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Challenges in Applying Nearest Neighbour Methods

Challenges in Applying Nearest Neighbour Methods. Valerie LeMay and Ian Moss. Western Mensurationists Meeting, June 21-23, 2009, Vancouver, Washington. Challenges and Opportunities in Applying Nearest Neighbour Methods. Valerie LeMay and Ian Moss.

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Challenges in Applying Nearest Neighbour Methods

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  1. Challenges in Applying Nearest Neighbour Methods Valerie LeMay and Ian Moss Western Mensurationists Meeting, June 21-23, 2009, Vancouver, Washington

  2. Challenges and Opportunities in Applying Nearest Neighbour Methods Valerie LeMay and Ian Moss Western Mensurationists Meeting, June 21-23, 2009, Vancouver, Washington

  3. Dr. Albert Stage

  4. Outline • The Good • The Bad • The

  5. Outline • The Good • The Bad • The Ugly • Million Dollar Baby (Quiz at the End)

  6. The Good: Multivariate Reference Data, X’s and Y’s → Calculate Variable-Space Distance using X’s Target Observation, X’s only Use Y values (or averages) from selected reference observation(s) as Estimates for the target observation Select one or more neighbours that have similar X values (Small distance metric)

  7. The Good: Integrate Data Sources Across Scales For forest Inventory, X’s can be LeMay and Moss

  8. The Bad: Spatial Registration Difficulties

  9. The Bad: Differences in Spatial Extents

  10. The Bad: Too Many Choices? • Choice of weights for auxilliary variables to find nearest neighbours as this affects outcome • How many neighbours to average in getting estimates of the Y’s for a target? • Which X variables to use, which relates to sources of data for the X’s? • Pre-stratification by X variables? • When are results good enough to use?

  11. The Bad: Estimators • Bias Corrections and Variance Estimators? Single Y variable Multiple Y’s given correlations among Y’s?

  12. The Ugly • Complexity of the y-variables (e.g. tree list of dbh and species; biomass of components) • Poor relationships between available X-variables and Y-variables of interest (e.g., stems per ha as the Y variable) • Scarcity of reference data in terms of number of observations and ranges of X-variables, relative to the number of target observations • Processing times: many Y’s and many target observations = very long processing • “Smoothing” of results for mapping when pixel-type data are used

  13. Million Dollar Baby • Research on estimators that have good statistical properties are being developed • Possible gains with new and more available remotely sensed imagery • Better GPS to spatially register data sources • New algorithms for smoothing using spatial clustering • Increased computing resources • Finally – we can link single-tree growth and yield models to the inventory

  14. The Quiz • Q1. What did the list of topics of the outline have in common? (Oral) • Q2. What was uniquely different about one of the items in the list of topics? Name two differences. • Q3. Graduate students only: What is the main take home message? (May be not obvious and remember to “think like a graduate student”) • Q4. What is the nearest neighbour for the target given in the next slide? Why? (You may think outside the box!) LeMay and Moss

  15. Target 2 4 3 5 7 1 6

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