CN102663512A - Simulation prediction method for dynamic evolution simulation of urban greenbelt - Google Patents
Simulation prediction method for dynamic evolution simulation of urban greenbelt Download PDFInfo
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Abstract
The invention provides simulation prediction method for dynamic evolution simulation of urban greenbelt based on geo-cellular automata and multi-agent, which belongs to a technical field of geographic modeling and urban planning. Firstly, change data is obtained by overlay analysis and transition analysis of urban greenbelt thematic maps at two periods of time. Time and space configuration rules of the urban greenbelt are established by combining with a statistical model of urban greenbelt change and based on a multi-agent system theory, and a dynamic simulation model of the dynamic urban greenbelt is configured by adopting cellular automata; a logistic regression model is configured by combining with evolution of nature rules; parameters of the module according to social economic data are adjusted and reasonable expansion and transition rules are determined ; and a model builder toolset is established based on a powerful spatial modeling and analysis capability of GIS, by which simulation and prediction of the urban greenbelt change trend is carried out. The method in the invention realizes dynamic evolution simulation for the urban greenbelt, and is capable of providing assistant decision supports for making land use policy for government and city planners.
Description
Technical field
The invention belongs to geographical modeling and city planning technical field, be specifically related to urban green space change dynamics Forecasting Methodology.
Background technology
The urban green space is the important component part of urban ecological system, has the key effect of improving the city living environment and keeping the ecologic equilibrium.The urban green space can strengthen the naturality of urban look for the city dweller provides good living environment on the one hand; For urban biology suitable ecologic environment, promotion city dweller and natural harmonious symbiosis is provided on the other hand.The urban green space variation model is on the basis that the urban green space changing condition is analyzed, to disclose amplitude and the characteristics such as speed and space distribution that the urban green space changes; Be that urban green space driving factors and mutual relationship thereof are analyzed; Seek the mechanism that the urban green space changes, the trend that prediction greenery patches, future city changes and to the influence of ecologic environment and socio-economic development.Cities and towns green space expansion is a microcosmic; Multiagent; The mobilism process of the long-term complicacy of natural and social fellowship also is downtown area, urban district and the concrete manifestation of frontier area, suburb Process of Urbanization on space layout, is the result of various factors combined action.The urban green space changes driving mechanism research and should consider more to express the process of self-organization of macroscopical greenery patches structure evolution through the individual behaviour decision making of microcosmic in the anthropomorphic dummy ground system with interacting.Therefore, how to include the behaviour decision making of micro entities in urban green space change modeling model, the decision-making of inquiring into micro entities will be the effective way that improves the evolution simulation precision to the influence that the urban green space changes.
Also there is following weak point in current urban green space dynamic similation Forecasting Methodology:
1. do not consider the spatial character in greenery patches,, and seem not enough on the space distribution prognosis modelling to the greenery patches often with the quantitative aspects of study limitation in the greenery patches expansion.Be difficult to guarantee that the greenery patches satisfies the reasonable distribution on the green space in process of construction.
2. in the prediction of urban green space, only consider the prediction under the natural conditions, and have the subject lack such as city dweller and government of appreciable impact to consider, make that the result and the truth of prognosis modelling are widely different in the greenery patches the urban green space.
Summary of the invention
The purpose of this invention is to provide the urban green space change dynamics forecast method that a kind of composite factor is comprehensive, simulation precision is higher, for the development plan of urban green space provides decision-making foundation based on MAS and CA.
The present invention is based on multiagent thought,, determine the evolution result jointly in conjunction with the microcosmic decision-making of micro entities and the natural evolvement of cellular automaton (CA) itself; Have zonal characteristics in view of the urban green space simultaneously, the present invention introduces the controlling factor layer as the external environment condition that cellular changes in model, and the CA rule of urban green space is changed with space, change of time urban green space that is virtually reality like reality more objectively.
Technical characterstic of the present invention is; Introduce the optimizing idea of MAS (multiagent); With government, resident as micro entities (Agent); Participate in the relevant decision-making that the greenery patches develops, Logistic (logic) the regression forecasting function of additional cellular automaton itself, the dynamic evolution of common simcity type of green space.
