CN113642764A - Village and town settlement space evolution simulation prediction method and computer equipment - Google Patents
Village and town settlement space evolution simulation prediction method and computer equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a village and town settlement space evolution simulation prediction method and equipment, wherein the method comprises the following steps: manufacturing a dependent variable layer and an independent variable layer required by a GeoDetector model; making an exclusion layer and other layers required by SLUTH model simulation; inputting the layers into a SLUTH model for parameter correction to obtain optimal parameters; setting the initial year and the terminal year of SLUTH model simulation, and simulating and predicting the village and town settlement space evolution of the research area by adopting the optimal parameters. According to the embodiment of the invention, the GeoDetector model and the SLUTH model are fused to simulate and predict the evolution of the village and town settlement space, the driving relation of different transformation driving forces to the village and town settlement space is embedded into the land utilization simulation model, 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 settlement space can be better described.
Description
Technical Field
The invention relates to the technical field of humanistic geography and urban and rural planning, in particular to a method for simulating and predicting spatial evolution of village and town settlement.
Background
Land utilization is an important index for reflecting spatial 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 humanistic geography and urban and rural planning. The dynamic change simulation of land utilization can analyze and predict the change process of the space of villages and towns, prejudge the expanding direction and scale of the construction land of the villages and towns, help land managers to formulate practical and effective development policies of the villages and towns, and the land utilization change simulation and prediction based on a Cellular Automata (CA) model are hot topics in the dynamic change simulation and prediction. One of the key problems and difficulties is how to identify the driving force for transformation of the village and town settlement space, and effectively fuse the driving force with a land utilization simulation model, so as to improve the precision of the simulation model and better predict the evolution trend of the village and town space.
However, with the prior art, the existing Cellular Automata (CA) model cannot simulate the village and town settlement space transformation power and the land utilization dynamic change on a space well to be embedded, so that the dynamic change process of the village and town construction land space expansion at different periods cannot be effectively predicted.
Disclosure of Invention
In view of the above technical defects, an object of the embodiments of the present invention is to provide a method and a computer device for simulating and predicting the spatial evolution of village and town settlement, so as to effectively fuse a village and town settlement space transformation power identification model and a land use dynamic change simulation model, realize the advantage complementation of the two models, and finally scientifically depict the development trend and spatial layout of future land use of village and town settlement.
In order to achieve the above object, in a first aspect, an embodiment of the present invention provides a method for simulating and predicting spatial evolution of village and town settlement, 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 a land utilization interpretation result of the remote sensing images;
s2: acquiring 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, extracting data in the dependent variable layer and the independent variable layer according to the sampling points, and exporting sampling results in an excel format;
s4: importing the sampling result into a GeoDetector model for calculation to obtain the driving strength of each driving factor on the village and town settlement space evolution;
s5: making an exclusion layer required by SLUTH model simulation according to the driving strength;
s6: manufacturing a slope layer, a land utilization layer, an urban range layer, a traffic layer and a mountain shadow layer required by SLUTH model simulation;
s7: inputting a plurality of layers of the SLUTH model into the SLUTH model for parameter correction to obtain the optimal parameters simulated by the SLUTH model;
s8: setting the initial year and the final year of SLUTH model simulation, and simulating and predicting the village and town settlement space evolution of the research area by adopting the optimal parameters.
As a specific embodiment of the present application, step S1 specifically includes:
s11: acquiring historical and current remote sensing images of a preset research area range in multiple periods;
s12: interpreting the remote sensing image of each period to obtain the current land utilization situation vector data of the village and town colony in a plurality of time periods;
s13: and manufacturing a dependent variable layer required by a GeoDetector model by utilizing a GIS platform according to the current land utilization vector data.
