more here.

SAC-scale Difficulty

SAC-scale Difficulty

New Locations API
Furthermore we introduce our new locations API. With this endpoint you are able to query any kind of point of interest (POI) derived from OpenStreetMap along a polyline, extent or polygon. We have added a prototypical feature for this in the updated OpenRouteService web app which comprises a plethora of categories and sub-categories.

Locations API

Locations API

If you are eager to play around with these new features feel free to browse to OpenRouteService.org and if you can’t wait to use the API directly, please sign up for a key at go.openrouteservice.org.

ORS in Action at Ministry of Justice Niedersachsen
Last but not least we would like to feature one further use case of the OpenRouteService in action. The developers of the JustizNavigator Niedersachsen have successfully implemented our API and are now using it in production. You can find the corresponding web-app here - please be aware that this is the first beta.

JustizNavigator Niedersachsen

GIScience Heidelberg recently contributed to the Annual International Conference, which was held in the premises of the Royal Geographical Society and at the Imperial College in London. Together with João Porto de Albuquerque from the University of Warwick, our member René Westerholt convened two workshop sessions about “spatial urban analytics” on Friday 01 September (link to Session 1 and link to Session 2). The topics covered were varied and ranged from methodological geostatistical work to the analysis of liveability in cities. The discussions were lively and provided a valuable impetus for the presenters as well as for the audience. Further, our member Tessio Novack contributed to one of the sessions, by presenting work on the “characterization of urban blocks and sidewalks based on Volunteered Geographic Information and image-based social media”.

The Humanitarian OpenStreetMap Team (HOT) is currently coordinating OSM mapping activities as response to Hurricane “Irma” which is affecting the islands of the Caribbean and Florida, as well as to the severe floods in Bangladesh. Besides these two heavily affected regions, in the early morning hours a severe earthquake with magnitude of 8.1 hit Mexico.

In order to support the mapping and humanitarian activities we updated our OpenStreetMap based Disaster OpenRouteService ( a special version of OpenRouteService.org) to the following routing regions:

- North America (incl. Mexico)
- Caribbean
- Bangladesh

The data in our service is currently updated every 24h (around 21:00 CET - thanks to Geofabrik!).

If you have some time on your hands, please also see the current projects for Hurricane Irma and the Bangladesh floods where you can support the mapping efforts remotely.

HeiGIT is generously supported through core funding from the Klaus Tschira Foundation Heidelberg.

This year the capital of Canada will become the gathering place for the Humanitarian OpenStreetMap Team (HOT) and Missing Maps community and partners.

September 12th/13th the Missing Maps members will come together for their annual meeting to discuss current projects, challenges, ideas and future plans. The next two days, September 14th/15th, the HOT Summit will provide the chance to meet the HOT community and to exchange ideas all around the use of OpenStreetMap (OSM) data for humanitarian and disaster management purposes. Breakout sessions, lightning talks, workshops, facilitated discussions, birds of a feather, training as well as working group sessions will allow to learn more about the community and partners, current ideas and projects and future plans.

HeiGIT/GIScience being a long-term supporter of the HOT and Missing Maps community, Missing Maps member and a partner of the Humaninitarian OpenStreetMap Team, Marcel Reinmuth and Melanie Eckle of the GIScience/ HeiGIT group will be joining the Missing Maps and HOT events and present current work around MapSwipe and MapSwipe Analytics, the use of OSM for crowdsourced damage assessment and the role and potential of research with and about HOT and Missing Maps.

Melanie recently joined the board of directors of the Humanitarian OpenStreetMap Team. Therefore she will furthermore take part in the in-person board meeting that will be organized the following weekend.

Sounds interesting? Use the opportunity to be a part, the HOT Summit Registration is still open!

HeiGIT is generously supported through core funding from the Klaus Tschira Foundation Heidelberg.

