www.viamichelin.com , RouteNet plan
www.routenet.com ,
OpenRouteService http:openrouteservice.org
and Naver – in South Korea
http:maps.naver.com . Brussels in Belgium was
used for most of the tests, but Seoul in Korea was chosen for one of the underground experiments.
When calculating the shortest path in Brussels both Figure 34 Bing and Google do not use a gallery as a short cut, whereas the
others do so; Bing does not show the route, but Google Maps does provide a name, but not a route. The conclusion must be
that some networks do include interior pathways accessed by above ground entrances in their shortest route calculations;
others did not have indoor networks at the time of the experiment.
The underground Myondong shopping centre in Seoul lies beneath a wide and very busy road, with entrances at either side
of the road. This made a good test of routing software: would the calculated path suggest going down, through the shopping
centre, and then up on the other side of the road? Only Naver was able to provide pedestrian routing in Seoul, and it did
successfully find the route; definitely a case of “Why did the chicken survive crossing the road? Because it’s route planner
did know the underground path
.”
Figure 35: Brussels Central Station to Ravensteingallerij using route planner a Bing ,b Google Maps, c Mappy, d Via
Michelin, e RouteNet and f OpenRouteService. From Vanclooster et al 2012.
A further test was conducted in Brussels to see if underground entrances and exits were part of the route planner’s database.
The test route went from the main railway station via an underground connection to the Ravensteingallerij see Figure
35. Only OpenRouteService used all available entrances and underground passageways to progress directly to the gallery.
The other networks were less successful. In many cases, for all route finders, the address translating process to convert to geo-
location on the map were rather simplified. For instance all the route planners used one entranceaddress point for route
planning, no matter what the destination of the query, while railway stations are well known for having several entrances,
owing to their considerable size, These tests show that the data for the route networks in these
Brussels and Seoul examples were incomplete when the tests took place, not that the route finder algorithms per se were
inadequate. All data providers add as much extra spatial data and information into their networks as they can, but update and
discovery take time, and this leads to inadequacy in some locations. Until the last few years most commercial data
providers did not use any VGI or CGI spatial data to update their networks. Now, more are doing so as it becomes very clear
that organisations such as OSM can provide accurate useful information. For example, Google Maps – definitely in the CGI
camp for data acquisition – has started to make increased efforts to use this huge human resource to improve its mapping.
4.2 Crowdsourcing Indoor Geodata
The inside of buildings provides a fairly difficult VGI survey environment because of the non-operation of most easily
understood GPS handheld devices. Other means are becoming possible using 3-axis accelerometers and the 3-axis
magnetometers available in many smart phones and even a piezometer implanted in a Nike running shoe Xuan et al,
2011. But, the kit and skills required are not yet commonplace. Added to the sudden change of scale, and therefore desired
locational accuracy, when moving across the interface from exterior survey to interior mapping there is the question, as
discussed by Vanclooster et al 2012, of address matching with possible multiple entrances to the same building. Lee 2009 has
considered the address interface problem and comments on the orderliness of some exterior addresses and the disorganised
nature of many interiors. One of the most prolific authors dealing with 3D city models
published in the last few year has been Goetz Goetz and Zipf, 2011 and 2012; Goetz, 2012 who discusses the CityGML
standard for storing and exchanging 3D city models, and the progress of modelling, mostly with German examples. As he
points out, different regions in the world have different desires and therefore different standards that need to be met in their 3D
models. According to Haklay 2010 and others OSM is often able to exceed official or commercial data sources in terms of
quality and quantity.
Figure 36: Levels of Detail LoD0 – LoD4 defined in CityGML specification
CityGML defines a number of Levels of Detail LoD for modelling purposes: LoD0 is the plan view of a 2.5D terrain
model, LoD1 is a simple extruded block rendition, LoD2 is textured with roof structures, LoD3 is a detailed architectural
model, and LoD4 is a full “walkable” 3D model. As we have seen, most routing systems focus on a 2D network
specification and cannot cope easily with a 3D model. Goetz proposes:
“the development of a web-based 3D routing system based on a new HTML extension. The visualization of rooms as
well as the computed routes is realized with XML3D. Since this emerging technology is based on WebGL and will
likely be integrated into the HTML5 standard, the developed system is already compatible with most common
browsers such as Google Chrome or Firefox. Another key difference of the approach presented is that all utilized
data is actually crowdsourced geodata from OSM
” Goetz, 2012.
