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Advances in Space Research 49 (2012) 1253–1264

Poverty assessment using DMSP/OLS night-time light satelliteimagery at a provincial scale in China

Wen Wang a,1, Hui Cheng a,1, Li Zhang b,⇑

a School of Environmental and Natural Resources, Renmin University of China, Beijing 100872, Chinab Department of Geography, King’s College London, Strand, London WC2R 2LS, UK

Received 6 September 2011; received in revised form 29 January 2012; accepted 30 January 2012Available online 8 February 2012

Abstract

All countries around the world and many international bodies, including the United Nations Development Program (UNDP), UnitedNations Food and Agricultural Organization (FAO), the International Fund for Agricultural Development (IFAD) and the Interna-tional Labor Organization (ILO), have to eliminate rural poverty. Estimation of regional poverty level is a key issue for making strategiesto eradicate poverty. Most of previous studies on regional poverty evaluations are based on statistics collected typically in administrativeunits. This paper has discussed the deficiencies of traditional studies, and attempted to research regional poverty evaluation issues using3-year DMSP/OLS night-time light satellite imagery. In this study, we adopted 17 socio-economic indexes to establish an integrated pov-erty index (IPI) using principal component analysis (PCA), which was proven to provide a good descriptor of poverty levels in 31 regionsat a provincial scale in China. We also explored the relationship between DMSP/OLS night-time average light index and the povertyindex using regression analysis in SPSS and a good positive linear correlation was modelled, with R2 equal to 0.854. We then lookedat provincial poverty problems in China based on this correlation. The research results indicated that the DMSP/OLS night-time lightdata can assist analysing provincial poverty evaluation issues.� 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: DMSP/OLS night-time light; Provincial scale; Socio-economic development; Principal component analysis; Poverty index

1. Introduction

Poverty is a general term describing living conditionsthat are detrimental to health, comfort, and economicdevelopment (Elvidge et al., 2009). After 30 years of eco-nomic transformation, China has now become the secondlargest economy and the second largest trading nation inthe world according to recent statistics of the World Bankand the World Trade Organisation. China’s Gross Domes-tic Product (GDP) has increased from 268.3 billion dollarsto 5.3 trillion dollars since 1978, meanwhile the gap

0273-1177/$36.00 � 2012 COSPAR. Published by Elsevier Ltd. All rights rese

doi:10.1016/j.asr.2012.01.025

⇑ Corresponding author. Tel.: +44 (0) 20 78482692; fax: +44 (0) 2078482287.

E-mail addresses: wen_wang2000@hotmail.com (W. Wang), chenghui-whu@163.com (H. Cheng), li.zhang@kcl.ac.uk (L. Zhang).

1 Tel.: +86 (0) 10 88893061; fax: +86 (0) 10 62511645.

between Western China and other regions has been increas-ing (Li et al., 2008). Not everyone has equally shared thefruits of Chinese economic reform. Poverty is still a signif-icant problem in China and it needs a long time and greatefforts to be solved. So accurate assessments of regionalpoverty levels are essential for the central governmentand local policy makers to obtain reliable up-to-date dataof the socio-economic situation and tackle regionalinequality problems.

Traditionally, regional socio-economic developmentassessment is based on statistics collected by local govern-ments. GDP is the most popular indicator of economic per-formance (Sutton and Costanza, 2002) and has been usedin a wide range of socio-economic development studies inChina. For example Jian et al. (1996) adopted GDP datato analyse the regional inequality trends. Li et al. (2004)applied it to evaluate economic standards of 31 provinces

rved.

1254 W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264

(or municipalities). Jin (2007) used GDP as one of theurban economic vitality indexes for quantitative economicanalysis of 50 Chinese cities. However, there are limits tothis type of data, as economic census is usually collectedonce every five years in China and it takes substantial man-power and generates huge amount of economic costs. Italso needs a long period to update existing data and some-times may become impossible because of various reasons,e.g. change of local administrative units. It cannot meetspecial demands either due to the lack of spatialinformation.

