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Towards assessing the quality of volunteered geographic information from OpenStreetMap for identifying critical infrastructures

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Identifying the assets of a community that are part of its Critical Infrastructure (CI) is a crucial task in emergency planning. However, this task can prove very challenging due to the costs involved in defining the methodology and gathering the necessary data. Volunteered Geographic Information from collaborative maps such as OpenStreetMap (OSM) may be able to make a contribution in this context, since it contains valuable local knowledge. However, research is still due to assess the quality of OSM for the particular purpose of identifying critical assets. To fill this gap, this paper proposes a catalogue of critical asset types, based on the analysis of different reference frameworks. We thus analyze how good the emergent OSM data model is for representing these asset types, by verifying whether they can be mapped to tags used by the OSM community. Results show that critical asset types of all selected sectors and branches are well represented in OSM.
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Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
Towards assessing the quality of volunteered geographic
information from OpenStreetMap for identifying critical
infrastructures
Benjamin Herfort
GIScience Chair, Heidelberg
University, Germany
Herfort@stud.uni-heidelberg.de
Melanie Eckle
GIScience Chair, Heidelberg
University, Germany
Eckle@stud.uni-heidelberg.de
João Porto de
Albuquerque
GIScience Chair, Heidelberg
University, Germany
Dept. of Computer
Systems/ICMC, University of
Sao Paulo, Brazil,
jporto@icmc.usp.br
Alexander Zipf
GIScience Chair, Heidelberg
University, Germany
ABSTRACT
Identifying the assets of a community that are part of its Critical Infrastructure
(CI) is a crucial task in emergency planning. However, this task can prove very
challenging due to the costs involved in defining the methodology and gathering
the necessary data. Volunteered Geographic Information from collaborative maps
such as OpenStreetMap (OSM) may be able to make a contribution in this
context, since it contains valuable local knowledge. However, research is still due
to assess the quality of OSM for the particular purpose of identifying critical
assets. To fill this gap, this paper proposes a catalogue of critical asset types,
based on the analysis of different reference frameworks. We thus analyze how
good the emergent OSM data model is for representing these asset types, by
verifying whether they can be mapped to tags used by the OSM community.
Results show that critical asset types of all selected sectors and branches are well
represented in OSM.
Keywords
Emergency planning, Critical Infrastructure, OpenStreetMap, Volunteered
Geographic Information, Disaster Management
INTRODUCTION AND BACKGROUND
With the increasing frequency of disasters caused by natural hazards, emergency
planning becomes a crucial task for every municipality. For instance, by the year
2015 the development of flood risk maps as well as alarm and emergency plans
will be compulsory within the European Union as decided in the Flood Directive
2007/60/EG (Parliament 2007).
In this context, the concept of Critical Infrastructure (CI) is commonly used for
referring to objects that must be considered in planning since they have a critical
role for society, either because of their importance for the functioning of a society
or due to their significance for emergency management in the case of a disaster
(Bouchon 2006; Organization of American States 1991). The main idea behind
this concept is to focus on essential assets in emergency planning, which could
Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
cause important damage in case of different types and severities of hazards.
However, there is no consensual definition of which infrastructures are critical
(Haemmerli & Renda 2010, Comes, Bertsch & French 2013), let alone consensual
methodologies to identify the individual assets that are part of a critical
infrastructure (Moteff & Parformak 2004). Since particular regions have
idiosyncratic conditions to be considered, it is usually expected that each
municipality will identify their own critical assets for developing their emergency
plans (Organization of American States 1991). Nevertheless, this task can prove
very challenging to municipalities, since they may lack resources for defining
customized methodologies for asset identification, as well as for building a
comprehensive and up-to-date information basis.
Collaborative maps such as OpenStreetMap (OSM) were suggested as a potential
source of Volunteered Geographic Information (VGI) for identifying elements at
risk of a community (Schelhorn et al. 2014). OSM is collaboratively defined by
citizens according to the Wikipedia principle, thus it may contain valuable local
knowledge and thereby present an alternative for the costly methods of obtaining
and maintaining official data. There are already reported cases in which OSM data
proved to be of major use for disaster risk management (Neis et al. 2010; Soden et
al. 2014). This is in line with the recent trend of citizens participating in disaster
response by creating own applications to gather and exchange knowledge or add
their information to official maps that are opened up for the public (Turoff et al.
2013). Nevertheless, the quality and credibility of information produced by
volunteers is still a major concern for emergency agencies and disaster
management professionals.