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This would appear to be a very useful contribution to both mapping and routing services, and the use of OSM to provide
much of the ground truth data would prove invaluable in terms of both time and money.
Figure 37: Increase in use of the “building” blue and the “indoor” key for OSM additions from 2007 up to 2011. From
Goetz and Zipf 2011. There are now about 45M tagged buildings in OSM with
approaching 0.5M added every week Goetz, 2012; not all are fully rendered, but many are well above LoD1. This rate of
building increase is currently higher than that for roads, which, although standing at about the same total, increases by perhaps
0.2M per week. See Figure 37, which shows the increase in usage of building and interior keys between 2007 and 2011.
Describing and modelling buildings requires a common understanding of terms and importance. Ontologies have been
developed to allow not only questioning, searching and joining, but also to allow routing and navigation. Yuan and Zizhang
2008 proposed an alternative navigation ontology, but one that did not concern itself about colour or other non-essentials to
navigation. Goetz’s ontology is kept simple, but allows realistic visualization of the building as well.
It would appear from the foregoing that much of the present work on buildings, both exterior and interior is being performed
by OSM crowdsource enthusiasts; many of them very skilled. Who should be the crowdsourced agents for making the
building models? Rosser et al 2012 say that perhaps the buildings occupants are those best placed to make the most
satisfactory models; to them at least, and who has a better claim?
As time passes and the number of building edits shown in Figure 34 increases exponentially, it is likely that much of the
activity will turn to editing and the improvement of the quality of OSM submitted buildings, where in some cases exuberance
may have dominated accuracy. The open source tools to make and use the models have been generated, the skills are present in
some quantity, and the ontologies are defined to allow the buildings to be more than a pretty object; they can become full
network models. 4.3
Indoor Google
Google in its various mapping guises has for many years been the mainstay for providing base maps for the crowdsource
community. The main commercial spatial data providers were for many years Navteq, TeleAtlas and Google. Historically,
Netherlands based TeleAtlas and North American Navteq were used in many navigation applications.
Since Nokia
http:en.wikipedia.orgwikiNavteq bought
Navteq in 2008 and TomTom acquired TeleAtlas, also in 2008 http:en.wikipedia.orgwikiTele_Atlas
, there has been a clear separation where each navigation specialist was responsible for
its own data sets. But in 2011 Garmin bought Navigon AG to capture the iOS and Android navigation apps market where
Navigon was dominant. This was an important step for Garmin because all portable navigation devices had been losing ground
to smartphone navigation apps, a market that Garmin had not entered before. Interestingly in June 2011 Navigon had
introduced a PoI package derived from the crowdsourced OSM project.
Figure 38: Interiors of Guildford Cathedral, showing the indoor online possibilities of Google’s Street View. A full walk about
is very possible. Google Guildford Cathedral
. Google changed from TeleAtlas data in 2009 to individually
conducted “Street View” data gathering for their US dataset. Reasons for this move were said to be the lack of accuracy and
coverage in the United States from the TeleAtlas data according to
http:blumenthals.comblog20091012google-replaces- tele-atlas-data-in-us-with-google-data
. By doing this Google confirmed its intention to remain a major contender for
providing spatial information. Until 2011 neither Nokia, TomTom, nor Google had showed much interest in acquiring a
ubiquitous indoor data capability. Google announced in October 2011 that Street View would now move inside buildings.
Throughout 2011 Google was trialling its data collection and photography systems of interiors with the help of stores and
offices. Meanwhile Street View was being expanded to many new countries; by May 2012 Israel was on the photographic
map and by October Street View coverage had broadened to 11 countries, including the US, UK, Sweden, Italy and Singapore,
and special collections have been launched for South Africa, Japan, Spain, France, Brazil, Mexico, Israel and other countries.
At the same time Street View had been enhanced to provide indoor tours of a number of places, for instance: Russias
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Catherine Palace and Ferapontov monastery, Taiwans Chiang Kai-shek Memorial Hall, Vancouvers Stanley Park, the interior
of Kronborg Castle in Denmark, and Guildford Cathedral in Figure 38.
