CN116757060A - Urban expansion simulation method, terminal equipment and storage medium - Google Patents

Urban expansion simulation method, terminal equipment and storage medium Download PDF

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CN116757060A
CN116757060A CN202310483509.5A CN202310483509A CN116757060A CN 116757060 A CN116757060 A CN 116757060A CN 202310483509 A CN202310483509 A CN 202310483509A CN 116757060 A CN116757060 A CN 116757060A
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urban
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urban land
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徐智邦
王豪伟
蓝婷
赵纯源
杨溢
李程鹏
朱天媛
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Institute of Urban Environment of CAS
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Abstract

The invention relates to a city expansion simulation method, terminal equipment and storage medium, wherein the method comprises the following steps: training urban land development potential models of all natural partitions; inputting space variable factor grid data of a target area on a prediction base period time point into a city land development potential model, and predicting to obtain city land development potential space data; carrying out space superposition calculation on urban land development potential space data, planning constraint space data, random factor space data and space neighborhood factor data to obtain urban land comprehensive conversion probability data; performing iterative simulation in each administrative division, and setting a land larger than the comprehensive conversion probability threshold as a new land; judging whether the total area of the newly added land of the administrative division is larger than the total amount of the expansion target, if so, finishing iteration; otherwise, after the comprehensive conversion probability threshold is adjusted, iterating again. The invention can better reflect the unit difference of the urban expansion from bottom to top and the control from top to bottom.