A growing body of research has been conducted to analyze the quality of OSM
data (Arsanjani et al. 2013; Barron et al. 2013; Haklay 2010; Hecht et al. 2013;
Neis & Zipf, 2012). While these research studies showed that the quality of OSM
data is in many regions comparable to official or commercial data sets (Neis &
Zipf, 2012), it is also clear that the OSM data poses challenges regarding the
heterogeneity and inconsistency of its semantic data structure (Mooney &
Corcoran 2012). OSM does not have a fixed semantic model for objects such as
an ontology or catalogue. Instead, OSM elements are assigned “tags” that are
represented in a freely-chosen key-value-structure, also called “features”.
Although there is no compulsory feature catalogue, there are general guidelines
regarding the attributes or “tags” to be used for specific object types. These
tagging guidelines are constructed by the OSM community and can vary for
different regions in the world and specific applications, e.g. the list of Map
Features Germany1. Furthermore, new tags have been proposed by the OSM
community for adding information on a wide range of topics related to disasters,
which are consolidated in a Humanitarian Data Model2 (Neis et al. 2010).
However, there is currently a lack of studies evaluating to what extend this
information is useful for effectively meeting the requirements of specific
applications and decision makers, and particularly for the domain of emergency
planning.
To fill this gap, the objective of this paper is to present first results towards a
method for assessing the quality of OpenStreetMap for the specific purpose of
identifying assets of critical infrastructure in support of emergency planning.
The remainder of the paper is organized as follows. First, the approach and
method employed in this study are described. Afterwards, first results of the
analysis of the OSM data structure are presented. Finally, the paper concludes
with final remarks and points out directions for future work.
1 http://www.wiki.openstreetmap.org/wiki/DE:Map_Features
2http://wiki.openstreetmap.org/wiki/Humanitarian_OSM_Tags/HDM_preset
Acess: January 30, 2015
Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
APPROACH AND METHOD
This paper addresses the following research question:
RQ: How good is the OSM data structure for identifying Critical Infrastructure
assets?
In order to answer this question, our approach starts with the examination of
existing frameworks about Critical Infrastructure for deriving a catalogue of asset
types of critical infrastructures that should be considered in emergency planning.
This catalogue is then used to verify whether the data structure of OpenStreetMap
is capable of representing the asset types contained in the catalogue. In doing so,
we adopt a top-down approach, which starts from the information needs of the
domain of emergency planning and go down to evaluate whether these needs are
fulfilled by the volunteered geographic information of OpenStreetMap.
Figure 1. Research Approach
Figure 1 schematically depicts our methodology which is divided into three main
components: (1) definition of a catalogue of critical sectors and branches using
existing frameworks; (2) definition of a catalogue of asset types contained in the
critical sectors and branches, based on object type reference catalogues; (3)
definition of a catalogue of OSM tags used for assets of Critical Infrastructure.
Finally, based on the catalogue of OSM tags, we answer our research question by
verifying to what extend the OSM data structure (i.e. the existing tags) is capable
of representing assets of critical infrastructures.
(1) Definition of a catalogue of critical sectors and branches
The German national strategy for protection of critical infrastructures
(Bundesministerium des Innern) functioned as a basis for the definition of the
catalogue of critical sectors and branches. The acquired sectors and branches were
compared to sectors and branches in international frameworks dealing with
critical infrastructures. These frameworks included the critical infrastructure
resilience strategy of the Australian government (2010), the council directive on
the identification and designation of European critical infrastructures and the
assessment of the need to improve their protection of the European Union (2009)
and the national strategy for the physical protection of critical infrastructures and
key assets of the US government (Bush 2003). Although there were differences in
the distinction between sectors and branches, especially comparing US and
European frameworks, we matched all initial sectors and branches to
corresponding categories in the reviewed frameworks. Doing so, we generated a
verified catalogue of critical sectors and branches.
(2) Definition of a catalogue of asset types of critical infrastructures
In a second step we merged object type reference catalogues and the previously
developed catalogue of critical sectors and branches to derive a catalogue of asset
types of critical infrastructures. To obtain a detailed selection of asset types we
regarded universal and disaster management specific reference catalogues. They
are described in the following.
The ALKIS reference catalogue includes all object types that are registered in the
German cadastre. This catalogue is used by municipalities as well as by
corporations on a national level. The LUBW framework was developed by the
Department for Environment, Climate and Energy Baden-Wuerttemberg
following the Flood Directive 2007/60/EG (Parliament 2007). The reference
catalogue focuses on flooding events and contains object types for risk assessment
Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
and emergency planning. Since the LUBW reference refers to asset types in the
ALKIS catalogue for the vast majority of asset types, we synthesize the catalogues
and name then “ALKIS” in the following of this paper.
