CN107807909A - A kind of Land in Regional Land purposes policy of regulation and control simulation and effect analysis method - Google Patents
A kind of Land in Regional Land purposes policy of regulation and control simulation and effect analysis method Download PDFInfo
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Abstract
The invention discloses a kind of simulation of Land in Regional Land purposes policy of regulation and control and effect analysis method, it is related to Land use and land cover change simulation field.This method passes through integrated system kinetic model, cellular Automation Model and GIS, the Real Data Exchangs between three are realized in the rank of grid, realize the effective interaction between figure number, improve the precision of land use change modeling, the application of extended model, more preferable aid is provided for the regulation and control of land policy, a variety of regions, a variety of dimensions, the simulation of the land use policy of regulation and control of a variety of yardsticks and effect analysis can be effectively used for.
Description
Technical field
The present invention relates to Land use and land cover change simulation field, more particularly to a kind of Land in Regional Land purposes policy of regulation and control
Simulation and effect analysis method.
Background technology
Land use policy of regulation and control is simulated and formulation of the effect analysis for government decision and planning all right and wrong all the time
It is often important.The change of land use efficiently manages limited soil largely by the interference and influence of human factor
Ground, the sustainable development of land use is realized, is had important practical significance.
GIS-Geographic Information System (GIS) since the advent of the world, it successively experienced starting-consolidation-popularization-flourishing four development rank
Section, 20th century computer software and hardware development, be more to speed up advancing GIS development process.GIS possesses at powerful space
Reason, computing and analysis ability, available for the acquisition of data, storage, management, analysis etc..GIS is widely used in urban construction, soil
In the natures and social phenomenon such as ground utilization, environmental management, macro-level policy-making.Although GIS possesses powerful function and is widely applied,
But GIS well can not enter element of time extension, can not effectively Capturing Models dynamic.This limitation,
So that GIS is simulating complicated phenomenon, such as land use change survey, environmental change are non-linear, are restricted on complication system.
System dynamics model (SD) is a kind of macro-kinetic model based on theory " from top to bottom ", in that context it may be convenient to
The complicated association between key element under various development scenes is simulated, the flow and feedback relationship between each key element are expressed, according to reality
The observation of world's data with existing is realized to future behaviour to establish Dynamic Simulation Model by corresponding computer software
Prediction.Meanwhile SD can be effectively introduced into various macroscopical driving factors.In recent years, increasing researcher begins to use SD to come
Simulate complicated Land.But SD is a macroscopical quantitative model, only considers the evolution on key element macro, do not examine
Consider individual microscopic behavior, and there is no space expression ability, spatial data can not be handled, it is also difficult to which expression and analysis are empty
Between reciprocation between data.
As the dynamics space system of a kind of " from bottom to top ", it is multiple that cellular Automation Model (CA) is widely used for simulation
Miscellaneous dynamical system.Four fundamentals, i.e. cellular, neighborhood, cellular state and transformation rule are included in CA.Cellular be in CA most
Substantially, minimum unit, although the state of cellular is the set of volume of data, each cellular at a time can only
There is a kind of state, for representing the current attribute of cellular.State is exactly the current property value of cellular, i.e., what cellular currently represented contains
Justice.Neighborhood refers to a range of other cellulars around cellular, and now more common has a mole neighborhood, von Neumann adjacent
Domain, and extension mole neighborhood.Transformation rule is the function for determining cellular subsequent time state, is CA core content.CA is gathered around
Have powerful spatial operation ability, meanwhile, CA be based on cellular unit, this intrinsic spatialities of CA cause it be easy to
GIS and remote sensing image etc. are integrated.In the past few decades, increasing scholar's research is various is based on CA and GIS
The complicated phenomenons such as integrated model is to simulate land change, group of cities develops.However, traditional CA has the weakness of following several respects：
(1) in the system for paying attention to macroscopical driving factors, CA can not consider that such as government regulation, economic development, technological progress, population move
Feedback effect of the Macroscopic Factors such as move to microscopic units.Conversely, the change of these microscopic units attributes can not be also fed back to macroscopic view
The influence of key element.(2) in the simulation process of whole system, the transformation rule of cellular is unalterable.But in application scenes
Under, change over time, the transformation rule of cellular is also required to change therewith.(3) in traditional CA, the conversion of all cellulars
Rule is all consistent, does not make a distinction special cellular, is such as in the plot of the scenic spots and historical sites, its land-use style is can not be with
Meaning change.
