CN109767046A - A kind of soil space Optimal Configuration Method and system - Google Patents
A kind of soil space Optimal Configuration Method and system Download PDFInfo
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Abstract
The invention discloses a kind of soil space Optimal Configuration Method and systems.This method comprises: obtaining sample data；The sample data includes the urbanization quality evaluation index in sample soil and the five big Subspace Distribution figures in sample soil；According to sample data training binary Logistic regression model, spatial distribution model is obtained；Obtain the urbanization quality evaluation index in soil to be configured；The spatial distribution of described five big subspaces is obtained by the spatial distribution model according to the urbanization quality evaluation index in the soil to be configured；According to the urbanization quality evaluation index in soil to be configured, the land area of described five big subspaces is calculated by GLUE-S model；According to the land area and the spatial distribution, configuration is optimized to soil to be configured.This method or system can plan as a whole production, life and ecological land space, optimize configuration to the land area of five big subspaces.
Description
Technical field
The present invention relates to Land allocation domains, more particularly to a kind of soil space Optimal Configuration Method and system.
Background technique
Ecological space, production space and living space constitute the entirety of National land space.The essence in " three lives " space is society
The dynamic mapping in land utilization space such as meeting process, economic process and ecological process, " three lives " bearing capacity determine " three
The layout and structure in life " space, parsing and the interaction mechanism for disclosing " three lives " bearing capacity are the weights for delimiting " three lives " space
Want premise." ecology-production-life bearing capacity " is Resources and eco-environment for appearance ability, the ability of economic activity and satisfaction
The organic synthesis body of the social development ability of certain living standard size of population.
Existing numerous and complicated National land space Optimization method, such as agricultural location theory, the theory are ground from differential land rent
Study carefully the variation of space layout brought by range attenuation.There are also use the land use structures such as linear programming model, system dynamics
Optimized model, but these method starting points are mostly production and vital function, consider not enough, largely there is important valence to ecosystem characterization
The land used of value is classified as unused land, so that unused land has very big randomness to the adjustment of farming land and construction land.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of soil space Optimal Configuration Method and systems.
To achieve the above object, the present invention provides following schemes:
A kind of soil space Optimal Configuration Method, which comprises
Obtain sample data；The sample data includes the urbanization quality evaluation index and sample soil in sample soil
Five big Subspace Distribution figures；The urbanization quality evaluation index includes vegetation coverage, the gradient, the soil organism, cities and towns
Distance, cities and towns away from river distance, cities and towns away from ecological space distance far from living space, cities and towns cities and towns away from production space away from
From and cities and towns away from road with a distance from；The five big subspace includes living space, ecological space, industrial production space, agricultural
Production space and guarantee space；
According to sample data training binary Logistic regression model, spatial distribution model is obtained；
Obtain the urbanization quality evaluation index in soil to be configured；
Institute is obtained by the spatial distribution model according to the urbanization quality evaluation index in the soil to be configured
State the spatial distribution of five big subspaces；
According to the urbanization quality evaluation index in soil to be configured, the five big subspace is calculated by GLUE-S model
Land area；
According to the land area and the spatial distribution, configuration is optimized to soil to be configured.
It is optionally, described that spatial distribution model is obtained according to sample data training binary Logistic regression model,
It specifically includes:
Using the urbanization quality evaluation index in the sample soil as the input of the binary Logistic regression model,
Obtain output data；
Judge the error of the five big Subspace Distribution figures in the output data and the sample soil whether in error threshold
In range；
If so, determining that the binary Logistic returns mould model is spatial distribution model；
If it is not, adjusting the parameter of the binary Logistic regression model, make the output data and the sample soil
Five big Subspace Distribution figures error within the scope of error threshold.
