CN115293974A - GDP estimation method based on big data combined with night light remote sensing image - Google Patents
GDP estimation method based on big data combined with night light remote sensing image Download PDFInfo
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Abstract
The invention belongs to the technical field of remote sensing images, and discloses a GDP estimation method based on big data combined with night light remote sensing images, which comprises the following specific steps: s1, extracting a light value from night light remote sensing image data; s1.1, obtaining night light remote sensing data; s1.2, clipping is carried out by using vector diagrams of administrative regions of all levels. The data availability of the invention is strong, the data required by the invention are only open remote sensing images and public vector diagrams of all levels of administrative regions and statistical communique data, and the credibility of the data source is high; according to the method, the GDP value of the year without statistical data can be reasonably estimated according to the fitting model and the night light remote sensing image; the method has high accuracy, can accurately acquire the real GDP value, is beneficial to the subsequent research of economic problems, and provides reliable data support for the decision making of city managers; the invention has higher implementation efficiency, can automatically acquire various data through a big data remote sensing technology, has less manual intervention and higher overall efficiency.
Description
Technical Field
The invention belongs to the technical field of remote sensing images, and particularly relates to a GDP estimation method based on big data combined with night light remote sensing images.
Background
Currently, how to accurately measure the urban development level, effective statistics of GDP has important significance. Chen &. Nordhaus (2011) and Henderson et al (2012) propose that combining official GDP statistics with night light brightness can result in a more accurate estimate of true GDP. An estimation equation is constructed by using the night light value to GDP, data are conveniently acquired by adopting a scientific mode of combining remote sensing with big data, and the method can be used as a decision basis of an urban manager and has great significance in solving urban problems.
In the last decade, due to the rapid development of data sharing policies and sensors, night-light remote sensing has become one of the hot branches of the remote sensing field. Noctilucent remote sensing has been widely applied in the research fields of humanistic geography, regional economy, geopolitics and the like, and even the financial industry begins to utilize noctilucent remote sensing data. Due to abundant data and low cost, satellite remote sensing is the mainstream means of noctilucent remote sensing. The remote sensing method is one of the most effective and widely used means for mapping and analyzing the socio-economic development status from a spatial perspective. The remote sensing image has the advantages of wide coverage, high efficiency, strong objectivity and the like, provides massive spatial information for human beings, and has rich application in the aspects of built-up area extraction, land extension detection, land utilization mapping, land coverage change analysis, urban landscape structure detection and urban spatial structure analysis. Among these research directions, estimation of total domestic product (GDP) using directly detected long-time sequence night lights is the most representative development direction for measuring human social activities.
On the data source, the DMSP-OLS data only provides data of 1992-2013, and does not include subsequent years. As a continuation of the DMSP-OLS data, NPP-VIIRS archived night light images of month 4 to date in 2012. The domestic satellite Lopa I is the first major noctilucent remote sensing satellite in the world, and the data of the Lopa I can be adopted for night light extraction in the latest year.
Disclosure of Invention
The invention aims to provide a GDP estimation method based on big data combined with a night light remote sensing image, so as to solve the problems in the background technology.
In order to achieve the above purpose, the invention provides the following technical scheme: a GDP estimation method based on big data combined with night light remote sensing images comprises the following specific steps:
s1, extracting a light value from night light remote sensing image data;
s1.1, acquiring night light remote sensing data;
s1.2, clipping by using vector diagrams of administrative regions at all levels;
s1.3, preprocessing the night lamplight remote sensing image;
s1.4, extracting a light value;
s2, crawling each-level administrative region statistical bulletin by using a big data technology, and matching and reducing GDP data;
s2.1, crawling current-year GDP values in each-level administrative area statistical bulletin;
s2.2, administrative region matching;
s2.3, performing respective-reduction and flattening processing on the matched GDP value;
s3, fitting and estimating the reduced GDP by using the light value data to obtain a GDP true value;
s3.1 fitting and estimating a night light data value and a GDP;
s3.2 solving the real GDP value.
Preferably, step S1.1 is to obtain the night light remote sensing data, download DMSP/OLS and NPP-VIIRS satellites, and domestic jialo a satellite monthly remote sensing data from the night light remote sensing map published by nasa, and synthesize the annual data with the grid calculator in ArcGIS.
Preferably, the night light remote sensing data obtained in step S1.1 is clipped, so that vector data of a research object is required, the vector data is in a shape format, the research object is selected to be a national province-city-county three-level administrative district, and the national three-level vector map data can be obtained from a resource ring center of a Chinese academy.
Preferably, the clipped night light remote sensing data acquired in step S1.2 is preprocessed:
(1) orthorectification:
inputting an image to be orthorectified in an Input scanner; inputting a Reference image into an Input Reference predictor; inputting a terrain data resampling method required by orthorectification in an Input DEM (digital elevation model) master to select a nearest neighbor method;
(2) radiation calibration:
selecting Radiometric Calibration in a Toolbox for Radiometric Calibration, and selecting cut data in File Selection;
(3) and (3) atmospheric correction:
opening a FLAASH Atmospheric Correction tool in a Toolbox, and inputting a series of parameters;
(4) projection conversion
And selecting a projection grid tool to directly convert according to the layers.
