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Next week the HOT Summit will take place. The conference takes already place for the sixth time and it is the fifth consecutive time that we from HeiGIT/GIScience Heidelberg do contribute a session. This years topic is 10 Years of Humanitarian OpenStreetMap: The Past, Present, and Future of Humanitarian Mapping. It will be a one day conference in conjunction with the Understand Risk conference and happens on Friday 4th December. The fully virtual conference is organized in three blocks across several time zones, to reflect a truly global event.

Benjamin Herfort from HeiGIT will participate together with Hannah Ker (MapAction) and Geoffrey Kateregga (HOT) in an extended dialogue session towards the sustainability of community engagement and data production in humanitarian mapping activities. In our session we want to explore what humanitarian mapping in OSM looks like and if it is serving the community in a sustainable way. You can take a look at our slides already here and get prepared with questions and comments in advance.

Marcel Reinmuth (HeiGIT) will contribute a lightning talk: OpenStreetMap for healthcare access.
In which he will present latest results on using OSM data for healthcare access estimations and a brief overview on what is missing in healthcare data in OSM. Here you can see the slides and links to further resources.

Map of Overall and Humanitarian Building Mapping in OSM since 2008

Map of Overall and Humanitarian Building Mapping in OSM since 2008

Related work and Links:

What is the idea behind the Notebook?

In the case of an emergency (e.g. floods, earthquakes, political crisis) it is important to know where the health facilities are located. Furthermore, it is important to identify which cities/districts have a reduced or no access at all to health facilities before an emergency. Many countries still posses a centralized health system, making the tasks of the emergency workers even more difficult. In order to get accurate information from the health facilities, we retrieve the latest data that is available in the OpenStreetMap database. Two years ago, we wrote a Blogpost where we introduced a Jupyter Notebook that used our openrouteservice (ORS) Isochrones API in order to determine the access to health facilities in Madagascar. The notebook has been improved and updated and is now ready to be used with the latest version of the ORS API.

Check out the new interactive version of the notebook in nbviewer.

The biggest improvement of this new Jupyter Notebook is the automation and globalization of the analysis. In other words, the user just has to insert the ISO-3 code and the name of the desired country at the begin of the script. For example, if we want to make an analysis  for Bolivia, we just need to insert the ISO-3 code (”BOL”) and the oficial name (”Bolivia”). This is a big improvement because the user doesn’t have to get his own data (e.g. shapefiles).  By entering the ISO-3 code, the user automatically downloads a geojson file with the administrative boundaries (admin_level 2), a geojson with the points of the health facilities from the ohsome API and finally, a population raster from worldpop.org.

Another important upgrade is the implementation of the Python module rasterstats. The module replaces an old script that was used for the statistics and it includes a function called zonal statistics.  The function returns the statistics of the raster. This allows us to count and sum up the population for each district or isochrone in an easy and sophisticated manner. Lastly, the results are displayed in a choropleth map with multiple layers. We implemented GeoPandas and Folium in this last part.

Analysis of two countries - Comparing Health Care Access for Azerbaijan and Czech Republic

Workflow

Let’s have a look at the script. In the following examples, we will apply the notebook in the Republic of Azerbaijan and the Czech Republic. The first step is to enter the ISO-3 code and the name of the country  that we want to analyse. The script will automatically download the boundaries, the health facilities (nodes) and the “World Population” raster.

After this step, the analysis begins. The first task of the analysis is to create a districts dictionary that will save, for example, the population data from the raster. The overview map will show the user how the health facilities are distributed in the country.

Another important step is to calculate the access to health facilities per district. For this step, the script grabs the isochrones that we got from the ORS API. Combined with the population data stored in the raster, we are then able to calculate how many persons have access to a health facility on a district level.

Finally, the script saves the output as a geojson file. In order to check if the data has been written properly, the script displays the dictionary that was created at the beginning as a Pandas DataFrame. The final choropleth map has three layers. It allows the user to switch between the population count and the percent of the population in each district that is able to reach the health facilities via car or foot in a certain amount of time. The cursor displays the name of the district and the data.

Fig 1. ISO-3 code and name from Azerbaijan

Fig 2. Administrative Boundaries from Azerbaijan and health facilities clusters

Fig 3. The final results from Azerbaijan displayed as a Pandas Dataframe

Results

The last step of the script is to display the results of the analysis in an interactive choropleth map with three layers. For example, we can observe that in Azerbaijan (see Fig. 4), the persons living in the west and in the capital, Kabu, have a better access to the health facilities.

If we make the analysis for the Czech Republic, we get the choropleth map depicted in Fig 5. Comparing the result in Azerbaijan to the result in the Czech Republic, we could assume that the health facilities in the Czech Republic are more evenly distributed. As a result of this, the percent of people that have access to a health facility in a district increases. This a very basic comparison that is easy to achieve with this new notebook.

