Mapping the Trend of Regional Inequality in China from Nighttime Light Data

Mapping the Trend of Regional Inequality in China from Nighttime Light Data Xiaomeng Jin, Chi Chen Abstract In the past 50 years, a series of reform policies accelerated the development in China, especially those coastal areas. As a result, the regional inequality is evident in China, from coastal areas to interior areas. This paper proposed a new way to estimate the trend of regional inequality in China through nighttime light remote sensing data. The results show the gap between eastern and western areas is evident, but it is not widening from 1992 to 2010. Keywords China, nighttime light data, regional inequality I. INTRODUCTION N the past 50 years, China experienced an exceptional I economic growth across the country. The annual growth rate of Gross Domestic Product (GDP) was 9.8 percent over the years 1978 to 1998, among the world s highest during that period. However, because of the favorable geographical locations and China s reform policies, the gap between eastern coastal areas and western cities is widening [1]. West Development Strategy, established by Chinese government in last decade, tends to favor the development in western China. Thus, a question that intrigues many researchers is whether there exists convergence or divergence in economic development across China [2]. Traditionally, regional inequality is estimated through some socioeconomic indicators, such as GDP, GDP per capital [3], real GDP per capital [4], with different methods, like Gini coefficient [5] and Theil index [6]. Unfortunately, because of the inaccessibility and limited accuracy of census data, there is always a debate on which indicator and what method should be used in the analysis of regional inequality, leading to different opinions on convergence or divergence. Recently, remote sensing technique has attracted a lot of attention in the socioeconomic field, especially the nighttime light remote sensing data [7]. The night-time light images are collected by the US Air Force Weather Agency and processed at the National Geophysical Data Centre (NGDC) of the National Ocean and Atmosphere Administration (NOAA) using Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) data. NGDC combines the cloud-free portions of nighttime orbital segments over a full year to generate annual Xiaomeng Jin is with the school of Remote Sensing and information engineering, Wuhan University, Wuhan, 430079 China (corresponding author to provide phone: 8615271900406; e-mail: jinxiaomeng@whu.edu.cn). Chi Chen is with the school of Remote Sensing and information engineering, Wuhan University, Wuhan, 430079 China (e-mail: yaoganchenchi@163.com). nighttime lights products [8]. Since nighttime light is a good indicator of economic activity at night, many researchers take advantage of such feature to deal with socioeconomic problems. For example, reference [9] modeled the distribution of income per capita at the sub-national level across the world. Reference [10] estimated Mexico s informal economy using DMSP nighttime light data. As remote sensing data has relatively high temporal and spatial frequency when compared with census data, remote sensing is distinguished to contribute to providing gridded information on socioeconomic parameters. This paper builds upon previous work to map the trend of regional inequality in China, including the research of [11], which proved the high correlation between nighttime light data and poverty. In this study, we used 19-year DMSP/OLS data from 1992 to 2010 to derive the trend of regional inequality in China by using statistical methods, proposing an alternative measurement for regional inequality. II. STUDY AREA AND DATA A. Study area 31 provinces and municipalities in main land China are selected to carry out this study. China has drawn much attention worldwide recently because of its unique development pattern and the high economic growth rate. According to World Bank (2010), China is the world s second largest economic entity. However, the progress of economic growth was uneven in China, in terms of geographical locations, time and type, which is detrimental to stable and sustainable development in China. Usually, the regional inequality may decompose into two aspects: the urban-rural disparity and the east-west inequality [3]. This study mainly focused on the east-west inequality at a provincial scale. B. Data The data we used in this study is a series of 19-year DMSP/OLS imageries from 1992 to 2010 downloaded from the website of National Geophysical Data Center at National Oceanic and Atmospheric Administration (http://www.ngdc.noaa.gov/dmsp/downloadv4composites.htm l). DMSP/OLS nighttime light data is annual nighttime cloud-free image composites of lights of the globe collected by the DMSP/OLS sensors on a low-earth orbiting satellite (at 833 km altitude above earth). Each grid in the composite data contains a digital number (DN) ranged from 0 to 63 to indicate the average nighttime light intensity observed within each year. 104