In view of the respective advantage and disadvantage of SD, CA and GIS, SD-CA-GIS is integrated, realized grand by some researchers
See in dimension and microcosmic dimension to the Evolution Simulation of various complication systems.But the model of structure is mostly between SD and CA
Loose couplings, it is relatively independent work between two submodels.Lack effective feedback mechanism between SD and CA, be typically all
Both integrated couplings on functional plane are realized by the characteristic of two submodels each over time and space, can not be realized
Being in communication with each other between both SD and CA data.It can not be effectively used for simulating, predict, showing geographical distribution, it is various dimensions, multiple dimensioned
The change of simulation land use, and in depth effect analysis is carried out to analog result.
The content of the invention
It is an object of the invention to provide a kind of simulation of Land in Regional Land purposes policy of regulation and control and effect analysis method, so as to solve
Certainly foregoing problems present in prior art.
To achieve these goals, the technical solution adopted by the present invention is as follows：
A kind of Land in Regional Land purposes policy of regulation and control simulation and effect analysis method, comprise the following steps：
S1, it is interpreted for the initial remote sensing image in time needed for survey region, obtains regional land use present situation figure；
S2, determine the transfer matrix probability of land use pattern jointly according to transfer matrix probability and Raphel method；
S3, obtain various land use patterns factor shadow driven on each cellular of cellular Automation Model in region
Loud probability；
S4, the macro-indicators calculated according to system dynamics model, generate Disturbance；
S5, runtime kinetic model, different simulation feelings are obtained according to population policy, economic development, land policy etc.
Macro-indicators under scape；
S6, the macro-indicators that gained is calculated in S5 are imported into cellular Automation Model, while the soil that will be generated in S1
Ground influences the Disturbance in probability and S4 using present situation figure, the transfer matrix probability in S2, the driving factors in S3 and imported
Into cellular Automation Model, and the macro-indicators that are calculated by system dynamics model control cellular Automation Model
Iteration operation；
S7, cellular Automation Model iteration to it is a certain setting the time after, the policy data of GIS vector quantizations is imported into cellular
In automaton model, or iteration result exported, carry out deep GIS spatial analysis；
S8, after the completion of iteration, land use macro-indicators are calculated, go to S5, while macro-indicators are imported into system and moved
In mechanical model, soil relevant parameter in micro-tensioning system kinetic model；After reaching the setting time, stop iteration and export final
As a result.
Preferably, S3 comprises the following steps：
S301, chooses the driving factors of influence area land use change, and generates in survey region space lattice to drive
The raster file of reason element distance, meanwhile, by all spatial data uniform ranges and coordinate system；
S302, stochastical sampling point is generated in survey region, is sampled, time needed for acquisition and driving factors figure layer
Sampled data；
S303, sampled data is imported into Logistic models, training Logistic regression models, and according to
The evaluation indexes such as the conspicuousness in Logistic regression results finally to determine to influence more significant driving factors；
S304, according to the driving factors chosen, sample point data is handled, and the sample point data after processing is imported into
Calculated in Logistic models, what the various land use patterns factor driven on each cellular in region that obtains influenceed
Probability.
Preferably, in S303, Logistic regression models use equation below：
Wherein,
PEDisturbance degree of the surrounding environment to cellular land-use style is represented,
α represents Logistic regression constants,
βiThe Logistic regression coefficients of a certain environmental key-element are represented,
XiRepresent the value of a certain environmental impact factor.