Multiagent system among the present invention adopts from the top-down idea about modeling of bottom, with traditional from down and on the modeling thinking be different.Its core is through the local detail model of reaction individual configurations function and circulation feedback and the correction between the overall situation performance, studies local variations in detail and how to appear out complicated global behavior suddenly.Multiagent system just can construct the system model with complication system 26S Proteasome Structure and Function according to the system's local detail that studies a question required, response rule and the various local behavior of Agent.Though microcosmic wherein is individual maybe be fairly simple, the global behavior that causes through reciprocation between the microcosmic individuality maybe be extremely complicated.In multiagent system, the global behavior that behavior that microcosmic is individual and reciprocation are showed emerges with nonlinear mode.The combination of individual behavior has determined global behavior, otherwise global behavior has determined individual environment of making a strategic decision again.
Urban green space dynamic similation Forecasting Methodology based on MAS and CA provided by the invention comprises the steps:
(1) standard Various types of data comprises greenery patches present situation figure layer, restriction figure layer, suitability figure layer figures data and statisticss such as population, planning.
(2) confirm Agent number in the model, confirm the microcosmic influence that each main body develops to the urban green space.Confirm influence the main body that the greenery patches develops and comprise two types in resident and government, according to the resident Agent of simulation and the selection preference of the Agent of government, the calculating soil utilizes the position effectiveness of unit and soil to utilize the unit by the probability of resident Agent selection as the greenery patches.
(3) combine Logic Regression Models to confirm influence that the urban green space develops apart from variable and neighborhood variable, and based on logistic (logic) probability of each variable calculating urban green space evolution.
(4) adopts the integrated model of multiagent and cellular automaton, calculate the comprehensive probability that the urban green space is developed, and setting threshold confirms greenery patches evolution result that this process realizes based on the toolbox modeling function among the Arcgis Model Builder.
(5) according to setting up model prediction following certain greenery area and space distribution of phase for the moment, through the contrast adjustment model parameter that distributes with actual greenery patches, up to obtaining optimization model.
Described urban green space change dynamics forecast method based on MAS and CA is that the various data that model is used have unified standard: with social statistics uniform datas such as the size of population of study area, age compositions to same dimension; Vector figure data with urban green space over the years; Controlling element uniform datas such as river, basic farmland, road are same projection and spatial dimension.Because the spatial data of Arcgis toolbox models treated is the grid type data layout, thereby need urban green space figure, communication chart, drainage map be carried out the data-switching from the vector to the grid.Spatial resolution (being the grid cell size) can consider that the present invention is divided into 20 * 20m according to the actual conditions and the model calculation efficient of study area; The tool box features conversion to Polygon order of utilization Arcgis software, cell Size selects 20m; Thereby urban green space, Shanghai Pudong New Area figure and each special layer are divided into 1935 * 1593 grid cells, corresponding one by one to guarantee each unit, urban green space and controlling factor layer grid cell.
Described urban green space change dynamics forecast method based on MAS and CA, its model have comprised a series of environmental element layers and some multiagent and immovable cellular automaton layers with features of movement.In model, the space is made up of the two-dimensional grid of n * n, and the Agent that is simulated comprises resident, government etc., and these Agent not only have characteristics such as movability, adaptability, continuation, also have behavior property and rule of conduct.Resident Agent can propose the greenery patches demand according to existence demand or personal inclination.The Agent of government exerts one's influence to the greenery patches expansion according to resident's suggestion and general plan, and considers influencing each other of resident's main body and governmental body.
Described urban green space change dynamics forecast method based on MAS and CA, the greenery patches evolution result in the model are the results that comprehensive multiagent and cellular automaton combine.And consider the controlling factor layer in the expansion of actual greenery patches, and the controlling factor layer is generally drawn by various data analyses such as basic farmland, traffic, drainage maps, and the control of key-course is played crucial effects to the probability of greenery patches expansion in the model in the model.
Described urban green space change dynamics forecast method based on MAS and CA; Its structure modeling method adopts the Model Building modeling function in the Arcgis toolbox to realize; In the Arcgis toolbox modelling; The grid computing function is adopted in the expansion of cellular automaton, and the regression parameter that each space variable and greenery patches change obtains by SPSS, utilizes the existing instrumental function of self-editing function and Arcgis to realize the greenery patches addressing function of multiagent.
