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In the context of the United Nations World Data Forum 2021 organised by the United Nations Statistics Division and UN member states a series of blogposts has been published by UNSTATS. Among them is one by Alexander Zipf about improving access to healthcare facilities through geoinformation and crowdsourcing. He introduces the Open Healthcare Access Map by HeiGIT, based on OpenStreetMap (OSM) and openrouteservice (ORS) and further tools and services dealing with OSM data for humanitarian aid like ohsome or the Humanitarian OSM Stats humstats.heigit.org.
For details follow the link or see below:


Improving Access to Healthcare Facilities Through Geoinformation and Crowdsourcing

If the COVID-19 pandemic has taught us anything over the past year, it is how important our healthcare system is. Everyone should have access to a doctor and know how to get to the nearest hospital in an emergency. To help accomplish this, the Heidelberg Institute of Geoinformation Technology (HeiGIT) has developed the Open Healthcare Access Map, a web application that calculates accessibility to healthcare infrastructure by combining traffic data with that infrastructure. Additional information about population distribution provides the user with insights into the health care supply in certain regions. By aggregating the information on an administrative level using hexagons, the Open Healthcare Access Map is able to paint a more accurate picture of the spatial distribution of healthcare in a country on different scales. Though currently a prototype, the application already supports over 80 different countries in various parts of the world.

Fig. 1: Overview of all available countries in Open Healthcare Access Map

While OpenStreetMap (OSM) does already offer a lot of data on the healthcare sector, the quality of such data differs in each country. Since OSM is sustained by volunteer mapping efforts, it is important to lower monetary barriers that prevent the emergence of mapping communities in some countries. In 2020, OSM Senegal received a micro-grant and was able to map 197 hospitals in Senegal and update information about the availability of emergency health services at facilities. With this data, users can calculate the impact this information has on emergency services and their capacity through HeiGIT’s openrouteservice, an open source routing service that consumes user-generated and collaboratively collected free geographic data to provide directions in the form of optimized routing and reachability analyses, time-distance matrixes, point-of-interest finding, as well as geocoding. In addition to standard openrouteservice, there are a number of other services provided by HeiGIT that can help improve the lives of people and assist in emergency worker activities. The openrouteservice client for disaster management, for example, is a specialized instance of openrouteservice which provides hourly updates to the road network in disaster areas so that accurate and up-to-date routes can be generated for use in humanitarian relief activities. In addition, HeiGIT’s approach to explore missing built-up areas in OSM using a combination of social and remote sensing, can be used to significantly reduce volunteer mapping efforts and the maintenance of data quality.

Geographic information data is very important for achieving the UN Sustainable Development Goals and needs to be integrated with other data and statistical information. Doing so helps determine to what extent SDGs have been reached and where further action is necessary. This is in line with HeiGIT’s goal of encouraging and facilitating the beneficial use of geoinformation for society. To achieve this goal, HeiGIT (in collaboration with the GIScience group of Heidelberg University) provides a range of in-house developed web services, mostly based on OpenStreetMap (OSM), such as the aforementioned openrouteservice, or the OSM history analytics platform ohsome.org which makes OSM history data more accessible for various data analytics tasks on a global scale. In 2020 HeiGIT was able to provide a new map in ohsomeHeX showing an overview of areas in Sub-Saharan Africa that are still missing vital information on healthcare facilities. Having access to this information is critical for healthcare and emergency workers in order to estimate the need the population may have for these facilities, especially during recurrent waves of the pandemic.

Fig. 2: ohsomeHeX with Completeness layer on Health Facilities in Sub-Sahara Africa

In line with this, the Humanitarian OSM Stats (Humstats) dashboard also provides several humanitarian organizations with detailed, weekly reports on their OSM mapping statistics, allowing them to focus their efforts where it is most needed.

If you are interested in learning more about the various humanitarian and especially healthcare access projects HeiGIT is working on, check out these blogposts:

New blogposts are posted on a regular basis on the HeiGIT News site. Would you like to give feedback? Please feel free to reach out via mail. We greatly appreciate it!

