CN110991497B - BSVC (binary sequence video coding) -method-based urban land utilization change simulation cellular automaton method - Google Patents
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Abstract
The invention relates to a BSVC (binary sequence virtual machine) method based urban land utilization change simulation cellular automaton method. Comprising the following steps: 1) Performing supervision classification on the remote sensing images to obtain a classification chart of urban land utilization; 2) Obtaining urban land utilization change driving factor data and preprocessing the data; acquiring effective sample points of a land utilization map and a driving factor by a random hierarchical sampling method; 3) Training the effective sample points by using a Bayes Space Variable Coefficient (BSVC) method to obtain a conversion rule of the cellular automaton; 4) Obtaining urban land utilization conversion probability according to CA conversion rules established by BSVC; 5) Build BSVC-based CA model (CA BSVC ) The method comprises the steps of carrying out a first treatment on the surface of the 6) By CA BSVC The model carries out simulation application and verification analysis on urban land utilization, and realizes evaluation through Overall Accuracy (OA) and figure of merit (FOM); 7) And outputting and storing the simulation result. Compared with the prior art, the invention has higher simulation precision and better urban land utilization change simulation capability.
Description
Technical Field
The invention relates to a method for simulating cellular automata by urban land utilization change, in particular to a method for simulating cellular automata by urban land utilization change based on a BSVC method.
Background
Urban growth reflects the change of natural surface to urban artificial area, and has great influence on human society and ecological environment. In city growth simulation, some models consider spatial non-stationarity, but the existing model methods cannot fully reflect the spatial heterogeneity characteristics of city dynamics.
The geographic Cellular Automaton (CA) is a bottom-up, self-organizing urban simulation model for urban land use history pattern reconstruction and future scene prediction. To define driver-based transformation rules, the CA model typically uses driver factors reflecting the biophysical, socioeconomic, and infrastructure of the urban dynamics. The driving factor based transformation rules are typically constructed using Logistic Regression (LR) which assumes that the impact of each factor on urban space growth is the same. This means that the logistic regression derived parameters have spatial invariance and do not reflect the spatial non-stationarity of the city growth. To solve the problem of spatial instability, some scholars have developed some spatial methods to construct the CA model. For example, partition-based CA modeling divides space into several sub-regions and then uses LR to handle urban growing spatial differences. The K-means and KNN-cluster are adopted for regional division according to the evolution rate, and the performance of the method is superior to that of the traditional CA model. By employing unique transformation rules in each sub-region, the partitioning approach may reflect spatial non-stationarity explicitly and implicitly. Although these methods may represent spatial non-stationarity, they may not adequately represent the spatial non-stationarity of urban growth due to the use of spatially invariant parameters.
The spatial coefficient of variation model (SVCM) can generate transformation rules with spatial non-stationarity by allowing coefficients to vary with spatial and positional changes, thereby defining the spatial non-stationarity of urban growth. In SVCM, geographic Weighted Regression (GWR) is a typical method to incorporate spatial non-stationarity into the parameterization without predefined region classification. GWR is a local statistical model that uses a distance weighted subset of samples to generate regression coefficients for each point that are sensitive to the location of the sample. Recent technology, the combination of GWR and CA models can more accurately simulate urban expansion than traditional CA models. Although this approach may take into account spatial non-stationarity, there are some features that may lead to modeling difficulties. First, since GWR regression depends on the properties of surrounding samples, data set outliers may affect regression coefficients through spatial enclave effects. Second, non-constant variances are widely present in space and may not meet the assumption of normal errors. In contrast, a Bayesian Spatial Variation Coefficient (BSVC) method can detect non-constant variances, detect the effects of outliers, and thereby mitigate the bias of coefficient estimates. Therefore, the BSVC method should be more suitable for city growth simulation in combination with the CA model.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for simulating cellular automata by urban land utilization change based on a BSVC method.
