#AAG2015 presentation on OSM attribute inconsistency and semantic heterogeneity

An intrinsic approach for the detection and correction of attributive
inconsistencies and semantic heterogeneity in OSM data
Martin Loidl | martin.loidl@sbg.ac.at
Stefan Keller| sfkeller@hsr.ch
AAG Annual Meeting – Workshop OpenStreetMap Studies
Chicago, April 24th 2015

 OSM bottom-up community approach
 Rudimentary data model and attribute structure (tagging scheme K = v)
 Attributes: recommendations ≠ conventions ≠ formalized standard
 No restriction of tag usage and definition
Problem Statement
2
http://www.openstreetmap.es

 Within one way
 Within a succession of ways (e.g. street)
Attributive Inconsistencies
3
highway = motorway
name = Kennedy Expressway
bicycle = yes
highway = motorway
ref = I 90
highway = motorway
name = Fisher Freeway
ref = I 90
highway = motorway
ref = I 90

 Different (correct) description for one and the same entity
 Specific to crowd-sourced data (≠ authoritative data follow
strict specifications)
Semantic Heterogeneity
4
highway = cycleway
foot = designated
width = 3
highway = path
bicycle = designated
foot = yes
highway = footway
surface = asphalt

 Considering attributive inconsistencies and semantic
heterogeneity is relevant for …
 Visualization (data rendering)
 Descriptive statistics (classification)
 Spatial analysis (e.g. routing)
 Improve results through
 Harmonization (remove semantic heterogeneities)
 Correction through estimation (gaps, inconsistencies)
Relevance
5

 Spatial data quality
 Standards (e.g. ISO 19157 = harmonization of
multiple preceeding standards) and extensive
body of literature  of limited use for OSM data
 Quality asssessment of OSM data
 Primarily focusing on positional accuracy and
geometrical completeness
 Reference data set and/or descriptive
statistics
 Comparable little work on attribute quality
Data Quality
6
Haklay 2010
Hochmair et al. 2015
Barron et al. 2014

 Why an intrinsic approach?
 Extrinsic approach requires reference data set,
which ideally has:
 Same geographical coverage
 Same data model and attribute structure
 [Koukoletsos et al. (2012): multi-stage process
to deal with it to a certain extent]
 Quality of reference data set (authoritative data
doesn‘t necessarily imply better data!)
 Data often created for very different purposes
Quality Assessment
7
Elsbethen (Austria):
authoritative data –
OSM data

 Exclusively based on respective data set (data-centered
approach)
 Makes use of:
 Redundancy
 Inherent logic, functionally related attributes
Intrinsic Approach
8
Translation into query
statements
highway = * surface = *
tracktype = *

Case Study Area
9
 4,600 km² in Austrian-Bavarian
boarder region
 ~ 22,600 km total network length
 Rural and urban areas
 Data preparation
 Extraction from OSM Database
(April 1st 2015)
 Conversion to topological correct
graph (edge-node) in GeoDB

Major Road Network
10
 Major road = motorway, primary, secondary
(incl. links)
 Consistent for road category (highway = *)
 Makes features mappable = primary
intent/purpose of OSM
 Attributes incomplete (n = 11,951 segments)
 name = *: 64.6%
 surface = *: 22.93% [ can be estimated: asphalt]
 maxspeed = *: 72.19%
 lanes = *: 57.86%
 Rather an issue of completeness than of
inconsistency and heterogeneity

Local Road Network
11
 Majority of ways in OSM
 Differences in terms of attribute
quality (existence, consistency etc.)
 Relevant e.g. for active modes of
transport (cycling, hiking etc.)
 In many cases more extensive
(spatial coverage, attribute details)
than authoritative data

 Define set of logical/legal contradictions
 Connect to corresponding tags
 Tag specification according to Wiki
 Query the dataset for contradictions
Attributive Inconsistencies
12
approx. 1 from 1,000
("tracktype" = 'grade3' or "tracktype" = 'grade4' or "tracktype" = 'grade5')
and "surface" = 'asphalt'

 Distribution of inconsistencies:
 Regional diversity (national laws?)
 Spatial clusters (local mapper/communities?)
Spatial Particularities
13
highway = residential
maxspeed = 80

 Correction without ground truthing = estimation
 Quality of estimation depends on number of functionally
related attributes
Correction of Inconsistencies
14

 How to map a mixed foot-/cycleway in OSM?
Heterogeneity
15
http://www.stadt-salzburg.at

 How to map a mixed foot-/cycleway in OSM?
 Co-existence vs. “tag war”
 Credibility and reputation (Flanagin & Metzger 2008)
Heterogeneity
16
("highway" = 'footway' and ("bicycle" =
'designated' or "bicycle" = 'yes' or
"bicycle" = 'official'))
OR
("highway" = 'cycleway' and ("foot" =
'designated' or "foot" = 'yes'))
OR
("highway" = 'path' and ("foot" =
'designated' or "foot" = 'official') and
("bicycle" = 'designated' or "bicycle" =
'official'))
OR
("highway" = 'track' and ("foot" =
'designated' or "foot" = 'official') and
("bicycle" = 'designated' or "bicycle" =
'official'))
669 segments
1,202 segments
2,655 segments
73 segments

 Different (correct) views on same entity
Heterogeneity
17
highway = cycleway
surface = asphalt
ref = BGL 3
foot = designated
segregated = no
Last editor: j_cook
highway = path
surface = asphalt
foot = designated
Last editor: pyram

18
highway = track
name = Treppelweg
surface = gravel
tracktype = grade2
foot = yes
bicycle = yes
width = 3
highway = path
name = Treppelweg
surface = gravel
tracktype = grade2
foot = designated
width = 3
http://www.bing.com/maps

 Define derived attributes that fit best for actual purpose
Harmonization of Heterogeneity
19
Loidl & Zagel (2014)

 OSMAXX
 Extracts OSM data
 Data cleaning (capital
letters etc.) and
harmonization
(generalization)
 Conversion to GIS formats
 For visualization and
geospatial analysis
Harmonization of Heterogeneity
20

 Inconsistency = quality issue
 Can be detected with intrinsic approach
 Heterogeneity = depends on purpose
 Definition of derived attributes
 Implement assessment routines during editing or in post-
processing?
 Tag recommender system during editing (Vandecasteele & Devillers 2014)
 Probabilistic approach and/or functionally related attributes
 Prevent from contradiction
 Data tuning in post-processing allows specification for actual purpose
 Combination  prevent – detect – repair (Herzog et al 2007)
 Data model issue  social complexity of OSM (Spielmann 2014)
Wrap-Up
21
@gicycle_
gicycle.wordpress.com

#AAG2015 presentation on OSM attribute inconsistency and semantic heterogeneity

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