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HeiGIT wants to serve you better. Therefore we are conducting user feedback surveys regarding our various services.

The Deadline has been extended to March 03rd! Take your chance!

If you have ever used one of our OpenStreetMap based Online Services (or will do so now) for whatever purpose, we’d be very happy if you took the time and filled out the respective survey.

THANK YOU! We will listen to your suggestions.

Find and test the services at HeiGIT.org We appreciate your feedback!

Welcome back to another #ohsome blog post written by our awesome student assistent Sarah! This time we will look at the completeness of railway network data of one specific city in OpenStreetMap, as well as its development. For this we looked at the city of Prague and its completeness of the operator tag. Furthermore, you’ll get to see the development of the railway network data of Prague in an animation (and can even learn how to make one yourself!). In case you haven’t read the last ohsome region of the month blogposts, you can find part 1 here & part 2 here.

Data:

As usual you will have to think of the boundaries you’re going to set in your analysis. For this you again have to get your hands on a spatial data set with the boundaries of Prague (e.g. from here) in the GeoJSON format. The dataset of interest in regard of our railway network analysis can be accessed by sending a request to the ohsome API.

Requests:

For the visualization of the evolution we decided to use the operator tag as indicator, so we can display the ratio of railway network with that information given, as well as the point in time where this information startet to get added and the point in time when it reached its maximum value. We created a snippet with the final cURL POST requests, as well as the parameter text files and further information here.

You will have to use two endpoints for getting the needed data. One is /elements/length/ratio for the part where you want to look at the ratio development over the years and the other one is /elementsFullHistory/geometry so you can access and visualize the whole evolution of railway network data (as given in the filter). With this data extraction request you’ll get all the changes to the railway network within your given timeframe, as well as the duration of validity of these changes, which comes in handy when working on the evolution animation.

Analytical Visualization:

endpoint: /elements/length/ratio

timestamp: 2009-01-01/2021-01-01/P1M

filter: type:way and railway in (rail,light_rail,subway,tram,narrow_gauge) and operator=*

Evolution Visualization:

endpoint: /elementsFullHistory/geometry

timestamp: 2009-01-01,2021-01-01

filter=type:way and railway in (rail,light_rail,subway,tram,narrow_gauge)

Here is the evolution of the railway network of the city of Prague:

As you can see there are two different colors in use. The blue lines symbolize the part of the railway network that does not carry any operator information and the yellow lines represent the part of the network that does have said information added. You might notice the slight “blinking” effect of some of the lines throughout the duration of the animation, which indicates that these lines got edited. For creating this visualization of the evolution you can use the QGIS native Temporal Controller. A short tutorial as well as an introduction to cosmetic options can be found in an additional snippet.

Data Exploration:

Below you can see the ratio development of the the operator tag in the City of Prague. The higher the value the better the covering of the railway network with this information, the highest possible value being 1 (so 100%):

Although the ratio values increase over the years they barely reach 25%. When looking at the datasets we got from our requests, the part of the railway network which actually bares the information of the operator tag seems rather „up-to-date“ as even the name change of the Správa železnic in January 2020 was implemented rather quickly after coming into effect. Yet some of the railway network does not bare the information of an operator, although they most likely belong with one of the two main operators that were named in the dataset, namely Správa železnic & Dopravní podnik hlavního města Prahy, e.g. parts of the metro network do not have the operator tag. The exact reason for that appears to be unclear.

There is a whole list given when looking at the source tag in the full-history dataset, with a lot of them appearing to be linked to the Czech Office for Surveying, Mapping and Cadastre (ČÚZK for short) who offers quite a bit of GIS data. Interestingly enough the operator count wasn’t really used until January 1st, 2012. Throughout the years the overall trend of the ratio values is positive with a few data jumps. Since October 1st of 2016 the ČÚZK has been modifying and updating the INSPIRE-dataset which also happened in connection to their participation of the European Location Framework (ELF) project. The availability of the data might be related for the better ratio values by the end of the given timeframe.

