GIS, GIScience, and Spatial Data: An American Perspective Michael F. Goodchild University of California, Santa Barbara Walton Fellow, NCG

Uptake of GIS n What’s in a name? –geospatial –facilities management –land information systems n Uptake in all areas that deal with the surface of the Earth n 3D GIS –mining, geophysics –atmospheric science

Why GIS? n Management, inventory, maintenance –in any domain where location is important n Implementing policy –general principles applied in local context –database = local conditions –procedures, algorithms = general principles –simulating outcomes, what-if scenarios n Evaluating policy –mapping outcomes –identifying areas for intervention

Identifying Ethnic Neighborhoods with Census Data: Group Concentration and Spatial Clustering: John R. Logan and Wenquan ZhangJohn R. LoganWenquan Zhang

Planning Scenario Visualization and Assessment: A Cellular Automata Based Integrated Spatial Decision Support System Roger White, Bas Straatman, and Guy Engelen Roger WhiteBas StraatmanGuy Engelen

Simulations n 1.8 vehicles per driveway n Driver behavior influenced by: –lane width –slope –view distances –traffic control mechanisms –information feedback –driver aggressiveness n 770 homes –clearing times > 30 minutes 2D clip 3D clip

Policy implications n Addition of new outlets n Better deployment of traffic control resources n Understanding the risk n Reduce cars used per household n Problems of shut-ins, elderly, latch-key kids

New technologies n Google Maps n Google Earth n Published APIs n Abundant data n A simple user interface

Animations n d2.mpg d2.mpg n Bedwell hike Bedwell hike n Alan Glennon’s geo/vantage Alan Glennon’s geo/vantage

New application domains n Public health –health service delivery location analysis –epidemiology cluster detection morbidity, mortality –data management data modeling –GIS in the GP’s office

New application domains n Disaster management –obvious case –all aspects

Stages of problem solving Problem definition Design Data acquisition Integration Analysis Interpretation Presentation

Why does it take so long? n Analysis at the speed of light n Why can't we solve problems in real time? n How can we make it faster? n Disaster management requires rapid response

A 5-stage model 1. Specify 2. Search 3. Assess 4. Retrieve 5. Open

A data model for disaster management n A prepared template n Rapidly populated –using prepared routines n Prepared analysis functions n Up and running within minutes

Computing in the presence of the subject matter n U = S –or S = U 1 through U n n Managing the disaster on the spot n Collaborative technologies n Augmented not virtual reality n Mobile, ubiquitous GIS –location-based services

The technologies of U = S n Portable, wearable devices –user interfaces n Positioning –the device knows where it is n Wireless communication n The Spatial Web –everything knows where it is –GPS, RFID, etc.

How does a system know where it is? n GPS onboard –cellphone n Triangulation from towers n Determined at system build time n IP address

Geographic information science n The science behind the systems n Research that will improve future GIS n

Current GIScience topics n Extending GIS representation –3D, time, uncertainty n Extending GIS analysis –simulation modeling –data mining –visualization n Spatial data infrastructures –standards –geoportals

GOS coverage, 1/05

Concluding points n Strong uptake –some important gaps n New technologies –Google Earth, Spatial Web n GIScience –an active research community n SDI –strong institutional framework