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In the last week Melanie Eckle, who is working at the HeiGIT / GIScience Research Group Heidelberg, was elected as a Board Member of the Humanitarian OpenStreetMap Team. Congratulations Melanie!

The newly elected Board Members Ahasanul Hoque, Pete Masters and Melanie Eckle will join Jorieke Vyncke and Dale Kunce. The Board is elected by HOT’s voting members and oversees the activity of the HOT community and maps out the long-term strategy. Melanie is not only an OSM enthusiast, she has already worked together with many HOT members from various countries and is a driving force to build connections between academia and practitioners. We are looking forward to tighten these partnerships to address to challenges Melanie pointed out:

  • How well do we know the HOT community, and what are their needs?
  • What is the impact of HOT’s work?
  • How can we better identify and meet the needs of potential data users?
  • How can we detect deficiencies in the OSM data, improve validation and data maintenance processes, and assure a certain level of data quality?
  • How can we better support and encourage local mapping communities, and link remote and local mappers?

Since many of these challenges and the work of HOT in general are closely related to the work of the GIScience Research Group and HeiGIT (e.g. its Humanitarian mapping division), we will support her whenever possible. Keep up the good work!

Our group member Jiaoyan Chen attended the 26th World Wide Web Conference from 3 April to 7 April in Perth, Australia. The topic of the conference includes web-related machine learning, social network, knowledge base, crowdsourcing, urban data mining, etc. He gave a 30-minues presentation in the co-conference Big 2017 as well as a poster presentation in the main conference about the project of DeepVGI. The publication titled as “DeepVGI: Deep Learning with Volunteered Geographic Information” will soon be published by ACM.

On Friday, our member René Westerholt held a talk at the Annual Meeting of the American Association of Geographers (AAG) in Boston. The talk which is entitled “Topological and scale-related issues in Twitter analyses through superimposed forms of spatial heterogeneity” was part of a special session on geographic data science organised by Alex Singleton from the University of Leeds. The talk reflected upon findings regarding methodological issues in the spatial analysis of tweets and discussed implications for future analytical strategies.

René presenting at the AAG meeting in Boston

René presenting at the AAG meeting in Boston

Our team member René Westerholt recently held a joint session with Dr Guibo Sun from Hong Kong University. The session on “spatial urban analytics” was part of the Geography colloquium at Harvard University. Both talks were dealing with methodological issues. Thereby, René emphasised on technical issues in the spatial analysis of social media data. Dr Sun presented results about implications of varying scales on measures of the built environment. You can find the slides as well as short abstracts online.

today the interdisciplinary working group “Open Government” of “Städtetag Baden-Württemberg” meets in Heidelberg for a workshop on Open Data. Among the invited speakers are for example

- Michael Winckler, Interdisziplinäres Zentrum für wissenschaftliches Rechnen Uni Heidelberg (IWR) talking about “Open Data aus Sicht einer Forschungseinrichtung”

- Prof. Dr. Alexander Zipf, GIScience Heidelberg / HeiGIT), with a presentation about “Open Data und Wissenschaft - Geoinformatik im Kontext zu offenen Daten

- Hartmut Gündra, Netzwerk Geoinformation der Metropolregion Rhein-Neckar e.V., Geonet.MRN
talking about „Open Data – Rohstoff der digitalen Wirtschaft“

Further presenters include Antje Göldner (Geschäfts- und Koordinierungsstelle GovData) and representatives of different citya dministrations (Heidelberg, Freiburg, Ulm, etc.)

We are looking forward to stimulating discussion and further steps on the road to open government data.

We cordially invite anybody interested to the first public GIScience Colloquium presentation on Monday for this summer term.

Correctly identifying the contextual units that influence geographic phenomena is a fundamental issue within spatial analysis. As ‘true casually relevant’ activity-related geographic contexts vary at the level of the individual by time, space, and activity, they are prone to errors of misspecification, which affect the validity of results. The uncertainty related to this issue is reduced today when widely available high-resolution mobility data is used to reconstruct individual activity spaces. Yet, as activity is mediated by spatial cognition, knowledge, and preferences, delineating these spaces using only objective spatio-temporal constructs may hamper the effort, i.e. considering spaces based only on physical accessibility and not on their behavioral relevance may constrain the extent to which uncertainty may be reduced. To establish this argument, this presentation would rely on three studies:

- a field experiment studying changes in activity patterns within a tourist attraction when visitors are exposed to different spatial information and geographical layouts;

- a model predicting visit probabilities within a road network via the integration of non-Euclidean time-space constructs into models of Probabilistic Time Geography;

- a procedure formalizing topologies of time-space consumption behaviors represented by movement trajectories as well as delimiting the activity spaces related to them.

The first study stresses the need to consider the cognitive-behavioral element when delineating geographical contexts, while the latter two studies constitute the means for this end.

Recently, Strava, a popular website and mobile app dedicated to tracking athletic activity (cycling and running), began offering a data service called Strava Metro, designed to help transportation researchers and urban planners to improve infrastructure for cyclists and pedestrians. Strava Metro data has the potential to promote studies of cycling and health by indicating where commuting and non-commuting cycling activities are at a large spatial scale (street level and intersection level). The assessment of spatially varying effects of air pollution during active travel (cycling or walking) might benefit from Strava Metro data, as a variation in air pollution levels within a city would be expected.