Version 4 includes data from five satellites: F10, F12, F14, F15, and F16. Thus, the consistency of nighttime light data may be open to doubt, since the DN of a pixel may be change with different satellites even if there is no real change in the ground. However, in this study we mainly focus on comparing the spatial disparities within imageries and capturing the approximate trend of regional inequality. The difference between satellites could be ignored. Also, we used the fundamental geographic administrative boundaries in a vector format (ESRI shapefile) from the website of the National Fundamental Geographic Information System. III. METHODS A. Pre-processing Pre-processing includes three basic steps: extraction, re-projection and subdivision. First, the fundamental geographic administrative boundaries are re-projected to share the same projection and coordinate system with DMSP/OLS data using ArcGis 10.0. Second, The DMSP/OLS night-time light imageries of China were extracted from the global DMSP/OLS night-time light data using the Extraction Tool of Spatial Analyst of ESRI ArcGIS 10.0 and the data was then re-projected to the China Lambert Conformal Conic Projection from the original geographic projection (Lat/Lon) using nearest neighbor resampling algorithm in ENVI 4.8 (as shown in Fig. 1). Thirdly, we subdivided the each year imagery to 31 imageries representing 31 provinces and municipalities in main land China through Crop Tool in ESRI ArcGIS 10. average light index (ALI) to represent the regional light intensity: (2) where B is the regional total luminance of nighttime light; N is the sum of the number of all the pixels with DN value ranging from 1 to 63. C. Regional inequality measurement Perhaps the most common measurement for variability assessment should be the variance or standard variation. According to the delineation of diverging clubs in China from [12], 31 provinces could be classified as three clubs: eastern club, central club and western club. Eastern club includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Heilongjiang, Jinlin and Liaoning. Central club includes Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan. Western club is composed of Nei Mongol, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. In order to measure both the divergence between clubs, as well as convergence within each club, we calculated three types of variance: 1) intra-class variance; 2) inter-class variance; 3) overall variance. Usually, inter-class variance reflects the divergence among clubs while intra-class variance reflects the convergence within clubs. The overall variance is a good indicator for the overall divergence within 31 provinces. The intra-class variance can be calculated as following (3): (3) where x t is the ALI for the provinces in club i in year t, and m i is the average ALI for all provinces in club i in year t; N i is the number of provinces in club i; S i,t is the unbiased variance of club i in year t. The inter-class variance can be calculated as following (4): Fig.1 DMSP/OLS nighttime light imagery of China in 2009 B. Establishment of annual average nighttime light index (ALI) OLS visible band data have a 6-bit quantization, with digital numbers (DNs) ranging from 0 to 63. The regional total luminance of nighttime light can be calculated using the follow (1) [11]: where B is the regional total luminance of night-time light; B i is the image DN value, ranging from 1 to 63; N i is the number of pixels that have a DN value of B i. The next step is to normalize the regional luminance for comparative purpose. We use the Eq. (2) to calculate the (1) (4) where N is the total number of provinces and municipalities in China; N i is the number of provinces and municipalities in club i; m i,t is the average of ALI in club i in year t; m t is the average ALI of all provinces and municipalities in year t. The overall unbiased variance can be calculated from following (5): where x i,t is ALI of province i in year t; m t is average ALI of all provinces and municipalities in year t; VAR t is the total variance in year t. D. Trend Analysis In this part, we use SPSS as a tool to analyze the trend of regional inequality in China from 1992 to 2010. First we use the regression analysis tool of SPSS to produce a regression line between overall variance and time, including R 2 to illustrate its significance level and the line s intercept and slope. After that, we calculated the regression line for inter-class and intra-class variance with the same method. In order to analyze the trend of (5) 105

spatial inequality from east to west, we used the variable ratio t to calculate the gap between clubs with following (6): Since inter-variance is an indicator of divergence between clubs, while intra-variance is an indicator of divergence within clubs, the gap between clubs is more evident if ratio is increasing. (6) IV. RESULTS A. Results for annual average ALI The trend of average ALI for the three clubs is shown in Fig.2. From the results, we can see the average ALI of eastern club increased much more quickly than that in central and western clubs. Also, the results show that the gap between eastern and western clubs is widening, while the gap between central and western club is constant. Fig. 3 Trend of intra-class variance in eastern club Fig.2 Average ALI in three clubs B. Intra-class variance The regression lines of intra-class variance of three clubs are shown in Fig. 3, Fig.4, and Fig.5. Results show that eastern club shows the most evident increasing trend, with a slope of 5.431 compared with a slope of 0.026 in central club and 0.157 in western club, indicating the trend of unbalanced development in eastern club. In contrast, the development among provinces or municipalities in central club (ranging from 1 to 4) and western club (ranging from 0.5 to 5) is stable, with almost consistent intra-class variance. Fig.4 Trend of intra-class variance in central club Fig.5 Trend of intra-class variance in western club C. Inter-class variance The regression line of inter-class variance is shown in Fig.6. The regression result shows a significance level of 0.760, with a 106