Described urban green space change dynamics forecast method based on MAS and CA is through the optimization of confirming and adjust implementation model of model parameter.There is a large amount of parameters in the Arcgis toolbox model; Each parameter all has direct relation with the operation of model; Thereby the setting of parameter and adjustment be the Core Feature of system, and the method for confirming model parameter is to know that through oneself the data in time come the optimization model parameter.Detailed process is: the urban green space data of certain 2 years (as 1996 and 2002) before known; Under certain parameter condition, let model running, when the sum of land unit in the sum of unit, greenery patches and the check data equates approximately, stop model running; Result and check data to model running compare; Comparative result according to simulated data and empirical data comes the adjustment model parameter, carries out computing once more with adjusted parameter, and Simulation result is analyzed again; Through continuous comparison, analysis, adjustment parameter, confirm that at last one overlaps comparatively proper model computing parameter.
Description of drawings
Fig. 1 is the urban green space Dynamic Evolution Model figure based on multiagent and CA of the present invention.
Fig. 2 is the evolution rule figure of the urban green space dynamic similation based on MAS and CA of the present invention.
Embodiment
Specifically can be summarized as following 5 steps.
(1) standard Various types of data comprises greenery patches present situation, restriction figure layer figures data and statisticss such as population, planning.
(2) confirm Agent number in the model, confirm the microcosmic influence that each main body develops to the urban green space.According to the resident Agent distribution situation of simulation, calculate the soil and utilize the position effectiveness of unit and soil to utilize the unit to be selected probability as the greenery patches by resident Agent.
(3) combine Logic Regression Models to confirm influence that the urban green space develops apart from variable and neighborhood variable, and based on the logistic probability model of each variable calculating urban green space evolution.
(4) adopt the comprehensive probability and the setting threshold of the integrated model calculating urban green space development of multiagent and cellular automaton to confirm greenery patches evolution result, this process realizes based on the toolbox modeling function among the Arcgis Model Builder.
(5) according to setting up model prediction following certain greenery area and space distribution of phase for the moment, through the contrast adjustment model parameter that distributes with actual greenery patches, up to obtaining optimization model.
Described urban green space change dynamics forecast method based on MAS and CA is characterized in that the various data that model is used have unified standard: with social statistics uniform datas such as the size of population of study area, age compositions to same dimension; Vector figure data with urban green space over the years; Controlling element uniform datas such as river, basic farmland, road are same projection and spatial dimension.Because the spatial data of Arcgis toolbox models treated is the grid type data layout, thereby need urban green space figure, communication chart, drainage map be carried out the data-switching from the vector to the grid.Spatial resolution (being the grid cell size) can consider that the present invention is divided into 20 * 20m according to the actual conditions and the model calculation efficient of study area; The tool box features conversion to Polygon order of utilization Arcgis software, cell Size selects 20m; Thereby urban green space, Shanghai Pudong New Area figure and each special layer are divided into 1935 * 1593 grid cells, corresponding one by one to guarantee each unit, urban green space and controlling factor layer grid cell.
Described urban green space change dynamics forecast method based on MAS and CA is characterized in that model has comprised a series of environmental element layers and some MMG multimastergroup body and immovable cellular automaton layers with features of movement.In model, the space is made up of the two-dimensional grid of n * n, and the Agent that is simulated comprises resident, government etc., and these Agent not only have characteristics such as movability, adaptability, continuation, also have behavior property and rule of conduct.Resident Agent can propose the greenery patches demand according to existence demand or personal inclination.The Agent of government exerts one's influence to the greenery patches expansion according to resident's suggestion and general plan, and considers influencing each other of resident's main body and governmental body.
Described urban green space change dynamics forecast method based on MAS and CA is characterized in that the greenery patches evolution result in this model is the result that comprehensive multiagent and cellular automaton combine.And consider the controlling factor layer in the expansion of actual greenery patches, and the controlling factor layer is generally drawn by various data analyses such as basic farmland, traffic, drainage maps, and the control of key-course is played crucial effects to the probability of greenery patches expansion in the model in the model.
Described urban green space change dynamics forecast method based on MAS and CA; It is characterized in that structure modeling method of the present invention adopts the Model Building modeling function in the Arcgis toolbox to realize; In the Arcgis toolbox modelling; The grid computing function is adopted in the expansion of cellular automaton, and the regression parameter that each space variable and greenery patches change obtains by SPSS, utilizes the existing instrumental function of self-editing function and Arcgis to realize the greenery patches addressing function of multiagent.
