990 likes | 1.24k Views
Open Source Software and IPM Decision Tools: Some history& advantages vs. proprietary closed source software, and how open source software can be used for web-based database/GIS/decision support systems. Leonard Coop, Research Associate Entomology Dept. and Integrated Plant Protection Center
E N D
Open Source Software and IPM Decision Tools: Some history& advantages vs. proprietary closed source software, and how open source software can be used for web-based database/GIS/decision support systems Leonard Coop, Research Associate Entomology Dept. and Integrated Plant Protection Center Oregon State University
Software development: what tools should I use? - Performance: features/power/learning curve, etc. - Cost - License restrictions - Interoperability/Extendability - Support
Unix - the basis for open source software - The core Unix approach: non-GUI, interoperable toolkit of single purpose applications - A hypothetical example: Web user uploads a data file for GIS/plotting cat rawdata.txt | datafix.pl | GRASS | gnuplot | pbmtogif | apache Web user sees a map + plot of data
Open vs non-open source options Internet/Infrastructure DNS MS DNS TCP/IP IPX/NetBEUI (archaic) HTTP/HTML none SunOne/Java J2EE MS .net Server programs Email: Sendmail MS Exchange Web: Apache MS IIS File sharing: Samba MS Network Portal system: Metadot/PHPnuke,etc $$$
Open vs non-open source options Client & client/server programs OS: Linux *BSD 95/98/ME/NT/2000/XP Web/Email: Mozilla MS Outlook Office Suite: Star/Open/K MS 95/97/2000/XP Photo: GIMP Photoshop Stats: R S+ Database: mySQL Oracle, DBII Web devel: perl,php,python ASP, Coldfusion GIS: GRASS ESRI ArcInfo, ArcView
Searching for anything on the internet: Google (runs on 2000+ Linux boxes)
Open Source Software: What is the idea behind it? - Open source computing emerged from academia - Software development is a science - knowledge is shared openly, enabling evolutionary progress - Publishing source code (methods), brings peer review and accountability to the process
1983 Richard Stallman: started the Free Software Foundation/GNU software project
Staroffice: Virtual clone of MS office; version 5.2 free for Linux/Windows
How compatible/useful is openoffice? From slashdot.org (Feb. 24, 2002):
Where does LINUX get its name? - From OSU (sort of): LINUX is named after its inventor Linus Torvalds, first developed in 1991 while a student in Helsinki Finland. He was named after Linus Pauling, Because he was a famous chemist, and was trained at OSU
Project Support Provided by: USDA Western Region IPM Grants Program (1996-98, 1999-2002) USDA Pest Management Centers - W. Region (2002-2004) IPPC (OSU Integrated Plant Protection Center) - state level IPM Commodity grants (Oregon Vegetable Commission, Oregon Essential Oil Growers League, Oregon Cherry Commission)
Weather data for pest management models: free or fee? • Weather networks: • 650+ sites in OR, WA, ID, MT, WY, AK, W. Canada • Agrimet, Hydromet (Bur. Reclamation) • METAR, COOP (national weather service) • RAWS (US Forest Service, BLM) • Grower networks (e. g. Adcon, Automata) • Key website at NWS-Missoula
Online IPM Decision tools using open source software - Pest Alert Systems - Portal System - Phenology Models - Phenology Maps - GIS examples with GRASSLinks - Decision Support Systems
Online IPM Decision tools using open source software - Pest Alert Systems - Portal System - Phenology Models - Phenology Maps - GIS examples with GRASSLinks - Decision Support Systems
IPPC/PM Centers NW Portal - Custom Channels plus email/pager/ICQ notification
Online IPM Decision tools using open source software - Pest Alert Systems - Portal System - Phenology Models - Phenology Maps - GIS examples with GRASSLinks - Decision Support Systems
PRISM - Parameter-elevation Regression on Independent Slopes Model located at Oregon State University www.ocs.orst.edu/prism
DD maps: downscaling algorithm from 4 to 0.5 km/cell (64-fold in 2 dimensions) Use the GIS to calculate local 5x5 weighted regression equations for each cell at the coarse resolution (4 km), model is cumulative DD = a + b(elevation) Save model parameters a & b as maplayers At the high resolution (0.5km), use model parameters to estimate cumulative DDs and use distance-weighted averages to smooth edges Display new downscaled maps
Gaussian smoothed map (based on 4 km resolution) Middle Mountain not apparent
Downscaled map - 0.5 km resolution Middle Mountain apparent
Preliminary validation of downscale algorithm:map-predicted vs actual DDs ------------------------------------------- Data sources correlation coefficient vs. actual DD orig (4km) gaussian downscaled ------------------------------------------- verification sites (included in mapmaking) (5 locations) 0.99 0.97 0.99 validation sites (excluded from mapmaking) (7 locations) 0.67 0.76 0.81 -------------------------------------------
Targeted DD maps: Downy Brome in Hermiston, Lygus bug in Ontario (Treasure Valley)