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Michael Auer et al. OSM History for Disaster Activation Monitoring
Towards Using the Potential of
OpenStreetMap History for
Disaster Activation Monitoring
Michael Auer
GIScience Research Group, Institute of
Geography, Heidelberg University
Melanie Eckle
GIScience Research Group, Institute of
Geography, Heidelberg University
Sascha Fendrich
GIScience Research Group, Institute of
Geography, Heidelberg University
Luisa Griesbaum
GIScience Research Group, Institute of
Geography, Heidelberg University
Fabian Kowatsch
GIScience Research Group, Institute of
Geography, Heidelberg University
Sabrina Marx
GIScience Research Group, Institute of
Geography, Heidelberg University
Martin Raifer
GIScience Research Group, Institute of
Geography, Heidelberg University
Moritz Schott
GIScience Research Group, Institute of
Geography, Heidelberg University
Rafael Troilo
GIScience Research Group, Institute of
Geography, Heidelberg University
Alexander Zipf
GIScience Research Group, Institute of
Geography, Heidelberg University
ABSTRACT
Over the last couple of years, the growing OpenStreetMap (OSM) database repeatedly proved its potential for various
use cases, including disaster management. Disaster mapping activations show increasing numbers of contributions,
but oftentimes raise questions related to the quality of the provided Volunteered Geographic Information. In order
to better monitor and understand OSM mapping and data quality, we developed the ohsome software platform that
applies big data technology to OSM full history data. OSM full history data monitoring allows detailed analyses
of the OSM data evolution and the detection of remarkable patterns over time. This paper illustrates the specific
potential of our platform for disaster activations by means of two case studies. Initial results demonstrate that
our flexible and scalable platform structure enables fast and easy information extraction and supports mapping
processes and data quality assurance.
Keywords
OpenStreetMap, OpenStreetMap History, Disaster management, Street network analyses
INTRODUCTION
Up-to-date geodata is a valuable source of information in the aftermath of a disaster, e.g., to coordinate disaster
response and to support routing and navigation in the affected areas. When using proprietary or authoritative
map data, it is difficult to provide such up-to-date information. In contrast, openly accessible maps such as
OpenStreetMap (OSM) enable collaborative and quick mapping of disaster-affected areas, thereby constituting
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Proceedings of the 15th ISCRAM Conference – Rochester, NY, USA May 2018
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Michael Auer et al. OSM History for Disaster Activation Monitoring
a valuable source of information. For example, after the Nepal earthquake 2015 more than 7 000 volunteers
contributed map data to the OSM database using current satellite imagery (Radford 2015). The Humanitarian
OpenStreetMap Team (HOT) coordinates such mapping activities through organized mapping tasks using the HOT
Tasking Manager (HOT Tasking Manager 2018) and communicating the needs of the relief organizations and the
affected population on the ground.
Over the last couple of years, OSM data repeatedly proved its potential for disaster applications, in the disaster
response as well as the other phases of the disaster cycle (Palen et al. 2015; Dittus et al. 2016b; Eckle et al. 2017).
The mapping activations that are supported by many volunteers with various levels of experience oftentimes raise
questions related to the quality of the provided Volunteered Geographic Information (VGI). Learning about the data
quality that can be expected in a disaster activation helps to evaluate the quality and fitness for purpose of the OSM
data. For example, in the case of disaster routing, missing or inaccurately represented streets directly affect the
routing quality.
Quality assurance mechanisms are directly integrated into the HOT workflow in the form of validations carried out
by experienced volunteers. Furthermore, such evaluations can be carried out extrinsically by measuring the data
quality of OSM in comparison to other data sets, e.g., proprietary or authoritative data (Haklay 2010; Neis and Zipf
2012; Neis, Zielstra, et al. 2013). While this method proved to be useful in providing measurable results, data to
conduct such comparisons are rarely available for the areas of interest (Mooney et al. 2010).
Therefore, data quality assessments need to be conducted intrinsically by making use of the OSM full history
data. These data sets contain all edits (being it additions, deletions or modifications of objects) that were made by
the OSM community since the beginning of OSM in 2004. While these data sets offer a great variety of crucial
information, their analysis is challenging due to the large amount of raw data and the evolving object taxonomy
in OSM. In order to address these challenges, we develop the ohsome software platform that applies big data
technology to the OSM full history data. This paper introduces the platform and illustrates its potential for disaster
activation monitoring by means of two case studies.