The 3DGeo research group is part of the AGU Fall Meeting from 13 - 17 December 2021 with the following topics and contributions:

1) Hypersurface Observation Network (Hyperon) — What it is and why we need it (Monday, 13 December 2021 16:00 - 18:00 CST)

In the terrestrial domain, large biogeochemical and energetic uncertainties surround the soil-plant-atmosphere continuum of forests, leading to wide disagreement in the projected land carbon sink and global carbon balance. This is largely due to an absence of global observation networks providing coincident information on the structure, composition, and function of forests and adjacent planetary boundary layer (PBL) over space and time. Such observations are critical to learning new models of biospheric processes and improving our understanding and predictions of land-atmosphere exchange. Simultaneously, a new generation of hyper-temporal, -spatial, - spectral, and -angular (hypersensing) surface reference networks are needed to learn inversions of air- and space-borne measurements that resolve radiative-transfer uncertainties related to these land-atmosphere exchanges.

Existing terrestrial networks such as FluxNet, SpecNet, PhenoCam, AeroNet, and ForestGeo remain limited to single measurement points and domains. For example, FluxNet typically records CO2 and H2O exchanges at a single point in space using domain-specific instruments, making the measurements ungeneralizable, unrepresentative, and thus unsuited to up-scaling to Earth observation records or diagnosing Earth system models. Furthermore, adding new measurements to these systems often requires costly new instruments. Absent are generalizable surface-atmosphere observation systems able to retrieve a growing variety of observables over space and time.

Toward addressing these needs in unified Earth observation and system modeling (EOSM), we propose the Hypersurface Observation Network (Hyperon) — formerly 5DNet — a modular, intelligent, robotic, and coincident surface-atmosphere observation system. Initially focusing on forests, Hyperon is intended to cover a variety of surface domains. While previously impractical, powerful new low-cost instruments and embedded computers designed for edge inference provide an exciting opportunity to realize this goal. We provide an early conceptual overview of Hyperon, reaching out to the community to develop standards for instrument and site configuration options and a decentralized governance model for ensuring free and open science.

Erickson, A., Kumar, S.V., Hudson, D.I., Stamnes, S., Puttonen, E., Junttila, S., Pirk, S., Höfle, B., Chrostowski, L., Eitel, J. & Calders, K. (2021): Hypersurface Observation Network (Hyperon) - What it is and why we need it. In: AGU Fall Meeting 2021. Vol. AGU21 (B15G-1499), pp. 1-1.


Additionally, there are two contributions on the methods of gully detection and monitoring. These are the core research subjects 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. Find more details in previous blog posts.

2) Spatio-temporal assessment of gully activity in Namibia using Sentinel-1 SAR and Tandem-X DEM products as an instrument for land degradation neutrality (Tuesday, 14 December 2021, 10:20 - 10:30 CST)

Gully erosion is one of the most dangerous environmental threats in (semi-) arid regions. In Africa, changes in rainfall and unfavourable land-use practices have accelerated gully growth in recent decades, leading to landscape fragmentation, unbalanced watersheds runoff dynamics and desertification. National and international organizations have undertaken plans to reverse this catastrophic development. Therefore, the identification and development of standardized measurement and analytical procedures to quantify gully changes is a great necessity. The democratization of Uncrewed Aircraft Vehicles (UAV) has generated new possibilities to derive detailed 4D models (i.e. 3D surface changes over time). However, gullies frequently affect very large and inaccessible areas, as is the case of Namibia, and more remote and scalable solutions are required. Sentinel-1 radar sensors offer possibilities to study physical soil features and small-scale terrain deformation patterns through the signal backscattered intensity (sigma) and the electromagnetic wave phase coherence. Time series of both variables were studied in a large gully in Namibia’s Kunene Region between July 2019 and June 2021, incorporating two rainy seasons. As radar coherence is higher in gully zones due to the high soil compaction and the consequent lack of vegetation, low coherence periods is a proxy of terrain changes and gully activity. In this sense, comparing 19/20 and 20/21 wet seasons, the first one was more active in its erosion activity, as coherence was 10-15% lower in the gully areas. These results correlate well with the closest meteorological station records, which registered a much higher rainfall in 2019/2020 (>300 mm) than in 2020/21 (<100 mm). Zonal analysis confirms through the absolute differences in sigma images between the beginning and end of each season that the 19/20 season was more active in the gully areas than the 20/21. This result indicates greater activity in the east margin of the gully especially in the zones close to its external walls, with a more stable interior area. Preliminary findings show good temporal linking with rainfall rates, however, accurate validation is required for the zonal analysis using ultra-high resolution UAV imagery and 3D point clouds captured in the study area before and after each rainy season.

