A review of OpenStreetMap data

Abstract

While there is now a considerable variety of sources of Volunteered Geographic Information (VGI) available, discussion of this domain is often exemplified by and focused around OpenStreetMap (OSM). In a little over a decade OSM has become the leading example of VGI on the Internet. OSM is not just a crowdsourced spatial database of VGI; rather, it has grown to become a vast ecosystem of data, software systems and applications, tools, and Web-based information stores such as wikis. An increasing number of developers, industry actors, researchers and other end users are making use of OSM in their applications. OSM has been shown to compare favourably with other sources of spatial data in terms of data quality. In addition to this, a very large OSM community updates data within OSM on a regular basis. This chapter provides an introduction to and review of OSM and the ecosystem which has grown to support the mission of creating a free, editable map of the whole world. The chapter is especially meant for readers who have no or little knowledge about the range, maturity and complexity of the tools, services, applications and organisations working with OSM data. We provide examples of tools and services to access, edit, visualise and make quality assessments of OSM data. We also provide a number of examples of applications, such as some of those used in navigation and routing, that use OSM data directly. The chapter finishes with an indication of where OSM will be discussed in the other chapters in this book, and we provide a brief speculative outlook on what the future holds for the OSM project.

Full text

CHAPTER 3
A Review of OpenStreetMap Data
Peter Mooney* and Marco Minghini†
*Department of Computer Science, Maynooth University,
Maynooth, Co. Kildare, Ireland, Peter.mooney@nuim.ie
†Department of Civil and Environmental Engineering, Politecnico di Milano,
Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Abstract
While there is now a considerable variety of sources of Volunteered Geo-
graphic Information (VGI) available, discussion of this domain is oen exem-
plied by and focused around OpenStreetMap (OSM). In a little over a decade
OSM has become the leading example of VGI on the Internet. OSM is not just
a crowdsourced spatial database of VGI; rather, it has grown to become a vast
ecosystem of data, soware systems and applications, tools, and Web-based
information stores such as wikis. An increasing number of developers, indus-
try actors, researchers and other end users are making use of OSM in their
applications. OSM has been shown to compare favourably with other sources
of spatial data in terms of data quality. In addition to this, a very large OSM
community updates data within OSM on a regular basis. is chapter provides
an introduction to and review of OSM and the ecosystem which has grown
to support the mission of creating a free, editable map of the whole world.
e chapter is especially meant for readers who have no or little knowledge
about the range, maturity and complexity of the tools, services, applications
and organisations working with OSM data. We provide examples of tools and
services to access, edit, visualise and make quality assessments of OSM data.
We also provide a number of examples of applications, such as some of those
How to cite this book chapter:
Mooney, P and Minghini, M. 2017. A Review of OpenStreetMap Data. In: Foody, G,
See, L, Fritz, S, Mooney, P, Olteanu-Raimond, A-M, Fonte, C C and Antoniou, V.
(eds.) Mapping and the CitizenSensor. Pp. 37–59. London: Ubiquity Press. DOI:
https://doi.org/10.5334/bbf.c. License: CC-BY 4.0

38 Mapping and the CitizenSensor
used in navigation and routing, that use OSM data directly. e chapter n-
ishes with an indication of where OSM will be discussed in the other chapters
in this book, and we provide a brief speculative outlook on what the future
holds for the OSM project.
Keywords
OpenStreetMap, geodata, open data, Volunteered Geographic Information
(VGI)
1 Introduction
e OpenStreetMap (OSM) project was founded in 2004 and has now posi-
tioned itself as the most famous example of Volunteered Geographic Informa-
tion (VGI) on the Internet (Jokar Arsanjani et al., 2015). While OSM is only
one of many well established and well known VGI projects (See et al., 2016),
it holds a dominant position in the VGI landscape. Chapter 2 of this book, by
See et al. (2017), gives an overview of dierent sources of VGI in the context of
its usage and characteristics. In recent years OSM has attracted very signicant
research attention (Mooney, 2015) and could almost be considered a eld of
research in its own right (Jokar Arsanjani et al., 2015); given the inuence of
OSM on the VGI and citizen sensor research landscape, this chapter will pro-
vide an introduction to and overview of the OSM project.
OSM was founded in 2004 by then MSc student Steve Coast, who created the
idea as part of a thesis dissertation. Around that time the concept of crowd-
sourcing, collaboration and Web-based co-production or creation of knowl-
edge was beginning to gain momentum. Coast’s idea was simple: if I collect
geographic data about my area – where I have local knowledge – and you
collect geographic data about your area– where you have local knowledge–
then these can be combined, and we can begin to build a spatial database of
a region. If this scales up to a larger crowd of people, then it is very possible
to crowdsource the mapping of the entire world. e OSM mission statement
grew out of this simple idea, which was to be a collaborative project that cre-
ated a free editable map of the world. Rather than the focus being on outputs
in the form of cartographic products and maps, the core of OSM is a spatial
database, which contains geographic data and information from all over the
world. Many authors and commentators have speculated on the ingredients
for the rapid and sustained success of OSM since 2004. A number of factors are
seen as having been inuential in OSM’s development. In the rst instance one
of these factors is Web 2.0, or the interactive web (O’Reilly, 2007), which facili-
tates the development of large scale collaborative projects that can see hun-
dreds or thousands of people contributing simultaneously– the most famous