It’s important to underline that this script has still some limitations. The topography and relief of a country (e.g. a mountain range) are not taken into account in the ORS API. We are looking forward to improve this aspect and build a notebook that is even closer to the reality.

If you have thoughts or ideas how we can better implement this notebook in order to provide an even more realistic result, don’t hesitate to contact us here: info@heigit.org

Fig 4. Health Care Access for Azerbaijan

Fig 5. Health Care Access for the Czech Republic

Related Work and Literature

Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1 and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels with similar characteristics in a pool of unlabeled data and gully objects are detected where high densities of gully pixels are enclosed by an alpha shape. Gully objects are used in subsequent iterations following a mechanism where the algorithm uses the most reliable pixels as gully training samples. The gully class continuously grows until an optimal scenario in terms of accuracy is achieved. Results are benchmarked with manually tagged gullies (initial gully labelled area <0.3% of the total study area) in two different watersheds (408 km2 and 302 km2, respectively) yielding total accuracies of >98%, with 60% in the gully class, Cohen Kappa >0.5, Matthews Correlation Coefficient >0.5, and ROC Area Under the Curve >0.89. Hence, our method outlines gullies keeping low false-positive rates while the classification quality has a good balance for the two classes (Gully/NoGully). Results show the most significant gully descriptors as the high temporal radar signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (NDVI) (21.8%).

Classification results for three gully probability classes (high, medium, low) for WS2. a) shows the classification results and b) the realgully locations used  for  validation  (black  dots),  initial  gully  label  (green  polygons)  used  for training.  c-e)  Detailed  view  of  gully  mapping  underlain  withMicrosoft® Bing™ MapsAerialimagery.

Classification results for three gully probability classes (high, medium, low) for WS2. a) shows the classification results and b) the real gully locations used for validation (black dots), initial gully label (green polygons) used for training. c-e) Detailed view of gully mapping underlain with Microsoft® Bing™ Maps Aerial imagery.

This research builds on previous studies to face the challenge of identifying and outlining gully-affected areas with a shortage of training data using global datasets, which are then transferable to other large (semi-)arid regions.

Find all details in the full papers:

Orti, M.V., Winiwarter, L., Corral-Pazos-de-Provens, E., Williams, J.G., Bubenzer, O. & Höfle, B. (2020): Use of TanDEM-X and Sentinel products to derive gully activity maps in Kunene Region (Namibia) based on automatic iterative Random Forest approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Orti, M.V., Negussie, K., Corral-Pazos-de-Provens, E., Höfle, B. & Bubenzer, O. (2019): Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia). Remote Sensing. Vol. 11 (11), pp. 1-22.

Methods of gully detection and monitoring are the core research subject of the PhD project of Miguel Orti in the 3DGeo Research Group on the development of gully identification and measurement methods combining remote sensing and crowdsourcing techniques.

This research is part of the DEM_HYDR2024 project supported by TanDEM-X Science Team, therefore we would like to express thanks to the Deutsches Zentrum für Luft-und Raumfahrt (DLR) as the donor for the used TanDEM-X datasets. We acknowledge the financial support provided by the Namibia University of Science and Technology (NUST) within the IRPC research funding programme and to ILMI for the sponsorship of field trips to identify suitable study areas. Finally, we would like to express gratitude towards Heidelberg University and the Kurt-Hiehle-Foundation for facilitating the suitable work conditions during this research.

India accounts for nearly 18% of the worlds population. The country is also one of the main carrier of the worlds disease burden. Despite the general increase in life expectancy and decreasing mortality due to communicable diseases and malnutrition in recent years, the numbers of non-communicable diseases are rising substantially. Cardiovascular diseases such as diabetes and ischemic attacks are on the rise (Dandona 2017). Throughout the country the socioeconomic and health diversity is high and the Indian health system from under substantial shortcomings relating to infrastructure, the quality, and availability of services (Angell et al. 2019; Bhargava and Paul 2018).

OSM health care facilities in India over time

OSM health care facilities in India over time

Access to health services is determined by various factors, such as affordability, availability, information, accessibility, and compatibility. Affordability and information are cited as the main barriers that determine access (Butsch, 2011). Therefore, it is important that data on the spatial location of healthcare facilities is made freely available to all citizens. OpenStreetMap as a central global place for free geodata can be a solution to this. Public health officials can employ the data on care infrastructure to support their decision making. Businesses can use it to built new services on top. Citizens can easily interact with the data, getting answers on questions where and how to access a care service provider in their local community.