Preferably, in S4, the Disturbance is calculated using equation below：
Ran=1+ (ln λ)δ,
Wherein,
λ is the random number between 0 to 1,
δ is for controlling Disturbance interference strength, is the integer between 1 to 10.
Preferably, S6 is implemented using equation below：
St+1=f (St,Nt,Pt,SDt+1),
Wherein,
St+1T+1 moment cellular states are represented,
StT cellular state is represented,
NtT neighborhood cellular state is represented,
PtThe national policy of t time spaces is represented,
SDt+1Represent in influence of the t+1 moment system dynamics model to cellular transformation rule,
F is cellular from t state to the transfer function of t+1 moment states.
Preferably, the transfer function is：
Wherein,
Represent that neighborhood cellular influences probability,
Nh,k,tInfluence probability for neighborhood cellular type to center cellular type,
Represent that ambient elements influence probability,
SEk,tInfluence probability of the surrounding environment to center cellular is represented,
SHk,tThe transition probability that cellular self-characteristic determines is represented,
Represent mandatory constraint caused by natural environment, CEp,k,tIt is binary data, wherein, CEp,k,t=
0 expression cellular k is obligated by natural environment, conversely, CEp,k,t=1；
Represent mandatory constraint caused by Humanistic Factors, CHr,k,tIt is binary data, wherein CHr,k,t=0
Cellular k is represented by the mandatory constraint of Humanistic Factors, conversely, CHr,k,t=1；
Represent mandatory constraint caused by national policy, CPu,k,tIt is binary data, wherein, CPu,k,t=
0 represents cellular k by the mandatory constraint of national policy, conversely, CPu,k,t=1；
Rk,tRepresent that Disturbance influences probability,
PSD,t+1Represent that driven factor influences probability in system dynamics model.
Preferably, in S7, the policy data of the GIS vector quantizations includes：Regional population's data, GDP data, each land used class
The statistics and/or environmental key-element vector data of type area；The GIS spatial analysis includes：Buffer zone analysis and/or stacked
Analysis.
The beneficial effects of the invention are as follows：Land in Regional Land purposes policy of regulation and control simulation provided in an embodiment of the present invention and effect point
Analysis method, by integrated system kinetic model, cellular Automation Model and GIS, realized in the rank of grid three it
Between Real Data Exchangs, realize the effective interaction between figure-number, improve the precision of land use change modeling, extend
The application of model, more preferable aid is provided for the regulation and control of land policy, a variety of regions, a variety of can be effectively used for
Dimension, the simulation of the land use policy of regulation and control of a variety of yardsticks and effect analysis.
Brief description of the drawings
Fig. 1 is model general frame；
Fig. 2 is the system flow of land use；
Fig. 3 is the building process schematic diagram of cellular automata submodule；
Fig. 4 is model iteration evolution process schematic diagram.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with accompanying drawing, the present invention is entered
Row is further described.It should be appreciated that embodiment described herein is not used to only to explain the present invention
Limit the present invention.
The invention provides a kind of integrated system kinetic model, cellular Automation Model and GIS Integrated research soil
The method of purposes, the Real Data Exchangs between three are realized in the rank of grid, realize between figure-number it is effective mutually
It is dynamic.
It is including as follows the embodiments of the invention provide a kind of simulation of Land in Regional Land purposes policy of regulation and control and effect analysis method
Step：
S1, it is interpreted for the initial remote sensing image in time needed for survey region, obtains regional land use present situation figure；
S2, determine the transfer matrix probability of land use pattern jointly according to transfer matrix probability and Raphel method；
S3, obtain various land use patterns factor shadow driven on each cellular of cellular Automation Model in region
Loud probability；
S4, the macro-indicators calculated according to system dynamics model, generate Disturbance；
S5, runtime kinetic model, different simulation feelings are obtained according to population policy, economic development, land policy etc.
