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
Type of GIS Models
The Modeling Process
The Role of GIS in Modeling
Integration of GIS and Other Computer Programs
14.3 Binary Models
Applications of the Binary Model
14.4 Index Models
The Weighted Linear Combination Method
Other Methods
Applications of the Index Model
Chapter 14 outline (1)

3. 14.5.1 Linear Regression Models 14.5.2 Logistic Regression Models
14.6 Process Models
Soil Erosion Models
Other Examples of Process Models
GIS and Process Models
Chapter 14 outline (2)

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
Chapter 14 Exercises

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. Deductive or 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 2 3 1 2 3 + + 2 ID
Type 1 2 3 21 18 6 1 3 ID 1 2 3 5 6 7
Suit
Type 21 18 4 2 1 3 4 5 7 6 4
Figure 14.1
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 1 1 4 1 1 1 3 3 2 4 4 3 2 2 3 3 3 3 4 3 3 4 4 4 4 4 4 3 3 4 4
Grid 1
Grid 2
([Grid1] = 3)
AND
([Grid2] = 3) =
Figure 14.2
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. Figure 14.3
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. ID
Suit 1 2 3
S_V
1.0
0.2
0.52 1 2 3 + 2 + 1 ID
Type 1 2 3 21 18 6
T_V
0.4
0.1
0.83 3 2 1 3 4 ID
S_V
T_V 7 5 6 1
1.0
0.4 2
1.0
0.1 3
0.2
0.1
0.46 4
0.5
0.1
0.64
0.14
0.26 5
0.5
0.4
0.44
0.56
0.68 6
0.5
0.8
Figure 14.4 7
0.2
0.8
(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 * *0.6).

15. 7 21 1 3 45 57
Input
grids 32 49 5 2 63 31
0.2
0.6
0.2
0.6
0.6
0.8
Standardize cell values
into scale
0.8
1.0
1.0
0.4
1.0
0.4
x 0.6
x 0.2
x 0.2
Multiply by
criterion weights
0.12
0.36
0.04
0.12
0.12
0.16
0.48
0.60
0.20
0.08
0.20
0.08
0.28
0.64
Calculate index values by summing weighted criterion values
0.88
0.76
Figure 14.5
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* * *0.2, or

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. WEPP (Water Erosion Prediction Project)
Conservation Reserve Program (CRP), Farm Service Agency (FSA) of the U.S. Department of Agriculture
California LESA model
WEPP (Water Erosion Prediction Project)
SWAT
Better Assessment Science Integrating point and Nonpoint Sources (BASINS) system
Chapter 14 websites

18. Thank You!
Chapter 14 websites