The aim of the invention can be achieved by the following technical scheme:
a BSVC method-based urban land utilization change simulation cellular automaton method comprises the following steps:
step 1: performing supervision classification on the remote sensing images to obtain land utilization classification diagrams of initial and final years;
step 2: obtaining urban land utilization change driving factor data, and obtaining a land utilization classification chart and effective sample points of the driving factor data after preprocessing;
step 3: training the effective sample points by using a Bayes space coefficient changing method to obtain a conversion rule of the cellular automaton;
step 4: obtaining urban land utilization conversion probability by utilizing conversion rules of cellular automata built according to BSVC training;
step 5: establishing a CA model based on BSVC (binary sequence-based virtual reality) by integrating transformation probability, cell field, random factors and limiting factors, namely CA BSVC A model;
step 6: by CA BSVC The model carries out simulation application and verification analysis on urban land utilization to obtain CA BSVC A model simulation result;
step 7: for CA BSVC And the model and the simulation result thereof respectively carry out precision assessment from two aspects of rule fitting and simulation result, and output and store the simulation result.
Further, the step 1 comprises the following sub-steps:
step 11: acquiring satellite remote sensing image and vector map data required by modeling, and carrying out space reference unification and geometric correction on the satellite remote sensing image and vector map data;
step 12: and acquiring land utilization classification diagrams of cities in the initial year and the ending year based on a spectrum angle supervision classification method by utilizing two-stage satellite remote sensing images.
Further, the step 2 comprises the following sub-steps:
step 21: selecting driving space factor data affecting urban land utilization change, wherein the driving space factor data comprises data of highways, railways, subways, primary highways and elevations and population;
step 22: obtaining variables of distances of expressways, railways, subways and primary roads in ArcGIS by using Euclidean distances through remote sensing image data, administrative division diagrams and road traffic diagrams;
step 23: and sampling the land utilization classification map and the factor graph layer by using a random hierarchical sampling method, and obtaining effective sample points of the land utilization classification map and the driving factor data for providing reliable sample data points for CA rule conversion.
Further, the step 4 specifically includes: the method for acquiring the transformation probability of the land by utilizing the transformation rule of the cellular automaton established according to BSVC training to acquire the transformation probability distribution of the land under the influence of the space variable under the set space resolution comprises the following steps:
assuming that s represents whether the cell state transitions from non-urban to urban from time t to t+1, then y is denoted as 1; s is marked as 0 when the state of the cell is unchanged from time t to t+1;
and calculating the conversion probability of the land by using the acquired space variable data.
Further, CA in the step 5 BSVC The core problem of the model is to determine whether to switch a cell from one state to another in the next step, and the corresponding description formula is:
in the method, in the process of the invention,
indicates the state of the cell at time t+1, < >>
Representing the cell state at time t, f representing the calculated total transition probability P g Is a comprehensive transfer rule of (1)，P d Representing land use conversion probability based on driving factors, < ->
Representing the impact of the domain, con represents the spatial suppression function.
Further, CA in the step 5 BSVC The land utilization conversion probability and the total conversion probability of the model based on the driving factors are calculated as follows:
wherein a is i Representing the weight, x, of the ith driving factor i Represents the ith driving factor, ε represents the fit residual, TIP represents the time delta parameter, and LAP represents the local adjustment parameter.
Further, CA in the step 5 BSVC The field of the model adopts Moore field, and the description formula is as follows:
in the method, in the process of the invention,
the total number of urban cells in the m.times.m domain is represented, and (j. Noteq.i) indicates that the central cell i does not participate in the calculation.
Further, the weight of the ith driving factor is calculated as:
in which W is ij Representing a row normalization matrix, I k A standard matrix representing k rows and k columns, u i Representing random disturbance, beta i Representing the matrix of model coefficients, σ, at position i 2 Representing variance, delta 2 Representing a scale factor controlling the smoothing effect, W i The (n×n) space matrix in the position i is represented, and X represents the dependent variable matrix (n×k).
Further, the step 6 specifically includes: CA implementation using UrbanCA software and MTALAB language BSVC And (3) a simulation and prediction process of the model is performed by using the CA model for M times by taking the land utilization pattern of a certain year as an initial state, wherein M represents the year difference between the initial and the end, and a simulation and prediction result of land utilization change is obtained.
Further, the step 7 includes the following sub-steps:
step 71: by comparison with the land utilization pattern of remote sensing classification, CA BSVC And (3) carrying out precision calculation and evaluation on the model simulation result, wherein the precision calculation indexes comprise: figure of merit FOM and overall accuracy OA;
step 72: CA will be CEO Superposing and evaluating the model simulation result and the remote sensing classification result, wherein the superposed result comprises the following steps: the actual and simulation are city Hit, non-city simulation is city False, city simulation is non-city Miss, non-city CR and Water;
step 73: and outputting and storing the simulation result in GIS software.