Description

Urban expansion simulation method, terminal equipment and storage medium
Technical Field
The present invention relates to the field of data processing, and in particular, to a city expansion simulation method, a terminal device, and a storage medium.
Background
Generally, urban expansion simulation methods and systems relate to Geographic Information Systems (GIS), artificial Intelligence (AI), cellular Automata (CA), remote Sensing (RS), and other technical means. The realization thinking is as follows: firstly, collecting urban land space range data at two historical time points of a city; secondly, analyzing quantitative relations between a newly added urban land area between two historical time points and various natural and social variables such as terrain, gradient, distance from a road and the like at an initial historical time point to obtain related parameters; thirdly, calculating the probability of converting the land at the prediction base period from non-urban land to urban land based on the obtained quantitative relation parameters and various natural and social variables of the prediction base period; fourth, the total amount of the urban expanding land of the expected target year is determined by using a total amount prediction model, then the land conversion probability obtained in the last step, the neighborhood land of each land, the random fluctuation factor and the macro constraint factor are comprehensively considered, the possible newly added urban land is determined by using a cellular automaton model and the like, and finally the urban land space range of the expected target year is obtained.
However, the existing city expansion simulation method mainly aims at a single city, part of the city expansion simulation method aims at the regional scale, and the comprehensive framework design aiming at the national scale is few. Urban expansion is a spatial process that is affected by both bottom-up and top-down, with bottom-up effects such as land being affected by the surrounding neighborhood, and top-down effects such as land development of an area being managed and constrained by administrative authorities. However, the top-down impact of urban expansion is not limited to urban and regional dimensions, but includes top-level designs of national dimensions and target decomposition between different scale levels. Therefore, the combined action of space units with different scales needs to be considered, and the urban expansion simulation method and system capable of meeting the whole national scale are researched. In particular, for urban expansion simulations, the complexity of the problem is multiplied from city, to region, to country, behind the simple spatial scale change. The method is mainly characterized in that the longitudinal scale difference and the transverse regional difference of the land dynamic driving are two major aspects. For longitudinal dimension differences: the dynamic change of the land in the urban scale is greatly affected by the neighborhood; on the other hand, when the regional scale is reached, the administrative unit management and control difference which can be ignored under the urban scale tends to be obvious, more uncertainty exists in the land dynamic change of the cross administrative unit, and the neighborhood effect is also very easy to be regulated by the administrative boundary, especially in the region with obvious local policy difference; if the national scale is raised, the land dynamic change of the area can be affected by remote network interaction besides the neighborhood effect. For lateral regional differences: due to the diversity of cities, spatial environment variables on which urban expansion under different natural environments and social conditions mainly depends may be quite different; compared with city and regional scales, the national scale space range is wider, the types of residents possibly involved are more, the geographic space heterogeneity is stronger, and the regional difference of land dynamic driving is more remarkable.
In summary, the existing urban expansion simulation model still lacks definition and design of systemization, layering and materialization of different scale space units affecting the urban expansion process, and lacks a method and system for simulating urban expansion for distinguishing urban expansion driving and controlling different space units.
Disclosure of Invention
In order to solve the problems, the invention provides a city expansion simulation method, terminal equipment and a storage medium.
The specific scheme is as follows:
a city expansion simulation method, comprising the steps of:
s1: dividing the target area into n natural partitions based on natural geographic conditions, and dividing the target area into m administrative partitions based on administrative boundaries;
s2: based on historical data, collecting space variable factor grid data corresponding to each natural partition and newly added land range grid data of a city to construct n training sets;
s3: constructing a back propagation neural network model, taking space variable factor raster data as model input data and newly added urban land scope raster data as model output data, respectively training the model through each training set, and taking the trained model as an urban land development potential model of each natural partition;
s4: inputting space variable factor grid data of a target area on a prediction base time point into a corresponding urban land development potential model according to a corresponding natural partition, and predicting to obtain a urban land development potential value of each grid unit in the target area, wherein the urban land development potential value is used as urban land development potential space data;
s5: calculating urban land density in a molar neighborhood of each grid in a target area at a predicted base period time point, and taking the urban land density as space neighborhood factor data;
s6: carrying out GIS space superposition calculation on urban land development potential space data, planning constraint space data, random factor space data and space neighborhood factor data to obtain urban land comprehensive conversion probability data;
s7: according to the national scale macro target on the predicted final time point, carrying out target decomposition of the regional level of the regional administration, and determining the total amount of the macro city land expansion target of each administrative division;
s8: the iterative simulation is carried out in each administrative division, and the specific flow is as follows:
setting a land with the urban land comprehensive conversion probability data larger than the comprehensive conversion probability threshold as a newly added land;
calculating the total area of the newly added land of each administrative division;
judging whether the total area of the newly added land of the administrative division is larger than the total amount of the expanded target land of the macroscopic city of the division, if so, setting that the iteration of the administrative division is completed, and entering S10; otherwise, entering S9;
s9: after adjusting the comprehensive conversion probability threshold, returning to S5;
s10: and after the iteration of all the administrative partitions is completed, merging the iteration results of the m administrative partitions to obtain the urban land space distribution pattern at the expected final time point.
Further, the space variable factor raster data comprises two types of natural environment and location development.
Further, in step S2, the space variable factor raster data in the training set is space variable factor raster data at the first history time point, and the corresponding newly added urban land space range raster data is urban land space range raster data newly added at the second history time point relative to the first history time point.
Further, the initial comprehensive conversion probability threshold is set from the maximum value of the comprehensive conversion probability of the urban land of the target area, and the value of the comprehensive conversion probability of the urban land of the target area is traversed from large to small in sequence during each adjustment.
The city expansion simulating terminal equipment comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the method according to the embodiment of the invention when executing the computer program.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method described above for embodiments of the present invention.
According to the technical scheme, the differential space unit setting for driving and controlling urban expansion is established, the regional training is performed based on natural conditions, and the iterative control is performed based on administrative regions, so that the unit difference of urban expansion from bottom to top and control from top to bottom can be reflected better.
Drawings
Fig. 1 is a flowchart of a first embodiment of the present invention.
Detailed Description