Below you can see the output dataset of the full-history extraction with the Správa železnic operator data highlighted in red and the Dopravní podnik hlavního města Prahy operator data highlighted in blue. The rest of the the railway network remains without an operator tag:

Interestingly enough most of the Metro Network (yellow highlighted lines) appears to be tagged with the operator information when looking at the picture. So at least the subway of Prague appears to have that tag added to it through the years. The “operator-less” part of the railway network however appears to be most of the cities tram network and only some parts of the railway=rail are tagged with operator information (highlighted in magenta).

Even though the ratio values itself are quite low, there is a lot of overall railway data given, especially at the beginning of the timeframe. When looking at the sources, it appears like there has been the opportunity to import data from e.g. orthophotos and datasets given by the Ústav pro hospodářskou úpravu lesů Brandýs nad Labem (ÚHÚL for short), so the Czech Forest Management Institute, or the ČÚZK. Furthermore, the source given for quite some data was Bing. So these input opportunities appear to be the reason why there is quite a lot data given from the start, but when taking the operator tag as our indicator of completeness into consideration, a great part of it appears to be incomplete for some reason. Note: the source=uhul:ortofoto is not being used anymore (since ~Summer 2015) but still had an impact on the dataset in the beginning of the timeframe looked at.

Conclusion:

At last, our region could ideally teach you how to animate a map yourself and has shown you an approach to a completeness analysis with a certain tag. Although the overall ratio values of the city of Prague are still quite small, the local mapping community appears to be rather motivated and active, so one can assume that there is a good chance for an operator tagged future for Prague.

Thank you for reading this months blogpost and stay tuned for there is more to come! As always, you can reach out to us via our email address ohsome(at)heigit(dot)org.

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:

Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. With more than 7,000 projects in 150 countries humanitarian mapping has become a global community effort. Due to this large amount of projects, it can be difficult to get an overview on mapping activity. This is why we worked on the “Humanitarian OSM Stats” website to make it easier to find the information you are looking for. It combines data from the open-source Tasking Manager hosted by the Humanitarian OpenStreetMap Team (HOT) and information from OpenStreetMap (OSM) that has been processed using the ohsome OSM History Data Analytics Platform developed by HeiGIT.

Let us take for example Médecins Sans Frontières (MSF), which is among the biggest institutional users of the Tasking Manager. Since 2016, MSF has created mapping campaigns both in response to emergency situations, such as the epidemic outbreaks in the Democratic Republic of Congo and cyclones in Mozambique, but also for pre-emptive mapping ahead of a possible programme development.

Goals

In this blog post we will pursue three relatively easy goals:

  • Find out where mapping projects are located.
  • Find out about specific regions on which mapping has been concentrated.
  • Download all the data needed for the things above.

How to get the information from the website

  1. Visit the humstats.org website and select your organization and click on “go”. This will direct you to a new site with the statistics for the selected organization.
  2. A very quick overview on the locations of projects can be obtained from the “Tasking Manager” section. For MSF, we learn that, in total, more than 450 projects have been organized through the Tasking Manager. We further see that mapping has been organized in 26 countries.
  3. More details about the mapping in each country is provided in the map below. The color represents the number of projects in a specific country (darker → more projects). You can hover with the mouse over the map and get more insights. For instance, MSF has organized 40 projects in Nigeria through which more than 850,000 buildings have been added to OSM. More than 8,500 volunteers contributed to this effort. Wow! MSF’s mapping projects have also contributed many roads and buildings in the Democratic Republic of Congo, Central African Republic and Chad to the map. In each of these countries, volunteers have added more than 300,000 buildings to OSM.
Step 1: Select your organization.
Step 2: Check overall Tasking Manager stats.
Step 3: Check countries on the map for number of project and contributors in the Tasking Manager and OSM statistics.

Download the data as a geojson file

If you are interested to get the data behind these numbers and plots continue reading. On the website we offer a list of files to download. The stats_per_country.geojson is the ones that you need for the purpose described in this blog post. For instance, for MSF, this file will be located here: https://humstats.heigit.org/api/export/msf/stats_per_country.geojson.

To be continued

This is the third blog post of a series of posts we are currently working on. If you are interested please reach out to us (benjamin.herfort@heigit.org) and we can try to cover your questions in a future post.

