1. Location Prediction and Spatial Data Mining (S. Shekhar)
Specific Project in
Evaluation of location prediction techniques
Towards high performance parallel implementation
AHPCRC Relevance – Projectile Target Interaction Portfolio
Increase lethality of weapons such as guided missiles
Location prediction for map matching
to check correctness of missile trajectory
To identify unanticipated obstacle
Towards possible rerouting
Army Relevance in general
Predicting global hot spots (FORMID)
Army land management endangered species vs. training and war games
Search for local trends in massive simulation data
Critical infra-structure defense (threat assessment)
Inferring enemy tactics (e.g. flank attack) from blobology
Locating enemy (e.g. sniper in a haystack, sensor networks)
Locating friends to avoid friendly fire

2. Location Prediction Problem Definition: Past Approaches:
Given: 1. Spatial Framework
2. Explanatory functions:
3. A dependent function:
4. A family of function mappings:
Find: A function
Objective: maximize classification accuracy
Constraints: Spatial Autocorrelation in dependent function
Past Approaches:
Non-spatial: logistic regression, decision trees, Bayesian
Assume independent distribution for learning samples
Auto-correlation => poor prediction performance
Spatial: Spatial auto-regression (SAR), Markov random field Bayesian classifier (MRF)
No literature comparing the two!
Learning algorithms for SAR are slow (took 3 hours for 5000 data points)!
Nest locations
Distance to open water
Vegetation durability
Water depth

3. Accomplishments Formal Results Experimental results
SAR - parametric statistics, provides confidence measures in model
MRF from non-parametric statistics
SAR : MRF-BC :: linear regression : Bayesian Classifier
Rewrite SAR as y = (QX)  + Q, where Q = (I- W)-1
SAR has linear class boundaries in transformed space (QX, y)
MRF-BC can represent non-linear class boundaries
Experimental results
MRF-BC can provide better classification accuracies than SAR
But solution procedure is very slow
Details in Recent paper in IEEE Transactions on Multimedia