Macro-indicators under scape；
S6, the macro-indicators that gained is calculated in S5 are imported into cellular Automation Model, while the soil that will be generated in S1
Ground influences the Disturbance in probability and S4 using present situation figure, the transfer matrix probability in S2, the driving factors in S3 and imported
Into cellular Automation Model, and the macro-indicators being calculated by system dynamics model are come iteration cellular Automation Model
Control operation；
S7, cellular Automation Model iteration to it is a certain setting the time after, the policy data of GIS vector quantizations is imported into cellular
In automaton model, or iteration result exported, carry out deep GIS spatial analysis；
S8, after the completion of iteration, land use macro-indicators are calculated, go to S5, while macro-indicators are imported into system and moved
In mechanical model, soil relevant parameter in micro-tensioning system kinetic model；After reaching the setting time, stop iteration and export final
As a result.
Wherein, in S2, determine that the transfer matrix of land use pattern is general jointly according to transfer matrix probability and Raphel method
Rate, the intensity that neighborhood cellular retains the action potency and center cellular of center cellular self type is obtained with this；Land use
Example is expressed as below in the transfer matrix probability of type：
| 2000/1990 | Arable land | Forest land | Construction land | Waters | Summation |
| Arable land | 0.90 | 0.04 | 0.05 | 0.01 | 1.00 |
| Forest land | 0.10 | 0.88 | 0.01 | 0.01 | 1.00 |
| Construction land | 0.19 | 0.01 | 0.77 | 0.03 | 1.0 |
| Waters | 0.21 | 0.06 | 0.09 | 0.64 | 1.00 |
Note：Row represents land use pattern in 2000, and row represent nineteen ninety land use pattern.
Neighborhood cellular can use to the action potency of center cellular, such as the probability for making center element dysuria with lower abdominal colic be changed to arable land
Equation below is calculated：Type is the number that type is forest land in the quantity * 0.90+ neighborhood cellulars ploughed in f=neighborhood cellulars
Measure the quantity * 0.21 that type in the quantity * 0.19+ neighborhood cellulars that type in * 0.10+ neighborhood cellulars is construction land is waters.
The calculation of other three kinds of land use patterns is identical with above-mentioned calculation.
The intensity that center cellular retains self type is：
Such as center cellular is arable land, then：
1) probability for remaining arable land is 0.90；
2) probability for remaining forest land is 0.04；
3) probability for remaining construction land is 0.05；
4) probability for remaining waters is 0.01；
Other three kinds of land-use style computational methods and center cellular for arable land when computational methods it is identical.
Wherein, S3 comprises the following steps：
S301, chooses the driving factors of influence area land use change, and generates in survey region space lattice to drive
The raster file of reason element distance, meanwhile, by all spatial data uniform ranges and coordinate system；
S302, stochastical sampling point is generated in survey region, is sampled, time needed for acquisition and driving factors figure layer
Sampled data；
S303, sampled data is imported into Logistic models, training Logistic regression models, and according to
The evaluation indexes such as the conspicuousness in Logistic regression results finally to determine to influence more significant driving factors；
Logistic models are operated in SPSS softwares, and sample point data is imported into SPSS softwares, selection
Corresponding independent variable (such as the distance to school, distance to park etc.) and dependent variable (land use pattern), will be calculated
Go out result, as a result in just comprising this, it is as shown in the table.
Change numbers in equation
S304, according to the driving factors chosen, sample point data is handled, and the sample point data after processing is imported into
Calculated in Logistic models, what the various land use patterns factor driven on each cellular in region that obtains influenceed
Probability.
In S303, Logistic regression models use equation below：
Wherein,
PEDisturbance degree of the surrounding environment to cellular land-use style is represented,
α represents Logistic regression constants,
βiThe Logistic regression coefficients of a certain environmental key-element (such as arriving the distance of highway, the distance to park) are represented,
XiRepresent the value of a certain environmental impact factor (such as to the distance value of hospital).
In S4, the Disturbance is calculated using equation below：
Ran=1+ (ln λ)δ,
Wherein,
λ is the random number between 0 to 1,
δ is for controlling Disturbance interference strength, is the integer between 1 to 10.
