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Topic 7: GIS Models and Modeling

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  1. Topic 7: GIS Models and Modeling 第七讲 GIS模型与建模

  2. Chapter 14 GIS Models and Modeling 14.1 Introduction 14.2 GIS Modeling 14.2.1 Type of GIS Models 14.2.2 The Modeling Process 14.2.3 The Role of GIS in Modeling 14.2.4 Integration of GIS and Other Computer Programs 14.3 Binary Models 14.3.1 Applications of the Binary Model 14.4 Index Models 14.4.1 The Weighted Linear Combination Method 14.4.2 Other Methods 14.4.3 Applications of the Index Model

  3. 14.5 Regression Models 14.5.1 Linear Regression Models 14.5.2 Logistic Regression Models 14.6 Process Models 14.6.1 Soil Erosion Models 14.6.2 Other Examples of Process Models 14.6.3 GIS and Process Models

  4. Applications: GIS Modeling Task 1: Build a Vector-based Binary Model Task 2: Build a Raster-based Binary Model Task 3: Build a Vector-based Index Model Task 4: Build a Raster-based Index Model

  5. Basic Elements of GIS Modeling • Types of GIS Models • The Modeling Process • The Role of GIS in Modeling • Integration of GIS and Other Modeling Programs

  6. Types of GIS Models • Descriptive or Prescriptive • 描述性的或说明性的 • 2. Deterministic or Stochastic • 确定性的或随机性的 • 3. Static or Dynamic • 静态的或动态的 • 4. Deductiveor Inductive • 演绎的或归纳性的

  7. The Modeling Process Step 1. Define the goals of the model Step 2. Break down the model into elements and define the properties of each elements and the interactions between elements Step 3. Implement (实现)and calibrate(修正) the model Step 4. Validate (验证)the model

  8. The Role of GIS in Modeling • First, a GIS is a tool that can process, display, and integrate different data sources including maps, digital elevation models (DEMs), GPS (Global Positioning System), images, and tables. • Second, models built with a GIS can be vector-based or raster-based. • Third, the distinction between raster-based and vector-based models does not preclude(排除) GIS users from integrating both types of data in the modeling process. • Fourth, the process of modeling may take place in a GIS or require the linking of a GIS to other computer programs.

  9. GIS and Other Modeling Programs • Loose coupling • Tight coupling • Embedded system

  10. GIS Models A binary model uses logical expressions to select map features from a composite map or multiple grids. The output of a binary model is in binary format: 1 (True) for map features that meet the selection criteria and 0 (False) for map features that do not. An index model calculates the index value for each unit area and produces a ranked map based on the index values. An index model is similar to a binary model in that both involve multi-criteria evaluation and both depend on map overlay operations in data processing. But an index model produces for each unit area an index value rather than a simple yes or no.

  11. ID Suit 1 3 1 2 2 3 ID Type 21 1 2 18 3 6 Type ID Suit 1 3 21 2 3 18 18 1 3 4 2 18 2 21 5 6 2 6 6 7 1 1 2 3 + + 2 1 3 2 1 3 4 7 5 6 4 Suit = 2 AND Type = 18 An illustration of a vector-based binary model. The two maps at the top are overlaid so that their spatial features and their attributes of Suit and Type are combined. A logical expression, Suit = 2 AND Type = 18, results in the selection of polygon 4 in the output.

  12. 1 4 1 1 1 3 1 1 2 3 4 4 2 3 2 3 3 4 3 3 4 3 3 4 3 4 4 4 4 4 3 4 Grid 1 Grid 2 ([Grid1] = 3) AND ([Grid2] = 3) = An illustration of a raster-based binary model. A query statement, ([Grid1] = 3) AND ([Grid2] = 3), results in the selection of 3 cells in the output.

  13. To build an index model with the selection criteria of slope, aspect, and elevation, the weighted linear combination method involves evaluation at three levels. The first level of evaluation determines the criterion weights (e.g., Ws for slope). The second level of evaluation determines standardized values for each criterion (e.g., s1, s2, and s3 for slope). The third level of evaluation determines the index (aggregate) value for each unit area.

  14. Suit ID S_V 1 3 1.0 1 0.2 2 2 3 0.52 ID Type T_V 21 0.4 1 2 18 0.1 3 0.83 6 1 2 3 + + 2 1 3 2 1 3 T_V S_V ID 4 7 5 1 0.4 1.0 6 0.1 2 1.0 3 0.1 0.2 0.46 4 0.1 0.5 0.64 0.14 5 0.4 0.5 0.26 0.56 0.44 6 0.8 0.5 0.68 7 0.8 0.2 (S_V * 0.4) + (T_V * 0.6) An illustration of a vector-based index model. First, the Suit and Type values of the two input maps are standardized from 0.0 to 1.0. Second, the two maps are overlaid. Third, a weight of 0.4 is assigned to the map with Suit and a weight of 0.6 to the map with Type. Finally, the index values are calculated for each polygon in the output by summing the weighted values. For example, Polygon 4 has an index value of 0.26 (0.5 *0.4+ 0.1*0.6).

  15. 45 1 3 57 7 21 Input grids 32 49 63 5 31 2 0.2 Standardize cell values into 0.0-1.0 scale 0.6 0.6 0.8 0.6 0.2 0.4 1.0 0.4 1.0 1.0 0.8 Multiply by criterion weights x 0.2 x 0.2 x 0.6 0.12 0.12 0.16 0.12 0.36 0.04 0.20 0.60 0.08 0.20 0.48 0.08 0.64 0.28 Calculate index values by summing weighted criterion values 0.88 0.76 An illustration of a raster-based index model. First, the cell values of each input grid are converted into the standardized scale of 0.0 to 1.0. Second, the index values in the output grid are calculated by summing the products of each grid multiplied by its assigned weight. For example, the index value of 0.28 is calculated from: 0.2*0.6 + 0.2*0.2 + 0.6*0.2, or 0.12 + 0.04 + 0.12.

  16. GIS Models A regression model relates a dependent variable to a number of independent (explanatory) variables in an equation, which can then be used for prediction or estimation. There are two types of regression model: linear regression and logistic regression. A process model integrates existing knowledge about the environmental processes in the real world into a set of relationships and equations for quantifying the processes. Modules or sub-models are often needed to cover different components of a process model. Some of these modules may use mathematical equations derived from empirical data, while others may use equations derived from laws in physics.

  17. Conservation Reserve Program (CRP), Farm Service Agency (FSA) of the U.S. Department of Agriculture http://www.fsa.usda.gov/dafp/cepd/crp.htm California LESA model http://www.consrv.ca.gov/DLRP/qh_lesa.htm/ WEPP (Water Erosion Prediction Project) http://topsoil.nserl.purdue.edu/nserlweb/weppmain/wepp.html SWAT http://waterhome.tamu.edu/NRCSdata/SWAT_SSURGO Better Assessment Science Integrating point and Nonpoint Sources (BASINS) system http://www.epa.gov/waterscience/BASINS/

  18. Thank You!