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Where is is the closed Covid-19 vaccination center and what is the best way to get there? A new route planning app helps you answer this questions by suggesting ways to the nearest vaccination center.

You can use this route planner now at https://impfzentrum.openrouteservice.org

You only have to enter a starting location or allow the automatic use of the position information on your smartphone and the route to the vaccination center can be displayed. The app also offers written navigation instructions and further information about the vaccination center. By default, a route is calculated by car, but you can also switch to pedestrian routing, different bike profiles or even wheelchair routing. If necessary, various other options are available for adapting the route planning.

The app works online and can be used in modern web browsers on a PC, newer smartphones or tablets. The data from OpenStreetMap (OSM) is used as the data basis for both route planning and also for the vaccination centers. You can report or add missing data directly to OpenStreetMap by yourself.

Please note that you need a personal vaccination appointment to be admitted. The appointments are made differently in the individual federal states. Also the closest vaccination center within your state may not the one you are formally assigned to. You can also select the correct vaccination center yourself in the App. The app is currently focusing on Germany, but could be use for other regions as well if the vaccination centers of that region have been added to OpenStreetMap or are available in a structured way.

The application is based on the powerful route planner openrouteservice.org (ORS) and is developed by the Heidelberg Institute for Geoinformation Technology (HeiGIT gGmbH) together with the GIScience Research Group at Heidelberg University and made available as open source and with a free Web-API. Help pages for using the app are available for desktop web browsers, iOS or Android smartphones. The vaccination center app is an adapted version of the new ORS web client (beta).

This first prototype version of the app is still under development and will be further improved. We appreciate your suggestions for improvements.

All the best for the vaccination, follow the hygiene rules and stay healthy!

In addition, the general availability and supply of the population with vaccination centers was previously calculated by HeiGIT in this study. Openrouteservice supports accessibility analyzes by calculating so-called isochrones. Another analysis examines the accessibility of pharmacies taking into account the 15 km rule.

…. 

Here we go again: The first #ohsome blog post of 2021. This time, one of our new student assistants Sarah was dealing with street networks and their quality in order to find out which of the selected regions has the most detailed info in OpenStreetMap as well as the best data consistency over the past 10 years. The regions of interest are different european cities of roughly the same size and population (Bern, Bordeaux, Luxembourg, Maastricht & Regensburg).

We have conducted two analyses on the given areas of interest: relative length of the street network (length of streets divided by size of region) and an attributive completeness analysis looking at the length of streets having information about the speed through the tag “maxspeed” compared to those without. In each of the two analysis, we compared the regions with each other. At the end of the second analysis coming in another blog post next week, we will then crown a winner. Let’s jump right into the first analysis!

Data:

The necessary OSM data is downloaded as usual from our global ohsome API instance. You can get the spatial data set used as the area of interest in our requests via this website. There is also a GeoJSON-file in the snippet that you can find here, which already contains the specific boundaries used in this analysis, as well as the used cURL requests and visualizations.

Request:

The first request gives the evolution of the density of the street network within the boundaries of 5 cities over the past 10 years. You are going to need to send a POST request for that dataset. We chose csv as output format as it is easy to load and further process in e.g. spreadsheet programs.

Parameters:

format = csv

filter = type:way and (highway in (motorway, motorway_link, trunk, trunk_link, primary, primary_link, secondary, secondary_link, tertiary, tertiary_link, unclassified, residential, living_street, pedestrian) or (highway=service and service=alley))

time = 2010-12-01/2020-12-01/P1M

Data exploration:

The first graphic shows the development of the density of the street network in each city over the years 2010-2020.

When looking at the overall development of density values for Bordeaux having the highest values in our analysis, one can conclude that the starting value was relatively low, though the highest in comparison to all other examined regions. The dataset shows moderate growth until April 2012 with a minor drop in August followed by a rather strong increase until December 2012. More or less stable values have set in in July 2014.

The meeting point of the Maastricht dataset with the mean density value (9419.37 m/km2) takes place between December 2016 and January 2017. Before that the values are below said mean and have been over it ever since. A minimum density was reached in April of 2014 with values continuously decreasing with some stronger ‚jumps’. Subsequent to this a short phase of increasing values which ends up in a stagnation around the winter months of the same year. The trend remains positive until it stagnates in July 2018 and displays only a slight decrease for the rest of the time.