In S4, Disturbance does not know to influence caused by being used as human factor, incident etc..
S6 is implemented using equation below：
St+1=f (St,Nt,Pt,SDt+1),
Wherein,
St+1T+1 moment cellular states are represented,
StT cellular state is represented,
NtT neighborhood cellular state is represented,
PtThe national policy of t time spaces is represented,
SDt+1Represent in influence of the t+1 moment system dynamics model to cellular transformation rule,
F is cellular from t state to the transfer function of t+1 moment states.
The transfer function is：
Wherein,
Represent that neighborhood cellular influences probability,
Nh,k,tInfluence probability for neighborhood cellular type to center cellular type,
Represent that ambient elements influence probability,
SEk,tInfluence probability of the surrounding environment to center cellular is represented,
SHk,tThe transition probability that cellular self-characteristic determines is represented,
Represent mandatory constraint caused by natural environment, CEp,k,tIt is binary data, wherein, CEp,k,t=
0 expression cellular k is obligated by natural environment, conversely, CEp,k,t=1；
Represent mandatory constraint caused by Humanistic Factors, CHr,k,tIt is binary data, wherein CHr,k,t=0
Cellular k is represented by the mandatory constraint of Humanistic Factors, conversely, CHr,k,t=1；
Represent mandatory constraint caused by national policy, CPu,k,tIt is binary data, wherein, CPu,k,t=0
Cellular k is represented by the mandatory constraint of national policy, conversely, CPu,k,t=1；
Rk,tRepresent that Disturbance influences probability,
PSD,t+1Represent that driven factor influences probability in system dynamics model.
In S7, the policy data of the GIS vector quantizations includes：Regional population's data, GDP data, each land-use style area
Statistics and/or environmental key-element vector data；The GIS spatial analysis includes：Buffer zone analysis and/or Overlap Analysis.
Specific embodiment：
The embodiments of the invention provide a kind of simulation of Land in Regional Land purposes policy of regulation and control and the method for effect analysis, such as city
City's land use differentiation etc., is implemented in accordance with the following steps：
Step 1：Data determine.Data of the present invention include present landuse map, regional population's data, GDP
The appropriate distribution of data, the statistics of each land-use style area, such as environmental key-element vector data, school, hospital, highway.
Step 2：Constructing system dynamics submodule.The system flow provided according to Fig. 2, constructing system dynamics submodule
The separate equations in block, the module mainly according to macroscopical population policy, economic policy and land policy come build population, GDP and
The total amount change of each land-use style.
Step 3：Build GIS submodules.The module, which is mainly used in import after macro policy isovector, to be currently running
Model.Meanwhile in-depth analysis research is carried out for caused intermediate data in simulation process, mainly include macro policy vector
Change, the buffer zone analysis of vector data, Overlap Analysis etc..
Step 4：Build cellular automata submodule.As shown in figure 3, soil is calculated using present situation figure according to dynamics of land
Using transfer matrix probability, influenceed in combination with each cellular calculated in Logistic regression results by environmental key-element general
Rate, then the variable for influenceing the conversion of cellular type extracted in system dynamics model is added, it is built into turning for cellular automata
Change rule.
Step 5：As shown in figure 4, moving model.Model brings into operation from the system dynamics submodule of macroscopic view first, obtains
Take the total demand of various land use pattern areas.Then the area requirements total amount value of macroscopic view is input to cellular automata
In module, select wherein select it is several act on the transformation rule of cellular automata, while input present landuse map, environment will
Element influences probability graph, and model starts iteration operation, and all kinds of land-use styles are distributed according to the control of macroscopical area requirements total amount
Space layout.Run in simulative iteration to when sometime putting, iteration is suspended.GIS submodules are run, by macro policy vector
Change, and import data in cellular automata submodule.Or the intermediate data in iterative process is exported into GIS submodules
In block, further spatial analysis is carried out.Iteration continues to run until the macroscopical total amount for reaching required, while calculates macroscopical each
Land-use style area, these values are fed back into system dynamics submodule, for micro-tensioning system kinetics equation, into lower a period of time
The computing at quarter, the simulation time until reaching setting, computing stop.