A Review of OpenStreetMap Data 39
example of this is Wikipedia. Secondly the availability of low-cost, high-quality
and high-accuracy Global Positioning System (GPS) means that consumers
or citizens can now collect geographic information using smart devices such
as their smartphones or dedicated GPS units; these geographic data can then
be uploaded and contributed to OSM. e third factor is related to the citizen
contributors: the OSM project welcomes anyone to register and take part as
a contributor. Contributors can span the entire spectrum of geographic and
Information Technology expertise: from beginner or newcomer to expert level
geographer or soware developer.
1.1 How Does One Contribute to OSM?
e OSM data model is very straightforward to understand. ere are three
primitive data types or objects: nodes, ways (polygons and polylines) and rela-
tions (logical collections of ways and nodes). A way is made up of at least two
nodes (for polylines) or three nodes (for closed polygons). A node represents
a geographic point feature and its coordinate is usually expressed as latitude
and longitude. Within OSM, every object must have at least one attribute or tag
(a key/value pair) assigned to it to describe its characteristics. ere are many
guides and tutorial documents on how one begins to map with OSM; recently
the company Mapbox provided an updated set of documentation for this1. e
OSM Map Features pages on the OSM wiki (OpenStreetMap, 2016) represent
the reference document describing the ocially adopted OSM tags. ese
tags have been agreed upon over the years and there are wiki pages written to
describe the likely usage and use case scenarios of each tag. OSM follows a folk-
sonomy approach to tagging, and, in theory, any tag can be associated with any
object (Ballatore and Mooney, 2015). Contributors are free to create their own
tags. As several authors have shown (Ballatore and Mooney, 2015; Ballatore
and Zipf, 2015), this can lead to disagreements amongst contributors or confu-
sion on how to use specic tags in certain geographic scenarios (for example
tagging an object representing an unpaved pedestrian footpath). Services such
as taginfo2 allow exploration and visualisation of the most frequently used tags
and their keys for the entire OSM database. e taginfo service is particularly
useful for understanding the style or structure of tags used on specic object
types, conceptualising the very wide range of values some keys are assigned in
tags and the spatial distribution of tags. Taginfo is constantly updated in near
real-time and stores the tags from every object in the global OSM database.
ere is no theoretical limit on the number of tags that can be assigned to any
object. Nodes that have a tag with a key name are usually called Points of Inter-
est (POI) and usually represent the position of some object or structure of gen-
eral interest. Keys in OSM can be internationalised to accommodate languages
other than English, which, due to OSM’s origins, has established itself as the
lingua franca of the project (Ballatore and Mooney, 2015).

40 Mapping and the CitizenSensor
ere are many soware tools available to automate the process of contrib-
uting data or editing existing data. e most widely used and popular is the
JOSM (Java for OSM) tool3, followed by the Web-based iD editor4; JOSM is
acknowledged as being a soware tool more suited to more experienced OSM
contributors while the iD editor is very straightforward to use and is integrated
into the OSM map homepage. New data submitted to OSM or existing data
edited within the OSM database are available for access almost immediately,
and the OSM map on the OSM homepage will render changes quickly (within
30 minutes). As we shall discuss in Section 2, there are many ways in which
one can access and download OSM data for other uses. On a more technical
level, every object within the OSM database (nodes, ways or relations) has sev-
eral data attributes including: a globally unique ID; a version number, which
indicates how many times the object has been edited; a timestamp of the most
recent edit; and the user ID and the username of the contributor who created
(or last edited) the object.
Anyone can sign up and register for free as a contributor to OSM. In July
2016, there were over 2.7M registered contributors, as outlined on the OSM
wiki5; upon sign-up, a contributor can begin contributing or mapping new
data in OSM or editing existing data stored in the OSM spatial database. How-
ever, it is not easy to automatically access attribute or demographic information
about these user contributors from the OSM database or associated services.
Several researchers (Neis et al., 2013 and references therein) have attempted to
classify and understand who the contributors are to OSM through analysis of
their editing and contribution patterns over a long period of time.
ere are multiple ways users can contribute data to OSM. e simplest one
is through the digitisation of objects (such as buildings, roads and rivers) that
are visible on openly licensed satellite imagery. e most used imagery, avail-
able by default in the OSM iD editor, is the one provided under a compatible
licence by Microso (Coast, 2010). While this way of contributing data allows
volunteers to map places even when remote from the mapped place, other
instruments, such as GPS receivers and paper-based tools like Field Papers6,
allow users to physically survey an area and then upload or insert the informa-
tion into the OSM database. One of the more controversial methods of contrib-
uting data to the OSM database is through the bulk import of suitably licensed
geographic data. e pros and cons of taking a geographic dataset produced
outside of OSM and importing it into the OSM database have been discussed
by many authors (Zielstra et al., 2013), and the issue remains a contentious one
amongst the OSM community. One of the most powerful arguments against
this bulk import is that it goes against the very ethos of OSM that data be col-
lected or mapped by OSM contributors based on an ability to verify the quality
of the data, ability itself founded on local knowledge, physical collection of the
data or geographic expertise. Many examples of bulk import are available on
the OSM wiki website7, with the TIGER data import of roads and highways

A Review of OpenStreetMap Data 41
into OSM United States and the CORINE LandCover map import into OSM
France amongst the most well known and controversial.
e remainder of this chapter is organised as follows: in the next section, we
provide an overview of how OSM is accessed, visualised and used in research,
soware development and other applications. In the nal section of the chapter,
we provide some concluding remarks and points for discussion on OSM; we
also outline where the reader will nd more discussion of and information on
OSM in the proceeding chapters of this volume. e overall purpose of this
chapter is to introduce readers unfamiliar with OSM to the project and the
types of applications it is currently used for. We let other chapters in this volume
to describe specic aspects of OSM (data quality, visualisation of OSM, motiva-
tions of contributors, etc.) in more technical detail.
2 Applications Using OSM Data
In the introductory section of this chapter, we mentioned that, while much
of the focus of OSM is on the maps and cartographic products derived from
the OSM data, the core product of OSM is the spatial database. is second
section will provide a comprehensive list of a number of projects, organisa-
tions, services, soware and applications that make direct use of OSM data,
with references and links provided at the end of the chapter. A number of such
lists and descriptions are available on the Internet (e.g. on the OSM wiki8), but,
to the authors’ knowledge, this is the rst list provided in an academic paper.
Due to the free and open availability of OSM data and the increasing popular-
ity of OSM worldwide, it would be impossible to list all of the existing projects
and applications. Making use of OSM data has become so easy and immediate
that new tools are created almost every day. Some of these applications become
very popular and well known while other applications are limited to single
languages or user groups. erefore we limit the items on this list to what we
consider from our knowledge of OSM to be the most popular, up-to-date and
successful applications based on OSM data. e description of each item on
the list serves as a reference and starting point for readers having no or limited
experience in OSM.
We understand that links to online services and websites change over time
and can become obsolete or broken. However, with this in mind, the list itself
serves as a commentary on the diversity of application areas where OSM is
used. We organise the list under the following headings: Data Download
Applications and Services, Education and Research Use of OSM, Disaster and
Humanitarian OSM, Government and Industry Usage, Visualisation of OSM
Data, Soware (OSM Editors, Routing Services, Vector Rendering, other ser-
vices), Quality Assurance for OSM, and Games and Leisure. For more applica-
tions and services, a very extensive list is maintained on the OSM wiki9.

