CN111080008B - Urban ecological vulnerability spatial prediction method based on GIS and CA simulation - Google Patents
Urban ecological vulnerability spatial prediction method based on GIS and CA simulation Download PDFInfo
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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
Technical Field
The invention relates to the technical field of urban planning, in particular to a GIS and CA simulation-based urban ecological vulnerability spatial prediction method.
Background
Ecological vulnerability is a focus of continued urban development. Predicting the distribution of urban ecological vulnerability space is an important support for developing urban development strategy. The existing prediction of urban ecological vulnerability space is mostly based on historical ecological disaster data to empirically judge vulnerability space distribution. The operation of vulnerability space data analysis in urban planning is concentrated on the operation of vector data, and quantitative urban ecological vulnerability space distribution analysis is lacked.
Disclosure of Invention
The main purpose of the invention is to provide a spatial prediction method for urban ecological vulnerability based on GIS and CA simulation aiming at the problems.
The invention adopts the following technical scheme to realize the aim: 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;
(6) Inputting urban current land cover raster data;
(7) Setting a neighborhood type;
(8) Setting a neighborhood CA cell;
(9) The statistical type is an average;
(10) 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 the study 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.
The method has the advantages that 1, the ecological vulnerability cell development rule can be quantified based on neighborhood grid probability statistics and superposition of the positive-to-negative distribution rule, and quantitative support is provided for predicting urban ecological vulnerability space.
2. The urban ecological vulnerability spatial prediction model tool interface based on GIS and CA simulation, which is developed by the invention, is simple, efficient and feasible to operate.
Drawings
FIG. 1 is a schematic diagram of a neighborhood analysis process;
FIG. 2 is a schematic diagram of a process for creating a positive Ethernet grid;
FIG. 3 is a schematic diagram of a process for creating a neighborhood ecological vulnerability space;
FIG. 4 is a schematic diagram of a process for creating a future development land neighborhood ecological vulnerability space;
FIG. 5 is a schematic diagram of a process for creating future urban land ecological vulnerability highest threshold spatial data;
FIG. 6 is a schematic flow chart of a spatial prediction method of urban ecological vulnerability;
FIG. 7 is a schematic diagram of a spatial prediction model tool interface for urban ecological vulnerability.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings and preferred embodiments. 1-7, a GIS and CA simulation-based urban ecological vulnerability spatial prediction method is developed secondarily by a Model builder tool based on a GIS platform, and the specific implementation mode is as follows:
the first step: setting up an initial model
1. Urban current land cover raster data is entered.
2. The raster data is connected to a focus statistics tool.
2.1 setting neighborhood types.
2.2 setting the neighbor CA cell values.
2.3 statistical type is average.
2.4 output neighborhood analysis result data (see fig. 1).
3. The positive ethernet grid tool is loaded.
3.1 output range was determined as study range.
3.2 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.
3.3 output of the Positive-Ether raster data (see FIG. 2)
4. The neighborhood analysis result data, the front grid data and the urban ecological vulnerability space evaluation result data are connected to a grid calculator tool. Map algebraic expression: neighborhood analysis data, front grid data, urban ecological vulnerability space evaluation result data and neighborhood ecological vulnerability space data (shown in figure 3) are output.
5. Processing future development land data. With the reclassification tool, the future development land type is set to be 1 and the non-future development land type value is set to be 0.
6. And (4) loading a multiplication tool, multiplying the neighborhood ecological vulnerability space data generated in the step (4) with the future development land data processed in the step (5), and outputting the neighborhood ecological vulnerability space data of the future development land (as shown in fig. 4).
7. Dividing the space level of the ecological vulnerability of the future development land neighborhood, and connecting the result data of the step 6 to a segmentation tool.
7.1 the number of output areas is set to 10.
7.2 segmentation method selects an Equal area.
8. And extracting the highest threshold value data of the ecological vulnerability space of the future development land neighborhood.
8.1 using a reclassification tool, assigning the highest vulnerability grade value to a new value of 1 and the other grade values to new values of 0.
8.2 outputting the highest-level raster data, namely the ecological vulnerability space highest threshold data of the future development land neighborhood (as shown in figure 5).
9. The tool is saved and named as a city ecological vulnerability spatial prediction model tool, and the technical method flow of the city ecological vulnerability spatial prediction model tool (shown in figure 6).
And a second step of: setting model parameters
1. And acquiring the focus tool variables 'neighborhood analysis' and 'statistic type', and setting the current land cover/neighborhood analysis/statistic type as a model parameter.
2. And acquiring an output range of the positive Ethernet raster data tool variable, and setting the output range as a model parameter.
3. The grid calculator tool variable "scope" is acquired, and the scope/ecological vulnerability space evaluation is set as a model parameter.
4. Setting future development land as model parameters.
5. The method comprises the steps of obtaining the dividing tool variables of dividing method and number of output areas, and setting the number of dividing methods/output areas as model parameters.
6. And acquiring a reclassification tool variable 'reclassification field', and setting the reclassification field as a model parameter.
And a third step of: deleting the initially set data values causes the model parameters to be all null values.
Fourth step: and the storage tool is used for adjusting the parameter sequence based on the attribute options and completing the urban ecological vulnerability spatial prediction model tool. Tool interface (see fig. 7).
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the 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.
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| CN106599601A (en) * | 2016-12-29 | 2017-04-26 | 中国科学院遥感与数字地球研究所 | Remote sensing assessment method and system for ecosystem vulnerability |
| KR101856490B1 (en) * | 2017-11-17 | 2018-05-10 | 노아에스앤씨 주식회사 | Method for processing disaster vulnerability information about heavy rain |
| CN109242260A (en) * | 2018-08-14 | 2019-01-18 | 黑龙江工程学院 | The construction method and verification method of landscape ecological vulnerability index |
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| KR101856490B1 (en) * | 2017-11-17 | 2018-05-10 | 노아에스앤씨 주식회사 | Method for processing disaster vulnerability information about heavy rain |
| CN109242260A (en) * | 2018-08-14 | 2019-01-18 | 黑龙江工程学院 | The construction method and verification method of landscape ecological vulnerability index |
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