Searching solutions to improve the processes in cartography production and updating, using OpenStreetMap as data source

1. OFFICIAL AND CROWDSOURCED
GEOSPATIAL DATA
INTEGRATION
Searching solutions to improve the processes in
cartography updating
By Jimena Martínez
Supervisors: Antonio Vázquez and Marianne de Vries

2. 2
Table of contents
Background
Problems
The idea
The steps to develop the idea, and an
example to show it

3. 3
Background
BCN200 BTN25 BTA5 MGCP
International
Local (Spanish
National (Spain) National (Spain) (Africa, Middle
Scope provinces)
East)
Cartography Cells (208 Spain: 6
Provinces Sheets Sheets
units countries)
Scale 1/200.000 1/25.000 1/5.000 1/50.000
Updating cycle 2 years 4 years 4 years 4 years
Spanish budget:
Budget 300.000 € 3.500.000 € 800.000 €
27.000.000 €

4. 4
Problems
1. Why official cartography is never enough updated?
Update
process
Satellite/ aerial Release date
images collecting date (2011 version)
Feb. 2011 Dec. 2011
Dec. 2010 May 2011
1st real change 2nd real change Off. Data reflects
1st change

5. 5
Problems
2. Why updating process is such long and expensive?
Traditional updating process
Vector cartography from last year.
Set of data sources against which
compare the cartography (images,
maps, raster, vector)
Reviewing the whole cartography
unit.
Too much time to review, not
much time to edit features.

6. 6
Problems
2. Why updating process is such long and expensive?
Madrid case (1/200k)
Time to update: 4 weeks 1 person
Features edited percentage: 30%
Time to edit this features: 1,5
weeks
Would be possible to save the
other 2,5 weeks?

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Problems
1. Why official cartography 2. Why updating process is such
As a result:
is never enough updated? long and expensive?
Reviewing the whole cartography
Traditional process based
against different data sources is Long process
on different data sources.
needed…
Data sources have different
to detect changes. Expensive process
dates (collecting dates)
Not always useful result
(if highly updated
cartography is needed)

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The idea
To develop a general
A system that finds
methodology to decide
where the official
whether crowdsourced
dataset need to be Saving costs
(OpenStreetMap) and
updated, and which and obtaining
official geodata could be
type of update needs better updated
integrated or not in order
each feature, without cartography
to use OSM to improve
reviewing the whole
the official cartography
cartography unit.
updating process.

9. 9
The idea
Data sources in the updating process Better updated
features (not always)
Official data Official data
Vector format
Not complete
OSM data
Not homogeneous
OSM to indicate
where to update

10. 10
The idea
Differences in updating processes
NMAs official data Crowdsourced data (OSM)
Government (NMA) Hours/days
Users/NMAs/companies
Updating &
MAP v.1. production
Months/
processes MAP v.1…v.n
Years
Tenders
(companies)
Updating processes
MAP v.2

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The idea
Differences in updating processes
Update
process
OSM update Satellite/ aerial OSM update Release date
images collecting date (2011 version)
Jan. 2011 Feb. 2011 June 2011 Dec. 2011
Dec. 2010 May 2011
1st real change OSM reflects 2nd real change OSM reflects Off. Data reflects
1st change 2nd change 1st change

12. 12
The idea Differences
Which dataset is “better”?
Official data OSM data
Which one is
better?
Some studies (Haklay 2008,
As a result OSM But, what
Zielstra&Zipf, 2010) take this data The desired
is not 100% happens with
set as the “truth” against which result will be:
complete that?
to compare OSM
Official data OSM data
Types of
updates

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The idea “Given enough
Questions to answer
eyeballs, all bugs are
shallow”
WHY OSM?
Accuracy data (Linus Law).
Amount of data. Updated data.
Comparative studies
WHAT features from OSM?
OSM not as features to take, but as indicators If not useful, not used: types of
to use. updates.
AIM 3
HOW to integrate OSM and official data?
Matching data models in a reference semantic Quality indicators (traditional and
model (domain ontology) Crowd quality parameters)
AIM 1 AIM 2

14. The idea: the proposed system 14
WEB
Update OSM
Official data set OSM data set
Semantic
INPUT specifications Specifications
Reference:
Domain Ontology
Feature class 1 Feature class 1 (50)
Feature class 2 Feature class 2 (80)
... Candidates ...
Feature class n Feature class n (N)
Matching process (feature classes filter)
Updating process
“Updating gaps” Feature class 1 Feature class 1 (50)
Feature class 2 Feature class 2 (80)
... ...
Types of updates
Feature class n Feature class n (N)
VGI teams/
Online updating
QC and QA (features filter)
Feature class 1 (30)
...
Crowd
Feature class n (N-M) ISO 19157
Quality

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The steps to reach the goal
And an example to show them
1 • Making the matching between data models
and features. Ontology approach
2 • To study Quality parameters to decide which
features could be used.
3 • Proposing a new updating process based on
flags and types of updates.