RELATED WORK
Monitoring disaster activations has already proved to be of great potential for the mapping community itself, and
for data users to get a better idea about the data quality. For the Nepal earthquake response in 2015, Anhorn
et al. (2016) presented a framework to iteratively validate and update OSM objects. The study focused on the
spatio-temporal dynamics of spontaneous shelter camps in OSM. Additionally, the accuracy of the mapping was
analyzed using a crowdsourcing approach. Dittus et al. (2016a) also presented a monitoring approach analyzing
the different levels of engagement of volunteers and the resulting impact on the data and user activity. As disaster
mapping activations are oftentimes criticized for a lack of data maintenance, Quattrone et al. (2016) conducted a
long-term monitoring study and assessed OSM data contributions in different countries. Besides monitoring during
disaster activations, several post-hoc analyses and evaluations have been conducted, e.g., for Haiti 2010, Typhoon
Haiyan 2013 (Zook et al. 2010; Soden and Palen 2014; Palen et al. 2015) and for the more recent activation in
Nepal 2015 (Poiani et al. 2016; Soden and Palen 2016).
With the iOSMAnalyzer, Barron et al. (2014) developed a first framework for intrinsic OSM analyses and provided
quality indicators for different use cases. Jokar Arsanjani et al. (2013) furthermore evaluated the evolution of
OSM data to investigate specific patterns. First comprehensive intrinsic OSM data analyses related to collaborative
disaster mapping have been conducted by Dittus et al. (2017), providing insights about mapper behavior, the
mapping community and effects on the contributed OSM data.
Openly accessible OSM analysis platforms like OSM Analytics (2018) (a web based interactive visualization of
OSM feature density) and EPIC-OSM (a collection of customize-able statistics over raw OSM history data by
Anderson et al. (2016)) already provide some quantitative information about data contributions and contribution
activity for, e.g., specific HOT projects, but do not provide a full picture of the history of OSM data. Existing
software tools to support intrinsic data quality assessments are typically limited by offering only subsets of analysis
methods, and/or operate only on small database extracts for a defined number of case study regions.
THE OHSOME OSM HISTORY ANALYTICS PLATFORM
OSM offers a large quantity of data that is also quite diverse in respect of feature variety and scale. One aspect
of this is the free taxonomy scheme which is based on tags consisting of key-value pairs that are defined by the
community itself. This causes the task of analyzing how the data evolved over time to be rather complex, also
explaining the lack of general purpose analysis software until now.
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Michael Auer et al. OSM History for Disaster Activation Monitoring
The aim of the ohsome platform (Ohsome 2018) that is presented in this paper is to implement a distributed database
and computing infrastructure that gives access to OSM history data and allows for fast parallel processing. In order
to permit arbitrary spatio-temporal analyses, all information that is present in the original OSM history data set and
all available metadata fields are herein considered. The ohsome platform is co-developed with the OSM History
Database (OSHDB). The contained data is divided into spatio-temporal data cubes which may be distributed over a
computing cluster infrastructure. In order to meet different user demands, the platform provides an application
programming interface (API) at each of the following three abstraction levels:
•
The ohsome-API implements a representational state transfer (REST) web service that permits one to
interactively answer and visualize common predefined research questions and analyses. That is, the analyses
may be conducted in a standard web browser. This API is most suitable for the general public and for
non-programmers.
•
The OSHDB-API exposes the MapReduce big data programming model (Dean and Ghemawat 2004) in the
Java programming language. The Java-API is more flexible than the REST-API but still accessible with basic
knowledge of the Java programming language. Most of the more advanced analyses can be realized using it.
•
A low level raw data access gives even more direct access to the OSM data objects stored in the database and
more fine-grained control over the data processing. However, a basic understanding of the internal database
structure is required in order to interpret the results of a data request correctly.
Besides the above-mentioned focus on intrinsic data quality metrics, further application of the platform include
exploratory data analyses such as visualizing OSM contributor activity and examining individual OSM objects,
the analysis of OSM contribution patterns, or general geo-statistics and visual analytics. Possible applications of
these functionalities and their potential are presented in the following two case studies. Their purpose is not to
provide comprehensive scientific insights, but to illustrate the kind of analyses that may be performed for scientific
investigations of OSM history data.