Specific running is to load GIS data first, sets the initial value of each parameter.Then it is pre- according to SD models
The area of each land-use style of next time is surveyed, the area value of each land-use style is passed into CA models, influences to turn in CA models
Change rule.During CA model runnings, CA iterations is controlled according to the area value of each land-use style, CA models stop
After iteration, the area value of each land-use style calculated in CA is fed back into SD models.The simulation of next time model is promoted afterwards
Computing, so repeatedly, until reaching the predetermined simulation time.During whole model running, it can suspend in arbitrary year and change
In generation, by the national policy after some vector quantizations, such as Xiong Anxinqu is set up, and is added, influence model future time repeatedly
Generation operation.Current analog result can also be exported simultaneously, the analysis and research of profound level are carried out in GIS software, or provide
Used to other people.
After model brings into operation, in t0Initial time, the land use pattern data for handling obtain are inputted in GIS first
With driving factors (distance, the distance to school as arrived park) to the influence probability (totally four of four kinds of each land use pattern
File), while the initial time of model running, end time, maximum population's limits value, city growth rate are set.
After primary condition is provided with, runtime kinetic model, simulation first obtains t1The total face of moment construction land
Product S, then runs cellular Automation Model, and by construction land gross area S come control the iterations of cellular automata (when
When the construction land area of evolution simulated in cellular automata reaching S, iteration stopping), after cellular automata iteration stopping, meter
Construction land gross area S ' in Cellular Automata Simulation evolution result is calculated, S ' is delivered to system dynamics model.
Model enters t2The Evolution Simulation at moment, construction land initial area in system dynamics model is arranged to S ',
Continue above-mentioned iteration.
Run to tnDuring the moment, Evolution Simulation is suspended, imports the national policy of vector quantization in GIS, such as national regulation
Cultivated land protection region, by this influence condition add after, continue to run with model.Or analog result is exported after pause,
Further deep spatial analysis and use are carried out in GIS related softwares.
Step 6：As a result export.Vector data in operation result is exported as into .shp forms or the ASCII lattice of standard
Formula, macrostatistics export as excel sheet formats, are used to carry out the analysis of profound level.
By using above-mentioned technical proposal disclosed by the invention, following beneficial effect has been obtained：The embodiment of the present invention carries
The Land in Regional Land purposes policy of regulation and control simulation of confession and effect analysis method, pass through integrated system kinetic model, cellular automata
Model and GIS, the Real Data Exchangs between three are realized in the rank of grid, realize between figure-number it is effective mutually
It is dynamic, the precision of land use change modeling is improved, the application of extended model, is provided preferably for the regulation and control of land policy
Aid, a variety of regions, a variety of dimensions, the simulation of the land use policy of regulation and control of a variety of yardsticks and effect can be effectively used for
Analysis.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
Depending on protection scope of the present invention.
Claims (7)
1. a kind of Land in Regional Land purposes policy of regulation and control simulation and effect analysis method, it is characterised in that comprise the following steps：
S1, it is interpreted for the initial remote sensing image in time needed for survey region, obtains regional land use present situation figure；
S2, determine the transfer matrix probability of land use pattern jointly according to transfer matrix probability and Raphel method；
S3, what the various land use patterns factor driven on each cellular of cellular Automation Model in region that obtains influenceed
Probability；
S4, the macro-indicators calculated according to system dynamics model, generate Disturbance；
S5, runtime kinetic model, obtained according to population policy, economic development, land policy etc. under different simulated scenarios
Macro-indicators；
S6, the macro-indicators that gained is calculated in S5 are imported into cellular Automation Model, at the same the soil generated in S1 is sharp
The Disturbance in probability and S4, which is influenceed, with the transfer matrix probability in present situation figure, S2, the driving factors in S3 imported into member
In cellular automaton model, and the macro-indicators being calculated by system dynamics model control cellular Automation Model repeatedly
Generation operation；
S7, cellular Automation Model iteration to it is a certain setting the time after, it is automatic that the policy data of GIS vector quantizations is imported into cellular
In machine model, or iteration result exported, carry out deep GIS spatial analysis；
S8, after the completion of iteration, land use macro-indicators are calculated, go to S5, while macro-indicators are imported into system dynamics
In model, soil relevant parameter in micro-tensioning system kinetic model；After reaching the setting time, stop iteration and export most to terminate
Fruit.
