CN116167661A - Land utilization change simulation credibility assessment method based on space dislocation - Google Patents
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Abstract
The invention relates to a land utilization change simulation credibility assessment method based on space dislocation, which comprises the following steps: collecting a land utilization classification map and a driving factor map for constructing a CA model; acquiring a land transition probability map based on an intelligent optimization algorithm, and establishing a CA model; simulating the city pattern by using the CA model and outputting and storing a simulation result; performing single numerical evaluation on the simulation result of the CA model by adopting a pixel-by-pixel comparison method; respectively generating a spatial distribution map of a simulation result state-static index and a simulation result state-change index by adopting a spatial dislocation-moving window analysis method; and outputting and storing the simulation result and the spatial distribution diagram as a credibility evaluation result. Compared with the prior art, the invention has the advantages that an evaluation chart for reporting the simulation precision/error of each pixel can be generated, the spatial distribution of the simulation precision and the error is displayed, the spatial heterogeneity of the precision can be captured, the reliability of the CA model and the remote sensing classifier can be detected, and the like.
Description
Technical Field
The invention relates to the field of land utilization change simulation, in particular to a land utilization change simulation credibility assessment method based on space dislocation.
Background
Cellular Automaton (CA) models are the most commonly used spatial simulation method for modeling and predicting urban dynamic growth. The reliability of the CA model is the basis for its modeling of historical patterns and prediction of future scenarios. In CA-based city growth simulation, model performance assessment is typically affected by data source and driver accuracy, CA parameter selection, and effectiveness of the assessment method. Therefore, evaluating simulation accuracy is important for discriminating whether the CA model can meet city planning and decision. The evaluation method of the model simulation result generally comprises visual discrimination, pixel-by-pixel comparison, spatial structure evaluation, landscape index calculation and the like.
For each simulation result graph, when a state-static (state-static) index and a state-change (state-change) index are generated by pixel-by-pixel comparison, each evaluation index corresponds to a numerical value. The values of these indicators are closely related to the modeling method and the area of investigation, and often the accuracy of the simulation cannot be estimated accurately. Since simulation results often produce spatial heterogeneity, the resulting precision results also produce similar spatial heterogeneity. For CA-based simulations, one evaluation index value that is higher in the global range may be lower in the simulation accuracy of the local area, and one evaluation index value that is lower in the global range may be higher in the simulation accuracy of the local area. Thus, single-value evaluation cannot reflect the simulation accuracy across spaces, resulting in failure to evaluate the simulation performance of the local area. Thus, the key problems that need to be solved at present are: how to evaluate the simulation results of the local area.
Disclosure of Invention
The invention aims to provide a land utilization change simulation credibility assessment method based on space dislocation, which not only can report quantitative assessment indexes of each pixel, but also can capture space heterogeneity of precision, is beneficial to detecting credibility of a CA model and a remote sensing classifier, and thus supports land resource management and decision.
The aim of the invention can be achieved by the following technical scheme:
a land utilization change simulation credibility evaluation method based on space dislocation comprises the following steps:
step 1) collecting a land utilization classification map and a driving factor map for constructing a CA model;
step 2) acquiring a land transition probability map based on an intelligent optimization algorithm, and establishing a CA model;
step 3) simulating the city pattern by using the CA model and outputting and storing a simulation result;
step 4) performing single numerical evaluation on the simulation result of the CA model by adopting a pixel-by-pixel comparison method;
step 5) generating a spatial distribution diagram of a simulation result state-static index by adopting a spatial dislocation-moving window analysis method;
step 6) generating a spatial distribution diagram of the simulation result state-change index by adopting a spatial dislocation-moving window analysis method;
step 7) outputting and storing the simulation result and the spatial distribution diagram as a credibility evaluation result.
Said step 1) comprises the steps of:
step 1-1), land utilization classification images are obtained by using a land satellite remote sensing image and a supervision classification method;
step 1-2) extracting a driving factor graph affecting urban expansion by using the vector data set and the raster image.