In the next blog post of this series we will take an even closer look at the MSF’s mapping activity in the Democratic Republic of Congo (DRC), Central African Republic (CAR) and Venezuela.

ATTENTION!! One week deadline extension. Are you working on GIS for disaster management? Hurry up! You have until Feb, 21 to submit your WIP or Practitioner paper to GIS Track.

Extended Submission deadline for WiP and Practitioner papers: February 21, 2021 - updated

Track: Geospatial Technologies and Geographic Information Science for Crisis Management (GIS)
https://www.drrm.fralinlifesci.vt.edu/iscram2021/files/CFP/ISCRAM2021-Track10-Geospatial_Technologies

https://www.drrm.fralinlifesci.vt.edu/iscram2021/call-papers.php

Track Description

With crisis and hazardous events being an “inherently spatial” problem, geospatial information and technologies have been widely employed for supporting disaster and crisis management. This was further highlighted during the response to the 2020 Coronavirus pandemic, which is relying extensively on spatial analysis for managing  the virus dissemination pathways and fighting against the virus propagation. Therefore, geospatial methods and tools – such as Spatial Decision Support Systems (SDSS), Geographic Information Systems (GIS) architectures, Volunteered Geographic Information (VGI), spatial databases, spatial-temporal methods, as well as geovisual analytics technologies –  have a great potential to contribute to, understand the geospatial characteristics of a crisis, estimate damaged areas, define evacuation routes, and plan resource distribution. Collaborative platforms like OpenStreetMap (OSM) have also been employed to support disaster management (e.g., in near real-time mapping). Nevertheless, all these geospatial big data pose new challenges for not only geospatial data visualization, but also data modeling and analysis; existing technologies, methodologies, and approaches now have to deal with data shared in various formats, different velocities, and uncertainties. Furthermore, new issues have been also emerging in urban computing and smart cities for making communities more resilient against disasters. In line with this year’s conference theme, the GIS Track particularly welcomes submissions addressing aspects of geospatial information in disaster risk and crisis research, and how this geospatial information should embrace the interdisciplinary nature of crisis situations. This includes exploring bridges between geospatial data science methods and tools and other related fields, including (but not limited to): computing disciplines (e.g. AI and machine learning), social sciences (e.g.  socio-spatial aspects of risk and resilience, community resilience, participation and governance) and humanities (e.g. spatial humanities and spatial digital arts). We seek conceptual, theoretical, technological, methodological, empirical contributions, as well as research papers employing different methodologies, e.g., design-oriented research, case studies, and action research. Solid student contributions are welcome.

Track topics are therefore focused on, but not limited to the following list:

– Geospatial data analytics for crisis management
– Location-based services and technologies  for crisis management
– Geospatial ontology for crisis management
– Geospatial big data in the context of disaster and crisis management
– Geospatial linked data for crisis management
– Spatially explicit machine learning and Artificial Intelligence for crisis management
– Urban computing and geospatial aspects of smart cities for crisis management
– Spatial Decision Support Systems for crisis management
– Individual-centric geospatial information
– Remote sensing for crisis management
– Geospatial intelligence for crisis management
– Spatial data management for crisis management
– Spatial data infrastructure for crisis management
– Geovisual analytics for crisis management
– Spatial-temporal modeling in disaster and crisis context
– Crisis mapping and geovisualization
– Collaborative disaster mapping, citizen participation
– Public policies and governance for geospatial information
– Case studies of geospatial analysis/tools during a pandemic situation
– Empirical case studies

Important Dates

In acknowledgement of the ongoing challenges posed by the COVID-19 pandemic, the original submission deadlines for ISCRAM 2021 have been extended by several weeks.

The 2021 ISCRAM conference invites three types of paper submissions:

  • Deadline passed: CoRe - Completed Research (from 4000 to 8000 words).
  • WiP - Work In Progress (from 3000 to 6000 words).
  • Practitioner (from 500 to 3000 words).

HeiGIT wants to serve you even better. Therefore we are conducting user feedback surveys regarding our various services.
If you have ever used one of our OpenStreetMap based Online Services (or will do so now) for whatever purpose, we’d be very happy if you took the time and filled out the respective survey.

THANK YOU! We will listen to your suggestions.