In comparison to traditional methods, satellite remotesensing has an advantage to provide efficient and accuracyspatial data for various physical and social science researchpurposes due to its high temporal resolution and extensivespatial coverage. Satellite imagery has been recognised tobe capable of mapping and analyzing socio-economicrelated issues with high accuracy since the late 1960s (e.g.

Tobler 1969; Welch 1980; Foster 1983) and the night-timeradiance data has been proven to be capable of providingstrong estimation of population, GDP and electricity con-sumption based on the strong correlation between lightsand human activities (Elvidge et al., 1997a,b). It shows agood potential in regional poverty analysis. The night-timelight images are collected by the US Air Force WeatherAgency and processed at the National Geophysical DataCentre (NGDC) of the National Ocean and AtmosphereAdministration (NOAA) using Defence MeteorologicalSatellite Program (DMSP) Operational Linescan System(OLS) data. NGDC combines the cloud-free portions ofnight-time orbital segments over a full year to generateannual night-time lights products (Elvidge et al., 1997a,bElvidge et al., 2001) that have been used in a range of stud-ies, such as GDP estimation and energy consumption anal-ysis (Elvidge et al., 1997a,b; Elvidge et al., 2001), mineralin-use stocks (Takahashi et al., 2010) and income proxy(e.g. Sutton and Costanza, 2002). Nakayama and Tanaka(1983) explored the relationship between the light diameterof a city and its economy. Elvidge et al. (1997b) Elvidgeet al. (1999) then found a close relationship betweennight-time light and human activity such as energy con-sumption and the important economic activity indicatorGDP. The strong relationship between economic activitiesand CO2 emissions with the total lit area were also revealedand mapped by (Doll et al., 2000). Later, Doll (2003) usedthe cumulative radiance value in the radiance-calibratednight-time image to develop an area-GDP relationship ata national scale for the United Kingdom. Sutton et al.(2007) made a similar attempt to estimate sub-nationalGDP for the United States, China, India and Turkey.Elvidge et al. (2009) produced a global poverty map usinga poverty index calculated by dividing population count(LandScan 2004) by the brightness of satellite observedlighting (DMSP/OLS night-time lights). The main socio-economic factors considered by most of these studies werepopulation, energy consumption, greenhouse gas emis-sions, urban sprawl, forest fires monitoring and light pollu-

tion. There are no studies on poverty issues of China at aprovincial scale using remote sensing data so far.

This study combines the 3-year DMSP/OLS night-timelight data with other socio-economic statistical indicatorsto establish DMSP/OLS night-time average light indexesat a provincial scale in China and analyse the relationshipbetween them and an integrated poverty index to explorethe spatially irregular distribution of social wealth ofChina. It may contribute to the effort of a more balancedregional development in China.

2. Data and methods

2.1. Study area

31 provinces and municipalities in mainland China(Fig. 1) have been selected to carry out this study. Therapid economical growth of these 31 regions in the last30 years has drawn worldwide attention and made Chinathe world’s second largest economy according to the WorldBank. Meanwhile, the uneven economic growth rate hascaused apparent economical inequality amongst differentregions and built up a big gap between the west and theeast (Li et al., 2008). The inequality is now recognised asa great barrier for the future sustainable economical devel-opment of the nation.

2.2. Socio-economic statistical data and fundamental

geographic data

The 3-year socio-economic statistical data (from 2007 to2009) for the selected 31 provinces and municipalities isobtained from the National Bureau of Statistics of China.The fundamental geographic administrative boundaries ina vector format (ESRI shapefiles) were downloaded fromthe website of the National Fundamental GeographicInformation System and their projections were reprojectedinto the China Lambert Conformal Conic Projection usingESRI ArcGIS 9.3. Both GDP data and administrative datawere then integrated into a geospatial database for furtheranalysis.