This interest on Google’s part in interiors and encouraging both Street View but also crowdsourced participation may herald the
start of complete routing systems that do understand 3D buildings, their interiors, the underground passages, and the car
parks that go to make up the modern city. It will then be very interesting to apply Vanclooster’s routing tests again and see
whether the 3D information gathered by VGI, CGI, or PGI means, allows accurate shortestfastest guidance through the
urban maze.
5 WHITHER SPATIAL ONTOLOGIES?
Shanahan 1995 is quoted in Bhatt 2008 as defining spatial ontology as:
“If we are to develop a formal theory of common sense, we need a precisely defined language for talking about shape,
spatial location and change. The theory will include axioms, expressed in that language, that capture domain-
independent truths about shape, location and change, and will also incorporate a formal account of any non-
deductive forms of common sense inference that arise in reasoning about the spatial properties of objects and how
they vary over time
.” The idea that common sense is vital and will be used while
reasoning with and about spatial objects is to be applauded. The understanding and semantics of geographical concepts vary
both between user communities – VGI or PGI based – and need ontological formal languages and structures to represent the
concepts used by any information community. Stock 2008 argues that, as human semantics can be extremely informal in
nature, “Perhaps NSDIs have been too formal and do not account for this human flexibility?”
Berners-Lee 2005 tried to estimate the effort involved in ontology creation. The table in figure 39 shows his results.
Figure 39: Berners-Lee 2005 Total cost of ontologies. This table assumes ontologies are evenly spread across orders of
magnitude, committee size is reasonably represented by logcommunity, time is community2, and costs are shared
evenly over the community. The scale column represents the community size from which the committee size and costs are
calculated. The general conclusion is that the cost of large efforts is huge, but it is borne by and benefits an even larger
group and so the cost per individual is very, very small indeed. A large effort is required to build an ontology for a particular
domain. This is a disincentive for groups who might create ontologies to suit their own circumstances. Also, why should
there be a single ontology, and why in English, which might act as a constraint to some non-English semantic ontologies
http:en.wikipedia.orgwikiLinguistic_relativity ? Perhaps
variety and semantic translation and interoperability would be a better approach? The figures in the table argue strongly in
favour of large group participation and might be thought to be a good prospect for professional crowdsourcing, as with standards
and software open source projects? GIS has used standard forms with attached labels since it
appeared as a discipline Evans, 2008. Researchers consider a good representation of the world assumes: there is only one type
of object in a given space; several in one space would be identical, and that everyone would use the same descriptive
terms and labels. In practice this is not so; we are all our own experts with different mental formulations of the world around
us brought about by different experiences and upbringing in society.
Despite this problem with diverse groups OSM has built an open ontology from scratch Dodge, 2011. Many of the people
most actively involved in the ontological development of OSM, whilst skilful and self-motivated, do not have a cartographic
background. This often leads to lively debates about the ontology for OSM and some new thoughts about how maps
should look and work. The difficulties are usually ironed out by social negotiation to determine the best understanding of the
objects in question. As time passes so OSM’s ontology is becoming more complex and useful, but partly as the result of
considerable mental anguish amongst the contributors and editors.
5.1
Vagueness
Much time and effort is involved in defining ontologies of spatial objects; possibly even more in recognising, defining and
categorising the objects in a formal manner so that they can enter an ontological description. A major problem of the real
world is that although an object is definitely present it can rarely be described by a single term, or multiple occurrences by the
same exact definition. Our desire to classify is confounded by vagueness of description.
A traditional example of this problem is: when does a pond become a river, become a lake, become a sea Third, 2008?
Scale is one factor, form is another, salinity might be a third, and so on. Our descriptors are unclear and suit our thinking,
perhaps. This problem of vagueness could be overcome by choosing to ignore insignificant parts, but insignificant is also
vague and undefined in most cases. Humans tend to skip over insignificant
deviations. A serious problem in developing an ontology is to define the terms to be used within it.
