CN106485360A - Segmental society's prediction of economic indexes method and system based on overall noctilucence remote sensing - Google Patents

Segmental society's prediction of economic indexes method and system based on overall noctilucence remote sensing Download PDF

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CN106485360A
CN106485360A CN201610901532.1A CN201610901532A CN106485360A CN 106485360 A CN106485360 A CN 106485360A CN 201610901532 A CN201610901532 A CN 201610901532A CN 106485360 A CN106485360 A CN 106485360A
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sol
amp
local
total amount
sei
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CN201610901532.1A
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李畅
罗荣
吴宜进
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华中师范大学
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    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"

Abstract

The present invention provides segmental society's prediction of economic indexes method and system based on overall noctilucence remote sensing, includings cutting image and extract the nighttime light intensity value total amount of target zone, the socio-economic indicator of extraction regional area;Assumed based on linear concordance, judge between the nighttime light intensity value total amount of global area and the nighttime light intensity value total amount of regional area, and between the nighttime light intensity value total amount of regional area and the socio-economic indicator of regional area, if there is the linear dependence meeting pre-conditioned threshold value;Build the segmental society's economic indicator quantitative inversion model based on overall noctilucence remote sensing when meeting, global area nighttime light intensity value total amount according to year to be predicted, it is predicted analyzing based on inverse model, the socio-economic indicator obtaining the accordingly regional area in year predicts the outcome, complete segmental society's economic dynamic development to estimate, estimation results automatically can be obtained in time, there is important market value.

Description

Segmental society's prediction of economic indexes method and system based on overall noctilucence remote sensing

Technical field

The invention belongs to remote sensing geoscience applications field, it is related to a kind of segmental society's economic indicator based on overall noctilucence remote sensing Quantitative inversion Forecasting Methodology and system.

Background technology

U.S.A. military affairs meteorological satellite (Defense Meteorological Satellite Program, DMSP) is carried Linear scanning operation system (Operational Line System, OLS) sensor optical multiplication is used at night due to it Pipe (PMT), thus electrically amplified to light very capable.Can to the light in city, flame even using its photoelectricity amplification characteristic The extremely low aura of visibility is detected, thus possesses the energy of the footprint clearly catching mankind's activity under dark background Power, becomes a kind of unique data that can monitor economical activities of mankind.Retrievable DMSP/OLS is to 2013 from 1992 Year long-term sequence data, it ensure that noctilucence remotely-sensed data has comparability between interregional, year border.Just because of this, DMSP/OLS noctilucence remotely-sensed data be widely used in the Urban Space patulous research of big regional extent, economy and population estimation, In the environmental problem assessment such as urban power consumption and energy resource consumption analysis, carbon emission and light pollution.

However, the spatial resolution of DMSP/OLS data is 2.7km, it is by 5 high-resolution on satellite Sensing data carry out averagely after obtained by, so low spatial resolution lead to existing at present based on DMSP/OLS data Application more can only concentrate in the range of the large area of more than city or city-level with research, and be based on DMSP/OLS night lamp The application of the zonule of light data and research then rarely have and are related to.Meanwhile, the existing big region based on DMSP/OLS data Modeling does not carry out tight theoretical proof yet, lacks the reasonability that theoretically ship type evaluation builds and science.

Content of the invention

Present invention aims to existing based on DMSP/OLS data in range of application (little yardstick) and model construction Deficiency on Theoretical Proof, provides a kind of economic indicator quantitative inversion Predicting Technique side of segmental society based on overall noctilucence remote sensing Case.

The technical solution adopted in the present invention provides a kind of segmental society's prediction of economic indexes based on overall noctilucence remote sensing Method, comprises the following steps:

Step 1, respectively using regional area and global area as target zone, cuts out the noctilucence remote sensing shadow of target zone Picture, extracts nighttime light intensity value total amount SOL in target zone some years;

Step 2, extracts socio-economic indicator SEI in regional area some yearsLocal

Step 3, based on linear concordance it is assumed that judging nighttime light intensity value total amount SOL of global areaGlobalWith office Nighttime light intensity value total amount SOL in portion regionLocalBetween, and regional area nighttime light intensity value total amount SOLLoca Socio-economic indicator SEI with regional areaLocalBetween, if there is the linear dependence meeting pre-conditioned threshold value, including Following sub-step,

Step 3.1, judges SOLGlobalWith SOLLocalBetween with the presence or absence of meeting the linear dependence of pre-conditioned threshold value, Correlation coefficient computing formula is as follows,

Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SOLGlobal)iIt is 1 year night lights total amount in global area,It is that in global area, night lights are total The expectation of amount;N expression of years sum, the value of i is 1,2 ..., N;

Step 3.2, judges SOLLocalWith SEILocalBetween with the presence or absence of meeting the linear dependence of pre-conditioned threshold value, Correlation coefficient computing formula is as follows,

Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SEILocal)iIt is 1 year socio-economic indicator value in regional area,It is that in regional area, social economy refers to The expectation of scale value;

Step 4, when the linear dependence condition in step 3.1 and step 3.2 is set up, distant based on overall noctilucence by building Segmental society's economic indicator quantitative inversion model of sense, for predicting segmental society's economic dynamic development, including following sub-step,

