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Noise pollution is a growing problem in many urban environments, affecting citizens’ daily life. It can reduce citizens’ happiness, increase their stress, and even people them get sick if they are exposed to noise pollution for a long period of time. In recent studies we investigate the use of crowdsourced data to derive noise polluted areas. Such information can also be used for generating routes that minimise exposure to noise.

Within urban environments, noise can be caused by many factors. Road traffic noise is one of the major noise sources. Besides, industrial areas, commercial places, and various buildings (such as chemical factories, power plants) also create noise. In the context of traffic noise emission, we mainly use OpenStreetMap data to derive the noise levels, according to the category of streets. A noise level is roughly approximated for each street segment based on the relevant attributes of roads (type of streets, traffic lights, number of lanes, speed limit, etc.) and surroundings. For example, a main street usually has more traffic than a residential street and thus has larger noise values and affects larger areas (buffers). This is inspired by earlier work by Martinelli and allows to estimate noise polluted areas and to generate a comprehensive coverage, currently only based on OpenStreetMap data. At the moment we investigate the quality of this modelled proxy data by comparison to official noise data in order to calibrate and improve those very first approximations.

With the initially derived noise information, a new experimental prototype feature ‘Quiet Routing’ is integrated into the LABS.OpenRouteService.org for evaluation purposes. This is adding to the “healthy” (stress reducing) routing theme, that has been introduced recently starting with the “prefer green areas” routing option.

The new ‘quite routing‘ feature in ORS can generate routes that avoid noisy areas. Moreover, an option of dynamically adjusting the weights is also added, allowing users to customize their routes and balancing between route distance and noise exposure. The same is now true for the “prefer green areas” option, that now also can be adapted individually by the user. For the sake of simplicity currently 10 levels are provided for each index. This leads to different weighting of the new attributes in comparison to the length of the route. So each user can decide individually how long a detour can be in comparison to the shortest route. Of course the results vary considerably with data quality and other factors what needs further ongoing investigations.

The feature also enhances the users’ awareness of noise along the route via the visualization of noise levels of route segments on the Web client. In addition to that these visualizations also help to understand the behaviour of the routing results according to the different weightings.

Currently the experimental prototype is available on the recently established LABS.openrouteservice.org platform together with other early features such as pedestrian routing through open spaces, the Places Location services API and others. These are availably initially only for all of Germany.

ORS Shortest Pedestrian Route without considering noise level

ORS Shortest Pedestrian Route without considering noise level

ORS QUITE Pedestrian Route considering noise

ORS QUITE Pedestrian Route considering noise

This work is inspired and supported through our cooperation project on urban stress (Psychogeography) with the Psychoepidemiologisches Zentrum (PEZ) at the Central Institute for Mental Health (ZI Mannheim) and the Intelligent Mobility Group at HeiGIT. The latter is kindly supported by the Klaus Tschira Foundation, Heidelberg through the core-funding for HeiGIT (Heidelberg Institute for Geoinformation Technology). Follow up research can for example analyse the effects of the individual choices and preferences, also further attributes are under consideration. We are looking forward to your feedback.

Open Position:

Software Engineer Geoinformation Technology / OpenStreetMap
Heidelberg Institute for Geoinformation Technology (HeiGIT)

In order to promote technology transfer and applied research in the area of ​​Geoinformatics, the Heidelberg Institute for Geoinformation Technology (HeiGIT) is currently being established with the support of the Klaus-Tschira Foundation. http://www.heigit.org. This is to be continued in the future as an independent institute. For this, we search a Software Engineer Geoinformation Technology. Depending on your experience the tasks are related to at least one of the following two areas:

A: Big Spatial Data Analytics (OSM, Social Web)

· Support designing and developing a queryable OSM Full History Geodatabase using Big Data Frameworks (Apache Ignite, Spark, Hadoop, etc.)

· Collaboration in the development of Web-based services for quality assessment and improvement of OpenStreetMap by data mining in OSM Full History data

· Development of methods and GI web services, especially for the analysis and data enrichment of heterogeneous geodata, especially from the social web, OpenStreetMap, remote sensing etc.

B: Route planning, Smart Mobility and Navigation Intelligence

· (Special) Routing with OSM, especially extensions of http://OpenRouteService.org based on Java

· Extension of the services infrastructure of various location-based services (LBS) using user-generated geodata, especially OSM.