Said step 2) comprises the steps of:
step 2-1) performing systematic sampling on the urban land utilization classification map and the driving factor map, and providing training samples for constructing CA conversion rules, wherein the function of the conversion rules is expressed as follows:
P·g～UrbanTranRule(State cu ,P tr ,NE,Res,Sto)
where Pg is the global transition probability, state cu Is the current cell state, P tr Is the transition probability defined by the driving factor, NE is the neighborhood effect, res is the global and local constraint, sto is the random factor;
step 2-2) searching CA parameters based on a cross entropy optimization algorithm CEO, and establishing a CA model.
Said step 2-2) comprises the steps of:
step 2-2-1) constructing an objective function which relates the CA model to the actual city growth;
step 2-2-2) constructing a group of random sequence samples according to probability distribution;
step 2-2-3) finding a better solution by updating the probability distribution parameters and the objective function, wherein in order to find the most suitable probability P tr (a) Automatically updated probability parameter p= { P 1 ,P 2 ,…,P k Expressed as:
in the method, in the process of the invention,
for the indication function, γ is a predefined small residual, a ij The j-th parameter which is the i-th driving factor, k is the population size of CEO;
step 2-2-4) when the CEO iteration is finished, obtaining an optimal solution a, namely CA parameters, and determining a CA model by the CA parameters.
Said step 3) comprises the steps of:
step 3-1) correcting the constructed CA model: under the GIS modeling and simulation environment, selecting an urban distribution pattern of a certain year as an initial state of a correction period, operating the constructed model for A times to obtain a simulation result of the urban distribution pattern, and outputting the simulation result, wherein A is the difference between the initial and final years of the model correction period;
step 3-2) verifying the constructed CA model: under the GIS modeling and simulation environment, selecting an urban distribution pattern of a certain year as an initial state of a verification period, operating the constructed model for B times to obtain a simulation result of the urban distribution pattern, and outputting the simulation result, wherein B is the difference between the initial and the final years of the model verification period.
Said step 4) comprises the steps of:
step 4-1), performing state-static index evaluation on the simulation result by adopting a column-linked matrix in a pixel-by-pixel comparison method;
step 4-2) generating four indexes by adopting real-analog superposition in a pixel-by-pixel comparison method: hit, miss, false positive, and correct rejection, and calculate a state-change indicator for assessing dynamic growth of the city based on the four indicators.
The status-stationary indicators include overall accuracy, user accuracy, producer accuracy.
The state-change indicators include quality factor, accuracy, recall.
Said step 5) comprises the steps of:
step 5-1) generating a basic evaluation index layer:
layer a. Both the real mode and the simulation result are city class Cor_Ur;
layer b. True mode is city class, simulation result is non-city class Fal _ur;
layer c. True mode is non-city class, simulation result is city class Fal _ NUr;
layer d. Real mode and simulation result are non-city class Cor_ NUr;
step 5-2) using spatial dislocation-moving window analysis: calculating a focus level index by adopting a focus statistics tool in ArcGIS;
step 5-3) generating an accuracy and error map: an evaluation chart of the state-stationary index focus level is generated by the following formula:
wherein, map mw () The layer is shown, SPOA shows the result of the spatialization of the overall accuracy, SPPA shows the result of the spatialization of the producer accuracy, SPUA shows the result of the spatialization of the user accuracy.
Said step 6) comprises the steps of:
step 6-1) generating a basic evaluation index layer:
layer a. Both simulation result and actual mode are city URHit;
layer b. Simulation results are urban and actual mode is non-urban URFalse;
layer c. Simulation result is non-city and actual mode is city URMiss;
layer d. Simulation result and actual mode are non-urban CR;
step 6-2) using spatial dislocation-moving window analysis: calculating a focus level index by adopting a focus statistics tool in ArcGIS;
step 6-3) generating an accuracy and error map: an evaluation chart of the state-change index focus level is generated by the following formula:
wherein, map mw () The layer is shown, SPFOM shows the spatialization result of the quality factor, SPPRE shows the spatialization result of the accuracy, and SPRE shows the spatialization result of the recall.
