sampling in i-tree concepts, techniques and applications
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Sampling in i-Tree
Concepts, techniques and applications
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Introduction
Sampling is so pervasive in i-Tree that we have factored it out for a separate discussionOverviewConceptsTechniquesApplications
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Concepts IRandom sampleData collection in which every member of the
population has an equal chance of being selectedPopulation = the set of people or entities to which
findings are to be generalized. The population must be defined explicitly before a
sample is taken
Can sometimes break population into subgroups (stratification) for better numbers
Mind tricks easily, so need rigorous method
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Source: http://www.negrdc.org/counties/madison/comprehensive-plans/newcomp/maps/8_01ExistLandUseMadisonCo.jpg
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Concepts IIVariance = (SD)2
Measure of how spread out the distribution is, i.e., how much individual samples vary
The less the individual measurements vary from the mean (average), the more reliable the mean
In an urban forest, different traits to investigate (variables) may have different variances
Species distribution (high?) vs. population size (low) Hurricane debris (high?) vs. ice storm debris (low)
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Source: Dave Nowak and Jeff Walton, personal communication (DRG data)
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Concepts III
Sample sizeWill need to be larger
the weaker the relationships to be detected the higher the significance level being sought the smaller the population of the smallest subgroup the greater the variance of the variables
Can be smaller as these factors change, especially as variance goes down
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Source: Dave Nowak, personal communication
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Standard error (SEM)The Standard Error (Standard Error of the Mean)
calculates how accurately a sample mean estimates the population mean.
Formula: SEM = SD/N , where SD = “standard deviation” of the sample, and N = sample size.
Note that as SD goes down or N goes up, SEM gets smaller—i.e., estimate becomes better.
Commonly represented by “±” after a number.
Concepts IV
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Source: blogaloutre http://www.ontabec.com/fatigue.jpg
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Techniques IGet random numbersTablesTelephone book
(final digits!)Electronic
randomizersOnline DesktopPDA
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Techniques IISelect plotsUse map techniques
Grid overlay for maps/photosSimple edge rulers also work
Pick randomly from listStreet, with replacementBlock number
Create random coordinates SpreadsheetGIS
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Techniques II
Easy way to get random list of street segments
Bring TIGER/Line files as shape file from ESRI into a GIS
Details in Appendix B of the Manual
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Techniques IIIReserveCreate more plots than neededSomething like 10%Take replacements from list in order
when plot must be thrown outNon-existentUnfindable Inaccessible
No bias!
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Application I
Inventory typesComplete Inventory
Costly, time-consumingPartial Inventory
Complete inventory of some forest segmentSample Inventory
Randomly-selected trees inventoried for large-scale interpretation
Cost-efficient Good for planning Not suitable for day-to-day field management
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Application I
Sample inventory benefitsIncrease public safetyFacilitate short- and long-term planningImprove public relationsJustify budgetsEstimate tree benefits
Large gain for small investment
i-Tree promotes the value of sampling
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Applications II
Manual sampling techniques valid, but tedious for larger areasi-Tree v. 1.0 will include applications to automate the process for two types of plots:Linear (street) plots/segments
STRATUM/MCTI, SDAPSpatial (park, any area) plots
UFORE
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Applications II
Linear plot selectorSTRATUM/MCTISDAP
Final testingRequirementsArcMap 8.3 or 9.0Polygon file delimiting study area
boundaryRoad shape file (TIGER/Line data)
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Applications II
Spatial plot selectorUFOREFinal testing
RequirementsArcMap 8.3 or 9.0Polygon file delimiting study area
boundaryRaster-based file of strata (e.g., land
uses) within study area Digital aerial photos (optional)
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Final sampling thoughts
Sampling is our friendBoth tool and product in i-TreeUnderstanding of validity of what i-Tree offers will depend critically on understanding the process and capability of sampling