CN113642764B - Village and town aggregation space evolution simulation prediction method and computer equipment - Google Patents
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Abstract
The embodiment of the application discloses a village and town aggregation space evolution simulation prediction method and equipment, comprising the following steps: a dependent variable layer and an independent variable layer required by the GeoDetector model are manufactured; making an exclusion layer and other layers required by SLEETH model simulation; inputting a plurality of layers into a SLEETH model for parameter correction to obtain optimal parameters; setting the initial year and the final year of SLEETH model simulation, and adopting optimal parameters to simulate and predict the village and town aggregation space evolution of a research area. According to the embodiment of the application, the GeoDetector model and the SLEETH model are fused to simulate and predict the evolution of the village and town aggregation space, and the driving relation of different transformation driving forces to the village and town aggregation space is inlaid into the land utilization simulation model, so that the advantage complementation of the two models is realized, the precision of the simulation model can be effectively improved, and the evolution trend of the village and town aggregation space can be better depicted.
Description
Technical Field
The application relates to the technical field of humane geography and urban and rural planning, in particular to a village and town aggregation space evolution simulation and prediction method.
Background
Land utilization is an important index reflecting the space change of villages and towns, is influenced by various transformation dynamics such as economy, society, population, policy and the like, and is a research focus in the fields of humane geography and urban and rural planning. Dynamic change simulation of land utilization can analyze and predict the change process of village and town space, pre-judge the expansion direction and scale of village and town construction land, help land managers to develop effective village and town development policies, and land utilization change simulation and prediction based on Cellular Automaton (CA) model are hot topics in the dynamic change simulation. One of the key problems and difficulties is how to identify the driving force of the village and town aggregation space transformation, and effectively fuse the driving force with the land utilization simulation model, so as to improve the precision of the simulation model and enable the simulation model to better predict the evolution trend of the village and town space.
However, with the prior art, the existing Cellular Automaton (CA) model has the problem that the dynamic change process of the space expansion of the village and town construction land in different periods cannot be effectively predicted because the village and town aggregation space transformation power and land dynamic change simulation cannot be well inlaid in space.
Disclosure of Invention
Aiming at the technical defects, the embodiment of the application aims to provide a village and town aggregation space evolution simulation prediction method and computer equipment so as to effectively fuse a village and town aggregation space transformation power identification model and a land utilization dynamic change simulation model, realize the advantage complementation of the two models and finally scientifically describe the development trend and space layout of future land utilization of villages and towns.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a village and town aggregation space evolution simulation prediction method, including:
s1: acquiring historical and current remote sensing images of a preset research area range, and manufacturing a dependent variable layer required by a GeoDetector model according to land utilization interpretation results of the remote sensing images;
s2: collecting driving factor data in a preset research area range, and manufacturing an independent variable layer required by a GeoDetector model according to the driving factor data;
s3: manufacturing sampling points by using a GIS platform, sampling and extracting the dependent variable layer and the data in the independent variable layer according to the sampling points, and exporting a sampling result in an excel format;
s4: the sampling result is imported into a GeoDetector model for calculation, and driving strength of each driving factor on village and town aggregation space evolution is obtained;
s5: manufacturing an exclusion layer required by SLEETH model simulation according to the driving strength;
s6: manufacturing a gradient layer, a land utilization layer, a city range layer, a traffic layer and a mountain shadow layer required by SLEETH model simulation;
s7: inputting a plurality of layers of the SLUTH model into the SLUTH model for parameter correction to obtain optimal parameters simulated by the SLUTH model;
s8: setting the initial year and the final year of SLEETH model simulation, and adopting the optimal parameters to simulate and predict the village and town aggregation space evolution of the research area.
As a specific embodiment of the present application, step S1 specifically includes:
s11: acquiring historic and current remote sensing images of a preset research area range in a plurality of periods;
s12: interpreting the remote sensing images in each period to obtain land utilization status vector data of villages and towns in a plurality of time periods;
s13: and according to the land utilization current situation vector data, utilizing a GIS platform to manufacture a dependent variable layer required by the GeoDetector model.