We offer an attractive position in an interdisciplinary dynamic team in a highly dynamic growth market. HeiGIT is and will be related closely to the Department of Geoinformatics which is Member of the Interdisciplinary Center for Scientific Computing (IWR) and the Heidelberg Center for the Environment (HCE). We offer a stimulating interdisciplinary research environment with many personal development opportunities.

We expect an above-average university degree in one of the subjects of Geoinformatics, Computer Science, Geography, Mathematics or similar. Apart from a strong team spirit, independence and high motivation, as well as excellent abilities for internal and external communication and presentation in (if possible) German and English this includes excellent competency in methodology and technology, and geoinformatics experience, especially in Web Development, either in at least one the areas of ​​navigation, mobility & routing (Java) or in the area of ​​Big Spatial Data Analytics with Ignite & Spark (Java) or Spatial Data Mining & Machine Learning.

The position is to be filled as soon as possible and initially limited to June 2019 with the option of sustainable extension. Please send application documents (CV, certificates, references, etc.) as soon as possible, (best before mid August 2017) to zipf@uni-heidelberg.de

job description HeiGIT OSM big data, routing as pdf

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

“Heatmapping”: Accessing Geodatabases of Building Stocks for the Development of Spatial Energy System Models

Sebastian Blömer
ifeu Institut für Energie- und Umweltforschung Heidelberg

Time and date: Mon, July 10, 2.15 pm
Venue: INF 348, Room 015, Department of Geography, Heidelberg University

To evaluate the technological and economic feasibility of the use of different heat sources, conversion and supply structures, the development of high-resoluted GIS-Models of the long term energy demand for floor heating and hot water in residential and non-residential buildings became an important field of research in recent years. In this talk, I will give an overview of spatial energy system models of the German building stock from my work at the ifeu - Institute for Energy and Environmental Research Heidelberg. First, I will present the main requirements of spatial heat demand models of buildings with regard to the leading research questions and set them in relation with available geodatabases. As an insight into the development of nationwide heat atlases, I will present the procedure of a nationwide GIS-model of the development of the long term heat demand in the German residential building stock based on proprietary building footprint data. I will conclude with an overview of my recent work on integrating 3D-data and information on non-residential buildings into GIS-based heat demand models. As an Input for discussion, I will present the results of an evaluation of different machine learning algorithms for the automatic classification of settlement structures to enrich building footprint data.

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

Research in psychology and public health shows that there are environmental factors that cause an area to impose more or less stress to a person. One example is that being surrounded by natural green areas (meadows, parks, trees and forests etc. or also blue water areas) has a relaxing influence to the mood of a person in contrast to walking through a highly urbanised non-natural area which broadly speaking puts more stress on someone. Therefore a route that may not be the shortest path, but one that prefers the existing of green land-use areas to some extend can be seen as the more stress-free and healthy choice.

If you want to plan such a more pleasant or healthy route, you now can activate the “prefer green areas” option in the “additional settings” section of pedestrian routing options of OpenRouteService.
For testing purposes this specific feature which is targeted towards urban areas is currently restricted to Germany only (similar to other experimental prototypes at the brand new labs.openrouteservice.org).

This feature is still experimental, as the results vary strongly with the calculation of the weights for the green score. The latter depends both on the way of preprocessing and algorithms, as well as the data completeness of the green area data in OpenStreetMap. So expect some interesting results sometimes. Yet, the first route computations look often quite reasonable and promising. But of course this is a subjective measure, as different people will prefer different settings and accept detours of different lengths in favour of a greener or more healthy route. So there is much need for further research and development. We will be working on giving the user the option to interactively change the weighting in a more dynamic and fine-grained manner than it is possible at the moment in order to truly personalise her tour.

The calculated green index has also been added to the enhanced visualisation options for the route segments on the Web client. This allows the user for different categories (like surfaces, way types, steepness, suitability and now also for the green index ) to interactively overlay for each route segment the specific values on the current route using adapted colour codings.

Enjoy testing this new features on OpenRouteService.org

ORS Shortest Pedestrian Route (Mannheim)

ORS Shortest Pedestrian Route (Mannheim)

ORS GREEN Pedestrian Route (Mannheim)

ORS GREEN Pedestrian Route (Mannheim)

This work was inspired and supported through our cooperation project on urban stress (Psychogeography) with the Psychoepidemiologisches Zentrum (PEZ) at the Central Institute for Mental Health (ZI Mannheim) and the Intelligent Mobility Group at HeiGITThe latter is kindly supported by the Klaus Tschira Foundation, Heidelberg through the core-funding for HeiGIT (Heidelberg Institute for Geoinformation Technology). Follow up research can for example analyse the effects of the individual choices and preferences, also further attributes are under consideration.

