CN116011319A - Urban expansion simulation method based on driving factor analysis - Google Patents
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Abstract
The invention discloses a city expansion simulation method based on driving factor analysis, which comprises the steps of firstly identifying influence factors of city expansion by using a constructed driving factor analysis frame, determining specific related data, collecting and preprocessing, secondly analyzing contribution degree of each factor in city expansion, combining an obtained contribution degree result with city space change data, establishing a city expansion probability model, then simulating a city expansion condition by using the probability model, and finally comparing a simulation result with real data to carry out accuracy verification. The method accurately simulates the urban expansion by identifying the driving factors of the urban expansion and the contribution degree thereof in the urban expansion and integrating the driving factors into the simulation model.
Description
Technical Field
The invention relates to the technical field of urban planning, in particular to a city expansion simulation method based on driving factor analysis.
Background
In the past decades, urban tide and surge are rolled up worldwide, one of the most attractive phenomena in the global modern development process is already realized, 30 hundred million people are predicted to flow into a central area of a city according to the existing data prediction, 70% of world population is predicted to reside in the city, urban mass is accompanied by the interconversion of different places and the continuous expansion of building land in the city, great influence is generated on the aspects of energy utilization, climate change, ecological safety and the like, and urban expansion simulation can be used as an important reference for land utilization planning and urban development decision in the future area to a certain extent, so that urban planning is the research focus in the current urban planning field.
The Cellular Automaton (CA) model has strong space operation capability, can effectively simulate land change, is considered as a very convenient and effective tool for researching urban expansion, is widely applied in the field of urban simulation, but is difficult to simulate the complex change of the whole city through a simple cell transformation rule when the CA model is used for urban expansion simulation due to the dynamics, openness and complexity of the urban system, and the CA model is combined with other methods in a certain degree, so that the transformation problem of cells during simulation is solved, the action of a driving factor in urban expansion is ignored, the integral simulation precision is improved to a certain degree if the influence of the driving factor is fully considered in the process of simulating urban expansion, and therefore, a method is needed for solving the driving factor for identifying urban expansion and the contribution degree problem thereof in urban change and simulating urban expansion by means of the driving factor analysis result.
In view of the above technical drawbacks and needs, the present inventors have completed the present invention through long-time studies and practices.
Disclosure of Invention
The invention aims to solve the problems, and designs a city expansion simulation method based on driving factor analysis.
The technical scheme for achieving the purpose is that the urban expansion simulation method based on the driving factor analysis comprises the following steps of:
step 1: establishing an urban expansion driving factor framework aiming at the condition of a research area, and determining specific factors according to the driving factor framework;
step 2: acquiring basic data of a research area, wherein the basic data comprise a data set A and a data set B;
step 3: preprocessing basic data;
step 4: determining the contribution degree of driving factors of the transformation between different types of the research area in the time sequence by utilizing a random forest algorithm;
step 5: generating an urban expansion simulation probability model, and predicting the plaque number of each place in a Y3-year research area;
step 6: generating city expansion change results of a research area Y3 years;
step 7: and (3) comparing the result of the Y3-year simulation with the Y3-year real data in the data set A, calculating Kappa coefficients to determine simulation error precision, repeating the steps 5-7 if the precision verification is not met, and ending the simulation if the precision verification requirement is met.
As a further description of the present technical solution, the step 1 includes the following steps:
step 1.1: constructing a driving factor framework, and analyzing driving influence factors including climate factors, geographic factors, socioeconomic factors and city development factors from the four-dimensional framework by combining the geographic position of a research area and specific expansion conditions;
step 1.2: and selecting different expansion driving factors according to the constructed driving factor framework aiming at a specific research area.
As a further description of the present technical solution, in the step 2, the data set a includes land use classification maps of the study areas Y1 year, Y2 year and Y3 year;
the data set B includes driving factor data for the study area determined according to step 1.
As a further description of the technical scheme, the step 3 includes unified coordinate system and unified row-column arrangement of all data, classification extraction of land utilization data in the city and processing of specific driving factor data.
As a further description of the technical scheme, in the step 4, according to the land utilization data of the year Y1 and the year Y2 in the data set a and the factor data of the research area processed in the step 3, determining the contribution degree of driving factors of the conversion among different types of the research area in the time sequence by using a random forest algorithm;
the method comprises the following steps:
step 4.1: extracting the change conditions of various types of lands by utilizing land utilization data of Y1 and Y2 years in the data set A and using an intersection algorithm in a geologic analysis tool;
step 4.2: sampling various land change areas by using the result of the step 4.1, mining various land utilization change probabilities by using a random forest algorithm in combination with data preprocessed by various factors of a research area, and finally determining the contribution degree of the various factors, wherein the formula is as follows:
in the middle of
Representing the probability of the r-th grid change to k-type land utilization; d is 0 or 1, a value of 1 indicates that the land utilization type is changed into k type land utilization, and a value of 0 indicates that the land utilization type is changed into other types; x represents a driving factor vector; i (·) is an indicator function of the decision tree set; h is a n (x) The prediction type of the nth decision tree of the vector x; m is the total number of decision trees.
