Awesome Jon,
We are all suffering of lack of sleep. We all need to be careful about this.
Great stuff. And quite important to assure fiability of our Navigation products.
It greatly help.
cheer
Pierre
De : kusala nine <[email protected]>
À : Pierre Béland <[email protected]>
Cc : hot <[email protected]>
Envoyé le : Mercredi 29 avril 2015 17h34
Objet : Re: [HOT] Nepal major road validation
hi - I'm still ploughing through the UN road distance map. It's good because
it's shown up some misclassifications but mostly placename differences and
incorrect locations. I've covered most of the high priority area and the good
news is that the distances largely agree with the OSRM distances even in some
of the remoter areas. I'm going to push on with some of the other areas and try
to correct placename locations and road connectivity as I find them. the table
below is the North/East section of the UN diagram. I have some data from the
west and will cover the higher priority areas first. Generally though the road
connectivity looks pretty good :-)
Jon.
kathmandu dunche 118 (117)kathmandu banepa 26 (30)banepa panauti 9 (6.8)
dhulikhel nepalthok 50 ? banepa lamidanda 19 (20) - lamidanda =
lamidadhalamidanda dolalghat 12 (11) dolalghat chautara 25 (26)dolalghat
lamosangu 19 (24)lamosangu kodari 37 (34)lamosangu charikot 18 (56) - are we
missing a more direct road here?charikot tamakosi 18 (19) tamakosi?(duplicate
placename - 27.6188, 86.0788)charikot ramechhap (airport) - 71 (57) [issues
with places marked in wrong places. kirnetar, tamakosi, ramechhap]
naubise galchhi 22 (23) - galchhi = gardo kholagalchhi malekhu 20 (22)malekhu
mugling 40 (37)
On Tue, Apr 28, 2015 at 1:22 PM, kusala nine <[email protected]> wrote:
Hi - just a bit more info on the validation on roads. I concentrated on Task 6
and got the following results from OSRM vs the UN document. OSRM are in
(brackets). All distances in km.
Kathmandu - Naubise 25 (30)Naubise - Palung 37 (43)Palung - Simbhanjyang 15
(18)Simbhanjyang - Bhainse 41 (41) note: Bhainse = Bhaise Dovan on
geonames...Bhainse - Hetauda 11 (12)Hetauda - Bharatpur 77 (76)Palung -
Kulekhani 21 (35) I think Kulekhani not yet fully captured and actual location
looks vague to me.
I'm using this to focus on digitising some of the Task 6 squares to get the
main road distances working. The area around Naubise may be dubious as two of
the distances are greater in OSRM. Please note that, as I said before
Kathmandu-Hetauda = 129 on the network map and 221 on the triangle map (unless
I'm suffering from lack of sleep??). Anyway, I'm moving on to task 3 (most of
task 5 is outside the area of the network map)... Hope this helps. I will also
keep digitising roads in task 6 according to these results.
Jon.
On Tue, Apr 28, 2015 at 11:11 AM, kusala nine <[email protected]> wrote:
I agree - a good methodology is to use the smaller tree diagram to validate in
dividual distances and look at where they differ significantly. The UN doc
itself is inconsistent. If you look at Kathmandu to Hetauda the tree diagram
distances don't add up to the distance in the triangular table (89km vs
211km!). The tree distances are much closer to the OSRM distances and you can
work on smaller ones. I managed to validate the distances within task 6 last
night fairly quickly. I'll write the results up and post them on later today.
Jon.
On Tue, Apr 28, 2015 at 11:02 AM, Pierre Béland <[email protected]> wrote:
Great
>From your table, I see many significative differences. Taking shorter segments
>would help to validate further I think.
cheers
Pierre
De : kusala nine <[email protected]>
À : hot <[email protected]>
Envoyé le : Mardi 28 avril 2015 1h47
Objet : Re: [HOT] Nepal major road validation
I did quick comparison just using names from the distance calculator from
kathmandu (results below) but now moved onto checking using the smaller network
diagram. this lets you concentrate on areas which are in the current task
manager tasks. Just finishd Task 6 last night and they look pretty good
covering Kathmandu, Hetauda, Bharatpur, Palung and Bhampali. I've since
digitised roads in a couple of key tiles based on km distance discrepancies and
when I've finished those I'll move on to task 3. The triangular distance table
is good but points to a lot of discrepancies in the longer distances which are
out of the target zones. I'm happy to digitise the triangular table though if
anyone wants to try the URL method to identify areas of difference. I'm using
NGAs geonames service to identify differences in place naming - what should we
do when there's a difference? e.g "Bhainsie = Bhaise Dovan". Can I attribute a
synonym?