CASE STUDY 1: NEPAL EARTHQUAKE 2015
On April 25 and May 12 of 2015, two major earthquakes with magnitudes of 7.8 (36 km east of Khudi) and 7.2
(19 km southeast of Kodari) and several minor tremors hit the central part of Nepal. In this case study we use the
ohsome platform to facilitate an exploratory analysis of the evolution of the OSM street network in the aftermath of
the Nepal earthquakes 2015 in scope of the disaster mapping activities. Thereby, we focus on the OSM’s fitness for
use for disaster routing applications.
Common intrinsic quality indicators concerning the street network are completeness of the data set, positional and
semantic accuracy and topological correctness (Neis and Zipf 2012; Barron et al. 2014). With respect to routing
applications, topological errors in OSM data are described as one of the major challenges (Neis, Singler, et al. 2010;
Sehra et al. 2016). For instance, junctions cannot be determined if intersecting OSM ways do not share a common
node (Neis and Zipf 2012). Sehra et al. (2017) assess the street navigability of OSM data in Northern India using
heuristic indicators and by analyzing road lengths as well as attribute completeness and semantic completeness.
In this case study the evolution of the OSM street network (OSM tag highway=*) is investigated for the Nepal
earthquake 2015 based on the following quality indicators:
•
Completeness: (a) street lengths aggregated for street categories and (b) percentage of streets which include
street names (e.g., Sehra et al. (2017)),
•
Topological correctness: (c) percentage of intersecting OSM ways that do not share a common node (d)
percentage of OSM endpoints close (distance
≤
1 m) but not connected to an OSM way (Jokar Arsanjani
et al. 2013; Neis and Zipf 2012; Neis, Zielstra, et al. 2013),
•User activity: (e) Number of users that edited OSM streets (Neis and Zipf 2012).
We examine the topological correctness in monthly time steps for the year 2015, the completeness and user activity
is investigated on a daily basis in order to evaluate the direct response phase. The results are presented in Fig. 1.
Volunteers started to map streets in OSM right after the April 2015 earthquake. Within seven days 3619 OSM users
added about 310 000 km to the OSM street network. The maximum number of contributors was reached two days
after the event. Thereafter, the number of mappers decreased from more than 1 000 on April 27, to about 100 per
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319
Michael Auer et al. OSM History for Disaster Activation Monitoring
0.3302
0.7926
1.2551
1e8 hwy=*
2.79
7.84
12.90
highway=road
5.92
11.34
16.77
hwy with name
0.83
2.21
3.59
junction without node
0.14
0.87
1.60
endpoint next to way
0
539
1078
number of contributors
Jan 1 Feb 1 Mar 1 Apr 1 May 1 June 1 July 1 Aug 1 Sept 1 Oct 1 Nov 1 Dec 1
0.008
0.013
0.019
impassable hwys
count % of all jcts. % of all hwys
% of all hwys
% of all endpts.
% of all hwys length [m]
2015
Figure 1. Intrinsic quality indicators, Nepal earthquake response 2015.
day one month later and about 25 contributors per day at the end of June. The Kathmandu district and surrounding
regions are the areas with the highest number of contributions. Fig. 2(left) shows the spatial distribution of the
mapping activities and the area for which specific road mapping tasks have been defined via the HOT tasking
manager.
Not only did the number of mapped streets increase, but also the number of topological errors (Fig. 2, right). A total
of 2 018 intersecting OSM streets without a common node have been detected before the earthquake on April 1,
increasing up to 6 332 on May 1. The percentage of such errors was relatively constant before the earthquake.
However, topological correctness of the data set has been continually deteriorated after the event. Regarding
OSM street categories we focused on the tag highway=road. This tag is considered to be temporary and is used
for unknown or unverified roads (OSM Wiki: Nepal 2018). During the direct disaster response of the OSM
community, the percentage of streets tagged as roads doubled from 6% to 12% within three days. This indicates
possible semantic inaccuracies in the post-disaster OSM data set. However, the percentage of streets with the
tag highway=road went back to 6% two weeks after the earthquake, meaning that the streets have been further
edited and validated. Furthermore, we assessed the attribute completeness for the OSM key name. The percentage
of streets with a name tag decreased from 13% down to 8% within 10 days after the earthquake on April 25. In
contrast to the streets tagged as highway=road, not much editing was done in the post-disaster data set and the
percentage of streets with a name attribute remained low throughout the year 2015.
