CN104992041B - A kind of urban sprawl Boundary Prediction method based on Space Syntax - Google Patents

A kind of urban sprawl Boundary Prediction method based on Space Syntax Download PDF

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CN104992041B
CN104992041B CN201510477655.2A CN201510477655A CN104992041B CN 104992041 B CN104992041 B CN 104992041B CN 201510477655 A CN201510477655 A CN 201510477655A CN 104992041 B CN104992041 B CN 104992041B
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line segment
built
grid
areas
model
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CN104992041A (en
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王海军
张安琪
夏畅
宋丹阳
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Wuhan University WHU
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Abstract

The invention discloses a kind of urban sprawl Boundary Prediction method based on Space Syntax, comprise the following steps：1）Draw line segment map（Segment Map）And extract built-up areas border；2）Establish fixed mesh unit；3）Ask and calculate Space Syntax spatial analysis variables, and be assigned to grid；4）Each grid is calculated to built-up areas frontier distance；5）Establish multiple linear regression model and examine；6）Urban sprawl Boundary Prediction.The inventive method constructs the quantitative model between built-up areas border and Space Syntax spatial analysis variables, and above-mentioned model is applied in urban sprawl Boundary Prediction, can provide new approaches for the research of Urban Planning Decision Making and urban border problem to a certain extent.

Description

A kind of urban sprawl Boundary Prediction method based on Space Syntax
Technical field
The present invention relates to urban sprawl Boundary Prediction method, more particularly to a kind of urban sprawl border based on Space Syntax Forecasting Methodology.
Background technology
Urbanization in China is constantly accelerated, and how to realize the effective control increased to city, is Study of Urban Important topic.Because city system is by multifactor effect, its interior space operation is complicated and changeable, in the past more than the research for city Overweight analysis economy and social factor, it is believed that the form and pattern of city space are their projections geographically, are economical Material city has been moulded with social processes.The model on the prediction enlargement border established based on this city analysis method is considered mostly Factor is numerous, and model is complex, as cellular Automation Model, SLEUTH models, CLUE-S models, two sorted logics return Mould, multi-variable logistic regression model etc..
The seventies in last century, London university professor Hillier propose Space Syntax, i.e., one kind is with spatial shape analysis Based on analyze city space method, for solve urban issues, there is provided new Research Thinking and means.Space Syntax is recognized For street road network is one of factor for determining motion in itself, and land use pattern, inhomogeneity are moulded by its influence to motion The soil of type can find suitable position under this motion, as commercial land can be moved to stream of people's junction of road network, and its His type soil can be moved to the less place of the stream of people.Space Syntax explains the behavior of people in space and spatial shape in itself Relation, it is believed that road is largely fixed the behavior of people.Domestic and foreign scholars are analyzed in city scope based on Space Syntax Great number of issues, such as in terms of pedestrian and vehicle flowrate analysis, the raiser such as Hiller, car trip by city street network it is reachable Property structure influence；A van Nes etc., using the cities and towns in Europe as research object, road construction and city are analyzed based on Space Syntax The relation of change, and study the influence that circumferential highway is distributed to retail shop.In terms of urban planning：Jose Julio Lima Etc. the city's spatial structure and social bond feature for analyzing last century end Berlin, the graduate Chetty R. in the Mai Kenxi whole world Influence of the social economy to living area form is analyzed Deng contrast U.S. different scales city.In terms of the differentiation of urban morphology general layout, For Hiller et al. by researching and proposing, the process of urban agglomerations is due to that irregular grid has unbalanced gravitation, in addition with A large amount of works, which are combined Space Syntax with GIS, is used for this aspect research.Other Space Syntax is also applied to urban land use Density, the structure of pedestrian traffic model, the distribution of criminal offence spatially etc..Since Space Syntax occurs, city In the range of achievement in research it is rich, but it is less to the achievement in research in terms of urban border extension to be currently based on Space Syntax.
The content of the invention
In view of the deficienciess of the prior art, the invention provides a kind of urban sprawl Boundary Prediction based on Space Syntax Method, this method can truer, simulating city expansions exactly.