The HAZUS reference catalogue was developed by the Federal Emergency
Management Agency of the United States Department of Homeland Security for
multi hazard loss estimation. It includes information on General Building Stock,
Essential Facilities, High Potential Loss Facilities, User Defined Facilities,
Transportation Systems and Utility Systems.
To define asset types of critical infrastructures we selected asset types from the
reference catalogues according to the following criterion. Do the asset types in the
reference catalogue refer to any of the sectors and branches ascertained in the
previous step? We independently classified all asset types within the ALKIS and
HAZUS references according to this criterion. One should empathize that we did
not classify the asset types according to their actual criticality. We rather
generated a catalogue of potentially critical asset types.
Next, identical object types from different reference catalogues were merged. The
final catalogue contains unique asset types of critical infrastructures that are listed
in at least one object type reference catalogue.
(3) Definition of a catalogue of OSM tags
In the third step we added OSM tags to the asset types of critical infrastructures
obtained in the second step. For doing so, we initially searched within the OSM
Map Features, the OSM Map Features Germany and Humanitarian Data Model
tagging guidelines to identify the proposed key-value-structure.
The OSM Map Features are a tagging guideline established by the OpenStreetMap
community. The OSM Map Features Germany is customized version of the above.
Both function as an informal standard, the former internationally, the latter
especially for describing features in Germany. The Humanitarian Data Model is a
tagging guideline developed for emergency management purposes, nevertheless it
is not used as widely as the other two guidelines.
Regarding asset types that are not listed within these tagging guidelines, we
further included other tags that are frequently used by the OSM community. We
used the taginfo api3 to determine whether the proposed key-value-structure is
adopted more than 1000 times by the OSM contributors and added this feature to
the catalogue if true. We applied this due to the fact that new features in OSM can
be introduced without proposing them on a wiki page, but solely by using them.
For this reason there are features that are used widely but not listed in the Map
Features tagging guidelines.
FIRST RESULTS
The results of step one and two of the method described in the previous section
are presented in Table 1. The resulting catalogue of asset types of critical
infrastructures consists of 9 sectors comprising 27 branches. Within the branches
there are 342 asset types. “Transport” contains by far the most asset types (108),
while “Finance and Insurance Industry” and “Telecommunication and
Communication Technology” are less represented (5, 12). The number of asset
types within the other sectors is less extreme (21-56).
Table 2 shows the number of asset types contained in the different reference
catalogues. While the reference catalogue from HAZUS only contains less than
half of all asset types (160), ALKIS and OSM include considerably more asset
types (267, 239).
3 https://taginfo.openstreetmap.org/ , Access: January 30, 2015
Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
Sectors
Branches
Energy (28)
Electricity (13); Oil (8); Gas (7)
e.g. power plant, substation, oil storage tank, gas pipeline
Telecommunication
and Communication
Technology (12)
Telecommunication (5); Communication Technology (7)
e.g. communication tower, antenna, telecommunication provider
Transport (108)
Aviation (14); Maritime Navigation (1); Inland Water Navigation
(35); Railway (25); Road (29); Logistics (4)
e.g. airport, harbor, station, railway bridge, railway tunnel,
motorway, highway
Health (21)
Medical Care (18), Medicine and Vaccine (2); Laboratory (1)
e.g. hospital, social facility, pharmacy, medical laboratory
Water (29)
Water Supply (21); Sewage Water Disposal (8)
e.g. water works, water reservoir, hydrant, wastewater plant
Nutrition (56)
Food Industry (33); Food Trade (23)
e.g. supermarket, department store, restaurant, farmland, flour
production
Finance and
Insurance Industry
(5)
Banks (4); Insurance (1)
e.g. credit institution, bank, atm, money exchange, insurance
company,
State and
Administration (47)
Government and Administration (28); Parliament (1); Justice (4);
Emergency (14)
e.g. Parliament, townhall, primary school, prison, police, fire
station
Media and Culture
(36)
Broadcasting (2); Cultural Assets (12); Symbolic Monuments
(22)
e.g. radio station, tv station, theatre, opera, church
Table 1. Catalogue of asset types of critical infrastructures, number of asset in each
branch in brackets
# OSM
# HAZUS
#ALKIS
# Catalogue
239
267
Table 2. Number of asset types contained in reference catalogues
Figure 2. Share of asset types for different reference catalogues and critical Sectors in
percent
Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
Figure 2 provides a more detailed view on these differences between the reviewed
reference catalogues. While HAZUS is by far the most suitable source for the
sectors “Health” and “Finance and Insurance Industry”, ALKIS performs best in
the sectors “Media and Cultures”, “Water”, “Energy” and “Transport”. In the
sectors “Water”, “Transport” and “Energy” HAZUS can provide only less than
half the total identified asset types. OSM provides more than 75% of all asset
types for the sectors “Media and Culture”, “Finance and Insurance Industry”,
“Water” and “Transport”. However, OSM and ALKIS cover only less than 50%
of all asset types of the sector “Health”.