42 Mapping and the CitizenSensor
2.1 Data Download Applications and Services
Regardless of the types of applications and visualisations that can be produced
with OSM, the applications and services that provide access to the data within
the OSM database are arguably the most important part of the OSM’s data
architecture. Geofabrik is one of the best known providers of access to OSM
data and provides access to continental-, national- and regional-sized data
extracts10; the data are uploaded very frequently (at least hourly) and are pro-
vided in a number of dierent formats. e OSM wiki provides access to the
so-called Planet.osm le11, which is the entire OSM database contained in one
very large XML or compressed format le. is le is updated every few days.
e wiki page lists many mirror servers providing access to the Planet.osm
le, with many of these servers providing the le updated on an hourly basis.
OSM also provides an API12 that allows extracting and saving raw data from/to
the OSM database. ere are API calls to create, read, update and delete map
data for OSM, and this provides soware developers and applications with
the most up-to-date data available. However, queries for very large amounts
of data (such as city- or country-sized) are discouraged and disallowed. e
Overpass API service13, with its popular frontend Overpass Turbo14, is a read-
only API that allows access to selected parts of the OSM map database; clients
send queries using a special API query language or using the graphical inter-
face provided by Overpass Turbo. e Overpass API also allows programmatic
calls for data extracts of arbitrary geographic size. e commercial company
Mapzen provides OSM data for download in city- or region-based extract sizes
from their Metro Extracts15 service: a number of data formats are provided
and their data extracts are updated on a weekly basis. A simple and popular
way to download small amounts of OSM data is provided on the OSM home-
page and consists in using its ‘export’ feature16. is allows users to browse
the OSM map and select small regions using a bounding rectangle, which can
then download OSM data to the calling device. All of the services mentioned
so far provide, as standard, OSM data in the default OSM XML data format17.
As most types of XML, OSM XML requires special soware tools in order to
be processed, and there are many options available for this task18. Data pro-
viders such as Geofabrik19 and Mapzen20 also provide OSM data in common
formats, such as SHP les: this allows users to process and visualise the data
using desktop GIS tools.
2.2 Education and Research Use of OSM
e ability to access the entire OSM spatial database on an hourly basis or even
more frequently has proved a great attraction for the research community over
the past number of years (Jokar Arsanjani et al., 2015). ere has been a steady
increase year-on-year of the number of papers being produced by the academic

A Review of OpenStreetMap Data 43
community in the domain of VGI, and OSM forms a major component of this
work. In 2015, one of the rst edited volumes on OSM as a research topic was
published (Jokar Arsanjani et al., 2015); the volume considered OSM’s role in
GIScience and contained a very wide range of research topics, from navigation
and routing to data quality and visualisation. Similarly, two EU COST Actions
focused on VGI that ran from 2012 to 2016, TD1202 ‘Mapping and the Citi-
zen Sensor’ (from where this volume comes)21 and IC1203 ‘ENERGIC’22, have
produced some excellent research around OSM. In other educational settings,
a repository such as TeachOSM23 provides a set of community- contributed
resources for teachers, trainers, educators and instructors who want to bring
OSM into their classrooms. e classroom can be a very important setting for
educating the next generation of OSM mappers or contributors. ere are many
examples, including ‘a world-record humanitarian mapathon that took place at
the Politecnico di Milano in northern Italy in March 2016’24: is mapathon
event involved over two hundred children from six elementary schools in the
Milan province. is mapathon resulted in the mapping of over 5000 buildings
in Swaziland (Ebrahim et al., 2016). More information can also be found in
Chapter 5 of this book, by Fritz et al. (2017).
2.3 Disaster and Humanitarian OSM
OSM data and mapping has been used extensively in recent disaster and
humanitarian emergencies and operations all over the world. e Humanitar-
ian OpenStreetMap Team (HOT)25 is a nonprot organisation leading the inter-
national eorts in community mapping projects. rough its open source Task-
ing Manager26, HOT coordinates online collaborative mapping based on OSM
when major disaster strikes anywhere in the world, such as during the Nepal
earthquake in 2015 and the Japan and Ecuador earthquakes in 2016; in regions
such as Nepal, OSM very oen is the only available source of mapping data and
cartography that rescuers and aid agencies can use. e Missing Maps project27
is an open, collaborative humanitarian project aiming to map the most vulner-
able places in the developing world. Missing Maps founders and members are
mainly humanitarian organisations (e.g. the American Red Cross and Doctors
Without Borders) and NGOs; the project’s volunteered mapping is again based
on OSM data and the HOT Tasking Manager. e University of Heidelberg
hosts the disastermappers project28, which aims to educate and train university
students about mapping in OSM for humanitarian purposes. Reaction time is
oen very quick and successful with OSM. Examples include a 5-day period
of mapping where the Humanitarian OSM Team and volunteers mapped over
100,000 buildings and hundreds of miles of roads in Guinea when Ebola broke
out in 201429. e eorts of the OSM community in times of humanitarian cri-
sis are easy to visualise, as snapshots of OSM data can be extracted to show the
eects of mapping before and aer a particular event. HOT shows the changes30