The technical solution adopted in the present invention is：A kind of urban sprawl Boundary Prediction method based on Space Syntax, it is special Sign is, comprises the following steps：
Step 1：Extract completed region of the city border and line segment map is drawn based on Space Syntax, its specific implementation includes following Sub-step：
Step 1.1：Extract built-up areas boundary graph, including history, present situation and planning three periods of year；
Step 1.2：Axial map is drawn, divides history, present situation and the planning width figure of year three；
Step 1.3：Axial map is translated into generation line segment map.
Step 2：Covered as research area, foundation in the region that urban land may be developed into using the existing built-up areas in city and its periphery The fixed mesh unit in lid research area；
Step 3：Space Syntax spatial analysis variables are calculated, and are assigned to grid；
Step 4：Each grid is calculated to different times built-up areas frontier distance D；
Step 5：Multiple linear regression model is established, determines model variable and corresponding parameter, and the polynary line to being established Property regression model is tested；
Step 6：Urban sprawl Boundary Prediction, its specific implementation include following sub-step：
Step 6.1：According to the multiple linear regression model established, using present situation road network and planned road network, by each grid Point substitutes into model to the value of dependent variable, calculates planning year each mesh point to planning year built-up areas frontier distance；
Step 6.2：Each mesh point is drawn to the isopleth of frontier distance, prediction result is visualized.
Preferably, the axial map described in step 1.2 is to draw to form using AutoCAD, described in step 1.3 It is that axial map is translated into line segment map using DepthmapX softwares that axial map is translated into generation line segment map, will Remove axial stubs parameter value is arranged to 25%.
Preferably, the specific implementation of step 3 includes following sub-step：
Step 3.1：Calculate the spatial analysis variables F of different times segment method, including the degree of integration under different spaces yardstick I, selectance C, line density TSL, nodal value NC, depth value TD and angle connection value AnCon, i.e.,：
F∈{Ir,Cr,TSLr,NCr,TDr, AnCon },
Wherein r takes different space scales, is determined according to completed region of the city area；
Step 3.2：Using《Urban Land Classification code GB/T 18507-2014》Middle path link is calculated up to degree effect point Method, the spatial analysis variables F for describing line segment is assigned to grid, is designated as X；Its specific implementation includes following sub-step：
Step 3.2.1：Its operating radius is calculated according to road grade, and is assigned to corresponding line segment, calculation formula is as follows：
D=s ÷ l；
Wherein：D is operating radius, and s is construction land area, and l is each rank road total length；
Step 3.2.2：According to the implication of spatial analysis variables index, when assigning it to each grid unit, not only to consider The line segment desired value size of the grid unit is served, also to consider its relative distance away from line segment；Line segment is to each grid list The influence of member is with range attenuation in its service radius, and depth value is increase；It is index mould to determine distance decay model Type：
Or
Wherein XjThe spatial analysis variables desired value obtained for grid unit j, FiFor line segment i spatial analysis variables index Value, diActual range for grid unit j to line segment i, d are line segment i operating radius, and n is the line segment for acting on grid cell j Sum.
Preferably, the spatial analysis variables F of different times segment method, step are calculated in step 3.1 using DepthmapX Programmed in 3.2 using ArcView and the spatial analysis variables F for describing line segment is assigned to grid, be designated as X.
Preferably, each grid is calculated to different times built-up areas frontier distance D using ArcGIS instruments in step 4.
Preferably, the specific implementation of step 5 includes following sub-step：
Step 5.1：Using each grid unit after urban sprawl away from built-up areas frontier distance as dependent variable, with before expanding away from building up Each spatial analysis variables are independent variable before and after area's frontier distance and expansion, build multiple linear regression model：
Wherein：D0、DtTo extend forward and backward each grid cell to built-up areas frontier distance, ω0For extension front border distance power Weight values, X0QUOTE X0* MERGEFORMAT be constant term,For spatial analysis variables before extension, ωiFor form before extension point Analyse variable weight value；For spatial analysis variables after extension；λiSpatial analysis variables weighted value after extension, m are independent variable sum；
Step 5.2：Each mesh point is to completed region of the city status on the border distance and history distance, history year and present situation year form Situational variables substitute into multiple linear regression model.The regression analysis in SPSS softwares is called, the mode for selecting variable to step into Model is built, can filter out influences the significant factor to dependent variable (away from completed region of the city status on the border distance), that is, meets sig ≤ 0.05 factor, not significant factor is rejected, determines degree of correlation between them, and then obtain model variable and corresponding parameter；
Step 5.3：The multiple linear regression model established is tested, its specific implementation includes following sub-step：
Step 5.3.1：Calculate the degree of fitting R of multiple linear regression model2With level of signifiance sig. indexs, testing model is intended It is right；
Step 5.3.2：Multiple linear regression model residual distribution is analyzed, examines residual error independence；
Step 5.3.3：Homogeneity variance is examined；
Step 5.3.4：Consistency check, using historical data, history road network and present situation road network, present situation is predicted, And contrasted with actual conditions, calculate Kappa coefficients.
