Birds of Feather Session 4: Context areas beyond traditional CS and GIS Participants (in alphabetical order): Peggy Agouris Arie Croitoru Pete Doucette Pasquale Franzese Michael Gould James Pustejovsky John Schnase

Context areas (1) Context areas : – areas in which research is carried out that can be linked with the current geospatial and geotemporal research – Includes: Earth & atmospheric sciences Defense & homeland security Forestry Agriculture Biology Arts

Context areas (2) A better interface with the following areas is needed: – GUI interface design / psychology – NeuroGeoSci (brain maps) – Artificial intelligence and machine learning, particularly in the context of modeling – Gaming – Linguistics – Information science/library science – English studies/history – …

Some observations on context areas Very different missions, goals and expectations The context of scale differs significantly We should promote spatial thinking across the board: – Developing spatial thinking for everyday activity

Why isn’t it happening? There are some significant barriers: – Semantics – Education and skill level – Data handling/sharing/ownership – Trust – Lack of incentive – Tendency towards multidisciplinary rather than interdisciplinary

The role of education The current approach in education plays a fundamental role in creating these barriers! A need to create a common knowledge/skill-set for spatial thinking – it has to be a bottom up process. We need to define the “fundamental laws” of the domain, e.g. – DBs – Vector processing – Raster processing – …

An emerging opportunity Google earth as a stepping stone – Creating a broad awareness – Creating new needs and demands – Brings coherence to the field

The evolving funding landscape Currently, Geospatial/GeoTemporal research is not a distinct funding category. A need for a new context-driven funding model. – Context should not be eliminated.

Environmental change As a case study (1) – increase in wildfires – Invasive species – Response requires rapid decision making, integration of data etc.

Environmental change As a case study (2) Requires a suite of tools that has not been fully developed: Change detection Modeling/studying/predicting phenomena Multi-scale analysis Temporal analysis Uncertainty

Environmental change As a case study (3) – There is a suite of tools that does not exist: Rapid deployment of data without continuous reliable communication Communication = computer + non-computer (enabling human communication) – Need to support various users with various levels of skills – Need to educate users/funding agencies/professionals.