Urban Growth Simulation A Case Study of Indianapolis

1. Urban Growth Simulation A Case Study of Indianapolis Sharaf Alkheder & Jie Shan School of Civil Engineering Purdue University March 10, 2005

2. OUTLINE • Introduction • Data and preprocessing • NN approach and implementation • Results and evaluation • Concluding remarks

3. INTRODUCTION • Urban growth is a complex process • Growth parameters include land use suitability, city development level, economical phase, etc... • A functional model to describe such a process is impossible • Neural Network (NN) is gaining popularity

4. INTRODUCTION (Cont’d) • Li and Yeh (2002) integrate NN, GIS and CA to simulate different development patterns • Pijanowskia et al. (2002) integrate Artificial NN and GIS to forecast the change in land use • Existing studies • Atlanta growth simulation by Yang and LO (2003) • Urban growth prediction for San Francisco and Washington by Clarke and Gydos (1998)

5. STATEMENT OF THE PROBLEM • Indianapolis exhibited accelerated urban growth over the last three decades • Such a growth makes a small part of Marion County in seventies to cover whole Marion County and parts of neighbor counties in 2003 • The objective of this work is to utilize NN algorithms to simulate urban growth boundaries

6. Methods • Simple Adaptive Linear NN (SALNN) and Back Propagation (BPNN) algorithms were used • Different city centers were selected • Three different datasets were used fortraining • Short and long-term predictions were made

7. DATAPREPARATION STUDY AREA/ INDIANAPOLIS

8. DATA • Six historical satellite images for Indianapolis • over 30 years were used: • - One Landsat MSS 60-meter image (1973) • - Five Landsat TM 30-meters image (1982, • 1987, 1993, 2000 and 2003) • All images were rectified and registered to Universal Transverse Mercator (UTM) NAD1983

9. IMAGE REGISTRATION • Although all images are georeferenced, co-registration is still needed • Second order polynomial transformation function was used • Projected images were resampled to 30 meters • 12 control points were used per image • The Landsat TM 2000 georefrenced image was used as the reference image

10. EXAMPLE OF REGISTERED IMAGES

11. IMAGE FUSION • A panchromatic 15-meter resolution image was fused with the 2003 XS low resolution images • Fusion is to produce an image with high both spectral and spatial resolution • Multiplicative method was used for fusion using all image bands • In fused image • spatial resolution is improved • spectral resolution may deteriorates in certain areas such as roads and residential areas

12. FUSION RESULTS Fused Original

13. FUSION RESULTS Spatial resolution improvement examples Fused Original

14. FUSION RESULTS Spectral resolution deterioration examples Fused Original

15. IMAGE CLASSIFICATION • Fused images and original images are respectively used for classification • Same training and testing conditions with 1:4 ratio were implemented for both classifications • Classification method: maximum likelihood; supervised • High resolution orthophotographs and USGS land classification maps were used as ground references • Seven classes were specified in the images: • - Water - Road - Residential - Commercial • - Forest - Pasture/grasses - Row crops

16. CLASSIFICATION RESULTS Original Fused

17. CLASSIFICATION RESULTS (Cont’d) • Some areas in the fused images were classified better than the original images, e.g. forest class • Other areas were deteriorated e.g. commercials • Classification accuracy of the original 2003 images was 89.14%, while it was 84.00% for the fused images • Higher overall classification accuracy is achieved for original image

18. DATA FOR NN SIMULATION • The six years historical urban growth boundaries of Indianapolis area were measured.

19. DATA FORNN SIMULATION • Two centers were selected • For every configuration, six measurements were recorded at each 3 degrees angle interval • A matrix of 120 by 6 measurements was prepared

20. NEURAL NETWORK ALGORITHMS • Three datasets were prepared for NN training: • - Real data set without interpolation • - 5 year interpolated data set • - 1 year interpolated data set • RBFN algorithm

21. NEURAL NETWORK ALGORITHMS • Two of the well-known NN algorithms were trained using the three prepared datasets for every center configuration • The adaptive linear NN as well as BP algorithms were used • Radial growth distance was predicted as a function of angular distribution and years • Short (3 years, for 2003) and long term (7 years, for 2000) predictions

22. NEURAL NETWORK ALGORITHMS SALNN Structure

23. NEURAL NETWORK ALGORITHMS BPNN Structure

24. NEURAL NETWORK GROWTH SIMULATION • For every center configuration, we produced the following outputs: • - SALNN & BPNN long term prediction (2000 based on 1973 to 1993) • - SALNN & BPNN short term prediction (2003 based on 1973 to 2000)

25. RESULTS (1) • SALNN vs. BPNN long term prediction (2000)/Center(a) • SALNN BPNN • Better Performance for SALNN for real data only • Close performance at the third dataset with SALNN being better • BPNN didn’t perform well at real data • Noticeable discrepancy between real and long-term predicted boundaries

26. RESULTS (2) • SALNN vs. BPNN short term prediction (2003)/Center(a) • SALNN BPNN • Better match between predictedand real boundaries • SALNN perform better than BPNN for all of the three data sets

27. RESULTS (3) • SALNN vs. BPNN long term prediction (2000)/Center(b) • SALNN BPNN • Some effect of center is clear on the predicted results • Third dataset produce the best results • SALNN performs better

28. RESULTS (4) • SALNNvs. BPNN short term prediction (2003)/Center(b) • SALNN BPNN • Better performance for SALNN • Center effect is less than for long term prediction

29. WEIGHTED NN URBAN GROWTH • Urban growth rate is faster in certain directions due to driving factors such as development probability • Weighted radial growth as a function of radial measurement and growth direction was used • Threshold should be met to implement the weighted growth modification • Better results were obtained were the real boundaries match the predicted ones very closely

30. RESULTS (5) • Weighted SALNNshort term prediction (2003) • Center (a) Center (b) • Very close match between real and predicted boundaries • The effect of center on prediction results minimized

31. RESULTSSUMMARY • The best growth prediction for the two algorithms and centers achieved using the third dataset • For both centers, the results showed that SALNN gave better results compared to the BPNN results • Under the limitation of the availability of the data the SALNN works better than BPNN. • Results of predictions is somewhat independent on the centers location especially for the third dataset • Weighted NN results are the best in term of matching the real and predicted boundaries

32. CONCLUSIONS • SALNN algorithm produced better results than BPNN given the limited size of the available data • Prediction results improved as the interpolation interval between the real data points gets smaller. • City center location has certain effect on the predicted urban growth pattern • Weighted NN improved the prediction results and minimized the effect of center location

33. CURRENT AND FUTURE WORK • Urban growth prediction using (X,Y) coordinates of the boundaries • Weighted NN simulation for the fact that growth is not the same in all directions • Urban growth errors NN training • Growth errors statistical modeling as a function of the radial distance, time and angles of growth • Cellular Automata and Fuzzy Logic simulation

34. QUESTIONS