The dataset for the city of Bern, displayed in a green line with triangles, also begins with a density below the mean value (8398.486 m/km2) but has a quick development to values above mean in February 2011 already. These higher values, including the maximum values from July 2013 to May 2015, remain consistent until about August 2017 from which they fall and stay below mean again. The minimum values were reached in January of 2019 and only increased very slightly ever since.

For Luxembourg, represented by the blue line with circles, one can basically record a steady increase with a few minor collapses and a bigger jump between January and February 2011 and a phase of stagnation around May 2013. The last city we looked at was Regensburg which mainly had two phases of density development that were separated by a stronger increase during June 2015 - May 2016. The density values have been above mean (6949.04 m/km2) ever since August 2015. There have been some data imports for the cities, e.g. in May 2015 by the Vermessungsamt der Stadt Bern, but they do not appear to have a significant impact on the density data.

For the second part of the exploration, we created two bar charts, which display the percentual growth (top graphic) of each city from the first to last timestamp, as well as the absolute delta values for each city (bottom graphic):

As Bern is the only city with a negative development, it has the lowest value in both graphics. This probably indicates that the street network of this city was already well developed prior to the starting timestamp of this analysis. We chose to visualize both, the percentual and absolute numbers, as the percentage-growth was more suitable for looking at the individual development of each city, whilst the absolute numbers are good for a comparison of the overall development. This becomes especially obvious when looking at Bordeaux, which only has a moderate individual development but in context of absolute numbers it is the front runner of all cities.

User data:

We have also conducted a user analysis subsequent to the analysis of the density values and their development. The main outcome of said analysis was that there appears to be no connection between the differences in the density values in regards to the number of active users doing any kind of edits (create, modify, delete) on the streets. Nevertheless, the following bar chart gives you an overview on the min, mean and max values of the numbers of users for each city using again a monthly resolution. The highest number of users in one month was reached in Regensburg in June 2020 with 28 users and the lowest number was reached in Maastricht, where there wasn’t any activity on said streets in June of 2012.

Ranking

As we have said it initially, here you see a ranking of the five cities based on the analyzed data:

Overall density of street network

  1. Bordeaux
  2. Maastricht
  3. Bern
  4. Luxembourg
  5. Regensburg
Thanks for reading the first ohsome street network analysis blogpost. Whilst this week there was a final ranking, next weeks blogpost will take up on this analysis for the coronation ceremony for the region of the month. Furthermore, there will be yet another analysis dealing with maximum speed, which will be taken into account when looking for a winner. Will Bordeaux be able to keep the first spot and be crowned “ohsome street network city of the month”? Can Regensburg give the red flag of the last spot to another city? Stay tuned and check our blog again next week to find it out!

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:

Prof. Alexander Zipf has been invited to give the keynote at GIS DAY 2021 at McGILL University (Montréal, Canada) on Tuesday 19. Jan. 2021. The theme of the whole GIS Day at McGill is Humanitarian Mapping and the talk will talk about:

“Analysing and Improving OpenStreetMap for Humanitarian Aid with Data Mining and GeoAI”

Agenda: (EST time zone)

12:00 p.m. Prof. Alexander Zipf (GIScience, Heidelberg University, Germany)

1:00 p.m. Hannah Ker: MapAction & OCHA

1:30 p.m. Anna Teach (HOT): HOT - the Humanitarian OpenStreetMap Team

2:00 p.m. Omaru Sefu (Facebook - Data for Good): Privacy Preserving Data Products for Humanitarian Response

2:30 p.m. Chris North (ESRI Canada): ESRI’s Covid-19 Dashboards

Based on newly available locations for missing COVID-19 vaccination centers the accessibility analysis has been updated.

Globales Gletschermonitoring - Chance & Herausforderungen
Der Vortrag zu diesem Thema von Dr. Isabelle Gärtner-Roer vom World Glacier Monitoring Service der Universität Zürich zu diesem Thema findet am Dienstag, den 19. Januar um 19 Uhr online statt.

