Spatialization Dataset of the GDP for the Yunnan
border area from the years 1992 to 2013
Lu, X.1,2 Li, J.1* Duan, P.1 Li, C.1 Cheng, F.1 Wang, J. l.1
1. College of Tourism and Geographical Sciences, Yunnan
Normal University, Kunming, 650500, China;
2. Key Laboratory of Virtual Geographic Environment and
Ministry of Education, Nanjing Normal University, Nanjing, 210023, China
Abstract: The
Yunnan border area is located on the southwestern border of Yunnan Province. It
is located in the economic corridors of Myanmar, India, China, and Bangladesh
and has an important geographical position. Therefore, it is critical to
implement the spatial fitting of the Gross Domestic Product (GDP) data in the
Yunnan border area with high precision. In this study, the Yunnan border area was used as the
research area, and DMSP/OLS nighttime light data, land use data, and Yunnan
provincial statistical data were used as data sources to implement the spatial
fitting of the GDP data for the research area from the years 1992 to 2013.
Saturation correction, mutual correction, annual fusion, interannual
correction, reprojection, resampling, and clipping were performed on nighttime
light data. Spatialization of the primary industry based on land use data was
implemented. Based on nighttime light data, a "classification
regression" method was used to implement the
spatialization of the secondary and tertiary industries to obtain the
spatialization of the GDP and verify the fitting results. The results show that
the error of the primary industry fitting value of all periods is 1.12% at the
maximum, and the fitting error is small. The relative error of the fitting of
the secondary and tertiary industries after classification regression is less
than 6.40% in each period, and the relative fitting error of the final GDP of
each period is less than 4.24%, with high accuracy. The dataset is stored in
the .tif file format. The spatial resolution of a single file is 1 km, and
there were a total of 22 groups of files; the amount of the data volume was
68.6 MB (when compressed to 1 file, it was 3.61 MB). The research results of
this dataset are published in the Journal of Geo-Information Science, Vol. 21,
No. 3, 2019 and Areal Research and Development, Vol. 39, No. 2, 2020.
Keywords: GDP spatialization; Yunnan border area;
Nighttime light data; Land use data; Geo-Information Science
1 Introduction
The border areas of
Yunnan are located in the economic corridors of China, India, Myanmar, and
Bangladesh. They serve as a bridge and link between China, South Asia and the
Southeast Asian countries for material, cultural, economic and trade exchanges.
Therefore, this geographical location is very important for China’s
development. The Gross Domestic Product (GDP) is an evaluation indicator that
measures a region’s economic development, gauges urban development and reflects
regional planning [1]. The traditional GDP statistical data is
limited by separations in administrative units, so it is difficult to obtain
very precise economic data that show the differences within the administrative
units, and it is impossible to conduct a comprehensive analysis in combination
with raster data such as ecological environment [2] data. Through
the spatial fitting of the GDP data, the above problems can be solved. With the
continuous progress and development of remote sensing technology, remote
sensing data such as nighttime light data and land use data are widely used in
the spatial fitting of GDP data. The DMSP/OLS nighttime light data acquired by
the US Defense Military Meteorological Satellite has been applied since the
1970s [3]. The data has strong photoelectric amplification
capabilities, can intuitively reflect human activities, and has the advantages
of extended time spans and wide spatial coverages. After continuous improvement
and updating, it has been applied to many aspects of development planning such
as urbanization monitoring, population analysis, energy consumption and GDP
estimation. Therefore, based on the DMSP/OLS nighttime light data, combined
with land use data and statistical data, this study constructed a spatial
dataset of the GDP in the Yunnan border area from the years 1992 to 2013. This
dataset reflects the economic differences within the administrative divisions
of the Yunnan border area and can be comprehensively analyzed with raster image
data such as natural ecological environment data.
2 Metadata of Dataset
The metadata of the
dataset [4] is summarized in Table 1. It includes the dataset’s full
name, short name, authors, geographical area, calendar years, temporal
resolution, spatial resolution, data format, data size, data files, data
publisher, and data sharing policy.
