Geospatial Feature Conflation:
Conceptual, Statistical, and Optimization Approaches
Real World Image
Funding for Geospatial Feature Conflation:
Conceptual, Statistical, and Optimization
A two-year research award to Mike Goodchild and Martin Raubal
10/1/2009–9/30/2011, with potential for renewal until 9/30/2014
Four research themes will be addressed in this project: (i) development of a relational-algebra framework and formalization for conflating heterogeneous geospatial data; (ii) statistical and optimization approaches for conflating multiple geospatial data sources; (iii) provenance characterization and uncertainty evaluation in geospatial data conflation; and (iv) conflation-based approaches to spatiotemporal reasoning.
By describing and modeling the process systematically in a consistent manner, theme (i) is critical for understanding major components in heterogeneous data conflation and for providing guidelines for choosing appropriate methods. Theme (ii) addresses the issues of non-optimality in existing conflation techniques and of increasing data accuracy using statistical and optimization approaches. Theme (iii) aims to represent uncertainty propagation and quality assessment in conflation through provenance characterization and modeling. Theme (iv) proposes to facilitate spatiotemporal reasoning by developing conflation-based approaches that take into consideration geospatial data from various sources to construct multiple constraints about geographic features in a multi-dimensional space-time context.
Project findings will be disseminated through: (i) presentations at academic conferences and NGA meetings, (ii) publications in relevant journals, (iii) development of open-source prototypes using the proposed approaches, (iv) development of a Web service for conflation that can be incorporated into service-oriented architectures, and (v) applications of these techniques and approaches to several real-world case studies.
The proposed research will provide a theoretical foundation to the integration of incompatible geospatial data. This problem is currently addressed by ad hoc solutions using dataset-sensitive techniques. It will develop novel approaches to implementing the conceptual framework, thus improving conflation results and enhancing spatiotemporal reasoning. The findings of this project will not only meet the requirement of creating higher-accuracy data from multiple sources, but will also offer a new direction for utilizing rich yet incompatible geospatial data to facilitate spatial reasoning. Its results will be of substantial benefit to NGA, scientific researchers, policy makers, and the general public.
The difficulty of conflation depends on many factors, such as complexity of representation and the volume and accuracy of the datasets involved. Specifically, incompleteness and inaccuracy of the original datasets, different reference systems, distinct generalizations and representations of reality, semantic issues of terminology and classification, various scales, and different purposes, as well as various time frames all create challenges in the use of geospatial data from heterogeneous digital sources.
Although there are several ad hoc solutions of digital geospatial data integration designed for particular datasets (e.g., Saalfeld, 1988; Samal, Seth, and Cueto, 2004; Walter and Fritsch, 1999), geospatial data conflation has not been systematically and adequately studied as a general and fundamental problem in geographic information science. This project seeks to investigate integration and assessment of incompatible geospatial data by developing a comprehensive framework for conflation from diverse sources, and by creating methods that can effectively and efficiently incorporate multiple-source data into a consistent structure. Specifically, we propose to address the following four research themes, and to extend to other research questions if time permits:
- A general conceptual and theoretical framework for conflation
- Statistical and optimization approaches to conflation
- Provenance characterization and uncertainty evaluation
- A conflation-based approach to spatiotemporal reasoning
For more information about this project, please see NGA research grant to Goodchild and Raubal.