Optionally, the urbanization quality evaluation index according to soil to be configured calculates institute by GLUE-S model
The land area for stating five big subspaces, specifically includes:
Obtain the remote sensing images in soil to be configured；
According to the urbanization quality evaluation index in soil to be configured, the remote sensing images are standardized, are obtained
To remote sensing index image；
According to the remote sensing index image, the pixel value of each urbanization quality evaluation index is determined；
Obtain soil urbanization comprehensive evaluation model；
According to the pixel value of each urbanization quality evaluation index, pass through the soil urbanization comprehensive evaluation model, meter
The urbanization index of each soil pixel in soil to be configured；
According to the urbanization index of each soil pixel in the soil to be configured, described five are calculated by GLUE-S model
The land area of big subspace.
Optionally, the expression formula of the soil urbanization comprehensive evaluation model are as follows:
Wherein I is the urbanization index of soil pixel, and n is evaluation index number, n=8；PiFor the weight of i-th of index,
XiFor the pixel value of i-th of evaluation index.
The present invention also provides a kind of soil space Optimizing Configuration System, the system comprises:
Sample data obtains module, for obtaining sample data；The sample data includes the urbanization matter in sample soil
Measure the five big Subspace Distribution figures in evaluation index and sample soil；The urbanization quality evaluation index includes vegetative coverage
Degree, the gradient, the soil organism, cities and towns distance, cities and towns far from river distance away from ecological space, cities and towns far from living space away from
From, distance of the cities and towns cities and towns away from a distance from production space and cities and towns away from road；The five big subspace includes living space, life
State space, industrial production space, agricultural production space and guarantee space；
Training module, for obtaining spatial distribution mould according to sample data training binary Logistic regression model
Type；
Evaluation index obtains module, for obtaining the urbanization quality evaluation index in soil to be configured；
Spatial distribution module passes through the sky for the urbanization quality evaluation index according to the soil to be configured
Between distributed model, obtain the spatial distribution of described five big subspaces；
Area calculation module passes through GLUE-S model for the urbanization quality evaluation index according to soil to be configured
Calculate the land area of described five big subspaces；
Module is distributed rationally, for being carried out to soil to be configured according to the land area and the spatial distribution
It distributes rationally.
Optionally, the training module, specifically includes:
Input unit, for being returned the urbanization quality evaluation index in the sample soil as the binary Logistic
The input for returning model, obtains output data；
Judging unit, for judging the output data and the error of the five big Subspace Distribution figures in the sample soil is
It is no within the scope of error threshold；
As a result determination unit, for the error when the output data and the five big Subspace Distribution figures in the sample soil
When within the scope of error threshold, determining that the binary Logistic returns mould model is spatial distribution model；
Adjustment unit, for being missed when the output data and the error of the five big Subspace Distribution figures in the sample soil
When outside poor threshold range, the parameter of the binary Logistic regression model is adjusted, makes the output data and sample soil
The error of the big Subspace Distribution figure of the five of ground is within the scope of error threshold.
Optionally, the area calculation module, specifically includes:
Remote sensing images acquiring unit, for obtaining the remote sensing images in soil to be configured；
Image processing unit, for the urbanization quality evaluation index according to soil to be configured, to the remote sensing images
It is standardized, obtains remote sensing index image；
Pixel value determination unit, for determining each urbanization quality evaluation index according to the remote sensing index image
Pixel value；
Model acquiring unit, for obtaining soil urbanization comprehensive evaluation model；
Urbanization exponent calculation unit passes through the soil for the pixel value according to each urbanization quality evaluation index
Urbanization comprehensive evaluation model in ground calculates the urbanization index of each soil pixel in soil to be configured；
Areal calculation unit passes through for the urbanization index according to each soil pixel in the soil to be configured
GLUE-S model calculates the land area of described five big subspaces.
Optionally, the expression formula of the soil urbanization comprehensive evaluation model are as follows:
Wherein I is the urbanization index of soil pixel, and n is evaluation index number, n=8；PiFor the weight of i-th of index,
XiFor the pixel value of i-th of evaluation index.