Described urban green space change dynamics forecast method based on MAS and CA is characterized in that the optimization of confirming and adjust implementation model through model parameter.There is a large amount of parameters in the Arcgis toolbox model; Each parameter all has direct relation with the operation of model; Thereby the setting of parameter and adjustment be the Core Feature of system, and the method for confirming model parameter is to know that through oneself the data in time come the optimization model parameter.Detailed process is: the urban green space data of known 1996 and 2002; Under certain parameter condition, let model running, when the sum of land unit in the sum of unit, greenery patches and the check data equates approximately, stop model running; Result and check data to model running compare; Comparative result according to simulated data and empirical data comes the adjustment model parameter, carries out computing once more with adjusted parameter, and Simulation result is analyzed again; Through continuous comparison, analysis, adjustment parameter, confirm that at last one overlaps comparatively proper model computing parameter.
Embodiment chooses the inner city, Shanghai City as the test block.Spatial data comprises remotely-sensed data and GIS data.Remotely-sensed data be 2003 with mutually SPOT5 image two time in 2007.The GIS data comprise Shanghai City present status of land utilization data in 2003,2007, overall city planning figure, land price figure, Pudong New District's turnpike road communication chart and school, hospital, park distribution plan.Society's data are that statistics bureau, 2007 Shanghai statistical yearbooks and the 5th census data are obtained from Shanghai City, mainly comprise demographic data and economic statistics data, and social data are to be unit with the street town.
Because dissimilar residents shows owing to the attribute of himself is different the totally different preference of choice of location and the difference of greenery patches quantity required, thereby makes different spatial decisions.Adopt multiple criteria judgment models (MCE), obtain the subjective preferences weight of choice of location factor of influence.Before definite choice of location factor of influence objective weight, need to eliminate the influence of each factor of influence dimension, promptly each factor is carried out normalization and handle.
Suppose to hold a resident Agent on each newly-increased urbanization grid, thereby also confirmed the number of different times resident Agent.Here an Agent has only reflected proportionate relationship, is not only to represent a people or one family, and physical meaning in the present invention is resident's quantity of on average holding in the grid.
The key issue of setting up the multiagent model be how to Agent carry out suitable abstract with describe, explain how all kinds of Agent survey external information and this is reacted and how external information influences the selection behavior between the Agent.In this research, mainly consider the Agent of government, resident Agent, other Agent put aside.The Agent of government plays the effect of macro adjustments and controls among the present invention, and it does not have space attribute.The locus of resident Agent is to be randomly dispersed on the survey region in original state, and behind the model running, resident Agent selects comparatively satisfied green space layout after consulting according to the preference of oneself and with the Agent of government is common.
(1) decision probability of governmental body
Government's macroscopic view overall city planning plays decisive action to the greenery patches variation in city, has guided the evolutionary process of entire city, has also determined urban development pattern.The macroscopic view planning of government seems particularly important in urban development.Government's macroscopic view city planning here mainly is meant to be planned the overall space distribution of all kinds of lands used of urban development in the following certain hour.
Here government organs are incorporated in the model as main body, the Agent of government has the responsibility of planning clay resource and reaction resident demand, and responsibility embodies both ways: government planning influences the transformation of land use pattern on the one hand; Resident's demand reciprocation is fine regulated the original planning of government in planning on the one hand.The relation of influencing each other between the multiagent, information interchange, cooperation is regulated government planning through resident's demand and is realized, to reach common understanding and to adopt certain action to influence the purpose of its environment of living in.The wish of government itself realizes through city planning among the Agent of government, and government and resident coordinate through resident Agent the wish of land used conversion in type to be affacted on the probability of the Agent of government to the soil conversion.
So temporal characteristics of planning itself and real data situation, the present invention adopts the immediate plan with legal effect as the government planning data.Suppose the as-is data according to the T1 time, done the T2 time immediate plan of (be generally T1 after about 5 years), each position receives the influence of city layout state variation to be so:
In the formula;
expression Agent of government decision probability; The greenery patches planning chart layer of
expression T2 time, the greenery patches present situation figure layer of
expression T1 time.
The spatial spread in greenery patches is selected to utilize mode to be converted into the comprehensive decisions such as complexity in greenery patches according to user demand and current soil, and the transition probability in greenery patches mainly receives current land use pattern, and the influence of facing the quantity in greenery patches in the territory.Comprehensive several kinds of factors, the greenery patches transition probability can be expressed as:
In the formula; The compatible coefficient of
expression type of green space;
influence for facing the territory,
is the growth suitability in greenery patches.
