Penn State UniversityMGIS 596a Capstone Proposal for

Kent Stanton

A Technology Roadmapfor Spatially Referenced 

Community Science

Technology Roadmap:An informal mechanism for sharing IS design practices and knowledge. I'm interested in system architectures and the process/methods used to provision information system(s). 

Community Science:Applied science at the intersection of research, management and policy. Community conveys a sense of  scale, a bounded geographic context, and a shared sense of purpose.

Spatially Referenced:GIS is Integrative - ubiquitous georeferencing - the GeoWeb

  Definitions

Phase One: 596c Independent Study

A Technology Roadmap for Spatially Referenced Community Science(paper In draft)

Phase Two: 596a, 596b

A pilot implementation of an information system framework to support the American Chestnut restoration project

  The MGIS Project

The American Chestnut Foundation (ACF) has hybridized American Chestnut trees and blight resistant Asian varieties. They plan to propagate those trees on a widespread basis.

The ACF needs to track tens of thousands of trees over several decades. This includes detailed environmental data and potentially genetic data for three species: the trees, the Chestnut Blight fungus, and the virus that attacks the fungus.

  Case Study: American Chestnut Restoration Project

Problem Statement:Information systems for projects like this are commonly designed and built on an ad-hoc basis resulting in limited utility and poor long term results. 

In ecosystem and sustainable resources management, some problems are "wicked". Wicked problems are characterized by divergent stakeholder goals, resource constraints and uncertain science. 

Adaptive management (AM) prescribes the use of iterative cycles of describing/predicting, doing, and monitoring/learning.  

Project actions are designed specifically to produce useful knowledge.

  Wicked Problems: Adaptive Management

Co-management emerged as a challenge to the "the primacy of expert-driven solutions" (Armitage)

Co-management places project control and implementation in the hands of stakeholders. Stakeholders are involved in every aspect of projects.

Adaptive co-management (ACM) is the combination of the two methods: "a natural evolution of the two methods" (Armitage)

But how can non-specialists participate in the research?

  AM + Co-management: Adaptive Co-Management (ACM)

eScience:interdisciplinary, collaborative, and data-intensive

Citizen Science:Students, amateur naturalists, activists and community groups participate in research and monitoring

Community Science:Adaptive co-management joined with citizen science to extend participation to all phases of research. GIS and the GeoWeb provide a common and appealing organizational structure.

The democratization of expertise brings challenges and opportunities

  eScience to Citizen Science to Community Science

How do we protect our Drinking water? • For that we need to know more about the watershed• And that means we need to know about the wetlands• So what effect will Purple Loosestrife have on the

ecological communities and functions of those wetlands? • What effects might come from a rapid shift in the water

temperature, or oxygen levels, as a result of climate change?

  What kinds of questions would a community ask?

"recent revolutionary advances in data and method are enabling unprecedented expansion of ecological investigation into areas of inquiry previously unapproachable due to lack of fine-detail, broad scale data on environmental conditions" Cushman and Huettmann, Ecological Knowledge, Theory and Information in Space and Time, 2009

  Data-intensive Science and Community Science

"Science is becoming increasingly dependent on data, yet traditional data technologies were not designed for the scale and heterogeneity of data in the modern world."

A conceptual architecture for community science integrates support for ACM with the emerging GeoWeb and data-intensive science.

Peter Fox, James Hendler: The Forth Paradigm, Semantic eScience: Encoding Meaning in Next-Generation Digitally Enhanced Science

  Ready, fire, AIM!

How is the near extinction of the American Chestnut tree similar to the Gulf Oil Spill?

In both cases a primary response was undertaken with little or no knowledge of the long-term effects the response would produce.

In the case of the Chestnut Blight, the widespread harvesting of healthy trees "before they get the blight and die" culled the trees with natural resistance. In retrospect it was the worst possible course of action.

In the Gulf oil spill, large quantities of chemical dispersant have been used to "break up the oil". What effects will this have?

Will we look back 50 years from now and say "that was the worst possible response?"

  Large scale environmental disasters...

We need to know what the fisherman know, what the beachcombers know, what the duck hunters know, what the marine biologists know, what the ecologists know, and what the oil companies know...

We need software tools for integrating, managing and analyzing data in various formats. We need improved methods for geo-referencing unstructured data. We need robust methods for temporal referencing.

Enter, The Semantic Web**and the other computing and communications technologies that support the GeoWeb and Community Science

  We need information and we need it fast...

Web 1.0: Web browser requests a "web page". The server sends back a file containing information and HTML that tells the browser how the information should be rendered.

Web 2.0 - The read/write web. The page is created dynamically probably using data from multiple servers and services. The emergence of social networking.

