This presentation was given in a session dedictated to OpenStreetMap studies during the annual meeting of the Association of the American Geographers (AAG) in Chicago, IL.

1. 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

2.  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

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

4.  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
bicycle = designated
surface = asphalt

5.  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

6.  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

7.  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

8.  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 = *

9. 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

10. 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

11. 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

12.  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'

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

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

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

16.  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

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

18. 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
bicycle = designated
width = 3
http://www.bing.com/maps

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

20.  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

21.  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