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The UndercoverEisAgenten team is still on the road introducing their most recent results to the public and scientific community.

On July 2, visitors to the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI) at the “Long Night of Sciences” Berlin + Potsdam tested an UndercoverEisAgenten permafrost crowdmapping application.

The UndercoverEisAgenten team also participated in the GI Salzburg 2022 conference from July 4th through 7thThey discussed their newest paper on the potential of Citizen Science for mapping landscape change in Arctic permafrost regions, which describes the results of a data analysis with visitor contributions from the MS Wissenschaft 2019 traveling exhibition.

Exhibition-goers experienced mapping permafrost in satellite imagery using an app as a part of an interactive exhibit drawing from the work of HeiGIT and AWI. The analysis results show that this task is more challenging for citizen scientists than others, e.g. building detection, as implemented in MapSwipe, an app developed with support of HeiGIT. The paper examines several approaches to facilitate the task for citizen scientists and informs the ongoing development of crowdmapping tools for the UndercoverEisAgenten project. It has been published in the German language AGIT Journal, an interdisciplinary open access journal for Applied Geoinformatics.

For those who missed the exhibition or look forward to their next opportunity to explore this burgeoning project, the UndercoverEisAgenten team will be attending the Explore Science event in Bremen coming September. The event is organized by the Klaus Tschira foundation and will take place September 8–10 in the Bürgerpark.

Do you know an African student hoping to do a PhD in GIScience at the University of Heidelberg?

The Robert and Christine Danziger Scholarship deadline has been extended to August 31. Up-and-coming doctoral students from Africa, especially Ghana and West and Central Africa, are invited to apply for financial support. Applicants can apply for a doctorate in the field of Geography (with a focus on Geoinformatics at Heidelberg University

More information here.

At long last, welcome to another addition of our How to become ohsome-series!
This one is special because we’re discussing a third-party application that uses ohsome API as back-end to accumulate historical OSM data. Although this innovation isn’t directly about our API, it’s close enough (and exciting enough) to merit inclusion and just one more opportunity for you to become ohsome yourself!

We’re talking about is-osm-uptodate which is, as the name suggests, all about checking if the OpenStreetMap data within your given input boundary is up to date. As this would be our first constant external user, we naturally just had to write a little shoutout blog post. The ohsome-team is more than happy to see that our product helps the OSM community gather needed data.
Is-osm-uptodate has been using ohsome API since the release of version 1.6 (v1.6 - Ohsome) on July 7th, 2021, emphasizing that the new API allowed the application to run “much faster” and “analyze wider areas”.

We decided to reach out to Francesco Frassinelli (nickname: FraFra), the main thinker behind is-osm-uptodate, to gain some insight into the application’s developers and its reach. In his thesis, Frassinelli compared ohsome API to other solutions which enable you to gather OSM historical data. He concluded that ohsome API was the “fastest publicly available API to extract the historical data” which was needed.  His thesis on the usage of OSM historical data was what lead to the development of the Is-osm-uptodate software. According to Frassinelli, between 50-100 unique users were active between June and July 2022.

Now onto to the application itself: Is-osm-uptodate is intuitive and easy to navigate. Users simply enter their parameters of choice using the same logic and selectors as when creating the usual ohsome request. Then, choose a boundary-geojson and the results are good to go, easily downloadable as either a geojson or json depending on how users would like to keep working with their output data.  Additionally, the application delivers rapid output, with requests fulfilled at a comparable time to ohsome. The output in the example below, which displays the first edits in all highways within the boundary of Neuenheim in Heidelberg, took only ~6 seconds to be generated. Furthermore, there appears to be no notable resource usage in the ohsome API cluster when sending a request with roughly the same scope as in our example. So additional requests via is-osm-uptodate are a “piece of cake” to the ohsome API.

In the figures above, the input filter conditions and boundary (upper) as well as the resulting map and output statistics (lower) are shown.

Definitely give Is-osm-uptodate a look- it’s truly ohsome (and awesome ;D)! The excitement doesn’t end there though, with the next addition to this series offering a tutorial for the ohsome dashboard with special focus on our shiny, new advanced settings option!

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:

Further reading:

Auer, M., Eckle-Elze, M., Fendrich, S., Griesbaum, L., Kowatsch, F., Marx, S., Raifer, M., Schott, M., Troilo, R., Zipf, A. Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring. Proceedings of the 15th ISCRAM Conference – Rochester, NY, USA (2018).

Minghini, M., Frassinelli, F. OpenStreetMap history for intrinsic quality assessment: Is OSM up-to-date?. Open geospatial data, softw. stand. 4, 9 (2019). https://doi.org/10.1186/s40965-019-0067-x

This June, MSF (Médecins Sans Frontières) GIS Week 2022 was held in person again in beautiful Prague. Nearly the entire MSF team dealing with geo-data and geospatial analysis congregated at the event, which presented a fantastic opportunity to learn more about the current use of GIS within MSF and to discuss its future strategic importance.

