Journal of Geographical Sciences >
The spatio-temporal heterogeneity of county-level economic development and primary drivers across the Loess Plateau, China
Liu Shijun (1996‒), Master Candidate, specialized in economic geography and regional studies. E-mail: 1324408210@qq.com |
Received date: 2020-03-31
Accepted date: 2020-07-23
Online published: 2021-05-25
Supported by
Shaanxi Provincial Planning Office of Philosophy and Social Science(2019D045)
Copyright
Unbalanced economic growth is a ubiquitous phenomenon while investigating the regional development at a large spatial scale. Therefore, it is of great significance to analyze the spatio-temporal pattern of regional economic growth and the drivers to understand and facilitate the economic development of low development areas. Taking a county as a fundamental study unit, we used the county-level per capita GDP data on the Loess Plateau from 2005 to 2017, and geographic variables such as slope, elevation, and population density to analyze the spatio-temporal differences and the driving factors of the county-level economic development in the Loess Plateau by employing both conventional and advanced quantitative methods including Exploratory Spatial Data Analysis (ESDA) and the geographic detector model. Our results suggested that: (1) The selected indicators, including absolute difference, the fluctuation of relative difference and total difference of economic development on the Loess Plateau, all show steady increasing trends, respectively. (2) There are 64.5% of the counties with economic development being below the average level of the whole Loss Plateau region. The relatively high developed counties are distributed in the “A”-shaped regions in Inner Mongolia Autonomous Region, Shaanxi, and Henan provinces, however, the low development counties are mainly located in the “V”-shaped regions in Gansu and Shanxi provinces. (3) GDP, investment in fixed assets and urbanization rate are the major driving factors influencing the regional economic development, and the combined effects are far greater than that of any individual factor.
Key words: county; economic development difference; driving factor; Loess Plateau
LIU Shijun , XUE Liang . The spatio-temporal heterogeneity of county-level economic development and primary drivers across the Loess Plateau, China[J]. Journal of Geographical Sciences, 2021 , 31(3) : 423 -436 . DOI: 10.1007/s11442-021-1851-y
Figure 1 Location of the Loess Plateau |
Table 1 The county-level economic difference of the Loess Plateau from 2005 to 2017 |
Coefficient of variation | Theil index | Moran's I | Coefficient of skewness | Standard deviation | |
---|---|---|---|---|---|
2005 | 0.8970 | 0.1492 | 0.4022 | 1.9926 | 11555 |
2006 | 0.9119 | 0.1493 | 0.4121 | 2.0377 | 14022 |
2007 | 0.9568 | 0.1545 | 0.4347 | 2.3423 | 18035 |
2008 | 0.9944 | 0.1581 | 0.4785 | 2.5692 | 23413 |
2009 | 1.0682 | 0.1576 | 0.4853 | 3.2159 | 28000 |
2010 | 0.9918 | 0.1418 | 0.4605 | 2.6865 | 30627 |
2011 | 0.9727 | 0.1373 | 0.4937 | 2.6151 | 36325 |
2012 | 0.9874 | 0.1365 | 0.5030 | 2.7705 | 42080 |
2013 | 0.9619 | 0.1270 | 0.5116 | 2.9628 | 43184 |
2014 | 0.9384 | 0.1250 | 0.5027 | 2.8944 | 43484 |
2015 | 0.9486 | 0.1236 | 0.5487 | 3.0198 | 43630 |
2016 | 0.9492 | 0.1189 | 0.5523 | 3.1181 | 45453 |
2017 | 0.9209 | 0.1260 | 0.5370 | 2.8887 | 49112 |
Figure 2 Changes of county-level economic differences on the Loess Plateau from 2005 to 2017 |
Table 2 Change and decomposition of county-level economic differences on the Loess Plateau from 2005 to 2017 |
2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total difference | 0.1492 | 0.1493 | 0.1545 | 0.1581 | 0.1576 | 0.1418 | 0.1373 | 0.1365 | 0.1270 | 0.1250 | 0.1236 | 0.1189 | 0.1260 | |