Big thanks to the whole team for both preparing the landuse data (similar to OSMlanduse.org) based on OpenStreetMap and enhancing the routing service and client.

Recently Barrons studied the effect of Amazon.com buying Whole Foods for US$13.7 billion. They used the OpenRouteService Isochrones API for an detailed accessibility analysis of the whole US. With OpenStreetMap based OpenRouteService and US Census data they calculated how much US population is covered within different driving times from the US wide network of the 444 Whole Food stores plus the AmazonFresh/Pantry locations. They conclude that over 70% of the U.S. population, roughly 224 million people would be within one-hour delivery by Amazon and for example all of Manhattan within 10 minutes. Barrons sees this as a potential game-changer for Amazon, which tries to strengthen it’s “last-mile” delivery network as well as grocery sales.

The analysis demonstrates the power of accessibility analysis using OpenRouteService Isochrones API (the new version has been introduced here) for business analysis, site selection and logistics in nearly any domain based on OpenStreetMap data. Just imagine the power of combining this kind of analysis with the new OpenRouteService Places location service API. A early sneak preview of parts of that POI location search is available on the recently started Labs.OpenRouteService.org for experimental new features.

Read the whole Barrons study at:
Amazon and Whole Foods: How 2-Day Shipping Could Become an Hour or Less - With its deal for Whole Foods, Amazon will be within an hour drive of 70% of Americans.”
http://www.barrons.com/articles/amazon-and-whole-foods-how-2-day-shipping-could-become-an-hour-or-less-1498682000

https://openrouteservice.org/

In the aftermath of a disaster, knowing the condition of buildings, infrastructure, and utilities is critical to both immediate response and long-term recovery efforts. The Humanitarian OpenStreetMap Team (HOT) is often asked to help identify damage to buildings and other assets in the affected region. In the past, limitations in post-disaster imagery and difficulties in identifying building damage from aerial views have hindered these efforts.

In a current research project, the GIScience Research Group / HeiGIT supports HOT, Stanford Urban Resilience Initiative (SURI), the World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR), and the University of Colorado, Boulder in improving how building damage information can be gathered through crowdsourcing. We intend for the methods and tools we are developing to better facilitate the contributions of online volunteers, and to maximize the impact of those contributions to disaster response and recovery processes.

In the scope of the project we developed three experiments that are geared toward better understanding the crowd’s ability to assess building damage from satellite imagery. The first survey is a building-level damage assessment set up in OSM, while the other two are novel area-based damage assessments.

Please find the experiments here:

  1. Building-level assessment
  2. Damage ranks
  3. Damage comparison

We are now seeking participation from you to test our approach and enable taking crowdsourced damage assessments further!

To learn about your experience and improve our survey, we would appreciate
if you could provide your feedback in the user survey that is linked in the
damage assessment experiments. The consent forms for this research can be found
here, please contact sloos@stanford.edu if you have any related question.

We appreciate your help and thank you in advance for your contribution!

Since the launch of the MapSwipe Analytics webpage three month ago we were able to improve our service and offer more detailed information on each project in MapSwipe. MapSwipe is a mobile App for crowdsourcing geographic information needed by humanitarian organisations like Red Cross and Doctors without Borders (MSF) introduced earlier. In particular the data can be used for priorizing areas within the HOT Tasking Manager.

Especially the new “agreement” layer helps to understand the quality of the MapSwipe data. Agreement values close to 1.0 indicate that there is a high agreement amongst volunteers. Vice versa, values close to 0 show that the MapSwipe volunteers disagreed. By using this layer we are able to find out, which areas need more attention in the mapping process.

Additionally you can toggle between the “Yes”, “Maybe” and “Bad Image” layers to visualize the number of contributions in each class. The “Progress” layer helps you to track the status of the project and depicts which areas are already covered and which areas are still to map.

Furthermore, we had a closer look at our mapping community. In total more than 16,000 people contributed to MapSwipe. However, not everyone is contributing equally. A special “Thank You!” goes to the long-term mappers, who are the backbone of MapSwipe and contribute most of the data. But also “MapSwipe Mapathons” are a great way to contribute and generate a lot of data (up to 50,000 results per day!), that is used to delineate populated areas.

So, dear MapSwipers please continue with you great efforts and tell you friends and family to use MapSwipe. :)

We recently launched the “MapSwipe Working Group” under the umbrella of the HOT Technology Working Group. If you are interested in becoming an active part of the developers community, don’t hesitate to contact us and have a look at the MapSwipe github repository.

