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 PDFInfo
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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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[C] OKTJSMMVPCPJKNUHFFFAOYSAN 0.000 description 1
 238000005516 engineering processes Methods 0.000 description 1
Classifications

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06Q—DATA 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
 G06Q10/00—Administration; Management
 G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
Abstract
Description
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 longterm sequence data, it ensure that noctilucence remotelysensed data has comparability between interregional, year border.Just because of this, DMSP/OLS noctilucence remotelysensed 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 highresolution 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 citylevel 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 socioeconomic indicator SEI in regional area some years_{Local}；
Step 3, based on linear concordance it is assumed that judging nighttime light intensity value total amount SOL of global area_{Global}With office Nighttime light intensity value total amount SOL in portion region_{Local}Between, and regional area nighttime light intensity value total amount SOL_{Loca} Socioeconomic indicator SEI with regional area_{Local}Between, if there is the linear dependence meeting preconditioned threshold value, including Following substep,
Step 3.1, judges SOL_{Global}With SOL_{Local}Between with the presence or absence of meeting the linear dependence of preconditioned threshold value, Correlation coefficient computing formula is as follows,
Wherein, (SOL_{Local})_{i}It is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SOL_{Global})_{i}It 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 SOL_{Local}With SEI_{Local}Between with the presence or absence of meeting the linear dependence of preconditioned threshold value, Correlation coefficient computing formula is as follows,
Wherein, (SOL_{Local})_{i}It is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SEI_{Local})_{i}It is 1 year socioeconomic 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 substep,
Step 4.1, set up following ideal model go forward side by side line parameter estimate,
SEI_{Local}=A+B × SOL_{Global}
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,
SEI_{Local}=A+B × SOL_{Global}+ ζ, ζ～N (0, σ^{2})
In formula, ζ is overall random error after disturbance, N (0, σ^{2}) 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 Socioeconomic indicator predict the outcome, complete segmental society's economic dynamic development and estimate.
And, preconditioned threshold value is 0.8, when 0.8 ＜  ρ ≤1 it is believed that SOL_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Height correlation.
And, in step 1 and step 5, the realization extracting nighttime light intensity value total amount includes following substep,
Step 1.1, the Geographical projections coordinate system of DMSP/OLS noctilucence remotelysensed 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, DN_{i}Represent the brightness value of the pixel of the ith grade in region, M_{i}Represent in region The pixel sum of the ith brightness degree, i is from 0 to n1.
And, step 5 gained predicts the outcome, and the forecast interval of its 1 α is：
Wherein, α is significance level, SOL_{0}For meeting the SOL of the global area of related condition, that is, to be predicted year Global area nighttime light intensity value total amount, SEI_{0}It is SOL=SOL_{0}The observation at place, represents the regional area in year to be predicted Socioeconomic indicator；For SEI_{0}Estimation, represent to be predicted year regional area socioeconomic indicator prediction knot Really, δ (SOL_{0}) 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 socioeconomic indicator SEI in regional area some years_{Local}；
Three module, for based on linear concordance it is assumed that judging the nighttime light intensity value total amount of global area SOL_{Global}Nighttime light intensity value total amount SOL with regional area_{Local}Between, and regional area nighttime light intensity Value total amount SOL_{Loca}Socioeconomic indicator SEI with regional area_{Local}Between, if exist and meet the linear of preconditioned threshold value Dependency, including with lower unit,
Unit 3.1, for judging SOL_{Global}With SOL_{Local}Between with the presence or absence of meeting the linear correlation of preconditioned threshold value Property, correlation coefficient computing formula is as follows,
Wherein, (SOL_{Local})_{i}It is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SOL_{Global})_{i}It 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 SOL_{Local}With SEI_{Local}Between with the presence or absence of meeting the linear correlation of preconditioned threshold value Property, correlation coefficient computing formula is as follows,
Wherein, (SOL_{Local})_{i}It is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SEI_{Local})_{i}It is 1 year socioeconomic 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,
SEI_{Local}=A+B × SOL_{Global}
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,
SEI_{Local}=A+B × SOL_{Global}+ ζ, ζ～N (0, σ^{2})
In formula, ζ is overall random error after disturbance, N (0, σ^{2}) 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 socioeconomic indicator of regional area predict the outcome, complete segmental society's economic dynamic development and estimate.
And, preconditioned threshold value is 0.8, when 0.8 ＜  ρ ≤1 it is believed that SOL_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Height 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 remotelysensed 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, DN_{i}Represent the brightness value of the pixel of the ith grade in region, M_{i}Represent in region The pixel sum of the ith brightness degree, i is from 0 to n1.