Web 3.0 - The Semantic Web. All of the above plus metadata that says what the data means. The metadata defines a "data api" for every website or page. 

Sometimes the metadata is useful to you directly, but more likely is is used by a software agent operating on your behalf.

  Ok, but what is the Semantic Web?

The raster data model: Each cell in a grid is associated with the value of some attribute. 

Geosimulation and cellular automata: Agents iterate over the cells examining the values and applying rules that are saved to a new grid. The new grid represents new knowledge.

The Semantic Web: Think of a grid where each cell represents a node on the Internet. Each node has some set of attributes. Nodes can be servers, pages or data segments embedded in a page and nodes can layered.

Your agent iterates over the nodes, applies rules, and creates new knowledge layers.

  For GISers, this can be put into familiar terms...

Traditional GIS data models associate attributes with object (discrete-vector) or field-based (continuous-raster) geographic representations.

Mosaics - Google Maps revealed a different approach (and ArcGIS 10 includes mosaic layers as a base layer type)

The mosaic data model blurs the vector-raster distinction. Need a different scale? Generate it. Missing or inaccurate geographic references or missing attribute values? Put those agents to work reprocessing the data.

  The GeoWeb: Ubiquitous Geographic Referencing

The Geoweb and supporting semantic-enabled technologies are available now

OpenLayers - An open source web mapping API (javascript) providing an abstraction on top of multiple geodata APIs: Google Maps, Open Streetmaps, ESRI, Microsoft, Yahoo, etc.

  

  Aren't you the one who promised us jet packs?

Semantic web capabilities implemented by intellegent data warehousing: Cloud Computing, BigTable, MapReduce

Ready made frameworks to support user and community activities and for managing and sharing knowledge: Drupal

The software and information engineering perspectives on all of this include: 

• Domain Specific Language: The various products are built on a common set of geoprocessing components and tools

• Data tied to extensible metadata: ArcCatalog

• Workflows and Models: Model Builder layers a visual language over the geoprocessing components to create  workflows and models

• Scriptable Command Line Tools: support repeatable automation

  I'm an open source advocate, but ESRI appears to get it

  Obligatory architecture diagram with a lot of lines and boxes...

The Data:• tree locations• genomic classifications: tree, fungus, virus• environmental factors: soil, moisture, competition, etc.• ecological communities• track health and status over time• integrate remote sensing data• integrate local and community knowledge

Support:Project ManagementCommunity Interaction and NetworkingData Visualization and AnalysisA Data API - Sharing Data with Others

  The ACF Chestnut Reintroduction Project

Drupal (Open Atrium) provides a framework for project management, communications and social networking

Custom Drupal modules to support data management

A map-centric UI embedded in Drupal using OpenLayers

Kepler and ESRI tools for data processing and data analysis

VUE (Visual Editing Environment) and Protege for the creation and management of semantic data

Google Data to put it all in the cloud for long term storage

  The Pilot Implementation

  References

Argent R. M., Components of Adaptive Management; C. Allan and G.H. Stankey (eds.), 2009, Adaptive Environmental Management: A Practitioner’s Guide, Springer Science and Business Media B.V.

Armitage, D., Berkes F., Doubleday N., 2007, Adaptive Co-Management : Collaboration, Learning, and Multi-Level Governance., University of British Columbia Press.

Bradshaw, G. A., Borchers J. G. 2000. Uncertainty as information: narrowing the science-policy gap. Conservation Ecology 4(1): 7. [online] URL: http://www.consecol.org/vol4/iss1/art7/

Carr, A. J. L. 2004. Why do we all need community science. Society and Natural Resources 17:841–849.

DeGrace, P., StahlL. H., 1990, Wicked Problems, Righteous Solutions. Prentice Hall, Yourdon Press.

Freinkel S., 2007, American Chestnut: The Life, Death, and Rebirth of the Perfect Tree, University of California Press.

Hey T., Tansley S., Tolle K. (editors), 2009, The Forth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research.

Jarrar, M., Meersman, R., 2008, Ontology Engineering -The DOGMA Approach; Advances in Web Semantics I, LNCS 4891, pp. 7–34, 2008.

Lee, K. N. 1993. Compass and gyroscope, integrating science and politics for the environment. Island Press, Washington, D.C., USA.

Mitchell, M. 2009, Complexity, Oxford Press

National Science Foundation, 2007, Cyberinfrastructure Vision for 21st Century Discovery.

Sankar K., Bouchard S.; 2009, Enterprise Web 2.0 Fundamentals, Cisco Press

Silvertown, J., 2009, A new dawn for citizen science, Trends in Ecology and Evolution Vol.24 No.9

  Questions?

and lest I forget...

many thanks to Doug Miller for his guidance and support