As a specific embodiment of the present application, step S5 specifically includes:
s51: according to the driving strength of each driving factor, weighting and superposing the layers of each driving factor in the research range by adopting a GIS platform to generate a village and town aggregation space evolution power distribution diagram;
s52: acquiring various vegetation, water areas, terrains and ecological management and control element layers in a research range, and carrying out weighted superposition on the layers by adopting a GIS platform to generate a village and town aggregate space ecological sensitivity distribution map;
s53: generating a village and town aggregation space evolution probability map by adopting a GIS platform to obtain the village and town aggregation space evolution power distribution map and a village and town aggregation space ecological sensitivity distribution map;
s54: reclassifying the data, and manufacturing the village and town aggregation space evolution probability map as an exclusion map layer required by SLEEUTH model simulation.
As a specific embodiment of the present application, step S6 specifically includes:
s61: obtaining DEM data in the range of a research area, and manufacturing the DEM data into a gradient layer and a mountain shadow layer;
s62: preparing land use current situation vector data in a plurality of periods as a land use layer;
s63: the method comprises the steps of manufacturing urban area range map layers by using village and town aggregation construction land vector data in a plurality of periods;
s64: the current road traffic vector data is drawn as a traffic layer.
As a specific embodiment of the present application, step S7 specifically includes:
and (3) carrying out parameter correction by adopting a forced Monte Carlo iterative calculation method, wherein the parameter correction is carried out in 4 stages of coarse correction, fine correction, final correction and simulation parameter acquisition, and a set of increased parameter sets obtained in each step are used for parameter correction of the next step, so that the optimal parameters simulated by the SLUTH model are finally obtained.
Further, as a preferred embodiment of the present application, the method further comprises:
consistency evaluation is carried out on the simulation results by using Kappa coefficients, when the simulation results are completely consistent with the reference, kappa reaches a maximum value of 1, and the larger the Kappa value is, the better the consistency is; when Kappa is more than or equal to 0.75, the consistency of the two is higher, and the change is smaller: when Kappa is more than or equal to 0.4 and less than or equal to 0.75, the consistency of the two is common, and when Kappa is more obvious and less than 0.4, the consistency of the two is lower, and the change is larger.
In a second aspect, an embodiment of the present application provides a computer device, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, and the memory is configured to store a computer program, where the computer program includes program instructions, and where the processor is configured to invoke the program instructions to perform the method of the first aspect.
By implementing the embodiment of the application, the GeoDetector model and the SLEETH model are fused to simulate and predict the evolution of the village and town aggregation space, and the driving relation of different transformation driving forces to the village and town aggregation space is inlaid into the land use simulation model, so that the advantage complementation of the two models is realized, the precision of the simulation model can be effectively improved, and the evolution trend of the village and town aggregation space can be better depicted.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a flowchart of a village and town aggregation space evolution simulation prediction method according to an embodiment of the present application;
FIG. 2 is a classification diagram of land utilization in Korean city;
FIG. 3 is a diagram of a GeoDetector model dependent variable (Y) layer;
FIG. 4 is a diagram of a GeoDetector model argument (X) layer;
FIG. 5 is a sample point diagram layer schematic;
FIG. 6 is an exclusion layer diagram of the SLEEUTH model;
FIG. 7 is all input layers of the SLEEUTH model;
FIG. 8 is a simulation result display diagram of the SLEEUTH model;
fig. 9 is a block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application 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 application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, an embodiment of the present application provides a village and town aggregation space evolution simulation and prediction method fusing a GeoDetector and a SLEUTH model, which mainly includes the following steps:
s1: and acquiring the history and current remote sensing images of the preset research area range in a plurality of periods.
S2: and interpreting the remote sensing image of each period to obtain land utilization state vector data of the village and town aggregation space in a plurality of time periods.
S3: and collecting driving factor data in the research area, and performing preliminary processing to form a data set.
S4: and (3) utilizing a GIS platform to manufacture the multi-stage land utilization current vector data obtained in the step (S2) into space growth change data of different time periods, and generating a dependent variable (Y) layer required by the GeoDetector model.
S5: and (3) preparing the driving factor data obtained in the step S3 into an independent variable (X) layer required by the GeoDetector model.
S6: and manufacturing sampling points by using a GIS platform, sampling and extracting the dependent variable layer and the data in the independent variable layer according to the sampling points, and exporting a sampling result in an excel format.
S7: and (3) importing the table data obtained in the step (S6) into a GeoDetector model for calculation to obtain the driving strength of each driving factor on the village and town aggregation space evolution.
