WG IV/3: Spatial Data Analysis, Statistics and Uncertainty Modelling
Nowadays, we are faced with an ever increasing quantity of spatially referenced data produced by official organizations as well as through crowdsourcing and geospatial information provided voluntarily by citizens (VGI). A great diversity of data is available, including data produced via space borne and aerial sensors, geosensors, volunteers, social networks and crowdsourcing. While spatial data have arguably long been “big”, the current context is especially critical, as the collection, integration, and analysis of big spatial data are increasingly impacted by the V’s: volume, velocity, veracity, validity, value, variability and variety. Big decisions made on the basis of big spatial data must be cognizant of the big uncertainties lurking within that data.
Given this broad challenge for GIScience, we identify one important goal for the next four years: to encourage further development of theory and methods to facilitate the integration, mining and validation of the data and their corresponding information, through the application of data fusion processes to optimize information extraction and facilitate informed decision making processes. A number of spatial data fusion algorithms have been proposed and implemented. Methods such as Demspter-Shafer theory of evidence, fuzzy logic, artificial neural networks, adaptive neuro fuzzy inference systems, rough set theory, granular computing, game theory, ordered weighted averaging operators are some of the smart spatial data fusion methods that can be used. In addition, various multi-criteria decision making approaches have been developed to support decision making processes.
Within this context, the ISPRS WG IV/3 will focus on smart spatial data fusion algorithms, spatial statistics, spatial analysis, data mining and optimization and their data quality and information uncertainty assessment.
Working Group Officers:
|Mahmoud R. Delavar|
Centre of Excellence in Geomatic Eng. in Disaster Management
School of Surveying and Geospatial Eng.
College of Eng.
University of Tehran
P.O. Box: 11155-4563,Tehran
Dept. of Geodesy and Geoinformation
Technical University Wien
Gußhausstr. 25-29 / E120 (CD0342)
|Jamal Jokar Arsanjani|
Department of Planning and Development
Aalborg University Copenhagen
A.C. Meyers Vænge 15
GIS COGIT Laboratory
French Mapping Agency
+33 1 43 98 62 36
Terms of Reference:
- Review data mining methods applicable to spatial data
- Review methods for spatial data quality assessment for the several types of spatial data
- Develop new algorithms and/or combination of methodologies to integrate different types of spatial data
- Develop methods to assess quality of information resulting from data integration
- Advance knowledge in assessing spatial data fitness for use
- Performance assessment of spatial data optimization methods
- Development and evaluation of innovative algorithms and software tools of interoperability in GIScience
- Evaluation of various multi-criteria decision making methods
- Review crowd sourced data and VGI
- Address challenges in big spatial data on GISciences
- Investigation and evaluation of spatial data fusion/integration methods
- Review spatial statistics methods and their uncertainty assessment
- Review interoperability in spatial data bases