RMSI - a GIS consulting company, contributing since 2018 to OSM - leads efforts on an import of data on healthcare facilities in India since April 2019. The data source of the import are three different archives published by the Indian government on their open data portal: https://data.gov.in/. The archives include facilities like: hospitals, clinics, health centers and blood banks.
In the following we explore in which state when health-related objects were imported. We use HeiGIT technology to assess the imports to OSM: the ohsome API and the ohsome dashboard.

Tagging scheme for healthcare facilities in OSM:

  • amenity=doctors :: min. 1 doctor present, non-inpatient care
  • amenity=clinic :: min. 10 doctors present, non-inpatient care
  • amenity=hospital :: inpatient care
  • healthcare=* :: tag to cover more details on healthcare facilities (e.g. midwives, nurses, hospices, centres, blood banks, birthing centres)

Note that the tag “healthcare=*” is often used complementary to the other tags and therefore we do not evaluate that tag separately.

ohsome Results

Before the import started, only 5399 hospitals and 1557 other facilities were represented by OSM in India. Beginning in spring 2019 we have found a surge in new facilities added, across all states.

OSM hospitals in India over time.

OSM clinics in India over time.

OSM doctors in India over time.

First significant imports started at the beginning of 2019 with the clinics tag, followed by the imports of hospitals in May 2019 and in (April) September 2019 with the doctors tag. For most Indian states the number of healthcare related objects has (min.) doubled and was carried out step by step. For example, at the beginning of our time series (March 2018), most doctors were tagged in Karnataka (SW-India) (53), this number changed by the end of May 2020 and rose from 53 to 73, and to 115 by the end of June 2020, where our time series ends. For other states - like Assam (NE-India) - only one doctor was tagged at the beginning of March 2018. Then the import started, and within one week there were about 100 new facilities.
The imports for hospitals draw a similar picture, but this tag shows the most imports, e.g. in Maharashtra (SW-India) the number of tagged hospitals rose from 682 to 6343 and they are still ongoing - what we can assume from the curve. For other states, like Andhra Pradesh (SE-India), the import stagnates since January. Another significant import of clinics happened in Telangana (southern India), where within one week the number of tags rose from 49 to 2252 in June 2019. Further imports for this state happened later in October/November 2019, but some tags were deleted in April 2020. In general, most significant imports happened in November 2019 and in some states they are ongoing.

Looking at the density of tagged healthcare facilities per state, the highest density per 100k inhabitants is given in Lakshadweep (tropical archipelago), followed by Goa (SW-India) and Puducherry (SE-India). On the other hand the lowest density per 100k inhabitants is given in Bihar (NE-India), Jammu and Kashmir (northern India) and Jharkhand (NE-India):

OSM healthcare facility density by Indian state.

Conclusion/Future

We have found that over the course of less than a year healthcare objects in India went from 6956 to 48101, including ~33000 hospitals. This is a huge increase in critically important geo data. Further, this data is now also easy accessible through the OSM ecosystem of diverse services. Based on this data, we were able to determine in more detail which Indian states have a high density of health care infrastructure and which have a low density of health care facilities. However, this is still a rather macro perspective. With the new data now in OSM, everyone can assess the distribution on a spatial scale that suits their use case best.

In an upcoming analysis we will focus on the attributes. What thematic information we can derive from health facilities in India. How dense are these facilities tagged. What is actually tagged, and furthermore for what facilities and where in India can we derive capacity information.

Related Work

“Local Knowledge” is constituting the exceptional value of Volunteered Geographical Information and thus also considered as an important indicator of data quality. We are interested in how much local information is captured in OpenStreetMap data. In this blog post we explore the temporal evolution of mapping in OSM and the information stored in its database, by taking an explorative  look at four different cities in Germany, Nepal and the Philippines: Heidelberg, Kathmandu, Pokhara and Manila.

Heidelberg is generally considered to be relatively well mapped and has experienced mapping activity over a decade for now. Mapping in Kathmandu has been impacted heavily by data created for disaster response in the aftermath of the 2015 earthquake disaster in Nepal. This resulted in a significant increase in activity from mappers around the world. As comparison, we also will take a look at Pokhara. Pokhara is Nepals second largest city and lays approximately 200 km west of Kathmandu and belongs to the more rural part of Nepal. Manila is the capital and the economical and cultural center of the Philippines.

The image below shows a potential classification of OSM data in regard to the types of information it might contain. This is mostly targeted towards a humanitarian mapping context and may need adaptation for a more general evaluation of mapping phases, but we use it here as an example only. While buildings and road network completeness are of interest for level 0-1 (mostly based on remote mapping such as in international humanitarian mapathons, the further levels 2-4 are considered to source from local knowledge.