Compared with the prior art, the invention has the following advantages:
(1) The invention considers the Bayesian space variable coefficient and constructs a CA model CA based on the Bayesian Space Variable Coefficient (BSVC) BSVC And is applied to simulation of urban land use variation. CA model CA by regressing the model with Geographic Weighting (GWR) GWR And (3) comparing the simulation results of the test pieces. CA (CA) BSVC Is superior to CA in terms of overall accuracy and graphics goodness GWR . The urban land utilization change model simulation method based on the cellular automata and the Bayesian space variable coefficients can better simulate and predict urban land utilization change dynamics.
(2) The invention relates to a BSVC-method-based urban land utilization change simulation cellular automaton method, which adopts influence factors mainly including space distance variables, natural factors and socioeconomic factor variables, and adopts a Bayesian space change coefficient CA model (CA BSVC ) The modeling effect of the model is superior to that of the geo-weighted regression CA model (CA GWR ) The simulation of urban land utilization change can be well completed.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of an example study area;
FIG. 3 is a diagram of urban land use driving factors;
FIG. 4 shows the actual urban land use variation and CA BSVC 、CA GWR Conversion probability maps of the two models;
FIG. 5 is a real urban land distribution and CA BSVC 、CA GWR Two models simulate urban land distribution maps.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The invention can be realized by the following technical scheme:
as shown in fig. 1, the overall flow of the method for simulating cellular automata by urban land utilization change based on a Bayesian Space Variable Coefficient (BSVC) method of the invention comprises the following steps:
1) Performing supervision classification on the remote sensing images by utilizing a spectrum angle method to obtain urban land utilization classification diagrams of initial and final years;
2) Obtaining urban land utilization change driving factor data and preprocessing; acquiring effective sample points of a land utilization map and a driving factor by a random hierarchical sampling method;
3) Training effective sample points by using a Bayes Space Variable Coefficient (BSVC) method to obtain a conversion rule of a Cellular Automaton (CA);
4) Obtaining urban land utilization conversion probability according to CA conversion rules established by BSVC;
5) Establishing a CA model (CA) based on BSVC by integrating transformation probability, cell neighborhood, random factors and limiting factors BSVC )；
6) By CA BSVC The model carries out simulation application and verification analysis on urban land utilization, and realizes evaluation through Overall Accuracy (OA) and figure of merit (FOM);
7) And outputting and storing the simulation result.
The step 1) specifically comprises the following steps:
8) Acquiring satellite remote sensing image and vector map data required by modeling, and carrying out space reference unification and geometric correction on the satellite remote sensing image and vector map data;
9) Acquiring urban land utilization classification diagrams of the initial year and the final year based on a spectrum angle supervision classification method by utilizing two-stage satellite remote sensing images;
the step 2) specifically comprises the following steps:
10 Firstly, selecting space factors which influence urban land utilization change, including data of expressways, railways, subways, primary highways, elevations and population;
11 The variables of the distances of the expressway, the railway, the subway and the primary road are obtained in the ArcGIS by using Euclidean distances through remote sensing image data, administrative division diagrams and road traffic diagrams.
The step 3) is specifically as follows:
12 Firstly, sampling a land utilization map and a factor map layer by using a random hierarchical sampling method, and providing reliable sample point data for CA rule conversion;
the step 4) is specifically as follows;
13 Training CA conversion rules on the obtained effective sampling point data by using Bayesian Space Variable Coefficients (BSVC);
14 Using established CA transformation rules, the transformation probability distribution under the influence of the spatial variable is obtained at a spatial resolution of 30 m. The method for obtaining the land transition probability data comprises the following steps:
assuming that s represents whether the cell state transitions from Non-Urban (Non-uban) to Urban (Urban) from time t to t+1, s is denoted as 1; s is marked as 0 if the state of the cell has not changed from time t to t+1. And calculating the land transition probability by using the acquired space variable data.
