Urban land-population dynamic coupling simulation method and system based on geographic element process
Through the simulation method based on geographical metaprocesses, the shortcomings of population-land interaction simulation in the existing technology are solved, and high-accurate land and population simulation is achieved, and scientific urban planning and sustainable development are supported.
Patent Information
- Application Number
- CN202510147842.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-11
- Publication Date
- 2025-05-23
AI Technical Summary
Existing urban human-land coupling simulation methods are difficult to accurately reflect the complex interaction between population and land, especially inadequate performance when describing heterogeneous spatial distribution and dynamic interactions, and difficult to capture the diversity of residence and transportation patterns when dealing with vast urban spaces.
The dynamic coupled simulation method of urban land-population based on geographical meta-process is adopted. By acquiring and preprocessing the basic data of land and population, a CA model and GMP model are constructed, the spatial gradient changes of land and population are analyzed, the punishment mechanism for land simulation is established, and population behavior is simulated under the framework of the ABM model, and the differential evolution algorithm is used for iterative prediction.
It improves the spatial accuracy of land simulation and the behavioral accuracy of population simulation, enhances the convergence efficiency and prediction accuracy of the model, supports the formulation of scientific and forward-looking urban planning strategies, optimizes urban resource allocation and promotes sustainable development.
Smart Images
Figure CN120031249A_ABST
Abstract
Claims
1. A method for simulating urban land-population dynamic coupling based on geographic meta-process, characterized in that: include: Obtain the basic land and population data of the study area, and pre-process the basic land and population data to obtain the circle structure that identifies the city center; Construct a CA model based on the circle structure that identifies the city center; The GMP model is used to analyze the spatial gradient changes of land and population, complete macro process modeling, and calculate the expected value of growth of each circle at the micro level; Based on the expected growth value of each circle calculated by the GMP model and the actual growth value of each circle in each iteration, the demand of each circle is determined, and a penalty mechanism for land simulation is established by integrating the nonlinear function with the CA model to adjust the growth in a soft constraint manner; Based on the circle structure that identifies the city center, under the ABM model framework, the behavior of the working and non-working population in choosing their work location, commuting mode, and residence location is simulated; And carry out urban form control influenced by social networks and urban form control based on carrying capacity; The differential evolution algorithm is used to adjust the GMP model parameters and iteratively predict the impact of land use change on population distribution under various expansion scenarios.
2. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The method for constructing the CA model includes: A number of newly added urban land and non-urban land were selected by random sampling method to construct a raster dataset including natural factors and socio-economic factors. The random forest algorithm combined with the genetic algorithm was used to calculate the probability of the influence of natural factors and socio-economic factors on the development suitability of the study area. The CA model was constructed by adding neighborhood influence, geographic constraints and random interference. The calculation formula for the conversion probability of land cells developing into urban land in the CA model is: ； in, P is the conversion probability of land cells in the CA model developing into urban land, P potent To develop suitability probabilities, is the neighborhood influence effect, For geographical constraints, is random interference.
3. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The GMP model is used to analyze the spatial gradient changes of land and population to complete the macro process modeling, including: The GMP model is used to subdivide urban growth into a series of substantial meta-processes with a fixed time span. In each growth stage, land and population elements are spatially expanded according to exponential probability. The density functions of urban land and population elements are: ； in, is the gradient parameter in the urban growth process, n is the cumulative number, r represents the distance from the city center, y It is the rate of change or growth level of urban element density.
4. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The method for establishing a penalty mechanism for land simulation includes: Based on the proportional relationship between the expected growth value of each circle calculated by the GMP model and the actual growth value of each circle in each iteration of urban land simulation, the growth demand of land cells in each circle is determined; The Sigmoid function is integrated with the CA model to establish a penalty mechanism for land simulation. The formula is as follows: ； in, Q (i,current) , Q (i,intial) and Q (i,expect) are the increased, initial and expected numbers of urban cells, N The iteration step size set for the model, x i For the i The demand for each iteration of each circle, y i is the penalty item for the corresponding circle, c and k To control the parameters of the Sigmoid function curve shape, is the conditional control function, D is the set critical threshold, indicating the critical condition for the growth of the current circle. D T is the multiple relationship between the current total amount of growth cells and the expected total amount of cells. D T Meet or exceed D The punishment mechanism is activated.
5. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The simulated behaviors of the working population and the non-working population in choosing a place of work include: Summarize the labor force data and total work area of each sector of the primary, secondary and tertiary industries, and calculate the average workspace area required for each employee; Multiply the base area of each building by the floor height to assess the building area of each enterprise; Calculate the labor demand of each enterprise in each department: ； in, LaborDemand e, f For Enterprises e In the department f The labor demand in Total Building Area e For Enterprises e of building area, Average Workplace Area f For Department f Average workspace area per employee in Calculate the labor demand ratio: ； in, Demand Ratio e For Enterprises e The labor demand ratio, MaximumDemand e For Enterprises e The maximum number of workers that can be accommodated, Current Capacity e For Enterprises e Current workforce size.
6. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The simulated commuting mode selection behaviors of the working population and the non-working population include: Set four modes of transportation, including walking or cycling, bus, subway and private car. The type of public transportation can only be selected when the starting point and destination are covered by bus or subway; Assuming that the working population commutes from work to home and the non-working population travels randomly, the choice of transportation mode is related to the economic cost and time cost of travel, and a multinomial logit function is used for calculation: ； Among them, the variable m Indicates the type of transportation mode. P m To select m The probability of a mode of transportation, It is m The choice weight of each mode of transportation, Cost eco and Cost time Respectively m The economic and time costs of these modes of transportation, a (m,eco) and a (m,time) Respectively m The economic cost coefficient and time cost coefficient of each mode of transportation, Cost r1 is the introduced random interference term, N m represents the set of all available transportation modes.
7. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The simulated behaviors of the working population and the non-working population in choosing where to live include: According to the personal preferences of residents and market conditions, three expected residential types are set, including the best comprehensive benefit residential, high-value residential and low-density comfortable residential. The roulette method is used to determine the residential choice. If residents cannot find a better place to live, they will keep their current location. Among them, the cost function of residential choice is: ； ； ； in, CBM Represents a way for residents to try to minimize their cumulative costs, including housing prices Cost house , commuting costs Cost travel , living expenses Cost living and distractors Cost r2 , interference Cost r2 Indicates unpredictable external factors, HV Representatives prioritize residents who acquire the most expensive properties within their search radius, LDC Residents who attach importance to the living space environment, Density house is the population density.
8. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The urban form control influenced by social networks and the urban form control based on carrying capacity include: Set social connectivity thresholds to control the interaction of each population unit with the defined neighborhood, adjust the admission conditions for new residents, and adjust the dynamics of residents according to the strength of social connections; assume that each population unit is within the neighborhood radius defined by it η within η 2 -1 unit interaction, social network connectivity threshold δ The formula for determining whether a unit can accommodate a new resident is as follows: ； if ； in, S ( v , j ) indicates a unit ( v , j )’s social connection strength, z and l Represent the coordinates of the neighborhood cells, N η ( v , j ) represents the neighborhood radius η Neighborhood cells within f ( C z,l , D z, l, v, j ) represents the influence of the quantized neighboring cells on the current cell, C z,l represents the neighborhood unit ( z , l )’s social relationship strength or degree of connectivity, D z,l,v,j From the neighborhood unit ( z , l ) to unit( v , j )’s physical distance or spatial relative position, t is the current time step, P t+1 ( v , j ) indicates time t +1 hour unit ( v , j ) population, P t ( v , j ) indicates time t Time unit ( v , j ) population density, Indicates population change; The maximum theoretical growth threshold and expected growth amount are calculated for each circle, and different density threshold ranges are set. By adjusting the growth coefficient within the interval, the population growth rate can be gradually regulated. The formula is as follows: , if ； ； in, h is an interval index, with a value range of 1≤ h ≤ n ',in n ' is the total number of intervals; Indicates i The circle is in h The growth coefficient of the interval, is the maximum value of the growth coefficient, indicating that i The ratio of the theoretical maximum growth threshold of a circle to the maximum value of the circle in the initial year. is the minimum value of the growth coefficient.
9. The urban land-population dynamic coupling simulation method based on geographic meta-process according to claim 1 is characterized in that: The differential evolution algorithm is used to adjust the GMP model parameters and iteratively predict the impact of land use changes on population distribution under various expansion scenarios, including: Using multi-period data on land and population density, we analyze the curve shapes and changing trends of the two, calculate the average distance fitting value between these curves, and set three land expansion scenarios: compact, stable, and sprawling. Through linear regression analysis, the average density distance in the future is inferred, and then the differential evolution algorithm is used to adjust the gradient parameters in the urban growth process of the GMP model. and cumulative number of n , ensuring that forecasts reflect actual urban development conditions; At the same time, the gray prediction method and linear calibration method are used to adjust key model parameters including departmental labor demand, wage levels, population carrying capacity of each circle and expected growth rate to ensure the accuracy of the simulation process.
10. An urban land-population dynamic coupling simulation system based on geographic meta-process, characterized in that: include: The data acquisition and preprocessing module is used to obtain the basic land and population data of the study area, and preprocess the basic land and population data to obtain the circle structure that identifies the city center; The model building module is used to build a CA model based on the circle structure that identifies the city center; The GMP model is used to analyze the spatial gradient changes of land and population, complete macro process modeling, and calculate the expected value of growth of each circle at the micro level; The land simulation module is used to determine the demand of each circle based on the expected growth value of each circle calculated by the GMP model and the actual growth value of each circle in each iteration, integrate the nonlinear function with the CA model, establish a penalty mechanism for land simulation, and adjust the growth in a soft constraint manner; Population simulation module, which is used to simulate the behavior of working and non-working population in choosing work locations, commuting methods and residential locations based on the circle structure of the city center under the ABM model framework; And carry out urban form control influenced by social networks and urban form control based on carrying capacity; The prediction module is used to adjust the GMP model parameters using the differential evolution algorithm and to iteratively predict the impact of land use changes on population distribution under various expansion scenarios.