Compared with prior art, the present invention has following technical effect that the present invention according to the city in the soil to be configured
Town quality evaluation index obtains the spatial distribution of described five big subspaces by the spatial distribution model；According to be configured
Soil urbanization quality evaluation index, the land area of described five big subspaces is calculated by GLUE-S model；According to institute
Land area and the spatial distribution are stated, configuration is optimized to soil to be configured.The present invention can plan as a whole production, life
With ecological land space, configuration is optimized to the land area of five big subspaces, to adapt to National land space management and research
It needs.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the flow chart of soil space of embodiment of the present invention Optimal Configuration Method；
Fig. 2 is the structural block diagram of soil space of embodiment of the present invention Optimizing Configuration System.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The object of the present invention is to provide a kind of soil space Optimal Configuration Method and systems, plan as a whole production, life and life
State land used space optimizes configuration to the land area of five big subspaces.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
As shown in Figure 1, a kind of soil space Optimal Configuration Method the following steps are included:
Step 101: obtaining sample data；The sample data include sample soil urbanization quality evaluation index and
The big Subspace Distribution figure of the five of sample soil；The urbanization quality evaluation index includes that vegetation coverage, the gradient, soil are organic
Distance far from living space of matter, cities and towns distance, cities and towns far from river distance, cities and towns away from ecological space, cities and towns cities and towns are away from production
The distance of distance and cities and towns away from road in space；The five big subspace includes living space, ecological space, industrial production sky
Between, agricultural production space and ensure space.
Step 102: according to sample data training binary Logistic regression model, obtaining spatial distribution model.Tool
Body includes:
Using the urbanization quality evaluation index in the sample soil as the input of the binary Logistic regression model,
Obtain output data；
Judge the error of the five big Subspace Distribution figures in the output data and the sample soil whether in error threshold
In range；
If so, determining that the binary Logistic returns mould model is spatial distribution model；
If it is not, adjusting the parameter of the binary Logistic regression model, make the output data and the sample soil
Five big Subspace Distribution figures error within the scope of error threshold.
Step 103: obtaining the urbanization quality evaluation index in soil to be configured.
Step 104: according to the urbanization quality evaluation index in the soil to be configured, passing through the spatial distribution mould
Type obtains the spatial distribution of described five big subspaces.
Step 105: according to the urbanization quality evaluation index in soil to be configured, calculating described five by GLUE-S model
The land area of big subspace.It specifically includes:
Obtain the remote sensing images in soil to be configured；Object-oriented method completion pair is carried out using high score image data first
Study the land use identification in area's three lives space.
The present invention uses living space quality, ecological space quality, industrial production space quality, agricultural production space matter
Amount ensures distance far from river of five, space subsystem and vegetation coverage, the gradient, the soil organism, cities and towns, cities and towns away from life
Distance away from production space of distance, cities and towns cities and towns of the distance, cities and towns of state space far from living space and cities and towns away from road away from
From eight indexs form urbanization quality appraisement system.For the tax power of each index weights, selected index is considered in this evaluation
Attribute and achievement data appropriate, determine and determine index weights using directly assigning weight based on expert graded.
According to the urbanization quality evaluation index in soil to be configured, the remote sensing images are standardized, are obtained
To remote sensing index image；When quantitative assessment, initial data is standardized, generally requires and chooses suitable object reference
Value.Usual way is to take sample maximum to the target value of positive type index；Target reference sampling to flyback type index
This minimum value；To moderate type index, determined according to international experience and national relevant regulations.It is noted that moderate type index
Target reference, it may be possible to a specific numerical value, it is also possible to a continuous interval value.For positive index, general feelings
Condition, standardized value=actual value/target reference；But when actual value is greater than target reference, standardized value is enabled to be equal to 1.It is right
In reverse index, ordinary circumstance, standardized value=target reference/actual value；But it when actual value is less than target reference, enables
Standardized value is equal to 1.For moderate type index, phase threshold method can be used, i.e., different standards is set in different sections, it is real
Optimum value of the actual value in reasonable interval, then corresponding standardized value is close or equal to 1, is in the standardization of reasonable interval
It is smaller to be worth amplitude of variation；Actual value is more biased to both ends, then corresponding standardized value is closer to 0.It completes to scheme based on the above method
As standardization, the remote sensing index image of standard is finally obtained.