Compared with the prior art, the invention has the following beneficial effects:
the method of the invention can recognize the quantity precision of each pixel under the condition of considering the influence of the neighborhood, but not only provides a single value to summarize the simulation quality and the model performance, and better reflects the urban growth effect of the CA model in different simulated areas, and has the advantages that: 1) An evaluation chart reporting the simulation accuracy/error of each pixel can be generated, and the spatial distribution of the simulation accuracy and error is displayed; 2) The relationship between the driving factor and the evaluation index can be quantified. The space evaluation method not only can be used for evaluating the simulation result of the CA model, but also can be used for evaluating the remote sensing image classifier.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a diagram showing a historical land use distribution map for an example area according to an embodiment of the present invention;
FIG. 3 is a driving factor graph of land use variation in 1995-2015;
fig. 4 is a simulation result and evaluation chart of 2005 and 2015;
FIG. 5 is a state-stationary index space pattern diagram of the simulation results of 2005;
FIG. 6 is a state-change index space pattern diagram of the 2005 simulation result;
FIG. 7 is a state-stationary index space pattern diagram of the 2015 simulation results;
fig. 8 is a state-change index space pattern diagram of the 2015 simulation result.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
Unlike global and local levels, moving window analysis is an efficient method of evaluating simulation results at the focus level. Some GIS scholars have applied this approach to detect local differences or similarities between simulation results and actual patterns and point out that moving window analysis can be used to evaluate the similarity in composition and spatial structure of land use categories. The method is characterized by a fuzzy evaluation using a single numerical evaluation index, such as Kappa. Compared with the accurate comparison of pixel-by-pixel comparison, the land utilization change simulation credibility evaluation based on spatial dislocation overcomes the defect that the traditional evaluation method can underevaluate the simulation precision. Therefore, the invention provides a land utilization change simulation credibility assessment method based on space dislocation, which is shown in fig. 1 and comprises the following steps:
step 1) collect land use classification maps covering the initial and final years of the study and driving factor maps affecting urban expansion for use in constructing the CA model.
Step 1-1) obtaining land utilization classification diagrams of initial and final years required to be input in CA model correction and verification periods by using a land satellite remote sensing image and adopting a supervision classification method in ENVI software.
Step 1-2) extracting a driving factor graph affecting urban expansion by using the vector data set and the raster image.
Specifically, the distances of key elements such as city centers, county centers, main roads and infrastructures are obtained in a GIS environment by using a Euclidean distance method by integrating data such as a topographic map, a population distribution map, an administrative division map, a road traffic map and a POI, so that driving factors influencing urban expansion are extracted.
And 2) acquiring a land transition probability map based on an intelligent optimization algorithm, and establishing a CA model.
Step 2-1) performing systematic sampling on the urban land utilization classification map and the driving factor map, and providing training samples for constructing CA conversion rules.
Cellular Automata (CA) is a generic name of a self-organizing model, and is automatically evolved by a bottom-up method and is formed by rules of model construction. From the current cell State (State cu ) Conversion probability (P) based on driving factor tr ) The global transition probability P.g and the transition rule UrbanTranRule, which are jointly determined by the Neighborhood Effect (NE), the global and local constraints (Res) and the random factor (Sto), can be expressed as:
P·g～UrbanTranRule(State cu ,P tr ,NE,Res,Sto) (1)
where Pg is the global transition probability, state cu Is the current cell state, P tr Is the transition probability defined by the driving factor, NE is the neighborhood effect, res is the global and local constraint, sto is the random factor.
Realizing CA model construction in UrbanCA software and global transition probability P all Can be calculated by the following formula:
wherein TIP represents a scaling parameter for adjusting the probability attenuation effect, the range is 0.0-0.1, and the larger the value is, the stronger the scaling effect is; whereas LAP represents a scaling parameter that adjusts for the neighborhood effect, ranging from 0.5 to 1.0, a smaller value indicates a weaker scaling effect.
Transition probability P determined by driving factor tr Is the core part of CA conversion rule, and represents the influence of driving factors on land utilization change and city development and influences the cell state at the next moment in a probability mode.
Step 2-2) searching CA parameters based on a cross entropy optimization algorithm CEO, and establishing a CA model.