44 Mapping and the CitizenSensor
in the OSM map that occurred aer the city of Tacloban in the Philippines was
devastated by the super typhoon Haiyan in 2013.
2.4 Government and Industry Usage
OSM is being used in industry and by government agencies around the world.
Indeed there is a large number of companies listed on the OSM wiki31 who
provide consultancy based on OSM data. is consultancy has a wide range of
applications, including Web-based mapping, Web GIS, data analysis, routing
and navigation, and data extraction. ere are several leading companies in
this domain including: Mapbox32, MapQuest33, Stamen34, Mapzen35, CampTo-
Camp36 and Geofabrik18. Most of these companies also provide OSM services
back to the OSM user community, including OSM data extracts, web-map lay-
ers for online mapping and specialist visualisation.
Government usage of OSM is more dicult to track unless it is advertised
and highlighted by the government agencies involved. From the opposite direc-
tion, there has been signicant use of government data in OSM, with several
high-prole data imports having been performed over the years. ese imports
are based on the imported data having an acceptable open data licence allowing
the corresponding geodata to be inserted into the OSM database. e imports
include: the TIGER (the Topologically Integrated Geographic Encoding and
Referencing system) data, produced by the US Census Bureau, in the USA;
plan.at in Austria; GeoBase as a complete map of Canada; and the CORINE
Land Cover map in France.
In 2013, New York City opened up many ‘high-value datasets to the pub-
lic, making it possible to use these data to improve OSM’37, facilitated and
assisted by Mapbox30. ‘In return, New York City’s GIS team is informed of
changes made in OSM related to their datasets, which helps keep their map
data current.’ is eectively made the New York City municipality a partici-
pant and contributor to OSM in the United States. MapGive38 is an initiative
of the US Department of State’s Humanitarian Information Unit, ‘mak[ing]
it easy for new volunteers to learn to map and get involved in online tasks’.
Portland’s TriMet trac authority uses OSM to power their multi-modal traf-
c planner39. e Gendarmerie Nationale (one of the national police forces in
France) uses OSM maps inside their police cars40. e CROWDGOV report
by Haklay et al. (2014) has a number of examples of governmental use of
OSM around the world. ere is still some reluctance by government agen-
cies to use VGI and OSM as a complement to their own sources of spatial
data (Olteanu-Raimond et al., 2017b); however, examples do exist, such as
the French National Address Database (BAN), which ‘associates each address
listed on the French territory (25 million addresses) with its geographic
coordinates’ (the database ‘does not contain any nominative data’). BAN is
the result of ‘an innovative collaboration model between public authorities’

A Review of OpenStreetMap Data 45
in France and OSM France ‘to build an essential reference for the economy,
society and public services’41.
2.5 Visualisation of OSM Data
From anecdotal evidence, visualisation of OSM data is certainly one of the
most popular applications of OSM data. Visualisation of OSM data is facili-
tated by the exible availability of the OSM data (see Section 2.1) and the very
wide range of visualisation tools available, which can natively process OSM
data directly or from a spatial database. ere is a vast number of examples, and
we provide a small selection here for the purposes of illustrating the breadth of
applications.
OpenTopoMap42 provides a topographic visualisation of OSM data com-
bined with SRTM elevation data. e map tiles in OpenTopoMap are avail-
able for use as a web-map layer in other applications. OpenCycleMap43 is an
OSM rendering ‘primarily aimed at showing information useful to cyclists’. e
OpenCycleMap global cycling map is based on data from OSM and is updated
frequently. e OpenCycleMap website indicates that ‘at low zoom levels, it is
intended for overviews of national cycling networks; at higher zoom levels, it
should help with planning which streets to cycle on, where cyclists can park
their bikes, etc.’ It is also available for use as a web-map layer in other applica-
tions. In a similar fashion, the Hike & Bike Map44 visualisation of OSM data
highlights hiking and biking routes by using a specic cartographic style to
highlight these routes. e OpenSnowMap45 is an OSM-based map rendering
of ski slopes and lis. It integrates OSM data, MODIS/Terra Snow Cover 8-Day
Global data46 and SRTM 90m Digital Elevation data. As of December 2016,
over 100,000 km of skiing trails have already been mapped. OsmHydrant47 is a
special map showing the position of hydrants, water tanks and suction points,
with the purpose of assisting local authorities and re departments. While
there is an emphasis on visualisation, it allows OSM contributors to map new
hydrants and edit the existing ones. As of July 2016, almost 45000 hydrants had
been added. OpenFireMap48 is an OSM rendering, highlighting ‘re stations,
hydrants, water tanks, and ponds used for reghting (suction points)’. It does
not provide editing facilities directly. e Stamen company in the United States
provides several cartographic variations on the standard OSM map representa-
tions. ese are available for use as web-map layers in other applications. ree
of the most popular web-maps provided by Stamen are the terrain represen-
tation49, the black and white representation50 and the very artistic watercolor
representation51. ere is also a good deal of visualisation of OSM in 3D: one
of the best examples is the OSM Buildings52 JavaScript library for visualising
OpenStreetMap building geometry on 2D and 3D maps. F4map53 is a French
company providing cartography and visualisation services: one of its products
is a 3D visualisation of the world using OSM data. In other types of visualisa-

46 Mapping and the CitizenSensor
tion, Kothic JS54 is an in-development new technology that renders OSM data
‘on the y’ using HTML5 without the need for raster tile images. Mapbox Stu-
dio55 is a suite of free and paid-for tools to produce ‘vector tiles’, which can be
rendered either server-side or client-side, with many dierent customisations
available according to the OSM data being used.
2.6 OSM-based Soware
As mentioned above, the OSM community has created a vast ecosystem of so-
ware tools and services. As is the case with the visualisation of OSM data, it is
not possible to give an in-depth list of soware. We have organised this sec-
tion into three subsections: OSM data editors, OSM-based routing services and
other services.
2.6.1 OSM Data Editors
OSM is an openly accessible spatial database which any contributor can supply
geodata to and whose existing data any contributor can also edit. It is therefore
very important that soware tools be available to support this editing work
for contributors. e OSM wiki contains an extensive list of OSM data editing
tools56 and a comparison of their characteristics. In this section we outline ve
of the most famous and well known OSM editors. e iD editor57 is a Web-based
editor for OSM and is the editor that is integrated into the OSM homepage. e
JOSM editor3 is a Java editor for OSM and is considered an editor for skilled
OSM contributors. It ‘supports loading GPX tracks, background imagery and
OSM data from local sources as well as from online sources and allows’ direct
editing of the OSM data; a number of plugins provide other advanced func-
tions. Potlatch58 is a ash-based web editor for OSM. Vespucci59 is the rst
OSM editor specically developed for small and large Android-based devices;
it provides a reasonably extensive set of editing functionalities, which makes it
usable on the eld by novice and experienced OSM contributors. Merkaartor60
is a desktop-based soware editor for OSM that is available for installation and
use on most operating systems; similarly to JOSM and Vespucci, Merkaartor
provides a wide range of functionalities.
2.6.2 OSM-based Routing Services
OSM-based routing services are soware-based solutions that use the data
in the OSM database for the purposes of generating routing and navigation
solutions. Routing and navigation is possible when objects in OSM have
attributes (tags) that are helpful in solving these problems. e ability to
apply attributes from dierent thematic areas on the same object (such as