Step 4.1, set up following ideal model go forward side by side line parameter estimate,

SEILocal=A+B × SOLGlobal

Wherein, A and B is the model parameter estimated;

Step 4.2, the model parameter estimated according to step 4.1, build the segmental society's economy based on overall noctilucence remote sensing Index quantification inverse model is as follows,

SEILocal=A+B × SOLGlobal+ ζ, ζ~N (0, σ2)

In formula, ζ is overall random error after disturbance, N (0, σ2) it is normal distribution, average is 0, and variance is σ;

Step 5, according to be predicted year global area nighttime light intensity value total amount, based on step 4 build based on Segmental society's economic indicator quantitative inversion model of overall noctilucence remote sensing is predicted analyzing, and obtains the regional area in accordingly year Socio-economic indicator predict the outcome, complete segmental society's economic dynamic development and estimate.

And, pre-conditioned threshold value is 0.8, when 0.8 < | ρ |≤1 it is believed that SOLGlobalWith SOLLocalOr SOLLocalWith SEILocalHeight correlation.

And, in step 1 and step 5, the realization extracting nighttime light intensity value total amount includes following sub-step,

Step 1.1, the Geographical projections coordinate system of DMSP/OLS noctilucence remotely-sensed data is converted to blue vigorous top grade area projection, Obtain DMSP/OLS Raster Images;

Step 1.2, according to the vector border figure layer of target zone, cuts out the DMSP/OLS Raster Images of target zone Figure;

Wherein, n represents pixel number of greyscale levels, DNiRepresent the brightness value of the pixel of the i-th grade in region, MiRepresent in region The pixel sum of the i-th brightness degree, i is from 0 to n-1.

And, step 5 gained predicts the outcome, and the forecast interval of its 1- α is:

Wherein, α is significance level, SOL0For meeting the SOL of the global area of related condition, that is, to be predicted year Global area nighttime light intensity value total amount, SEI0It is SOL=SOL0The observation at place, represents the regional area in year to be predicted Socio-economic indicator;For SEI0Estimation, represent to be predicted year regional area socio-economic indicator prediction knot Really, δ (SOL0) be predictive value uncertainty.

The present invention provides a kind of segmental society's prediction of economic indexes system based on overall noctilucence remote sensing, including following mould Block:

First module, cuts out the night of target zone for respectively using regional area and global area as target zone Light remote sensing image, extracts nighttime light intensity value total amount SOL in target zone some years;

Second module, for extracting socio-economic indicator SEI in regional area some yearsLocal

Three module, for based on linear concordance it is assumed that judging the nighttime light intensity value total amount of global area SOLGlobalNighttime light intensity value total amount SOL with regional areaLocalBetween, and regional area nighttime light intensity Value total amount SOLLocaSocio-economic indicator SEI with regional areaLocalBetween, if exist and meet the linear of pre-conditioned threshold value Dependency, including with lower unit,

Unit 3.1, for judging SOLGlobalWith SOLLocalBetween with the presence or absence of meeting the linear correlation of pre-conditioned threshold value Property, correlation coefficient computing formula is as follows,

Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SOLGlobal)iIt is 1 year night lights total amount in global area,It is that in global area, night lights are total The expectation of amount;N expression of years sum, the value of i is 1,2 ..., N;

Unit 3.2, for judging SOLLocalWith SEILocalBetween with the presence or absence of meeting the linear correlation of pre-conditioned threshold value Property, correlation coefficient computing formula is as follows,

Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SEILocal)iIt is 1 year socio-economic indicator value in regional area,It is that in regional area, social economy refers to The expectation of scale value;

4th module, for judging that linear dependence condition is set up when unit 3.1 and unit 3.2, by building based on complete Office noctilucence remote sensing segmental society's economic indicator quantitative inversion model, for predicting segmental society's economic dynamic development, including with Lower unit, unit 4.1, set up following ideal model go forward side by side line parameter estimate,

SEILocal=A+B × SOLGlobal

Wherein, A and B is the model parameter estimated;

Unit 4.2, the model parameter estimated according to unit 4.1, build the segmental society's economy based on overall noctilucence remote sensing Index quantification inverse model is as follows,

SEILocal=A+B × SOLGlobal+ ζ, ζ~N (0, σ2)

In formula, ζ is overall random error after disturbance, N (0, σ2) it is normal distribution, average is 0, and variance is σ;

5th module, for the global area nighttime light intensity value total amount according to year to be predicted, based on the 4th module Build is predicted analyzing based on segmental society's economic indicator quantitative inversion model of overall noctilucence remote sensing, obtains corresponding year The socio-economic indicator of regional area predict the outcome, complete segmental society's economic dynamic development and estimate.

And, pre-conditioned threshold value is 0.8, when 0.8 < | ρ |≤1 it is believed that SOLGlobalWith SOLLocalOr SOLLocalWith SEILocalHeight correlation.