Preferably, prediction result is visualized using ArcGIS instruments in step 6.2.
Compared with prior art, the present invention has advantages below and beneficial effect：
The inventive method constructs the quantitative model of path space attribute and completed region of the city border, and should by above-mentioned model For that in urban sprawl Boundary Prediction, can be provided newly for the research of Urban Planning Decision Making and urban border problem to a certain extent Thinking, contribute to it is truer, grasp road network construction and urban sprawl relation exactly, can also simulated as cellular automata etc. Constraints during urban sprawl.
Brief description of the drawings
Fig. 1：For the line segment map of different times in present example, (a) is road network line segment map in 2002, and (b) is Road network line segment map in 2008, (c) are planning year road network line segment map；
Fig. 2：For the built-up areas border map (2002 and 2008) of different times in present example；
Fig. 3：For the line segment map spatial analysis variables Distribution value figure of different times in present example, with global degree of integration Exemplified by value, (a) is global degree of integration distribution map in 2002, and (b) is global degree of integration distribution map in 2008, and (c) is complete for planning year Office's degree of integration distribution map；
Fig. 4：For different times fixed mesh element variable Distribution value figure in present example, using the overall situation integrate angle value as Example, (a) are global degree of integration distribution map in 2002, and (b) is global degree of integration distribution map in 2008, and (c) is whole for the planning year overall situation Right distribution map；
Fig. 5：It is that each grid variate-value under different spaces yardstick in present example is related to what it is to built-up areas frontier distance Spend, (a) to integrate angle value and the built-up areas frontier distance degree of correlation, (b) is to select angle value and the built-up areas frontier distance degree of correlation, (c) it is nodal values and the built-up areas frontier distance degree of correlation, (d) is TSL values and the built-up areas frontier distance degree of correlation, and (e) is deep Angle value and the built-up areas frontier distance degree of correlation；
Fig. 6：For model residual test result figure in present example, (a) is the residual error histogram of model, and (b) is Residual error P-P schemes；
Fig. 7：For 2008 in present example away from built-up areas frontier distance predicted value and true Distribution value scatter diagram；
Fig. 8：Boundary Prediction figure is expanded for 2008 Nian Mianhu towns in present example；
Fig. 9：To plan that Boundary Prediction figure is expanded in Nian Mianhu towns in present example；
Figure 10：To use 2008 Nian Mianhu towns built-up areas of the inventive method prediction to expand border and reality in present example The comparison diagram on border border；
Figure 11：For in present example use the inventive method prediction planning Nian Mianhu towns built-up areas expand border with The comparison diagram on border in 2008.
Embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with the accompanying drawings and embodiment is to this hair It is bright to be described in further detail, it will be appreciated that implementation example described herein is merely to illustrate and explain the present invention, not For limiting the present invention.