Cross Entropy Optimization (CEO) CEO is a heuristic algorithm that is currently widely used to search for differences between target values and their predicted values, and in CA modeling, an objective function guided model may be used to implement automatic parameterization. Specifically, the method comprises the following steps:
step 2-2-1) constructing an objective function which relates the CA model to the actual city growth;
step 2-2-2) constructing a group of random sequence samples according to probability distribution;
step 2-2-3) find a better solution (i.e., CA parameters) by updating the probability distribution parameters and the objective function. To find the most suitable probability P tr (a) Automatically updated probability parameter p= { P 1 ,P 2 ,…,P k Expressed as:
in the method, in the process of the invention,
for the indication function, γ is a predefined small residual, a ij The j-th parameter which is the i-th driving factor, k is the population size of CEO;
step 2-2-4) when the CEO iteration is finished, obtaining an optimal solution a, namely CA parameters, and determining a CA model by the CA parameters.
And 3) simulating the city pattern by using the CA model and outputting and storing the simulation result.
Step 3-1) correcting the constructed CA model: under the GIS modeling and simulation environment, selecting an urban distribution pattern of a certain year as an initial state of a correction period, operating the constructed model for A times to obtain a simulation result of the urban distribution pattern, and outputting the simulation result, wherein A is the difference between the initial and final years of the model correction period.
Step 3-2) verifying the constructed CA model: under the GIS modeling and simulation environment, selecting an urban distribution pattern of a certain year as an initial state of a verification period, operating the constructed model for B times to obtain a simulation result of the urban distribution pattern, and outputting the simulation result, wherein B is the difference between the initial and the final years of the model verification period.
And 4) carrying out single numerical evaluation on the simulation result of the CA model by adopting a pixel-by-pixel comparison method.
And 4-1) carrying out state-static index evaluation on the simulation result by adopting a column-connected matrix in the pixel-by-pixel comparison method. In the present embodiment, the status-stationary index includes an Overall Accuracy (OA), a Producer Accuracy (PA), a User Accuracy (UA).
Step 4-2) generating four indexes by adopting real-analog superposition in a pixel-by-pixel comparison method: hit (URHit), miss (urMiss), false positive (urrase) and Correct Rejection (CR), and calculates a state-change indicator for assessing the dynamic growth of the city based on the four indicators. In this embodiment, the state-change indicators include figure of merit (FOM), accuracy (PRE), recall (RE).
The step 4) is specifically as follows: and (3) measuring whether the state of each pixel in the simulation result is matched with the actual city pattern by using a pixel-by-pixel comparison method. And selecting the model as an index for quantitative evaluation of the CA model. OA is a single numerical probability that represents the agreement of the simulation results with the actual city pattern, and can be expressed as:
wherein p is nn Is the number of pixels of correct simulation, SUM is the number of all pixels, t cn Is the number of urban pixels in the actual result.
Spatial overlapping of the start year actual map, the end year actual map, and the end year simulated map may reveal differences in structural patterns. The true-simulated overlap generates four metrics, including hit (URHit), miss (URMiss), false alarm (urfliase), and Correct Rejection (CR). Wherein URHit indicates that a cell is in a city state in both the actual mode and the simulation result, URMiss indicates that a cell is in a city state in the actual mode and is in a non-city state in the simulation result, urfliase indicates that a cell is in a non-city state in the actual mode and is in a city state in the simulation result, and CR indicates that a cell is in a non-city state in both the actual mode and the simulation result. The index generated by the above spatial superposition can be used to calculate a state-change index, such as FOM, for assessing urban dynamic growth:
where FOM represents the ability of the CA model to capture new urbanized pixels.
And 5) generating a spatial distribution diagram of the simulation result state-static index by adopting a spatial dislocation-moving window analysis method.
The moving window analysis generally uses a certain pixel as a center, sets a window with a fixed size and shape, performs statistical calculation in the window, and returns the calculation result to the pixel in the center of the window. The calculation process is repeated for each pixel, and a new grid surface layer can be generated. If the moving window analysis is used for model evaluation, spatial distribution results of accuracy and error can be obtained.
