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Improvement of Spatial Data Quality Using the Data Conflation

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Improvement of Spatial Data Quality Using the Data Conflation
Silvija Stankute, Hartmut Asche -Geoinformation Research Group, Department of Geography, University of Potsdam

Published in: Technology, Education

Improvement of Spatial Data Quality Using the Data Conflation

  1. 1. <ul><li>Improvement of </li></ul><ul><li>spatial data quality through data conflation </li></ul><ul><li>Silvija Stankute, Hartmut Asche </li></ul><ul><li>Geoinformation Research Group </li></ul><ul><li>Dept of Geography | University of Potsdam | Germany </li></ul>ICCSA 2011 | GEOG-AN-MOD 2011 | University of Santander | 20-23/06/2011
  2. 2. Summary <ul><li>Motivation: Spatial data quality matters </li></ul><ul><li>Spatial data quality: Definition, indicators </li></ul><ul><li>Data conflation: Optimising spatial data quality </li></ul><ul><li>Data conflation at work: Inserting a roundabout </li></ul><ul><li>Conclusion: What‘s the merit of data conflation? </li></ul>
  3. 3. <ul><li>Introduction of digital mapping techniques and GIS in the 1960s made quality of digital spatial data an issue in geoinformation processing (GI) </li></ul><ul><li>Error and uncertainty in spatial data identified as potential problems in GI processing uncommon in production and use of paper maps </li></ul><ul><li>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 </li></ul><ul><li>Objective of this work is to present data conflation as one option in GI processing for improvement of spatial data quality </li></ul>1 Motivation Spatial data quality matters
  4. 4. OpenStreetMap Analog topo map 1:10K Brandenburg Viewer 1 Motivation Spatial data quality matters Potsdam in different spatial datasets
  5. 5. <ul><li>Geodata quality </li></ul><ul><li>ISO 8402: totality of characteristics of a product that bear on its ability to satisfy stated or implied needs > fitness-for-use </li></ul><ul><li>Definition of spatial data quality necessitates information on (a) geodata used, ( b) user requirements </li></ul><ul><li>Fitness-for-use: data meet requirements of target application </li></ul><ul><li>Geo data quality indicators </li></ul><ul><li>Completeness </li></ul><ul><li>Logical consistency </li></ul><ul><li>Positional accuracy </li></ul><ul><li>Temporal accuracy: accuracy of reporting time of data </li></ul><ul><li>Semantic/thematic/attribute accuracy </li></ul><ul><li>Information on geodata quality included in metadata </li></ul>2 Spatial data quality Definition, indicators
  6. 6. <ul><li>D ata acquisition </li></ul><ul><li>Different methods for spatial data acquisition developed by spatial data producers result in different </li></ul><ul><li> data types </li></ul><ul><li> data formats </li></ul><ul><li> semantic information of geodata </li></ul><ul><li>Consequence: multiplicity of spatial data </li></ul><ul><li>Problem: multiple data use of specific datasets </li></ul><ul><li>Option: data integration or data conflation applied to existing datasets instead of continuous acquisition of new spatial data with above faults </li></ul>2 Spatial data quality Data acquisiton
  7. 7. <ul><li>Objective </li></ul><ul><li>Automated merge of heterogenous geodata to application requirements to produce best-fit dataset for any specific application </li></ul>source dataset SDS target dataset TDS output dataset 3 Data conflation Optimising spatial data quality missing data inserted data
  8. 8. <ul><li>One spatial object, different data models </li></ul><ul><li>Real world spatial data transformed into computer-readable digital data model representing spatial features as (a) points, (b) lines or (c) areas (polygons) </li></ul><ul><li>Modelling of real world spatial data can result in different data models of identical real world object: traffic roundabout </li></ul>3 Data conflation Optimising spatial data quality
  9. 9. One spatial object, multiple geometry OpenStreet Map TeleAtlas ATKIS 3 Data conflation Optimising spatial data quality
  10. 10. 4 Data conflation at work Conceputal framework <ul><li>Substituting roundabout for road crossing </li></ul><ul><li>Inserting roundabout in dataset where roundabout modelled as road crossing = not defined as roundabout </li></ul><ul><li>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 </li></ul><ul><li>When 3 edges are identified which have the same node (start or end point of edge), this intersection is part of roundabout </li></ul>
  11. 11. 4 Data conflation at work Automated workflow Producing best-fit dataset dataset 1 dataset 2 pre-processing pre-processing object assignment new dataset data sources
  12. 12. <ul><li>(a) edge tracing for identification of roundabout in input data-set 1, (b) search for roundabout access/exits in input dataset 2 </li></ul><ul><li>Merge access/exits with corresponding points on crossroads </li></ul>4 Data conflation at work Semantic accuracy <ul><li>Inserting roundabout in target dataset </li></ul><ul><li>Inserting roundabout </li></ul>
  13. 13. <ul><li>All access or exits of roundabout found in first input dataset </li></ul><ul><li>Corresponding edges in second input dataset also detected. </li></ul><ul><li>Geometrical information about new objects can be assigned to target dataset </li></ul>4 Data conflation at work Geometric completeness <ul><li>Assigning geometric information </li></ul><ul><li>Inserting roundabout </li></ul>
  14. 14. <ul><li>After completion of merge process of 2 or more datasets (points, lines, polygons) completeness of input data is always increased </li></ul><ul><li>Prerequisite: one of the input datasets must have more infor-mation than the other(s) </li></ul><ul><li>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 </li></ul><ul><li>Consequence: Datasets generated by conflation can only be complete in terms of geometrical information </li></ul>4 Data conflation at work Data quality optimised
  15. 15. 4 Data conflation at work Data quality optimised <ul><li>Real world spatial data: 8 buildings </li></ul><ul><li>Source dataset in-cludes information on 6 buildings (geo-metry, use) </li></ul><ul><li>Target dataset in-cludes information on 5 buildings (geo-metry, floors) </li></ul><ul><li>End dataset com-plete with geometric information </li></ul><ul><li>Geometric objects of both input datasets have 100% thematic completeness </li></ul>
  16. 16. <ul><li>Conflation methods allow the improvement of positional and temporal accuracy of spatial data </li></ul><ul><li>Positional accuracy of a dataset can be increased with the information provided by another input dataset </li></ul><ul><li>If both datasets show major variance from the corresponding real world objects, arithmetic average of all input datasets can increase this quality element </li></ul><ul><li>Temporal accuracy can be improved if metadata provide infor-mation about actuality of spatial data </li></ul><ul><li>Data conflation facilitates multiple use of quality spatial data which can be generated automatically to application require-ments from existing suboptimal datasets </li></ul>5 Conclusion What‘s the merit of data conflation?
  17. 17. Thank you for your attention Questions? Comments? Feedback? Contact Hartmut Asche | gislab@uni-potsdam.de Dept 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

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