Table
1 Metadata summary of
“Dataset of spatialization of GDP of the Yunnan border area from 1992 to 2013”
Items
|
Description
|
Dataset full name
|
Dataset of spatialization of the GDP of the Yunnan
border area from 1992 to 2013
|
Dataset short name
|
YunnanBorderGDP1992-2013
|
Authors
|
Lu, X. AAS-6714-2020, College of Tourism and Geographical Sciences,
Yunnan Normal University, lx_rsgis@163.com
Li, J. AAS-6000-2020, College of Tourism and Geographical Sciences,
Yunnan Normal University, keguigiser@163.com
Duan, P. College of Tourism and Geographical Sciences, Yunnan Normal
University, duanpingshai@163.com
Li, C. College of Tourism and Geographical Sciences, Yunnan Normal University,
lichen924541412@163.com
Cheng, F. College of Tourism and Geographical Sciences, Yunnan Normal University,
chengfeng_rs@163.com
Wang, J. L. College of Tourism and Geographical Sciences, Yunnan Normal
University, jlwang@ynnu.edu.cn
|
Geographical region
|
Yunnan border area, including eight prefectures including Baoshan City,
Honghe Hanizuyizu Autonomous Prefecture, Lincang City, Dehong Daizujingpozu Autonomous
Prefecture, Nujiang Lisuzu Autonomous Prefecture, Xishuangbanna Daizu
Autonomous Prefecture, Pu’er City and Wenshan City
|
Year
|
1992-2013
|
(continued)
Items
|
Description
|
Temporal resolution
|
Year
|
Spatial resolution
|
1km
|
Data format
|
.tif
|
Data size
|
68.6 MB (before compression), 3.61 MB (after compression)
|
Data files
|
1992 GDP spatialization Product data - 2013 GDP spatialization Product
data
|
Foundation(s)
|
National Key Research and Development Program of China (SQ2018YFE011725);
National Natural Science Foundation of China (41561048)
|
Computing environment
|
Arcgis
|
Data publisher
|
Global Change Research Data Publishing & Repository,
http://www.geodoi.ac.cn
|
Address
|
No. 11A, Datun Road, Chaoyang District, Beijing 100101, China
|
Data sharing policy
|
Data from the Global Change Research Data Publishing & Repository includes
metadata, datasets (data products), and publications (in this case, in the
Journal of Global Change Data & Discovery). Data sharing policy includes:
(1) Data are openly available and can be free downloaded via the Internet;
(2) End users are encouraged to use Data subject to citation; (3) Users, who
are by definition also value-added service providers, are welcome to
redistribute Data subject to written permission from the GCdataPR Editorial
Office and the issuance of a Data redistribution license; and (4) If Data are
used to compile new datasets, the ‘ten percent principal’ should be followed
such that Data records utilized should not surpass 10% of the new dataset
contents, while sources should be clearly noted in suitable places in the new
dataset[5]
|
Communication
and searchable system
|
DOI, DCI, CSCD, WDS/ISC, GEOSS, China GEOSS
|
3 Methods
3.1 Introduction and preprocessing of raw data
The DMSP/OLS
nighttime light data includes three kinds of image data products, which are
Average Visible, Stable Lights, & Cloud Free Coverages data, Average Lights
X Pct data, and Global Radiance Calibrated Nighttime Lights data. Average
Visible, Stable Lights, & Cloud Free Coverages data includes three data
types, namely, average visible light image, stable light image, and cloud free
coverages data. The data used in this study is the stable light image. The
phase is from the years 1992-2013, the DN value range is 0-63, and the pixel
size is 0.008333°.
Because the sensor of these data product
has not been calibrated on the satellite, the different light images obtained
by each sensor lacks continuity and comparability, and a saturation of the
pixel DN value exists in the central area of the city [6-7].
Therefore, this study combines the methods of Elvidge, Wu, Liu, etc. [8-10]
to perform saturation correction, mutual correction, annual fusion, interannual
correction, reprojection for the Albers projection, resampling for 1000 meters,
and clipping of the nighttime light data. Calibration steps: (1) Do a mutual
calibration and saturation calibration. Perform mutual correction and
saturation correction on the image to be corrected, from the years 1992-2013,
and the data of the 2006 F16 sensor in the data from the radiation calibration
product. The reference area is Jixi City, Heilongjiang Province. The
calibration model is:
(1)
where DN is the brightness value before the
correction, a,b,c are regression
coefficients, and DNcorrect is the
corrected DN value.
(2) Annual
fusion. The data obtained by different sensors in the same year is
inconsistent, and some of the images corrected after step (1) are fused within
the year according to the following formula (2).
(2)
where
and
represent the DN value of the i-pixel acquired by two different sensors in the n year after the mutual calibration and
the saturation correction, respectively, and DN(n,i) represents the DN of the i-pixel after the n-year
fusion and correction of the image within the year.
(3) Interannual
correction. After the fusion within the year, there is still the phenomenon of
incomparable images between different years.
The correction formula is given in formula (3).
(3)
In
the formula, DN(n-1,i),
DN(n,i), DN(n+1,i),
respectively represent the DN values of the image pixel i in n-1 year, n year, n+1 year.
3.2 Algorithm principle
The
correlation between the primary industry and the land use data for the fields
of cultivated land, forest, grass and water is high [11]. Therefore,
the agricultural, forestry, animal husbandry, and fishery output value data in
the primary industry are modeled respectively with the corresponding land types
in the land use data. The modeling formula is as follows:
(4)
In
formula (4), GDP1ij represents
the output value of the first industry in the j grid of the i
administrative region; Gkij
represents the four kinds of output values for k = 1-4, respectively corresponding to the
serial numbers of agricultural, forestry, animal husbandry and fishery output
values in the primary industry.
The brightness
of the DMSP/OLS nighttime light data has a certain correlation with the
secondary and tertiary industries, and the distribution of different light
brightness values of night lights can be used to fit the secondary and tertiary
industries. The fitting model is as follows:
(5)
GDP23
represents the fitting value of the secondary and tertiary industries,
and a is the
fitting coefficient. Where SOL stands for:
(6)
where Nm represents the number of pixels whose brightness
value is m, and Bm represents the brightness value of the pixel itself.