And, in the first module and the 5th module, the realization extracting nighttime light intensity value total amount includes adopting to place an order Unit,

Unit 1.1, for being converted to blue vigorous top grade area by the Geographical projections coordinate system of DMSP/OLS noctilucence remotely-sensed data Projection, obtains DMSP/OLS Raster Images;

Unit 1.2, for the vector border figure layer according to target zone, cuts out the DMSP/OLS grid shadow of target zone As figure;

Wherein, n represents pixel number of greyscale levels, DNiRepresent the brightness value of the pixel of the i-th grade in region, MiRepresent in region The pixel sum of the i-th brightness degree, i is from 0 to n-1.

And, the 5th module gained predicts the outcome, and the forecast interval of its 1- α is:

Wherein, α is significance level, SOL0For meeting the SOL of the global area of related condition, that is, to be predicted year Global area nighttime light intensity value total amount, SEI0It is SOL=SOL0The observation at place, represents the regional area in year to be predicted Socio-economic indicator;For SEI0Estimation, represent to be predicted year regional area socio-economic indicator prediction knot Really, δ (SOL0) be predictive value uncertainty.

The having the beneficial effect that of technical scheme that the present invention provides:For existing noctilucence remote sensing image research method and content Present in not enough it is proposed that a kind of new overall situation based on nighttime light data is to Local Quantitative inverse model Forecasting Methodology, And theoretically the convertibility of overall noctilucence remote sensing image to local noctilucence remote sensing image has been carried out with proof of deriving.In this base On plinth, the ideal quantitative inverse model proposing is optimized, is also with RANSAC algorithm and improves the quality of data, and to model The uncertainty of estimation makes quantitative analyses, has expanded the range of application of noctilucence remotely-sensed data, learns and other for studying geography The research from the overall situation to local problem of ambit provides a kind of new approaches, and estimation socio-economic indicator is more objective, keeps away Exempt from the interference of anthropic factor.Different from conventional statistics mode, need many mass data, take a large amount of manpower and materials moneys Source, using technical scheme provided by the present invention, can be according to the global area nighttime light intensity value total amount in current year, automatically Change ground and obtain estimation results in time, there is important market value.

Brief description

Fig. 1 is the process principle figure of the embodiment of the present invention.

Fig. 2 is that the Hubei Province (global area) of the embodiment of the present invention is illustrated with Wuhan City's (regional area) noctilucence remote sensing image Figure.

Fig. 3 be the embodiment of the present invention rejecting abnormalities value before and after from SOLHubeiTo Wuhan City GDPWuhanLinear regression pre- Survey model construction schematic diagram, the Linear Regression Forecasting Model before wherein Fig. 3 a is rejecting abnormalities value builds schematic diagram, Fig. 3 b is to pick Except the Linear Regression Forecasting Model after exceptional value builds schematic diagram.

Fig. 4 is GDP before and after the rejecting abnormalities value of the embodiment of the present inventionWuhanWith SOLHubeiForecast interval comparison schematic diagram, figure 4a is GDP before rejecting abnormalities valueWuhanWith SOLHubeiForecast interval schematic diagram, Fig. 4 b is GDP after rejecting abnormalities valueWuhanWith SOLHubeiForecast interval schematic diagram.

Specific embodiment

Technical scheme for a better understanding of the present invention, does further to the present invention with reference to the accompanying drawings and examples Describe in detail.

With reference to Fig. 1, the embodiment of the present invention taking the Wuhan City's GDP quantitative inversion based on Hubei Province's noctilucence remote sensing as a example, for Nighttime light intensity value total amount (Sum of Lights, SOL) and socio-economic indicator (Socio-Economic Indicators, SEI), the nighttime light intensity value total amount of global area is designated as SOLGlobal, in corresponding experimental example it is SOLHubei;The nighttime light intensity value total amount of regional area is designated as SOLLocal, it is SOL in corresponding experimental exampleWuhan;Partial zones The socio-economic indicator in domain is designated as SEILocal, it is GDP in corresponding experimental exampleWuhan.

The nighttime light intensity value of embodiment derives from DMSP/OLS noctilucence remotely-sensed data, can also adopt when being embodied as Other data, are not limited only to DMSP/OLS.

The step being implemented based on segmental society's prediction of economic indexes method of overall noctilucence remote sensing in embodiment is as follows:

Step 1, respectively using regional area and global area as target zone, using the extracting tool cutting in ArcGIS Go out the noctilucence remote sensing image of target zone, and pass through nighttime light intensity value total amount (Sum of Lights, SOL) computing formula Obtain the SOL in target zone some years.

Implement including following sub-step,

Step 1.1, the Geographical projections coordinate system of DMSP/OLS noctilucence remotely-sensed data is converted to blue vigorous top grade area projection, Obtain DMSP/OLS Raster Images;

Step 1.2, according to the vector border figure layer of target zone, cuts out target zone from step 1 acquired results DMSP/OLS Raster Images figure;

Step 1.3, by counting summation method, obtains the calculating of the nighttime light intensity value total amount SOL value of target zone Method is as follows:

Wherein, n represents pixel number of greyscale levels, and DMSP/OLS noctilucence data record is 6 gray-scale maps, therefore its gray level is 26 powers, i.e. 64 grades.DNiRepresent the brightness value of the pixel of the i-th grade in region, MiRepresent the i-th brightness degree in region Pixel sum, from 0 to n-1, therefore embodiment takes from 0 to 63 i, can use 1 to 63 during calculating.