The step 5) is specifically as follows;
15 Based on the data of step 14), establishing a geographic CA model based on BSVC and GWR, specifically comprising the following steps: the CA model determines whether to next transition a unit from one state to another, the cell state at time t+1 can be expressed as:
in the method, in the process of the invention,
indicates the state of the cell at time t+1, < >>
Representing the cell state at time t, f representing the calculated total transition probability P g Comprehensive transfer rules of P d Representing land use conversion probability based on driving factors, < ->
Representing the impact of the domain, con represents the spatial suppression function.
The Time Increment Parameter (TIP) is utilized to counteract the decay of the transition probability based on the driving factor, and the Local Adjustment Parameter (LAP) is utilized to counteract the increase in the neighborhood impact. Thus, the transition probability and the total transition probability based on the driving factor can be given by the following formulas:
wherein a is i Representing the weight, x, of the ith driving factor i The i-th driving factor is represented, epsilon represents the fitting residual error, TIP represents the time increment parameter, the value range of which is 0.0-0.1, LAP represents the local adjustment parameter, and the value range of which is 0.5-1.
For evaluation of the influence of the neighborhood, the CA model is more of a regular neighborhood of square or circle, such as the Moore neighborhood of m can be expressed as:
in the method, in the process of the invention,
representing the total number of urban cells in the m×m domain, (j+.i) representing that the central cell i does not participate in the calculation, the invention selects Moore5×5 as the cell neighborhood.
The limiting factor Con indicates that the cells are subject to some limitation including large-area water bodies, basic farms, ecological protection areas, parks, greenbelts, etc., and cannot develop and transform into urban cells. Con can be expressed as:
Con＝Bin(cell i (t)～available)
wherein Con is 0 or 1,0 indicates that the cell cannot be developed as a city cell, and 1 indicates that the cell can be developed as a city cell.
The random factor R is used to simulate a cell state transition caused by an uncertain factor, such as a cell that has no urban cell nearby, and the random factor increases the probability of development, and the cell is converted from a non-urban state to a urban state. The random factor R is expressed as:
R＝1+(lnr) a
wherein R represents a random number between 0 and 1, a represents a control parameter of a random factor R, and the value is an integer between 0 and 10.
Driving factor based land utilization conversion probability P determined by space variable d Is the core of the transformation rules, which represents the impact of these factors on land utilization and affects the cell state at the next moment by way of probability. If a Geographic Weighted Regression (GWR) is used to obtain the CA parameter, the weight a of the ith driving factor i The inner can be calculated by the following formula:
W i Y＝W i Xβ i +ε i
in which W is i Representing an (n) spatial matrix in position i; y is an explanatory variable vector (n×1); x is a dependent variable matrix (n X k); beta i Representing a matrix of model coefficients at location i; epsilon i Representing random errors.
If CA parameters are obtained by using Bayesian space-variant coefficients (BSVC), the weight a of the ith driving factor i The method can be modified as follows:
in which W is ij Representing a row normalization matrix, I k A standard matrix representing k rows and k columns, u i Representing random disturbance, beta i Representing the matrix of model coefficients, σ, at position i 2 Representing variance, delta 2 Representing a scale factor controlling the smoothing effect, W i The (n×n) space matrix in the position i is represented, and X represents the dependent variable matrix (n×k).
The total conversion probability P of land utilization is calculated according to the above formula g . In actual calculation, the MTALAB language is utilized to complete the parameter calculation of GWR and BSVC, and the calculation result and the set threshold value P are used thd A comparison is made to determine if the cell will be transformed at one time. Probability of transition P when cell i gi,t Greater than the set threshold P thd Converting into city type, otherwise, maintaining the state of the cell unchanged, and correspondingly describing the formula as follows:
the step 6) is specifically as follows;
16 CA implementation using urbanaca software and MTALAB language BSVC And (3) selecting a land utilization pattern of a certain year as an initial state, and operating the CA model for M times (the initial and ending years are different) to obtain a simulation and prediction result of land utilization change.
