Environmental and Ecological Statistics

, Volume 11, Issue 2, pp 113–138 | Cite as

Multiscale advanced raster map analysis system: Definition, design and development

  • G.P. Patil
  • J. Balbus
  • G. Biging
  • J. JaJa
  • W. L. Myers
  • C. Taillie


This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.

echelons and families of echelons surface topology and upper level sets geographic surveillance elevated cluster detection change detection and regional change patterns analysis multiscale assessment regional echelon partitions hotspots critical areas corridors outbreaks multicriteria rankings and fuzzy ranks posets Hasse diagrams and linear extensions elevated cluster prioritization environmental factor prioritization multicriteria comparisons and decisions multiscale landscape pattern metrics scaling domains multiscale fragmentation profiles eigen values as fractals modeling and simulation devices Markov random fields multivariate disjunctive indicator geostatistics and hierarchical Markov transition matrix models uncertainity analysis confidence statements statistical significance inferential geoinformatics userfriendly software system for multiscale map and surface analysis geospatial data management data mining data analysis visualization and communication MARMAP 


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© Kluwer Academic Publishers 2004

Authors and Affiliations

  • G.P. Patil
    • 1
  • J. Balbus
    • 2
  • G. Biging
    • 3
  • J. JaJa
    • 4
  • W. L. Myers
    • 1
  • C. Taillie
    • 1
  1. 1.Center for Statistical Ecology and Environmental Statistics, Department of StatisticsThe Pennsylvania State UniversityUniversity Park
  2. 2.Center for Risk Science and Public Health, Department of Occupational and Environmental HealthGeorge Washington UniversityWashington
  3. 3.ESPM-Division of Ecosystem SciencesUniversity of CaliforniaBerkeley
  4. 4.University of Maryland Institute for Computer StudiesUniversity of MarylandCollege Park

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