As a specific embodiment of the present application, step S5 specifically includes:
s51: according to the driving strength of each driving factor, carrying out weighted superposition on the image layers of each driving factor in the research range by adopting a GIS platform to generate a village and town settlement space evolution power distribution map;
s52: acquiring various vegetation, water area, terrain and ecological control element layers in a research range, and performing weighted superposition on the layers by adopting a GIS platform to generate a village and town settlement space ecological sensitivity distribution map;
s53: generating a village and town settlement space evolution probability map by adopting a GIS platform and the village and town settlement space evolution power distribution map and the village and town settlement space ecological sensitivity distribution map;
s54: and reclassifying the data, and making the village and town settlement space evolution probability map as an exclusion map layer required by SLUTH model simulation.
As a specific embodiment of the present application, step S6 specifically includes:
s61: obtaining DEM data in a research area range, and making a slope layer and a mountain shadow layer;
s62: making the current land utilization state vector data in a plurality of periods into a land utilization layer;
s63: making the village and town settlement construction land vector data in a plurality of periods into a city range map layer;
s64: and making the traffic vector data depicting the current road into a traffic map layer.
As a specific embodiment of the present application, step S7 specifically includes:
and correcting the parameters by adopting a forced Monte Carlo iterative computation method, wherein the parameter correction is divided into 4 stages of coarse correction, fine correction, final correction and simulation parameter acquisition, and a set of increased parameter set obtained in each step is used for parameter calibration in the next step to finally obtain the optimal parameters simulated by the SLUTH model.
Further, as a preferred embodiment of the present application, the method further comprises:
using a Kappa coefficient to evaluate the consistency of the simulation result, when the simulation result is completely consistent with the reference, the Kappa reaches the 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 0.75, the consistency of the two is general, and when the change is more obvious and the Kappa is 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 invention 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, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method of the first aspect.
By implementing the embodiment of the invention, the GeoDetector model and the SLUTH model are fused to simulate and predict the evolution of the village and town settlement space, and the driving relation of different transformation driving forces to the village and town settlement space is embedded into the land utilization simulation model, so that the advantages of the two models are complemented, the precision of the simulation model can be effectively improved, and the evolution trend of the village and town settlement space can be better described.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below.
Fig. 1 is a flowchart of a village and town settlement space evolution simulation prediction method according to an embodiment of the present invention;
FIG. 2 is a Korean city land use classification diagram;
FIG. 3 is a diagram of a dependent variable (Y) layer of a GeoDetector model;
FIG. 4 is a diagram of the layer of the GeoDetector model argument (X);
FIG. 5 is a schematic diagram of a sampling point pattern;
FIG. 6 is a schematic diagram of an exclusion layer of the SLUTH model;
FIG. 7 is all input layers of the SLUTH model;
FIG. 8 is a graph showing simulation results of the SLUTH model;
fig. 9 is a block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a village and town settlement space evolution simulation and prediction method fusing GeoDetector and SLEUTH models, 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 the current land utilization state vector data of the village and town settlement space in a plurality of time periods.
S3: and collecting driving factor data in the research area, and performing primary processing to form a data set.
S4: and (4) making the multi-stage land utilization current situation vector data obtained in the step (S2) into space growth change data of different time periods by using a GIS platform, and generating a dependent variable (Y) layer required by the GeoDetector model.
S5: the driving factor data obtained in step S3 is created as an argument (X) layer required for the GeoDetector model.
S6: and manufacturing sampling points by using a GIS platform, extracting data in the dependent variable layer and the independent variable layer according to the sampling of the sampling points, and exporting sampling results in an excel format.
S7: and 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 settlement space evolution.
S8: and according to the driving strength of each driving factor obtained in the step S7, performing weighted superposition on the map layers of each driving factor in the research range by adopting a GIS platform to generate the village and town settlement space evolution power distribution map.
S9: and acquiring various vegetation, water area, terrain and ecological control element layers in a research range, and performing weighted superposition on the layers by adopting a GIS platform to generate a village and town settlement space ecological sensitivity distribution map.