From our own team, Benjamin Herfort and Marcel Reinmuth participated in sessions, offered a hands-on workshop concerning accessibility analysis, and facilitated a discussion round on participatory mapping. We presented the current version of the Sketch Map Tool and its applications in Madagascar and in a waterproofing data project. Afterwards, participants considered the potential for these types of tools to collect local data in collaboration with communities. The accessibility analysis session covered a brief theoretical introduction into two methods: cost-raster-based and network-based accessibility. The post-presentation conversation focused on limitations of the methods in the field and data quality challenges. The second portion of the accessibility session featured a hands-on exploration of the methods in QGIS using examples from the field such as access to water and health.

The event was complemented by an evening Mapathon hosted by the local Czech Missing Maps community, which presented an opportunity to develop skills in validation and validator training with special attention the the many JOSM plugins used for this purpose. Over 50 people attended the Mapathon and joined conversations about the potential value of creating footprints derived from deep learning approaches to improve OSM data coverage and how MapSwipe could help with this task.

You can find further resources and material here:

Materials of the accessibility workshop: https://heibox.uni-heidelberg.de/d/929fa3680ff648ecb65a

SketchMapTool: https://www.geog.uni-heidelberg.de/gis/sketchmaptool.html

OpenHealthCareAccessMap: https://apps.heigit.org/healthcare_access/#/

We call for applications to postdoctoral positions within the Heidelberg Mannheim Health and Life Science Alliance “Innovation Campus” for Inter-institutional project.

The Central Institute of Mental Health (ZI), Prof. A. Meyer-Lindenberg, the GIScience Research Group at Heidelberg University (Prof. A. Zipf), the 3DGeo Group (Prof. B. Höfle); and the Department of Biodiversity and Plant Systematics (Prof. A. Koch) at Heidelberg University propose a joint research project, that shall investigate the relationship between “Urban nature experience, biodiversity and mental health”
(Project Proposal. Nr. 55 (page 154ff)

If you are a PostDoc in the mentioned fields and interested in such work, please do apply for this project according to the details given here, referring to project Nr 55 and the respective PI:

Call for “Inter-Institutional Postdoctoral Positions” of the “Innovation Campus Heidelberg Mannheim Health &Life Sciences”:

https://www.health-life-sciences.de
(In addition you may also want to inform the relevant PI, e.g. Prof Zipf (GIScience) or Prof Höfle (3DGeo) about your application by email.)

Deadline for your applications is 22 September 2022, 5 pm CET.

Below you find a short summary of the project idea (Proposal No 55):

Environmental influences in urban areas pose risk to mental health (Lederbogen et al., 2011; Tost et al., 2015), making the identification and promotion of protective factors a high priority for urban public mental health and policy making. Urban nature experience and biodiversity are epidemiologically proven protective factors for mental health (Tost et al., 2019), but the daily-life psychological and neural mechanisms are poorly understood. The aim of this interdisciplinary research platform is the identification, quantification and neurobehavioral mechanistic inquiry of the causal subcomponents of nature experience and biodiversity as urban protective factors. For this purpose, the research platform strategically combines the psychiatric neuroscience research line of the Central Institute of Mental Health, Mannheim, with the research lines of the departments of GIScience and Geoinformatics and the department of Biodiversity and Plant Systematics at Heidelberg University. Extending our previous ecological neuroscience work we will 1) apply established geoinformatics and genomic approaches to quantify real-life urban environmental exposures including urban green space and biodiversity in the living environments of study participants, 2) record aspects of momentary psychological well-being (e.g., affective valence, stress experiences) with e-diaries while 500 adolescents and adults from the Rhine-Neckar metropolitan region go about their daily routines, thereby continuously collecting sensor data (e.g., geographic position, physical activity, heart rate, ambient noise, temperature) and, 3) examine brain function and established neural markers of environmental risk and protective factors of participants using functional magnetic resonance imaging. The acquired data will be analyzed in a multimodal transdisciplinary approach including stakeholders to quantify individual exposures to urban green space and biodiversity in everyday life, demonstrate protective effects of urban nature experience and biodiversity on well-being and mental health, and establish a direct mechanistic link to neural processing of environmental stimuli. In addition to providing critical basic knowledge for the development of new therapeutic and preventive programs for mental health, this transdisciplinary research platform will inform policy decisions on the design and maintenance of urban ecosystems for improving mental health and establish the Heidelberg Mannheim region as an internationally visible research area for the mental health consequences of rapidly changing biodiversity.