Based on the
nighttime light index, the error between the fitting results of the global
modeling of the secondary and tertiary industries and the statistical data is
large. Therefore, in order to reduce the deficiency of the global modeling the
study uses the concept of "classification regression", that is,
adding the relative error to the fitting model for the global modeling; the
formula is as follows:
(7)
GDPS is the statistical value of
the secondary and tertiary industries in the study area. The specific process
of "classification regression" modeling is:
1. According to
the initial fitting equation, filter out the counties with |δ|<25% to form
the first equation. These counties are fitted with the first equation.
2. Divide the
counties where |δ|>25% into two parts, where δ>25%, rebuild the fitting
model’s overestimated section, and in counties where δ<-25% rebuild the
fitting model’s underestimated section;
3. After building
the model through the above two steps, if there still exist counties with
|δ|>25%, and if the number is greater than 10% of the total fitting model
number, then continue to partition the model until the fitting equation is
|δ|<25% or the number of the remaining fitting model is less than 10%; then
the modeling is terminated.
The primary
industry model fitting results obtained above, and the
secondary and tertiary industries model fitting results are overlaid to obtain
the GDP fitting results. The obtained fitting results still will have large
fitting errors in some counties. Therefore, a linear correction within the
county area is performed on the obtained GDP fitting results. The correction
model is as follows (8):
(8)
In the formula, GDPcorrect represents the
corrected GDP raster data, GDPestimated
is the estimated GDP data of each grid, GDP* represents the statistical GDP
data of the county, and GDPall
is the estimated GDP data of the corresponding county.
4 Results and validation
4.1 Data Composition
The composition of
the GDP datasets in the Yunnan border area is shown in Table 2, and the spatial
distribution is shown in Figure 1[12-13].
4.2 Data results
From the years 1992 to 2013, the spatialization distribution of the GDP
in the Yunnan border area is shown in Figure 1. From the years 1992 to 2004,
the regions with a higher GDP and
Table 2 The composition of the GDP dataset of
Yunnan border area
Dataset name
|
Dataset time
|
Spatial
resolution
|
GDP dataset products in Yunnan border area
|
1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013
|
1km
|


Figure 1 Visualization of the spatial
distribution of GDP in Yunnan border area from 1992 to 2013
better economic development
were mainly distributed in the eastern region of the border area of the
Yunnan - Honghe Hanizuyizu Autonomous Prefecture with scattered distributions
in the western part area. Since 2005, the economic development of Baoshan City,
Dehong Daizujingpozu Autonomous Prefecture and Xishuangbanna Daizu Autonomous Prefecture has gradually improved,
while the economic development of Lincang City and Pu'er City in the central
region has lagged behind other regions. After 2010, the high-value pixel area
of the GDP gradually spread throughout the border areas, and the economic development
level of each prefecture gradually increased. In terms of counties, counties
with high GDP values such as Mile county, Gejiu
city, and Kaiyuan city have always been at the forefront of economic
development in the border regions. In particular, Mile county
has been ranked first in GDP in the border areas since 2001. After 2001,
Jinghong and Baoshan, Tengchong, Jianshui, and Mengzi developed gradually. The
GDP pixel value increased year by year, and the high-value pixel area gradually
spread out from the center of the city. The low value GDP pixel areas are
mainly concentrated in counties such as Gongshan, Fugong, Ximeng and Lianghe,
and the economic development is slow for these areas.
4.3
Validation of data results
The error between the fitting
value of the primary industry, the fitting value of the secondary and tertiary
industries, the fitting value of the GDP and the corresponding GDP statistics
can be compared and analyzed. The error analysis results show that: the
relative error of the primary industry fitting value of the fitting data in
each period can reach 0.09%, at minimum, and the maximum does not exceed 1.12%.
The error is small and the fitting accuracy is high. It shows that the primary
industry is feasible according to the land use data and the precision is high.
The maximum error of the unclassified regression fitting of the secondary and
tertiary industries is -29.09%, and the
relative error of the fitting after classification regression is only -6.40%, and the minimum can
reach -0.40%. It shows
that the classification regression fitting method greatly improves the fitting
accuracy of the secondary and tertiary industries. The relative error of the
final GDP fitting is only -4.24% at the
maximum, with a higher precision and better fitting accuracy. The fitting
results can be used for subsequent data calculations, analysis and other
applications.
5 Discussion and Conclusion
This research is based on the use of land use data
to fit the GDP of the primary industry by using the DMSP/OLS nighttime light
data and a classification regression fitting method to fit the secondary and
tertiary industries. The fitting period is from the years 1992 to 2013. By
verifying the accuracy of the fitting results, the results show that the
fitting accuracy of each industry is high. The relative error of the fittings
in the primary industry is not higher than 1.12%, the relative error of the
fittings in the secondary and tertiary industries are not more than 6.40%, and
the relative error of the final GDP fitting is only -4.24%, and the fitting effect is good.
Additionally, the inversion accuracy is high. These data can be further used
for the analysis of economic differences within administrative divisions and
may be combined with other raster image data for spatial calculations and
analysis.
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