When being embodied as, target zone is designated area, as needed, can make respectively for regional area or global area Extract nighttime light intensity value total amount for target zone.Referring to Tu2Zhong Hubei Province (global area) and Wuhan City's (regional area) Noctilucence remote sensing image.

Step 2, determine weigh regional area socio-economic conditions index (Socio-Economic Indicators, SEI), as GDP or population etc., implementation can obtain partial zones by consulting the country statistical yearbook of index of correlation Domain accordingly some annual socio-economic indicators SEILocal

According to formula (1) and ASSOCIATE STATISTICS yearbook, it is calculated 1998,2000,2003,2005,2006, 2008,2009,2010,2011, the SOL in Deng10Nian Jian Hubei Province in 2012 and Wuhan City's socio-economic indicator GDP, as shown in table 1 below,

Table 1 Hubei Province SOL (SOLHubei) and Wuhan City GDP (GDPWuhan) (unit:Ten thousand yuan)

Step 3, based on linear concordance it is assumed that according to step 1 and step 2 gained historical data, judging global area Nighttime light intensity value total amount (SOLGlobal) and regional area SOL (SOLLocal) between, and the SOL of regional area (SOLLocal) and regional area socio-economic indicator (Socio-Economic Indicators, SEI) (SEILocal) between, it is No have stronger linear dependence, including following sub-step,

Step 3.1, judges SOLGlobalWith SOLLocalBetween calculate with the presence or absence of stronger linear dependence, correlation coefficient Formula is as follows:

In formula (2), (SOLLocal)iIt is 1 year night lights total amount in regional area,It is regional area night lamp The average of light total amount, by (the SOL of NLocal)iIt is averaged and obtain, (SOLGlobal)iIt is that in global area, 1 year night lights is total Amount,It is the expectation (i.e. arithmetic average) of night lights total amount in global area;N expression of years sum, the value of i is 1, 2,…,N.

Step 3.2, judges SOLLocalWith SEILocalBetween calculate with the presence or absence of stronger linear dependence, correlation coefficient Formula is as follows:

In formula (3), SOLLocalIt is the night lights total amount of regional area, (SOLLocal)iIt is 1 year night in regional area Light total amount,It is the average of regional area night lights total amount, (SEILocal)iIt is 1 year social economy in regional area Desired value,It is the expectation (i.e. arithmetic average) of socio-economic indicator value in regional area.

The embodiment of the present invention proposes, and during | ρ |=0, shows SOLGlobalWith SOLLocalOr SOLLocalWith SEILocalCompletely not Related;It is believed that SOL during 0 < | ρ | < 0.3GlobalWith SOLLocalOr SOLLocalWith SEILocalUncorrelated;0.3 < | ρ |≤0.5 When it is believed that SOLGlobalWith SOLLocalOr SOLLocalWith SEILocalLower correlation;It is believed that SOL during 0.5 < | ρ |≤0.8GlobalWith SOLLocalOr SOLLocalWith SEILocalSignificantly correlated;It is believed that SOL during 0.8 < | ρ |≤1GlobalWith SOLLocalOr SOLLocalWith SEILocalHeight correlation.

Embodiment sets related condition threshold value as 0.8, and when being embodied as, those skilled in the art may be set to other values. Meeting related condition threshold value, assert that having stronger linear dependence condition sets up, and can enter step 4, if be unsatisfactory for Then it is unsuitable for this method, stop flow process.According to formula (2) and formula (3), calculate SOLHubeiWith SOLWuhanAnd SOLWuhan With GDPWuhanCorrelation coefficient be respectively 0.9748 and 0.0.9122, two correlation coefficienies be satisfied by ρ ∈ (0.8,1], step 3.1 Set up with the linear dependence condition in step 3.2.

Step 4, when the linear dependence condition in step 3.1 and step 3.2 is set up, distant based on overall noctilucence by building Sense segmental society's economic indicator quantitative inversion model to estimate segmental society's economic dynamic development, including following sub-step,

Step 4.1, for building the segmental society's economic indicator quantitative inversion model based on overall noctilucence remote sensing, builds first Ideal model go forward side by side line parameter estimate:

According to SOLGlobalWith SOLLocalBetween and SOLLocalWith SEILocalBetween there is stronger dependency, can build Regression model is as follows:

SOLLocal=aDN+bDN×SOLGlobal(4)

SEILocal=aLocal+bLocal×SOLLocal(5)

In formula, SOLGlobalRepresent the SOL, SOL of global areaLocalRepresent the SOL, SEI in regional areaLocalExpression office SEI in portion region, aDNWith bDN, aLocalWith bLocalIt is coefficient.

Formula (4) is substituted in formula (5), obtains SEILocal:

SEILocal=aLocal+bLocal×(aDN+bDN×SOLGlobal) (6)

Further, make A=aLocal+bLocal×aDN, B=bLocal×bDN, then obtain ideal model as follows:

SEILocal=A+B × SOLGlobal(7)

Wherein, A and B is the model parameter estimated.

SEILocalWith SOLGlobalAnd SEILocalWith SOLLocalDependency equivalence principle prove as follows:

In formula, ρ is the correlation coefficient between the SEI and the SOL of regional area of regional area, and ρ ' is the SEI of regional area The correlation coefficient and SOL of global area between, it follows that the correlation coefficient ρ from local SOL to local SEI with complete Correlation coefficient ρ between office SOL to local SEI ' equal.