Bennett 2008 discussed the standpoint theory of vagueness and showed how apparently impossibly difficult semantic
problems – is this a river or an estuary? Is this a heap or merely a pile? – could be expressed using supervaluation semantics
Bennett, 2008 to enable vague human language to be logically interpreted by a set of possible precise interpretations
called precisifications, providing a very general framework within which vagueness could be analysed within a formal
representation and handled by computer algorithm. Implementation of this process is not at all trivial, despite the
human ability to make instant judgements. For instance processing geometric shapes to provide qualitative shape
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but is not precise, nor is it complete. There are many ways of storing data, here on paper or in a GIS, and several ways of
structuring the data using a variety of syntactic and schematic models. What is needed, as proposed by Stock and others listed
earlier in this text, is the ability to integrate models based on global schema and perhaps to use Bennett’s standpoint theory to
help remove vagueness from the mix. Mary McRae, until 2010 working with OASIS Organization
for the Advancement of Structured Information Standards
, is well known for a presentation containing the slide “Standards
are like parachutes: they work best when theyre open”, possibly derived from LE Modesitt Jr, or was it Frank Zappa, or
Elvis? The point is well made, however. Open standards are essential if people are to be willing to use them. They must be,
according to OSGeo, freely and publicly available, non- discriminatory, with no license fees, agreed through formal
consensus, vendor neutral, and data neutral. Note that open standards does not mean open source. Standards are usually
documents; sources tend to be software. See the paper from OSGeo on this subject – Open Source and Open Standards at
http:wiki.osgeo.orgwikiOpen_Source_and_Open_Standards .
The OGC recommends many geospatial standards to users, including GML Geography Markup Language as its base. See
http:en.wikipedia.orgwikiGeography_Markup_Language .
GML is the XML grammar for expressing geospatial features. It offers a system for data modelling and as such is used as a basis
for scientific applications and for international interoperability. It is at the heart of INSPIRE Infrastructure for Spatial
Information in the European Community, the European initiative, and for GEOSS Global Earth Observation System of
Systems
. See http:inspire.jrc.ec.europa.euindex.cfm
and http:www.earthobservations.orggeoss.shtml
. New standards from OGC are continuing, with community-specific application
schemas introduced to extend GML. GML 3.0 is a generic XML defining points, lines, polygons and coverages. It extends GML
to model data related to a city using CityGML for representation, storage and exchange of virtual 3-D models.
There was a workshop in January 2013: CityGML in National Mapping
. See http:www.geonovum.nlcontentprogramme-
workshop-national-mapping .
Other standards include: the Web Feature Service WFS, for requesting geographical features; the Web Map Service WMS,
for requesting maps using layers, to be drawn by the server and exported to the client as images; and the Styled Layer
Descriptor
SLD which provides symbolisation and colouring for feature and coverage data.
The OGC is an international organisation, but so is ISO, and both generate standards for the geodata community to use. The
OGC was started with mainly industrial, commercial and some research membership, whereas ISO, in the form of Technical
Committee 211 ISOTC211, was instituted as a completely independent body with members delegated by participating
nations, usually but not always from appropriate national standards bodies. Both operated in the same field but,
fortunately, for many years OGC and ISO have cooperated closely in standards specification to the benefit of both.
Recently ISOTC211 has published the text for standard 19157, Geographic information — Data quality
, to specify standards for ensuring that quality of geographic information can be
implemented, measured and maintained. The data quality elements consist of: completeness of features; logical
consistency , adherence to data structure rules; positional
accuracy within a spatial reference system; thematic accuracy
of quantitative and qualitative attributes; temporal quality of a time measurement; usability element based on user
requirements; and lineage: provenance of the data. Interestingly, all but logical consistency need to be verified by some ground
truth action. The standard proposes that different methods of sampling see Figure 41 will be necessary for different data
types.
Figure 41: Choosing a sampling strategy for quality checking by logical feature or by areal selection ISOTC19157.
There is flexibility as to which strategy is chosen based on either a complete population survey, probabilistic or
judgemental sampling procedure. The choice depends on the data being tested.
6
ACCURACY, QUALITY, AND TRUST
Figure 42: MODIS top and GLC-2000 bottom satellite images covering a 20km x 20km area around the UK town of
Milton Keynes, showing automatic classification by different but standard approaches, to indicate similar land cover classes.
In both images deep pink is cropland or natural vegetation, red is urban and built up areas.
Volume XL-1W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany
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Volume XL-1W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany
417
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Volume XL-1W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany
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Are VGI contributors ever going to desire to exploit fully the geospatial standards available; or indeed to have any interest in
them? How far can loose knit organisations like OSM impose structure and completeness on their members; and, should they?