Disaster relief routing applications rely on up-to-date OSM information. Thus, this information is crucial to
effectively support relief organizations. This exploratory analysis shows that the collaborative mapping efforts
provided a large amount of street network data after the Nepal earthquake 2015. In the direct-response phase this
was achieved at the expense of data quality. However, contributors continued their mapping activities and corrected,
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Michael Auer et al. OSM History for Disaster Activation Monitoring
Figure 2. Evolution of the OSM street network in Nepal 2015; the extent of the HOT tasks which focused on
street mapping is marked in red (left). Heatmap showing the density of crossing OSM ways without common node
(right).
e.g., the possible semantic inaccuracies due to streets tagged as roads to a certain amount. The presented quality
indicators were calculated in a few minutes, providing high potential for near-real time quality analyses and, thus,
facilitating feedback mechanisms to the OSM mapping community that can help to address issues in a timely
manner. This can help to improve data quality and, thus, applications like OSM routing services to support disaster
response.
CASE STUDY 2: COMPARATIVE STUDY OF HOT ACTIVATIONS
As described above, HOT was activated for several disasters and provided their support with an increasing number
of mapping volunteers over the last years. In each of these activations, main contributions are provided in the
response phase of the disasters which generally lasts for more than four weeks after the launch of an activation.
While the numerous contributions proved to be of great value for the coordination of disaster management, the VGI
character of the data and the different levels of experience of the contributing mappers cause uncertainty regarding
the usability of the data in the field.
These kind of questions can be addressed using the ohsome platform, e.g., by conducting a follow-up analysis to
assess further changes and, therefore, validations that had to be conducted. To achieve this, we extract OSM objects
that were added during the direct disaster response period and analyze changes that were applied to the data after the
activation period. Because information about buildings and roads are often the most valuable for disaster response,
we focus our analyses thereon. The three HOT disaster activations that were selected are the Haiti earthquake
activation 2010, the Typhoon Haiyan activation 2013 and the Nepal earthquake activation 2015. While the Typhoon
WiPe Paper – Geospatial Technologies and Geographic Information Science for Crisis Management (GIS)
Proceedings of the 15th ISCRAM Conference – Rochester, NY, USA May 2018
Kees Boersma and Brian Tomaszewski, eds.
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Michael Auer et al. OSM History for Disaster Activation Monitoring
3 6 9 12 15 18 21 24 27 30
0
5
10
15
20
25
30
Haiti Philippines Nepal
months after the disaster response
% of created objects changed
Figure 3. Comparison of changes to data that was created during activations (accumulated).
Haiyan and Nepal activation were among the largest in sense of contributors and scope, comparing these activations
to the earliest activation in Haiti enables assessing possible developments of the mapping community over time.
Similar to the event-response on the ground, mapping also undergoes different stages of activity: pre-event, event,
event-response and post-event. The three events are characterized through some key statistics for the respective
periods. While the pre-event period shows average numbers of 0.94 (Haiti), 675.18 (Philippines) and 369.46 (Nepal)
total contributions per day, these numbers rise to maxima of 12 156, 76 047 and 186 854, respectively, shortly after
the event. However this has to be seen in relation with the total area of the countries (Haiti: 27 750 km
2
, Philippines:
300 000 km
2
, Nepal: 147 181 km
2
(Central Intelligence Agency 2018) as well as the amount of OSM-Data available
at the start of the activation (Haiti: 1 653 elements, Philippines: 460 079, Nepal: 1 357336). The three events are
furthermore characterized through their respective activation outreach with Haiti showing a mean of 33 unique
mappers per day during the event-response while the Philippines and Nepal responses both have averages nearly
three times as high (Philippines: 96; Nepal: 81).
This exemplary follow-up study, run within minutes and with only little coding effort on the ohsome platform, can
help to estimate, e.g., the quality of the added objects and the maintenance of the added data. Quality is high if
the data does not need to be changed shortly after the event while maintenance increases the number of changes
on the long term. Figure 3shows the percentage of OSM objects created during the event-response period and
changed during the post-event period for our three case-studies. This indicates that quality of the added data is high
because a maximum of 15% of the objects were changed until one year after the event. The percentages in the more
recent Philippines and Nepal activations indicate even higher data quality. At the same time maintenance seems
to take place because these numbers still rise two years after the event. Speaking in total numbers 18 185 objects
were changed in Haiti (Philippines: 42 747, Nepal: 46 720) within 30 months after the disaster activations. These
changes were contributed by 233 unique mappers in Haiti while the Philippines were corrected and maintained by
943 and Nepal by 1 639 unique mappers. Hence while data quality rises, the number of mappers also rises whereby
the mean contribution-share of each mapper decreases.