S10: and generating a village and town settlement spatial evolution probability map by adopting a GIS platform according to the maps obtained in the step S8 and the step S9.
S11: and reclassifying the data, and respectively making the village and town settlement space evolution probability map obtained in the step S10 as an exclusion map layer required by SLUTH model simulation.
S12: making other data layers required by SLUTH model simulation: grade (Slope), land use (Landuse), Urban area (Urban), Transportation (Transportation), mountain shadow (Hillshade).
S13: inputting a plurality of layers of the SLUTH model into the SLUTH model, and correcting parameters by adopting a forced Monte Carlo iterative computation method (Brute-force Monte Carlo method), wherein the parameter correction is divided into 4 stages of coarse correction, fine correction, final correction and simulation parameter acquisition, a set of increased parameter set obtained in each step is used for parameter calibration in the next step, and the optimal parameters simulated by the SLUTH model are finally obtained.
S14: and carrying out land use evolution simulation verification of the 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, simulating to generate a land use map of the current year, and carrying out comparative 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: using a Kappa coefficient to evaluate the consistency of the simulation result, when the result is completely consistent with the reference, the Kappa reaches the 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 0.75, the consistency of the two is general, and when the change is more obvious and the Kappa is less than 0.4, the consistency of the two is lower and the change is larger.
S16: and simulating and predicting the village and town settlement space evolution of the research area by using the model subjected to consistency evaluation as an optimal parameter and taking the layer data of the current year as an initial year.
S17: and obtaining the simulation and prediction result of the spatial evolution of the village and town settlement in the target year.
Further, for a better understanding of the embodiments of the present invention, the following examples are set forth:
(1) through 30-meter precision satellite image interpretation, land use classification vector data of 4 periods (2000, 2005, 2013, 2018) of the wei nan korea city, shanxi province are obtained, as shown in fig. 2.
(2) Extracting map spots of construction land for settlement of villages and towns in the last period (2013 to 2018), calculating space variation by taking villages and towns as units, and making a dependent variable (Y) map layer required by a GeoDetector by utilizing a GIS platform, as shown in FIG. 3.
(3) Selecting independent variable factors (X), namely dynamic factors, required by the GeoDetector, such as general population (X1), population density (X2), urban area distance (X3), road distance (X4) and the like, respectively manufacturing the factors into corresponding layers, processing each layer into 5 grades by using a natural breakpoint method, and assigning the reclassification values to 1, 2, 3, 4 and 5, as shown in figure 4.
(4) Sampling point image layers with the distance of 300 meters (10 times precision and adjustable according to the area size) are manufactured by utilizing a fishing net establishing module with the sampling function in a GIS data management tool, and sampling results in an excel format are derived by utilizing a sampling module for extraction and analysis in a spatial analysis tool, as shown in figure 5.
(5) And importing 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, the space difference of the dependent variable (Y) is explained to the extent that a certain power factor (X) is large, the larger the q value is, the stronger the explanatory power of the dependent variable Y by the independent variable X is, otherwise, the weaker the explanatory power is, and the expression is as follows:
in the formula: h 1, …, L being a hierarchy, i.e. classification or partition, of variable Y or factor X; n is a radical ofhAnd N is the number of units of layer h and the whole area respectively;
and σ2Respectively of layer h and of the whole zoneVariance of Y value. SSW and SST are the sum of intra-layer variance and total variance of the whole area, respectively.
The q values of the GeoDetector operation results X1, X2, X3 and X4 are respectively as follows: and after normalization processing, the ratio is used as the weight of each power factor.
Wherein, the GeoDetector operation result 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 exporting the dynamic factor layers into a 30-meter precision 8-bit gray GIF file format, namely, the dynamic factor layers can be used as SLUTH model exclusion layers (figure 6). And the attribute value of the pixel of the excluded layer is 0-100, which represents the impossibility that the area can be converted into the settlement construction land. The higher the value of the exclusion map layer is, the more unlikely the region is to be converted into the settlement construction land, if the value is 100, the region is completely impossible to be reconstructed into the settlement construction land, otherwise, if the value is 0, the probability that the region is converted into the settlement construction land is the maximum. In the 8-bit gray GIF format, the color stretching range is 0-255, corresponding to 0-100 of the layer pixel attribute assignment.