Furthermore, the results from OSM are more homogeneous considering different
sectors (min: 38.1%, max: 83.3%) than the results from ALKIS (min: 28.6%,
max: 100%) and HAZUS (min: 24.1%, max: 100%).
Finally, we examined the relationship among our catalogue, OSM, HAZUS and
ALKIS using Venn diagram (See Figure 3). The bold dashed circles cover all
asset types of critical infrastructures listed in our catalogue.
Figure 3. Venn diagram of asset types for different reference catalogues
Figure 3 shows that the HAZUS and ALKIS reference catalogues contain
different asset types to a great extent. The HAZUS reference shares about one
third of all asset types listed in ALKIS, while vice versa the ALKIS reference
shares about 55% of all asset types listed in HAZUS. On the other hand OSM
covers and about 69% of all asset types from HAZUS and about 75% of all asset
types from ALKIS.
Further analysis shows that OSM bears great potential to add asset types to the
reviewed reference catalogues. A combined list of asset types from OSM and
ALKIS will cover about 78% of HAZUS. A combined list of asset types from
OSM and HAZUS will cover even 82% of ALKIS. Beyond that, there are 71 asset
types that can neither be found in HAZUS nor in ALKIS, but in OSM (e.g. cable
distribution cabinet, road under construction, automated external defibrillator, fire
extinguisher).
FINAL REMARKS AND FUTURE WORK
This paper presents first results towards a method for assessing the quality of
OpenStreetMap for the specific purpose of identifying assets of critical
infrastructure in support of emergency planning. First results show that critical
asset types of all selected sectors and branches are well represented in OSM.
OSM provides good results for most of the critical sectors compared to other
reference catalogues from HAZUS and ALKIS. The high number of asset types in
OSM shows that the OSM data structure is suitable to represent critical
infrastructures in a sophisticated way. In total, OSM reaches a level of detail that
is comparable to other reference catalogues.
However, regarding different critical sectors the results also show that other
reference catalogues perform better than OSM in specific areas. Especially in the
sector “Health”, HAZUS contains much more asset types. In this manner, it
should be noticed that using OSM as an exclusive source will exclude a
significant number of asset types of critical infrastructures.
However, our results show that OSM bears great potential to add a high number of
asset types to both HAZUS and ALKIS reference catalogues.
Further research should build upon these first results to assess the fitness for use
Herfort et al.
Towards assessing the quality of volunteered geographic information from OpenStreetMap for
identifying critical infrastructures
Short Paper Planning, Foresight, and Risk Analysis
Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
of the OSM data for identifying assets of critical infrastructure. For doing this, the
data that is actually produced by volunteers should be analyzed for different case
studies, in order to address the local heterogeneity in the quality and completeness
of the OSM data. This is crucial to validate our hypothesis out of the scope of our
analysis (Germany and USA) and for filtering incorrect data.
Furthermore, if the quality of the data is confirmed, we envisage the design of a
decision-support system that would be able to retrieve data automatically from
OSM and present it to the user for assisting emergency planning, based on the
catalogue presented in this paper.
ACKNOWLEGDEMENTS
The authors are thankful to Landesanstalt für Umwelt, Messungen und
Naturschutz Baden-Württemberg (LUBW) for providing funding for this research
within the “Klimopass” program. The authors would like to thank Svend-Jonas
Schelhorn for his support regarding the analysis of the reference catalogues. J. P.
Albuquerque is grateful for CAPES (grant no. 12065-13-7) and Heidelberg
University (Excellence Initiative II / Action 7) for supporting his contribution to
this research.