Im Rahmen der internationalen Gletscherbeobachtung werden Gletscher in den verschiedensten Regionen systematisch und kontinuierlich beobachtet. Weltweit werden Massenbilanzen von über 150 Gletschern und Längenänderungen von über 500 Gletschern bestimmt. Zusätzlich ermöglichen Fernerkundungsdaten die flächendeckende Kartierung der Gletscher und die Bestimmung von Volumenänderungen. Während die Messmethoden und -,möglichkeiten immer umfangreicher werden, verändert sich der eigentliche Forschungsgegenstand drastisch. Weltweit verlieren die meisten Gletscher an Masse und schmelzen zurück, deutlich sichtbar für Wissenschaftler und Laien. In den letzten Jahren mussten die Messungen an einigen Gletschern eingestellt werden, womit wichtige Indikatoren für den Klimawandel verloren gehen. Wo möglich, sollten neue Messprogramme auf (noch) größeren und höher liegenden Gletschern installiert werden. Diese Maßnahmen müssen von den nationalen Gletschermessprogrammen frühzeitig erkannt und geplant werden. Zudem sollen die schwindenden Messnetz-Gletscher umfassend dokumentiert werden, da sie künftigen Generationen als wichtiges, historisches Zeugnis des Klimawandels dienen mögen, wie zuletzt der Ok-Gletscher in Island, der im August 2019 offiziell „beerdigt wurde“. Die Chancen und Herausforderungen der Bereitstellung der Gletscherdaten sowie der Datennutzung sollen auf nationaler wie auf internationaler Ebene beleuchtet werden.

Die Veranstaltungsreihe findet im Wintersemester 2020/21 online statt.
Zugang hierzu haben Mitglieder der HGG und angemeldete Schulklassen. Der Zugangscode wird den Mitgliedern und Neumitgliedern per Mail oder per Post zugeschickt. Für das Wintersemester 2020/21 bieten wir für Neumitglieder einen reduzierten Mitgliedsbeitrag in Höhe von 6 € für Studierende und 12 € für vollzahlende Mitglieder an. Das Anmeldeformular finden Sie zum Download auf der HGG-Homepage oder Sie können es per Mail unter <hgg@UNI-HEIDELBERG.DE anfordern.

http://hgg.urz.uni-heidelberg.de/mitglied.php

Die Vorträge dieses Semester:
Der Klimawandel und seine Konsequenzen für die Nutzungssysteme stehen seit einigen Jahrzehnten im Zentrum der wissenschaftlichen Debatte und medialer Berichterstattung. Nicht erst seit den Klimastreiks der Fridays-for-Future-Bewegung und den wiederkehrenden Berichten des IPCC (Intergovernmental Panel on Climate Change) bilden die Probleme an der Schnittstelle zwischen Klimawandel, Wasserverfügbarkeit und Nachhaltigkeit wichtige Aufgaben einer verantwortungsbewussten und zukunftsorientierten Geographie.
HGG

Current lockdown regulations in Germany state that - in many but not all federal states - travel in COVID-19 hotspot regions is only allowed up to 15km distance. This has raised concerns since a 15km radius has been perceived by some as a serious constraint especially in rural regions. Clearly, this might prevent visits to friends and family - but this is of course the intention of the regulation which aims at a reduction of person to person contacts to reduce the virus spread. A different question is if basic needs can still be fulfilled given a radius of 15km. The regulations as far as known allow travel with a reasonable reason such as going to work, to a supermarket, a doctor or a pharmacy even if that is farther than 15km.

Still, it is an interesting question how far basic supplies are away from the inhabitants in Germany since this could be seen as an indicator for the equivalence of living conditions that are written in the German constitution. We looked at this by the example of pharmacies. OpenStreetMap (OSM) contained recently around 18.856 pharmacies in Germany which were concentrated in regions of higher population density. However, pharmacies are well enough distributed that only a small part of Germany is farther than 15km away from the next pharmacy (maximum distance ~19km).

Pharmacy locations derived from OpenStreetMap. Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org

These areas are furthermore only sparsely populated, especially in parts of Brandenburg and Mecklenburg-West Pomerania. According to the census from 2011 these areas are home to less than 2900 inhabitants. More than 76 million inhabitants live closer than 5km to the next pharmacy.

These distances can be interpreted as distances from the front door to the pharmacy. The lockdown regulations are a bit fuzzy with respect to how to measure the 15km distance but at least for several federal states they seem to be interpreted as distance from the boundary of the settlement - so lockdown relevant distances would even be smaller.

The number of pharmacies in OSM seems relatively complete if we look at the development of pharmacies reported in OSM over time. The decrease in recent years could be related with the close-down of pharmacies in rural areas as part of a concentration process. The calculations are based on the open-source OSM history analytics platform ohsome by HeiGIT.

The counts were queried for each district and each month using the ohsome API.

The counts were queried for each district and each month using the ohsome API. https://ohsome.org

According to the Deutsche Apotheker Zeitung 18,987 pharmacies were reported in Germany in March 2020. This matches relatively well with the 18,856  pharmacies that were present in OSM at the beginning of January 2021, some may have closed since then. And finally here is the challenge: can you find the (potentially?) missing 131 pharmacies and add them to OSM?