Find and test the services at HeiGIT.org We appreciate your feedback!

HeiGIT.org

In collaboration with the Institute of Earth Sciences at Heidelberg University, the 3DGeo group reconstructed underwater speleothems in a cave in Yucatán, Mexico. The so-called “Hells Bells” are fascinating formations in several sinkholes, at the boundary layer between fresh- and saltwater.

The full 3D mesh model obtained from several thousand photographs taken by a professional cave diver and underwater photographer can be interactively viewed and downloaded from heidICON: https://heidicon.ub.uni-heidelberg.de/detail/1264341

More information on the Hells Bells can be found in the dissertation of Dr. Simon Ritter:

Ritter, S. M. (2020): Unravelling the formation of Hells Bells: underwater speleothems from the Yucatán Peninsula in Mexico, Dissertation, Universität Heidelberg. doi:10.11588/heidok.00027813

3D View of the Hells Bells

When and where do changes occur in dynamic natural landscapes? A new method has been published that enables the automatic extraction of surface changes from entire time series of 3D point clouds. The developed method of spatiotemporal segmentation extracts changes regarding their surface change history, which makes it particularly useful for natural scenes that are subject to continual changes with smooth surface morphology. In the article, the method is used in a coastal monitoring application to extract accumulation and erosion forms on a sandy beach. The beach site was acquired by terrestrial laser scanning at hourly intervals over six months.

Spatially and temporally overlapping change forms extracted using spatiotemporal segmentation (Anders et al., 2021)

Spatially and temporally overlapping change forms extracted using spatiotemporal segmentation (Anders et al., 2021)

Fully automatic spatiotemporal segmentation of the 15 billion 3D points in 3,000 epochs yielded more than 2,000 so-called 4D objects-by-change representing temporary accumulation or erosion forms. The detected objects will provide the basis for detailed change analysis, for example of sediment transport on the beach linked to external drivers of wind, wave and anthropogenic forcing.

Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R., Vos, S. E., Höfle B. (2021). Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes. ISPRS Journal of Photogrammetry and Remote Sensing, 173, pp. 297-308. DOI: 10.1016/j.isprsjprs.2021.01.015.

Free article access until 30th March 2021 via this link: https://authors.elsevier.com/a/1cYWO3I9×1fKmO

To stay updated, follow us on ResearchGate!

The background of 4D change analysis within the Auto3Dscapes project is presented in this video: https://youtu.be/Fdwq-Cp0mFY

The research is carried out in cooperation with researchers from TU Delft in the frame of the CoastScan project. The PhD project Auto3Dscapes is supported by the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp).

The Ohsome Quality analysT (short OQT) is the name of a new software implemented by HeiGIT that is based on the #ohsome framework. Its main purpose is to compute quality estimations on OpenStreetMap (OSM) data. Any end user such as humanitarian organisations, public administrations, as well as researchers or any other institution or party interested in OSM quality can use the OQT to get hints on the quality of OSM data for their specific region and use case. The code will soon be made available as open source, but already now you can access the first proof of concept version via oqt.ohsome.org.

The Idea behind the OQT

The original idea for the OQT developed out of a simple use case: Having a one-click tool that can give information on the quality of the OSM data for a specific purpose. With OQT we want to address questions like:
Can I rely on the completeness of building data from OSM in a region where I want to organize a vaccination campaign in the near future? How complete is the road network in a specific region when I want to use it for car routing? How detailed are health care facilities mapped in OSM and which attributes are likely to be missing?

There are other tools available that work with OSM data in regards to the data quality. Software like Osmose, or the OSM Inspector or similar are used in the field of quality assurance with the main purpose to improve the mapping quality of OSM. The general topic of data quality though is on a different complexity level when it comes to the fitness for purpose of the data. Here, the main question is:
Is the OSM data good enough for my specific use case and within my area of interest?
To give support in answering that question, HeiGIT is developing an hopefully easy to understand quality analysis software that applies a simplification on the complexity on quality measurements by using a traffic light system with green-yellow-red as indicators. This can be achieved with as few as three clicks on the available OQT web page (not yet the aforementioned one-click solution, but we are just getting started…).