The evidence from this review would suggest that the majority of the work is done by competent committed people who do try
to produce completed maps, with full attribute sets, and careful editing. They may be unpaid, but they appear to be anything but
uncaring And, they are fast. There seems to be no question that timely update of features that interest them is a major strength
of the VGI mapping process. The numbers of VGI participants who are involved in any
particular project can be absolutely staggering, even if the distribution of effort means only relatively few do most; a few
thousand
or so perhaps? Raymond 2001 discusses what he calls Linus’ Law Linus Torvald, of Linux. He says in the
Cathedral and the Bazaar pp14-18: “Given enough eyeballs,
all bugs are shallow .” In open source developments, many
programmers are involved in the development, scrutiny, and testing the code, so software becomes increasingly better
without formal quality assurance procedures. In mapping, perhaps this can be translated into the number of enthusiastic
contributors that work on a given theme in a given area Haklay et al, 2010.
Most bazaar dwellers including most of us, dear friends are either not aware or at most partially aware of the standards we
are trying to implement, but the web software tries its best to prod us into good behaviour, so no problem really? Will they
map the bits others can’t or don’t want to reach? Almost certainly. Will they do things we, the supposed professionals
have not thought of? Oh yes, but not always for the best, perhaps. Those rural areas are a real problem if one is thinking
of backing up a national mapping organisation with VGI help. Volunteers are colourful and bumptious and the results a bit
prickly, but if their enthusiasm adds value – by no means necessarily only or at all in monetary terms – and is reasonably
accurate, then why not? Volunteers have a good editing record as seen in all the
community projects using OSM. Is VGI a major opportunity for national mapping agencies? Yes
6.2
Quality in VGI
Figure 51: from Grira et al 2009, volunteer freedom of interaction compared with authoritarian constraint.
The relationship between the actors in mapping is illustrated on a theoretical basis in Figure 51, taken from Grira et al 2009.
The graph in Figure 51 can be used to position the trends, tools, products, and processes in geospatial activity. Grira invoked
two axes: one from authoritarian to volunteered, which might be called a power axis, where many people, volunteers
individually have little power, but a few organisations, national mapping have considerable authority but also considerable
constraint on their actions. The other axis is one of capability: where full capability is exhibited by volunteers in the form of
mashups developed for a disaster mapping site perhaps, using open source software, considerable ingenuity and a large user
base; and the other end of the same axis would, amongst other consist of internet surfers viewing contributed maps. The
national mapping agency lies to the right in the diagram, including both the highly trained surveyor in the field
influencing changes in a map, and in the constrained and limited corner print shop, where the map is printed correctly, or perhaps
not. Grira considered there were three gaps threatening the success
of volunteered efforts in relation to institutions: − A normative gap – the difference between quality expected
by end users and quality standards developed by organisations, for instance ISO 19115 on metadata
compliance. Very, very few volunteers will have looked at, let alone read and understood the specification.
− A technological gap – GIS or map servers provide geographic information; it may well be an uphill struggle
for users or volunteers to upload feedback and comments about data quality. In the age of VGI, institutions that
supply mapping will need to reconsider their role in relation to users.
− A cognitive gap – volunteers need to be able to use less static types of metadata, and introduce their own
terminology. At the same time producers must realise that user perception of the meaning of quality is often confused
with that of accuracy see section on trust later in this paper.
Figure 52: From Cooper 2011. African based 2D VGI taxonomy, and the advent of the crowdsourced SDI.
Cooper et al 2011 took a far more practical approach to the problem of a 2D taxonomy of VGI, with one axis called
Determination of Specifications instead of Power, and the
other axis Type of Data instead of Capability, and the entries are real web sites rather than theoretical activities, but the principles
of construction are the same.
