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--></style><title>Re: [Talk-us] Dirt Roads (formerly: Abandoned
railway)</title></head><body>
<blockquote type="cite" cite>
<blockquote>I think the other half of the equation, however, is
actually getting this<br>
fixed across the country. At present it appears to be just a small
number of mappers doing it in their areas; the US is a big place,
and at the current rate it's not going to be fixed any time soon.
Drive-by tools like<br>
MapRoulette are generally a good solution for systemic data
quality</blockquote>
<blockquote>problems, but in this case I think the problem's too big
for that. ... Anything else?</blockquote>
</blockquote>
<div><br></div>
<div>I certainly agree: the US is large, our mapper density here
is low. That makes for slow-to-build map data.</div>
<div>To which Michael Patrick replies:</div>
<div><br></div>
<blockquote type="cite" cite>Imports. The bulk of the roads in the OSM
USA came from the US Census, but fundamentally, the TIGER data base
was primarily designed to support census activities. Besides the the
Census Bureau, there are many other federal agencies such as the BLM,
BIA, DOD, etc. and their congruent state agencies that have available
detailed GIS dataset available. (Continues...)</blockquote>
<div><br></div>
<div>Yes, this is true. Sometimes such (federal, or state, or
local, like a city GIS department) data are quite useful, sometimes
such data (as TIGER) are not useful. OSM's TIGER data, many of
us agree, are noisy, tagged in a uniform way (residential) when that
is less than optimal, and have been discussed many times as "need
to be corrected." Correcting old, noisy TIGER data is
possible, even by a bot, but either way, manually it is huge work (and
we hardly have THAT many willing volunteers), nor have we sustained an
effort to take a systematic approach to entering newer, better data,
which at least partly likely means a carefully written and deployed
bot. Yes, this COULD be done, and may eventually, but it is a
lot of work. Let's consider it a medium-term goal.</div>
<div><br></div>
<div>In short, there are lots of good data out there that might be
imported. But, federal data (while sometimes good, sometimes
bad/obsolete/noisy) often cover only federal land, state/county/local
data are "patchy" and just that: local, and our import
process is detailed and takes time, people, effort, consensus and
dedication. So, results are around what we have here in the
USA: a mishmash of noisy federal data that hangs over much of
the rural, "TIGER desert" areas like an old spider web, and
shiny gems of smaller pockets of attention (local areas, counties,
even states) where dedicated volunteers polish up the data to be
fairly useful (beautiful, routable, commercializable, extendable...).
Good, but we must do better.</div>
<div><br></div>
<div>There is no magic bullet. We want excellent data that are
correct and up-to-date. We have a vast fifty states in which to
do this: the fourth-largest country on Earth by area. Yet,
it has been said many times: OSM in the USA has a relatively low
density of users. Yes, our data get better, but not quickly.
Specific and targeted projects that identify and project-manage
specific sub-areas, with good discussion, consensus and
roll-up-our-sleeves work is what is going to correct this. This
will take time, let's just agree to that.</div>
<div><br></div>
<div>I'd like to see (more) well-identified, well-prioritized,
even-novices-can-do-this-if-they-want such projects emerge and be
displayed in our wiki (or someplace) so that fired-up OSM volunteers
itching to map can "shop along the shelf," pick out a
sub-project that gives chew-and-digest satisfaction (whether it lasts
a day, a week or a month) and results in that warm feeling of
accomplishment (beautiful, high quality data as useful results) once
done. Now, THAT'S a crowd-sourced mapping project! We're
getting there, though in a low gear. Discussions like these,
some identification, some organization, some inspiration, and we will
rev it up faster. Elephants are best eaten one bite at a time.
(A metaphor, not literal)</div>
<div><br></div>
<div>SteveA</div>
<div>California</div>
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