As a further description of the technical scheme, in the step 5, an artificial neural network algorithm is utilized to combine the result of the step 4 and the land utilization data of the years Y1 and Y2 in the data set A to generate an urban expansion simulation probability model, and the plaque number of each place in the study area of the year Y3 is predicted;
step 5.1: generating various kinds of variation probability models of the research area by using the driving factor contribution degree in the step 4.2 and combining land utilization data of Y1 and Y2 years in the data set A and using an artificial neural network learning model in machine learning;
step 5.2: and generating the plaque amount of each type of the Y3-year research area by using land utilization data of Y1 and Y2 years in the data set A and using a Markov prediction model.
As a further description of the technical scheme, in the step 6, the probability model and the predicted plaque number generated in the step 5 are utilized, and the urban expansion change result of the research area Y3 years is generated by means of cellular automaton simulation in combination with the land utilization data of the year Y2 in the data set a.
As a further description of the present technical solution, in step 7, the Kappa coefficient evaluates the difference between the simulated city expansion result and the real city, the value range of the Kappa coefficient is between 0 and 1, the closer the Kappa coefficient is to 1, the better the consistency is, the closer the simulated result is to the actual situation, and the calculation formula is as follows:
wherein P0 represents the duty cycle of the correct analog ground class; pc represents a randomly expected accuracy; pp is the correct rate for perfect simulation, i.e. 1.
The method has the beneficial effects that the driving factors for identifying the urban expansion and the contribution degree thereof in the urban expansion are increased, the urban expansion is simulated by means of the driving factor analysis, and the rule probability problem of variation among different types in the simulation process can be better compensated by utilizing the driving factor analysis result, so that the accuracy of the result is improved.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a plot of land utilization and individual factor data processing results for an embodiment of the present invention;
figure 3 is a graph of simulated 2020 results for an embodiment of the present invention.
Detailed Description
The invention is specifically described below with reference to the accompanying drawings, as shown in fig. 1, a city expansion simulation method based on driving factor analysis, comprising the following steps:
step 1: establishing an urban expansion driving factor framework aiming at the condition of a research area, and determining specific factors according to the driving factor framework; the step 1 comprises the following steps:
step 1.1: constructing a driving factor framework, and analyzing driving influence factors including climate factors, geographic factors, socioeconomic factors and city development factors from the four-dimensional framework by combining the geographic position of a research area and specific expansion conditions;
step 1.2: and selecting different expansion driving factors according to the constructed driving factor framework aiming at a specific research area.
Step 2: acquiring basic data of a research area, wherein the basic data comprise a data set A and a data set B; in said step 2, data set a includes land use classification maps of study areas Y1, Y2 and Y3 years;
the data set B includes driving factor data for the study area determined according to step 1.
Step 3: preprocessing basic data; in the step 3, the method comprises the steps of unifying a coordinate system and a row-column arrangement of all data, classifying and extracting land utilization data in the city and processing specific driving factor data.
Step 4: determining the contribution degree of driving factors of the transformation between different types of the research area in the time sequence by utilizing a random forest algorithm; in the step 4, according to the land utilization data of the Y1 and Y2 years in the data set A and the factor data of the research area processed in the step 3, determining the contribution degree of driving factors transformed among different types of the research area in the time sequence by using a random forest algorithm;
the method comprises the following steps:
step 4.1: extracting the change conditions of various types of lands by utilizing land utilization data of Y1 and Y2 years in the data set A and using an intersection algorithm in a geologic analysis tool;
step 4.2: sampling various land change areas by using the result of the step 4.1, mining various land utilization change probabilities by using a random forest algorithm in combination with data preprocessed by various factors of a research area, and finally determining the contribution degree of the various factors, wherein the formula is as follows:
in the middle of
Representing the probability of the r-th grid change to k-type land utilization; d is 0 or 1, a value of 1 indicates that the land utilization type is changed into k type land utilization, and a value of 0 indicates that the land utilization type is changed into other types; x represents a driving factor vector; i (·) is an indicator function of the decision tree set; h is a n (x) The prediction type of the nth decision tree of the vector x; m is the total number of decision trees.