Btw, you may also be interested in our short analysis of accessibility of COVID-19 vaccitation centers in Germany.

Some of our earlier work related to public health / accessiblity using OSM:

Time series of topographic point clouds offer great possibilities to advance our understanding of dynamic landscapes. To exploit the full information these 4D datasets contain on spatial and temporal properties of natural surface changes, the 3DGeo research group is developing methods for 4D change analysis. These methods are required to answer fundamental questions on the dynamics of natural landscapes:

(1) When and where do changes occur?

(2) How do changes occur (e.g. magnitudes, directions)?

(3) How (well) can we detect changes?

An important improvement that was just published by Williams et al. (2021) is to consider the direction of changes in the detection process. Within this article, we present a data-driven method to find a dominant movement direction (DMD) by considering local changes in multiple directions. Our work shows that it is important to consider the direction along which change is measured. So far, comparisons of the topography are often done in the direction of the local surface. But natural change processes often do not occur orthogonal to the surface, for example in the case of rock falls or rock glacier creep.

test

Measurement of change between two point clouds along different directions. (a) Movement of a sliding block in between pc1 and pc2, (b) orientation of surface normals resulting in negative change in the area where the block has moved from, and positive change in the area that the block has moved into. The magnitude of this change equates to the height of the block, rather than the magnitude of movement, (c) multi-directional change estimates (red crosses) that are clustered in the direction of movement (red squares). The magnitude of movement along the dominant (mean) direction equates to displacement.

Multi-directional change quantification provides an important alternative view of movement where

(1) movement processes operate in a direction that is not surface-normal,

(2) the underlying process(es) may not be known, and

(3) movement(s) across the point cloud scene is not oriented along a single axis

Find all details in the full paper (get free access here until 9 February 2021)

Williams, J.G., Anders, K., Winiwarter, L., Zahs, V., Höfle, B. (2021): Multi-directional change detection between point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 172, pp. 95-113. https://doi.org/10.1016/j.isprsjprs.2020.12.002.

In another study, we tackle the challenge that topographic change at a given location usually results from multiple processes operating over different timescales (Ulrich et al., 2021). Interpretations of surface change are often based upon single values of movement, measured over a fixed time period or in a single direction. For the example of a rock glacier, we improve this by assessing movement along different directions (normal to the surface and in the direction of rock glacier flow) over different temporal intervals. By this, we are able to show that change processes feature different magnitudes and directions over different intervals and to separate different processes of change that are superimposed and would hence be hidden in longer observation periods. The article also highlights the importance of multi-directional change and increased temporal resolution in the setup of future observation networks.

Ulrich, V., Williams, J.G., Zahs, V., Anders, K., Hecht, S., Höfle, B. (2021): Measurement of rock glacier surface change over different timescales using terrestrial laser scanning point clouds. Earth Surface Dynamics. Vol. 9, pp. 19-28. https://doi.org/10.5194/esurf-9-19-2021.

Stay tuned for new methods on other aspects of 4D change analysis coming soon!

In the meantime, check out a recent video on 4D change analysis within the Auto3Dscapes project. Or find out what is being done on 4D change analysis in the GEODYNAMO4D and AHK-4D projects.

The Horizon 2020 LandSense project was concluded successful. Please find a selection of the produced publications and deliverables here. The project has enabled our group to pursue quality aspects of voluntarily collected geo information data and to ramp up efforts related to OSMlanduse.
Together with the University of Nottingham (Giles Foody) and Institut national de l’information géographique et forestière France (Ana-Maria Olteanu-Raimond) the GIScience Heidelberg group was leading the quality assurance and quality control of the largest Horizon 2020 Citizen Science Project, providing accuracy, performance and benchmark services for six citizen science pilot projects.

One of the key aspects for the quality estimation was the collection of reference data for OSMlanduse where you can find the latest edition of our mapathon campaigns here. For OSMlanduse and its integration with our mapathon data as well as additional auxiliary remote sensing data we’re using HeiGIT’s OSM big data processing platform ohsome an in-house labor of love, handcrafted in Java and Python, using only hand picked, organic, free license libraries (https://github.com/GIScience). We are currently concluding results and making produced models and validation data collected publicly available.

We do cordially thank for the excellent collaboration among our partners and are thankful for the extended collaboration within follow up projects!

