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Die Fachtagung Katastrophenvorsorge wird jedes Jahr vom Deutschen Roten Kreuz organisiert, mit dem Ziel eine Dialogplattform für verschiedene Akteuren der nationalen und internationalen Katastrophenvorsorge zu schaffen.

In Kooperation mit dem Bundesamt für Bevölkerungsschutz und Katastrophenhilfe (BBK) und dem Zentrum für satellitengestützte Kriseninformation (ZKI), haben wir vom HeiGIT den Workshop „Geodaten in der Katastrophenvorsorge“ durchgeführt. Anhand konkreter Beispiele aus der Forschung und Praxis konnte so die Bedeutung und der Nutzen von Geoinformationen für die Katastrophenvorsorge erläutert werden.

Weitere Informationen, Bilder und Illustrationen der Tagung mit Graphic Recording finden Sie in der Dokumentation zu Fachtagung Katastrophenvorsorge 2018 (S. 39-43).

In a previous blog post we performed a conceptual compliance analysis between OSM data and several tagging-guidelines using the OSHDB API. The results were visualized in a line chart, comparing the different compliance ratio over several months. The following analysis focuses on a spatial representation of the conceptual compliance. It is conducted for the “iD-editor” data reference on a yearly basis. We use a shapefile containing a grid of equal area 10×10 km cells over the region of interest. The cells have unique ids. The compliance rate is calculated individually for each cell.

In our former post on computing a compliance rate, between 2015 and 2016 we saw a reduction in the compliance of the OSM data with the iD and JOSM editor. This was caused by a special tag (de:strassenschluessel), which is used almost exclusively in Mecklenburg Western Pomerania and is therefore not directly supported by the presets of OSM’s generic iD editor. In the spatial visualization the effect of this tag is visible as well. Between 2015 and 2016 we see a clear decline of cells with the highest compliance.

More details about the implementation of this analysis can be found in this recent article on heigit.org:

https://heigit.org/spatial-conceptual-compliance-analysis-with-the-openstreetmap-history-database-oshdb/

If you have any questions or want to give us feedback, don’t hesitate to contact us via info@heigit.org.

Related work: Ballatore, A. ; Zipf, A. (2015): A Conceptual Quality Framework for Volunteered Geographic Information. In COSIT 2015 Conference on Spatial Information Theory XII October 12-16, 2015, Lecture Notes in Computer Science.

This blog post is the start of a series of posts, which describe what you are able to do using the ohsome framework developed at the Heidelberg Institute of Geoinformation Technology (HeiGIT). OpenStreetMap (OSM), the biggest open map of our world, offers not only the current state of the data, but the whole historical evolution of it in a temporal resolution of down to one second. This rich source of data can be retrieved in the GeoJSON data format using the newest version of the ohsome API and then directly integrated into QGIS. Via the usage of the QGIS plugin TimeManager, it is possible to visualize the development of the displayed features over time. The following gif shows the evolution of buildings in Heidelberg (ways with the key “building”) from 2008-01-01 till 2018-01-01 in a monthly interval. Do you recognize the first mapped building of Heidelberg from this gif?

klick the picture to see the animated gif

The green ones are the buildings with the OSM tag “addr:city” and the red ones those without. Every time a building is displayed thicker, it has been edited in the respective month.

What do you have to do to get such a visualization of your city? There are only 3 steps necessary:

1) Data extraction using the ohsome API

For getting the needed data, we send a request to the Apache Ignite instance of Germany using this URL (https://api.ohsome.org/v0.9-ignite-germany/elementsFullHistory/geometry?bboxes=8.6463%2C49.3714%2C8.7193%2C49.4361&keys=building&properties=tags%2Cmetadata&showMetadata=false&time=2008-01-01%2C2018-01-01&types=way”). The important part is the spatial parameter “bboxes”, which you would have to adapt to your specific region.

2) Importing and preparing the GeoJSON data in QGIS

QGIS directly supports the GeoJSON data format. The only thing you would have to do is take the response file of the ohsome API and load it into QGIS, e.g. via drag-and-drop. Then you should choose the Polygons to import. Next, you need to specify an adequate reference system. We chose “Europe_Albers_Equal_Area_Conic” but feel free to choose one that fits best to your use case. Before starting to use the data with the TimeManager plugin, we recommend you to save the GeoJSON data as a GeoPackage file. To apply the different colors based on the “addr:city” tag, we defined an SQL function “addr:city” IS NOT NULL and colored the two values appropriately.