And, the 5th module gained predicts the outcome, and the forecast interval of its 1 α is：
Wherein, α is significance level, SOL_{0}For meeting the SOL of the global area of related condition, that is, to be predicted year Global area nighttime light intensity value total amount, SEI_{0}It is SOL=SOL_{0}The observation at place, represents the regional area in year to be predicted Socioeconomic indicator；For SEI_{0}Estimation, represent to be predicted year regional area socioeconomic indicator prediction knot Really, δ (SOL_{0}) 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 remotelysensed 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 socioeconomic 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 SOL_{Hubei}To Wuhan City GDP_{Wuhan}Linear 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 invention_{Wuhan}With SOL_{Hubei}Forecast interval comparison schematic diagram, figure 4a is GDP before rejecting abnormalities value_{Wuhan}With SOL_{Hubei}Forecast interval schematic diagram, Fig. 4 b is GDP after rejecting abnormalities value_{Wuhan}With SOL_{Hubei}Forecast 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 socioeconomic indicator (SocioEconomic Indicators, SEI), the nighttime light intensity value total amount of global area is designated as SOL_{Global}, in corresponding experimental example it is SOL_{Hubei}；The nighttime light intensity value total amount of regional area is designated as SOL_{Local}, it is SOL in corresponding experimental example_{Wuhan}；Partial zones The socioeconomic indicator in domain is designated as SEI_{Local}, it is GDP in corresponding experimental example_{Wuhan}.
The nighttime light intensity value of embodiment derives from DMSP/OLS noctilucence remotelysensed 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 substep,
Step 1.1, the Geographical projections coordinate system of DMSP/OLS noctilucence remotelysensed 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 grayscale maps, therefore its gray level is 26 powers, i.e. 64 grades.DN_{i}Represent the brightness value of the pixel of the ith grade in region, M_{i}Represent the ith brightness degree in region Pixel sum, from 0 to n1, 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 socioeconomic conditions index (SocioEconomic 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 socioeconomic indicators SEI_{Local}；
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 socioeconomic indicator GDP, as shown in table 1 below,
Table 1 Hubei Province SOL (SOL_{Hubei}) and Wuhan City GDP (GDP_{Wuhan}) (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 (SOL_{Global}) and regional area SOL (SOL_{Local}) between, and the SOL of regional area (SOL_{Local}) and regional area socioeconomic indicator (SocioEconomic Indicators, SEI) (SEI_{Local}) between, it is No have stronger linear dependence, including following substep,
Step 3.1, judges SOL_{Global}With SOL_{Local}Between calculate with the presence or absence of stronger linear dependence, correlation coefficient Formula is as follows：
In formula (2), (SOL_{Local})_{i}It 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 N_{Local})_{i}It is averaged and obtain, (SOL_{Global})_{i}It 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 SOL_{Local}With SEI_{Local}Between calculate with the presence or absence of stronger linear dependence, correlation coefficient Formula is as follows：
In formula (3), SOL_{Local}It is the night lights total amount of regional area, (SOL_{Local})_{i}It is 1 year night in regional area Light total amount,It is the average of regional area night lights total amount, (SEI_{Local})_{i}It is 1 year social economy in regional area Desired value,It is the expectation (i.e. arithmetic average) of socioeconomic indicator value in regional area.
The embodiment of the present invention proposes, and during  ρ =0, shows SOL_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Completely not Related；It is believed that SOL during 0 ＜  ρ  ＜ 0.3_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Uncorrelated；0.3 ＜  ρ ≤0.5 When it is believed that SOL_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Lower correlation；It is believed that SOL during 0.5 ＜  ρ ≤0.8_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Significantly correlated；It is believed that SOL during 0.8 ＜  ρ ≤1_{Global}With SOL_{Local}Or SOL_{Local}With SEI_{Local}Height 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 SOL_{Hubei}With SOL_{Wuhan}And SOL_{Wuhan} With GDP_{Wuhan}Correlation 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 substep,
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 SOL_{Global}With SOL_{Local}Between and SOL_{Local}With SEI_{Local}Between there is stronger dependency, can build Regression model is as follows：
SOL_{Local}=a_{DN}+b_{DN}×SOL_{Global}(4)
SEI_{Local}=a_{Local}+b_{Local}×SOL_{Local}(5)
In formula, SOL_{Global}Represent the SOL, SOL of global area_{Local}Represent the SOL, SEI in regional area_{Local}Expression office SEI in portion region, a_{DN}With b_{DN}, a_{Local}With b_{Local}It is coefficient.