When being embodied as, when carrying out Linear Regression Forecasting Model parameter estimation, existing RANSAC algorithm can be utilized, that is, Make a judgment criterion first against input data, iteratively reject those and differ larger data with estimation parameter, so After retain correct input data Linear Regression Forecasting Model solved.This judgment criterion requires one in the data of M group Ensure that under fixed confidence probability that at least one group data is entirely point in model, that is, need to meet following relation:

D=1- (1- (1- θ)q)M(9)

In formula, θ represents the error rate (i.e. the shared ratio in initial data of exterior point) of data, and q is computation model parameter The minimum data amount needing, d is confidence probability, and M is the number of groups of samples.According to the practical situation of experimental data, by setting Error rate θ of the confidence probability d data of experimental data calculates minimum sampling number and estimates final model parameter.

For example utilize existing RANSAC algorithm routine, by 10 years Hubei Province SOL (SOLHubei) and Wuhan City GDP (GDPWuhan) in data input RANSAC algorithm routine, according to formula (7), setting confidence probability setting d is 0.90 and maximum Iterationses are 45 times, weed out the 7th point and the 8th point, i.e. 2009 and 2010 annual datas, obtain the estimation parameter of model It is respectively -3977.725,0.013.

Step 4.2, the model parameter estimated according to step 4.1, build the segmental society's economy based on overall noctilucence remote sensing Index quantification inverse model, as follows:

SEILocal=A+B × SOLGlobal+ ζ, ζ~N (0, σ2) (10)

In formula, SEILocalIt is the SEI of regional area, SOLGlobalIt is the SOL of global area, A and B is parameter to be estimated, ζ is Overall random error after disturbance, N (0, σ2) it is normal distribution, average is 0, and variance is σ.

The disturbance existing in view of real data, can consider the noise on slope and intercept, build actual disturbance Under GTLQTM Disturbance Model.Therefore, SEILocalAnd SOLLocalExpression formula can be write as:

In formula (11) and formula (12), i is for indicating i-th observation data, and m, n are the denominator of slope, and e', f' are to cut Away from ωi'、μi'、ηi'、λi' it is the Disturbance being respectively added in slope and intercept, Normal Distribution, SEILocalWith SOLGlobalExpression formula as follows:

Wherein, a, b are slope and the corresponding multinomial coefficient of intercept.

By replacement, draw under actual disturbed conditions, the night lights remote sensing image quantitative inversion of the overall situation to local turns Change expression formula as follows:

SEILocal=A+B × SOLGlobal+ζ (14)

In formula (14), SEILocalIt is the SEI of regional area, SOLGlobalIt is the SOL of global area, A and B is parameter to be estimated, ζ is overall random error after disturbance.Determined according to segmental society's economic indicator that formula (14) obtains based on overall noctilucence remote sensing Amount inverse model, as formula (10).

In embodiment, according to formula (10), build the segmental society's warp based on overall noctilucence remote sensing after rejecting abnormalities value Ji index quantification inverse model is y=-3977.725+0.013 × x, and y corresponds to SEILocal, x correspondence SOLGlobal.Rejecting abnormalities Scatterplot before and after value is as shown in Figure 3.According to formula (10), before rejecting abnormalities value, build regression model y=-1598.1072+ 0.0091 × x (as shown in Figure 3 a), coefficient of determination R2=0.6453, the regression model y=-3977.7251 after rejecting abnormalities value + 0.013 × x (as shown in Figure 3 b), coefficient of determination R2=0.8709, after rejecting abnormalities value, the goodness of fit of model significantly improves.

Step 5, according to be predicted year global area nighttime light intensity value total amount, based on step 4 build based on Segmental society's economic indicator quantitative inversion model of overall noctilucence remote sensing is predicted analyzing, and obtains the regional area in accordingly year Socio-economic indicator predict the outcome, complete segmental society's economic dynamic development and estimate.

The global area nighttime light intensity value total amount extracting mode in year to be predicted, consistent with step 1.

In practice, if adopting conventional statistics mode, needing many mass data, taking a large amount of manpower and materials moneys Source, the statistical result of socio-economic indicator often obtains for delayed several years.Using method provided by the present invention, can be according to current The global area nighttime light intensity value total amount in year, automatically obtains estimation results in time.

Using technical solution of the present invention to the segmental society's economic indicator quantitative inversion based on overall noctilucence remote sensing building Model is predicted analyzing, and the forecast interval of its 1- α is:

In formula, SOL0For meeting the SOL of the global area of related condition, i.e. the global area night lamp in year to be predicted Light intensity value total amount, SEI0It is SOL=SOL0The observation at place, i.e. the socio-economic indicator of the regional area in year to be predicted;For SEI0Estimation, that is, the socio-economic indicator of regional area in year to be predicted predict the outcome;δ(SOL0) it is predictive value The uncertainty of (valuation) SOL it is preferable that being calculated as follows,

Wherein,It is standard deviation estimate, t obeys student distribution,Represent student distributionQuantile,It is to go through The average of all time SOL, total sum of deviation square in history dataN is the number of observed value, that is, The total time sum of historical data, SOLiFor the global area nighttime light intensity value total amount of 1 year in historical data, degree of freedom For N-2, α is significance level.