Step 5: generating an urban expansion simulation probability model, and predicting the plaque number of each place in a Y3-year research area; in the step 5, an artificial neural network algorithm is utilized to combine the result of the step 4 and the land utilization data of the Y1 and Y2 years in the data set A to generate an urban expansion simulation probability model, and the plaque quantity of each place in the Y3 year research area is predicted;
step 5.1: generating various kinds of variation probability models of the research area by using the driving factor contribution degree in the step 4.2 and combining land utilization data of Y1 and Y2 years in the data set A and using an artificial neural network learning model in machine learning;
step 5.2: and generating the plaque amount of each type of the Y3-year research area by using land utilization data of Y1 and Y2 years in the data set A and using a Markov prediction model.
Step 6: generating city expansion change results of a research area Y3 years; in the step 6, the probability model and the predicted plaque number generated in the step 5 are utilized, and the urban expansion change result of the research area Y3 years is generated by means of cellular automaton simulation by combining the land utilization data of Y2 years in the data set A.
Step 7: comparing the result of the Y3-year simulation with the Y3-year real data in the data set A, calculating Kappa coefficients to determine simulation error precision, repeating the steps 5-7 if the precision verification is not met, and ending the simulation if the precision verification requirement is met;
in the step 7, the Kappa coefficient evaluates the difference between the simulated city expansion result and the real city, the value range of the Kappa coefficient is between 0 and 1, the closer the Kappa coefficient is to 1, the better the consistency is, the closer the simulated result is to the actual situation, and the calculation formula is as follows:
wherein P0 represents the duty cycle of the correct analog ground class; pc represents a randomly expected accuracy; pp is the correct rate for perfect simulation, i.e. 1.
The present invention will be further described in detail below in conjunction with the following examples, for the purpose of facilitating understanding and practicing the present invention by those of ordinary skill in the art, it being understood that the examples described herein are for the purpose of illustration and explanation only and are not intended to limit the invention.
Examples: the technical scheme adopted by the invention is as follows: a city expansion simulation method based on driving factor analysis comprises the following steps:
step 1: the Dongguan city is taken as a research area, and specific factor data included in urban expansion of the area is determined by means of an urban expansion driving factor framework, and the specific factor data are shown in the following table:
step 2: acquiring relevant data of Dongguan city, which comprises a data set A: a classification chart of land utilization in Dongguan city in 2010, 2015 and 2020; data set B: DEM data, average air temperature data, average precipitation data, average person GDP data, population density data, and road data at all levels of Dongguan city;
step 3: preprocessing the collected data, including unified coordinate system and unified row-column arrangement of all the data, resampling the Dongguan city land utilization data, extracting a research area of data such as air temperature, precipitation and the like, performing gradient analysis on DEM data to generate gradient data, and performing Euclidean distance calculation on each level of road data, wherein the generated land utilization data and each factor processing result are shown in figure 2;
step 4: determining the contribution degree of driving factors transferred between different places of a research area in a time sequence by utilizing a random forest algorithm and combining the 2010 and 2015 land utilization data in the data set A and the driving factor data processed in the step 3, wherein the ratio of the contribution degree of each factor is shown in the following table:
step 5: and (3) generating the Dongguan city expansion simulation probability model by combining the driving factor contribution degree generated in the step (4) and land utilization data in 2010 and 2015 in the data set A by using the artificial neural network model. And based on the two-stage land utilization data, generating the plaque quantity (unit: number) of various places in Dongguan city in 2020 by using a Markov prediction model, wherein the plaque quantity prediction result is as follows:
step 6: and 5, generating a city expansion change result in Dongguan city 2020 by means of cellular automata simulation by combining 2015 land utilization data in the data set A by using the probability model and the predicted plaque quantity generated in the step, wherein the result is shown in fig. 3;
step 7: and comparing the simulation result in 2020 with the real data in the data set A, calculating Kappa coefficient to determine simulation error precision, wherein the final precision coefficient result is 0.945, and the precision coefficient result is more than 0.8 and meets the simulation precision requirement. Compared with the Kappa value of 0.886 of the simulation precision of the traditional method, the method has the advantage of improving the simulation precision by about 6 percent.
According to the method, the driving factors for identifying the urban expansion and the contribution degree of the driving factors in the urban expansion are increased, the urban expansion is simulated by means of driving factor analysis, and the problem of the regular probability of variation among different types in the simulation process can be better solved by using the driving factor analysis result, so that the accuracy of the result is improved.
The above technical solution only represents the preferred technical solution of the present invention, and some changes that may be made by those skilled in the art to some parts of the technical solution represent the principles of the present invention, and the technical solution falls within the scope of the present invention.