|
place | OSM | UN | difference | Comments… |
| baitadi | 842 | 1168 | -326 | |
| biratnagar | 396 | 549 | -153 | |
| chandragadhi | 473 | 624 | -151 | |
| terhathum | 505 | 654 | -149 | |
| ilam | 538 | 685 | -147 | |
| dharan | 403 | 545 | -142 | |
| birganj | 135 | 276 | -141 | |
| hetauda | 82 | 221 | -139 | |
| rajbiraj | 317 | 456 | -139 | |
| gaighat | 314 | 452 | -138 | |
| dhankuta | 467 | 595 | -128 | |
| janakpur | 295 | 378 | -83 | |
| tulsipur | 401 | 441 | -40 | |
| narayanghat | 133 | 144 | -11 | |
| bhairahawa | 274 | 279 | -5 | |
| dhunche | 115 | 117 | -2 | |
| dhulikhel | 31 | 32 | -1 | |
| lumbini | 300 | 301 | -1 | |
| trishuli bazar | 69 | 70 | -1 | |
| salyan | 504 | 503 | 1 | |
| kodari | 116 | 113 | 3 | |
| pokhara | 202 | 198 | 4 | |
| chautara | 87 | 82 | 5 | |
| jiri | 188 | 176 | 12 | |
| butwal | 272 | 257 | 15 | |
| tansen | 311 | 296 | 15 | |
| Birendranagar | 595 | 575 | 20 | |
| dipayal | 836 | 816 | 20 | |
| mahendranagar | 706 | 684 | 22 | |
| dhangadhi | 676 | 650 | 26 | |
| nepalgunj | 525 | 499 | 26 | |
| taplejung | 887 | 835 | 52 | |
| gorkha | 197 | 140 | 57 | |
| dailekh | 664 | 605 | 59 | |
| krishnagar | 831 | 334 | 497 | |
| baglung | no route | 271 | | Baglung not connected - closest major place is
pokhara |
| Barhabise | no place | 111 | | no such place. Closest OSM place is Mangalsen
by wikipedia's coordinates. |
| kakarbhitta | no place | 618 | | not inOSM. Closest is charali = 1036km
(charali-kakarbhitta=11km) |
| sindhullmadhi | no place | 387 | | still looking…. |
On Tue, Apr 28, 2015 at 12:53 AM, Andy Anderson <[email protected]> wrote:
Hi, Andrew,
The documentation says “The results are network distance, i.e. travel-time, in
10th of seconds.” It’s hard to translate that into distances without the speeds
assigned to road segments. If one knows the type of road and what the “common”
speed is for that type of road one can make an estimate, but it could still be
quite far off. Do you have any guidance on how to get road segment information?
If one needs to download the road data itself, then the length of the segments
should be there, too.
— Andy
On Apr 27, 2015, at 6:44 PM, Andrew Buck <[email protected]> wrote:
> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA1
>
> The OSRM distance table calculator is documented here...
>
> https://github.com/Project-OSRM/osrm-backend/wiki/Server-api#distance-ta
> bles
>
> Basically you feed it a url with a bunch of lat/lon pairs are
> parameters (say for the 10 largest cities in the area) and then it
> creates a 10x10 table of the distances from every city to every other
> city. So we can duplicate the table in the top right corner of the
> PDF, we just need someone to get the lat/lon of each of those cities
> and feed them into the api call documented above. If our distances
> are signifigantly greater than the PDF distances listed for any
> entries then that probably indicates a problem in our data on the road
> between those cities.
>
> - -AndrewBuck
>
>
> On 04/27/2015 07:57 AM, Pierre Béland wrote:
>> http://un.org.np/node/10028, shows road distance of major cities
>> from kathmandu. Look at the pdf files that shows distance of
>> various road segments. Any volunteer to use this data and compare
>> with OSRM's distance matrix tool? If distances are significantly
>> different, this would indicate missing connecting roads or wrong
>> tag like path.
>>
>> Pierre
>>
>>
>>
>>
>>
>> _______________________________________________ HOT mailing list
>> [email protected] https://lists.openstreetmap.org/listinfo/hot
>>
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