According to formula (15), make α=0.05, obtain 95% confidence interval as shown in Figure 4.Comparison diagram 4a and Fig. 4 b, figure In 4b, 95% confidence interval band is narrower than the confidence interval band in Fig. 4 a, and this illustrates on the whole, after rejecting abnormalities value The constructed model regression model more constructed than before rejecting abnormalities value more can explain sample situation.

Secondly, the minima of the x before and after rejecting abnormalities value in comparison sheet 2, maximum, the uncertain area at average three Between, x before rejecting abnormalities valueminUncertain interval at=372019 is (2376.96,5512.57), and amplitude of variation is 7889.53, xmaxUncertain interval at=953738 is (1749.74,9516.04), and amplitude of variation is 7766.3, xmean Uncertain interval at=591793 is (47.7678,7477.54), and amplitude of variation is 7429.77;After rejecting abnormalities value xminUncertain interval at=372019 is (2299.77,3432.44), and amplitude of variation is 5732.21, xmax=953738 The uncertain interval of place is (3518.76,9283.2), and amplitude of variation is 5764.44, xmeanUncertainty at=569746 Interval is 5292.21 for (783.86,6076.07) amplitude of variation, and after rejecting abnormalities value, uncertain interval is obviously reduced, and with The minima of x, maximum, at average three as a example, the reduction amplitude substantially 2000 of its indeterminacy section.

Before and after table 2 rejecting abnormalities value, Wuhan City's GDP true value is compared with predictive value

When being embodied as, method provided by the present invention can realize automatic running flow process based on software engineering, and mould may also be employed Massing mode realizes corresponding system.The embodiment of the present invention provides a kind of segmental society's economic indicator based on overall noctilucence remote sensing pre- Examining system, including with lower module:

First module, cuts out the night of target zone for respectively using regional area and global area as target zone Light remote sensing image, extracts nighttime light intensity value total amount SOL in target zone some years;

Second module, for extracting socio-economic indicator SEI in regional area some yearsLocal

Three module, for based on linear concordance it is assumed that judging the nighttime light intensity value total amount of global area SOLGlobalNighttime light intensity value total amount SOL with regional areaLocalBetween, and regional area nighttime light intensity Value total amount SOLLocaSocio-economic indicator SEI with regional areaLocalBetween, if exist and meet the linear of pre-conditioned threshold value Dependency, including with lower unit,

Unit 3.1, for judging SOLGlobalWith SOLLocalBetween with the presence or absence of meeting the linear correlation of pre-conditioned threshold value Property, correlation coefficient computing formula is as follows,

Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SOLGlobal)iIt is 1 year night lights total amount in global area,It is that in global area, night lights are total The expectation of amount;N expression of years sum, the value of i is 1,2 ..., N;

Unit 3.2, for judging SOLLocalWith SEILocalBetween with the presence or absence of meeting the linear correlation of pre-conditioned threshold value Property, correlation coefficient computing formula is as follows,

Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SEILocal)iIt is 1 year socio-economic indicator value in regional area,It is that in regional area, social economy refers to The expectation of scale value;

4th module, for judging that linear dependence condition is set up when unit 3.1 and unit 3.2, by building based on complete Office noctilucence remote sensing segmental society's economic indicator quantitative inversion model, for predicting segmental society's economic dynamic development, including with Lower unit, unit 4.1, set up following ideal model go forward side by side line parameter estimate,

SEILocal=A+B × SOLGlobal

Wherein, A and B is the model parameter estimated;

Unit 4.2, the model parameter estimated according to unit 4.1, build the segmental society's economy based on overall noctilucence remote sensing Index quantification inverse model is as follows,

SEILocal=A+B × SOLGlobal+ ζ, ζ~N (0, σ2)

In formula, ζ is overall random error after disturbance, N (0, σ2) it is normal distribution, average is 0, and variance is σ;

5th module, for the global area nighttime light intensity value total amount according to year to be predicted, based on the 4th module Build is predicted analyzing based on segmental society's economic indicator quantitative inversion model of overall noctilucence remote sensing, obtains corresponding year The socio-economic indicator of regional area predict the outcome, complete segmental society's economic dynamic development and estimate.

Each module implements and can be found in corresponding steps, and it will not go into details for the present invention.

Above content is that the present invention is said with that does further describes it is impossible to assert that the present invention's is concrete in conjunction with the embodiments Implement to be only limited to these explanations.It should be appreciated by those skilled in the art, without departing from the feelings being defined by the appended claims Under condition, various modifications can be carried out in detail, all should be considered as belonging to protection scope of the present invention.