3) Creating the time series visualization using the TimeManager plugin

After you’re finished with setting an adequate color scheme, you can load the layer into the TimeManager. The start time is stored in the field “@validFrom” and the end time in the field “@validTo”. You can define additional animation and time format options as you like and then your animation should be ready to test within QGIS. TimeManager also offers the possibility to export the video as individual .png files for Windows, or directly as an animated gif or video for Linux or OSX.

The data used to create the gif was downloaded from our Germany Ignite instance. Besides that, we also have an instance using data from Nepal. If you have a request for a specific region, or any other suggestion or feedback, please do not hesitate to contact us via email under info@heigit.org. Stay tuned for further posts of this series to get to know more about our ohsome framework.

F-B Mocnik participates in the Complex Networks 2018 conference, presenting work about the impact of space on network representations.  Street networks have been examined in respect to their structure.  F-B Mocnik has previously examined networks from various domains, thereby demonstrating that the polynomial volume law applies to many of them.  The presentation at the conference focuses on the geographical domain only.  Thereby, it examines the dimension of street networks, which turns out to be very stable.

F-B Mocnik: The Polynomial Volume Law of Complex Networks in the Context of Local and Global Optimization. Scientific Reports 8(11274), 2018

Christmas is just around the corner and the disastermappers heidelberg are organizing another workshop on the use of OpenStreetMap data. As last year they will present different ways to craft personal Christmas gifts based on OpenStreetMap features.

Additionally they will also show how to use open satellite imagery archives captured by Sentinel and Landsat mission. Together we will create posters, postcards, calendars or everything you need to imprint a T-shirt.

We will show you how!

When: 13.12.2018, 4 pm
Where: Small PC-Pool + SR, Institute of Geography, Heidelberg University,
Berliner Straße 48, Heidelberg

Snacks & drinks are provided.
No prior knowledge needed. Please bring your own laptop + mouse.

We are happy to hereby share latest information about the 5th HOT Summit- the annual gathering of the Humanitarian OpenStreetMap Team community- which after great events in Washington, Brussels, Ottawa and Dar es Salaam, will next year come to the picturesque city of Heidelberg, September 19th and 20th.

View from Old Bridge to the Castle

HOT Summit 2019 as well as the immediately preceding State of the Map, the global OpenStreetMap conference, will be hosted by Heidelberg University. Therefore, members of the OSM and HOT community will have the chance to visit both events and spend a whole 5 days to mingle, share ideas, discuss current topics- and most importantly Bridge the Map, the theme for the State of the Map 2019. Find more about the Summit 2019 and get latest updates here.
Interested in supporting the Summit Working Group and/ or the local team and to help discuss, plan and develop ideas for HOT Summit 2019? There are different ways to support remotely as well as locally, and we are always looking forward to hearing your ideas. Just ping us on summit@hotosm.org.
Or are you interested in supporting the event through sponsorship? Please find further information here and in the Sponsorship Prospectus.

HOT Summit 2018 Participants in Dar es Salaam National Museum

A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations behind behaviors. The aim of a recently published study is to model and understand human activity patterns within urban areas using both spatiotemporal and cognitive psychology methods to measure both human behavior patterns and the underlying motivations. We first propose a framework that enables us to analyze the spatiotemporal patterns of urban human activities, infer the associated semantic patterns that represent the motivations driving human mobility choices and behaviors, and measure the similarity between human activities. We then construct a human activity network based on the similarity to depict human activity patterns. The framework is applied to a case study of Toronto, Canada, where geotagged tweets are used as a proxy for human activities to explore activity patterns. The analysis of the human activity network shows that 61% of tweeter users follow similar activity patterns. Our work provides a new tool for better understanding the way individuals interact with urban environments that could be applied to a variety of urban applications.

