CN111080008B - Spatial prediction method of urban ecological vulnerability based on GIS and CA simulation - Google Patents

Abstract

The invention relates to the technical field of urban planning, in particular to a urban ecological vulnerability spatial prediction method based on GIS and CA simulation, which comprises the following steps: creating a neighborhood analysis of the current land cover data; creating a positive ethernet distribution grid; loading ecological vulnerability space evaluation data; identifying a future development land neighborhood ecological vulnerability space; dividing the ecological vulnerability space grade of the future development land neighborhood; extracting the highest threshold value data of the ecological vulnerability space of the future development land neighborhood; and constructing an urban ecological vulnerability spatial distribution prediction initial model based on model builder, deleting initial data, and completing a model tool for predicting urban ecological vulnerability spatial distribution prediction. The method avoids the defect of empirical judgment in the traditional planning, so as to quantitatively predict the urban ecological vulnerability spatial distribution, and becomes logic of vulnerability spatial probability according to the neighborhood in the process of predicting the urban ecological vulnerability spatial distribution.

Description

Spatial prediction method of urban ecological vulnerability based on GIS and CA simulation

Technical field

The invention relates to the technical field of urban planning, and in particular to a spatial prediction method of urban ecological vulnerability based on GIS and CA simulation.

Background technique

Ecological vulnerability is the focus of sustainable urban development. Predicting the spatial distribution of urban ecological vulnerability is an important support for formulating urban development strategies. Existing predictions of urban ecological vulnerability space are mostly based on empirical determination of vulnerability spatial distribution based on historical ecological disaster data. The operation of vulnerability spatial data analysis in urban planning focuses on the operation of vector data, and there is a lack of quantitative spatial distribution analysis of urban ecological vulnerability.

Contents of the invention

The main purpose of the present invention is to provide a spatial prediction method of urban ecological vulnerability based on GIS and CA simulation in response to the above problems.

In order to achieve the above purpose, the present invention adopts the following technical solution: a spatial prediction method of urban ecological vulnerability based on GIS and CA simulation, which is characterized by: including the following steps:

Step 1: Create neighborhood analysis and use focus statistics;

(6) Input urban current land cover raster data;

(7) Set the neighborhood type;

(8) Set up neighborhood CA cells;

(9) The statistical type is average;

(10) Output raster feature proximity, including neighborhood value values and null values 0;

Step 2: Create a normal distribution and use the Create Normal Grid tool;

(1) The output range is determined as the research range.

(2) Output a raster with normal (Gaussian) distributed random values. The formula used is

where x is a raster value variable, f(x) is the function value where x occurs, e is a constant equal to 2.71828..., σ and μ are overall parameters.

Step 3: Identify the neighborhood vulnerability space using a raster calculator;

(1) Input the neighborhood analysis data created in step 1, the orthographic raster data created in step 2, and externally load the urban ecological vulnerability spatial evaluation result data;

(2) The map algebra expression is: neighborhood analysis data + orthographic raster data + urban ecological vulnerability spatial assessment result data;

Step 4: Identify the ecological vulnerability space in the neighborhood of future development land;

(1) Use the reclassification tool and set the future development land type value to 1 and the non-future development land type value to 0;

(2) Use the multiplication tool to multiply the results of step 3 with the future development land data. The output data is the ecological vulnerability space of the future development land neighborhood;

Step 5: Divide the spatial levels of ecological vulnerability in the neighborhood of future development land and use segmentation tools;

(1) The number of output areas is set to 10;

(2) Select Equal area as the segmentation method;

Step 6: Extract the spatial highest threshold data of ecological vulnerability in the neighborhood of future development land;

(1) Use the reclassification tool to assign the value with the highest vulnerability level a new value of 1, and assign the values of other levels a new value of 0;

(2) Output the highest level raster data, which is the highest threshold data of ecological vulnerability space in the neighborhood of future development land;

Step 7: Connect the tools in steps 1-6 based on the model builder, build the initial model, delete the initial data, and complete the urban ecological vulnerability spatial prediction model tool interface.

The beneficial effects of the present invention are: 1. The present invention can quantify the development rules of ecological vulnerability cells based on neighborhood grid probability statistics and superimposed normal distribution rules, and provide quantitative support for predicting urban ecological vulnerability space.

2. The urban ecological vulnerability spatial prediction model tool interface based on GIS and CA simulation developed by this invention is simple, efficient and feasible to operate.

Description of drawings

Figure 1 Schematic diagram of the neighborhood analysis process;

Figure 2 is a schematic diagram of the process of creating a orthographic grid;

Figure 3 Schematic diagram of the spatial process of creating neighborhood ecological vulnerability;

Figure 4 is a schematic diagram of the spatial process of creating ecological vulnerability in the neighborhood of future development land;

Figure 5 is a schematic diagram of the process of creating spatial data for the highest threshold of ecological vulnerability of future urban land;

Figure 6 Schematic flow chart of the urban ecological vulnerability spatial prediction method;

Figure 7 Schematic diagram of the urban ecological vulnerability spatial prediction model tool interface.