S8: and (3) according to the driving strength of each driving factor obtained in the step (S7), carrying out weighted superposition on the layers of each driving factor in the research range by adopting a GIS platform, and generating a village and town aggregation space evolution power distribution diagram.
S9: and obtaining various vegetation, water areas, terrains and ecological management and control element layers in the research range, and carrying out weighted superposition on the layers by adopting a GIS platform to generate a village and town aggregate space ecological sensitivity distribution map.
S10: and (3) generating a village and town aggregation space evolution probability map by adopting the GIS platform and the maps obtained in the step S8 and the step S9.
S11: reclassifying the data, and respectively manufacturing the village and town aggregation space evolution probability map obtained in the step S10 into an exclusion map layer required by SLEETH model simulation.
S12: making other data layers required by SLEEUTH model simulation: slope (Slope), land use (Landuse), city range (Urban), traffic (Transportation), mountain shadow (Hillshade).
S13: inputting a plurality of layers of the SLUTH model into the SLUTH model, carrying out parameter correction by adopting a forced Monte Carlo iterative calculation method (Brute-force Monte Carlo method), wherein the parameter correction is carried out in 4 stages of coarse correction, fine correction, final correction and simulation parameter acquisition, and a set of increased parameter sets obtained in each step are used for parameter calibration of the next step, so that the optimal parameters simulated by the SLUTH model are finally obtained.
S14: and carrying out land use evolution simulation verification of a research area by adopting the optimal parameters obtained by final correction, taking the layer data of the 1 st stage as the initial year of simulation, generating a land use map of the current year in a simulation manner, and carrying out comparison analysis on the pixel scale and the actual current land use of the current year so as to quantitatively evaluate the accuracy of model simulation.
S15: consistency evaluation is carried out on the simulation results by using Kappa coefficients, when the results are completely consistent with the reference, kappa reaches a maximum value of 1, and the larger the Kappa value is, the better the consistency is; when Kappa is more than or equal to 0.75, the consistency of the two is higher, and the change is smaller: when Kappa is more than or equal to 0.4 and less than or equal to 0.75, the consistency of the two is common, and when Kappa is more obvious and less than 0.4, the consistency of the two is lower, and the change is larger.
S16: and simulating optimal parameters by using the model after consistency evaluation, and simulating and predicting village and town aggregation space evolution of the research area by taking the layer data of the current year as an initial year.
S17: and obtaining simulation and prediction results of the regional evolution of the village and town in the target year.
Further, for a better understanding of embodiments of the present application, the following is exemplified:
(1) Land use classification vector data of 4 periods (2000, 2005, 2013, 2018) of south korea city, the province of shanxi are acquired through 30 m precision satellite image interpretation, as shown in fig. 2.
(2) The map spots for village and town aggregate construction in the last period (2013 to 2018) are extracted, the space variation is calculated by taking villages and towns as units, and a GIS platform is used for manufacturing a dependent variable (Y) map layer required by a GeoDetector, as shown in figure 3.
(3) The independent variable factors (X) required by the GeoDetector, namely the dynamic factors, such as the general population (X1), population density (X2), urban distance (X3), road distance (X4) and the like, are selected, each factor is respectively manufactured into corresponding layers, each layer is processed into 5 grades by a natural breakpoint method, and the grades are reclassified to be 1, 2, 3, 4 and 5, as shown in figure 4.
(4) The sampling dot diagram layer with 300 m spacing (10 times precision and adjustable according to the size of the region) is manufactured by utilizing the creating fishing net module with the sampling function in the GIS data management tool, and the sampling module for analysis is extracted from the space analysis tool to derive the sampling result in the excel format, as shown in fig. 5.
(5) And (3) introducing the sampling result into a GeoDetector for operation to obtain each power factor q value of the factor detector. The q value is also called factor explanatory power, the value is between 0 and 1, which indicates how much a certain power factor (X) explains the space difference of the dependent variable (Y), the larger the q value is, the stronger the explanatory power of the independent variable X to the dependent variable Y is, and the weaker the explanatory power is, the expression is:
wherein: h=1, …, L is a stratification, i.e. a classification or partition, of the variable Y or factor X; n (N) h And N is the number of units of layer h and the full area respectively;sum sigma 2 The variance of the Y values for layer h and full region, respectively. SSW and SST are the sum of intra-layer variances and the total variance of the whole region, respectively.