Compatible coefficient is changed in the greenery patches of different land use type
| Land use pattern | Plough | Residential estate | The greenery patches | The commercial land | Industrial land | Other lands used | Water body |
| The compatible coefficient in greenery patches | 0.7 | 0.3 | 1 | 0.2 | 0.2 | 0.3 | 0.1 |
TaFor in the 9*9 window area serviceably, G is the area in greenery patches in the window.
(2) resident Agent and choice behavior thereof
Along with the progressively raising of public participation dynamics, resident Agent can propose the greenery patches demand according to existence demand or personal inclination.Be ready high slightly price such as the part high-salary stratum and select the more better residential district of high ambient of green percentage, cause government possibly make corresponding planning simultaneously.Resident Agent can carry out restriction to model from the regional area angle.Reach basic demand such as resident's requirement greenery patches in the scope of activities of oneself.The demand of supposing the green areas of average per-person share is S, certain legal figure then in the k resident's number be num, so should the zone in the greenery patches demand be:
Suppose actual having
, so
Wherein,
is resident Agent to legal figure then all cellulars (i, j) probability of effect in the k.When oneself satisfies the demands through having
; Promptly when
; Whether the greenery patches transforms; Attitude that the resident is that it doesn't matter, showing probability is 0; When having
when not satisfying the demands, the resident hopes that the cellular quantity in greenery patches increases to some extent.
(3) city dweller Agent and the relationship analysis of the Agent of government mutual-action behavior
1) Agent of government and resident Agent microcosmic are consulted
Government can utilize the soil of situation and future plan to utilize situation to compare according to present soil, this place, provides different acceptance probabilities.The number of times of being applied for when a zone is many more, and its received probability will increase; This has demonstrated fully government also considers the public's wish comprehensively in macroscopic view planning requirement.Its equation expression is following:
In the formula: P
AcceptThe probability of being accepted by government for geographic position Lij; P
AcceptBe the original acceptance probability of government; G is the number of times that this ground is applied for, P
1Be every application once, the acceptance probability that government increased; H is with L
IjBe the grid number of having been accepted in 7 * 7 neighborhood windows of center, P by government
2Be grid of being accepted by government of every increase in 7 * 7 neighborhood windows, the acceptance probability that government increased.
2) resident is to the influence of the Agent of government
Behavior between resident and the government is inevitable also to be interactional, and in model, we suppose that resident Agent and the behavior between the Agent of government in this zone exists interactive relationship.To be converted into the probability in greenery patches high more if the resident Agent that distributes in unit, a certain non-greenery patches neighborhood in should the zone accepts other lands used, and then this unit is selected also high more as the probability in greenery patches by the Agent of government.Its equation expression is following:
In the formula:
P x For the unit x in this zone is selected the initial probability as the greenery patches by resident Agent, n is the quantity of the interior resident Agent that distributes of 9 * 9 neighborhoods of unit x,
QiFor i resident Agent in 9 * 9 neighborhoods of unit x converts the decision probability in greenery patches into to other lands used,
WiBe weight, the inverse that belongs to the distance of unit and unit x with this resident Agent is represented.
(4) cellular automaton combines with multiagent system
In main body, resident Agent hopes that the environment greenery patches of being lived covers green increase on the one hand, so that life is more comfortable; The Agent of government does not hope commercial, the industrial land of large tracts of land change yet on the other hand.Thereby near near water system, the residential block, the movable concentrated area of the city center stream of people might preferentially be converted into the urban green space and build the zone.It is then smaller that shopping centre, shielded farming land are converted into the probability of urban green space.Can find out thus; The factor that influences resident Agent decision-making be mainly apart from Town Center's distance, apart from the major urban arterial highway road equidistant variable of distance and neighborhood variablees such as agricultural protection land used neighborhood density, residential land neighborhood density; Mostly these variablees are continuous variable and classified variable; Consider the influence of statistical considerations and restrictive factor in addition, introduce variablees such as the small towns density of population.Can obtain the contribution rate of each influence factor by Logic Regression Models.The general type of logistic regression is following:
In the formula:
Be independent variable, representative influences each class variable of resident Agent decision-making respectively, and n is the independent variable number, in this model,
Represent agricultural protection land used neighborhood density, construction land neighborhood density respectively, apart from the municipal highway distance, apart from Town Center's distance, the small towns density of population, land price etc.,
YBe the linear function of independent variable, represent the soil to utilize state, its value can only be 0 or 1,
YBe to represent that the soil of adding up utilized the unit that the conversion of other lands used to the urban green space taken place at 1 o'clock,
YBe to represent no change at 0 o'clock; A is a constant, with parameter
b 1 , b 2 ..., b n Be all regression coefficient to be asked.The probability that
representative is changed to the urban green space from other lands used through resident Agent decision-making back unit x.In Logic Regression Models, probable value can be the nonlinear function of explanatory variable, and this is multiagent urban green space variation model and uses a strictly increasing function,
With
YThe increase of value and increasing.Regression coefficient
b 1 -b n Showed each resident Agent decision variable for probable value
Contribution rate, if just, then its corresponding independent variable increase helps the growth of probable value, on the contrary then effect is opposite.