According to the remote sensing index image, the pixel value of each urbanization quality evaluation index is determined；
Obtain soil urbanization comprehensive evaluation model；The expression formula of the soil urbanization comprehensive evaluation model are as follows:
Wherein I is the urbanization index of soil pixel, and n is evaluation index number, n=8；PiFor the weight of i-th of index,
XiFor the pixel value of i-th of evaluation index；
According to the pixel value of each urbanization quality evaluation index, pass through the soil urbanization comprehensive evaluation model, meter
The urbanization index of each soil pixel in soil to be configured；
According to the urbanization index of each soil pixel in the soil to be configured, described five are calculated by GLUE-S model
The land area of big subspace.
CLUE-S model is mainly made of two modules, respectively space module and non-space module, spatial analysis module
Based on various rasterizing spatial datas, according to the probability of land use, the competition class of various land use patterns and soil
Using transformation rule, space distribution: non-space land use demand module is carried out to the land use demand file in simulation time
The main variation for calculating the land use pattern quantity as caused by land use demand factor in research area, or calculate specified
Land use scene under Land Demand, Land Demand can be fixed against other mathematical models and Statistic analysis models and carry out
It calculates.Non-empty Land Demand is configured to spatially by the space distribution module of this model, reaches quantity and Spatial optimum allocation
Purpose.
Distribution of the land use demand in space module is the comprehensive empirical analysis to land use, Spatial variability
And dynamic analog realization.Wherein, it is standby with it mainly to disclose land utilization space distribution for empirical analysis and Spatial variability
The relationship of driving factors and space constraints factor is selected, generating different land use type probability distribution is suitable for figure, measures different soil
The suitable degree that ground use pattern is distributed in each space cell.Space module can also be according to the reality of research area's land use
Situation defines the complexity that one group of rule converts different land use and controls, and can such as guarantee to study area by rule
Interior protects land used that transformation etc. does not occur in being expected.
Step 106: according to the land area and the spatial distribution, configuration being optimized to soil to be configured.
The optimization area for the land quantity that the land area being calculated in step 105 can be calculated with following methods
It compares and analyzes.
Grey linear programming model (GLP model) objective function f (x) is passed through using maximization of economic benefit as target pursuing
While Ji maximizing the benefits, to protect farmland and based on Ensuring Food Safety, setting arable land " policy guidance " scene." political affairs
Under plan guidance type " scene, one group of mesh is arranged must not be lower than total Farmland index that Fushun City is assigned as target in quantity of cultivated land
Equation is marked, corresponding land use quantitative structure optimal scheme is constructed.The land use data of this model is 2014
Remote sensing image interpretation obtains, that is, the time point of as-is data is 2014.
GLP model is grown up on the basis of linear programming and gray model, due to optimizing disposition of land itself
The grey characteristics having, therefore the Land use structure type value in certain following region can be solved by the model, it is full to seek
The programme of sufficient greatest benefit.The mathematic(al) representation of grey linear programming model are as follows:
Wherein f (x) is objective function, value represent region land deterioration integrated risk degree f (x } smaller, then Land in Regional Land
Degeneration integrated risk is smaller, and the ecological benefits in soil are higher；cjFor the benefit coefficient of decision variable: xjFor decision variable, herein
As land use pattern, unit hm2.Its one group of solution { xjBe known as optimal solution, i.e., optimal land use structure aijIt is about
Beam coefficient, that is, decision variable coefficient；bjTo constrain constant, that is, resource constraint amount；M is constraint equation number；N is decision variable number.