Q values of the GeoDetector operation results X1, X2, X3 and X4 are respectively: the ratio after normalization treatment is the weight of each power factor.
The result of the GeoDetector operation is shown in Table 1:
(6) And (3) carrying out weight assignment on each dynamic factor layer by using a weighted sum module of superposition analysis in a GIS space analysis tool, and then deriving the dynamic factor layers into a 30-meter-precision 8-bit gray scale GIF file format, namely, eliminating the layers as a SLUTH model (figure 6). The value of the pixel attribute of the excluded image layer is 0-100, which indicates the impossibility that the region can be converted into the landing construction land. The higher the value of the excluded layer, the less likely the region is to be converted into the landing construction land, if the value of 100 is the region is completely impossible to reconstruct into the landing construction land, otherwise, if the value of 0 is the region is the largest possibility of being converted into the landing construction land. In the 8bit gray scale GIF format, the color stretching range is 0-255, which corresponds to 0-100 of image layer pixel attribute assignment.
(7) SLEUTH model simulation requires 6 layers, namely Slope (Slope), land utilization (land use), exclusion layer (expression), city range (Urban), traffic (Transportation), mountain shadow (Hillshade). The method comprises the steps of obtaining 30 m precision DEM data of Korean cities, manufacturing the DEM data into a gradient layer and a mountain shadow layer, manufacturing the current situation vector data of 4 time nodes into a land utilization layer, manufacturing the current situation vector data of 4 time nodes for village and town construction into a city range layer, manufacturing the current situation road traffic vector data of 4 depicted time nodes into a traffic layer, and exporting all the layers into an 8-bit gray scale GIF file format with uniform coordinates and uniform resolution, as shown in fig. 7.
(8) Inputting each layer into SLUTH model for model calibration, adopting OSM_NS as best fitting goodness index of the determined model in the calibration stage,
OSM_NS＝compare×pop×edges×clusters×xmean×ymean。
wherein, compare is the ratio of the total number of simulated town pixels to the total number of actual town pixels in the last year, pop is the least square regression correlation coefficient value of the ratio of the number of simulated town pixels to the number of actual town pixels in the calibration year, edges is the least square regression correlation coefficient value of the ratio of the number of simulated town boundaries to the number of actual town boundaries in the calibration year, clusters is the least square regression correlation coefficient value of the ratio of simulated town clusters to the number of actual town clusters in the calibration year, xmean is the least square regression correlation coefficient value of the ratio of the average x coordinate value of simulated town pixels to the average x coordinate value of actual town pixels in the calibration year, and ymean is the least square regression correlation coefficient value of the ratio of the average y coordinate value of simulated town pixels to the average y coordinate value of actual town pixels in the calibration year.
(9) The method comprises the following three calibration stages of coarse calibration, fine calibration and coarse calibration to obtain a group of optimal parameters: the diffusion coefficient was 31, the reproduction coefficient was 65, the dispersion coefficient was 1, the gradient coefficient was 62, and the road coefficient was 44.
(10) Inputting optimal parameters, setting the simulation start year of the SLEETH model to 2018 and the simulation end year to 2030, and obtaining the simulation prediction results of the regional evolution of the villages and towns in the Korean city from 2018 to 2030, as shown in FIG. 8.
By implementing the village and town aggregation space evolution simulation and prediction method, the GeoDetector model and the SLEETH model are combined to simulate and predict the evolution of the village and town aggregation space, and the driving relations of different transformation driving forces to the village and town aggregation space are embedded into the land utilization simulation model, so that the precision of the simulation model can be effectively improved, and the evolution trend of the village and town aggregation space can be better depicted.
Based on the same inventive concept, an embodiment of the present application provides a computer device, as shown in fig. 9, the user mobile terminal may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and a memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected by a bus 105. The memory 104 is used for storing a computer program comprising program instructions, which the processor 101 is configured to invoke for performing the method of the above-described method embodiment part.
It should be appreciated that in embodiments of the present application, the processor 101 may be a central processing unit (Central Processing Unit, CPU), a deep learning graphics card (e.g., NPU, injedag GPU, google TPU), other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 102 may include a keyboard or the like, and the output device 103 may include a display (LCD or the like), a speaker or the like.