A kind of urban sprawl Boundary Prediction method based on Space Syntax provided by the invention, comprises the following steps：
Step 1：Extract completed region of the city border and line segment map is drawn based on Space Syntax, its specific implementation includes following Sub-step：
Step 1.1：Using ArcGIS softwares, solved by visual observation based on the remote sensing image for passing through radiant correction and geometric correction Translate extraction built-up areas boundary graph, including history, present situation and planning three periods of year；
Step 1.2：Using AutoCAD softwares, axial map is drawn, divides history, present situation and planning three periods of year；
Step 1.3：With DepthmapX softwares, axial map is translated into line segment map, by Remove axial Stubs parameter value is arranged to 25%；
Step 2：The instrument in ArcGIS is called, urban land may be developed into the existing built-up areas in city and its periphery Region is research area, establishes the fixed mesh unit in covering research area；
Step 3：Space Syntax describes road network by spatial analysis variables, is calculated using DepthmapX softwares empty Between syntax spatial analysis variables；Realize and border is expanded based on Space Syntax predicted city, need to be by road index by certain rule It is assigned to grid；Specific implementation includes following sub-step：
Step 3.1：The spatial analysis variables F for calculating different times segment method, including different spaces chi are asked using DepthmapX Degree of integration I, selectance C, line density TSL, nodal value NC, depth value TD and angle connection value AnCon under degree etc., i.e.,：
F∈{Ir,Cr,TSLr,NCr,TDr, AnCon...... }, wherein r can use different space scales, be built according to city Determined into area's area, such as：R ∈ 100 meters, and 300 meters, 500 meters, 1000 meters, 1500 meters, 2000 meters ...；
Step 3.2：Using《Urban Land Classification code GB/T 18507-2014》Middle path link is calculated up to degree effect point Method, the spatial analysis variables F for describing line segment is assigned to grid, is designated as X；Its specific implementation includes following sub-step：
Step 3.2.1：Its operating radius is calculated according to road grade, and is assigned to corresponding line segment, calculation formula is as follows：
D=s ÷ l；
Wherein：D is operating radius, and s is construction land area, and l is each rank road total length；
Step 3.2.2：According to the implication of spatial analysis variables index, when assigning it to each grid unit, not only to consider The line segment desired value size of the grid unit is served, also to consider its relative distance away from line segment；Line segment is to each grid list The influence of member is with range attenuation in its service radius, and depth value is increase；It is index mould to determine distance decay model Type：
Or
Wherein XjThe spatial analysis variables desired value obtained for grid unit j, FiFor line segment i spatial analysis variables index Value, diActual range for grid unit j to line segment i, d are line segment i operating radius, and n is the line segment for acting on grid cell j Sum.
Step 4：Each grid is calculated to different times built-up areas frontier distance D using ArcGIS instruments；
Step 5：Multiple linear regression model is established, determines model variable and corresponding parameter, and the polynary line to being established Property regression model is tested；Its specific implementation includes following sub-step：
Step 5.1：Using each grid unit after urban sprawl away from built-up areas frontier distance as dependent variable, with before expanding away from building up Each spatial analysis variables are independent variable before and after area's frontier distance and expansion, build multiple linear regression model：
Wherein：D0、DtTo extend forward and backward each grid cell to built-up areas frontier distance, ω0For extension front border distance power Weight values, X0For constant term,For spatial analysis variables before extension, ωiFor spatial analysis variables weighted value before extension；To expand Spatial analysis variables after exhibition；λiSpatial analysis variables weighted value after extension, m are total number of variable；
Step 5.2：Each mesh point is to completed region of the city status on the border distance and history distance, history year and present situation year form Situational variables substitute into multiple linear regression model.The regression analysis in SPSS softwares is called, the mode for selecting variable to step into Model is built, can filter out influences the significant factor to dependent variable (away from completed region of the city status on the border distance), that is, meets sig ≤ 0.05 factor, not significant factor is rejected, determines degree of correlation between them, and then obtain model variable and corresponding parameter. The process of urban sprawl, it is usually associated with the constantly improve of city road network, especially urban fringe road network；And urban fringe road network Development and can improve urban fringe land used regional conditions, so as to promote the fast development on built-up areas border.Due to local road Improving for net answers prioritizing selection anti-the effect for being affected by it region much larger than integrally, thus be accordingly used in the variable for establishing model Reflect the local variable of Regional Road Network change.
Step 5.3：The multiple linear regression model established is tested, its specific implementation includes following sub-step：
Step 5.3.1：Calculate the degree of fitting R of multiple linear regression model2With level of signifiance sig. indexs, testing model is intended It is right；
Step 5.3.2：Multiple linear regression model residual distribution is analyzed, examines residual error independence；
Step 5.3.3：Homogeneity variance is examined；
Step 5.3.4：Consistency check, using historical data, history road network and present situation road network, present situation is predicted, And contrasted with actual conditions, calculate Kappa coefficients.
Step 6：Urban sprawl Boundary Prediction, its specific implementation include following sub-step：
Step 6.1：According to the multiple linear regression model established, using present situation road network and planned road network, by each grid Value (the significant factor of influence screened by step 5) desired value to dependent variable of point substitutes into model, and it is each to calculate planning year Mesh point to planning year built-up areas frontier distance；
Step 6.2：Each mesh point is drawn to the isopleth of frontier distance using ArcGIS, and prediction result is visualized.