A Review of OpenStreetMap Data 47
a road or a street) means that dierent routing applications can be easily
developed.
e Open Source Routing Machine (OSRM)61 is a C++ routing engine for
nding ‘shortest paths in road networks’. It supports car, bicycle and walk modes
and is ‘easily customized through proles’. GraphHopper62 is a company based
in Germany focused on delivering the ‘fastest possible routing algorithms’ and
‘privacy protection’ using open source soware for their customers. eir open
source routing library and server includes elevation data and allows routing
for several dicult vehicle types. e MapQuest Directions API63 is oered
by the US company MapQuest and calculates ‘point-to-point, multipoint, and
optimized routes’. e API can be used by any application, and the directions
are based on OSM data. OpenRouteService64 is a routing service developed by
the GIScience Research Group at Heidelberg University (Germany); it provides
routing capabilities for dierent categories (including wheelchairs users), fea-
tures an advanced graphic interface and is also available in a mobile version.
Kurviger65 is a specialised routing service for motorcyclists, which computes
optimal paths considering the topography of the terrain. It is only available in
German. Cruiser for Android66 is an Android-based mapping and navigation
application. Wheelmap.org67 is an open and free online map of wheelchair-
accessible places. While it is not actually a routing application per se, it provides
information on the wheelchair-accessibility of public places, which is very use-
ful for wheelchair users, by allowing contributors to directly edit OSM to pro-
vide accessibility information. ViaMichelin68 is a ‘wholly owned subsidiary of
the Michelin Group’69; it ‘designs, develops and markets digital travel assistance
products and services for road users in Europe’, and the German version of
their route planner uses an OSM Outdoor Layer visualisation70. INRIX Trac71
is a commercial product for navigation and trac information that uses OSM
data; the application learns the preferences and daily routines of the user, and,
based on the learned activities, makes a daily personalised itinerary with the
anticipated tours and frequently used routes.
2.6.3 Other Services
In this section, we provide some links to other services that use OSM but do not
necessarily t neatly inside our classications. In OSM, nodes that have spe-
cic tags are oen called POI amongst contributors and users of OSM. ere
is no absolute set of tags that qualify as indicating a POI, but usually a POI will
have tags related to amenities, such as buildings, shopping, education or build-
ings with cultural and historical signicance. e OpenPoiMap72 provides a
map-based visualisation of all POI in OSM for any part of the world: POI are
presented as individual layers, which can be turned on or o, and, based on
what visualisation information the map provides, contributors can then edit
the POI data directly in OSM using the links provided on the interface. e

48 Mapping and the CitizenSensor
Places! service73 attempts to present a visualisation of the analysis of patterns
in place names within given countries based on the OSM database for those
countries. For example, Places! tries to nd patterns in the spatial distribution
of places in Switzerland containing the term ‘berg’ or places in the United King-
dom containing the term ‘hill’ in their name. e analysis is performed oine
and updated regularly.
e OSM Analytics74 application recently launched by HOT provides inter-
active functionality to analyse how specic OSM features are mapped in a spe-
cic region. is tool allows the user to select the geographic region of interest
and shows a graph of the mapping activity in that region. It is possible to select
a specic time interval to view the number of newly mapped or edited features
in that period; the map will highlight the matching buildings, as related to this
time interval. is tool is a very useful way to obtain a high-level view of how
OSM developed in a particular region. Finally, the Show-Me-e-Way applica-
tion75 is an interactive web application that displays near real-time edits per-
formed by contributors to OSM. e application loads recent edits and displays
them by jumping to the particular region where the edit was made. is type
of visualisation is possible owing to the fact that very recent edits submitted to
OSM by contributors are immediately available for access by anyone who con-
nects to the OSM API or other services listed in Section 2.6.
2.7 Quality Assurance for OSM
e quality of OSM data is under constant scrutiny by the scientic commu-
nity. e quality of data in OSM is one of the major concerns that industry and
authoritative agencies such as National Mapping Agencies (NMAs), Land and
Cadastral Agencies and other types of government agencies have about OSM
(Olteanu-Raimond et al., 2017b). In practice, there is no single set of metrics
or criteria against which OSM can be measured that will satisfy all users for the
myriad of possible end applications. e quality of the OSM data and suitability
for a particular application, purpose or use case is very much dependent on the
characteristics of the problem being tackled. e OSM community recognises
the importance of data quality, and a very wide range of tools and applications
have been developed to tackle this issue. In this section, we provide some intro-
duction to a small number of these. A comprehensive list is maintained on the
OSM wiki76.
BBBike and Geofabrik deliver the OSM Map Compare tool77, which allows
visual comparison of OSM map layers with other popular mapping systems
such as Google, Bing, HERE, ESRI, etc. e web map interface allows users to
visually compare any region in OSM with the corresponding mapping in the
other popular systems. IGN France (French National Institute of Geographic
and Forest Information) provides a very similar system to Map Compare with

