Iccsa stankuteha180611

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© stankute, asche·ifg·uni·potsdam 2011

Improvement of spatial data quality through data conflation

Silvija Stankute, Hartmut AscheGeoinformation Research GroupDept of Geography | University of Potsdam | Germany

ICCSA 2011 | GEOG-AN-MOD 2011 | University of Santander | 20-23/06/2011

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Summary

1. Motivation: Spatial data quality matters

2. Spatial data quality: Definition,

indicators

3. Data conflation: Optimising spatial data

quality

4. Data conflation at work: Inserting a

roundabout

5. Conclusion: What‘s the merit of data

conflation?

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Introduction of digital mapping techniques and GIS in the 1960s made quality of digital spatial data an issue in geoinformation processing (GI)

Error and uncertainty in spatial data identified as potential problems in GI processing uncommon in production and use of paper maps

Ongoing development from 1980s to design and implement data transfer standards which include data quality information hitherto available on the margins of paper maps only

Objective of this work is to present data conflation as one option in GI processing for improvement of spatial data quality

1 Motivation Spatial data quality matters

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OpenStreetMap Analog topo map 1:10K Brandenburg Viewer

1 Motivation Spatial data quality matters

Potsdam in different spatial datasets

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Geodata quality ISO 8402: totality of characteristics of a product that bear

on its ability to satisfy stated or implied needs > fitness-for-use

Definition of spatial data quality necessitates information on (a) geodata used, (b) user requirements

Fitness-for-use: data meet requirements of target application

Geodata quality indicators Completeness Logical consistency Positional accuracy Temporal accuracy: accuracy of reporting time of data Semantic/thematic/attribute accuracy Information on geodata quality included in metadata

2 Spatial data quality Definition, indicators

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Data acquisition Different methods for spatial data acquisition developed

by spatial data producers result in different data types data formats semantic information of geodata

Consequence: multiplicity of spatial data

Problem: multiple data use of specific datasets

Option: data integration or data conflation applied to existing datasets instead of continuous acquisition of new spatial data with above faults

2 Spatial data quality Data acquisiton

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Objective Automated merge of heterogenous

geodata to application requirements to produce best-fit dataset for any specific application

source dataset SDS

target dataset TDS output dataset

3 Data conflation Optimising spatial data quality

missing data

inserted data

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One spatial object, different data models Real world spatial data transformed into computer-

readable digital data model representing spatial features as (a) points, (b) lines or (c) areas (polygons)

Modelling of real world spatial data can result in different data models of identical real world object: traffic roundabout


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One spatial object, multiple geometry

OpenStreetMap

TeleAtlas

ATKIS


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4 Data conflation at work Conceputal frameworkSubstituting roundabout for road crossing

Inserting roundabout in dataset where roundabout modelled as road crossing = not defined as roundabout

Detecting “missing” roundabout by identifying position of crossings in input datasets: roundabout identified if minimum of 3 edges of road network have identical start and end point

When 3 edges are identified which have the same node (start or end point of edge), this intersection is part of roundabout

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4 Data conflation at work Automated workflowProducing best-fit dataset

dataset 1

dataset 2

pre-processing

pre-processing

object assignment

new datasetdata sources

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(a) edge tracing for identification of roundabout in input data-set 1, (b) search for roundabout access/exits in input dataset 2

Merge access/exits with corresponding points on crossroads

4 Data conflation at work Semantic accuracyInserting roundabout in target dataset

Inserting roundabout

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All access or exits of roundabout found in first input dataset

Corresponding edges in second input dataset also detected.

Geometrical information about new objects can be assigned to target dataset

4 Data conflation at work Geometric completenessAssigning geometric information

Inserting roundabout

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After completion of merge process of 2 or more datasets (points, lines, polygons) completeness of input data is always increased

Prerequisite: one of the input datasets must have more infor-mation than the other(s)

Not all new geometry objects of target dataset include infor-mation on thematic attributes, hence completeness of target dataset can never be complete in terms of thematic information

Consequence: Datasets generated by conflation can only be complete in terms of geometrical information

4 Data conflation at work Data quality optimised

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4 Data conflation at work Data quality optimised Real world spatial

data: 8 buildings Source dataset in-

cludes information on 6 buildings (geo-metry, use)

Target dataset in-cludes information on 5 buildings (geo-metry, floors)

End dataset com-plete with geometric information

Geometric objects of both input datasets have 100% thematic completeness

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Conflation methods allow the improvement of positional and temporal accuracy of spatial data

Positional accuracy of a dataset can be increased with the information provided by another input dataset

If both datasets show major variance from the corresponding real world objects, arithmetic average of all input datasets can increase this quality element

Temporal accuracy can be improved if metadata provide infor-mation about actuality of spatial data

Data conflation facilitates multiple use of quality spatial data which can be generated automatically to application require-ments from existing suboptimal datasets

5 Conclusion What‘s the merit of data conflation?

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Thank you for your attention

Questions? Comments? Feedback?

Contact Hartmut Asche | [email protected] of Geography | University of Potsdam

| GER Web www.geographie.uni-potsdam.de/geoinformatik

ICCSA 2011 | GEOG-AN-MOD 2011 | University of Santander | 20-23/06/2011

Iccsa stankuteha180611

Technology

data integration

spatial data acquisition

data quality information

spatial data producers

spatial data qualitydefinition

indicators data conflation

present data conflation

merit of data conflation