2. Land in Regional Land purposes policy of regulation and control simulation according to claim 1 and effect analysis method, it is characterised in that S3
Comprise the following steps：
S301, chooses the driving factors of influence area land use change, and generate in survey region space lattice to drive because
The raster file of plain distance, meanwhile, by all spatial data uniform ranges and coordinate system；
S302, stochastical sampling point is generated in survey region, is sampled, time needed for acquisition and the sampling of driving factors figure layer
Data；
S303, sampled data is imported into Logistic models, train Logistic regression models, and return according to Logistic
The evaluation indexes such as the conspicuousness in fruit are summed up finally to determine to influence more significant driving factors；
S304, according to the driving factors chosen, sample point data is handled, and the sample point data after processing is imported into
Calculated in Logistic models, what the various land use patterns factor driven on each cellular in region that obtains influenceed
Probability.
3. Land in Regional Land purposes policy of regulation and control simulation according to claim 2 and effect analysis method, it is characterised in that
In S303, Logistic regression models use equation below：
<mrow>
<msub>
<mi>P</mi>
<mi>E</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>+</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mi>z</mi>
</mrow>
</msup>
</mrow>
</mfrac>
</mrow>
<mrow>
<mi>z</mi>
<mo>=</mo>
<mi>&alpha;</mi>
<mo>+</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>&beta;</mi>
<mi>i</mi>
</msub>
<msub>
<mi>X</mi>
<mi>i</mi>
</msub>
</mrow>
Wherein,
PEDisturbance degree of the surrounding environment to cellular land-use style is represented,
α represents Logistic regression constants,
βiThe Logistic regression coefficients of a certain environmental key-element are represented,
XiRepresent the value of a certain environmental impact factor.
4. Land in Regional Land purposes policy of regulation and control simulation according to claim 1 and effect analysis method, it is characterised in that S4
In, the Disturbance is calculated using equation below：
Ran=1+ (ln λ)δ,
Wherein,
λ is the random number between 0 to 1,
δ is for controlling Disturbance interference strength, is the integer between 1 to 10.
5. Land in Regional Land purposes policy of regulation and control simulation according to claim 1 and effect analysis method, it is characterised in that S6
Implemented using equation below：
St+1=f (St,Nt,Pt,SDt+1),
Wherein,
St+1T+1 moment cellular states are represented,
StT cellular state is represented,
NtT neighborhood cellular state is represented,
PtThe national policy of t time spaces is represented,
SDt+1Represent in influence of the t+1 moment system dynamics model to cellular transformation rule,
F is cellular from t state to the transfer function of t+1 moment states.