A Review of OpenStreetMap Data 49
their Ma Visionneuse78 application, which allows OSM to be compared with
IGN layers, amongst others; this is particularly useful for comparison between
French web map layers. e OSM Inspector79, also by Geofabrik, provides an
overlay of potential errors or data quality problems onto an OSM map. ese
problems include: very long ways (polylines); self-intersecting ways, polygons
or polylines, which are represented by only one node; and polygons or pol-
ylines that have duplicate nodes contained within them.
Taginfo2 is a very popular Web-based application that displays up-to-date
statistics about the tags used in the OSM database, e.g. which tags are used, how
many times they are used, where a certain tag occurs, etc. Taginfo is particu-
larly useful for nding problems with the keys or values in tags, the popularity
of tags, where specic tags are used and which other tags are used in combina-
tion with them. e use of taginfo to nd problems with tagging relates to its
very comprehensive listing of the ranking of popularity/application of values to
specic keys in tags. is can quickly allow an OSM expert to identify instances
of an incorrect assignment of values in tags that has an overall eect on tag
data quality. Taginfo does not provide any information on errors relating to
geometry or topology. Osmose80, an acronym for OpenStreetMap Oversight
Search Engine, is a quality assurance tool available to detect issues in OSM data;
it is also useful for integrating third-party datasets. It tries to detect anomalies
in the data and then display them on an OSM map, from which contributors
can x or update them. Keep Right81 is one of the oldest quality assurance tools
in OSM. It displays automatically detected errors on the OSM map or in a list
format, and it detects a very wide set of error types, including geometry errors,
topological errors, attribution errors and other general OSM errors.
MapRoulette82 is a Web-based application that proposes challenges to x
errors in OSM. Each challenge represents a set of tasks, and OSM contributors
can x the errors by performing edits in OSM in the usual way. e challenges
vary in diculty, allowing contributors to choose the types of errors that they
feel condent about xing. e xing is very heavily focused on the contribu-
tors’ interpretation of information from aerial imagery. DeepOSM83 attempts
to detect problems in OSM road networks using neural networks. e system
downloads satellite imagery and the corresponding OSM data that show roads/
features for that area. is allows DeepOSM to generate training and evalu-
ation data for the neural networks, which then calculate predictions of mis-
registered roads in OSM.
e Grass&Green project (Ali et al., 2016) asks OSM contributors to cor-
rect tagging or classication of land use features involving grass or green areas.
is application provides a two-screen interface, where an OSM feature is
highlighted on the standard OSM web-map layer and in aerial imagery. e
user (who needs to have an OSM account) must then provide an appropriate
classication for this entity by choosing what he/she believes is correct from
the list of classications: grass, park, garden, forest and meadow. e JOSM

50 Mapping and the CitizenSensor
Validator84 ‘is a core feature of JOSM which checks and xes invalid data’ that
have been contributed to OSM or are being contributed for the rst time. e
validator checks and xes a wide variety of problems, including topological
errors, unclosed polygons and overlapping areas.
Academic research has produced a wide range of quality assessment and
comparison tools for OSM (Ostermann and Granell, 2017). One of the most
recently published is that of Brovelli et al. (2017): this open source soware tool
provides an automated comparison of street network data in OSM with that in
an authoritative dataset. Users of the tool must provide the authoritative dataset
for comparison.
2.8 Games, Leisure and General Public Information
In this nal section of applications for OSM, we describe a mixture of appli-
cations that use OSM for the purposes of games, leisure or general public
information.
‘Collapse– e Division Game’85 is a simulation game based on open data-
sets (including OSM data), created by Ubiso to introduce the environment
upon which the new online action game ‘TomClancy’s e Division’ (for
Windows, Playstation and Xbox)86 is based. e user is the rst person in the
world infected with a virus, and the game realistically simulates the diusion
of the virus until the collapse of society; OSM data relating to health facili-
ties, societal infrastructure and transportation are used in the simulation. e
OSM game Kort87 is very similar to MapRoulette79, with the exception that Kort
drives a gamication approach to OSM error xing. Kort was developed for
usage mainly on mobile devices but also works well on most browsers. For both
solving tasks and checking existing solutions, points (so-called Koins) can be
earned. e goal is to continually rise through the ranks of the high-score list.
Additionally, players are also awarded medals for their eorts. At the time of
writing, there are over 2,000 active players having solved almost 50,000 tasks.
e solutions to tasks must be evaluated and accepted by other users before
they are submitted to the OSM database.
In a YouTube video88, an OSM contributor provides a video-based visualisa-
tion of the contribution of nodes to OSM over the period 2004–2016. Nodes
in OSM that have had more editing activity on them are coloured using a heat-
map approach. is timelapse video and many others listed on the OSM wiki89
provide a very good high-level overview of how OSM has developed since its
inception. e node density map by tyrasd90 provides a static visual overview
of how many nodes are mapped within any OSM region. Lukas Martinelli91
produced a Global Noise Pollution map based on the urban infrastructure
data in OSM for cities and urban areas. GoodCityLife is a group of freelance
researchers in urban dynamics who use OSM to produce visualisations. One
such visualisation is their Smelly Maps92, which uses the underlying OSM data

A Review of OpenStreetMap Data 51
for a city or region to calculate if there is likely to be nasty odours or smells in
a locality. Bahnhof.de93 is the website providing information about railway sta-
tions in Germany; OSM is used as the base layer for the mapping on this infor-
mation website. e ight simulation soware World2XPlane by X-Plane94,95
is also worth mentioning; this soware takes OSM data and converts the data
into scenery for X-Plane. It uses as much information as possible to generate
highly realistic scenery.
3 Conclusions and Discussion
In this chapter, we have provided an overview of the OSM project. As men-
tioned in the introduction, OSM is probably the most famous example of VGI
on the Internet today. Even at the time of writing (during the summer of 2016),
the project continued to grow and expand, with over 2.7M registered contribu-
tors/users and almost 3.4B nodes of data, which made up almost 350M poly-
gons and polylines. Around 37,000 contributors are active in OSM during a
typical month. OSM can certainly claim to be the largest freely and openly
accessible database of geographic data in the world. Indeed its rate of growth
in terms of geographic data and frequency of contributions and editing brings
OSM into the realm of geographic big data (Leonelli, 2014). When one consid-
ers the extended OSM ecosystem of open source soware, data download ser-
vices, data visualisation services, wiki help systems, mailing lists and forums,
OSM serves as a very suitable starting point for any discussion on VGI. Indeed
one could speculate on how VGI would have developed if OSM had been absent
from this space. is chapter has attempted to give the reader who is new to
OSM an introduction to the OSM ecosystem while providing the reader famil-
iar with OSM an overview of where OSM currently stands in the world of VGI.
In the remaining chapters of this book, OSM will be mentioned and dis-
cussed in many dierent ways. In Chapter 4, Touya et al. (2017) address the
challenges of automated mapmaking using VGI as the input data, and the
authors consider OSM as a key source, but not the only source, of this VGI data.
Chapter 2, See et al. (2017) has already indicated that there are many sources of
VGI available today. While OSM is open data and is licensed under the Open
Data Commons Open Database License (ODbL), there are privacy and ethical
issues around the reuse of OSM data. In OSM, one is free to copy, distribute,
transmit and adapt OSM data, as long as credit is provided to OSM and its con-
tributors. If one alters or builds upon the data, then the resultant data must also
be distributed under the same licence. Chapter 6 tackles some of these issues
for OSM and VGI in general (Mooney et al., 2017). In Chapter 8, Antoniou
and Skopeliti (2017) consider how the concept of quality has evolved in OSM
over time through the analysis of the evolution of OSM data specications and
of OSM editors. e very evolution and changes over time to the OSM ecosys-
tem can inuence the quality of OSM data. Related to this theme, Chapter 9,