17 A land use change result based on the two model simulations and predictions is output.
The step 7) is specifically as follows;
18 For CA) BSVC Land utilization results of model simulation are evaluated in terms of simulation accuracy from two aspects of rule fitting accuracy and simulation result respectively
Firstly, comparing the simulation result with the land utilization pattern of remote sensing classification, and calculating the accuracy of the simulation result, wherein the main indexes include graphic goodness (FOM) and Overall Accuracy (OA). Decomposing the overall accuracy into two types, namely a city (Hit) and a non-City (CR), and decomposing errors into two types, namely ignorance (Misses) and substitution (False), wherein the ignorance errors refer to urban cells which are actually cities but are simulated to be non-cities, namely, the CA model cannot capture; an alternative error refers to a city cell that is actually non-city but modeled as a city, i.e., the CA model erroneously increases.
Superposing the simulation result and the remote sensing classification result, wherein the superposition result comprises 5 types: the actual and simulated are city (Hit), actual non-city simulated as city (False), actual city simulated as non-city (Miss), actual and simulated are non-City (CR), and Water (Water). Based on visual discrimination display, contrast CA BSVC The simulation result of the model differs from the actual classification result.
The step 8) specifically comprises the following steps:
19 Outputting and storing the simulation result in GIS software.
The practical embodiment of the invention is as follows:
taking the land utilization of the combined fertilizer city in 2008-2018 as a case, the regional position of the case is shown in figure 2. For testingSyndrome CA BSVC The effectiveness of the model in land use change simulation, in which case the CA model (CA GWR ) As a comparison object, the change process of the synchronous urban land utilization is simulated, and the result shows that CA BSVC Is superior to CA in simulation effect GWR And (5) a model. A method for simulating cellular automata based on the urban land utilization change of Bayesian space variable coefficients comprises the following steps:
1) Firstly, remote sensing image data of the combined fertilizer city 2008 and 2018, administrative division diagrams and road traffic diagrams are selected to be used as basic data for training CA rule conversion and obtaining land transition probability;
2) Sampling the values of the space variables, the initial year and the end year state values of land use according to remote sensing image data by using a random hierarchical sampling method;
3) Calculating the distance, elevation and population data (table 1) from the expressway, the railway, the subway and the primary road by using the remote sensing images of each year, the administrative division layer and the road traffic layer and using Euclidean distance;
4) The method is used for supervising and classifying the fertilizer combination remote sensing image by utilizing a spectrum angle method, so that land utilization patterns are interpreted;
5) And realizing Bayesian Space Variable Coefficients (BSVC) and Geographic Weighted Regression (GWR) by using the effective sample points and the space variable values obtained by using a random hierarchical sampling method in actual calculation by using MATLAB language. Table 1 shows goodness of fit for the two models, indicating CA BSVC The model is better; table 2 shows transformation rule parameters for both models and fig. 3 shows urban land transformation for both models;
6) Using the obtained land transition probability and CA conversion rule (FIG. 4), a geographic CA model CA based on BSVC and GWR is established BSVC And CA GWR ；
7) With 2008 state as initial value, CA is used respectively BSVC And CA GWR The model was run 10 times to predict 2018 land use change (fig. 5);
8) Comparing the simulated and predicted result with the real city growth, and analyzing the change of the Overall Accuracy (OA) and the figure of merit (FOM); table 2 shows that the overall accuracy of the invention is improved by 0.5% and the figure of merit for the change of interest is improved by 23.5% compared to the GWR based CA model;
9) And outputting and storing the visualized result.