(5) urban green space evolution multiagent decision making package rule
Comprehensive above each several part result is in conjunction with multiagent system and cellular Automation Model, then position candidate
L Ij Selected at random by t resident Agent, and can be expressed with following formula by the final probability of government permission exploitation:
In the formula:
P Total Be that t resident Agent selects the geographic position at random under maximization of utility
L Ij Probability,
P Gov * ij is a position candidate
L Ij By the probability of government's acceptance,
P Ca Be the transition probability of this position cellular,
aWith
bBe respectively the weight of influence separately.
(6) model parameter confirms and adjustment
There is a large amount of parameters in the Arcgis toolbox modeling tool; Each parameter all has direct relation with the operation of model; Thereby the setting of parameter and adjustment be the Core Feature of system, and the present invention confirms that the method for model parameter is to know that through oneself the data in time come the optimization model parameter.Detailed process is: oneself knows the urban green space data of 1996 and 2002; Under certain parameter condition, let model running, when the sum of unit, greenery patches in the sum of unit, greenery patches and the check data equates approximately, stop model running; Result and check data to model running compare; Comparative result according to simulated data and empirical data comes the adjustment model parameter, carries out computing once more with adjusted parameter, and Simulation result is analyzed again; Through continuous comparison, analysis, adjustment parameter, confirm that at last one overlaps comparatively proper model computing parameter.
Claims (3)
1. based on the urban green space of geographical cellular automaton and the multiagent simulating and predicting method that dynamically develops, it is characterized in that may further comprise the steps:
(1) based on ArcGIS software specifications Various types of data, comprise greenery patches present situation figure layer, restriction figure layer, suitability figure layer pattern data, and population, planning statistics;
(2) confirm main body number in the model, confirm the microcosmic influence that each main body develops to the urban green space; Confirm influence the main body that the greenery patches develops and comprise two types in resident and government, according to the resident's main body of simulation and the selection preference of governmental body, the calculating soil utilizes the position effectiveness of unit and soil to utilize the unit by the probability of resident's main body selection as the greenery patches;
(3) combine Logic Regression Models to confirm influence that the urban green space develops apart from variable and neighborhood variable, and based on the logic probability of each variable calculating urban green space evolution;
(4) adopt the comprehensive probability and the setting threshold of the integrated model calculating urban green space development of multiagent and cellular automaton (CA) to confirm greenery patches evolution result, this process realizes based on the toolbox modeling function among the Arcgis Model Builder;
(5) according to setting up model prediction following certain greenery area and space distribution of phase for the moment, through the contrast adjustment model parameter that distributes with actual greenery patches, up to obtaining optimization model.
2. Forecasting Methodology according to claim 1 is characterized in that in the step (1), and the various data-switching that model is used are to have unified standard: the size of population, the age composition social statistics uniform data of study area are arrived same dimension; With the vector figure data of urban green space over the years, comprise that river, basic farmland, road controlling element uniform data are same projection and spatial dimension; The vector type Data Format Transform of urban green space figure, communication chart, drainage map is arrived the grid type data layout, to meet the Data Format of Arcgis toolbox models treated.
3. Forecasting Methodology according to claim 1 is characterized in that in the step (5), through the optimization of confirming and adjust implementation model of model parameter; Detailed process is: according to former certain the urban green space data in 2 years; Under certain parameter condition, let model running, when the sum of land unit in the sum of unit, greenery patches and the check data equates approximately, stop model running; Result and check data to model running compare; Comparative result according to simulated data and empirical data comes the adjustment model parameter, carries out computing once more with adjusted parameter, and Simulation result is analyzed again; Through continuous comparison, analysis, adjustment parameter, confirm that at last one overlaps comparatively proper model computing parameter.
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