slope of 0.876. We can assume that the gap between coastal and inland provinces is widening from 1992 to 2010, especially after 1995. places in eastern clubs, like Liaoning, Jilin, Hainan, and Heilongjiang, do not have large advantages over other provinces in central and even western clubs. D. Overall variance Fig. 6 Trend of inter-class variance The regression result of overall variance is shown in Fig. 7. The regression line has a slope with 3.139. The R 2 is 0.806, indicating a high linear increasing trend with the inter-class variance. Fig. 7 Trend of overall variance E. Regional Inequality analysis results From the results above, we can see clearly that all kinds of variance show an increasing trend. Thus, it is necessary to integrate intra-class variance and inter-class variance together to illustrate whether the gap between every two clubs is widening. The ratio trend is shown in Fig. 8. To our surprise, the result does not show any increasing trend from 1992 to 2010. It may result from two aspects: 1) the increasing trend of inter-class variance is not evident enough; 2) the increasing variance within eastern club counteracts the increasing overall variance. Also, from the results of ALI, we can see that the main contributors to the large variance in eastern club are those economically developed areas, such as Shanghai, Beijing, and Tianjin, where policies favored the development there. Other Fig 8. Ratio trend from 1992 to 2010 V. DISCUSSION ON ACCURACY Accuracy is always a major problem for researches in remote sensing, especially in quantitative field. The accuracy of our study is mainly influenced by five factors: 1) the rough resolution of DMSP/OLS imageries; 2) noise points, like forest fires; 3) approximate statistics; 4) the difference between satellites; 5) the difference between ALI and the intensity of economic activity. First, as for the resolution, our study focuses on a provincial scale, which is a sum of the regional pixels instead of an individual pixel. Thus the problem of mixing pixels can be ignored here. Second, since the composite has already replaced background noise with values of zero, the only kind of noise points like forest fires are from the ground itself, not the sensors. Unfortunately, such problem cannot be solved without external data source. Limited by available data, this study does not consider such noise. Thirdly, as the statistical method we use here is based on a long period and 31 provinces in China, the economic development of a single city or even a province cannot be reflected in this study. However, this study mainly focuses on capturing the general trend of regional inequality in China. Abnormalities should be eliminated through regression. Fourth, in theory, the difference between F10, F12, F14, F15 and F16 will lead to the inconsistency between different imageries instead of within one image. However, there are significant differences in the radiometric signatures across the different DMSP/OLS satellites in reality, even in the same year. When there are NTL observations from two different satellites available in the same year, there may be systematic significant deviation one from another [13]. In the future, more accurate calibrated NTL data will be helpful to solve this problem. Fifth, there is always a debate on whether remote sensing data could reflect socio-economic problems since they tend to be far away from each other. Admittedly, limited by the temporal and spatial resolution of remote sensing imageries, accurate quantification of socioeconomic indices, such as GDP, GNP and even the 107

poverty index remains to be difficult. However, when remote sensing data is used in macro-level estimation, it is both convenient and objective. Like the studies of [7], [9] and [11], they all illustrate the efficiency of remote sensing, especially for the areas where census data is limited and inaccurate. In our study, since we do not produce the accurate quantification for the extent of regional inequality, the results are convincing when they are used to analyze macro-scale trend. VI. CONCLUSION This study is based on previous studies to map the trend of regional inequality of China in the past 20 years. Regional inequality is a favorite topic in economic field since the unstable economic development will lead to unbalanced resource distribution. If regional inequality continues, poor people will be even poorer while rich people will be wealthier, leading to a larger gap between poor and rich, generating social instability. Mapping the trend of regional inequality can help policy makers better adjust the regional development. This study is unique from other studies on regional inequality because we use remote sensing techniques to answer whether divergence exists in China. As a result, we conclude that though there is still a huge gap between eastern areas and western areas, the gap is not widening. However, the increasing trend of intra-class variance in eastern club reveals the gap between eastern and western areas is resulted from few individual regions, such as Shanghai, Beijing, Tianjin, and Guangdong, instead of overall development in eastern areas. Even if some provinces are neighborhoods from geographical location, their economic growth rate is different, like Beijing and Hebei. Usually, regional inequality in China results from the urban-rural inequality and the eastern-western inequality. In this study, the phenomenon described above partly shows the urban-rural gap tends to be the major component instead of east-west inequality. Also, under such circumstance, we can conclude geographic location is no longer the most important factor that influences the economic growth. Policies, on the other hand, can play an important role in adjusting the trend of regional inequality. Thus, only if policies favored the development in western areas, we can expect convergence of economic development in China in the future. This study proposes an alternative to measurement of regional inequality, which does not require the census data. Thus, for the areas where census data is inaccessible or inaccurate, this method could be a desirable estimation for regional inequality. However, challenges remain in this study. First, noise points are not eliminated from the imageries, and oversaturation is unavoidable. This may require additional data to be integrated in the future study. 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