The variable of model is set as land use pattern, and decision variable setting considers and present land classification system as far as possible
It combines.According to the feature in Wanghua District three lives space and the requirement of overall plan for land use, 5 decision variables are set altogether:
xl(ecology), x2(agricultural production), x3(life), x4(guarantee), x5(industrial production).
Constraint condition is mainly extracted according to " five district overall plans for land use (2006-2020) ", specific as follows.
1) area-constrained: all land-use style area summations are 337.51 square kilometres；
2) total Farmland constrains: cultivated area reaches the year two thousand twenty planning value or more；
3) construction land constrains: planning that total amount limitation residential area land used and urban construction are used according to land used for urban and rural construction projects area
The summation on ground；
4) ecological land constrains: forest land area reaches the year two thousand twenty planning area or more；
5) actual conditions and economic restriction (compared with status): increased according to town site, traffic safety engineering, do not utilized
The conditions such as ground area reduction are constrained.
Constraint condition:
x2≥10.7614
x5+x3≤86.0082
X1+x3+x4=26.1087
xi≥0
Objective function f (x } it is economic benefit objective function.In conjunction with yearbook data, according to the phase of different land use type
Unit area output, which establishes economic benefit objective function, to be determined to benefit flexible strategy coefficient.By single during statistics 2004-2014
Total output value in plane product, it is 16% that growth rate, which is calculated, and therefore 2014 can obtain for 86.00045 hundred million yuan/km2.
Unit area total industrial output value to the year two thousand twenty is 265.986 hundred million yuan/km2, therefore obtains each type by what formula calculated
Unit area output, to obtain economic benefit function:
F (x)=33.248x1+16.6242x2+6.650*x3+9.974*x4+265.986x5
According to the grey principle of GLP model, the upper limit, lower limit and the middle limit in constraint condition section are taken respectively, designs more cover dies
Type is run using Lingo software and obtains optimal solution.Finally obtain the soil benefit of five big subsystem space the year two thousand twenty of three lives space
It is (raw to be respectively as follows: 51.950km2 (ecological space), 40.024km2 (agricultural production space), 47.698km2 with quantity optimization area
Space living), 9.369km2 (ensureing space), 40.876km2 (industrial production space), realize to the excellent of land use quantitative structure
Change.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: the present invention according to it is described to
The urbanization quality evaluation index in the soil of configuration obtains the space of described five big subspaces by the spatial distribution model
Distribution；According to the urbanization quality evaluation index in soil to be configured, described five big subspaces are calculated by GLUE-S model
Land area；According to the land area and the spatial distribution, configuration is optimized to soil to be configured.Energy of the present invention
It is enough to plan as a whole production, life and ecological land space, configuration is optimized to the land area of five big subspaces, to adapt to territory sky
Between manage and research needs.
As shown in Fig. 2, the present invention also provides a kind of soil space Optimizing Configuration System, the system comprises:
Sample data obtains module 201, for obtaining sample data；The sample data includes the urbanization in sample soil
Five big Subspace Distribution figures of quality evaluation index and sample soil；The urbanization quality evaluation index includes vegetative coverage
Degree, the gradient, the soil organism, cities and towns distance, cities and towns far from river distance away from ecological space, cities and towns far from living space away from
From, distance of the cities and towns cities and towns away from a distance from production space and cities and towns away from road；The five big subspace includes living space, life
State space, industrial production space, agricultural production space and guarantee space.
Training module 202, for obtaining spatial distribution according to sample data training binary Logistic regression model
Model.
The training module 202, specifically includes:
Input unit, for being returned the urbanization quality evaluation index in the sample soil as the binary Logistic
The input for returning model, obtains output data；
Judging unit, for judging the output data and the error of the five big Subspace Distribution figures in the sample soil is
It is no within the scope of error threshold；
As a result determination unit, for the error when the output data and the five big Subspace Distribution figures in the sample soil
When within the scope of error threshold, determining that the binary Logistic returns mould model is spatial distribution model；
Adjustment unit, for being missed when the output data and the error of the five big Subspace Distribution figures in the sample soil
When outside poor threshold range, the parameter of the binary Logistic regression model is adjusted, makes the output data and sample soil
The error of the big Subspace Distribution figure of the five of ground is within the scope of error threshold.