Furthermore, the ohsome platform was used to detect the type of edits that have been applied to the OSM data
(see Fig. 4). Thereby possible patterns, the evolution of the data and differences between the three events can be
investigated. Comparing the activations, variations in type of edits become apparent. Figures 4a and 4c show that
most edits after the Haiti earthquake were tag changes, while most edits after the Nepal earthquake consisted in
geometry changes. These patterns could represent the ad hoc character of the Haiti activation that caused a lack
of coordination and structure, e.g., regarding tagging schemes, that had to be corrected afterwards. Less changes
in the later activations could show experience in activation coordination and training, and therefore less need for
corrections. On the other hand, frequently updated satellite data, used as mapping-aid can also lead to a high
number of geometry changes if the alignment of imagery varies.
While ohsome cannot directly provide information about all factors influencing mapping in OSM, it enables
detecting patterns that can then be further analyzed through qualitative assessments of the data and the activation
WiPe Paper – Geospatial Technologies and Geographic Information Science for Crisis Management (GIS)
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Michael Auer et al. OSM History for Disaster Activation Monitoring
3 6 9 12 15 18 21 24 27 30
0
20,000
40,000
60,000
80,000
months after the disaster response
number of changes made by category
(a) Haiti
3 6 9 12 15 18 21 24 27 30
0
20,000
40,000
60,000
80,000
(b) Philippines
3 6 9 12 15 18 21 24 27 30
0
20,000
40,000
60,000
80,000
(c) Nepal
3
9
15
21
27
0
60000
other changes
tag changes
geometry changes
deletions
Figure 4. Number of changes by type for the three selected disaster responses (accumulated).
itself. Its combination with other data sets and contextual information can thereby in the future deliver further
insights.
CONCLUSION AND OUTLOOK
We presented two case studies which demonstrate the potential of the ohsome OSM history analytics platform to
extract valuable information from the OSM history data, including mapping processes and data quality related to
disaster management. Due to its flexibility and scalability, the platform facilitates these kind of analyses of the
OSM data evolution and the detection of contribution patterns in VGI data.
The exploratory analysis of the street network in the aftermath of the Nepal earthquake 2015 shows that the
collaborative mapping efforts could provide a large amount of data within a short time. However, this was at first
achieved at the expense of data quality. Additionally, the evolution of OSM objects that were added during the very
active direct event-response phase was analyzed for Haiti 2010, Haiyan 2013 and Nepal 2015. The results indicate
that maintenance of the added OSM data takes places as the total number of follow-up edits increases in the long
term. Comparing the three activations reveals a higher percentage of OSM objects changed in the aftermath of the
Haiti activation. This could represent the ad hoc character of the first HOT activation, whereas the more recent
activations indicate a higher data quality.
The ohsome OSM history analytics platform is not limited to post-hoc disaster response analyses but can also be
applied to gain deeper insights into OSM data at all four stages of disaster management (mitigation, preparedness,
event, response). Likewise to disasters, OSM data itself changes during these phases. Such patterns and changes
can be analyzed using our platform and enable addressing further questions related to recovery, preparedness and
mitigation: Is the OSM data that was added and/or changed during the response period of an event maintained in
and after disaster recovery? What influence does preparedness mapping have for disaster response and data quality?
Further extensions of the database and data analysis framework will make it possible to analyze the OSM data in
respect to its metadata such as changeset attributes or user-to-user communication via OSM notes and changeset
discussions. While in this study we have shown the potential of the OSM history platform for post-hoc analyses,
future work will also facilitate monitoring OSM data contributions during disaster activation in near-real time.
Quasi-live OSM data updates (by implementing a mechanism that applies OSM’s minutely data updates to our
database) will make it possible to create new tools that help to actively improve OSM data quality by providing
feedback-loops between data producers and consumers. Timely data quality assessment during disaster response
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can help to align training materials, coordination of mapping activities as well OSM-based applications like disaster
relief routing.
ACKNOWLEDGMENTS
This work is supported by the Klaus Tschira Foundation (KTS), Heidelberg. We thank the anonymous reviewers
for their helpful suggestions.
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