Based on the traditional pixel-by-pixel comparison method, the method for realizing the state-static index space visualization mainly comprises the following steps:
step 5-1) T-based 2 The real land utilization pattern and the simulated land utilization pattern at the moment are used for obtaining four basic evaluation index TIF layers:
layer a. Both the real mode and the simulation result are city class Cor_Ur;
layer b. True mode is city class, simulation result is non-city class Fal _ur;
layer c. True mode is non-city class, simulation result is city class Fal _ NUr;
and d, the real mode and the simulation result are of non-city type Cor_Nur.
Step 5-2) using spatial dislocation-moving window analysis: and (3) inputting each index layer extracted in the step (5-1) by adopting a focus statistics tool in the ArcGIS, setting the shape and the size of a moving window, determining a numerical statistics mode, calculating a focus level index, and using the generated result to calculate a space visualization result of each state-static index.
Step 5-3) generating an accuracy and error map: an evaluation chart of the state-stationary index focus level is generated by the following formula:
wherein, map mw () The layer is shown, SPOA shows the result of the spatialization of the overall accuracy, SPPA shows the result of the spatialization of the producer accuracy, SPUA shows the result of the spatialization of the user accuracy.
And 6) generating a spatial distribution diagram of the simulation result state-change index by adopting a spatial dislocation-moving window analysis method.
Based on the traditional pixel-by-pixel comparison method, the method for realizing the state-change index space visualization mainly comprises the following steps:
step 6-1) T-based 1 Real land utilization pattern at moment, T 2 The real land utilization pattern and the simulated land utilization pattern at the moment are used for generating a basic evaluation index layer for acquiring a space visualization result of the state-change index:
layer a. Both simulation result and actual mode are city URHit;
layer b. Simulation results are urban and actual mode is non-urban URFalse;
layer c. Simulation result is non-city and actual mode is city URMiss;
and d, the simulation result and the actual mode are non-urban CR.
Step 6-2) using spatial dislocation-moving window analysis: and (3) inputting each index layer extracted in the step (6-1) by adopting a focus statistics tool in the ArcGIS, setting the shape and the size of a moving window, determining a numerical value statistics mode, and using the generated result to calculate a space visualization result of each state-change index.
Step 6-3) generating an accuracy and error map: an evaluation chart of the state-change index focus level is generated by the following formula:
wherein, map mw () The layer is shown, SPFOM shows the spatialization result of the quality factor, SPPRE shows the spatialization result of the accuracy, and SPRE shows the spatialization result of the recall.
Step 7) outputting and storing the simulation result and the spatial distribution diagram as a credibility evaluation result.
In the embodiment, a land satellite remote sensing image and a vector data set are adopted to acquire urban land patterns in Ningbo city 1995, 2005 and 2015 and driving factors influencing urban expansion. CA conversion rules are built in UrbanCA software based on a CEO method, a land conversion probability map of Ningbo city is generated, and a CEO-CA model is built to simulate city expansion in 1995-2015. After a single numerical value evaluation result is obtained by adopting a pixel-by-pixel comparison method, the simulation results in the correction and verification stages are evaluated by utilizing the proposed space evaluation method, and SPOA, SPPA, SPUA, SPFOM, SPPRE and SPRE of the simulation results in 2005 and 2015 are obtained.
In step 1), land satellite remote sensing images of 1995, 2005 and 2015 and data such as administrative division diagrams, traffic road network diagrams, population diagrams, topography diagrams and POIs are collected by using Ningbo city as a research area and serve as basic data for constructing CA models in research.
Land utilization classification is carried out on Landsat remote sensing images of Ningbo market by adopting a supervision classification method in ENVI software, so that a real city space pattern diagram of Ningbo market in 1995, 2005 and 2015 is generated, as shown in FIG. 2.
Based on the administrative district demarcation layer, the road traffic layer and the infrastructure layer, the Euclidean distance in the space analysis tool is utilized to calculate the distance from each cell to the city center, the district county center, the trunk road, the education institution, the medical facilities and the like in the GIS environment, and grid diagrams of topography, population and the like are obtained to form a driving factor diagram for influencing the expansion of the urban space of Ningbo city, as shown in tables 1 and 3.