Evaluation index obtains module 203, for obtaining the urbanization quality evaluation index in soil to be configured.
Spatial distribution module 204, for the urbanization quality evaluation index according to the soil to be configured, by described
Spatial distribution model obtains the spatial distribution of described five big subspaces.
Area calculation module 205 passes through GLUE-S mould for the urbanization quality evaluation index according to soil to be configured
Type calculates the land area of described five big subspaces.
The area calculation module 205, specifically includes:
Remote sensing images acquiring unit, for obtaining the remote sensing images in soil to be configured；
Image processing unit, for the urbanization quality evaluation index according to soil to be configured, to the remote sensing images
It is standardized, obtains remote sensing index image；
Pixel value determination unit, for determining each urbanization quality evaluation index according to the remote sensing index image
Pixel value；
Model acquiring unit, for obtaining soil urbanization comprehensive evaluation model；The soil urbanization overall merit mould
The expression formula of type are as follows:
Wherein I is the urbanization index of soil pixel, and n is evaluation index number, n=8；PiFor the weight of i-th of index,
XiFor the pixel value of i-th of evaluation index；
Urbanization exponent calculation unit passes through the soil for the pixel value according to each urbanization quality evaluation index
Urbanization comprehensive evaluation model in ground calculates the urbanization index of each soil pixel in soil to be configured；
Areal calculation unit passes through for the urbanization index according to each soil pixel in the soil to be configured
GLUE-S model calculates the land area of described five big subspaces.
Distribute module 206 rationally, for according to the land area and the spatial distribution, to soil to be configured into
Row is distributed rationally.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (8)
1. a kind of soil space Optimal Configuration Method, which is characterized in that the described method includes:
Obtain sample data；The sample data include sample soil urbanization quality evaluation index and sample soil five
Big Subspace Distribution figure；The urbanization quality evaluation index includes vegetation coverage, the gradient, the soil organism, cities and towns far from river
Distance far from living space of distance, cities and towns of the distance, cities and towns of stream away from ecological space, distance of the cities and towns cities and towns away from production space with
And distance of the cities and towns away from road；The five big subspace includes living space, ecological space, industrial production space, agricultural production
Space and guarantee space；
According to sample data training binary Logistic regression model, spatial distribution model is obtained；
Obtain the urbanization quality evaluation index in soil to be configured；
Described five are obtained by the spatial distribution model according to the urbanization quality evaluation index in the soil to be configured
The spatial distribution of big subspace；
According to the urbanization quality evaluation index in soil to be configured, the soil of described five big subspaces is calculated by GLUE-S model
Ground area；
According to the land area and the spatial distribution, configuration is optimized to soil to be configured.
2. soil space Optimal Configuration Method according to claim 1, which is characterized in that described according to the sample data
Training binary Logistic regression model, obtains spatial distribution model, specifically includes:
Using the urbanization quality evaluation index in the sample soil as the input of the binary Logistic regression model, obtain
Output data；
Judge the error of the five big Subspace Distribution figures in the output data and the sample soil whether in error threshold range
It is interior；
If so, determining that the binary Logistic returns mould model is spatial distribution model；
If it is not, adjusting the parameter of the binary Logistic regression model, make the five of the output data and the sample soil
The error of big Subspace Distribution figure is within the scope of error threshold.
3. soil space Optimal Configuration Method according to claim 1, which is characterized in that described according to soil to be configured
Urbanization quality evaluation index, the land area of described five big subspaces is calculated by GLUE-S model, is specifically included:
Obtain the remote sensing images in soil to be configured；
According to the urbanization quality evaluation index in soil to be configured, the remote sensing images are standardized, are obtained distant
Feel index image；
According to the remote sensing index image, the pixel value of each urbanization quality evaluation index is determined；
Obtain soil urbanization comprehensive evaluation model；
According to the pixel value of each urbanization quality evaluation index, by the soil urbanization comprehensive evaluation model, calculate to
The urbanization index of each soil pixel in the soil of configuration；
According to the urbanization index of each soil pixel in the soil to be configured, described five big sons are calculated by GLUE-S model
The land area in space.