Preferably, the light value of the image preprocessed in the step S1.3 is extracted, and the average light value and the total light intensity counted by the subareas are displayed in a table;
s1.3, carrying out pretreatment, wherein the pretreatment step comprises the following steps: orthorectification, radiometric calibration, atmospheric rectification, gaussian filtering, data enhancement, etc.
S1.3.1 orthorectification: inputting an image to be orthorectified, inputting a reference image, inputting topographic data required by orthorectified, and selecting a nearest neighbor method by a resampling method;
s1.3.2 radiation calibration: carrying out radiometric calibration on the image, and converting the DN value of the image into radiance;
s1.3.3 atmospheric correction: converting the radiance of the image into an expression reflectivity, reading parameters from a metadata file, and executing atmospheric correction;
s1.3.4 gaussian filtering: carrying out weighted average on the whole image, and substituting the weighted average gray value of the pixel in the neighborhood determined by convolution with each pixel in the convolution scanning image for the value of the central pixel point of the template;
s1.3.5 data enhancement: the method comprises the steps of improving the contrast and the image definition of an object and a non-object in an image by using a space domain method based on linear stretching;
s1.4, extracting a light value, extracting the light value from the image preprocessed in the step S1.3, and displaying the average light value and the total light intensity of the subarea statistics by a table.
Preferably, in the step S2, the GDP year data in each administrative area statistical bulletin is crawled by a big data retrieval module in combination with each administrative area table batch web page, and the year can be updated to the latest year of the statistical bulletin.
Preferably, in the step S2, the data crawled from the statistical bulletin are sorted and matched, and meanwhile, the GDP indexes of the past years of each province are prepared to determine the base year.
Preferably, the GDP data crawled in step S2.1 is subtracted in step S2 in combination with the yearly published GDP indices of the provinces.
Preferably, fitting is performed by the night light data obtained in step S1.3 and the subtracted GDP data obtained in S2.3, and the formula is as follows:
model 1 (multiple linear regression model):
y＝a 2 x 2 +a 1 x 1 +a 0 x 0
converting the unary linear regression equation into a multiple linear regression model:
wherein:
model 2 (multiple logistic regression model):
y＝EXP(b 1 x 2 +b 2 x 1 +b 0 x 0 )
substituting the data GDP in the step S2.3 as y, substituting the average light value and the total light value extracted by remote sensing in the step S1.3 as x into the model, fitting to obtain coefficients a and b, and obtaining the fitting model.
Preferably, step S3.2 continues to repeat the preceding steps with night light data for all years, including the latest year, the night light data for all the existing years measuring the true GDP value.
The invention has the following beneficial effects:
the data availability of the invention is strong, the data required by the invention are only open remote sensing images and public vector diagrams of all levels of administrative regions and statistical communique data, and the credibility of the data source is high;
according to the method, the GDP value of the year without statistical data can be reasonably estimated according to the fitting model and the night light remote sensing image;
the method has high accuracy, can accurately acquire the real GDP value, is beneficial to the subsequent research of economic problems, and provides reliable data support for the decision making of city managers;
the invention has higher implementation efficiency, can automatically acquire various data through a big data remote sensing technology, has less manual intervention and higher overall efficiency.
Drawings
FIG. 1 is a flow chart of an estimation method according to the present invention;
FIG. 2 is a table of night light data extraction results according to the present invention;
FIG. 3 is a graph of the fitting effect of the present invention;
fig. 4 is a diagram illustrating the operation of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to a GDP estimation method based on big data combined with night light remote sensing images, which mainly comprises the following steps:
1. extracting a light value from the night light remote sensing image data;
2. crawling each level of administrative region statistical bulletin by a big data technology, and matching and reducing GDP data;
3. fitting and estimating the GDP subjected to the reduction by using the light value data to obtain a GDP true value;
the specific implementation flow of the present invention is shown in fig. 1, and the specific implementation details of each part are as follows:
1. extracting a light value from the night light remote sensing image data:
the data sources mainly comprise three satellites, namely a DMSP/OLS satellite, an NPP-VIIRS satellite and a Jialoyi satellite, and the downloading website comprises the following parts: DMSP/OLS (http:// www.ngdc.noaa.gov/DMSP/downloa d. Html); NPP-VIIRS (https:// ngdc.noaa.gov/eog/viii rs/download _ dnb _ comp sites.html); gama Luo No. one (http:// 59.175.109.173
Cutting the downloaded image by using a Chinese administrative division vector diagram, and synthesizing monthly data into annual data by using a grid calculator in ArcGIS;
and preprocessing the night light remote sensing data, including orthorectification, radiometric calibration, atmospheric correction, projection conversion and the like.
Extracting the night light value, wherein the extraction result is shown in figure 2;
and (4) crawling each level of administrative region statistical bulletin by utilizing software big data such as Python and the like, and extracting GDP values.
Using stata.17 software to reduce the GDP value and fitting the equation, the fitting graphs of equation 1 and equation 2 are shown in fig. 3, equation 2 is shown as a whole R 2 0.84, time series fitting inter-group R 2 At 0.948, fitting equation 2 works well, showing only the results of equation 2, which are shown below.
