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Predicting Locations Using Map Similarity(PLUMS): A Framework for Spatial Data Mining Sanjay Chawla(Vignette Corporation) Shashi Shekhar, Weili Wu(CS,

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Presentation on theme: "Predicting Locations Using Map Similarity(PLUMS): A Framework for Spatial Data Mining Sanjay Chawla(Vignette Corporation) Shashi Shekhar, Weili Wu(CS,"— Presentation transcript:

1 Predicting Locations Using Map Similarity(PLUMS): A Framework for Spatial Data Mining Sanjay Chawla(Vignette Corporation) Shashi Shekhar, Weili Wu(CS, Univ. of Minnesota) Uygar Ozesmi(Ericyes University, Turkey) http://www.cs.umn.edu/research/shashi-group

2 Outline Motivation Application Domain Distinguishing characteristics of spatial data mining Problem Definition Spatial Statistics Approach Our approach: PLUMS Experiments, Results, Conclusion and Future Work

3 Motivation Historical Examples of Spatial Data Exploration –Asiatic Cholera, 1855 –Theory of Gondwanaland –Effect of fluoride on Dental Hygiene A potential application in news –Tracking the West Nile Virus

4 Application Domain Wetland Management: Predicting locations of bird(red-winged blackbird) nests in wetlands Why we choose this application ? –Strong spatial component –Domain Expertise –Classical Data Mining techniques(logistic regression, neural nets) had already been applied

5 Application Domain: Continued.. Nest Locations Distance to open water Vegetation DurabilityWater Depth

6 Unique characteristics of spatial data mining Spatial Autocorrelation Property

7 Unique characteristics…cont Average Distance to Nearest Prediction(ADNP):

8 Location Prediction:Problem Formulation Given: A spatial framework S. – Explanatory functions, – Dependent function F –A family F of learning model function mappings Find an element Objective: maximize (map_similarity = classification_accuracy + spatial accuracy) Constraints: spatial autocorrelation exists

9 Spatial Statistics Approach 1. 2. 2”Logistic Regression:

10 Spatial Stat: Solution Techniques Least Square Estimation: Biased and Inconsistent Maximum Likelihood: Involve computation of large determinant(from W) Bayesian: Monte Carlo Markov Chain(e.g. Gibbs Sampling)

11 Our Approach

12 Experiment Setup

13 Result(1)

14 Result(2)

15 Conclusion and Future work PLUMS >> Classical Data Mining techniques PLUMS State-of-the-art Spatial Statistics approaches Better performance(two orders of magnitude) Try other configurations of the PLUMS framework and formalize!

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