(7) The SLUTH model simulation needs 6 layers, namely Slope (Slope), land utilization (Landau), Exclusion layer (Exclusion), Urban area (Urban), traffic (Transportation) and mountain shadow (Hillshade). The method comprises the steps of obtaining 30-meter accuracy DEM data of a Korean city, making a slope map layer and a mountain shadow map layer, making 4 time node land utilization current situation vector data into a land utilization map layer, making 4 time node village and town settlement construction land vector data into a city range map layer, drawing 4 time node current situation road traffic vector data to make a traffic map layer, and exporting all map layers into an 8-bit gray GIF file format with unified coordinates and consistent resolution, as shown in figure 7.
(8) Inputting each layer into a SLUTH model for model calibration, adopting OSM _ NS as the optimal goodness-of-fit index of the determined model in the calibration stage,
OSM_NS＝compare×pop×edges×clusters×xmean×ymean。
in the formula, match is the ratio of the total number of the simulated townsized pixels in the last year to the total number of the actual townsized pixels in the last year, pop is the least square regression correlation coefficient value of the ratio of the number of the simulated townsized pixels to the number of the actual townsized pixels in the calibration year, edges is the least square regression correlation coefficient value of the ratio of the number of the simulated townsized pixels to the number of the actual townsized pixels in the calibration year, clusters is the least square regression correlation coefficient value of the ratio of the simulated townsized pixels to the actual townsized pixels in the calibration year, xmean is the least square regression correlation coefficient value of the ratio of the average x-coordinate value of the simulated townsized pixels to the average x-coordinate value of the actual townsized 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 the simulated townsized pixels to the average y-coordinate value of the actual townsized pixels in the calibration year.
(9) Obtaining a group of optimal parameters through three calibration stages of rough calibration, fine calibration and rough calibration: the diffusion coefficient is 31, the spawning coefficient is 65, the spread coefficient is 1, the slope coefficient is 62, and the road coefficient is 44.
(10) Inputting optimal parameters, setting the SLUTH model to simulate the initial year to be 2018 and the final year to be 2030, and obtaining the simulated prediction result of the spatial evolution of the village settlement from 2018 to 2030 in the Korean city, as shown in FIG. 8.
By implementing the method for simulating and predicting the evolution of the village and town settlement space, the method of fusing the GeoDetector model and the SLUTH model is adopted to simulate and predict the evolution of the village and town settlement space, and the driving relation of different transformation driving forces to the village and town settlement space is 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 settlement space can be better described.
Based on the same inventive concept, an embodiment of the present invention provides a computer device, as shown in fig. 9, where the user mobile terminal may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected via a bus 105. The memory 104 is used for storing a computer program comprising program instructions, the processor 101 being configured for invoking the program instructions for performing the methods of the above-described method embodiment parts.
It should be understood that, in the embodiment of the present invention, the Processor 101 may be a Central Processing Unit (CPU), a deep learning graphics card (e.g., NPU, england GPU, google TPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an FPGA (Field-Programmable Gate Array) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc. 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 device type information.
In a specific implementation, the processor 101, the input device 102, and the output device 103 described in the embodiment of the present invention may execute the implementation manner described in the embodiment of the village and town settlement space evolution simulation and prediction method provided in the embodiment of the present invention, and details are not described herein again.