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

Our Project Publications

land use of Europe, Heidelberg and a countryside in Utrecht

Related & earlier work at GIScience/HeiGIT (selection)

Updated based on newly available vaccination center locations at the 14th of January 2021.

Vaccination of a sufficiently large share of the population is considered the most important action to fight the spread of SARS-CoV -2 and resulting COVID-19 infections. Germany has started together with the majority of EU member states at the 27th of December 2020 its vaccination campaign. After an initial phase in which mobile teams vaccinate inhabitants of home for the elderly as well as stuff in homes for the elderly and hospitals vaccination centers will be in charge of vaccinating the population. The vaccination centers will later on presumably supported by vaccination by medical practitioners. Till when, easy access to the vaccination centers is one important factor for a successful vaccination campaign. Access to vaccination is by appointment - the procedures differ between the individual federal states.

Most federal states in Germany have announced the location of the vaccination centers. The OpenStreetMap community has immediately added this information to its data base - details can be found in the corresponding OSM Wiki page. One center was missing in Baden-Württemberg (Stuttgart III) , one in Bavaria (Kitzingen), a second center in Gera (Thuringia) and two centers were likely missing in Mecklenburg-West Pomerania.

We at HeiGIT have used this information to calculate the accessibility to these centers by means of the openrouteservice. Based on its isochrones functionality we calculated the time required to reach the centers by cars. We distinguished two cases:

  1. unconstrained access to the vaccination centers
  2. access constrained by federal state - i.e. we assumed that only inhabitants of the federal state that host the vaccination center are allowed to access the center
According to news reports the second option is more likely.
In practice access might be further regulated in so far as that vaccination centers might be responsible for one or several districts (German Stadt- und Landkreise). Since details were not known the restrained to investigate this scenario further.
In a later step isochrones were intersected with the 1sqkm population grid based on the latest zensus in 2011.

The map of driving distances indicates that while large parts of Germany have access to the vaccination centers in reasonable driving distances some regions have to face longer driving distances. These are located in less densely populated areas. Please keep in mind that that a few vaccination centers could not be located so far.

The map shows the driving distance to the next vaccination center. State boundaries were considered as barriers for this analysis.

Overall ~27% of inhabitants in the federal states live in driving distance of 10 minutes or less, ~70% in driving distance of 20 minutes or less and 91.6% in driving distance of 30 minutes or less. However, this implies that 6.8 million inhabitants have to face a driving time of more than 30 minutes from which ~190,000 inhabitants have to drive more than one hour - one way.

Population in different driving distances to covid-19 vaccination centers in Germany. Based on the most recent information on the locations of vaccination centers. The analysis considered the borders of the federal states as barriers.

If we look at the individual federal states separately we see an uneven distribution of driving time: especially inhabitants of Brandenburg will have to undertake relatively long journeys to the next vaccination center in the federal state since 43.8% of the inhabitants are living in a driving distance of more than half an hour, from which 4.7% are living farther away than 50 minutes. Inhabitants of Mecklenburg-West Pomerania presumably will also have to face longer average driving distances.

If vaccination centers could also serve inhabitants of other federal states driving distances especially in Brandenburg and Mecklenburg-West Pomerania would improve (results not shown).

Population in different driving distances to COVID-19 vaccination centers in Germany for each federal state separately. Based on the most recent information on the locations of vaccination centers. The analysis considered the borders of the federal states as barriers.

You might also be interested in our analysis of accessibility to pharmacies in Germany with 15km covid-19 restriction,

Some of our earlier work on healthcare / accessiblity using OSM:

The GIScience Research Group with the 3D Geospatial Data Processing Group and the team at HeiGIT (Heidelberg Institute for Geoinformation Technology) do send you the best wishes for a peaceful holiday season and a successful New Year. Thank you for your cooperation, efforts and interest in our work.

We hope with you that we will be able to return to normality at some point in 2021 and meet again in good health.

As in previous years, we have decided to donate money to those who are less well off, instead of sending gifts to our friends and partners. The Klaus Tschira Foundation has again provided HeiGIT gGmbH with a total of 5,000 euros for this purpose. Because of the challenges the pandemic poses on many people this year HeiGIT donated the money to those suffering depression (Deutsche Depressionshilfe).

Last year HeiGIT donated the same amount from KTS to ACTT via Charity4Aid. ACTT is an organisation supporting schools in Tanzania with refurbished computers and ICT training. It took some time, but the refurbished computers finally arrived at schools in Tanzania and we received those nice thank you pictures below. You are welcome! A big thank you to all who helped making this possible.

Christmas greetings!

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