Orti, M., Höfle, B., Bubenzer, O. & Negussie, K. (2021): Spatio-temporal assessment of gully activity in Namibia using Sentinel-1 SAR and Tandem-X DEM products as an instrument for land degradation neutrality. In: AGU Fall Meeting 2021. Vol. AGU21 (GC43E-08), pp. 1-1.

3) Identifying and describing the impact of gully erosion in the livelihoods and properties of traditional Himba communities in Kaokoland (Namibia) as a driver of regional migration (Tuesday 16 December 13:25 - 13:30 CST)

Gully erosion is an accelerator of land degradation and one of the most critical agents threatening the environment in Namibia’s north-western region. Large gullies dominating alluvial valleys expand each year during the short but intense rains, leading to a reduction of arable land and grazing areas, a destruction of roads, cattle paths, agricultural facilities and houses, prompting territorial fragmentation and the geographical isolation of local communities. In contrast, gullies can also act as a linear oasis while providing several benefits to their inhabitants. This research aims to describe the mutual influences between a large gully and the local communities in a valley extended towards the south from Opuwo, inhabited by the same native Himba families for several generations. In-situ surveys show that the gully is a general concern in the area due to the insecurity and direct physical risk it poses to humans and their domestic animals. The second factor of distress is the accelerating land degradation in the valley, leading to the disappearance of grazing areas, forcing local shepherds to travel further in their transhumance. Ortho-imagery and spatial analysis shows that 10% of the houses, 25% of the Kraals and 50% of the gardens are less than 50 metres away from the gully border, and therefore they are in current or potential risk of abandonment, forcing eventual re‑settlements and migrations. Moreover, indigenous knowledge arises that the gully also offers a few advantages, like its ability to store water during the dry season. These benefits are frequently seen as a trap or an associated risk for the animals and children getting in the gully. To this end, it is noticeable that as the gully affects the communities and its livelihoods, they also acts as a driver of development for the gully through its agricultural and livestock practices. This is evident by the appearance of the gully heads on paths, ditches, and domestic animals’ routes, along with endemic overgrazing for decades. In summary, this research identified these prevalent human-nature dynamics and attempted to provide recommendations that can reverse accelerated degradation in the long term while describing the present and potential future of the Himba people inhabiting these fragile lands in Kaokoland.

Orti, M., Castillo, C., Bubenzer, O. & Höfle, B. (2021): Identifying and describing the impact of gully erosion in the livelihoods and properties of traditional Himba communities in Kaokoland (Namibia) as a driver of regional migration. In: AGU Fall Meeting 2021. Vol. AGU21 (NH22A-04), pp. 1-1.

Under pressure from the decision of the Federal Constitutional Court in spring 2021, the German government has amended its climate targets. The new coalition also wants to stick to these and pursue the goal of greenhouse gas neutrality by 2045. Emissions are to be reduced by 65 percent by 2030 compared to 1990 (Presse- und Informationsamt der Bundesregierung (2021)).

Soon, all domains of our society will have to become climate neutral. In view of rising prices for emission certificates, climate-damaging products and work processes will become continuously more expensive. Companies that fail to make the transition to climate-friendly production will no longer be competitive. Furthermore, in a society that is becoming increasingly aware of the consequences of climate change, companies that are harmful to the climate will suffer from image damage in comparison to their counterparts that operate more sustainably.

What does this mean for science institutes? Sooner or later, they too will have to be climate neutral. Cost-intensive audits will have to be carried out to quantify emissions and to identify reduction potentials. The tool Pledge4Future is the answer to these challenges: a free web tool for scientific working groups to calculate, visualise and reduce per capita CO2 emissions in the long term. While the tool emphasises the collaborative and independent aspect of climate action, it also aims to amplify the ambitions of institutes and research groups through benchmarking.