In this project the PostDoc at the GIScience Research Group at Heidelberg University will generate new geographical and environmental information (e.g., high resolution land use maps) through the combination and fusion of crowdsourced geodata, remote sensing imagery and laser scanning (LIDAR) as well as data from the Social Web and further public and government sources using state of the art machine learning methods. Here we integrate heterogeneous spatial data from public, private, and crowdsourced sources to calculate high resolution spatial databases with relevant attributes for environmental (e.g., noise, air quality, green space, biodiversity) and socioeconomic factors. For the proposed project we will derive a high-quality biodiversity dataset for the study area from those heterogeneous sources, including ambulatory assessments on how people roam and perceive this biodiversity. To produce this map, the heterogeneous spatial information from the different data sources (local administration, private sector, academia, crowdsourcing, Citizen Science) needs to be georeferenced, quality controlled, and spatially disaggregated towards a common spatial reference system. We use machine learning methods for data fusion and prediction of relevant biodiversity classes. The effects of this information for mental health are then calculated and spatially correlated with subject data collected by project partners.

Highly precise 3D data are particularly suited to derive a detailed description of the urban landscapes and vegetation, including proxies for biodiversity and also in-situ perception of the physical environment. For this the 3DGeo Research Group Heidelberg investigates and develops novel computational methods for the geographic analysis of 3D/4D point clouds. Our datasets are acquired by cutting-edge Earth observation technology (e.g., laser scanning, photogrammetry and radar). We aim at increasing the understanding of geographic phenomena and human-environment interactions by observing and analyzing them in full 3D, in near real-time with high spatial and temporal resolution.

Earlier related work at GIScience Heidelberg:

Projects:

meinGrün - information and navigation on urban green spaces in cities https://www.geog.uni-heidelberg.de/gis/meingruen_en.html
Psychogeography – Psychoepidemiology and HealthGIS in the Metropole Region Rhein-Neckar (Psychoepidemiologisches Zentrum PEZ @ ZI Mannheim) https://www.geog.uni-heidelberg.de/gis/psychogeographie.html

Related earlier Publications at GIScience & 3DGeo Heidelberg University:

  • Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. ISPRS Int. J. Geo-Inf. 2021, 10, 251. https://doi.org/10.3390/ijgi10040251
  • C. Geiss, E. Brzoska, P. Aravena Pelizari, S. Lautenbach, H. Taubenböck (2022): Multi- target Regressor Chains with Repetitive Permutation Scheme for Characterization of Built Environments with Remote Sensing, International Journal of Applied Earth Observations and Geoinformation, 106, https://doi.org/10.1016/j.jag.2021.102657 .
  • H. Lee, B. Seo, A. F. Cord, M. Volk, S. Lautenbach (2022), Using crowdsourced images to study selected cultural ecosystem services and their relationships with species richness and carbon sequestration, Ecosystem Services, 54,2022,https://doi.org/10.1016/j.ecoser.2022.101411.
  • Li, H. J. Zech, C. Ludwig, S. Fendrich, A. Shapiro, M. Schultz, A. Zipf (2021): Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning.. International Journal of Applied Earth Observation and Geoinformation, Vol 104, 2021, 102571.
    https://doi.org/10.1016/j.jag.2021.102571.
  • Ludwig, C.; Fendrich, S.; Zipf, A. (2020): Regional variations of context‐based association rules in OpenStreetMap. Transactions in GIS. Wiley.
    DOI: https://doi.org/10.1111/tgis.12694
  • Reichert, M., Brüßler, S., Reinhard, I. et al. The association of stress and physical activity: Mind the ecological fallacy. Ger J Exerc Sport Res 52, 282–289 (2022). https://doi.org/10.1007/s12662-022-00823-0
  • Reichert M., Giurgiu M., Koch E., Wieland L. M., Lautenbach S., Neubauer A. B., von Haaren-Mack B., Schilling R., Timm I., Notthoff N., Marzi I., Hill H., Brüßler S., Eckert T., Fiedler J., Burchartz A., Anedda B., Wunsch K., Gerber M., Jekauc D., Woll A., Dunton G. F., Kanning M., Nigg C. R., Ebner-Priemer U., Liao Y. (2020): Ambulatory assessment for physical activity research: State of the science, best practices and future directions, Psychology of Sport & Exercise, https://doi.org/10.1016/j.psychsport.2020.101742.
  • Reichert, M.; Braun, U.; Gan, G.; Reinhard, I.; Giurgiu, M.; Ma, R.; Zang, Z.; Hennig, O.; Koch, E. D.; Wieland, L.; Schweiger, J.; Inta, D.; Hoell, A.; Akdeniz, C.; Zipf, A.; Ebner-Priemer, U.W.; Tost, H.; Meyer-Lindenberg, A.(2020): A neural mechanism for affective well-being: Subgenual cingulate cortex mediates real-life effects of nonexercise activity on energy. Science Advances.
    https://doi.org/10.1126/sciadv.aaz8934
  • Reichert, M., Braun, U., Lautenbach, S., Zipf, A., Ebner-Priemer, U., Tost, H., Meyer-Lindenberg, A. (2020): Studying the impact of built environments on human mental health in everyday life: methodological developments, state-of-the-art and technological frontiers. Current Opinion in Psychology 32, 158-164. https://doi.org/10.1016/j.copsyc.2019.08.026
  • Tost, H., Reichert, M., Braun, U., Reinhard, I., Peters, R., Lautenbach, S., Hoell, A., Schwarz, E., Ebner-Priemer, U., Zipf, A., Meyer-Lindenberg, A., 2019. Neural correlates of individual differences in affective benefit of real-life urban green space exposure. Nature Neuroscience. https://doi.org/10.1038/s41593-019-0451-y
  • Reichert, M., Tost, H., Reinhard, I., Schlotz, W., Zipf, A., Salize, H.-J., Meyer-Lindenberg, A., & Ebner-Priemer, U. W. (2017). Exercise vs. non-exercise activity: e-diaries unravel distinct effects on mood. Medicine & Science in Sports & Exercise, 49(4): 763-773.
  • Reichert, M., Tost, H., Reinhard, I., Zipf, A., Salize, H., Meyer-Lindenberg, A., Ebner-Priemer, U.W. (2016): Within-subject associations between mood dimensions and non-exercise activity: An ambulatory assessment approach using repeated real-time and objective data. Frontiers in Psychology. 7:918. DOI:10.3389/fpsyg.2016.00918
  • Li, H.; Ghamisi, P.; Rasti, B.; Wu, Z.; Shapiro, A.; Schultz, M.; Zipf, A. A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks. Remote Sensing. 2020, 12, 2067. DOI: https://doi.org/10.3390/rs12122067
  • Törnros, T., Dorn, H., Reichert, M., Ebner-Priemer, U., Salize, H.-J., Tost, H., Meyer-Lindenberg, A., Zipf, A. (2016): A comparison of temporal and location-based sampling strategies for GPS-triggered electronic diaries.” Geospatial Health. Vol 11, No 3. DOI:10.4081/gh.2016.473.
  • Reichert, M., Törnros, T., Hoell, A., Dorn, H., Tost, H., Salize, H.-J., Meyer-Lindenberg, A., Zipf, A., Ebner-Priemer, U. W. (2016). Using Ambulatory Assessment for experience sampling and the mapping of environmental risk factors in everyday life. Die Psychiatrie. 2/2016. 94-102.
  • Dorn, H., Törnros, T. & Zipf, A. (2015): Quality Evaluation of VGI using Authoritative Data – A Comparison with Land Use Data in Southern Germany. ISPRS International Journal of Geo-Information. Vol 4(3), pp. 1657-1671, doi: 10.3390/ijgi4031657
  • Törnros, T., Dorn, H., Hahmann, S., and Zipf, A. (2015): Uncertainties of completeness measures in OpenStreetMap - A Case Study for buildings in a medium-sized German city, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 353-357, doi:10.5194/isprsannals-II-3-W5-353-2015.
  • Dorn, H., Törnros, T., Reichert, M., Salize, H.J., Tost, H., Ebner-Priemer, U., Meyer-Lindenberg, A., Zipf, A. (2015): Incorporating Land Use in a Spatiotemporal Trigger for Ecological Momentary Assessments. In: Car, A., Jekel, T., Strobl, J., Griesebner, G. (Eds.), GI_Forum 2015 – Geospatial Minds for Society (pp. 113-116). Journal for Geographic Information Science, 1.
  • Foshag, K., Aeschbach, N., Höfle, B., Winkler, R., Siegmund, A. & Aeschbach, W. (2020): Viability of public spaces in cities under increasing heat: A transdisciplinary approach. Sustainable Cities and Society 59, pp. 1-10. DOI: https://doi.org/10.1016/j.scs.2020.102215.
  • Antonova, S., Thiel, C., Höfle, B., Anders, K., Helm, V., Zwieback, S., Marx, S., Boike, J. (2019): Estimating tree height from TanDEM-X data at the northwestern Canadian treeline. Remote Sensing of Environment, 231. https://doi.org/10.1016/j.rse.2019.111251
  • Yan, Y., Schultz, M., Zipf, A. (2019): An exploratory analysis of usability of Flickr tags for land use/land cover attribution, Geo-spatial Information Science (GSIS), Taylor & Francis. https://doi.org/10.1080/10095020.2018.1560044
  • Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., (2015): Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. In: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M., (eds) OpenStreetMap in GIScience: experiences, research, applications. ISBN:978-3-319-14279-1, pp. 37-51, Springer Press.
  • Lee, H., Seo, B., Koellner, T., Lautenbach, S. (2019): Mapping cultural ecosystem services 2.0 - potential and shortcomings from unlabeled crowd sourced images. Ecol. Indic. 96, 505–515.
  • Schultz, M., Voss, J., Auer, M., Carter, S., and Zipf, A. (2017): Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 63, pp. 206-213. DOI: 10.1016/j.jag.2017.07.014.
  • Li, H. & Zipf, A. (2022): A conceptual model for converting openstreetmap contribution to geospatial machine learning training data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 253–259. DOI:10.5194/isprs-archives-XLIII-B4-2022-253-2022
  • Ludwig, C.; S. Lautenbach, W.-M. Schömann & A. Zipf (2021): Comparison of simulated fast and green routes for cyclists and pedestrians. 11th International Conference on Geographic Information Science (GIScience 2021). K. Janowicz & J. Verstegen (eds.); pp. 3:1–3:15. DOI:10.4230/LIPIcs.GIScience.2021.II.3
  • T. Václavík, S. Lautenbach, T. Kuemmerle, R. Seppelt (2019): Mapping Land System Archetypes to Understand Drivers of Ecosystem Service Risks, in Atlas of Ecosystem ServicesDrivers, Risks, and Societal Responses, Schröter, M., Bonn, A., Klotz, S., Seppelt, R., Baessler, C. (Eds.), Springer, 69-85, https://www.springer.com/us/book/9783319962283

