Of course, (vector) tiling is an inevitable phase of any efficient mapping 
system covering large areas. But tiled data representation of the RW objects 
has many drawbacks/limitations especially when it comes to error detection, 
spatial analyses, data volume and so on and this is what I want to comment very 
shortly.
Assume, we understand the same thing when mentioning notions like tile-frame, 
data-tile (shortly - tile), tile grid, tiling and so on. Though, this is risky 
and could cause misunderstandings (just try to find a well-founded tile 
definition, you will be surprised). Anyway, the last point in this related 
article https://www.mapbox.com/developers/vector-tiles/
indicates that a vector tile is an extract from an input object data layer 
(like primary roads, lakes, rivers, buildings.) provided by clipping along the 
tile-frame and restructuring (the data format, compression, languages. here are 
totally irrelevant). Consequently, tiles are mutually independent and are 
adding huge extra fragmentation (to the already fragmented) source data. At the 
same time, most of the spatial analyses and error detection issues require 
large/un-fragmented data (model of the RW objects), except maybe in some 
trivial cases. 
Here are some examples. Assume the task/analyses needs all roundabouts on a 
long primary road or building-layouts in a larger area, or the water surface of 
a large lake or all islands/holes in a river, just to mention some. Because of 
the tiling based fragmentation, the related tile based search/retrieval is 
practically impossible and so is the task. Example gratia, many of the 
islands/holes on a river source data will simply disappear after tiling (if a 
tile-frame edge crosses a hole the corresponding border line will be clipped 
into segments and these will get outer area border roles in restructuring).
Regards, Sandor

P.S. Besides the mentioned limitations, the tiling based fragmentation may 
cause unwanted rendering effects too. If interested, there are more details, 
examples and arguments here
https://drive.google.com/file/d/0B6qGm3k2qWHqTlREaEN3QnF0T0k/view?usp=sharing


-----Original Message-----
From: [email protected] [mailto:[email protected]] 
Sent: 05 September 2015 14:00
To: [email protected]
Subject: dev Digest, Vol 126, Issue 3

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Today's Topics:

   1. OSM QA Tiles for data analysis (Matt Greene)


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Message: 1
Date: Fri, 4 Sep 2015 16:18:48 -0700
From: Matt Greene <[email protected]>
To: [email protected]
Subject: [OSM-dev] OSM QA Tiles for data analysis
Message-ID:
        <caorm0a8cr+g68vffudyafgejxtd7bfq7874sexxdvnai2me...@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

As announced yesterday on Mapbox's blog ( 
https://www.mapbox.com/blog/osm-qa-tiles/), a full-spectrum vector tileset of 
OpenStreetMap is now publicly available for download at 
http://osmlab.github.io/osm-qa-tiles/.

We are generating these tiles with libosmium - Jochen wrote about this just 
this week (
https://lists.openstreetmap.org/pipermail/dev/2015-September/028671.html)
and tippecanoe.

At Mapbox we're using this QA tiles to discover disconnected roads, alignment 
errors, vandalism, etc. This is further facilitated by using these tiles in 
conjunction with Turf.js (http://turfjs.org/), the javascript-based geospatial 
analysis toolset, and Tile Reduce ( https://github.com/mapbox/tile-reduce), 
which can run the analysis across all tiles in a given area.

We're opening this work with the spirit of sharing approaches to improve the 
map - I'd love to get some feedback from members on here regarding this release 
and its potential to provide meaningful QA for OpenStreetMap.

Cheers,
Matt Greene
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