The project emerged from a sustainability working group mainly consisting of members of HeiGIT and the GIScience research group during The Climate Challenge Hackathon organised by Scientists4Future Heidelberg and the Goethe Institute. It emerged from the two-stage evaluation process as the winner of 8,000 EUR in project funding. A first demo version on the website offers a glimpse of some of the future services. The beta test phase is scheduled to start in spring 2022. If you would like to be a beta tester, please contact info@pledge4future.org.

As part of the festival “When Machines Dream the Future” of the “Generation A=Algorithmus” project of the Goethe Institute, Scientists4Future Heidelberg and Pledge4Future members Sarah Lohr, Felix Munzlinger and Veit Ulrich presented the project in a live interview on 13 November. On 14 November, Pledge4Future took part in a critical live panel discussion on the pros and cons of using artificial intelligence.

Author: Sarah Lohr

Date: 10.12.2021

Einladung Vortrag Online:

Dienstag, 14. Dezember 2021, 19:15 Uhr
Melanie Eckle-Elze, Benjamin Herfort, Dr. Carolin Klonner

Digitale Geographie im Katastrophenmanagement

Klimawandel, Bevölkerungswachstum, Verstädterung und weitere zunehmende Landnutzungsveränderungen führen dazu, dass immer mehr Menschen in Risikogebieten leben. Um die vorhandenen Risiken zu verstehen und angepasste (Vorsorge-) Maßnahmen zu ergreifen, bedarf es der Zusammenführung von Expertenwissen als auch der Vor-Ort-Kenntnisse der Einwohner. Wie kann die Bevölkerung selbst mit ihrem “analogen” lokalen Wissen zur Katastrophenvorsorge beitragen? Und kann auch aus der Ferne “digitale” Unterstützung geleistet werden? In unserem Vortrag werden wir auf diesbezügliche aktuelle Forschungsprojekte der Abteilung Geoinformatik und des HeiGIT eingehen und über die Aktionen der disastermappers heidelberg Initiative berichten.

Projekte wie MapSwipe bieten die Möglichkeit, Satellitenbilder auf eine spielerische Art zu klassifizieren und zu markieren, wo sich Gebäude befinden. Das Sketch Map Tool wiederum kann genutzt werden, um direkt vor Ort mit der betroffenen Bevölkerung Informationen über vergangene Katastrophen wie z.B. Hochwasserereignisse zu sammeln. Dabei wird das räumliche lokale Wissen auf Karten eingezeichnet und im Anschluss digital verarbeitet. So wird analoges Wissen auf eine schnelle Art und Weise digital nutzbar. Die studentische Initiative disastermappers heidelberg wurde gegründet, um den Austausch von Student*innen, den Mitarbeiter*innen des Instituts und internationalen Organisationen und Gruppen, die im Bereich Katastrophenmanagement arbeiten, zu fördern. Hierfür bieten die disastermappers zahlreiche Veranstaltungen rund um das Thema Katastrophenmanagement an, bei denen Interessierte teilnehmen und aktive Beiträge leisten können.

Den Flyer mit den Vortragsthemen finden Sie unter folgendem Link:

Die HGG Abendvorträge werden bis auf weiteres online stattfinden.
Zugang hierzu haben Mitglieder der HGG und angemeldete Schulklassen.
Der Zugangscode wird den Mitgliedern und Neumitgliedern per Mail oder per Post zugeschickt.
Der Mitgliedsbeitrag beträgt 12€ für Studierende und 25€ für vollzahlende Mitglieder.
Das Anmeldeformular finden Sie zum Download auf der HGG-Homepage
oder auf Nachfrage per Mail hgg@UNI-HEIDELBERG.DE

Selected related publications:

Stellenausschreibung Universität Heidelberg – GIScience

Wissenschaftliche Mitarbeiter:in Geoinformatik - Projekt GeCO

GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion

Du hast Interesse an Klimawandel, Treibhausgasemissionen und innovativen Geoinformatik-Methoden?
Im Rahmen des vom Heidelberg Center for the Environment (HCE) durch die Exzellenzstrategie geförderten Kooperationsprojektes GeCO suchen wir baldmöglichst nach einer wissenschaftlichen Mitarbeiter:in (m/f/d). Die Abteilung Geoinformatik entwickelt im Projekt GeCO Methoden zur Generierung von räumlich hoch-aufgelösten CO2 Emissionsinventaren mittels Methoden aus Spatial Data Science und Machine Learning (insb. Deep Learning). Ziel ist es Eingabedaten für die nachfolgende Modellierung des atmosphärischen Transports durch die Projektpartner aus der Umweltphysik zu erstellen. Hierzu werden mehrere Geodatensätze zu Landnutzung und weiterer relevanter Quellen für Treibhausgase (Industrie, Verkehr, Wohnen, Abfall, Landwirtschaft etc.) genutzt. Eine wichtige Datenquelle stellt dabei OpenStreetMap (OSM) dar, welches Geodaten zu Gebäuden, Industrieanlagen, Verkehrsinfrastruktur und Landnutzung enthält, die die Grundlage für die Verortung von Emissionen liefern. Die Frage der Datenqualität wird untersucht und für die Bewertung der OSM-Objekte verwendet. Vgl.: https://www.geog.uni-heidelberg.de/gis/geoco.html

Wir bieten eine attraktive Stelle (Teilzeit) in einem interdisziplinär ausgerichteten dynamischen Team und in einem hochaktuellen Forschungsgebiet. Die Abteilung ist u.a. Mitglied im Interdisziplinären Zentrum für Wissenschaftliches Rechnen (IWR) der Universität und Gründungsmitglied des Heidelberg Center for the Environment (HCE). Das An-Institut HeiGIT gGmbH setzt die Forschungsergebnisse in praxisnahe Anwendungen um. Die Exzellenz-Universität Heidelberg bietet in einer der attraktivsten Städte Deutschlands ein besonders anregendes interdisziplinäres Forschungsumfeld mit vielen Entwicklungsmöglichkeiten und attraktiven Weiterbildungsangeboten.

Wir erwarten ein überdurchschnittlich abgeschlossenes Universitätsstudium oder eine Promotion in einem der Fächer Geoinformatik, Informatik, Geographie oder ähnlichen Disziplinen. Erforderlich sind neben ausgeprägtem Teamgeist und hoher Motivation, ausgezeichnete und breite Methodenkompetenz und Forschungserfahrungen im Bereich Geoinformatik, insbesondere in einigen der oben genannten Gebiete (Spatial Data Science, Geodata Fusion, Spatial Disaggregation, Machine Learning, Deep Learning, Programmierung, GeoDB), effektiver und effizienter Umgang mit sehr großen heterogenen Geodatensätzen, vertiefte Kenntnisse von OpenStreeetMap und die Fähigkeit zum selbständigen wissenschaftlichen Arbeiten und zum Projektmanagement, sowie ausgezeichnete Fähigkeiten zur Kommunikation und Präsentation.

Die Stelle ist baldmöglichst zu besetzen und zunächst bis 08/2023 befristet (Teilzeit). Die Vergütung erfolgt nach TV-L E13. Aussagekräftige Bewerbungsunterlagen (Zeugnisse, Referenzen, etc.) senden Sie baldmöglichst bis spätestens 10. Jan. 2022 - bzw. solange bis die Position besetzt ist - digital an knorr@uni-heidelberg.de. Es besteht die Option zum Thema eine Promotion zu schreiben. Schwerbehinderte werden bei gleicher Eignung bevorzugt berücksichtigt.

Wir freuen uns auf Deine Bewerbung!

GIScience Research Group

Heidelberg University

Prof. Dr. Alexander Zipf

Institute of Geography · INF 368 · 69120 Heidelberg

PDF: ausschreibunggisciencegeco2021.pdf

See also related Open Positions at HeiGIT gGmbH.

This week on 29 November, it was announced that our team member Nina Krašovec received Nahtigal Award from the Faculty of Arts of the University of Ljubljana (UL) for her master’s thesis “Detection of standing dead trees using leaf-on and leaf-off UAV-borne laser scanning point cloud data in mixed forests”. The research was conducted under the supervision of Assist. Prof. Dr. Blaž Repe (UL) in collaboration with the 3DGeo group and co-supervision of Prof. Dr. Bernhard Höfle.