To be below base using Jie Yang County Guangdong Provice Jiexi County Mian Hu towns remote sensing images in 2002,2008 and planned road network figure Plinth, predict Mian Hu towns on, planning year (i.e. 2015) built-up areas expansion border in 2008 using the inventive method.
Step 1：Mian Hu towns built-up areas road network line segment map, such as Fig. 1, the side interpreted by visual observation are drawn based on remote sensing image Method, extract Mian Hu towns built-up areas, such as Fig. 2 respectively in ACRGIS.
Step 2：Using Mian Hu towns built-up areas and its periphery can expansible region as research object, establish 30 meters * 30 meters Grid, area's endosymbiosis will be studied into 41400 fixed meshes.
Step 3：Calculate spatial analysis variables, such as Fig. 3；Using exponential decay model, form is divided by ArcView programmings Analyse variate-value and assign grid, as a result such as Fig. 4.
Step 4：Calculate different times research area in each mesh point to built-up areas frontier distance, wherein, inside built-up areas away from From value take opposite number participate in research, in order to analyze each mesh point expansion situation and away from urban border distance between dependency relation.
Step 5：Model is established and examined；
Pass through SPSS softwares, distance and its Space Syntax of the analysis each grid points of different year to Mian Hu towns town boundary Correlation between situational variables, the obtained degree of correlation the results are shown in Table 1, and confidence level is much smaller than 0.05, therefore in confidence level In the case of 95%, it is believed that corresponding spatial analysis variables and urban border are significantly correlated, this proof with spatial analysis variables come It is feasible to predict town boundary distance.
Each mesh point portion forms situational variables of table 1 are with arriving town boundary apart from degree of correlation R statistical forms
The variable degree of correlation under analysis different spaces yardstick, such as Fig. 5, each spatial analysis variables are related to town boundary respectively Degree is presented is gradually reduced trend with spatial scaling up.
It is reduced equation because obtained spatial analysis variables are numerous, takes influence significantly within 2002 and 2008, and it is related Spend high angle connection value (Ancon) and 100 meters, 300 meters, 500 meters, 800 meters, degree of integration (I) value, section under 1000 metrical scales Points (NC) value, depth value (TD), selection angle value (C), the distance (jl02) to town boundary in 2002 are used as independent variable, arrive The distance of town boundary in 2008 makees the method that dependent variable is progressed into SPSS softwares using variable and builds multiple linear regression Equation.
For further to equation simplification, variable entering order and the equation model degree change feelings of comprehensive consideration Systematic selection Correlation between condition and variable, it is final to determine that 11 spatial analysis variables are used for equation structure and are：Jl02,02 year and 08 year NC100, NC300, TD500, I800, AnCon.
The coefficient and confidence level of variable in the model of table 2
Obtained forecast model is：
Dt=-68.708+0.823 × D0-3.752*I8000-1.612×I800t-24.892×
AnCon0+90.085×AnCont+84.474×NC1000-122.336×NC100t-
6.877×NC3000+18.755×NC300t
In formula：
D0、Dt--- the forward and backward each grid cell of expansion to built-up areas frontier distance；
I8000、I800t--- local degree of integration (800 meters) corresponding to expansion is forward and backward；
ANCon0、ANCont--- angle connection value corresponding to expansion is forward and backward；
NC1000、NC100t--- local nodes number (100 meters) corresponding to expansion is forward and backward；
NC3000、NC300t--- local nodes number (300 meters) corresponding to expansion is forward and backward.
The parameter level of signifiance in model is respectively less than 0.05, it is seen that parameter has a significant impact to dependent variable, model adjustment R afterwards2For .885, sig.=.000<0.05, in the case of confidence level 95%, it is believed that models fitting works well.
Model testing to being established：
1. residual test.It is in normal distribution that can obtain residual error by Fig. 6, and residual error P-P figure distributions are linear, it was demonstrated that residual error is independent. Residual test result proves that models fitting works well.
