What is the typical positional
error in a street centerline database? The following maps show the
drive path (broken black line) of a VITAL test vehicle superimposed
on two test data bases (red).
red base map lines in this illustration are from vendor Knopf Engineering
(Visalia CA), which claims an accuracy of about 0.6 metres, 55% of the
time. Driving eastbound and westbound down a major artery (horizontal
line), the vehicle track straddles the centerline, as it should.
When the vehicle takes U-turns on side streets, the correspondence between
the vehicle path and database is striking. On the freeway (oblique
parallel lines left of center) the vehicle path runs almost exactly down
the centerline of the northbound and southbound carriageways. If
all street network data were this accurate, interoperability problems would
reference database with the same drive path as above, significant positional
errors are evident in the database. Note the gross generalization
of the ramp off the northbound carriageway of the freeway. The grid
interval is 200m.
This database is more typical
of the commercial offerings currently on the market. In fairness
to these map database vendors, they serve a different market, and their
price is more than an order of magnitude below that of the engineering-grade
Disagreement the Interoperability
Each commercial database is
in error by roughly 10-50 metres in most cases. The errors at a given
place are of different magnitudes and directions, because vendors use different
methods (e.g. base maps from various sources, aerial photography, GPS)
to construct and to update their data.
A graphic overlay is a simple
yet dramatic way to illustrate the extent of disagreement, and the kinds
of locations where disagreement is most likely to occur. In the illustrations
below, data bases for a section of Goleta (suburban Santa Barbara) are
overlaid in pairs, one in red, the other in green. Discordances of
up to 100 metres are routinely observed
in some neighbourhoods, while in other areas there is better agreement.
In the case of Map 5 (B vs F), one is known to be derived from the other;
this is evident from the close agreement.
|It is fair to
argue that when two maps disagree, one or both contain error. Examining
a small section of one of the above maps closely, we find two types of
These are the visible errors
that are immediately evident from the overlay maps. In addition there
are disagreements on the spellings of street names and aliases,
prefixes and suffixes, address ranges, link connectivities
and attributes such as the number of lanes.
error of inclusion/exclusion:
streets that appear in one data base and not the other
Origins of Disagreement
Disagreement originates due
In addition there are always
instrumentation and human errors.
Lack of standard control procedures
in surveying engineering plans for road construction are often laid out
with reference to a local ground control station, but there is no immediate
need from an engineering viewpoint to tie those station coordinates into
a wider civic, state or national coordinate system. As a result,
maps develop piecemeal, and the components are subsequently collaged with
inevitable stretching and twisting.
Scale requirements data may
be gathered from small scale (1:100,000) maps or aerial photography, for
a general purpose survey; but those data are inappropriate for navigation
or other applications that require large scale (1:25,000) scale surveys.
can be studied by identifying a set of points in one data base, finding
the corresponding points in the other data base, and measuring the vector
or positional difference between the two points. The easiest points
of correspondence to study are intersections. They can be automatically
identified with reference to the names of the intersecting streets (the
process is not error-proof see our comments on the Cross-Streets
In the figures below, intersections
have been identified automatically within a small neighborhood in Goleta,
California. The databases are in red and green, the vectors in black.
If one database can be considered
true (usually by virtue of having been surveyed at a larger scale), then
the vectors are a measure of the distribution of error in the other
|The set of black
vectors may be considered a sample of a vector field, i.e. at every point
on the map there is a local error, of which the yellow vectors represent
a sample. At VITAL we are trying to characterize error fields
in different ways. If the magnitude and direction of error is consistent
in one portion of the map, it would suggest that that area was surveyed
or digitized at the same time, by the same operator, with a constant error.
Once we have a satisfactory means of modelling error, we can consider the
error at any point to be a function of two components: (a) systematic error
as characterized by the field, and (b) random error at the point.
Error due to (a) can be corrected, leaving only random residual error.
Two of our technical
reports (Church et al, 1998; Funk et al, 1998) describe our most recent
efforts in error modeling.