TABLE 1 Driving factors affecting Ningbo City development
| Name of the name | Category(s) | Source | Interpretation of the drawings |
| Distance to city center | Center of the machine | National base geographic data | Influence of Ningbo municipal administration center on urban development |
| Distance to town center | Center of the machine | National base geographic data | Influence of urban center of Ningbo city on city development |
| Distance to main road | Network system | OpenStreetMap road data | Influence of Ningbo city major roads on city development |
| Population of people | Density of | WorldPOP | Population density of each pixel |
| Topography of the ground | Elevation of the sea | Terrain remote sensing product | Elevation of each pixel |
| Distance to hospital | POI | Hundred degree map | Influence of Ningbo city hospitals on city development |
| Distance to school | POI | Hundred degree map | Influence of Ningbo city school on city development |
In the steps 2) -4), using a real city mode of Ningbo city in 1995 as an initial state, and adopting a CEO-CA model to simulate a city pattern in 2005; and the real city mode in the year 2005 of Ningbo city is taken as an initial state, and the CEO-CA model is adopted to simulate the city pattern in the year 2015. The simulation results were evaluated for single values, and table 2 and fig. 4 show the simulation results and accuracy.
TABLE 2 evaluation of simulation result accuracy during correction and verification period
In step 5) and step 6), as for the simulation result of the CA model, the accuracy of the focus level is visualized using a moving window analysis method, and statistical data (range, average value, and SD) thereof are analyzed. Tables 3 and 4 are statistical information of the CA model correction and validation period state-rest and state-change space evaluation indexes, respectively.
TABLE 3 statistics on correction period State-still and State-change spatial assessment indices
TABLE 4 statistics on verification period State-still and State-change space assessment indicators
Fig. 5 and 6 are spatial evaluation graphs of simulation results of CA model correction period, respectively, in which fig. 5 shows SPOA, SPPA and SPUA of simulation results in 2005, and fig. 6 shows SPFOM, SPPRE and SPRE of simulation results in 2005.
Fig. 7 and 8 are spatial evaluation diagrams of simulation results of CA model verification period, respectively, in which fig. 7 shows SPOA, SPPA and SPUA of simulation results of 2015, and fig. 8 shows SPFOM, SPPRE and SPRE of simulation results of 2015.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by a person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. The land utilization change simulation credibility evaluation method based on space dislocation is characterized by comprising the following steps of:
step 1) collecting a land utilization classification map and a driving factor map for constructing a CA model;
step 2) acquiring a land transition probability map based on an intelligent optimization algorithm, and establishing a CA model;
step 3) simulating the city pattern by using the CA model and outputting and storing a simulation result;
step 4) performing single numerical evaluation on the simulation result of the CA model by adopting a pixel-by-pixel comparison method;
step 5) generating a spatial distribution diagram of a simulation result state-static index by adopting a spatial dislocation-moving window analysis method;
step 6) generating a spatial distribution diagram of the simulation result state-change index by adopting a spatial dislocation-moving window analysis method;
step 7) outputting and storing the simulation result and the spatial distribution diagram as a credibility evaluation result.
2. The land use variation simulation reliability assessment method based on spatial dislocation according to claim 1, wherein said step 1) comprises the steps of:
step 1-1), land utilization classification images are obtained by using a land satellite remote sensing image and a supervision classification method;
step 1-2) extracting a driving factor graph affecting urban expansion by using the vector data set and the raster image.
3. The land use variation simulation reliability assessment method based on spatial dislocation according to claim 1, wherein said step 2) comprises the steps of:
step 2-1) performing systematic sampling on the urban land utilization classification map and the driving factor map, and providing training samples for constructing CA conversion rules, wherein the function of the conversion rules is expressed as follows:
P·g～UrbanTranRule(State cu ,P tr ,NE,Res,Sto)
where Pg is the global transition probability, state cu Is the current cell state, P tr Is the transition probability defined by the driving factor, NE is the neighborhood effect, res is the global and local constraint, sto is the random factor;
step 2-2) searching CA parameters based on a cross entropy optimization algorithm CEO, and establishing a CA model.
