towards rule-guided classification for volunteered geographic information

Ahmed Loai Ali*, Falko Schmid, Zoe Falomir, and Christian Freksa

University of Bremen – Cognitive System research group (CoSy)

Contact: * [email protected]

Geospatial Week @ ISSDQ 2015


The advanced technologies & the power of crowd-

sourcing.

VGI:

one form of UGC, but UGGC

contents with implicit/explicit spatial references

evolved to play a vital role in GIScience.


Implicit/Explicit VGI:

Micorbolgs, OpenStreetMaps, WikiMapia, etc..

Potential data source & support various applications:

Map provisions

Urban planning

Land use mapping

Crises management

Environmental monitoring

Support humanities


Explicit–VGI:

Users act as observers, collect, share, and maintain information about

geographic features, generating digital maps.

They are mostly untrained and non-experts, however they are interested in

contributing geographic information.


The needed quality for reliable

services.

The main wheel between data

production & consumption

VGI has heterogeneous quality

due to:

Volunteers participants

Various technologies

Loose contribution mechanism

Data

Quality

Data

Production

Data

Utilization


Standard measures

Consistency

Completeness

Positional accuracy

Thematic accuracy

Temporal accuracy

Evolved measures:

Conceptual quality

Fitness of use


extrinsic

intrinsic

Social

Geographic

Crowdsourcing


Classification


A facet of thematic accuracy

Problematic classification results in:

Inaccurate/ Incomplete results

Rich data with limited use


Rule-guided classification approach

To improve the classification quality

Through guiding:

Improve the contributors experiences

Direct the contributors towards consistent classification


Considering OSM data set, large amount of data with

good quality

Identical entities should be classified similarly – at

least within a country level

The appropriate classification of an entity is strongly

depending on

Entity characteristics

Geographic context


Grass-related entities classification

Park, Garden, Meadow, Grass, Forest, Wood


Germany data set (December 2013)

10 most densest cities

3724 forest

3030 garden

7336 grass

4277 park

4445 meadow

1454 wood


Topological investigation


Predictive association rule extraction

Class(E, C) R(E, F) (support, confidence)

An entity E

R ϵ {‘contains’, ‘meet’, ’disjoin’, ’coveredBy’ or ‘overlap’}

F a set of features [f1,f2, …]

C ϵ {‘park’, ’garden’, ’grass’, … .}


Topological investigation

Step 1: find frequent rule items

Step 2: generate rules

Step 3: filter rules

Step 4: develop a classifier


9193 rules:

4100 describe the forest class

215 describe the garden class

745 describe the grass class

506 describe the meadow class

2938 describe the park class

689 describe the wood class

Classification plausibility and ambiguity


Pruning:

remove redundant rules based on confidence

Filtering rules based on confidence:

confidence ≥ 50%

Ranking 1st

& 2nd

recommendations:

consider top 2 recommendations

Classification assumption:

maximum confidence per class


The entity matches with:

Park 92%

Meadow 46%

Grass 32%

Forest 13%

Garden 12%


60%

50%

75%

55%

0%

25%

0%

10%

20%

30%

40%

50%

60%

70%

80%

all rules rules with conf >= 50&

Classification accuracies by various hypothesis

1st recommendations 1st & 2nd recommendations not matched


38%

71% 72%

42%

80%

7%

67%

81%

87%

58%

94%

15%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

forest garden grass meadow park wood

Classification accuracies per class

1st recommendation 1st & 2nd recommendation


81%

87%

94%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

forest garden grass meadow park wood

Classification accuracies per class

1st recommendation 1st & 2nd recommendation


Grass

Park Park


Park

Grass Grass


Garden

Meadow Garden


Now, it becomes applicable

We launched Grass&Green

A web tool that guide participants towards the most appropriate

classification

It is online under www.opensciencemap.org/quality

Simple interface

Definitions and descriptions are provided (textual & visual)

Multiple-classification plausibility

Contribute directly to OSM project

http://www.opensciencemap.org/quality


@grass_and_green

Grass&Green

[email protected]

www.opensciencemap.org/quality

http://www.opensciencemap.org/quality


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Contributions to Grass&Green tool in 24 days

active contribution

inactive contribution

users

@grass_and_green

Grass&Green


23%

67%

10%

Agreement with Recommendations

Agree

Partial Agree

Not Agree

@grass_and_green

Grass&Green


Data mining is a promising methodology to VGI quality

analysis

Recommendation systems is required

Towards features- and location- customized systems

Fuzziness of spatial concepts towards data evaluations


Thank you !!!

?

[email protected]

@grass_and_green

Grass&Green

towards rule-guided classification for volunteered geographic information

Data & Analytics