2. homogeneity variance examines (F inspections).Table 3 is the result of F significance tests, and F is the variance of data, sig. tables The level of signifiance of representation model.Model sig=.000<0.05, F assay is notable, refuse the former degree of correlation be 0 it is assumed that and Regression sum of square accounts for the 88.5% of total sum of squares, i.e. regression model explains the 88.5% of total sum of squares, and models fitting effect is good It is good.
The multivariate regression models analysis of variance table of table 3
3. consistency check.One group of predicted value, i.e., the prediction of each mesh point to border in 2008 are can obtain after establishing model Distance, and by this Prediction distance compared with its actual range, evaluation and foreca result.
A) degree of correlation
Fig. 7 is using actual range as X-axis, and Prediction distance is the scatter diagram of Y-axis, be distributed in figure substantially in slope be 1 it is straight Line, R=.941 illustrate that predicted value is good to actual value fitting effect.
B) Kappa coefficients
Whether inside built-up areas it is classified according to mesh point, " prediction classification " is to be less than or equal to Prediction distance 0 grid points, i.e. the point assignment 1 inside prediction built-up areas, the point more than 0 i.e. outside built-up areas are entered as -1, together Reason obtains " original is classified " by partitioning standards of actual distance.The crosstab of the classification of grid point prediction and former classification results is established, is obtained It is .760 to Kappa coefficient values, close to 1, both are highly consistent.
C) prediction result contrasts with actual conditions
Prediction result is imported into ARCGIS, Visualization such as Fig. 8 is carried out to result, by prediction, built-up areas are 2002 To it is to the south during 2008, east expansion is obvious, based on due south, southwest and due east direction, several no expansions to the west of built-up areas.Figure 10 be the comparison diagram of prediction results in 2008 and actual boundary, by Tu Ke get, the actual expansion trend in built-up areas and prediction case base This is consistent.The prediction to Urban Land Expansion direction and trend can be realized to a certain extent using this method, and city is expanded Border is opened to be fitted.
Step 6：Plan Nian Mianhu towns built-up areas Boundary Prediction；
The variate-value of 2008 and each mesh point in planning year is substituted into regression equation, tries to achieve planning year each mesh point to building up Area's Boundary Prediction distance, prediction result is imported into ArcGIS, generation prediction result figure (see Fig. 9).Figure 11 expands side for planning year Boundary's prognostic chart and actual boundary comparison diagram in 2008, as seen from the figure, the south orientation expansion by the east of in this period of Mian Hu towns built-up areas Based on, it is northern also slightly to expand supplemented by east.
It should be appreciated that the part that this specification does not elaborate belongs to prior art.
It should be appreciated that the above-mentioned description for preferred embodiment is more detailed, therefore can not be considered to this The limitation of invention patent protection scope, one of ordinary skill in the art are not departing from power of the present invention under the enlightenment of the present invention Profit is required under protected ambit, can also be made replacement or deformation, be each fallen within protection scope of the present invention, this hair It is bright scope is claimed to be determined by the appended claims.

Claims (6)

  1. A kind of 1. urban sprawl Boundary Prediction method based on Space Syntax, it is characterised in that comprise the following steps：
    Step 1：Extract completed region of the city border and line segment map is drawn based on Space Syntax, its specific implementation includes following sub-step Suddenly：
    Step 1.1：Extract built-up areas boundary graph, including history, present situation and planning three periods of year；
    Step 1.2：Axial map is drawn, divides history, present situation and the planning width figure of year three；
    Step 1.3：Axial map is translated into generation line segment map；
    Step 2：The region that may develop into urban land using the existing built-up areas in city and its periphery is established covering and ground as research area Study carefully the fixed mesh unit in area；
    Step 3：Space Syntax spatial analysis variables are calculated, and are assigned to grid；
    Specific implementation includes following sub-step：
    Step 3.1：Calculate the spatial analysis variables F of different times segment method, including degree of integration I under different spaces yardstick, choosing Degree of selecting C, line density TSL, nodal value NC, depth value TD and angle connection value AnCon, i.e.,：
    F∈{Ir,Cr,TSLr,NCr,TDr, AnCon },
    Wherein r takes different space scales, is determined according to completed region of the city area；
    Step 3.2：Using《Urban Land Classification code GB/T 18507-2014》Middle path link divides algorithm up to degree effect, The spatial analysis variables F for describing line segment is assigned to grid, is designated as X；Its specific implementation includes following sub-step：