Claims (8)

1. a kind of segmental society's prediction of economic indexes method based on overall noctilucence remote sensing is it is characterised in that comprise the following steps:
Step 1, respectively using regional area and global area as target zone, cuts out the noctilucence remote sensing image of target zone, Extract nighttime light intensity value total amount SOL in target zone some years;
Step 2, extracts socio-economic indicator SEI in regional area some yearsLocal
Step 3, based on linear concordance it is assumed that judging nighttime light intensity value total amount SOL of global areaGlobalWith partial zones Nighttime light intensity value total amount SOL in domainLocalBetween, and regional area nighttime light intensity value total amount SOLLocaWith office Socio-economic indicator SEI in portion regionLocalBetween, if there is the linear dependence meeting pre-conditioned threshold value, including following Sub-step,
Step 3.1, judges SOLGlobalWith SOLLocalBetween with the presence or absence of meeting the linear dependence of pre-conditioned threshold value, related Coefficient formulas are as follows,
ρ = Σ i = 1 N [ ( SOL L o c a l ) i - SOL L o c a l ‾ ] · [ ( SOL G l o b a l ) i - SOL G l o b a l ‾ ] Σ i = 1 N ( ( SOL L o c a l ) i - SOL L o c a l ‾ ) 2 · Σ i = 1 N ( ( SOL G l o b a l ) i - SOL G l o b a l ‾ ) 2 , ρ ∈ [ - 1 , 1 ]
Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is regional area night lights total amount Average, (SOLGlobal)iIt is 1 year night lights total amount in global area,It is night lights total amount in global area Expect;N expression of years sum, the value of i is 1,2 ..., N;
Step 3.2, judges SOLLocalWith SEILocalBetween with the presence or absence of meeting the linear dependence of pre-conditioned threshold value, phase relation Number computing formula is as follows,
ρ = Σ i = 1 N [ ( SOL L o c a l ) i - SOL L o c a l ‾ ] · [ ( SEI L o c a l ) i - SEI L o c a l ‾ ] Σ i = 1 N ( ( SOL L o c a l ) i - SOL L o c a l ‾ ) 2 · Σ i = 1 N ( ( SEI L o c a l ) i - SEI L o c a l ‾ ) 2 , ρ ∈ [ - 1 , 1 ]
Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is regional area night lights total amount Average, (SEILocal)iIt is 1 year socio-economic indicator value in regional area,It is socio-economic indicator value in regional area Expectation;
Step 4, when the linear dependence condition in step 3.1 and step 3.2 is set up, by building based on overall noctilucence remote sensing Segmental society's economic indicator quantitative inversion model, for predicting segmental society's economic dynamic development, including following sub-step,
Step 4.1, set up following ideal model go forward side by side line parameter estimate,
SEILocal=A+B × SOLGlobal
Wherein, A and B is the model parameter estimated;
Step 4.2, the model parameter estimated according to step 4.1, build the segmental society's economic indicator based on overall noctilucence remote sensing Quantitative inversion model is as follows,
SEILocal=A+B × SOLGlobal+ ζ, ζ~N (0, σ2)
In formula, ζ is overall random error after disturbance, N (0, σ2) it is normal distribution, average is 0, and variance is σ;
Step 5, according to the global area nighttime light intensity value total amount in year to be predicted, based on step 4 structure based on the overall situation Segmental society's economic indicator quantitative inversion model of noctilucence remote sensing is predicted analyzing, and obtains the society of the accordingly regional area in year Meeting prediction of economic indexes result, completes segmental society's economic dynamic development and estimates.
2. according to claim 1 the segmental society's prediction of economic indexes method based on overall noctilucence remote sensing it is characterised in that: Pre-conditioned threshold value is 0.8, when 0.8 < | ρ |≤1 it is believed that SOLGlobalWith SOLLocalOr SOLLocalWith SEILocalHeight phase Close.
3. the segmental society's prediction of economic indexes method based on overall noctilucence remote sensing according to claim 1 or claim 2, its feature exists In:In step 1 and step 5, the realization extracting nighttime light intensity value total amount includes following sub-step,
Step 1.1, the Geographical projections coordinate system of DMSP/OLS noctilucence remotely-sensed data is converted to blue vigorous top grade area projection, obtains DMSP/OLS Raster Images;
Step 1.2, according to the vector border figure layer of target zone, cuts out the DMSP/OLS Raster Images figure of target zone;
S O L = Σ i = 1 n - 1 ( D N ) i × M i
Wherein, n represents pixel number of greyscale levels, DNiRepresent the brightness value of the pixel of the i-th grade in region, MiRepresent i-th in region The pixel sum of brightness degree, i is from 0 to n-1.
4. according to claim 3 the segmental society's prediction of economic indexes method based on overall noctilucence remote sensing it is characterised in that: Step 5 gained predicts the outcome, and the forecast interval of its 1- α is:
Wherein, α is significance level, SOL0For meeting the SOL of the global area of related condition, i.e. the overall situation in year to be predicted Region nighttime light intensity value total amount, SEI0It is SOL=SOL0The observation at place, represents the society of the regional area in year to be predicted Can economic indicator;For SEI0Estimation, the socio-economic indicator of regional area representing year to be predicted predicts the outcome, δ (SOL0) be predictive value uncertainty.
5. a kind of segmental society's prediction of economic indexes system based on overall noctilucence remote sensing is it is characterised in that include with lower module:
First module, for respectively, using regional area and global area as target zone, the noctilucence cutting out target zone is distant Sense image, extracts nighttime light intensity value total amount SOL in target zone some years;
Second module, for extracting socio-economic indicator SEI in regional area some yearsLocal
Three module, for based on linear concordance it is assumed that judging nighttime light intensity value total amount SOL of global areaGlobalWith Nighttime light intensity value total amount SOL of regional areaLocalBetween, and regional area nighttime light intensity value total amount SOLLocaSocio-economic indicator SEI with regional areaLocalBetween, if there is the linear correlation meeting pre-conditioned threshold value Property, including with lower unit,
Unit 3.1, for judging SOLGlobalWith SOLLocalBetween with the presence or absence of meeting the linear dependence of pre-conditioned threshold value, Correlation coefficient computing formula is as follows,
ρ = Σ i = 1 N [ ( SOL L o c a l ) i - SOL L o c a l ‾ ] · [ ( SOL G l o b a l ) i - SOL G l o b a l ‾ ] Σ i = 1 N ( ( SOL L o c a l ) i - SOL L o c a l ‾ ) 2 · Σ i = 1 N ( ( SOL G l o b a l ) i - SOL G l o b a l ‾ ) 2 , ρ ∈ [ - 1 , 1 ]
Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is regional area night lights total amount Average, (SOLGlobal)iIt is 1 year night lights total amount in global area,It is night lights total amount in global area Expect;N expression of years sum, the value of i is 1,2 ..., N;
Unit 3.2, for judging SOLLocalWith SEILocalBetween with the presence or absence of meeting the linear dependence of pre-conditioned threshold value, phase Close coefficient formulas as follows,
ρ = Σ i = 1 N [ ( SOL L o c a l ) i - SOL L o c a l ‾ ] · [ ( SEI L o c a l ) i - SEI L o c a l ‾ ] Σ i = 1 N ( ( SOL L o c a l ) i - SOL L o c a l ‾ ) 2 · Σ i = 1 N ( ( SEI L o c a l ) i - SEI L o c a l ‾ ) 2 , ρ ∈ [ - 1 , 1 ]
Wherein, (SOLLocal)iIt is 1 year night lights total amount in regional area,It is regional area night lights total amount Average, (SEILocal)iIt is 1 year socio-economic indicator value in regional area,It is socio-economic indicator value in regional area Expectation;
4th module, for judging that linear dependence condition is set up when unit 3.1 and unit 3.2, by building based on overall night Segmental society's economic indicator quantitative inversion model of light remote sensing, for predicting segmental society's economic dynamic development, including to place an order Unit,
Unit 4.1, set up following ideal model go forward side by side line parameter estimate,
SEILocal=A+B × SOLGlobal
Wherein, A and B is the model parameter estimated;
Unit 4.2, the model parameter estimated according to unit 4.1, build the segmental society's economic indicator based on overall noctilucence remote sensing Quantitative inversion model is as follows,
SEILocal=A+B × SOLGlobal+ ζ, ζ~N (0, σ2)
In formula, ζ is overall random error after disturbance, N (0, σ2) it is normal distribution, average is 0, and variance is σ;
5th module, for the global area nighttime light intensity value total amount according to year to be predicted, based on the 4th module construction Based on segmental society's economic indicator quantitative inversion model of overall noctilucence remote sensing be predicted analyze, obtain accordingly year office The socio-economic indicator in portion region predicts the outcome, and completes segmental society's economic dynamic development and estimates.
6. according to claim 5 the segmental society's prediction of economic indexes system based on overall noctilucence remote sensing it is characterised in that: Pre-conditioned threshold value is 0.8, when 0.8 < | ρ |≤1 it is believed that SOLGlobalWith SOLLocalOr SOLLocalWith SEILocalHeight phase Close.
7. the segmental society's prediction of economic indexes system based on overall noctilucence remote sensing according to claim 5 or 6, its feature exists In:In first module and the 5th module, the realization extracting nighttime light intensity value total amount includes adopting with lower unit,
Unit 1.1, for the Geographical projections coordinate system of DMSP/OLS noctilucence remotely-sensed data is converted to blue vigorous top grade area projection, Obtain DMSP/OLS Raster Images;
Unit 1.2, for the vector border figure layer according to target zone, cuts out the DMSP/OLS Raster Images of target zone Figure;
S O L = Σ i = 1 n - 1 ( D N ) i × M i
Wherein, n represents pixel number of greyscale levels, DNiRepresent the brightness value of the pixel of the i-th grade in region, MiRepresent i-th in region The pixel sum of brightness degree, i is from 0 to n-1.
8. according to claim 7 the segmental society's prediction of economic indexes system based on overall noctilucence remote sensing it is characterised in that: 5th module gained predicts the outcome, and the forecast interval of its 1- α is:
Wherein, α is significance level, SOL0For meeting the SOL of the global area of related condition, i.e. the overall situation in year to be predicted Region nighttime light intensity value total amount, SEI0It is SOL=SOL0The observation at place, represents the society of the regional area in year to be predicted Can economic indicator;For SEI0Estimation, the socio-economic indicator of regional area representing year to be predicted predicts the outcome, δ (SOL0) be predictive value uncertainty.
CN201610901532.1A 2016-10-14 2016-10-14 Segmental society's prediction of economic indexes method and system based on overall noctilucence remote sensing CN106485360A (en)

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