How to use the OQT

The website serves as a simple entry point to the OQT. The user can select a predefined area (this will be more flexible in future versions of course) and choose a respective quality report. The report can then be viewed in a following browser window (by clicking “Get Quality Report“). An example of how this view currently looks like can be seen in the following graphic.

In the background, a request is sent to an API when the quality report has been requested. This starts the processing and computations for the required indicators depending on the selected quality report. As the current examples on the web page are using precomputed indicators only, the response should be received relatively fast and is then displayed in a window that looks like the following graphic.

The results give an overall quality value for the selected area and topic, as well as for the individual indicators. They are displayed using a simple traffic light system (green-yellow-red) together with a textual explanation and individual graphs. There, you have a visual expression of the respective indicator and can also see the threshold curves for e.g. a yellow or red quality, as shown for the ghspop-comparison for building-count indicator in the graphic above.

In the upcoming download-as-pdf feature, those graphs will also be included in a high resolution with additional explanations. In the following two sections explanations on the two key features - indicator and report are given.

Indicator

An indicator is the basis of giving an estimation on the quality within the OQT. The current first POC release 0.1 features in total five working indicators. There are intrinsic indicators (using OSM data only) like the POI density, or the mapping saturation of buildings (looking at the development over time and if some saturation can be dedected), as well as extrinsic indicators, which are using OSM data together with other data sources like the mentioned global-human-settlement layer population data comparison with the OSM building count.
The output of every indicator is a normalized value between the scale of 0 to 1. This value is then categorized into a labelling schema of green-yellow-red to give an easier understanding on what a specific value can mean. Currently, this categorization is predefined for the different indicators, but the users will be able to customize this in future versions.

Report

The combination of a set of indicators is called a report in the OQT. It has again it’s own quality value & label, which is computed through weighting the respective indicator values. The weighting schema is another feature, that is currently predefined, but will be customizable soon. This means that users shall be able to weight the individual indicators for the overall quality value in future versions, which will allow to fine tune the setting for different use cases.

The current proof of concept version includes initially the following three draft reports: simple report (see example), remote mapping level one, and the sketchmap-fitness report, which was developed in the waterproofing project.

As mentioned in the introduction, one main goal of the OQT is to bring different quality developments based on OpenStreetMap data “under one hood”. This ensures that valuable results coming out of a research project are not forgotten once it is finished and can be used instead on a long-term basis and potentially on a bigger scale.

Conclusion & Next Steps

Currently the Ohsome Quality analysT can be described as a functioning OSM data quality analysis software, accessible via a web interface, giving an estimate on the quality of OpenStreetMap data for specific regions using a set of quality indicators that can be combined to quality reports. What different components are working behind the web interface and how they can be accessed (e.g. via an API or command line interface) will soon be described in another blog post about the OQT.

Please remember that this is a very first proof of concept implementation of the OQT. Future plans include adding a richer set of indicators, being able to customize the weighting of the different indicators, processing bigger areas of interest (e.g. using a national/hex-grid on global scale) and of course going open source to GitHub. Please get in touch if you have further ideas, or want to give feedback or contributions to the development, ideally via a mail to ohsome(at)heigit(dot)org.

https://oqt.ohsome.org

In the past 10 years, the collaborative maps of OpenStreetMap (OSM) have been used to support humanitarian efforts around the world as well as to fill important data gaps for implementing major development frameworks such as the Sustainable Development Goals (SDGs). In a recently accepted paper we provide a comprehensive assessment of the evolution of humanitarian mapping within the OSM community, seeking to understand the spatial and temporal footprint of these large-scale mapping efforts. The paper is published in the Scientific Reports Journal by Nature and available as “open access” https://www.nature.com/articles/s41598-021-82404-z :

This analysis encompasses all humanitarian mapping projects organized through the HOT Tasking Manager since 2012 (start of the available data), enabling us — for the first time — to cast a longitudinal perspective on the intersecting effects of mapping efforts, socio-economic, and demographic characteristics. This analysis provides critical insights into the achievements of humanitarian mapping so far, by combining data about OSM humanitarian mapping with statistics on global population distribution and human development at the sub-national level.