    Step 3.2.1：Its operating radius is calculated according to road grade, and is assigned to corresponding line segment, calculation formula is as follows：
    D=s ÷ l；
    Wherein：D is operating radius, and s is construction land area, and l is each rank road total length；
    Step 3.2.2：According to the implication of spatial analysis variables index, when assigning it to each grid unit, not only to consider to service In the line segment desired value size of the grid unit, its relative distance away from line segment is also considered；Line segment is to each grid cell Influence is with range attenuation in its service radius, and depth value is increase；It is exponential model to determine distance decay model：
    Or
    Wherein XjThe spatial analysis variables desired value obtained for grid unit j, FiFor line segment i spatial analysis variables desired value, di Actual range for grid unit j to line segment i, d are line segment i operating radius, and n is the line segment sum for acting on grid cell j；
    Step 4：Each grid is calculated to different times built-up areas frontier distance D；
    Step 5：Multiple linear regression model is established, determines model variable and corresponding parameter, and the multiple linear to being established returns Model is returned to test；
    Specific implementation includes following sub-step：
    Step 5.1：Using each grid unit after urban sprawl away from built-up areas frontier distance as dependent variable, with expand before away from built-up areas side Each spatial analysis variables are independent variable before and after boundary's distance and expansion, build multiple linear regression model：
    Wherein：D0、DtTo extend forward and backward each grid cell to built-up areas frontier distance, ω0To extend front border distance weighting value, X0For constant term, Xt0For spatial analysis variables before extension, ωiFor spatial analysis variables weighted value before extension；XtnFor shape after extension State situational variables；λiFor spatial analysis variables weighted value after extension, m is independent variable sum；
    Step 5.2：Each mesh point is to completed region of the city status on the border distance and history distance, history year and the morphological analysis of present situation year Variable substitutes into multiple linear regression model；The regression analysis in SPSS softwares is called, the mode for selecting variable to step into is established Model, filter out influences the significant factor to dependent variable, rejects not significant factor, determines degree of correlation between them, and then obtain To model variable and corresponding parameter；
    Step 5.3：The multiple linear regression model established is tested, its specific implementation includes following sub-step：
    Step 5.3.1：Calculate the degree of fitting R of multiple linear regression model2With level of signifiance sig indexs, testing model degree of fitting；
    Step 5.3.2：Multiple linear regression model residual distribution is analyzed, examines residual error independence；
    Step 5.3.3：Homogeneity variance is examined；
    Step 5.3.4：Consistency check, using historical data, history road network and present situation road network, present situation is predicted, and with Actual conditions contrast, and calculate Kappa coefficients；
    Step 6：Urban sprawl Boundary Prediction, its specific implementation include following sub-step：
    Step 6.1：According to the multiple linear regression model established, using present situation road network and planned road network, by each mesh point Model is substituted into the value of dependent variable, calculates planning year each mesh point to planning year built-up areas frontier distance；
    Step 6.2：Each mesh point is drawn to the isopleth of frontier distance, prediction result is visualized.
  2. 2. the urban sprawl Boundary Prediction method according to claim 1 based on Space Syntax, it is characterised in that：Step Axial map described in 1.2 is to draw to form using AutoCAD, and axial map is translated to generation line described in step 1.3 Section map is that axial map is translated into line segment map using DepthmapX softwares, by Remove axial stubs parameter Value is arranged to 25%.
  3. 3. the urban sprawl Boundary Prediction method according to claim 1 based on Space Syntax, it is characterised in that：Step The spatial analysis variables F of different times segment method is calculated in 3.1 using DepthmapX, is programmed in step 3.2 using ArcView The spatial analysis variables F for describing line segment is assigned to grid, is designated as X.
  4. 4. the urban sprawl Boundary Prediction method according to claim 1 based on Space Syntax, it is characterised in that：Step 4 It is middle to calculate each grid to different times built-up areas frontier distance D using ArcGIS instruments.
  5. 5. the urban sprawl Boundary Prediction method according to claim 1 based on Space Syntax, it is characterised in that：Step Described in 5.2 is sig≤0.05 on the significant criterion of dependent variable influence.
  6. 6. the urban sprawl Boundary Prediction method according to claim 1 based on Space Syntax, it is characterised in that：Step Prediction result is visualized using ArcGIS instruments in 6.2.
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