Fig. 1: Image from Twitter post by Rebecca Firth from HOT

In the following we will compare different aspects of development of OSM Data, including

  • completeness of road network and buildings (level 0-1)
  • exploratory analysis of local information for facilities and POIs (level 3)
  • overall information richness (level 4)

Examining this evolution should give us some insights on how long it takes volunteers to provide local information (especially in a context where mapping started with remote mapping) and how far the process is at the different locations. In order to perform this analysis the ohsome API by HeiGIT was utilized to access the OSM full history data. The API provides different endpoints to extract and aggregate data about the objects, users and single contributions.

NOTE: In addition to this Blogpost a Jupyter Notebook (source code) was released, which allows u to generate an interactive map and plots for regions of your choice.

Level 0-1: Roads and Buildings

Road network

Especially humanitarian mapping in OSM often started with roads and buildings, which can be traced remotely from satellite imagery. The situation is different in areas where there is already an existing strong OSM community for a longer time. For example in total, over 1.75 million kilometers of highway were mapped in Kathmandu. The related graph shows clearly the impact of the 2015 earthquake: the road network increased by approximately 15% directly after the disaster and by 30% until today. Pokharas increase since the earthquake is even bigger, with doubling its mapped length of highway objects since when. Especially two spikes are noticeable: one in the direct aftermath of the earthquake and one in the year 2016. The development of Heidelbergs road network length, showed in contrast a more constant development, with a small growth rate over the last decade. Manilas road network showed seemed to be still in the phase of active mapping: its length increased by approximately 7% over the last year.

Buildings

The mapping of buildings in comparison to the length of the road network showed a slightly delayed development. After initial mapping of buildings in 2011, only very few buildings were mapped in Pokhara until about one year after the earthquake. Afterwards the number of mapped buildings showed a rapid growth over a few months in the second quarter of 2016. Kathmandu showed three main growth events: one in the end of 2012, marking the first noticeable amount of contributions, one directly following the earthquake in 2015 and another smaller one in 2019. Manila showed a steady data evolution, with the exception of one spike in 2019 which indicates that similar to the road network buildings are still not mapped completely.

Level 3: Temporal Evolution of Facilities and POIs

Facilities

The third level is characterized by information about facilities. Here we take a look at the temporal development of educational facilities, access to drinking water, healthcare facilities and information about the road network (in this case bridges and tunnels). The plot below shows the total count of objects belonging to these groups.

While Heidelberg showed a more or less constant behavior, the 3 other cities showed a more irregular growth pattern. Manila experienced a steady increase over the last 10 years, which further accelerated in the last one to two years. This indicates an ongoing mapping of facilities and infrastructure. Kathmandu’s graph showed a strong increase between 2012 and 2013 and has since experienced irregular phases of growth which slowly leveled out recently. Notable is, that the 2015 earthquake response mapping didn’t had a significant impact on this development. Pokhara’s development, started slow and has grown between 2016 and 2018, before leveling out afterwards.

Point of Interests

As point of interest (POI) we consider objects containing the tags name and amenity. The amount of those are of interest to understand the amount of information besides geometries, facilities and critical infrastructure. A comparison to the development of roads and buildings indicates that the mapping of POIs followed the mapping of essential map features like buildings and roads. In particular the development for Kathmandu and Pokhara, which experienced short concentrated periods of highway and road mapping, showed a delayed evolution in regard to the mapping of POIs. This might indicate that mapping buildings and roads and mapping POIs were two separate processes. Manila and Heidelberg showed at least some form of co-occurence between the mapping of buildings and roads and the mapping of POIs which might indicate a simultaneous mapping process of the different features.

Level 4: Overall Information Richness

Temporal Evolution for Buildings and Roads

Following the scheme above, the main characteristic of the fourth level is a high amount of stored information in the objects in the map.  We will take a look at the number of additional tags per object. For definition of Richness of VGI data see Ballatore & Zipf (2015).

The graph below shows clearly that the amount of additional tag information in Heidelberg is very high for roads and buildings. For instance, more than 50% of the buildings contain five or more tags. Manila, Kathmandu and Pokhara had a significant lower portion of buildings and streets containing additional information. An exception was the road network of Manila which was comparable to Heidelberg.

Spatial Distribution for Buildings and Roads

(the map can only be viewed using the Jupyter Notebook)

Exploring the spatio-temporal domain using the leaflet map below, shows that Manila and Heidelberg both showed alternating pattern of activity over a longer stretch of time. Pokhara and Kathmandu instead showed region wide morex extensive activity over short periods.

This suggests, that Heidelberg and Manila, both had a variety of spatially separated processes, while Kathmandu and Pokhara, were affected events covering the whole cities.