Formula (4) is substituted in formula (5), obtains SEI_{Local}:
SEI_{Local}=a_{Local}+b_{Local}×(a_{DN}+b_{DN}×SOL_{Global}) (6)
Further, make A=a_{Local}+b_{Local}×a_{DN}, B=b_{Local}×b_{DN}, then obtain ideal model as follows：
SEI_{Local}=A+B × SOL_{Global}(7)
Wherein, A and B is the model parameter estimated.
SEI_{Local}With SOL_{Global}And SEI_{Local}With SOL_{Local}Dependency 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 (SOL_{Hubei}) and Wuhan City GDP (GDP_{Wuhan}) 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：
SEI_{Local}=A+B × SOL_{Global}+ ζ, ζ～N (0, σ^{2}) (10)
In formula, SEI_{Local}It is the SEI of regional area, SOL_{Global}It is the SOL of global area, A and B is parameter to be estimated, ζ is Overall random error after disturbance, N (0, σ^{2}) 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, SEI_{Local}And SOL_{Local}Expression formula can be write as：
In formula (11) and formula (12), i is for indicating ith observation data, and m, n are the denominator of slope, and e', f' are to cut Away from ω_{i}'、μ_{i}'、η_{i}'、λ_{i}' it is the Disturbance being respectively added in slope and intercept, Normal Distribution, SEI_{Local}With SOL_{Global}Expression 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：
SEI_{Local}=A+B × SOL_{Global}+ζ (14)
In formula (14), SEI_{Local}It is the SEI of regional area, SOL_{Global}It 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 SEI_{Local}, x correspondence SOL_{Global}.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 R^{2}=0.6453, the regression model y=3977.7251 after rejecting abnormalities value + 0.013 × x (as shown in Figure 3 b), coefficient of determination R^{2}=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 Socioeconomic 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 socioeconomic 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, SOL_{0}For 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, SEI_{0}It is SOL=SOL_{0}The observation at place, i.e. the socioeconomic indicator of the regional area in year to be predicted；For SEI_{0}Estimation, that is, the socioeconomic indicator of regional area in year to be predicted predict the outcome；δ(SOL_{0}) 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, SOL_{i}For the global area nighttime light intensity value total amount of 1 year in historical data, degree of freedom For N2, 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 value_{min}Uncertain interval at=372019 is (2376.96,5512.57), and amplitude of variation is 7889.53, x_{max}Uncertain interval at=953738 is (1749.74,9516.04), and amplitude of variation is 7766.3, x_{mean} Uncertain interval at=591793 is (47.7678,7477.54), and amplitude of variation is 7429.77；After rejecting abnormalities value x_{min}Uncertain interval at=372019 is (2299.77,3432.44), and amplitude of variation is 5732.21, x_{max}=953738 The uncertain interval of place is (3518.76,9283.2), and amplitude of variation is 5764.44, x_{mean}Uncertainty 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 socioeconomic indicator SEI in regional area some years_{Local}；
Three module, for based on linear concordance it is assumed that judging the nighttime light intensity value total amount of global area SOL_{Global}Nighttime light intensity value total amount SOL with regional area_{Local}Between, and regional area nighttime light intensity Value total amount SOL_{Loca}Socioeconomic indicator SEI with regional area_{Local}Between, if exist and meet the linear of preconditioned threshold value Dependency, including with lower unit,
Unit 3.1, for judging SOL_{Global}With SOL_{Local}Between with the presence or absence of meeting the linear correlation of preconditioned threshold value Property, correlation coefficient computing formula is as follows,
Wherein, (SOL_{Local})_{i}It is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SOL_{Global})_{i}It 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 SOL_{Local}With SEI_{Local}Between with the presence or absence of meeting the linear correlation of preconditioned threshold value Property, correlation coefficient computing formula is as follows,
Wherein, (SOL_{Local})_{i}It is 1 year night lights total amount in regional area,It is that regional area night lights are total The average of amount, (SEI_{Local})_{i}It is 1 year socioeconomic 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,
SEI_{Local}=A+B × SOL_{Global}
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,
SEI_{Local}=A+B × SOL_{Global}+ ζ, ζ～N (0, σ^{2})
In formula, ζ is overall random error after disturbance, N (0, σ^{2}) 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 socioeconomic 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.
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