The committee of the Department of Geography acknowledged her outstanding research and nominated her for the award. The Faculty received 30 theses from the departments. The committee thoroughly reviewed all the submitted works, evaluations from the supervisors and justifications from the departmental committees and selected four recipients of the Nahtigal Award. All four were also nominated for the prestigious Prešeren Prize for Students of the University of Ljubljana (one of whom received it).

The Nahtigal award is in memoriam of Rajko Nahtigal, a Slovenian Slavicist, philologist, academic and pedagogue. He was the first dean of the Faculty of Arts as well as the first president of the Slovenian Academy of Sciences and Arts.

We want to congratulate also the other awardees for their outstanding work.

Der nächste Vortrag der Heidelberger Geographischen Gesellschaft HGG findet am Dienstag, 02. November 2021, 19:15 Uhr ONLINE statt:

Dienstag, 30. November 2021, 19:15 Uhr
Prof. Dr. Alexander Brenning (Universität Jena)

Hangrutschungsmodellierung unter dem Einfluss von Klima- und Landnutzungswandel mit Data-Science-Methoden

Regionalskalige empirische Gefährdungsanalysen für Naturgefahren nutzen Innovationen der Datenwissenschaften, um gefährdete Reliefeinheiten präziser und effizienter zu identifizieren. Im Kontext von Hangrutschen kommen Methoden des maschinellen Lernens insbesondere bei der automatisierten Kartierung von Rutschungsinventaren sowie der Erstellung von Gefahrenhinweiskarten zum Einsatz. Die Frage der Abschätzung der Auswirkungen von Klima- und Landnutzungswandel steht ferner zunehmend im Fokus. Herausforderungen ergeben sich dabei aufgrund von Verzerrungen in Eingangsdaten, Modellüberanpassung und der Notwendigkeit von Extrapolationen. Gegenüber reinen Black-Box-Modellen versprechen hybride Modellierungsansätze, die prozessbasierte Elemente integrieren, eine verbesserte Plausibilität, Interpretierbarkeit und Übertragbarkeit. Der Vortrag gibt einen Überblick über methodische Ansätze anhand von Fallstudien aus aktuellen Forschungsprojekten.

HGG-Programm im Wintersemester 2021/22

On Friday, November 26th, Dr. Carolin Klonner (GIScience Research Group) and Melanie Eckle-Elze (HeiGIT) will be supporting the Urban Context Unit of German Red Cross (GRC) in the “Data and Digitalisation in Urban Humanitarian Action” session at the Virtual Conference of the Red Cross Red Crescent Urban Collaboration Platform 2021.

The focus of the session is on the potentials and limitations of digital solutions in the context of humanitarian assistance. Participants will have the chance to learn about different developments and deployments of digital solutions in urban humanitarian action but also the related complexities and challenges.

The panel and audience will discuss and share experiences about the (dis) advantages of digital solutions in humanitarian assistance and potential approaches to collaboratively overcome current hindrances.

Sounds interesting? If you want to join the discussion, please follow the link and join us tomorrow at 1 pm CET. We are very looking forward to a lively discussion.

Registration for our Innsbruck Summer School of Alpine Research 2022 is now open (until 15 January 2022). If you want to learn innovative practical and methodological skills to characterize complex terrain and object features using close-range and remote sensing techniques - apply now!

The Summer School in the Ötztal Alps in Austria will be the fourth edition after three successful implementations in 2015, 2017, and 2019.

All details regarding learning objectives, keynote speakers, contents and program, registration and deadlines are given on the Summer School website: https://www.uibk.ac.at/geographie/summerschool/2022/

The 3DGeo Research Group will be co-organizing the summer school and lead assignments about 4D monitoring of high-mountain phenomena (4D rocks!).

At long last, welcome back to a new blog post of the How to become ohsome-series. As it’s been quite a while since you got an introduction to how to access the ohsome API, we would like to pick up this topic one more time this month. The former post with different ways to access the ohsome API is to be found here. Some new tools are available to help you analyze OpenStreetMap data. Below is a brief overview of the ohsome-py package, the ohsome R package, the ohsome QGIS plug-in ohsomeTools and ohsome2x. For each of the clients, we provide an example of how to query for monthly counts of school buildings (→ building=school) which are mapped as building outline (→ type:way) within a given boundary (”yourboundary.geojson“).