LandSense Questionnaire

http://bit.ly/2upEw7a <- LandSense Questionnaire

LandSense aims to develop an online marketplace where companies that develop IT solutions using LULC data in one of three themes - urban, rural, and forestry, can acquire this information and further develop their products. By completing this questionnaire, you help us develop and further enhance our platform. Thank you very much in advance! This will take up to 5 minutes of your time! We will make sure that your information stays private! *Land Use/Land Cover data refers to data acquired from satellites which is analysed and can be used e.g. by farmers to derive relevant insights helping them support their farming decisions and enhance their organizations


we cordially invite everybody interested to our next open GIScience colloquium talk

Adaptive Trip Planning

Dr. Thomas Liebig
TU Dortmund, Artificial Intelligence Unit

Time and date: Tue, September 12, 10:00 am
Venue: INF 348, Room 015, Department of Geography, Heidelberg University

Route planning makes direct use of geographic data and provides beneficial recommendations to the public. In real-world the schedule of transit vehicles is dynamic and delays in the schedules occur. Incorporation of these dynamic schedule changes in multi-modal route computation is difficult and requires a lot of computational resources. We present an approach that extends the state-of-the-art for static transit schedules, Transfer Patterns, for the dynamic case. Therefore, we amend the patterns by additional edges that cover the dynamics. Our approach is implemented in the open-source routing framework OpenTripPlanner and compared to existing methods in the city of Warsaw. Our results are an order of magnitude faster then existing methods. In Addition, urban areas are increasingly subject to congestions. Most navigation systems and algorithms that avoid these congestions consider drivers independently and can, thus, cause novel congestions at unexpected places. Pre-computation of optimal trips (Nash equilibrium) could be a solution to the problem but is due to its static nature of no practical relevance. In contrast, we present an approach to avoid traffic jams with dynamic self-organizing trip planning. We apply reinforcement learning to learn dynamic weights for routing from the decisions and feedback logs of the vehicles. In order to compare our routing regime against others, we validate our approach in an open simulation environment (LuST) that allows reproduction of the traffic in Luxembourg for a particular day. Additionally, in two realistic scenarios: (1) usage of stationary sensors and (2) deployment in a mobile navigation system, we perform experiments with varying penetration rates. All our experiments reveal that performance of the traffic network is increased and occurrence of traffic jams are reduced by application of our routing regime.

Further dates and details: http://www.geog.uni-heidelberg.de/gis/veranstaltungen_en.html

The 3D spatial data processing group had the opportunity to collaborate with the team of Prof. Tzu-Ping Lin from National Cheng Kung University, Tainan, Taiwan, within the frame of a study dealing with carbon emission reduction measures in urban environments.

One option of reducing carbon emission by buildings is to install photovoltaic (PV) panels in order to meet the energy needs of buildings. The Voxel Octree Solar Toolkit (VOSTOK), which is developed at the 3D spatial data processing group, was applied in order to calculate estimates of solar potential on the roofs in various urban structures.

Main advantages of VOSTOK are its ability to take into account mutual shading between buildings, and the free choice of temporal resolution. Subsequently, a seasonal carbon budget on a monthly basis is provided in the study, leading to recommendations such as the installation of PV in areas with homogeneous building height distribution.

Exemplary estimations of the solar potential on roofs in Tainan. (a) Monthly sum of solar irradiation for January, (b) Monthly sum of solar irradiation for July.

Exemplary estimations of the solar potential on roofs in Tainan. (a) Monthly sum of solar irradiation for January, (b) Monthly sum of solar irradiation for July.

Check out the manuscript for a detailed description of the study:

Tzu-Ping Lin, Feng-Yi Lin, Pei-Ru Wu, Martin Hämmerle, Bernhard Höfle, Sebastian Bechtold, Ruey-Lung Hwang, Yu-Cheng Chen, Multiscale analysis and reduction measures of urban carbon dioxide budget based on building energy consumption, Energy and Buildings, Volume 153, 2017, Pages 356-367, ISSN 0378-7788, http://dx.doi.org/10.1016/j.enbuild.2017.07.084.

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