We conclude with three recommendations directed at the humanitarian mapping community:

  1. Improve methods to monitor mapping activity and identify where mapping is needed.
  2. Rethink the design of projects which include humanitarian data generation to avoid non-sustainable outcomes.
  3. Remove structural barriers to empower local communities and develop capacity.

Key Findings

Figure 1 provides context and motivation for our research by showing a chronology of key events in humanitarian mapping since 2010, the year HOT was founded in the aftermath of the devastating earthquake in Haiti. It shows the immediate impact that post-disaster community “activations” have had on the contributions to OSM. Besides these punctual events, there are several additional factors with more long-term effects such as the introduction of new tools, the availability of open satellite imagery for mapping in OSM and the number of organizations involved. Furthermore, political frameworks set the policy agenda and form the institutional space in which local communities, national and international NGOs operate.

Figure 1: Sketch of the evolution of humanitarian mapping in OSM in regard to major disaster activations, the socio-technical development of the community and global political frameworks. The plots at the bottom show the number of buildings and highways added to OSM.

The spatio-temporal statistical analysis of OSM’s full history since 2008 showed that humanitarian mapping efforts added 60.5 million buildings and 4.5 million roads to the map. Overall, mapping in OSM was strongly biased towards regions with very high Human Development Index. However, humanitarian mapping efforts had a different footprint, predominantly focused on regions with medium and low human development. Despite these efforts, regions with low and medium human development only accounted for 28% of the buildings and 16% of the roads mapped in OSM although they were home to 46% of the global population. Our results highlight the formidable impact of humanitarian mapping efforts such as post-disaster mapping campaigns to improve the spatial coverage of existing open geographic data and maps, but they also reveal the need to address the remaining stark data inequalities, which vary significantly across countries.

We furthermore analyzed OSM contributions on the national and sub-national level to statistically check which factors have been driving and prohibiting mapping activity. This has been complemented by an investigation of the temporal evolution of humanitarian mapping activity in OSM. For further details please refer to the paper in Scientific Reports by Nature, which also provides a more comprehensive discussion of the results and the methodology applied.

Figure 2: Spatial distribution of the number of (a)-(b) buildings and (c)-(d) highways added to OSM between 2008/01/01 - 2020/05/20 in regard to overall and humanitarian mapping activities. Further the maps depict the spatial distribution of (e) SHDI and (f) population density.

Please note that our insights about humanitarian mapping in OSM only provide an incomplete picture which lacks an on-the-ground perspective and neglects other remote mapping tools, since we considered only the mapping that was organized through the HOT Tasking Manager. For instance, humanitarian mapping that has been organized by local residents on the ground is not considered here. This limitation is accompanied by the fact that our analysis only focused on two types of mapped objects (buildings, highways). Mapping in OSM comes with a much greater variety of potential map objects (e.g. health facilities, schools, water points), which can add particular value in comparison to other geographic data sets. We are aware of the fact that our definition of humanitarian mapping is therefore oversimplified and the results must be taken with a grain of salt. In many regions of the world there is no clear distinctive line between humanitarian and non-humanitarian mapping activities as the humanitarian and non-humanitarian OSM communities are not disjoint.

Further Work

More statistics to monitor humanitarian mapping can be found at the Humanitarian OSM Stats webpage and in this blogpost series.

You can find out more about the work of João Porto de Albuquerque here and about the work of Jennings Anderson here.
Further scientific work from HeiGIT and GIScience Research Group:
Herfort, B.; Li, H.; Fendrich, S.; Lautenbach, S.; Zipf, A. Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning. Remote Sens. 2019, 11, 1799.

Raifer, M.; Troilo, R.; Kowatsch, F.; Auer, M.; Loos, L.; Marx, S.; Przybill, K.; Fendrich, S.; Mocnik, F.; Zipf, A (2019): OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data. Open Geospatial Data, Software and Standards. 4, 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.

Auer, M.; Eckle, M.; Fendrich, S.; Kowatsch, F.; Marx, S.; Raifer, M.; Schott, M.; Troilo, R.; Zipf, A. (2018): Comprehensive OpenStreetMap History Data Analyses- for and with the OSM community. Talk at the State of the Map conference 2018, Milan.