In the modern technological age, mobile application are necessary to reach out to the masses. Therefore, it’s critical for any web application to be available through mobile devices. In the case of ohsomeHeX, enabling use on mobile devices requires overcoming the challenging of screen space use. The application utilizes significant screen space for its side panels to show user-selected datasets as well as for the bottom panel which shows statistics for the selected datasets.

In an attempt to solve this issue, we provide a lite-mode application for any smaller-screen devices. This lite-mode is a limited functionality ohsomeHeX application wherein one can use the time-slider to view temporal changes for an OSM topic over time as well as select between tons of topics available on ohsomeHeX. Users of lite-mode are unable to use certain features such as clicking on the hex/cells on the map to generate datasets for further region-specific analysis. Lite-mode is designed to be an option for getting an overview of OSM changes of topics on the go. However, for a drilled-down analysis, one must still use a full-scale application.

At the start of ohsomeHeX, the application checks for the device’s screen size. If the screen size is discovered to be mobile screen, i.e. screen-width smaller than 600px, then a prompt appears allowing the user to switch to lite-mode. If one is using a permalink to open ohsomeHeX, then the user’s selected Topic, Map Extents and Time Period are preserved when switching to lite-mode.

Welcome back to a new installment of the ohsome Region of the Month blog series! This will be part two our posts on hiking related tags and this time, we’ll investigate the connection between user activity and said tags. To do this, we’ll use mountain time as a reference area and incorporate insights from part one of this series (find it here). If this post sparks your interest, make sure to check out our other blog posts including this personal favorite covering coastal lengths. We also have some fantastic content on the functionalities of the ohsome APIincluding the How to become ohsome series.

Now, let’s get started!

Data

As in our previous work, we added input geoJSON and other information to a snippet for your convenience. The data used can be found here.

These are the filter conditions used in our requests:

format=csv

filter=type:way and (highway in (path, track, footway) and not footway in (sidewalk, crossing))

time=2007-12-01/2022-05-01/P1M

Two requests were sent, one to create each figure, and the endpoints used are: /elements/length/contributions/count

Andes

In the figures above and following in the blog post you can see the development of length and contribution count values on a monthly scale (black line) as well as a linear trend line (blue line).

For the Andes, there is an upwards trend with usually increased contribution activity for the months of April through August. There are also no particularly prominent changes in length with the exception of the 2008-2009 period, when there is a fairly large increase. This is possibly related to an early forming of the mapping community. The slope of the graph increases over time, as also reflected in the linear trend. The rather strong increase in length starting in 2021 could potentially be related to an ongoing address import for Chile.

Appalachian Mountains


Beginning in July 2009, a very large increase can be seen for about half a year, followed by a drop to the previous level. Around April 2020, values are noticeably higher as the contribution activity increases on average. Although there is not an “ideal” seasonal pattern, there is often an increase over spring and the summer months, whereas the values drop more often throughout November and December. Increased activity over the summer months can be found, for example, in the years 2009, 2012, 2015-2018 and from 2020. As mentioned at the start, the values are permanently increased.