Conclusion

Mapping patterns in Kathmandu and Pokhara were clearly distinct from those in Heidelberg, both with respect to the temporal development of buildings and roads and the amount of tags. This indicates that a lower amount of local knowledge was present in the OSM data of the two cities. Mapping in Manila showed at least some resemblance in comparison to the development of Heidelberg, but also contains overall less information yet and the buildings and road network are still undergoing constant mapping.

In case you are interested to learn more about ohsome take a look at the How to become OHSOME series or take a look at the literature below. In case you want to take a look at a region of your choice, just add your bounding box in the cell of the jupyter notebook and rerun the cells.

Links:

OSHDB and ohsome API git repositorys

Humanitarian OSM Stats Global statistics for Humanitarian Open Street Map Team projects

ohsome HeX- Open Street Map History Explorer

Literature

Raifer, M., Troilo, R., Kowatsch, F., Auer, M., Loos, L., Marx, S., Przybill, K., Fendrich, S., Mocnik, F.-B.& Zipf, A. (2019): OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data.Open Geospatial Data, Software and Standards 2019 4:3. https://doi.org/10.1186/s40965-019-0061-3

Auer, M.; Eckle, M.; Fendrich, S.; Griesbaum, L.; Kowatsch, F.; Marx, S.; Raifer, M.; Schott, M.; Troilo, R.; Zipf, A. (2018): Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring. ISCRAM 2018. Rochester. NY. US.

Grinberger, A. Y; Schott, M; Raifer, M.; Troilo, R.; Zipf, A. (2019): Analyzing the spatio-temporal patterns and impacts of large-scale data production events in OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proc. of the Academic Track at the State of the Map 2019, 9-10. Heidelberg.

Ballatore, A. and Zipf, A. (2015): A Conceptual Quality Framework for Volunteered Geographic Information. COSIT - CONFERENCE ON SPATIAL INFORMATION THEORY XII. October 12-16, 2015. Santa Fe, New Mexico, USA. Lecture Notes in Computer Science, pp. 1-20.

Ludwig, C, Fendrich, S, Zipf, A. Regional variations of context‐based association rules in OpenStreetMap. Transactions in GIS. 2020; 00: 121. https://doi.org/10.1111/tgis.12694

Erhalten Sie mit ohsomeHeX wertvolle Einblicke in die Qualität und den Entwicklungsprozess von OpenStreetMap-Daten!

(see for english version here)

Das HeiGIT Big Data Team freut sich zur Exploration der Veränderungen der globalen Daten in OpenStreetMap nun eine Version 1.0 von ohsomeHeX , dem OSM History eXplorer zu veröffentlichen, die eine komplett überarbeitete Benutzeroberfläche bietet. Diese macht es einfacher alle relevanten Informationen im Auge zu behalten: Wählen Sie ein oder mehrere Themen aus, wählen Sie verschiedene Interessenbereiche aus und lassen Sie ohsomeHeX Vergleichsdiagramme für Sie erstellen. Beobachten Sie die Entwicklung Ihres OSM Datenthemas im Zeitverlauf und vergleichen Sie es mit dem Benutzeraktivitätsdiagramm, um Hinweise zur Qualität der OSM-Daten in Bezug auf Zeit, Raum und Thema zu erhalten.

Das neue Layout von ohsomeHeX folgt der Designidee des Open Design, bei dem der Benutzer - wie aus Dashboard-Anwendungen bekannt - alle Informationen auf einmal erhält. Im Gegensatz dazu hatten frühere Versionen ein minimalistisches Design, bei dem Informationen nur auf Anfrage berechnet wurden. Aufgrund des Feedbacks der Benutzer wurde ohsomeHeX neu gestaltet, und wir hoffen, dass damit für neue und erfahrene Benutzer die Benutzerfreundlichkeit und damit die Zugänglichkeit der zugrunde liegenden Informationen verbessert wurden. Die Anwendung richtet sich an Wissenschaftler, OSM Praktiker und die interessierte Öffentlichkeit und hilft bei der Untersuchung zeit-räumlicher Muster verschiedener ausgewählter Themen aus OSM-Daten.

Was ist Neu?

Grafikfeld:
Das neues Grafikfeld behält die zentrale Stellung. Neben den räumlichen Mustern, die der Benutzer in der Karte visuell untersuchen kann, werden hier die spezifischeren Ergebnisse eines benutzerdefinierten Drilldowns angezeigt. Mehrere Auswahlen können einfach in einem gemeinsam genutzten Diagramm verglichen werden.

Anzeige Gesamtflächen:
Jede benutzerdefinierte Auswahl wird jetzt durch die Anzahl der ausgewählten Features und eine Bereichsanzeige angereichert, die für die Interpretation der Ergebnisse eines Vergleichs zwischen verschiedenen interessierenden Bereichen wichtig ist.