It should be noted that, in the embodiments of the present invention, please refer to the foregoing method embodiments, which are not described herein again.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A simulation and prediction method for spatial evolution of village and town settlement 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 a land utilization interpretation result of the remote sensing images;
s2: acquiring 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, extracting data in the dependent variable layer and the independent variable layer according to the sampling points, and exporting sampling results in an excel format;
s4: importing the sampling result into a GeoDetector model for calculation to obtain the driving strength of each driving factor on the village and town settlement space evolution;
s5: making an exclusion layer required by SLUTH model simulation according to the driving strength;
s6: manufacturing a slope layer, a land utilization layer, an urban range layer, a traffic layer and a mountain shadow layer required by SLUTH model simulation;
s7: inputting a plurality of layers of the SLUTH model into the SLUTH model for parameter correction to obtain the optimal parameters simulated by the SLUTH model;
s8: setting the initial year and the final year of SLUTH model simulation, and simulating and predicting the village and town settlement space evolution of the research area by adopting the optimal parameters.
2. The village-town settlement spatial evolution simulation prediction method according to claim 1, wherein the step S1 specifically comprises:
s11: acquiring historical and current remote sensing images of a preset research area range in multiple periods;
s12: interpreting the remote sensing image of each period to obtain the current land utilization situation vector data of the village and town colony in a plurality of time periods;
s13: and manufacturing a dependent variable layer required by a GeoDetector model by utilizing a GIS platform according to the current land utilization vector data.
3. The village-town settlement spatial evolution simulation prediction method according to claim 1, wherein the step S5 specifically comprises:
s51: according to the driving strength of each driving factor, carrying out weighted superposition on the image layers of each driving factor in the research range by adopting a GIS platform to generate a village and town settlement space evolution power distribution map;
s52: acquiring various vegetation, water area, terrain and ecological control element layers in a research range, and performing weighted superposition on the layers by adopting a GIS platform to generate a village and town settlement space ecological sensitivity distribution map;
s53: generating a village and town settlement space evolution probability map by adopting a GIS platform and the village and town settlement space evolution power distribution map and the village and town settlement space ecological sensitivity distribution map;
s54: and reclassifying the data, and making the village and town settlement space evolution probability map as an exclusion map layer required by SLUTH model simulation.
4. The village-town settlement spatial evolution simulation prediction method according to claim 2, wherein the step S6 specifically comprises:
s61: obtaining DEM data in a research area range, and making a slope layer and a mountain shadow layer;
s62: making the current land utilization state vector data in a plurality of periods into a land utilization layer;
s63: making the village and town settlement construction land vector data in a plurality of periods into a city range map layer;
s64: and making the traffic vector data depicting the current road into a traffic map layer.
5. The village-town settlement spatial evolution simulation prediction method according to claim 1, wherein the step S7 specifically comprises:
and correcting the parameters by adopting a forced Monte Carlo iterative computation method, wherein the parameter correction is divided into 4 stages of coarse correction, fine correction, final correction and simulation parameter acquisition, and a set of increased parameter set obtained in each step is used for parameter calibration in the next step to finally obtain the optimal parameters simulated by the SLUTH model.
6. The village-town settlement spatial evolution simulation and prediction method according to any one of claims 1-5, wherein said method further comprises:
using a Kappa coefficient to evaluate the consistency of the simulation result, when the simulation result is completely consistent with the reference, the Kappa reaches the 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 0.75, the consistency of the two is general, and when the change is more obvious and the Kappa is less than 0.4, the consistency of the two is lower and the change is larger.
7. 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 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596100A (en) * | 2022-11-08 | 2023-08-15 | 天津大学 | Carbon sink monitoring and early warning method based on land utilization change simulation |
| CN116596100B (en) * | 2022-11-08 | 2024-01-23 | 天津大学 | Carbon sink monitoring and early warning method based on land utilization change simulation |
| CN117371963A (en) * | 2023-12-06 | 2024-01-09 | 浙江数维科技有限公司 | Automatic checking method and system for homeland investigation evidence-providing photos |
| CN117371963B (en) * | 2023-12-06 | 2024-02-23 | 浙江数维科技有限公司 | Automatic checking method and system for homeland investigation evidence-providing photos |
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