Wei Huang & Songnian Li (2019) An approach for understanding human activity patterns with the motivations behind, International Journal of Geographical Information Science, 33:2, 385-407, DOI: 10.1080/13658816.2018.1530354

Related earlier work:

Nowadays, several research projects show interest in employing volunteered geographic information (VGI) to improve their systems through using up-to-date and detailed data. The European project CAP4Access was one of the successful examples of such international-wide research projects that aimed to improve the accessibility of people with restricted mobility using crowdsourced data. In this project, OpenStreetMap (OSM) was used to extend OpenRouteService. However, a basic challenge that this project tackled was the incompleteness of OSM data with regards to certain information that is required for wheelchair accessibility (e.g. sidewalk information, kerb data, etc.). In the following article, we present our approach in awareness raising and using tools for tagging accessibility data into OSM database for enriching the sidewalk data completeness. Several experiments have been carried out in different European cities, and discussion on the results of the experiments as well as the lessons learned are provided. The lessons learned provide recommendations that help in organizing better mapping party events in the future. We conclude by reporting on how and to what extent the OSM sidewalk data completeness in these study areas have benefited from the mapping parties by the end of the project.

Illustrations of portable ramp mapped on Wheelmap

Illustrations of portable ramp mapped on Wheelmap

Further reading:

Mobasheri, A., Zipf, A., Francis. L. (2018). OpenStreetMap Data Quality Enrichment through Awareness Raising and Collective Action Tools – Experiences from a European project. Journal of Geo-spatial Information Science, 21:3, 234-246, DOI: 10.1080/10095020.2018.1493817

The article is part of a special issue on Crowdsourcing for Urban Geoinformatics of the Journal of Geo-spatial Information Science. Amongst interesting other papers this issue includes some more articles from our group:

Further selected publications on the topic of OSM/VGI for accessibility:

Conceptual compliance measures to what degree contributors of volunteered geographic information (VGI) are using proposed tagging-standards. Here, we look into OpenStreetMap (OSM) as the most well-known example for VGI. In OSM the most important tagging guideline is defined by its wiki. In addtion, OSM editors like iD or JOSM provide presets (default options to adhere to tagging standards).

This analysis is based on the paper “A Conceptual Quality Framework for Volunteered Geographic Information” by Andrea Ballatore and Alexander Zipf. By using the OpenStreetMap History Database (OSHDB), developed at the Heidelberg Institute for Geoinformation Technology, we can analyze how compliant the tagging of highway objects of the OSM data is with these tagging-standards. The OSHDB allows us to further analyze the temporal evolution of these compliance values:

Figure 1: Conceptual Compliance of OSM history data of Mecklenburg Western Pomerania with the tagging-guidelines for highway-objects of the editors iD, JOSM and the OSM Wiki.

An example of such a data compliance evolution is shown in Figure 1, where in the time between mid of 2015 and 2016 we see a reduction of the compliance rate for the iD and the JOSM editor. A further investigation reveals that the decrease is mainly caused by one special OSM tag (de:strassenschluessel), which was then used in Mecklenburg Western Pomerania. That tag is not part of the iD and JOSM tagging-guidelines, leading to a decrease in the corresponding compliance values.

A recent article on heigit.org explains the analysis in more details and showcases how the OSHDB API can be used to for doing (conceptual) quality analysis of OSM data:

heigit.org/conceptual-compliance-analysis-with-the-openstreetmap-history-database-oshdb/

Related work: Ballatore, A. ; Zipf, A. (2015): A Conceptual Quality Framework for Volunteered Geographic Information. In COSIT 2015 Conference on Spatial Information Theory XII October 12-16, 2015, Lecture Notes in Computer Science. UC Santa Barbara.

Understanding how citizens interact with transportation system is a key to solving a variety of urban issues in general and traffic congestion in particular. Recently, scholars have put efforts on the pertinent work ranging from developing traffic predictors to understanding human mobility and activity patterns. Multiple types of data have been used, of which crowdsourced data (e.g. social media data) plays an essential role. Due to the limitation of traffic information extraction from social media data raised in the existing work, a paper that recently has been published online (limited time free access) aims to develop an approach which allows us to explore the potential influence of human activities on daily traffic congestions through linking human activities derived from geotagged tweets to the daily traffic conditions.

The result of a case study of Toronto, Canada exhibits that entertainment related activities are more likely to appear during evening peak hours, while it seems that morning rush hours are less sensitive to human activities. In addition, it is learned that the activities involved in international events tend to have a long-term impact on urban traffic. This work provides a new tool for urban planners and policy makers to deal with complex urban issues effectively using low-cost social media data and sheds light on the research on analyzing urban traffic and urban dynamics based on crowdsourced data.

Wei Huang, Shishuo Xu, Yingwei Yan, Alexander Zipf (2018): An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada,
Cities, ISSN 0264-2751, https://doi.org/10.1016/j.cities.2018.07.001.

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