:)

Further references:

Herfort, B., Reinmuth, M., Porto de Albuquerque, J., Zipf, A. (2017): Towards evaluating the mobile crowdsourcing of geographic information about human settlements. 20th AGILE conference 2017, Wageningen, Netherlands.

Herfort B, Eckle M, Reinmuth M et al. The democratisation of humanitarian mapping: insights into the MapSwipe app and data quality [version 1; not peer reviewed]. F1000Research 2017, 6:704 (slides) (doi: 10.7490/f1000research.1114077.1)

MSF Scientific Days Talk on Youtube: https://youtu.be/Oxu5M6gzvuo

This work has kindly been supported by the Klaus Tschira Foundation, Heidelberg through the core-funding for HeiGIT (Heidelberg Institute for Geoinformation Technology).

https://laco-wiki.net
Heidelberg
https://laco-wiki.net/c/hei_artificial_mapat # artificial non - vegetated

Toulouse
https://laco-wiki.net/c/tls_artificial_mapat # artificial non - vegetated

Vienna
https://laco-wiki.net/c/vie_artificial_mapat # artificial non - vegetated

Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most important crowdsourced mapping platforms, in a recent study (1) we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation of people with limited mobility. See (2) for background information. The completeness of OSM data is being assessed regarding sidewalk information. Relevant attributes for sidewalk information such as sidewalk width, incline, surface texture, etc. are considered, and through both extrinsic and intrinsic quality analysis methods (3), the results of fitness for use of OSM data for routing services of disabled persons are presented. Based on that empirical results, it is concluded that OSM data of relatively large spatial extents inside the studied cities could be an acceptable region of interest to test and evaluate wheelchair routing and navigation services, as long as other data quality parameters such as positional accuracy and logical consistency are checked and proved to be acceptable. We present an extended version of OSMatrix web service (4) and explore how it is employed to perform spatial and temporal analysis of sidewalk data completeness in OSM. The tool is beneficial for piloting activities, whereas the pilot site planners can query OpenStreetMap and visualize the degree of sidewalk data availability in a certain region of interest. This would allow identifying the areas that data are mostly missing and plan for data collection events. Furthermore, empirical results of data completeness for several OSM data indicators and their potential relation to sidewalk data completeness are presented and discussed.

(1) Mobasheri, A.; Sun, Y.; Loos, L.; Ali, A.L (2017): Are Crowdsourced Datasets Suitable for Specialized Routing Services? Case Study of OpenStreetMap for Routing of People with Limited Mobility. Sustainability 2017, 9(6), 997; doi:10.3390/su9060997

(2) Zipf, A.; Mobasheri, A.; Rousell, A.; Hahmann, S. (2016): Crowdsourcing for individual needs—The case of routing and navigation for mobility-impaired persons. In European Handbook of Crowdsourced Geographic Information; Capineri, C., Haklay, M., Huang, H., Antoniou, V., Kettunen, J., Ostermann, F., Puves, R., Eds.; Ubiquity Press: London, UK, 2016; pp. 325–337

(3) Barron, C., Neis, P. & Zipf, A. (2013): A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis , Transactions in GIS, Volume 18, Issue 6, December 2014, Pages: 877–895 DOI: 10.1111/tgis.12073.

(4) Roick, O., Loos, L. & Zipf, A. (2012): Visualizing spatio-temporal quality metrics of Volunteered Geographic Information – A case study for OpenStreetMap. Geoinformatik 2012. Mobilität und Umwelt. Braunschweig. Germany.

OSM Wheelchair Routing for Europe at https://OpenRouteService.org
ORS with Open Spaces Pedrestrian Routing: https://Labs.OpenRouteService.org

OSMatrix for Europe: http://osmatrix.uni-hd.de

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

Semantic Signatures for Social Sensing in Smart Environments

Prof. Dr. Krzysztof Janowicz
Associate Professor for Geographic Information Science and Geoinformatics at the Geography Department of the University of California, Santa Barbara, USA

Time and date: Mon, July 3, 2.15 pm
Venue: INF 348, Room 015, Department of Geography, Heidelberg University

In this talk we will introduce the framework of semantic signatures in the context of social sensing and illustrate how its combination of deductive and inductive methods provides new insights into key questions of geographic information retrieval, reverse geocoding, place name disambiguation, similarity reasoning, and so forth. We will discuss these topics in the context of bottom-up, sensor-rich environments.

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

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