52 Mapping and the CitizenSensor
by Skopeliti et al. (2017), considers how quality in VGI can be visualised and
communicated eectively, with signicant research work having already been
carried out on this topic using OSM as the case-study. As discussed earlier in
this chapter, OSM has a very exible and easy-to-understand approach to the
contribution of new geographic data or editing of existing data in the OSM
database. Chapter 10 considers best practices for VGI data collection, and Min-
ghini et al. (2017) propose in that chapter that the lack of protocols and the
exibility of contribution is not necessarily a good thing in terms of produc-
ing consistently high-quality VGI data. Chapter 11 (Bastin et al., 2017) consid-
ers VGI data management and suggests ways in which OSM can be integrated
into the so-called Semantic Web, where all OSM’s data would be converted
to Linked Data. Finally, Chapter 13 (Olteanu-Raimond et al., 2017a) discusses
VGI and the role of NMAs, with OSM oen seen as a rival or competitor to the
geographic data services provided by these agencies. As is obvious from this
overview of the remaining chapters of the book, a deep scientic discussion of
VGI is impossible without reecting on and considering the impact and inu-
ence of OSM. is is certainly very likely to continue for many years to come.
3.1 e Future of OSM
OSM’s greatest strength will always be its huge pool of contributors. ousands
of these contributors have collected and generated some of the world’s best
street and topographic data without expensive teams of professional surveyors
or world-class equipment. As the world and the urban and natural environment
change every day, OSM contributors have the ability to depict this changing
world in a map and a database that belong to them. OSM may not yet have the
advanced types of features that Google Maps has– street-view images, multi-
modal navigation, social recommendations, etc.– but it may soon have. Mapil-
lary96,97, which is a service for crowdsourcing street-level photographs using
smartphones and computer vision, has almost 70 million geotagged street-level
photographs at the time of writing. Mapillary shares the open data ethos of
OSM and they can work well together (Juhász and Hochmair, 2016). Very simi-
larly, eorts are in place to link OSM elements with their corresponding Wiki-
pedia pages and Wikidata items. As an example, the WTOSM98 (Wikipedia To
OSM) service developed by the Italian OSM community automatically identi-
es Wikipedia pages that can be linked (by means of tags) to OSM elements.
Mature services such as OpenRouteService provide navigation services based
wholly on OSM’s database. One of the factors in the evolution of OSM over
the past decade or so has been the ability of the project to adapt and expand in
the face of technological advancements in other areas of ICT and Open Source
Soware. Web service access to the OSM database or its mirrors has improved
and is very stable, allowing developers to build an array of applications using
the data directly from the database.

A Review of OpenStreetMap Data 53
ere are some challenges for OSM going forward. ese challenges are
a mixture of factors based on the social and technological aspects of VGI
(Mooney, 2015). Contributors can make edits to the OSM global database with-
out any real controls or moderation at the point of contribution. Despite the
fact that there are many applications available for an a posteriori quality check
(see Section 2.7), as long as edits can be made without initial controls the issue
of OSM data quality will remain a contentious one. Relatively unknown con-
tributors from an unknown crowd supplying geospatial data is a concern to end
users and stakeholders such as NMAs, government agencies and commercial
companies. ere have been many instances in the past where large amounts
of OSM data have been deleted by new or inexperienced contributors. Some
authors have considered the problem of automated detection of instances of
vandalism and of the purposeful deletion of data in OSM (Neis et al., 2012).
Many local OSM communities have long debated the wish and need to imple-
ment tools for checking and approving contributions (e.g. by more experienced
contributors or by the community itself). However, such an implementation
would be clearly against the very same nature of the OSM project, and no for-
mal actions are yet in place in this regard.
Several academic studies have shown that for specic regions of the world,
OSM has reached a very high and mature level of completeness and spatial
accuracy compared to data from sources such as NMAs (Dorn et al., 2015).
One of the major challenges will be to sustain the contributor motivation for
editing and maintaining the OSM database into the future (Budhathoki and
Haythornthwaite, 2012). Every day sees less white space or empty places on
the OSM map. Similar scenarios are being observed in Wikipedia (Jankowski-
Lorek et al., 2016). e task of being an OSM contributor is changing from that
of being the contributor of brand new geodata to OSM to that of map garden-
ing (McConchie, 2016; Sinton, 2016); in this latter case, contributors are not
necessarily involved in contributing new material to OSM but are attending to
the upkeep and update of the existing geometry and attribute data (tags) in the
database.
As geolocation is further embedded into social media, user-generated con-
tent on the Internet, etc., issues of privacy and ethics can be raised (Blatt, 2015),
and the work outlined in Chapter 6 of this book (Mooney et al., 2017), high-
lighting these problems in relation to VGI, will become critical; currently, very
little work has been undertaken by the research community into privacy and
ethics in VGI. In the nal chapter of one of the rst edited volumes dedicated
to OSM, Mooney (2015) advises that the academic community has a signicant
role to play in the future of OSM; through scientic research and investigation,
the academic community is encouraged to feed its results and experiences back
directly into the OSM community and become more closely involved in the
day-to-day workings of the OSM ecosystem. is model has been very success-
ful in the open source soware community, and this can extend to the OSM
world.