Claims (8)
1. The city expansion simulation method based on the driving factor analysis is characterized by comprising the following steps of:
step 1: establishing an urban expansion driving factor framework aiming at the condition of a research area, and determining specific factors according to the driving factor framework;
step 2: acquiring basic data of a research area, wherein the basic data comprise a data set A and a data set B;
step 3: preprocessing basic data;
step 4: determining the contribution degree of driving factors of the transformation between different types of the research area in the time sequence by utilizing a random forest algorithm;
step 5: generating an urban expansion simulation probability model, and predicting the plaque number of each place in a Y3-year research area;
step 6: generating city expansion change results of a research area Y3 years;
step 7: and (3) comparing the result of the Y3-year simulation with the Y3-year real data in the data set A, calculating Kappa coefficients to determine simulation error precision, repeating the steps 5-7 if the precision verification is not met, and ending the simulation if the precision verification requirement is met.
2. The urban expansion simulation method based on driving factor analysis according to claim 1, wherein the step 1 comprises the steps of:
step 1.1: constructing a driving factor framework, and analyzing driving influence factors including climate factors, geographic factors, socioeconomic factors and city development factors from the four-dimensional framework by combining the geographic position of a research area and specific expansion conditions;
step 1.2: and selecting different expansion driving factors according to the constructed driving factor framework aiming at a specific research area.
3. The urban expansion simulation method based on driving factor analysis according to claim 1, wherein in the step 2, the data set a includes land use classification maps of the study areas Y1 year, Y2 year and Y3 year;
the data set B includes driving factor data for the study area determined according to step 1.
4. The method for simulating urban expansion based on driving factor analysis according to claim 1, wherein the step 3 comprises the steps of unifying a coordinate system of all data, arranging the data in a uniform row and column, classifying and extracting land utilization data in the city and processing specific driving factor data.
5. The urban expansion simulation method based on driving factor analysis according to claim 1, wherein in the step 4, the driving factor contribution degree of the transformation between different types of the research areas in the time sequence is determined by using a random forest algorithm according to the land utilization data of Y1 and Y2 years in the data set A and the factor data of the research areas processed in the step 3;
the method comprises the following steps:
step 4.1: extracting the change conditions of various types of lands by utilizing land utilization data of Y1 and Y2 years in the data set A and using an intersection algorithm in a geologic analysis tool;
step 4.2: sampling various land change areas by using the result of the step 4.1, mining various land utilization change probabilities by using a random forest algorithm in combination with data preprocessed by various factors of a research area, and finally determining the contribution degree of the various factors, wherein the formula is as follows:
in the middle of
Representing the probability of the r-th grid change to k-type land utilization; d is 0 or 1, a value of 1 indicates that the land utilization type is changed into k type land utilization, and a value of 0 indicates that the land utilization type is changed into other types; x represents a driving factor vector; i (·) is an indicator function of the decision tree set; h is a n (x) The prediction type of the nth decision tree of the vector x; m is the total number of decision trees.
6. The urban expansion simulation method based on the driving factor analysis according to claim 1, wherein in the step 5, an artificial neural network algorithm is utilized to combine the result of the step 4 and the land utilization data of the years Y1 and Y2 in the data set A to generate an urban expansion simulation probability model, and the plaque number of each area in the study area of the year Y3 is predicted;
step 5 comprises the steps of:
step 5.1: generating various kinds of variation probability models of the research area by using the driving factor contribution degree in the step 4.2 and combining land utilization data of Y1 and Y2 years in the data set A and using an artificial neural network learning model in machine learning;
step 5.2: and generating the plaque amount of each type of the Y3-year research area by using land utilization data of Y1 and Y2 years in the data set A and using a Markov prediction model.
7. The urban expansion simulation method based on driving factor analysis according to claim 1, wherein in the step 6, the probability model generated in the step 5 and the predicted plaque number are utilized to generate the urban expansion change result of the research area Y3 years by means of cellular automaton simulation in combination with the land utilization data of the data set a of Y2 years.
8. The city expansion simulation method based on driving factor analysis according to claim 1, wherein in the step 7, the Kappa coefficient evaluates the difference between the simulation city expansion result and the real city, the Kappa coefficient has a value ranging from 0 to 1, the closer the Kappa coefficient is to 1, the better the description consistency, the closer the simulation result is to the actual situation, and the calculation formula is as follows:
wherein P0 represents the duty cycle of the correct analog ground class; pc represents a randomly expected accuracy; pp is the correct rate for perfect simulation, i.e. 1.
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Inventor after: Su Meirong Inventor after: Qin Chenghao Inventor after: Yue Wencong Inventor after: Xu Chao Inventor after: Teng Yanmin Inventor before: Su Meirong Inventor before: Zhu Yajun Inventor before: Rong Qiangqiang Inventor before: Huang Qianyuan |