The length values rise enormously directly in the beginning 2008 and remained steady for the rest of 2008, this development is visible in the contributions graph as well. However, the is a second peak within the contributions graph around the end of 2009 which is not displayed in the length graph. Until 2011, the increase in length is rather stagnant, the values remain at about the same level. Thereafter, however, the slope steepens. It is a generally continuously increasing trend with slightly higher increases commonly observed over spring/summer months

Atlas Mountains


In the Atlas Mountains there is only low contribution activity with a slight increase towards the end of the graph (around 2020).  However, there is an outstanding peak between July and November 2019, which is also reflected in the monthly length graph. Potentially, the cause could potentially be a larger import and it seems and it seems likely that at least the majority of the contributions here correspond to the contribution type creation.

Many of the smaller annual peaks occur mainly during the second half of the year, which can be seen especially well in 2009. The length plot also displays the peak of 2019, but beyond that a first, less intensive increase between November 2018 and February 2019 is already visible. In general, the values for the Atlas Mountains are rather lower and a truly notable increase in length is only observable 2012. Again, it is noticeable that the activity takes place mainly in the second half of the year, while in particular May to July represent months of rather low activity.

European Alps


The contributions plot shows an increasing trend of general activity over the considered period. Starting from April 2020 onwards, we see higher values are visible, which coincides with the beginning of the measures against the pandemic coming into force around this time. Looking at the development per year, it can be seen that especially warmer months, such as April/May and July/August and additionally December/January, show an increased contribution activity. The latter could be related to winter tourism.

There are two distinct peaks, between May and August 2009, and between May and September 2011. Looking at the temporal evolution of hiking-related tags in their length, no particular seasonality emerges. The values increase continuously, with no months or seasons standing out in particular.

Himalaya


Contribution activity for the Himalayan Mountains increases over the period considered, with two peaks in between March - August 2015 and July - October 2017. Similar to much of the other regions, there has been an increase in activity since 2020, with no particular seasonality evident apart from several lighter increases between April and September. The peaks shown in the contributions graph for 2015 and 2017 are also visible in the length plot, but there is another larger jump in data between July and November 2018. The severe peak in 2015 is most likely related to the earthquake that took place around the time. The length does increase over the period, but in stages rather than continuously.

New Zealand Southern Alps


In the case of the New Zealand Southern Alps, prominent peaks occur in June 2015, June 2016, and March - May 2017, when the contribution activity appears particularly high. As before, activity rises from around April 2020. All peaks are followed by a sharp dip, so this is not a trend of generally increased activity. Also, the increased contributions that occur in 2020 subside again in the following year. Depending on the year, increased contribution activity is often found over December and January, i.e. over the summer. In addition, increased activity can also be seen in April or between September and October.

In the case of length, the linear trend and the exact monthly development are very close. In some cases, however, jumps can be seen, e.g. August to October 2009, March to May 2019 or April to August 2020, all of which can also be seen in the contributions plot. In these cases, it seems that there is indeed an explicit increase in tagging activity. Consistent jumps in a specific time of the year running through the time series are also not evident here. Lastly, the slight stagnation in length values starting in 2021 might be related to a finalization in hiking-related tags within the frame of LINZ imports.


That’s it for our ohsome regions of the month. Each of them shows a somewhat positive trend where both the general length and contribution activity are on the rise. In addition, many examples probably also reflect the effects of the pandemic, which could explain many instances of increased activity from the first half of 2020. In some cases, we may see seasonality effects, for example in the case of the European Alps.

Thank you for reading the second part of the hiking-related tags series. Stay tuned for more ohsome content!


Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more 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 resources can be found here:

New data description paper on our tree point cloud dataset was just published

Today, our data description paper was published in the open access Journal Earth System Science Data:

Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Fassnacht, F. E., and Höfle, B. (2022): Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests, Earth Syst. Sci. Data, 14, 2989–3012, https://doi.org/10.5194/essd-14-2989-2022.

Workflow for the generation of the dataset.

Workflow for the generation of the dataset.

Laser scanning from different acquisition platforms allows the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve information about forest structure and individual tree properties. In the frame of the SYSSIFOSS project, we acquired airborne, UAV-borne and terrestrial laser scanning data in German mixed forests. From these overlapping point clouds, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics.

Overview of single tree measurements recorded in the field and derived from the point clouds.

Overview of single tree measurements recorded in the field and derived from the point clouds.

Our dataset may serve as a benchmark dataset for algorithms in forestry research, in particular automated tree segmentation, tree species classification of forest inventory metric prediction.

With our data description paper, we enable the reuse of our dataset by providing important information on data collection and processing and by presenting a thorough quality analysis.

The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher:

Weiser, H., Schäfer, J., Winiwarter, L., et al. (2021): Terrestrial, UAV-borne and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements. PANGAEA, https://doi.org/10.1594/PANGAEA.942856.