54 Mapping and the CitizenSensor
Notes
1 https://www.mapbox.com/blog/redesigned-osm-mapping-guides/
2 https://taginfo.openstreetmap.org
3 https://josm.openstreetmap.de
4 http://wiki.openstreetmap.org/wiki/ID
5 http://wiki.openstreetmap.org/wiki/Stats
6 http://eldpapers.org
7 http://wiki.openstreetmap.org/wiki/Import/Catalogue
8 https://wiki.openstreetmap.org/wiki/Main_Page
9 http://wiki.openstreetmap.org/wiki/List_of_OSM-based_services
10 http://download.geofabrik.de/
11 http://wiki.openstreetmap.org/wiki/Planet.osm
12 http://wiki.openstreetmap.org/wiki/API
13 http://wiki.openstreetmap.org/wiki/Overpass_API
14 https://overpass-turbo.eu/
15 https://mapzen.com/data/metro-extracts/
16 http://wiki.openstreetmap.org/wiki/Export
17 http://wiki.openstreetmap.org/wiki/OSM_XML
18 http://wiki.openstreetmap.org/wiki/Soware/Desktop
19 https://www.geofabrik.de/
20 https://mapzen.com/products/#data
21 http://www.citizensensor-cost.eu
22 http://vgibox.eu
23 http://teachosm.org/en/
24 https://hotosm.org/updates/2016-03-09_200_kids_map_swaziland_for_
malaria_elimination
25 https://hotosm.org
26 http://tasks.hotosm.org
27 http://www.missingmaps.org
28 https://disastermappers.wordpress.com/
29 https://www.mapbox.com/blog/ebola-mapping-progress/
30 http://pierzen.dev.openstreetmap.org/hot/leaet/OSM-Compare-before-
aer-philippines.html#12/11.2197/124.9925
31 http://wiki.openstreetmap.org/wiki/Commercial_OSM_Software_and_
Services
32 https://www.mapbox.com
33 http://www.mapquest.com
34 http://maps.stamen.com
35 https://mapzen.com
36 http://www.camptocamp.com/en/
37 https://www.mapbox.com/blog/nyc-and-openstreetmap-cooperating-
through-open-data/
38 http://mapgive.state.gov/

A Review of OpenStreetMap Data 55
39 http://trimet.org/#/planner
40 https://twitter.com/Gendarmerie/status/691947889103392768
41 http://www.modernisation.gouv.fr/sites/default/files/fichiers-attaches/
ban_cp_150415_en.pdf
42 http://opentopomap.org
43 http://opencyclemap.org/
44 http://hikebikemap.org
45 http://www.opensnowmap.org
46 https://nsidc.org/data/MOD10A2
47 https://www.osmhydrant.org
48 http://openremap.org
49 http://maps.stamen.com/terrain
50 http://maps.stamen.com/toner
51 http://maps.stamen.com/watercolor
52 http://osmbuildings.org/
53 http://www.f4map.com/
54 https://github.com/kothic/kothic-js
55 https://www.mapbox.com/mapbox-studio/
56 http://wiki.openstreetmap.org/wiki/Editors
57 http://wiki.openstreetmap.org/wiki/ID
58 http://wiki.openstreetmap.org/wiki/Potlatch_2
59 http://wiki.openstreetmap.org/wiki/Vespucci
60 http://wiki.openstreetmap.org/wiki/Merkaartor
61 http://project-osrm.org
62 https://graphhopper.com
63 https://developer.mapquest.com/products/directions
64 http://www.openrouteservice.org
65 https://kurviger.de
66 https://wiki.openstreetmap.org/wiki/Cruiser
67 http://wheelmap.org/en/map#/?zoom=14
68 http://www.viamichelin.de/
69 https://en.wikipedia.org/wiki/Michelin
70 http://www.thunderforest.com/maps/outdoors/
71 https://www.engadget.com/2016/03/30/inrix-trac-app-uses-ai-to-learn-
your-driving-habits
72 http://openpoimap.org
73 http://bgrsquared.com/places
74 http://osm-analytics.org
75 https://osmlab.github.io/show-me-the-way/
76 http://wiki.openstreetmap.org/wiki/Quality_assurance
77 http://mc.bbbike.org/mc/
78 http://mavisionneuse.ign.fr/visio.html?lon=3.46539&lat=46.044673&zo
om=15&num=4&mt0=ign-cartes&mt1=ign-scexstandard&mt2=google-
map&mt3=osmfr

56 Mapping and the CitizenSensor
79 http://tools.geofabrik.de/osmi
80 http://wiki.openstreetmap.org/wiki/Osmose
81 http://wiki.openstreetmap.org/wiki/Keep_Right
82 http://maproulette.org
83 https://libraries.io/github/trailbehind/DeepOSM
84 http://wiki.openstreetmap.org/wiki/JOSM/Validator
85 http://collapse-thedivisiongame.ubi.com
86 http://tomclancy-thedivision.ubi.com/game/en-us/home/
87 http://play.kort.ch
88 https://www.youtube.com/watch?v=FdRO-QZaWX8
89 http://wiki.openstreetmap.org/wiki/Timelapse_videos
90 http://tyrasd.github.io/osm-node-density/
91 http://lukasmartinelli.ch/gis/2016/04/03/openstreetmap-noise-pollution-
map.html
92 http://goodcitylife.org/smellymaps/
93 http://www.bahnhof.de/bahnhof-de/Karlsruhe_Hbf.html?hl=karlsruhe
94 http://www.flightsim.com/vbfs/content.php?16301-OpenStreetMap-
Tutorial
95 http://www.x-plane.com/desktop/home/
96 http://blog.mapillary.com/update/2016/05/24/use-mapillary-editing-
OSM.html
97 http://www.openstreetmap.org/user/Blackbird27/diary/38711
98 http://geodati.fmach.it/gfoss_geodata/osm/wtosm/en_US/index_2.html
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