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Proceedings of the ISCRAM 2015 Conference - Kristiansand, May 24-27
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At the time of writing most governments around the whole world are prioritising the limiting of the spread of the Covid-19 epidemic through a series of lockdown measures affecting schools, sports, leisure, commercial and travel activities. The gradual reversal of these measures is being carried out individually by countries and regions based on the public health emergency advice and other epidemiological modelling outcomes. As is the case in any humanitarian crisis situation, there is a need for specific, accurate and up-to-date geospatial information to facilitate emergency response, management of the overall response and public information. OpenStreetMap (OSM) has played a pivotal and often leading role in national and international humanitarian and citizen-led responses to crises such as natural disasters over the last decade or so. OSM can take these leading roles for a number of potential reasons, including; geospatial data availability under an open access license, facilitating the development of open services such as mapping, routing, etc. This paper critically assesses and reflects upon this new context and challenge for OpenStreetMap and humanitarian mapping from a number of different positions which are outlined as follows: OpenStreetMap usage as a cartographic basemap in Covid-19 related applications; Applications or services using OpenStreetMap data; Initiatives or applications aimed at the collection of new OSM data immediately relevant to the Covid-19 pandemic response or management; Import of authoritative geospatial data into OpenStreetMap where there are gaps in the OpenStreetMap database for a particular country or region; and, Academic research about the role or usage of OpenStreetMap in the Covid-19 response.
... A) WiFi-hotspots, B) skateboard sites, C) playgrounds, D) soccer fields 32 In contrast to OGD, crowdmapped data represents people's individual perception, value, and use of infrastructure and their interaction with their surroundings (Herfort et al. 2015;Mahabir et al. 2017, Quinn andDutton 2014). It is based on (local) people's specific local (spatial) knowledge, including aspects such as previous experience and contemporaneous knowledge, as well as subtleties of people's behavior, local cultural and institutional structures (McCall et al. 2015). ...
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To develop child- and youth-friendly cities, data on infrastructure relevant for this subgroup of society is an important asset. The data can be obtained by crowdmapping approaches like OpenStreetMap (OSM). Even though OSM is used for many purposes, the question is whether it is a valuable source of data on child- and youth-relevant urban infrastructure. And, if so, how OSM can support the call for child- and youth-friendly cities. This was examined in the Austrian city of Salzburg. Based on a specially created list of urban features, OSM was analyzed for tags that would be useful to describe the relevant elements and for data available on these elements. The results show that tags exist to describe the majority (80%) of the about 40 element types identified. Although OSM holds abundant data on the elements, data gaps exist. Comparing the OSM data to Open Government Data (OGD) revealed that both OSM and OGD, together deliver a more complete picture of the relevant elements. To increase young people’s contributions to OSM and to make better use of the potential of OSM (e.g. availability of child- and youth-specific information) the following measures are needed: increase OSM’s publicity, address their motivation to add data, support them in mapping, and build their spatial literacy skills. The definitive version is available at https://journals.openedition.org/articulo/4296
... There are several reasons for the implementation of participation projects and projects allowing for participatory spatial data contribution: Data that are otherwise not available or difficult to access can be received. This refers to people's local spatial knowledge and how they perceive, value, and use infrastructure or resources (International Fund for Agricultural Development [IFAD] 2009;Herfort et al. 2015;Hennig 2017). It creates the opportunity for data to be gathered on larger geographic scales and over longer periods of time than what is possible with traditional data collection approaches (Cohn 2008). ...
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The advances in web technology have triggered new modes of participation. In the context of online participation the use of geospatial technologies has received increasing attention. This includes possibilities for spatial data contribution by the public. Here, awareness and consideration of the motivations that drives people to take action are important aspects in the implementation of participatory projects including the development of tools that support spatial data collection and reporting. Even though literature provides extensive background on why people participate and how to address their motivations, the question is to which extent project designers, in fact, take this into account. Results of a questionnaire and an analysis of websites and web applications reveal that project designers consider motivations in their initiatives to varying degrees. To increase the extent to which motivational factors are considered and addressed in project implementation two approaches, participatory design and user interface design patterns, are discussed.
... Since OSM uses a free tagging system, this permits the users to use an unlimited number of attributes to describe each feature. Thus, tags define the type of a feature, but also describe its further properties [16][17][18][19][20] . ...
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Semantic web technologies play a significant role in many open data initiatives, including geo-mapping projects and platforms. At the same time, semantic principles are also promoted as a key enabling factor for multi-domain analyses of critical infrastructures as well as for improved emergency response. This chapter reviews the recent literature on ontology-based analysis and management of critical infrastructures, and proposes the use of ontology processing techniques to bridge the gap between infrastructure knowledge representation and available (often general-purpose) open data sources. In particular, it discusses an approach for matching a given critical infrastructure ontology to an ontology built on OpenStreetMap (OSM) tags that enables structured access to the associated geographical dataset.
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