Detailed ways

The specific implementation manner of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. As shown in Figures 1 to 7, a spatial prediction method of urban ecological vulnerability based on GIS and CA simulation is secondary developed based on the Model builder tool of the GIS platform. The specific implementation method is as follows:

Step 1: Build the initial model

1. Enter the current urban land cover raster data.

2. Connect the raster data to the Focus Statistics tool.

2.1 Set the neighborhood type.

2.2 Set the neighborhood CA cell value.

2.3 The statistical type is average.

2.4 Output the neighborhood analysis result data (Figure 1).

3. Load the orthographic grid tool.

3.1 The output range is determined as the research range.

3.2 Output a raster with normal (Gaussian) distributed random values. The formula used is

where x is a raster value variable, f(x) is the function value where x occurs, e is a constant equal to 2.71828..., σ and μ are overall parameters.

3.3 Output orthographic raster data (Figure 2)

4. Connect the neighborhood analysis result data, Zhengtai raster data, and urban ecological vulnerability spatial assessment result data to the raster calculator tool. Map algebra expression: neighborhood analysis data + orthographic raster data + urban ecological vulnerability spatial evaluation result data, and output neighborhood ecological vulnerability spatial data (Figure 3).

5. Process land use data for future development. Use the reclassification tool to set the future development land type value to 1 and the non-future development land type value to 0.

6. Load the multiplication tool, multiply the neighborhood ecological vulnerability spatial data generated in step 4 with the future development land data processed in step 5, and output the neighborhood ecological vulnerability spatial data of future development land (Figure 4).

7. Divide the spatial levels of ecological vulnerability of future development land neighborhoods and connect the result data of step 6 to the segmentation tool.

7.1 The number of output areas is set to 10.

7.2 Select Equal area for segmentation method.

8. Extract the spatial highest threshold data of ecological vulnerability in the neighborhood of future development land.

8.1 Use the reclassification tool to assign a new value of 1 to the value with the highest vulnerability level, and assign a new value of 0 to other levels.

8.2 Output the highest level raster data, which is the highest threshold data of ecological vulnerability space in the neighborhood of future development land (Figure 5).

9. Save the tool and name it as the Urban Ecological Vulnerability Spatial Prediction Model Tool, the technical method process of the Urban Ecological Vulnerability Spatial Prediction Model Tool (Figure 6).

Step 2: Set model parameters

1. Obtain the focus instrumental variables "Neighborhood Analysis" and "Statistical Type", and set the current land cover/neighborhood analysis/statistical type as model parameters.

2. Obtain the tool variable "output range" of the normal raster data and set the output range as the model parameter.

3. Obtain the raster calculator tool variable "scope" and set the scope/ecological vulnerability spatial evaluation as model parameters.

4. Set future development land as model parameters.

5. Obtain the segmentation tool variables "segmentation method" and "number of output regions", and set the segmentation method/number of output regions as model parameters.

6. Obtain the reclassification tool variable "reclassification field" and set the reclassification field as a model parameter.

Step 3: Delete the initial set data values so that all model parameters are null.

Step 4: Save the tool, adjust the parameter order based on attribute options, and complete the urban ecological vulnerability spatial prediction model tool. The interface of the tool (Figure 7).

The above are only preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications can also be made. should be regarded as the protection scope of the present invention.

Claims (1)

1. A city ecological vulnerability spatial prediction method based on GIS and CA simulation is characterized in that: the method comprises the following steps:

step 1: creating a neighborhood analysis, and using a tool to perform focus statistics;

(1) Inputting urban current land cover raster data;

(2) Setting a neighborhood type;

(3) Setting a neighborhood CA cell;

(4) The statistical type is an average;

(5) Outputting grid element proximity, wherein the grid element proximity comprises a neighborhood value and a null value 0;

step 2: creating a positive-ethernet distribution, and using a tool for creating a positive-ethernet grid;

(1) The output range is determined as a research range;

(2) Outputting a grid with a normal (Gaussian) distribution of random values using the formula

Where x is the grid value variable, f (x) is the function value at which x occurs, e is a constant equal to 2.71828 …, σ and μ are overall parameters;

step 3: identifying a neighborhood vulnerability space, the tool employing a grid calculator;

(1) Inputting the neighborhood analysis data created in the step 1, and externally loading the front grid data created in the step 2 into the urban ecological vulnerability space evaluation result data;

(2) The map algebraic expression is: neighborhood analysis data, front grid data, urban ecological vulnerability space evaluation result data;

step 4: identifying a future development land neighborhood ecological vulnerability space;

(1) Setting the future development land type to be 1 and the non-future development land type value to be 0 by using a reclassification tool;

(2) Multiplying the result of the step 3 with future development land data by using a multiplication tool, wherein the output data is a future development land neighborhood ecological vulnerability space;

step 5: dividing the ecological vulnerability space grade of the future development land neighborhood, and applying a dividing tool;

(1) The number of output areas is set to 10;

(2) The segmentation method comprises the steps of selecting an Equal area;

step 6: extracting the highest threshold value data of the ecological vulnerability space of the future development land neighborhood;

(1) Applying a reclassification tool to assign a new value 1 to the value with the highest vulnerability grade, and assigning a new value 0 to the values of other grades;

(2) Outputting the highest-level raster data, namely the highest threshold value data of the ecological vulnerability space of the future development land neighborhood;

step 7: and (3) connecting the tools in the steps 1-6 based on model builder, constructing an initial model, deleting initial data, and completing the urban ecological vulnerability spatial prediction model tool interface.