Bearbeitbare Auswahltitel:
Jetzt ist es möglich, den benutzerdefinierten Auswahlen auch benutzerdefinierte Namen zu geben. Beispiel: Wenn ein Benutzer versucht, die Anzahl der Krankenhäuser in Berlin und Prag zu analysieren, hat der Benutzer jetzt die Möglichkeit, benutzerdefinierte Namen im Vergleich zum Standardnamen der Auswahl anzugeben, zB „Anzahl der Krankenhäuser“ . Diese Titel spiegeln sich auch in der Diagrammlegende wider, sodass Benutzer die Bedeutung der Diagramm besser erkennen können.

Auswahlfarbe:
Jede Auswahl erhält eine eindeutige Farbe, mit der die ausgewählten Zellen auf der Karte angezeigt und auch auf die Diagramme angewendet werden. Als zukünftige Funktion planen wir, den Benutzern die Möglichkeit zu geben, eine benutzerdefinierte Farbe für ihre Auswahl auszuwählen.

Anzeige Mehrfachauswahl:
Jetzt können mehrere Auswahlen gleichzeitig in der Karte angezeigt werden. Dies ist besonders praktisch, wenn Benutzer daran interessiert sind, verschiedene Regionen nach demselben Thema oder derselben Region, aber unterschiedlichen Themen zu vergleichen. Zum Beispiel: Vergleich von  “Length of highway=residential” mit “Length of highway=service” von Paris. Dieses Verhalten kennen Personen, die mit “Ebenen” in GIS-Software arbeiten.

<a href=”http://k1z.blog.uni-heidelberg.de/files/2020/11/paris_highway.png”<

Wir hoffen, dass diese Bemühungen Ihre Erfahrung mit ohsomeHeX verbessern. Wir freuen uns auf Ihr Feedback! Seien Sie gespannt auf zukünftige Updates und Funktionen. OhsomeHeX basiert auf der Ohsome-API von HeiGIT und der zugrunde liegenden OpenStreetMap-History-DatenBank OSHDB.

Wenn Sie mehr über das HeiGIT ohsome Framework erfahren möchten, zögern Sie nicht, uns über ohsome (at) heigit.org zu kontaktieren oder sich direkt an ein Mitglied unseres Teams zu wenden. Weitere Informationen zur Ohsome OpenStreetMap History Data Analytics-Plattform und weitere Beispiele zur Verwendung der Ohsome-API finden Sie hier:

Eine in Science Advances veröffentlichte Studie liefert neue Erkenntnisse zum Zusammenhang von körperlicher Aktivität und Wohlbefinden im Alltag. Dazu wurde unter anderem untersucht, welche Hirnregionen dabei eine Rolle spielen.

Körperliche Aktivität macht glücklich und ist wichtig, um auch psychisch gesund zu bleiben. Forscherinnen und Forscher des Zentralinstituts für Seelische Gesundheit (ZI) in Mannheim, des Karlsruher Instituts für Technologie (KIT) und der Universität Heidelberg untersuchten, welche Hirnregionen dabei eine zentrale Rolle spielen. Die Ergebnisse zeigen, dass schon Alltagsaktivitäten wie Treppensteigen einen deutlichen Nutzen für das Wohlbefinden haben, insbesondere auch bei Menschen, die anfällig für psychiatrische Erkrankungen sind.

Sich zu bewegen, verbessert das körperliche Wohlbefinden und die geistige Gesundheit erheblich. Wie sich schon alltägliche Aktivitäten wie Treppensteigen, Spazieren gehen oder zur Straßenbahn laufen auf die eigene Befindlichkeit auswirken, war bisher wenig untersucht worden. Unklar ist bis jetzt insbesondere, welche Gehirnstrukturen daran beteiligt sind. Ein Forschungsteam am Zentralinstitut für Seelische Gesundheit in Mannheim, am Institut für Sport und Sportwissenschaft (IfSS) des KIT und der Abteilung Geoinformatik am Geographischen Institut der Universität Heidelberg fokussierte in seiner Studie Alltagsaktivitäten, die den größten Anteil unserer täglichen Bewegung ausmachen. „Schon das alltägliche Treppensteigen kann helfen, sich wach und energiegeladen zu fühlen und damit das Wohlbefinden zu steigern“, erläutern die beiden Erstautoren der Studie, Dr. Markus Reichert, der am ZI in Mannheim und am KIT forscht, und Dr. Urs Braun, Leiter der Arbeitsgruppe Komplexe Systeme in der Psychiatrie am ZI.