At the top right in Figure 52 is an as yet probably non-existent, possibly national, perhaps international, crowdsourced SDI
where users contribute data according to tight specifications from the SDI custodian, who would then subject the VGI to full
quality assurance processes. This concept has become a distinct possibility as open source software platforms become both more
Volume XL-1W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany
420
integrated and users more sophisticated. In the same quadrant OSM can be found: a crowdsourced product but with less
draconian specifications. In the lower right quadrant are regional or national point location databases for Points of
Interest
, such as Precint Web, a South African crime mapping site. The lower left quadrant contains sites which require
minimal custodial activity and contain location data – such as the Google Panoramio site that holds photos for Google Earth,
without much location editing or content checking, and hence fairly prone to error. The top left quadrant is effectively empty
as fundamental data sets Base data are very unlikely to be held by and for an individual user.
National mapping agencies provide data of generally higher quality than VGI output, as they have better trained staff, better
kit, they understand and are concerned about standards, and survey is their job of work. But, national responsibility for
national coverage at a particular range of scales might result in significant delays before they can update data in certain areas.
Most surveys keep a record of known changes that have occurred on map sheets and hope to update the maps when these
changes have reached a specified threshold level. This requires the agency both to notice change has taken place, presumably
by aerial photo survey or ground detection, and is quite costly. If at the very least the public could be persuaded to form part of
the notification process, then VGI might be the best available method to document changes when they happen and
simultaneously result in revision requests being submitted to the relevant agency. A stage more helpful, but risky perhaps to the
fundamental SDI, would be VGI data gathering and mapping initiatives, heading towards the fully crowdsourced SDI region
in Figure 52. Poor quality data might result from VGI, but it might also be produced by commercial contractors. In all cases
– VGI, internal or contracted – systems are needed to check the data quality, presumably to ISO 19157 and other standards. The
provision of metadata to ISO 19115 standard?, peer pressure and review should keep VGI contributors on the straight and
narrow. If it does not, then software tools are being developed to assess the quality aspects of imported data and to ensure their
consistency with the national depository. These tools are not yet fully functional see previous sections in
this paper but are showing promise in the research laboratory. ISO 19158 on Quality Assurance of Data Supply Jakobsson,
2011, now issued as a published standard 2012, is a major attempt to ensure validation of data input to measurable quality
standards. The standard allows three different levels of accreditation to be certified, and these would pass with the data
to all user generated output. It will be particularly useful for assessing data transfer potential between countries for the
INSPIRE programme within Europe. Whether the ISO 19158 specification is framed sufficiently widely to be able to be used
to certify VGI data – even data as robust as OSM data seems to be – is an interesting question. Yet this goal must be achieved if
VGI data is to enter the marble halls of NSDIs. Perhaps NSDIs should recognise levels of trustworthiness for
data, rather than set absolute standards? The question of how to assign trustworthiness indicators to datasets that do not meet all
standards requirements and whether this might be a solution where data is needed but better is not yet available, is
considered later in this paper. 6.3
Trust in VGI
User trust is different in form from agency trust as the user has different expectations and is concerned about different aspects
of VGI than the agency. The former wants thematic accuracy most of all with some, possibly mostly, topological regard for
locational consistency, whereas the agency tends to reverse this order, or even insist on both. Goodchild 2009 considered
“uncertainty regarding the quality of user-generated content is often cited as a major obstruction to its wider use
”, and considered that crowdsourcing ventures should always publish
assessments of quality. With the advent of the appropriate trustworthy ISO standards perhaps this has now become
possible? Stark 2010 tried an address matching method to asses
accuracy, quality and trust. The Swiss canton of Solothurn had over 90,000 geocoded addresses acting as the reference set
based on national cadastral survey data. If people using an address matching service are going to trust it then the address
location displayed must point reasonably closely to the mailbox location on map or photo. Goodchild 2007 thought failure in
achieving good locational matches to be a major vulnerability to acceptance of a VGI approach to generating location data.
Figure 53: Part of canton of Solothurn, from Stark 2012. The cadastral map at the top shows correct house locations. At the
bottom are the attempts by Google Maps, Bing and Yahoo Maps to locate addresses on an air photo background, with the
reference locations displayed. Stark used Google Maps, Bing and Yahoo Maps to locate the
addresses, and then compared these locations with the cadastral survey derived information see Figure 53. Over 95 of the
addresses were found for all three open WMS geocoders but the locations given were quite suspect in some cases. Figure 53
demonstrates a particularly poor area, where Bing and Yahoo
Volume XL-1W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany
421
Maps linearly road. Google
probably beca shows the dist
Figure 54 Dis the whole can
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6.4 Do