For further illustration of the various embodiments, the invention is provided with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments and together with the description, serve to explain the principles of the embodiments. With reference to these matters, one of ordinary skill in the art will understand other possible embodiments and advantages of the present invention.
The invention will now be further described with reference to the drawings and detailed description.
Embodiment one:
the embodiment of the invention provides a city expansion simulation method, as shown in fig. 1, comprising the following steps:
s1: the target area is divided into n natural partitions based on natural geographic conditions, and the target area is divided into m administrative partitions based on administrative boundaries.
The target region is a region for urban expansion simulation prediction.
S2: based on historical data, spatial variable factor grid data corresponding to each natural partition and newly added land range grid data of the city are collected to construct n training sets.
The training set construction method in this embodiment is as follows:
s201: acquiring space variable factor grid data corresponding to each natural partition of the first historical time point and related to urban expansion;
s202: respectively acquiring urban land space range raster data corresponding to each natural partition of the first historical time point and the second historical time point, and calculating newly added urban land range raster data of each natural partition in the period from the first historical time point to the second historical time point;
s203: and constructing training sets based on the space variable factor grid data of each natural partition and the land area grid data of the newly added city to obtain n training sets.
The spatial variable factor raster data in this embodiment includes two categories of natural environment and location development. Common natural environmental factors such as DEM, gradient, annual precipitation, annual average temperature, etc.; common location development factors are, for example, distance to roads, distance to railroads, distance to traffic stations, distance to city centers, distance to built-up areas, and density of open spaces in cities.
S3: and constructing a Back Propagation Neural Network (BPNN) model, taking space variable factor grid data as model input data and newly added urban land scope grid data as model output data, respectively training the model through each training set, and taking the trained model as an urban land development potential model of each natural partition. Finally, n urban land development potential models are obtained.
S4: and inputting the space variable factor grid data of the target area on the prediction base time point into a corresponding urban land development potential model according to the corresponding natural partition, and predicting to obtain the urban land development potential value of each grid unit in the target area as urban land development potential space data.
The spatial variable factor grid data input in step S4 is consistent with the spatial variable factor type of the input data of the model during the training of the model in step S3.
S5: and calculating urban land density in the molar neighborhood of each grid in the target area at the prediction base period time point as space neighborhood factor data.
The calculation formula of the urban land density is as follows:
wherein, (P) Ω ) i Representing the neighborhood city land density of grid i; con (S) j =rban) is used to determine whether the grid j in the neighborhood is urban land, and if yes, return 1; n represents the number of grids corresponding to the neighborhood side length, and can be obtained by converting the side length and the grid size.
S6: and carrying out GIS space superposition calculation on the urban land development potential space data, the planning constraint space data, the random factor space data and the space neighborhood factor data to obtain urban land comprehensive conversion probability data.
Spatial superposition is the multiplication of the four data on each grid.
S7: and (3) according to the national scale macroscopic target on the predicted final time point, carrying out target decomposition of the regional level of the regional administration, and determining the total amount of the macroscopic city land expansion target of each administrative division.
S8: the iterative simulation is carried out in each administrative division, and the specific flow is as follows:
setting a land with the urban land comprehensive conversion probability data larger than the comprehensive conversion probability threshold as a newly added land;
calculating the total area of the newly added land of each administrative division;
judging whether the total area of the newly added land of the administrative division is larger than the total amount of the expanded target land of the macroscopic city of the division, if so, setting that the iteration of the administrative division is completed, and entering S10; otherwise, S9 is entered.
S9: after the comprehensive conversion probability threshold is adjusted, the process returns to step S5.
In this embodiment, the initial integrated transition probability threshold is set starting from the maximum value of the urban land integrated transition probability of the target area, and the values of the urban land integrated transition probabilities of the target area are traversed sequentially from large to small at each adjustment.
Returning to the step S5, the urban land density in the mole neighborhood of each grid can be recalculated based on the newly added urban land to obtain new space neighborhood factor data, then the step S6 is carried out to obtain new urban land comprehensive conversion probability data, and then the steps S7-S8 are carried out, and the newly added urban land is updated again based on the new urban land comprehensive conversion probability data, so that the cycle is carried out until the condition is met.
S10: and after the iteration of all the administrative division is finished, merging the iteration results of the m administrative division to obtain a space distribution pattern of the urban land at the expected final time point, thereby finishing urban expansion simulation.
The embodiment of the invention considers the space unit difference of bottom-up driving and top-down control in the urban expanding process. In the aspect of bottom-up driving, partitioning is carried out according to the difference of natural geographic conditions, so that the urban expansion history process is trained in a partitioning way; secondly, in the aspect of top-down control, partitioning is carried out according to the range of administrative division, so that the urban expansion simulation iteration is controlled in a partitioning way; in conclusion, different partition strategies are adopted for training and iteration, and different space units driven and controlled in the city expansion process are reflected more objectively.
Compared with the prior art, the method establishes differential space unit arrangement for driving and controlling urban expansion, and can better reflect unit differences of urban expansion from bottom to top and control from top to bottom. From test results, the simulation accuracy result of urban expansion is improved by adopting the strategy, and meanwhile, the calculation speed is improved by adopting the strategy of regional training and iterative control, so that the urban expansion simulation of large scale, particularly national scale, can be better carried out.
Embodiment two:
the invention also provides city expansion analog terminal equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the steps in the method embodiment of the first embodiment of the invention are realized when the processor executes the computer program.
Further, as an executable scheme, the city expansion simulation terminal device may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The city expansion analog terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the above-described composition structure of the urban expansion simulation terminal device is merely an example of the urban expansion simulation terminal device, and does not constitute limitation of the urban expansion simulation terminal device, and may include more or less components than the above, or may combine some components, or different components, for example, the urban expansion simulation terminal device may further include an input/output device, a network access device, a bus, etc., which is not limited by the embodiment of the present invention.
Further, as an executable scheme, the processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the city expansion analog terminal device, and connects various parts of the entire city expansion analog terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the city expansion simulation terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The present invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the above-described method of an embodiment of the present invention.
The modules/units integrated in the urban expansion analog terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (6)