Besondere Relevanz haben die Forschungsergebnisse gerade in der derzeitigen Situation mit Corona-Beschränkungen und dem bevorstehenden Winter. „Aktuell leiden wir unter starken Einschränkungen des öffentlichen Lebens und unserer sozialen Kontakte, was sich auf unser Wohlbefinden niederschlagen kann“, sagt Professorin Heike Tost, Leiterin der Arbeitsgruppe Systemische Neurowissenschaften in der Psychiatrie am ZI und eine der zentralen Autorinnen der Studie, „da kann es helfen, öfter mal Treppe zu steigen, um sich besser zu fühlen.“

Alltagsaktivitäten steigern „Wachheit“ und „Energiegeladenheit“

„Die Untersuchungen wurden durch eine neuartige Kombination verschiedener Forschungsmethoden im Alltag und im Labor möglich“, sagt Professor Ulrich Ebner-Priemer, Leiter der Arbeitsgruppe mHealth Methoden in der Psychiatrie am ZI in Mannheim sowie stellvertretender Leiter des IfSS und Leiter des Mental mHealth Lab am KIT. Eingesetzt wurden Alltagserhebungsverfahren (sogenanntes Ambulantes Assessment) mit Bewegungssensoren und Smartphone-Abfragen zum Wohlbefinden, die anhand von Geolokalisationsdaten ausgelöst wurden, sobald sich die Studienteilnehmer bewegten.

Mit diesen Alltagserhebungsverfahren wurde bei 67 Personen der Einfluss der Alltagsaktivität auf die Wachheit und Energiegeladenheit über sieben Tage hinweg erfasst. Dabei zeigte sich, dass sie sich direkt nach alltäglicher Aktivität wacher und energiegeladener fühlten. Wachheit und Energiegeladenheit wiederum waren nachweislich wichtige Komponenten des Wohlbefindens und der psychischen Gesundheit der Studienteilnehmerinnen.

Gehirnareale für Alltagsbewegung und Wohlbefinden identifiziert

Kombiniert wurden diese Analysen bei einer weiteren Gruppe von 83 Personen mit Magnetresonanztomografie am ZI. Dabei wurde das Volumen der grauen Hirnsubstanz vermessen, um herauszufinden, welche Areale im Gehirn für diese Alltagsprozesse eine Rolle spielen. Wichtig für das Zusammenspiel von Alltagsbewegung und affektivem Wohlbefinden ist ein Bereich der Großhirnrinde, der subgenuale Anteil des Anterior Cingulären Cortex. Diese Hinrnregion spielt eine zentrale Rolle bei der Regulation von Emotionen und der Widerstandsfähigkeit gegenüber psychiatrischen Erkrankungen. Von den Autorinnen und Autoren der Studie wurde diese Hirnregion nun als ein entscheidendes neuronales Korrelat identifiziert, das den Zusammenhang von körperlicher Aktivität und subjektiver Energiegeladenheit vermittelt. „Personen, die ein geringeres Volumen an grauer Hirnsubstanz in dieser Region aufwiesen und ein erhöhtes Risiko haben, an psychiatrischen Erkrankungen zu leiden, fühlten sich einerseits weniger energiegeladen, wenn sie körperlich inaktiv waren“, beschreibt Heike Tost die Ergebnisse, „aber andererseits nach alltäglicher Bewegung deutlich energiegeladener als Personen mit größerem Hirnvolumen.“

Spezifischer Nutzen von körperlicher Aktivität im Alltag

Professor Andreas Meyer-Lindenberg, Vorstandsvorsitzender des ZI und Ärztlicher Direktor der Klinik für Psychiatrie und Psychotherapie, schlussfolgert, dass „die Ergebnisse damit auf einen spezifischen Nutzen von körperlicher Aktivität im Alltag für das Wohlbefinden hinweisen, insbesondere bei Menschen, die anfällig für psychiatrische Erkrankungen sind.“ Zukünftig könnten die in der Studie gewonnenen Ergebnisse im Alltag dazu führen, dass eine auf dem Smartphone installierte App bei sinkender Energie die Nutzer zu Bewegung stimulieren soll, um das Wohlbefinden zu steigern. „Langfristig ist in Studien zu klären, ob sich durch Alltagsbewegung kausal das Wohlbefinden und das Hirnvolumen verändern lassen und inwieweit diese Ergebnisse helfen könnten, psychiatrische Erkrankungen zu vermeiden und zu therapieren,“ sagt Urs Braun.

