1. The University of Texas at Dallas
Implementation of Neural network Interpolation in ArcGIS and Case Study for Spatial-Temporal Interpolation of Temperature
Master Project
POEC 6389
Xiaogang Yang
GIS Program
The University of Texas at Dallas
Instructor Dr. Fang Qiu
July, 2005

2. Outline Introduction. Objectives.
Theory of neural network interpolation.
Software design and implementation.
Case study: spatial-temporal interpolation of temperature
Conclusion.

3. Introduction The concept interpolation
Example: temperature, rainfall, Ozone, Housing price.
Most common used interpolation techniques: IDW, Spline, Kriging, Tin.
Spatial interpolation: 2D, 3D.
Spatial-temporal interpolation.
Major vendor’s application of interpolation: ESRI: IDW, Spline, Kring, Polynomial Mapinfo: IDW, TIN.
Most interpolation application is 2D based, few of them are 3D interpolation.
No application involving spatial-temporal interpolation.

4. Theory of neural network interpolation
Potential algorithm for spatial-temporal interpolation: Neural network
Neural network algorithm and back-Propagation (BP) model
Network Training: Forwards and Backwards

5. Software design and implementation
Integrated with ESRI ArcGIS: “Neural Network Extension”
Programming language: .NET platform, VB.net, ArcObject
Released Dynamic Linked Library (DLL).
Friendly user interface.
Standard data input and output: ESRI data format: Shape file, geodatabase, raster
Easy to use.

6. Software interface
Neural Network Extension

7. Software interface

8. Software interface

9. Software interface

10. Software interface

11. Software interface

12. Case study: spatial-temporal interpolation of temperature: Data Source
Study area: southern California, 26 stations.
Temperature data: daily temperature records of year (1997, 1998 and 1999).
Elevation Data: -40 ~3443 feet.
Station Group: train group and verification group

13. Case study: spatial-temporal interpolation of temperature
Multi dimensional interpolation
Spatial interpolation: 2D, 3D
Spatial-Temporal interpolation: 2D-temporal and 3D-temporal
Analysis and comparison.

14. Case study: spatial-temporal interpolation of temperature: 2D Interpolation by ESRI tools
IDW power 2 (Jan,1,1997)
IDW power 4 (Jan,1,1997)
Spline Regulation (Jan,1,1997)
Spline Tension (Jan,1,1997)
Global polynomial (Jan,1,1997)
Local polynomial (Jan,1,1997)
The R2 value of IDW and Sprine is high but very low for polynomial

15. Temperature vs. Elevation Day1

16. Case study: spatial-temporal interpolation of temperature: Neural network 2D Interpolation
NN 2D: Day 1,1997 R2 =
NN 2D: Day 50,1997 R2 =
NN 2D: Day 100,1997 R2 =
NN 2D: Day 150,1997 R2 =
NN 2D: Day 200,1997 R2 =
NN 2D: Day 250, R2 =
The distribute pattern switch a lot, and it is too general to provide the detail info.
Not good at all.

17. Neural network interpolation 2D Day1
Temperature distribution change raptly along with train loop
30000 loops R2 =
loops R2 =

18. Case study: spatial-temporal interpolation of temperature: Neural network 3D Interpolation
NN 3D: Day 1,1997 R2 =
NN 3D: Day 50,1997 R2 =
NN 3D: Day 100,1997 R2 =
NN 3D: Day 150,1997 R2 =
NN 3D: Day 200,1997 R2 =
NN 3D: Day 250,1997 R2 =

19. Neural network interpolation 3D Day1

20. Neural network interpolation 3D Day50

21. Neural network interpolation 3D Day100

22. Neural network interpolation 3D Day150

23. Neural network interpolation 3D Day200

24. Neural network interpolation 3D Day250

25. Case study: spatial-temporal interpolation of temperature: Neural network 2D-temporal Interpolation
NN 2D-T: Day 50,1997 R2 =
NN 2D-T: Day 100,1997 R2 =
NN 2D-T: Day 1,1997 R2 =
NN 2D-T: Day 200,1997 R2 =
NN 2D-T: Day 250,1997 R2 =
NN 2D-T: Day 150,1997 R2 =

26. Case study: spatial-temporal interpolation of temperature: Neural network 3D- temporal Interpolation
NN 3D-T: Day 100,1997 R2 =
NN 3D-T: Day 1,1997 R2 =
NN 3D-T: Day 50,1997 R2 =
NN 3D-T: Day 150,1997 R2 =
NN 3D-T: Day 200,1997 R2 =
NN 3D-T: Day 250,1997 R2 =

27. Neural network interpolation 3D-temporal Day1

28. Neural network interpolation 3D-temporal Day50

29. Neural network interpolation 3D-temporal Day100

30. Neural network interpolation 3D-temporal Day150

31. Neural network interpolation 3D-temporal Day200

32. Neural network interpolation 3D-temporal Day250

33. Comparison The interpolation result of 2D is unrealistic
Elevation play major effect on the temperature distribution.
3D interpolation generate better result
Temperature varies with time, The spatial-temporal (2D and 3D) interpolation takes time as an independent parameter, it can capture the trend of temperature overall.
For each specific time, 3D give the best result.

34. Project Conclusion
Neural network can be used to interpolation GIS data on multi-dimensional based interpolation for GIS dataset, 2D, 3D, spatial-temporal, even higher dimensions
The neural network interpolation application provide a very useful interpolation tool for GIS user.
The case study of spatial-temporal interpolation give very interesting result.
Spatial-temporal interpolation could be used to interpolate the irregular GIS dataset, such at housing sale price.

35. Thanks!