4. A land use change simulation reliability assessment method based on spatial dislocation according to claim 3, wherein said step 2-2) comprises the steps of:
step 2-2-1) constructing an objective function which relates the CA model to the actual city growth;
step 2-2-2) constructing a group of random sequence samples according to probability distribution;
step 2-2-3) finding a better solution by updating the probability distribution parameters and the objective function, wherein in order to find the most suitable probability P tr (a) Automatically updated probability parameter p= { P 1 ,P 2 ,…,P k Expressed as:
in the method, in the process of the invention,
for the indication function, γ is a predefined small residual, a ij The j-th parameter which is the i-th driving factor, k is the population size of CEO;
step 2-2-4) when the CEO iteration is finished, obtaining an optimal solution a, namely CA parameters, and determining a CA model by the CA parameters.
5. The land use variation simulation reliability assessment method based on spatial dislocation according to claim 1, wherein said step 3) comprises the steps of:
step 3-1) correcting the constructed CA model: under the GIS modeling and simulation environment, selecting an urban distribution pattern of a certain year as an initial state of a correction period, operating the constructed model for A times to obtain a simulation result of the urban distribution pattern, and outputting the simulation result, wherein A is the difference between the initial and final years of the model correction period;
step 3-2) verifying the constructed CA model: under the GIS modeling and simulation environment, selecting an urban distribution pattern of a certain year as an initial state of a verification period, operating the constructed model for B times to obtain a simulation result of the urban distribution pattern, and outputting the simulation result, wherein B is the difference between the initial and the final years of the model verification period.
6. The land use variation simulation reliability assessment method based on spatial dislocation according to claim 1, wherein said step 4) comprises the steps of:
step 4-1), performing state-static index evaluation on the simulation result by adopting a column-linked matrix in a pixel-by-pixel comparison method;
step 4-2) generating four indexes by adopting real-analog superposition in a pixel-by-pixel comparison method: hit, miss, false positive, and correct rejection, and calculate a state-change indicator for assessing dynamic growth of the city based on the four indicators.
7. The method for evaluating reliability of land use variation simulation based on spatial misalignment of claim 6, wherein the status-stationary index comprises overall accuracy, user accuracy, and producer accuracy.
8. The method for evaluating reliability of land use variation simulation based on spatial dislocation according to claim 6, wherein said state-variation index comprises quality factor, accuracy, recall.
9. The method for evaluating the reliability of land use change simulation based on spatial dislocation according to claim 7, wherein said step 5) comprises the steps of:
step 5-1) generating a basic evaluation index layer:
layer a. Both the real mode and the simulation result are city class Cor_Ur;
layer b. True mode is city class, simulation result is non-city class Fal _ur;
layer c. True mode is non-city class, simulation result is city class Fal _ NUr;
layer d. Real mode and simulation result are non-city class Cor_ NUr;
step 5-2) using spatial dislocation-moving window analysis: calculating a focus level index by adopting a focus statistics tool in ArcGIS;
step 5-3) generating an accuracy and error map: an evaluation chart of the state-stationary index focus level is generated by the following formula:
wherein, map mw () The layer is shown, SPOA shows the result of the spatialization of the overall accuracy, SPPA shows the result of the spatialization of the producer accuracy, SPUA shows the result of the spatialization of the user accuracy.
10. The land use variation simulation reliability assessment method based on spatial dislocation according to claim 8, wherein said step 6) comprises the steps of:
step 6-1) generating a basic evaluation index layer:
layer a. Both simulation result and actual mode are city URHit;
layer b. Simulation results are urban and actual mode is non-urban URFalse;
layer c. Simulation result is non-city and actual mode is city URMiss;
layer d. Simulation result and actual mode are non-urban CR;
step 6-2) using spatial dislocation-moving window analysis: calculating a focus level index by adopting a focus statistics tool in ArcGIS;
step 6-3) generating an accuracy and error map: an evaluation chart of the state-change index focus level is generated by the following formula:
wherein, map mw () The layer is shown, SPFOM shows the spatialization result of the quality factor, SPPRE shows the spatialization result of the accuracy, and SPRE shows the spatialization result of the recall.
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