Die aktuelle Studie ist in der Zeitschrift Science Advances erschienen:

Originalpublikation:
Markus Reichert, Urs Braun, Gabriela Gan, Iris Reinhard, Marco Giurgiu, Ren Ma, Zhenxiang Zang, Oliver Hennig, Elena Koch, Lena Wieland, Janina Schweiger, Dragos Inta, Andreas Hoell, Ceren Akdeniz, Alexander Zipf, Ulrich Ebner-Priemer, Heike Tost and Andreas Meyer-Lindenberg: A neural mechanism for affective well-being: Subgenual cingulate cortex mediates real-life effects of nonexercise activity on energy. Science Advances. DOI: 10.1126/sciadv.aaz8934

Related earlier work:

Tost, H., Reichert, M., Braun, U., Reinhard, I., Peters, R. , Lautenbach, S., Andreas, H., 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 exposureNature Neuroscience. https://doi.org/10.1038/s41593-019-0451-y

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

Koch E. D., H.  Tost, U. Braun, G. Gan, M.  Giurgiu, I. Reinhard, A.  Zipf, A.  Meyer‐Lindenberg, U.  Ebner‐Priemer, M.  Reichert (2020): Relationships between Incidental Physical Activity, Exercise, and Sports with subsequent Mood in Adolescents. The Scandinavian Journal of Medicine & Science in Sports. https://doi.org/10.1111/sms.13774

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. (pdf)

These days so much is different- also conferences. Carolin Klonner and Marcel Reinmuth attended the GeOnG 2020 virtually. Thanks to the organizers!

It worked well and there was great exchange between the participants. The workshop on experiences in defining health catchment areas brought together diverse stakeholders and approaches. Marcel Reinmuth contributed from the scientific open technology perspective to the workshop. He shared his latest findings on travel time to health facilities in Sub-Saharan Africa.

The lightning talk “OpenStreetMap Sketch Map Tool - The Future of OpenStreetMap Field Papers” presented by Carolin Klonner shows a new participatory mapping method to visualize and formalize local knowledge and is available online.

Maps showing population density and travel time to the nearest hospital for adults aged 60 years or  older, by sub-Saharan African region

Geldsetzer, P.; Reinmuth, M.; O Ouma, P.; Lautenbach, S.; A Okiro, E.; Bärnighausen, T.; Zipf, A. (2020): Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis https://doi.org/10.1016/S2666-7568(20)30010-6

Klonner, Hartmann, Djami, Zipf, A. (2019). “Ohsome” OpenStreetMap Data Evaluation: Fitness of Field Papers for Participatory Mapping In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 35-36. Heidelberg.

Related work

During the EuroSDR workshop we will present our OSMlanduse product (earlier post) to the land use (LU) and land cover community (LC) and highlight class accuracies and a benchmark comparison towards existing national authoritative products. Accuracy estimated to be presented are based on more than 7k reference points collected in the past month through a permanently open validation campaign. The campaign was featured on Octobers the EuroRegions week and GeoNet MRN meetup.

The mapathon comes in four themes: nature, urban, agriculture or expert. While the expert campaign may be addressed exclusively by application professionals the themes nature, urban, agriculture can be done by anyone that is enthusiastic about geography. Contribute here and choose your flavor.

If you have not registered for EuroSDR you can directly join the validation event held on 24.11.2020, 14:00 – 15:30 by contacting Michael Schultz to receive login credentials. Given that the validation effort is open permanently visit it directly here.

Related Work:

Neue Konzepte zur Bewältigung der Klimakrise und zur Gestaltung der Zukunft standen im Mittelpunkt des “Digitalen Herbstcamp” von PRIO1, einem neu gegründeten Klima-Netzwerk.

Die Teilnehmer*innen – zwischen 16 und 25 Jahre alt – entwickelten vom 13. bis 15. November 2020 im Rahmen von parallel laufenden Workshops Ideen und konkrete Aktivitäten zu den Handlungsfeldern “Wohlstand und Glück in einer nachhaltigen Welt”, “Städte der Zukunft”, “Zukunft in Bewegung”, “Nachhaltige Mode vs. Fast Fashion”, “Energiewende, effiziente Energiegewinnung und -nutzung”, “Globale Krisen erfordern globale Lösungen”, “Klima – das Triggerwort des 21. Jahrhunderts?” und “Anbau, Böden und Chemie: Wie können wir die Welt und uns gut ernähren?”.

Das Herbstcamp startete am Freitag mit dem Vortrag “Zukunft in der Klimakrise?! – Wo stehen wir und wie geht es weiter?” von Prof. Dr. Antje Boetius vom Alfred-Wegener-Institut für Polar- und Meeresforschung (AWI) und wurde am Sonntag beschlossen mit einem Talk von Dr. Nicole Aeschbach vom TdLab Geographie (Geographisches Institut der Universität Heidelberg) zum Thema “Zukunft gestalten - Wie wollen wir leben?”.

Das Klima-Netzwerk PRIO1 wird getragen von der Klimastiftung für Bürger (Betreiberin der Klima Arena in Sinsheim); die Dietmar Hopp Stiftung und die Klaus Tschira Stiftung (KTS) sind Initiatoren und Förderer von PRIO1.

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