6. Land in Regional Land purposes policy of regulation and control simulation according to claim 5 and effect analysis method, it is characterised in that institute
Stating transfer function is：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>h</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>8</mn>
</munderover>
<msub>
<mi>N</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&times;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>SE</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&times;</mo>
<msub>
<mi>SH</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&times;</mo>
<munderover>
<mo>&Pi;</mo>
<mrow>
<mi>p</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>q</mi>
</munderover>
<msub>
<mi>CE</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>&times;</mo>
<munderover>
<mo>&Pi;</mo>
<mrow>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>s</mi>
</munderover>
<msub>
<mi>CH</mi>
<mrow>
<mi>r</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&times;</mo>
<munderover>
<mo>&Pi;</mo>
<mrow>
<mi>u</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>v</mi>
</munderover>
<msub>
<mi>CP</mi>
<mrow>
<mi>u</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&times;</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&times;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>S</mi>
<mi>D</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Wherein,
Represent that neighborhood cellular influences probability,
Nh,k,tInfluence probability for neighborhood cellular type to center cellular type,
Represent that ambient elements influence probability,
SEk,tInfluence probability of the surrounding environment to center cellular is represented,
SHk,tThe transition probability that cellular self-characteristic determines is represented,
Represent mandatory constraint caused by natural environment, CEp,k,tIt is binary data, wherein, CEp,k,t=0 represents
Cellular k is obligated by natural environment, conversely, CEp,k,t=1；
Represent mandatory constraint caused by Humanistic Factors, CHr,k,tIt is binary data, wherein CHr,k,t=0 represents
Cellular k is by the mandatory constraint of Humanistic Factors, conversely, CHr,k,t=1；
Represent mandatory constraint caused by national policy, CPu,k,tIt is binary data, wherein, CPu,k,t=0 represents
Cellular k is by the mandatory constraint of national policy, conversely, CPu,k,t=1；
Rk,tRepresent that Disturbance influences probability,
PSD,t+1Represent that driven factor influences probability in system dynamics model.
7. Land in Regional Land purposes policy of regulation and control simulation according to claim 1 and effect analysis method, it is characterised in that S7
In, the policy data of the GIS vector quantizations includes：Regional population's data, GDP data, the statistics of each land-use style area
And/or environmental key-element vector data；The GIS spatial analysis includes：Buffer zone analysis and/or Overlap Analysis.
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| CN109447422A (en) * | 2018-10-08 | 2019-03-08 | 北京百分点信息科技有限公司 | A kind of policy simulation system and method based on system dynamics model |
| CN109671003A (en) * | 2018-12-21 | 2019-04-23 | 南京泛在地理信息产业研究院有限公司 | A kind of global land use and windy and sandy soil sequence space method of integrated GCAM and CA |
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| CN114819112A (en) * | 2022-06-30 | 2022-07-29 | 南京北斗创新应用科技研究院有限公司 | Land use suitability probability generation method considering spatial partition |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109447422A (en) * | 2018-10-08 | 2019-03-08 | 北京百分点信息科技有限公司 | A kind of policy simulation system and method based on system dynamics model |
| CN109671003A (en) * | 2018-12-21 | 2019-04-23 | 南京泛在地理信息产业研究院有限公司 | A kind of global land use and windy and sandy soil sequence space method of integrated GCAM and CA |
| CN109671003B (en) * | 2018-12-21 | 2023-04-25 | 南京泛在地理信息产业研究院有限公司 | Global land utilization and land cover sequence spatialization method integrating GCAM and CA |
| CN110009257A (en) * | 2019-04-17 | 2019-07-12 | 青岛大学 | Multiple dimensioned variable window cellular Automation Model based on urban traffic blocking sprawling analysis |
| CN110009257B (en) * | 2019-04-17 | 2023-09-08 | 青岛大学 | Multi-scale variable window cellular automaton model based on urban traffic congestion spreading analysis |
| CN110390129A (en) * | 2019-06-11 | 2019-10-29 | 同济大学 | The quantitative evaluation method of land use strategies validity based on GeoSOS-FLUS |
| CN110363356A (en) * | 2019-07-17 | 2019-10-22 | 长安大学 | A kind of water and soil resources space and time optimization configuration method of ecology-oriented |
| CN112651661A (en) * | 2021-01-11 | 2021-04-13 | 中国科学院空天信息创新研究院 | Digital simulation method and system for village and town settlement space development |
| CN114819112A (en) * | 2022-06-30 | 2022-07-29 | 南京北斗创新应用科技研究院有限公司 | Land use suitability probability generation method considering spatial partition |
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