The database of single tree point clouds and metrics is also available via https://pytreedb.geog.uni-heidelberg.de/, showcasing the web frontend of pytreedb, a Python software package providing a library and API for the storage and sharing of tree point clouds and measurements.

pytreedb webserver homepage

SYSSIFOSS is a joint project between the Institute of Geography and Geoecology (IFGG) of the Karlsruhe Institute of Technology (KIT) and the 3DGeo Research Group of Heidelberg University. The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number: 411263134. Find further details about the SYSSIFOSS project on the project website, in recent blogposts, or on Twitter (#SYSSIFOSS).

Heidelberger Geoinformatiker machen im Projekt „SocialMedia2Traffic“ geokodierte Informationen nutzbar

Navigationsdienste benötigen aktuelle Verkehrsinformationen, um geeignete Routen zu ermitteln und die Fahrzeit möglichst genau zu berechnen. Dafür können nun auch frei zugängliche Daten aus Sozialen Medien und der Weltkarte OpenStreetMap genutzt werden. Ein entsprechendes System, mit dem sich aus ihnen die Verkehrsgeschwindigkeit abhängig von der Tageszeit ableiten lässt, haben jetzt Geoinformatikerinnen und Geoinformatiker der Universität Heidelberg und des Heidelberg Institute for Geoinformation Technology (HeiGIT) entwickelt. Im Rahmen des Projekts „SocialMedia2Traffic“ integrierten die Wissenschaftler diese Informationen für elf Städte – darunter Berlin, London und New York – in den HeiGIT-Dienst „openrouteservice“, um die Routenplanung zu verbessern und die Berechnung der Ankunftszeit zu präzisieren.

Two routes at different time of day generated using openrouteservice

Abbildung 1: Zwei Routen zu unterschiedlichen Tageszeiten, berechnet durch den openrouteservice unter Berücksichtigung der modellierten Verkehrsgeschwindigkeit. Abbildung: Abteilung Geoinformatik

Mit Hilfe von Methoden des maschinellen Lernens entwickelten die Forscherinnen und Forscher unter der Leitung von Prof. Dr. Alexander Zipf innerhalb der einjährigen Projektlaufzeit Modelle, die auf Grundlage von geokodierten Daten aus dem Kurznachrichtendienst Twitter und dem frei verfügbaren Kartensystem OpenStreetMap die Verkehrsgeschwindigkeit auf innerstädtischen Straßen bestimmen können. Diese Informationen sind kostenlos und frei nutzbar, anders als die Geodaten, die üblicherweise in Navigationsdienste einfließen. Die stadtbezogenen Modelle ziehen aus der räumlichen Dichte der Tweets in der Nähe von Straßen und der damit einhergehenden menschlichen Aktivität Rückschlüsse auf den daraus resultierenden Verkehrsfluss.

Die Wissenschaftler des Geographischen Instituts der Universität Heidelberg und des HeiGIT verwendeten dafür Standortinformationen aus insgesamt zehn Millionen Tweets aus der Zeit von Januar 2018 bis März 2020, mit denen die Tweet-Dichte berechnet wurde. Nach diesem Prinzip könnten in Zukunft auch Daten aus weiteren Social-Media-Plattformen in derartige Modelle integriert werden. Zusätzlich wurden pro Stadt mehrere Tausend Autofahrten simuliert – basierend auf der Bevölkerungsverteilung und OpenStreetMap-Daten. Aktuell arbeiten die Forscherinnen und Forscher nun daran, die Genauigkeit ihres Systems weiter zu verbessern und für weitere Städte nutzbar zu machen.

„Mit dem von uns entwickelten System lässt sich nicht nur die Genauigkeit von freien Navigationsdiensten erhöhen. Die Modellergebnisse könnten außerdem genutzt werden, um Fuß- und Radrouten abseits viel befahrener Straßen vorzuschlagen oder räumlich hochaufgelöste Karten zu verkehrsbedingten CO2-Emissionen zu erstellen“, erklärt Prof. Dr. Alexander Zipf, Leiter der Abteilung Geoinformatik am Geographischen Institut der Universität Heidelberg und Geschäftsführer des von der Klaus Tschira Stiftung getragenen Heidelberg Institute for Geoinformation Technology.

Die Arbeit des HeiGIT zielt darauf, den Wissens- und Technologietransfer im Bereich Geoinformatik zu verbessern. Die Mitglieder des Instituts entwickeln dazu intelligente Routing- und Navigationsdienste für nachhaltige Mobilität und stellen Geodaten für die Unterstützung humanitärer Einsätze zur Verfügung. Sie nutzen zudem innovative Dienste aus dem Spatial Data Mining und Maschinellem Lernen, um nutzergenerierte Geodaten – zum Beispiel OpenStreetMap – zu analysieren, zu verarbeiten und zu visualisieren.