4. soil space Optimal Configuration Method according to claim 3, which is characterized in that the soil urbanization synthesis is commented
The expression formula of valence model are as follows:
Wherein I is the urbanization index of soil pixel, and n is evaluation index number, n=8；PiFor the weight of i-th of index, XiFor
The pixel value of i-th of evaluation index.
5. a kind of soil space Optimizing Configuration System, which is characterized in that the system comprises:
Sample data obtains module, for obtaining sample data；The sample data includes that the urbanization quality in sample soil is commented
Five big Subspace Distribution figures of valence index and sample soil；The urbanization quality evaluation index includes vegetation coverage, slope
Distance far from living space of degree, the soil organism, cities and towns distance, cities and towns far from river distance, cities and towns away from ecological space, cities and towns
Distance and cities and towns distance away from road of the cities and towns away from production space；The five big subspace include living space, ecological space,
Industrial production space, agricultural production space and guarantee space；
Training module, for obtaining spatial distribution model according to sample data training binary Logistic regression model；
Evaluation index obtains module, for obtaining the urbanization quality evaluation index in soil to be configured；
Spatial distribution module passes through the space point for the urbanization quality evaluation index according to the soil to be configured
Cloth model obtains the spatial distribution of described five big subspaces；
Area calculation module is calculated for the urbanization quality evaluation index according to soil to be configured by GLUE-S model
The land area of the five big subspace；
Module is distributed rationally, for being optimized to soil to be configured according to the land area and the spatial distribution
Configuration.
6. soil space Optimizing Configuration System according to claim 5, which is characterized in that the training module, it is specific to wrap
It includes:
Input unit, for returning mould for the urbanization quality evaluation index in the sample soil as the binary Logistic
The input of type, obtains output data；
Judging unit, for judge the output data and the sample soil five big Subspace Distribution figures error whether
Within the scope of error threshold；
As a result determination unit, for being missed when the output data and the error of the five big Subspace Distribution figures in the sample soil
When in poor threshold range, determining that the binary Logistic returns mould model is spatial distribution model；
Adjustment unit, the error for working as the output data with the five big Subspace Distribution figures in the sample soil is in error threshold
When being worth outside range, the parameter of the binary Logistic regression model is adjusted, makes the output data and the sample soil
The error of five big Subspace Distribution figures is within the scope of error threshold.
7. soil space Optimizing Configuration System according to claim 5, which is characterized in that the area calculation module, tool
Body includes:
Remote sensing images acquiring unit, for obtaining the remote sensing images in soil to be configured；
Image processing unit carries out the remote sensing images for the urbanization quality evaluation index according to soil to be configured
Standardization obtains remote sensing index image；
Pixel value determination unit, for determining the pixel of each urbanization quality evaluation index according to the remote sensing index image
Value；
Model acquiring unit, for obtaining soil urbanization comprehensive evaluation model；
Urbanization exponent calculation unit passes through the soil city for the pixel value according to each urbanization quality evaluation index
Town comprehensive evaluation model calculates the urbanization index of each soil pixel in soil to be configured；
Areal calculation unit passes through GLUE-S for the urbanization index according to each soil pixel in the soil to be configured
Model calculates the land area of described five big subspaces.
8. soil space Optimizing Configuration System according to claim 7, which is characterized in that the soil urbanization synthesis is commented
The expression formula of valence model are as follows:
Wherein I is the urbanization index of soil pixel, and n is evaluation index number, n=8；PiFor the weight of i-th of index, XiFor
The pixel value of i-th of evaluation index.
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