作者： Bryan C. Pijanowski, Daniel G. Brown, Bradley A. Shellito, Gaurav A. Manik 報告同學：詹傑閔

1. Using neural networks and GIS to forecast landuse changes: a Land Transformation Model應用GIS及類神經元網路預測土地利用變化之研究:一種土地轉換模式 作者：Bryan C. Pijanowski, Daniel G. Brown, Bradley A. Shellito, Gaurav A. Manik 報告同學：詹傑閔 1

3. Introduction • This paper illustrates how combining geographic information systems (GIS) and artificial neural networks (ANNs) can aid in the understanding the complex process of land use change. A GIS-based Land Transformation Model (LTM) to forecast land use change over large regions.

4. Background • ANNs(Artificial Neural Networks) ANNs were developed to model the brain’s interconnected system of neurons so that computers could be made to imitate the brain’s ability to sort patterns and learn from trial and error, thus observing relationships in data.

5. Background • A. simple perceptron • B. The multi-layer perceptron (MLP) classifying linearly separable data and performing linear functions The MLP consists of three layers: input, hidden, and output

6. Background • GIS(Geographic Information System) • Geographic Information System is an advanced computer software technology. It is a variety of spatial information collection, storage, analysis and visualization of information processing and management system. • In the international study on land use change, mainly in support of GIS through remote sensing images of different periods or land-use diagram space Diejia operation, obtained the land use types during the transfer matrix, and then analyzes the status of land use change .

7. Background

8. Background • The LTM follows four sequential steps

9. Background • LTMhas many factors,such as political, administrative, economic, cultural, human behavior and the environment, small roads, residential streets, rivers, lakes and so on. • LTM based on GIS technology is used to predict the large regional scale land use change. It uses a large number of socio-economic, political and environmental data and other information as the basis for the social, economic, political, ecological environment, land planners and resource managers to provide the necessary information.

10. Background • To recapitulate, LTM model in the following aspect is a powerful tool: • When the social, economic and spatial variables • driving the land use change occurs, the detection • of a variety of mechanisms. • (2) To predict the future potential for land use change.(3) Assessment of the government management • system and policies on land use and development • patterns.

11. Methods • Study area and data sources • Michigan’s Grand Traverse Bay Watershed (GTBW) was selected as the test site for this project. The GTBW, located in the northwestern portion of Michigan’s Lower Peninsula, is one of the most rapid population growth and land use change regions in the USA. • From 1970 to 1997, resident population in the watershed nearly doubled. Traverse City, with a resident population of approximately 18,000 (oftentimes having a seasonal tourist population exceeding 500,000) is the largest city in the watershed.

12. Methods • Map of Michigan’s Grand Traverse Bay Watershed counties and important locations within the watershed.

13. Methods

14. Methods • GIS-based predictor variables

15. Methods • Maps of the 10-predictor variables used for the training exercise. Ten predictor variables and the exclusion zones were compiled in Arc/Info Gridformat using the LTM GIS Avenue interface. • format (Table 1; Fig. 3) using the LTM GIS Avenue interface.

16. Methods • Maps of the 10-predictor variables used for the training exercise.

17. Methods • Maps of the 10-predictor variables used for the training exercise.

18. Methods • Maps of the 10-predictor variables used for the training exercise.

19. Methods • Maps of the 10-predictor variables used for the training exercise.

20. Methods • ANN-based integration • ANNs were applied to the prediction of land use change in four phases: • Use of GIS spatial data layer as the input layer ANNs • The use of small areas of historical data as the sample • data • (3) The extended network, all of the study area using • historical data • (4) The use of information obtained predictions

21. Methods • LTM application of the model is broadly as follows: The re-classification of land types with the code, and information and data of the drawings after the available information regarding the transport network, rivers, lakes, coastline location, etc. As a combination of GIS-based LTM model input data.

22. Methods • In the establishment of GIS-based land use change model, based on past historical data of a large number of mass analysis, we can see land use change and population trends; then the impact of land use in the population, political, economic, transportation and other elements of graph Overlay analysis to determine the elements of the new integrated impact of land use. The conclusions of the data will be generated as the ANNs spatial data layer input data to predict.

23. Methods • An overlay of model predictions and observed changes in an area southwest of Traverse City in Grand Traverse County.

24. Results and discussion • Watershed-scale land use projections • Theseprojections illustrate how the ANN could be trained on relationships betweenurbanization and all of the predictor variables that occurred in Grand Traverse County and, through our approach, applied to the same predictor variables scaledto a larger region to provide reasonable results for these counties in the watershed.

25. Results and discussion • ThisFig. shows the results of this regional forecast of land use changes

26. Results and discussion • Land use change prediction is the use of many different periods to obtain source of information on their comprehensive analysis and comparison, based on the changes that change the type of region and it is a data-based learning and analysis process, which is in line with ANN technology features. • ANN analysis of information processing capabilities of the GIS to make up for the lack of dynamic data analysis. It can be developed based on historical data to a certain variation of induction, and then to predict.

27. Conclusions • We made several assumptions in order to keep the model simple: (1) the pattern of each predictor variable remained constant beyond 1990. (2) spatial rules used to build the interactions between the predictor cells and potential locations for transition are assumed to be correct and remain constant over time. (3) the neural network itself was assumed to remain constant over time. (4) the amount of urban per capita undergoing a transition is assumed to be fixed over time.

28. Conclusions • Changes using GIS and ANN to ANN forecasting method takes advantage of the high degree of complexity of mapping abilityand Strong self-organizing and adaptive learning capacity ability to multi-source data fusion , detection accuracy and efficiency in a greater increase. • During the network training, the use of existing GIS data-aided training samples Selected to achieve the automation of sample points on the part of the selection of training samples can improve the efficiency of selection.

29. Conclusions • When using ANN predicted by the size and timing across the region-wide restrictions on the length, because some of the major impact Difficult to determine the characteristics of elements, so the prediction is not entirely accurate. • The need for the use of GIS data generated by way of a deeper level, We believe that with the continuous progress of science and technology will be more accurate forecasting results.