Access via python with the ohsome-py package:

If you want to analyze data with Python, the ohsome-py package can be your tool of choice and can be easily installed using pip. How it works is listed here, as well as an explanation of how to use it. For an even more in-depth introduction to using the ohsome-py package, you can check out this use case about public green spaces.

A request with ohsome-py could look like this:

import ohsome
import geopandas as gpd
client = ohsome.OhsomeClient()

bpolys = gpd.read_file("/yourpath/yourboundary.geojson")
example = client.elements.count.post(bpolys=bpolys,
                                      filter="type:way and building=school")
example_df = response.as_dataframe()

Access via R with the ohsome R package:

If you prefer to work with R, don’t worry, we have got you covered! One other of our projects is the ohsome R package, which allows you to send requests to the ohsome API via R. Again, you can find a very detailed explanation on how to install and use it here.

The ohsome R package is currently at an experimental stage. You can install it from Github.  While all kinds of queries to the ohsome API are possible with the package, it works most comfortably for OSM elements aggregation at the moment. A new version with full functionality is expected to be submitted to CRAN in the very near future.

A request with the ohsome R package could look like this:


htbo_example <- read_sf("yourboundary.geojson")
query <- ohsome_elements_count(
    boundary = htbo_example,
    filter = "type:way and building=school",
example <- ohsome_post(query)

Access via ohsomeTools:

If you want to analyze your data with QGIS and visualize it on a map, then the ohsome QGIS Plug-In (ohsomeTools) is the right tool for you. With it you can access the ohsome API directly in QGIS instead of sending a separate request and loading the data into your GIS. Please note that only QGIS v3.14 or newer is supported!

A very convenient feature of this tool is the automatic activation of the QGIS native temporal controller, if the geometry is suitable. Note that ohsomeTools has not yet been released in a public repository, but this will happen as soon as a suitable version of it is ready.

Again, you can find a short introduction on how to install and use the tool here. As you will surely notice, sending a request as well as using the output-file, is possible with very few clicks and in short time, which makes this tool a great addition for QGIS based examinations.

A request with ohsomeTools could look like this:

Access via ohsome2x:

Last but not least, our nodeJS client “ohsome2x” must not be left out. It allows you to access the ohsome API and store the output using either a command-line tool called “ohsome2x-cli”, or you can use it as library in your nodeJS scripts (JavaScript and TypeScript). For a small number of boundaries, you can request the ohsome API in one go and store the results in a simple GeoJSON output file, but the strength of ohsome2x is batch processing of thousands or millions of boundaries coming from a PostGIS DB. ohsome2x can then step by step query the ohsome API with parts of your input data and also cares about storage and useful indexing of the results in a PostGIS output table.

Of course you can find more information about installation and usage on the npm-registry and it’s repository.

A request with ohsome2x could look like this:

  "ohsomeQuery": {
    "queryType": "elements/count/groupBy/boundary",
    "filter": "building=school and type:way",
    "time": "2010-01-01/2021-11-01/P1M"
  "source": {
    "geometryId": "id",
    "name": "yourboundary.geojson",
    "store": { "path": "yourboundary.geojson", "type": "geojson" }
  "target": {
    "horizontalTimestampColumns": false,
    "createGeometry": true,
    "transformToWebmercator": false,
    "storeZeroValues": true,
    "computeValuePerArea": true,
    "name": "htbo_ohsome2x_example_output.geojson",
    "store": { "path": "htbo_ohsome2x_example_output.geojson",
    "type": "geojson" }

Below you can see visualizations of the output dataset from our example request (count of school-buildings):

This first visualization was created with the OSM Boundaries for the arrondissements of Paris, the request was sent with ohsome2x and the visualization was generated with QGIS.

The second visualization used the OSM boundaries of Paris as well, the request and plot were both generated with R.

Thank you for reading our new blog post of the How to become ohsome” series! We hope it was a helpful addition to the previous posts.

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:

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