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1st step: making the matching
Comparing data models
NMA data model OSM data model
Format Database, shp XML (.osm)
Node Node
(Geometric) Primitives Arc Way Tag
Face Relations
Feature class Table, file Primary tag (key)
Feature (each object) Row Primary tag (value)
Attribute Column Tag (key)
Values (domains) Cells Tag (value)

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1st step: making the matching
An approach (based on H. Uitemark)
A1 A1: building of interest
1. Official dataset C1 Legend C1: motorway
D1 D1: toll motorway
A B
Real world Candidates:
C D
{[(A1,A2), (A1,B2)], [(C1,C2), (C1,D2)], [(D1,C2), (D1,D2)]}
D2 A2 A2: building, church
2. OpenStreetMap Legend B2: building, school
C2 B2
C2, D2: highway, motorway

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1st step: making the matching
The example: motorways (BCN Spain-OSM)
Something“superior” and
semanticisneededto
compare 2 data models

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1st step: making the matching
Using ontologies. First approach
Official OSM data
data set set
matching matching Ontology
Ontology Domain Ontology
(OSMONTO)

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1st step: making the matching
Using ontologies. Second approach
Official OSM data
data set set
mapping Ontology
Domain Ontology
(OSMONTO)
ODEMapster
R2RML

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2nd step: quality study
Studying the quality: traditional parameters
van Oort (2006) Haklay (2008) ISO 19157 (2011)
Completeness Completeness Completeness
Logical consistency Logical consistency Logical consistency
Positional accuracy Positional accuracy Positional accuracy
Attribute accuracy Attribute accuracy Thematic accuracy
Temporal quality Temporal quality Temporal quality
Semantic Accuracy Semantic Accuracy
Usage, purpose and Usage, purpose and
Usability element
constraints constraints
Lineage Lineage Lineage (19115)
Variation in Quality
Meta-quality
Resolution (≈ scale)

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2nd step: quality study
Studying the quality: (some) crowd quality parameters
Maué (2007). PGIS Haklay (2008) van Exel (2010) Others
Reputation of contributors Longevity of engagement User quality Lineage
• Local knowledege
Number of editions on a • Experience
Information assymetry • Recognition Homogeneity in Quality
feature
Number of contributors Feature related quality Time between editions
on a feature • Lineage on a feature
• Possitional accuracy
Number of bugs fixed • Semantic accuracy

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2nd step: quality study
Higher quality
Lower quality
Some methods to measure traditional quality (pos. accuracy)
Buffer width:
Perkal • Possitional accuracy
Until blue is totally
(1966) • Interpretation of epsilon band inside orange
Goodchild • Possitional accuracy
and Buffer width:
• Complete data sets are needed
Hunter Until blue is 90-
(1997) • A higher quality dataset is needed 95% inside orange
• Possitional accuracy (OS-OSM) Buffer width:
Haklay • Complete data sets are needed. He Two buffers.
(2008) completed OSM Compare de
• Suposed OS is higher quality than OSM overlap areas
• No complete data (and nobody is going Buffer width:
BCNSpain- to complete). Neither BCN nor OSM Could be
OSM • Don´t know which data set is better impossible to
(OSM to update BCN) achieve 90-95%

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2nd step: quality study
Example: measures of positional accuracy on motorways
BCN Spain
OSM

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2nd step: quality study
Example: measures of positional accuracy on motorways
% length of BCN motorways within
the OSM buffer
90%
% of BCN roadswithinthe OSM buffer
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 2 3 4 5 6 7 8 9 10 15 20 25 30 50 100 200 500
Bufferwidth(m)
A 500 m buffer around OSM is needed to reach
80% of the BCN length within the buffer= lack of
completeness in OSM dataset
BCN Scale 1/200k (buffer must be ≈ 20m, which
means 73% of the length wihtin the OSM buffer)

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2nd step: quality study
Example: measures of positional accuracy on motorways
% length of OSM motorways within the
BCN buffer
% of OSM roadswithinthe BCN buffer
100%
80%
60%
40%
20%
0%
1 2 3 4 5 6 7 8 9 10 15 20 25 30 50 100 200 500
Bufferwidth(m)
A 25m buffer around BCN is needed to reach 90% of
the OSM length within the buffer.
In this case the method works because every OSM
motorways are also in BCN dataset.

27. 27
Higher quality
2nd step: quality study Lower quality
Some methods to measure traditional quality (completeness)
• Based on boundary box on each feature
• 300 m radius to find candidates to
match If the Bbox
OSL Musical • Additionally, levenshtein distance matches, then
Chairs (streets) the street
Algorithm name is
• A higher quality data set is needed to
compared
compare
• http://humanleg.org.uk/code/oslmusic
alchairs/
• Not useful for motorways or long
BCNSpain- features.
OSM • Useful for streets or polygons
• Convex hull could be used instead Bbox

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2nd step: quality study
Conclusions about traditional quality
Which parameter comes before? • Complete data set (not a measure of
Completeness or Possitional completeness) is needed to measure positional
accuracy accuracy
It is been proved that OSM is not • Congrats!
complete
• OSM not as features to take, but as indicators
It brings me to the first statement to use
• It doesn´t matter if OSM is not complete
• “Updating gaps”: which include the lack of
A new approach completeness of OSM

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3rd step: purpose updating process
Traditional classification of updates
Add
Updates Delete
Geometry
Modify
Attributes

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3rd step: purpose updating process
Proposed classification of updates
Don´t need to be
YES
updated
ROAD_ATT (offic) =
YES
ROAD_ATT (OSM)
Updating Attribute
ROAD_G (official)= NO
gap, type I updating
ROAD_G (OSM)
Updating Classification
YES
gap, type II updating
Official data ROAD_G (official)=
NO
OSM data OTHER_G (OSM)
Doesn´t exist in Updating
OSM can´t be
used, but
OSM gap, type III
adviced.
NO
Automatically
Doesn´t exist in Updating
updating from
official dataset gap, type IV
OSM?

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The result
Madrid case (1/200k)
Time to update: 1,5 weeks, 1
person
Features edited percentage: 30%
Time saved: 2,5 weeks
Costs saved: 40%

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Next steps
Find the best method to compare both data sets and
try it in different data sets (based on TQ and CQ)
Obtaining automatically different types of updating
gaps.
Look for a better way to compare data models
(ontology approach)
Try an automatic method to update the updating gaps
based on OSM.

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Thank you!
Dank je wel!
Gracias!
J.MartinezRamos@student.tudelft.nl