Shanxi | Difference | 0.0327 | 0.0339 | 0.0334 | 0.0321 | 0.0312 | 0.0292 | 0.0269 | 0.0276 | 0.0257 | 0.0241 | 0.0234 | 0.0227 | 0.0236 |
Proportion | 21.92% | 22.71% | 21.62% | 20.30% | 19.80% | 20.59% | 19.59% | 20.22% | 20.24% | 19.28% | 18.93% | 19.09% | 18.73% | |
Inner Mongolia | Difference | 0.0065 | 0.0068 | 0.0077 | 0.0081 | 0.0088 | 0.0086 | 0.0078 | 0.0077 | 0.0078 | 0.0072 | 0.0066 | 0.0058 | 0.0059 |
Proportion | 4.36% | 4.55% | 4.98% | 5.12% | 5.58% | 6.06% | 5.68% | 5.64% | 6.14% | 5.76% | 5.34% | 4.88% | 4.68% | |
Henan | Difference | 0.0046 | 0.0039 | 0.0063 | 0.0062 | 0.0058 | 0.0061 | 0.0060 | 0.0058 | 0.0055 | 0.0057 | 0.0057 | 0.0050 | 0.0056 |
Proportion | 3.08% | 2.61% | 4.08% | 3.92% | 3.68% | 4.30% | 4.37% | 4.25% | 4.33% | 4.56% | 4.61% | 4.21% | 4.44% | |
Shaanxi | Difference | 0.0346 | 0.0301 | 0.0310 | 0.0313 | 0.0286 | 0.0230 | 0.0251 | 0.0245 | 0.0221 | 0.0214 | 0.0206 | 0.0205 | 0.0220 |
Proportion | 23.19% | 20.16% | 20.06% | 19.80% | 18.15% | 16.22% | 18.28% | 17.95% | 17.40% | 17.12% | 16.67% | 17.24% | 17.46% | |
Gansu | Difference | 0.0293 | 0.0318 | 0.0292 | 0.0289 | 0.0270 | 0.0220 | 0.0230 | 0.0211 | 0.0203 | 0.0186 | 0.0181 | 0.0173 | 0.0207 |
Proportion | 19.64% | 21.30% | 18.90% | 18.28% | 17.13% | 15.51% | 16.75% | 15.46% | 15.98% | 14.88% | 14.64% | 14.55% | 16.43% | |
Qinghai | Difference | 0.0029 | 0.0026 | 0.0027 | 0.0027 | 0.0026 | 0.0036 | 0.0026 | 0.0023 | 0.0021 | 0.0023 | 0.0026 | 0.0025 | 0.0029 |
Proportion | 1.94% | 1.74% | 1.75% | 1.71% | 1.65% | 2.54% | 1.89% | 1.68% | 1.65% | 1.84% | 2.10% | 2.10% | 2.30% | |
Ningxia | Difference | 0.0086 | 0.0091 | 0.0083 | 0.0075 | 0.0073 | 0.0066 | 0.0061 | 0.0059 | 0.0056 | 0.0072 | 0.0053 | 0.0051 | 0.0052 |
Proportion | 5.76% | 6.10% | 5.37% | 4.74% | 4.63% | 4.65% | 4.44% | 4.32% | 4.41% | 5.76% | 4.29% | 4.29% | 4.13% | |
Interregional | Difference | 0.0300 | 0.0312 | 0.0359 | 0.0414 | 0.0461 | 0.0426 | 0.0398 | 0.0416 | 0.0378 | 0.0385 | 0.0413 | 0.0400 | 0.0401 |
Proportion | 20.11% | 20.83% | 23.24% | 26.13% | 29.38% | 30.13% | 29.00% | 30.48% | 29.85% | 30.80% | 33.42% | 33.64% | 31.83% |
Figure 3 Regional decomposition of county-level economic differences on the Loess Plateau from 2005 to 2017 |
Figure 4 Spatial difference of county-level economy on the Loess Plateau in 2005 and 2017 |
Table 3 Driving factors of county-level economic development on the Loess Plateau |
Driving factors | Calculation |
---|---|
Average slope (degree) (X1) | With ArcGIS partition statistics |
Average elevation (m) (X2) | With ArcGIS partition statistics |
Population density (person/km2) (X3) | Resident population/county area |
Urbanization rate (%) (X4) | Urban population/total population |
GDP (ten thousand yuan) (X5) | Statistical yearbook query |
Fixed assets investment of the whole society (ten thousand yuan) (X6) | Statistical yearbook query |
Highway density (km/km2) (X7) | The total length of the county road/the area of the county |
Railway density (km/km2) (X8) | Total length of railways in the county/area of the county |
Figure 5 Explanatory power of driving factors of county- level economic development on the Loess Plateau |
Table 4 Interaction of driving factors of county-level economic development on the Loess Plateau |
X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | |
---|---|---|---|---|---|---|---|---|
X1 | 0.1624 | |||||||
X2 | 0.3063 | 0.0533 | ||||||
X3 | 0.2870 | 0.1231 | 0.0397 | |||||
X4 | 0.3410 | 0.3429 | 0.3074 | 0.2360 | ||||
X5 | 0.5467 | 0.6032 | 0.6186 | 0.5922 | 0.4646 | |||
X6 | 0.3806 | 0.4102 | 0.4007 | 0.4386 | 0.5232 | 0.2748 | ||
X7 | 0.2692 | 0.1441 | 0.1312 | 0.3442 | 0.6416 | 0.4037 | 0.0617 | |
X8 | 0.2432 | 0.1307 | 0.1180 | 0.2878 | 0.5598 | 0.3350 | 0.1448 | 0.0535 |
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