1. The city expansion simulation method is characterized by comprising the following steps of:
s1: dividing the target area into n natural partitions based on natural geographic conditions, and dividing the target area into m administrative partitions based on administrative boundaries;
s2: based on historical data, collecting space variable factor grid data corresponding to each natural partition and newly added land range grid data of a city to construct n training sets;
s3: constructing a back propagation neural network model, taking space variable factor raster data as model input data and newly added urban land scope raster data as model output data, respectively training the model through each training set, and taking the trained model as an urban land development potential model of each natural partition;
s4: inputting space variable factor grid data of a target area on a prediction base time point into a corresponding urban land development potential model according to a corresponding natural partition, and predicting to obtain a urban land development potential value of each grid unit in the target area, wherein the urban land development potential value is used as urban land development potential space data;
s5: calculating urban land density in a molar neighborhood of each grid in a target area at a predicted base period time point, and taking the urban land density as space neighborhood factor data;
s6: carrying out GIS space superposition calculation on urban land development potential space data, planning constraint space data, random factor space data and space neighborhood factor data to obtain urban land comprehensive conversion probability data;
s7: according to the national scale macro target on the predicted final time point, carrying out target decomposition of the regional level of the regional administration, and determining the total amount of the macro city land expansion target of each administrative division;
s8: the iterative simulation is carried out in each administrative division, and the specific flow is as follows:
setting the land of the city with the comprehensive conversion probability data larger than the comprehensive conversion probability threshold as the newly added land of the city;
calculating the total area of the newly added urban land of each administrative division;
judging whether the total area of the newly added urban land of the administrative division is larger than the total amount of the macro urban land expansion target of the division, if so, setting that the iteration of the administrative division is completed, and entering S10; otherwise, entering S9;
s9: after adjusting the comprehensive conversion probability threshold, returning to S5;
s10: and after the iteration of all the administrative partitions is completed, merging the iteration results of the m administrative partitions to obtain the urban land space distribution pattern at the expected final time point.
2. The urban expansion simulation method according to claim 1, wherein: the space variable factor raster data comprises two types of natural environment and regional development.
3. The urban expansion simulation method according to claim 1, wherein: and S2, the space variable factor grid data in the training set is the space variable factor grid data of the first historical time point, and the corresponding newly added urban land space range grid data is the urban land space range grid data newly added by the second historical time point relative to the first historical time point.
4. The urban expansion simulation method according to claim 1, wherein: the initial comprehensive conversion probability threshold value is set from the maximum value of the urban land comprehensive conversion probability of the target area, and the value of the urban land comprehensive conversion probability of the target area is traversed from large to small in sequence during each adjustment.
5. A city expansion simulation terminal device is characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, which processor, when executing the computer program, carries out the steps of the method according to any one of claims 1 to 4.
6. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 4.
CN202310483509.5A 2023-04-28 2023-04-28 Urban expansion simulation method, terminal equipment and storage medium Pending CN116757060A (en)

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