Table 1 CA GWR And CA BSVC Model parameter comprehensive statistics
Table 2 CA GWR And CA BSVC Accuracy result analysis of (a)
Note that: variation= (BSVC-GWR)/gwr×100
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (4)
1. A BSVC method-based urban land utilization change simulation cellular automaton method is characterized by comprising the following steps:
step 1: performing supervision classification on the remote sensing images to obtain land utilization classification diagrams of initial and final years;
step 2: obtaining urban land utilization change driving factor data, and obtaining a land utilization classification chart and effective sample points of the driving factor data after preprocessing;
step 3: training the effective sample points by using a Bayes space coefficient changing method to obtain a conversion rule of the cellular automaton;
step 4: obtaining urban land utilization conversion probability by utilizing conversion rules of cellular automata built according to BSVC training;
step 5: establishing a CA model based on BSVC (binary sequence-based virtual reality) by integrating transformation probability, cell field, random factors and limiting factors, namely CA BSVC A model;
step 6: by CA BSVC The model carries out simulation application and verification analysis on urban land utilization to obtain CA BSVC A model simulation result;
step 7: for CA BSVC The model and the simulation result thereof are respectively subjected to precision assessment from two aspects of rule fitting and simulation result, and the simulation result is output and stored;
the step 2 comprises the following sub-steps:
step 21: selecting driving space factor data affecting urban land utilization change, wherein the driving space factor data comprises data of highways, railways, subways, primary highways and elevations and population;
step 22: obtaining variables of distances of expressways, railways, subways and primary roads in ArcGIS by using Euclidean distances through remote sensing image data, administrative division diagrams and road traffic diagrams;
step 23: sampling the land utilization classification map and the factor graph layer by using a random hierarchical sampling method to obtain effective sample points of the land utilization classification map and the driving factor data for providing reliable sample data points for CA rule conversion;
the step 4 specifically includes: the method for acquiring the transformation probability of the land by utilizing the transformation rule of the cellular automaton established according to BSVC training to acquire the transformation probability distribution of the land under the influence of the space variable under the set space resolution comprises the following steps:
assuming that s represents whether the cell state transitions from non-urban to urban from time t to t+1, then y is denoted as 1; s is marked as 0 when the state of the cell is unchanged from time t to t+1;
calculating the conversion probability of the land by using the acquired space variable data;
CA in the step 5 BSVC The core problem of the model is to determine whether to switch a cell from one state to another in the next step, and the corresponding description formula is:
in the method, in the process of the invention,
indicates the state of the cell at time t+1, < >>
Representing the cell state at time t, f representing the calculated total transition probability P g Comprehensive transfer rules of P d Representing land utilization conversion probability based on driving factors, Ω i t represents the influence of the domain, con represents the spatial suppression function;
CA in the step 5 BSVC The land utilization conversion probability and the total conversion probability of the model based on the driving factors are calculated as follows:
wherein a is i Representing the weight, x, of the ith driving factor i Representing the ith driving factor, epsilon representing the fitting residual, TIP representing the time increment parameter, LAP representing the local adjustment parameter;
CA in the step 5 BSVC The field of the model adopts Moore field, and the description formula is as follows:
in the method, in the process of the invention,
representing the total number of urban cells in the m×m domain, (j+.i) representing that the central cell i does not participate in the calculation;
the weight of the ith driving factor is calculated as follows:
in which W is ij Representing a row normalization matrix, I k A standard matrix representing k rows and k columns, u i Representing random disturbance, beta i Representing the matrix of model coefficients, σ, at position i 2 Representing variance, delta 2 Representing a scale factor controlling the smoothing effect, W i The (n×n) space matrix in the position i is represented, and X represents the dependent variable matrix (n×k).
2. The method for simulating cellular automata for urban land use change based on BSVC method according to claim 1, wherein said step 1 comprises the following sub-steps:
step 11: acquiring satellite remote sensing image and vector map data required by modeling, and carrying out space reference unification and geometric correction on the satellite remote sensing image and vector map data;
step 12: and acquiring land utilization classification diagrams of cities in the initial year and the ending year based on a spectrum angle supervision classification method by utilizing two-stage satellite remote sensing images.
3. The method for simulating cellular automata for urban land use variation based on BSVC method according to claim 1, wherein said step 6 specifically comprises: CA implementation using UrbanCA software and MTALAB language BSVC The simulation and prediction flow of the model selects the land utilization pattern of a certain year as an initial state and utilizes the CA model to run M times, wherein M represents the year difference between the initial and the end to obtain the landUsing varying simulations and predictions.
4. The method for simulating cellular automata for urban land use change based on BSVC method according to claim 1, wherein said step 7 comprises the following sub-steps:
step 71: by comparison with the land utilization pattern of remote sensing classification, CA BSVC And (3) carrying out precision calculation and evaluation on the model simulation result, wherein the precision calculation indexes comprise: figure of merit FOM and overall accuracy OA;
step 72: CA will be CEO Superposing and evaluating the model simulation result and the remote sensing classification result, wherein the superposed result comprises the following steps: the actual and simulation are city Hit, non-city simulation is city False, city simulation is non-city Miss, non-city CR and Water;
step 73: and outputting and storing the simulation result in GIS software.
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