According to the results, the fitting formula is as follows
Y i ＝exp[(-5.06e-16)L i 2 +(1.12e-7)L i ]Equation 2
When the formula 2 is viewed as a whole, the effect is good, and the formula 2 can be used for estimating the area with the night light data but without the GDP data.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A GDP estimation method based on big data combined with night light remote sensing images is characterized by comprising the following specific steps:
s1, extracting a light value from night light remote sensing image data;
s1.1, obtaining night light remote sensing data;
s1.2, clipping by using vector diagrams of administrative regions at all levels;
s1.3, preprocessing the night lamplight remote sensing image;
s1.4, extracting a light value;
s2, crawling each-level administrative region statistical bulletin by using a big data technology, and matching and reducing GDP data;
s2.1, crawling current-year GDP values in each-level administrative area statistical bulletin;
s2.2, administrative region matching;
s2.3, performing respective-reduction and flattening processing on the matched GDP value;
s3, fitting and estimating the reduced GDP by using the light value data to obtain a GDP true value;
s3.1 fitting and estimating a night light data value and a GDP;
s3.2 solving the real GDP value.
2. The GDP estimation method based on big data combined with night light remote sensing images according to claim 1, wherein the GDP estimation method comprises the following steps: step S1.1, night light remote sensing data is obtained, DMSP/OLS and NPP-VIIRS satellites and domestic Jia Luo I satellite monthly remote sensing data are downloaded from a night light remote sensing image published by nasa, and the data are synthesized into annual data by a grid calculator in ArcGIS.
3. The GDP estimation method based on big data combined with night light remote sensing images according to claim 2, wherein: and (2) cutting the night light remote sensing data acquired in the step (S1.1), wherein the vector data of the research object is required to be in a shape file format, the research object is selected to be a national province-city-county three-level administrative district, and the national three-level vector map data can be acquired from a national hospital resource ring center.
4. The GDP estimation method based on big data combined with night light remote sensing images according to claim 1, wherein: preprocessing the clipped night light remote sensing data acquired in the step S1.2:
(1) orthorectification:
inputting an image to be orthorectified in an Input scanner; inputting a Reference image into an Input Reference predictor; inputting a terrain data resampling method required by orthorectification in an Input DEM (digital elevation model) master to select a nearest neighbor method;
(2) radiation calibration:
selecting Radiometric Calibration in a Toolbox for Radiometric Calibration, and selecting cut data in File Selection;
(3) and (3) atmospheric correction:
opening a FLAASH Atmospheric Correction tool in a Toolbox, and inputting a series of parameters;
(4) projection conversion
And selecting a projection grid tool to directly convert according to the layers.
5. The GDP estimation method based on big data combined with night light remote sensing images according to claim 1, wherein: and S1.3, extracting a light value from the image after preprocessing, and displaying the average light value and the total light intensity of the partitioned statistics by a table.
6. The GDP estimation method based on big data combined with night light remote sensing images according to claim 1, wherein: in the step S2, GDP year data in each administrative area statistical bulletin is crawled by combining a big data retrieval module with each administrative area form batch webpage, and the year can be updated to the latest year of the statistical bulletin.
7. The GDP estimation method based on big data combined with night light remote sensing images according to claim 6, wherein the GDP estimation method comprises the following steps: and S2, sorting and matching the data crawled out by the statistical bulletin, preparing the GDP indexes of the past years of each province, and determining the base year.
8. The GDP estimation method based on big data combined with night light remote sensing images according to claim 7, wherein the GDP estimation method comprises the following steps: and in the step S2, the GDP data crawled in the step S2.1 is subtracted by combining the GDP indexes published every year.
9. The GDP estimation method based on big data combined with night light remote sensing images according to claim 5, wherein: fitting is carried out on the night light data obtained in the step S1.3 and the reduced GDP data obtained in the step S2.3, and the formula is as follows:
model 1 (multiple linear regression model):
y＝a 2 x 2 +a 1 x 1 +a 0 x 0
converting the unary linear regression equation into a multiple linear regression model:
wherein:
model 2 (multiple logistic regression model):
y＝EXP(b 1 x 2 +b 2 x 1 +b 0 x 0 )
substituting the data GDP in the step S2.3 as y, substituting the average light value and the total light value extracted by remote sensing in the step S1.3 as x into the model, fitting to obtain coefficients a and b, and obtaining the fitting model.
10. The GDP estimation method based on big data combined with night light remote sensing images according to claim 1, wherein the GDP estimation method comprises the following steps: and S3.2, continuously repeating the steps by using the night light data of all the years including the latest year, and measuring the real GDP value by using the night light data of all the existing years.
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| CN115713691A (en) * | 2022-11-21 | 2023-02-24 | 武汉大学 | Pixel-level electric power popularity estimation method and device based on noctilucent remote sensing |
| CN115713691B (en) * | 2022-11-21 | 2024-01-30 | 武汉大学 | Noctilucent remote sensing-based pixel-level power popularity rate estimation method and device |
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