In order to explore the potential of Strava Metro data in research of active travel and health, we investigated spatial patterns of non-commuting cycling activities and associations between cycling purpose (commuting and non-commuting) and air pollution exposure at a large scale. Additionally, we estimated the number of non-commuting cycling trips according to environmental characteristics that may help identify cycling behavior. Researchers who are undertaking studies relating to cycling purpose could benefit from this approach in their use of cycling trip data sets that lack trip purpose. We used the Strava Metro Nodes data from Glasgow, United Kingdom in an empirical study.

Empirical results reveal some findings that (1) when compared with commuting cycling activities, non-commuting cycling activities are more likely to be located in outskirts of the city; (2) spatially speaking, cyclists riding for recreation and other purposes are more likely to be exposed to relatively low levels of air pollution than cyclists riding for commuting; and (3) the method for estimating the number of non-commuting cycling activities works well in this study. The results highlight: (1) a need for policymakers to consider how to improve cycling infrastructure and road safety in outskirts of cities; and (2) a possible way of estimating the number of non-commuting cycling activities when the trip purpose of cycling data is unknown.

Sun, Y., & Mobasheri, A. (2017). Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data. International Journal of Environmental Research and Public Health, 14(3), 274.

Asher Yair Grinberger (Jerusalem) has been awarded a prestigious Alexander von Humboldt PostDoc Fellowship. He will join the GIScience Research Group Heidelberg later this summer to work for two years on developing theory & methods related to Big Spatial Data. We are looking forward working with him in Heidelberg.
But you have the chance to meet him already next week in Heidelberg:
He will start the GIScience colloquium for SoSe 2017 already on Mon, April 3, 14pm in INF 348, lecture room, with a talk about “The Identification of Geographic Activity Contexts: Considering Behavioral Effects“. Feel welcomed to join and stay tuned for the next presentations.

The MapSwipe App is widely used by many volunteers who donate their time and brain capacity to find buildings or roads on satellite imagery. Just recently we counted the 10,000,000th contribution! After only 6 months since MapSwipe was launched!
The outcome of these efforts are mainly used by humanitarian organisations like MSF, Netherlands Red Cross or CartONG to create HOT Tasking Manager tasks. Recently we have been busy to reveal the power of the data produced. For doing so we focus on several projects:

1. MapSwipe Analytics website: http://mapswipe.geog.uni-heidelberg.de

MapSwipe Analytics is a website that brings together everything related to MapSwipe projects. This includes a visualisation of the results (e.g. settlement layer), but also additional characteristics like agreement among volunteers, which is an important indicator for quality. Furthermore, you can monitor the progress of individual MapSwipe projects.

The map view for all projects:

The detailed “analytics” view:

Please note that we are still in the beta mode. We are working on improving the design as well as the analytical features. Your feedback is always welcome. Just post an issue to our GitLab repository: https://gitlab.com/giscience/MapSwipe/MapSwipeAnalytics

2. MapSwipeTools: https://gitlab.com/giscience/MapSwipe/MapSwipeTools

Since, at least three different people work on every MapSwipe tile (that are the little squares) further aggregation of the answers of one tile is needed. But also adjacent tiles marked with the same answer need to be put together. We need to filter out unreliable answers and finally derive geometries that are ready to use in the HOT Tasking Manager. Since, one main goal of MapSwipe is to support the OSM Mapping using the HOT Tasking Manager, we improved our algorithms to minimize the number of tasks and overall area, but still maintain high accuracy.

We provide you with the tools (written in python) to process the data on your own. This gives you the possibility to download and process MapSwipe data at any time and as often as you like. As we are trying to improve the scripts step by step, have a look at the GitLab repo and always get the latest version.

Left: The “old” algorithm derives larger polygons, which tend to be too small or too thin. Right: Using the “new” algorithm the polygons produces polygons that suit better for the OSM mapping.

3. MapSwipe Data Repository: http://mapswipe.geog.uni-heidelberg.de/download/

Sometimes it is not handy or possible to process data on your own. That’s the reason we created the MapSwipe Data repository, where you can just download the processed data from our server. We will update the data every 12 hours. If you cannot wait that long, have a look at the previous section.

The data comes in various categories. The most important ones are shown on the map:

aggregation –> settlement layer
bad_image –> cloud Layer
final –> Tasking Manager task geometries

4. Research Activities:

Besides the development of tools and websites we are also digging deeper into the relationship between data quality and intrinsic characteristics of the MapSwipe data.
Our short paper “Towards evaluating the mobile crowdsourcing of geographic information about human settlements” was accepted for the AGILE conference 2017. Our study identifies several factors that may cause disagreement between volunteers (e.g. bad imagery, dependence on individual users) and thus reduce the reliability of the information they produce. However, such disagreement cases appear not to be random. Their spatially clustered distribution suggests that they are systematically caused by underlying factors.

The insights of this initial study may be used to indicate which types of classification tasks are not well understood by volunteers and tell us where to improve the MapSwipe App.

Herfort, B., Reinmuth, M., Porto de Albuquerque M.J. and Zipf, A. (2017): Towards evaluating the mobile crowdsourcing of geographic information about human settlements. AGILE 2017 International Conference on Geographic Information Science. Wageningen. NL. (accepted).

As this is very much work in progress, stay tuned for more!

PS: You don’t have the MapSwipe App? Get it here: https://mapswipe.org/

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

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