Raifer, M. (2017): OSM history analysis using big data technology. Talk at the State of the Map conference 2017, Aizuwakamatsu.

Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. With more than 7,000 projects in 150 countries humanitarian mapping has become a global community effort. Due to this large amount of projects it can be difficult to get an overview on mapping activity. This is why we worked on the “Humanitarian OSM Stats” website to make it easier to find the information you are looking for. It combines data from the open-source Tasking Manager hosted by the Humanitarian OpenStreetMap Team (HOT) and information from OpenStreetMap (OSM) that has been processed using the ohsome OSM History Data Analytics Platform developed by HeiGIT.

This is the second blog post of a series of posts we are currently working on. If you are interested please reach out to us (benjamin.herfort@heigit.org) and we can try to cover your questions in a future post.

Again we will investigate the humanitarian mapping projects organized by Tanzania Development Trust (TDT). TDT is a volunteer run UK registered charity that has been supporting grassroots projects in rural Tanzania for 45 years. Getting lost while visiting these projects, and learning how the lack of maps hampered the work of their local rep and female genital mutilation (FGM) survivor and activist Rhobi Samwelly, chair Janet Chapman started Crowd2Map in 2015. Since then over 20,000 volunteers (as we will find out in this post) have added data to OSM and helped save an estimated 3000 girls from being cut. Anyone with an internet connection is very welcome to join TDT, so get in touch!

Goals

In this blog post we will pursue three relatively easy goals:

  • Find out how long (e.g. the number of days) users have contributed to the mapping efforts of an organization (here we use Tanzania Development Trust).
  • Find out how the mapping efforts are distributed among users.
  • Find out which users have contributed most in the last month.
  • Download all the data needed for the things above.

How to get the information from the website

  1. Visit the humstats.org website and select your organization and click on “go”. This will direct you to a new site with the statistics for the selected organization.
  2. Scroll down to the “Users” section or click on in in the header. The first figures visualizes the so-called “survival-rate”. It can give insights into long term involvement and motivation of users. For TDT around 10% of all volunteers used the Tasking Manager on 5 days or more. As for many crowdsourcing or volunteer projects there are many people who just contribute once, for TDT this represents about 70% of the users. Most mapping projects have the goal to grow the community of mappers. The “survival-rate” can help to find out if this is actually happening and if more users contribute more often.
  3. Scroll down a bit further and take a look a the next graphs, the so-called “Lorenz curve”. This plot can tell us how the mapping in the Tasking Manager is distributed among users. A straight line would mean that every user does the same amount of work. The more curved the line is the more work is done by a small group of people. For TDT we can see that 90% of the users did 31% of the mapping and 6% of the validation. Conversely, this also means that 10% of the users did 69% of the mapping and 94% of the validation. Also this observation is somehow expected in a volunteered mapping project (e.g. see the 1% rule). But again it can help us to understand how sustainable the mapping community is. Ideally there is a large group of users who contribute and the overall mapping should not only depend on the motivation of few individuals.
  4. Finally let’s take a look at who contributed most. Here we will consider validation in the Tasking Manager, which is a crucial step to ensure that the data added to OSM is of high quality. In the last month (in the example December 2020) 12 users validated tasks. With a great margin “pedr0faria” and “siwilde” are leading the way when it comes to validation in this month. I think this is the time for a big “Thank You”. Hopefully other users will be able to catch up in the next months.
Step 1: Select your organisation.
Step 2: Get the “survival” rate.
Step 3: Get the “Lorenz curve” of user contributions.
Step 4: Find out about most active users.

Download the data in a spreadsheet

If you are interested to get the data behind these numbers and plots continue reading. On the website we offer a list of files to download. The user_activity_sessions_per_month.csv is the one that you need for the purpose described in this blog post. For instance for Tanzania Development Trust this file will be located here: https://humstats.heigit.org/api/export/tdt/users_activity_sessions_per_month.csv (There is also a file which provides these numbers on a weekly basis.)

To be continued

In the next blog post of this series we will take a closer look at the geographic locations of mapping projects. (For this we will choose a different organization, since all of TDT’s projects are based in Tanzania, this would have been too easy. ;))

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