The memory 104 may include read only memory and random access memory and provides instructions and data to the processor 101. A portion of the memory 104 may also include non-volatile random access memory. For example, the memory 104 may also store information of device type.
In a specific implementation, the processor 101, the input device 102, and the output device 103 described in the embodiments of the present application may execute the implementation described in the embodiments of the village and town aggregation space evolution simulation prediction method provided in the embodiments of the present application, which is not described herein again.
It should be noted that, in the embodiment of the present application, the computer device is a more specific workflow and related details, please refer to the foregoing method embodiment, and the details are not described herein.
While the application 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 application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (2)
1. A village and town aggregation space evolution simulation prediction method is characterized by comprising the following steps:
s1: acquiring historical and current remote sensing images of a preset research area range, and manufacturing a dependent variable layer required by a GeoDetector model according to land utilization interpretation results of the remote sensing images;
s2: collecting driving factor data in a preset research area range, and manufacturing an independent variable layer required by a GeoDetector model according to the driving factor data;
s3: manufacturing sampling points by using a GIS platform, sampling and extracting the dependent variable layer and the data in the independent variable layer according to the sampling points, and exporting a sampling result in an excel format;
s4: the sampling result is imported into a GeoDetector model for calculation, and driving strength of each driving factor on village and town aggregation space evolution is obtained;
s5: manufacturing an exclusion layer required by SLEETH model simulation according to the driving strength;
s6: manufacturing a gradient layer, a land utilization layer, a city range layer, a traffic layer and a mountain shadow layer required by SLEETH model simulation;
s7: inputting a plurality of layers of the SLUTH model into the SLUTH model for parameter correction to obtain optimal parameters simulated by the SLUTH model;
s8: setting the initial year and the final year of SLEETH model simulation, and adopting the optimal parameters to simulate and predict the village and town aggregation space evolution of a research area;
the step S1 specifically comprises the following steps:
s11: acquiring historic and current remote sensing images of a preset research area range in a plurality of periods;
s12: interpreting the remote sensing image of each period to obtain land utilization status vector data of villages and towns in a plurality of time periods;
s13: according to the land utilization current situation vector data, utilizing a GIS platform to manufacture a dependent variable layer required by a GeoDetector model;
the step S5 specifically comprises the following steps:
s51: according to the driving strength of each driving factor, weighting and superposing the layers of each driving factor in the research range by adopting a GIS platform to generate a village and town aggregation space evolution power distribution diagram;
s52: acquiring various vegetation, water areas, terrains and ecological management and control element layers in a research range, and carrying out weighted superposition on the layers by adopting a GIS platform to generate a village and town aggregate space ecological sensitivity distribution map;
s53: generating a village and town aggregation space evolution probability map by adopting a GIS platform to obtain the village and town aggregation space evolution power distribution map and a village and town aggregation space ecological sensitivity distribution map;
s54: reclassifying the data, and manufacturing the village and town aggregation space evolution probability map as an exclusion map layer required by SLEEUTH model simulation;
the step S6 specifically comprises the following steps:
s61: obtaining DEM data in the range of a research area, and manufacturing the DEM data into a gradient layer and a mountain shadow layer;
s62: preparing land use current situation vector data in a plurality of periods as a land use layer;
s63: the method comprises the steps of manufacturing urban area range map layers by using village and town aggregation construction land vector data in a plurality of periods;
s64: the current road traffic vector data is drawn to be a traffic layer;
the step S7 specifically comprises the following steps:
carrying out parameter correction by adopting a forced Monte Carlo iterative calculation method, wherein the parameter correction is carried out in 4 stages of coarse correction, fine correction, final correction and parameter acquisition, and a set of increased parameter sets obtained in each step are used for parameter correction of the next step to finally obtain optimal parameters simulated by a SLUTH model;
consistency evaluation is carried out on the simulation results by using Kappa coefficients, when the simulation results are completely consistent with the reference, kappa reaches a maximum value of 1, and the larger the Kappa value is, the better the consistency is; when Kappa is more than or equal to 0.75, the consistency of the two is higher, and the change is smaller: when Kappa is more than or equal to 0.4 and less than or equal to 0.75, the consistency of the two is common, and when Kappa is more obvious and less than 0.4, the consistency of the two is lower, and the change is larger.
2. A computer device comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of claim 1.
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