Topic 7: GIS Models and Modeling

第七讲

GISGIS 模型与建模

Chapter 14 GIS Models and Modeling14.1 Introduction14.2 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 Programs14.3 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

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

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

Basic Elements of GIS Modeling

1. Types of GIS Models

2. The Modeling Process

3. The Role of GIS in Modeling

4. Integration of GIS and Other Modeling Programs

Types of GIS Models

1. 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

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

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.

GIS and Other Modeling Programs

Loose coupling

Tight coupling

Embedded system

GIS ModelsA 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.

12

3

12

3

  

 4

    

ID Suit

123

31

2

ID Type

12

3

21

18

6

12

34

5 67

Suit = 2 AND Type = 18

ID1

2

3

56

7

Suit Type

331

2

2

1

211818

21

66

4 2 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.

1 1 1 4

3 2 4 4

3 3 3 4

4 4 4 4

1 1 1 3

3 2 2 3

3 3 4 4

3 3 4 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.

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.

12

3

12

3

0.640.46

0.140.26

0.440.68

0.56

12

34

56

7

(S_V * 0.4) + (T_V * 0.6)

+

+

ID Suit

1

2

3

3

1

2

S_V

1.0

0.2

0.52

ID Type

1

2

3

21

18

6

T_V

0.4

0.1

0.83

ID

1

2

3

4

5

6

7

S_V

0.2

1.0

1.0

0.2

0.5

0.5

0.5

T_V

0.4

0.1

0.1

0.1

0.4

0.8

0.8

 

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).

7 21

32 49

1 3

5 2

45 57

63 31

0.2 0.6

0.8 1.0

0.2 0.6

1.0 0.4

0.6 0.8

1.0 0.4

0.12 0.36

0.48 0.60

0.04 0.12

0.20 0.08

0.12 0.16

0.20 0.08

0.28 0.64

0.88 0.76

x 0.6 x 0.2 x 0.2

Inputgrids

Standardize cell valuesinto 0.0-1.0 scale

Multiply bycriterion weights

Calculate index values by summing weighted criterion values 

  

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.

GIS ModelsA 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.

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/

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