Das Projekt „SocialMedia2Traffic“ wurde im Rahmen der Innovationsinitiative mFUND des Bundesministeriums für Digitales und Verkehr mit rund 100.000 Euro gefördert. Mit dieser Initiative unterstützt das Ministerium seit 2016 datenbasierte Forschungs- und Entwicklungsprojekte für die digitale und vernetzte Mobilität der Zukunft. Die Projektförderung wird ergänzt durch eine Vernetzung zwischen Akteuren aus Politik, Wirtschaft, Verwaltung und Forschung und durch die Bereitstellung von offenen Daten auf dem Portal mCLOUD.

Die Ergebnisse sind in einer interaktiven Web-Karte visualisiert und über die mCLOUD oder die SM2T API zum Download verfügbar.

Related projects and publications:

Over the last two years, we have witnessed the ever-fast growth of micro-mobility services (e.g., e-bikes and e-scooters), which brings both challenges and innovations to the traditional urban transportation systems. For example, they provide an opportunity to better address the “last mile” problem due to their convenience, flexibility and zero emission. As such, it is essential to understand why and how urban dwellers use these micro-mobility services across space and time. To tackle this challenge, we, a joint research team from Heidelber University, HeiGIT, Utrecht University, University of Warwick, and Tongji University, recently published a paper in Computers, Environment and Urban Systems.

In this paper, we aim to understand spatiotemporal trip purposes of urban micro-mobility through the lens of dockless e-scooter user behavior. We first develop a spatiotemporal topic modeling method to infer the underlying trip purpose of dockless e-scooter usage. Then, using Washington, D.C. as a case study, we apply the model to a dataset including 83,002 valid user trips together with 19,370 POI venues and land use land cover data to systematically explore the trip purposes of micro-mobility across space and time in the city. The results confirm a set of uncovered 100 Trips Topics as an informative and effective proxy of the spatiotemporal trip purposes of micro-mobility users. Though the proposed approach cannot yet fully replace traditional OD surveys in understanding user trip purposes considering the sophisticated socio-economic driving factors, it provided a promising low-cost and mode-oriented complement to standard OD user surveys. As potential applications, the modeling approach developed and the insights shared in this paper will be helpful for city authorities and policymakers to evaluate the impact of micro-mobility service in addition to existing public transit network and adjusting their transportation regulations accordingly, for dockless e-scooter companies to optimize their vehicle deployment by wisely reallocating their vehicle fleets, and also for mobile APP providers to estimate trip purposes based on their mobility patterns and provide timely POI recommendations

In conclude, our findings in this work provide enlightening insights for city authorities and dockless e-scooter companies into more sustainable urban transportation planning and more efficient vehicle fleet reallocation at a city-level.

Li, H., Yuan, Z., Novack, T., Huang, W., Zipf, A., (2022) Understanding spatiotemporal trip purposes of urban micro-mobility from the lens of dockless e-scooter sharing. Computers, Environment and Urban Systems, 96, 101848, June 2022, https://doi.org/10.1016/j.compenvurbsys.2022.101848

Previous related work:

  • Huang, W. and Li, S. (2018): An approach for understanding human activity patterns with the motivations behind. International Journal of Geographical Information Science (IJGIS), 2018.
    https://doi.org/10.1080/13658816.2018.1530354
  • Huang, W., Xu, S., Yan, Y. and Zipf, A. (2018): An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada. Cities, 2018. https://doi.org/10.1016/j.cities.2018.07.001
  • Novack T., R. Peters, A. Zipf (2018): Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets. ISPRS International Journal of Geo-Information, 7(3), 117-134. DOI:10.3390/ijgi7030117.
  • Yan, Y., M. Eckle, C.-L. Kuo, B. Herfort, H. Fan and A. Zipf (2017): Monitoring and Assessing Post-Disaster Tourism Recovery Using Geotagged Social Media Data. International Journal of Geo-Information, ISPRS IJGI. 6(5), 144; doi:10.3390/ijgi6050144
  • Rousell A. and Zipf A. (2017): Towards a landmark based pedestrian navigation service using OSM data. International Journal of Geo-Information, ISPRS IJGI, 6(3): 64.
  • Steiger, E., B. Resch, J. Porto de Albuquerque, A. Zipf (2016): Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps. Transportation Research Part C: Emerging Technologies. Vol 73, Dec.16, pp 91–104. http://dx.doi.org/10.1016/j.trc.2016.10.010
  • Zipf, A. (2002): Location aware mobility support for tourists. Trends & Controversies. IEEE Intelligent Systems. Journal. Special Issue on “Intelligent Systems for Tourism”. November/December 2002. S.57-59.


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