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PODS: Interpreting Spatial and Temporal Environmental Information Edoardo (Edo) Biagioni University of Hawai’i at Mānoa The Challenge • Endangered plants grow in few locations • Hawai'i has steep weather gradients: the weather is different in nearby locations • A single weather station doesn’t help, so • Have many sensors (PODS) • Make them unobtrusive: rock or log • Resulting in lots of data Sample Terrain What’s a POD, anyway? Inside a “Rock” light Internal: voltage wind (bend) temperature humidity Computer & Radio Batteries Data Collection • • • • • Wind, Rain, Temperature, Light, Moisture At each pod Every 5 minutes to 1 hour, for years Images at some of the pods Networking challenge: getting the data back without discharging the batteries • How to make sense of all this data? Spatial Patterns • Wet and dry areas have different plants • Cold and warm areas have different plants • Where is the boundary? The boundary will be different for different plant species • Does cloud cover matter? • Does wind matter? Pollinators, herbivores Temporal Patterns • Is this a warm summer? Winter? • Is it a warm summer everywhere, or just in some places? • Does it rain more when it is warmer? • What events cause flowering? • How long does it take the plant to recover after an herbivore passes? Endangered: Silene Hawaiiensis Who needs the Information? • Scientists (botanists) • High-School Students • Virtual Tourists What use is the Information? • Study the plants, prevent decline • Determine what is essential for the plant’s survival: e.g., how will global warming affect it? • Locate alternative areas • Watch what happens, instead of trying to reconstruct what happened • Capture rare phenomena How is the data communicated? • Graphs, maps, tables • Tables unwieldy for large numbers of PODS • Graphs need many different scales • Maps can help intuitive understanding • Ultimately, need to find useful patterns Picture of weather data, from web • http://weather.yahoo.com/graphics/satellite/east_usa.html Simple Map Blue: rain Big Blue: recent rain Cyan: cool, dry Red: warm, dry http://red2.ics.hawaii.edu/cgi-bin/location Graphs vs. Maps • Graphs • Good for recognition of temporal patterns • Can summarize a lot of data very concisely • Mostly for homogeneous data • Maps • Good for recognition of spatial patterns • Can summarize a lot of data very concisely • Good for heterogeneous data Strategies • Data Mining: search data for patterns, try to match to plant distribution • Machine Learning: try to predict new data. If prediction is wrong, something unpredicted (unpredictable!) is happening • Better maps, incorporating lots of data including images, but in a way that supports intuitive analysis Better Map Blue: rain Red: temperature Yellow: sunlight Plant population Not (yet) automated on the web… Where to go from here • Plant “surveillance”: being there, remotely • Data Collection is only the essential first step • Data Analysis must be supported by appropriate tools • Find out what really matters in the life of an endangered plant Acknowledgements and Links • Co-Principal Investigators: Kim Bridges, Brian Chee • Students: Shu Chen, Michael Lurvey, Dan Morton, Bryan Norman, and many more • http://www.botany.hawaii.edu/pods/ pictures, data • http://www.ics.hawaii.edu/~esb/pods/ these slides, the paper • [email protected]