2.3. DMSP/OLS night-time lights data

DMSP/OLS night-time data are annual night-timecloud-free image composites of lights of the globe collectedby the DMSP/OLS sensors on a low-earth orbiting satellite(at 833 km altitude above earth). DMSP operates satellitesin sun-synchronous orbits with night-time overpasses at 8–10 pm local time. With a swath width of 3000 km and 14orbits per day, each OLS instrument is capable of generat-ing a complete coverage of night-time data in a 24-hourperiod. The OLS is an oscillating scan radiometer withtwo spectral bands. The visible band straddles the visibleand near-infrared (VNIR) portion of the spectrum (0.5–0.9 lm) and the thermal band covers the 10.5–12.5 lmspectrum range. At night, the visible band is intensified

Fig. 1. Administrative map of China.

W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264 1255

using a photomultiplier tube (PMT) to permit detection ofclouds illuminated by moonlight. The light intensificationenables observation of faint sources of VNIR emissionsat night on the earth’s surface, including cities, towns, vil-lages, gas flares, heavily lit fishing boats, and fires (Elvidgeet al., 1997a,b). The low-light-sensing capabilities of theOLS at night permit the measurement of radiances downto 10�9 W/cm2/sr. NGDC has developed algorithms toremove areas contaminated by sunlight, moonlight, solarglare and fires and produced high quality global cloud-freecomposites of DMSP night-time light emissions with aver-age intensity (digital number recorded at the sensor) since1994 (Elvidge et al., 1997a,b). The spatial resolution ofthe data is reasonably high, 2.8 km at full mode and

0.56 km at fine mode. The high contrast and spatial resolu-tion of the data makes it a tool to identify regions ofintense human activity (Croft, 1978).

The version 4 DMSP/OLS night-time image productsfrom 2007 to 2009 (30 arc seconds spatial resolution),released by NOAA-NGDC in 2010 at http://www.ngdc.noaa.gov/dmsp/ downloadV4composites.html,were used for this study. The data is derived by multiplyingthe average visible band digital number (DN) of cloud-freelight detections with the percent frequency of light detec-tion. The inclusion of the percent frequency of detectionterm normalizes the resulting digital values for variationsin the persistence of lighting. The original global DMSP/OLS night-time light image of 2009 is shown in Fig. 2.

Fig. 2. Global DMSP/OLS night-time light image obtained in 2009.

1256 W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264

2.4. Methods

2.4.1. Establishment of DMSP/OLS night-time average lightindex (ALI)

The DMSP/OLS night-time light imageries of China(Fig. 3) were extracted from the global DMSP/OLSnight-time light data using the Extraction Tool of SpatialAnalyst of ESRI ArcGIS 9.3 and the data was then repro-jected to the China Lambert Conformal Conic Projectionfrom the original geographic projection (Lat/Lon) usingnearest neighbour resampling algorithm. Each pixel inthe imageries has a DN value ranging from 0 to 63. HigherDN values associate with more intense lights.

The regional total luminance of night-time light can becalculated using the follow Eq. (1) (Zhao et al., 2011):

B ¼X63

i¼1

Bi � N i ð1Þ

where B is the regional total luminance of night-time light;Bi is the image DN value, ranging from 1 to 63; Ni is thenumber of pixels that have a DN value of Bi.

Poverty is caused by comprehensive aspects of the socio-economic situations. Administrative areas can also affectthe poverty evaluation results in different regions. As theabove total luminance of night-time light can only repre-sent an intuitive impression of socio-economic activitiesat night for a region, we used an average light index

Fig. 3. DMSP/OLS night-time light

(ALI) that can better represent the average level of differentregions in this study, as shown in Eq. (2).

L ¼ B=N ð2Þwhere L is the average light index (ALI); B is the regionaltotal luminance of night-time light; N is the sum of the num-ber of all the pixels with DN value ranging from 1 to 63.

2.4.2. Establishment of integrated poverty index (IPI) using

principal component analysis (PCA)Principal components analysis (PCA) is a type of factor

analysis method that can be used to reduce large dataset.Based on statistics, PCA transfers a given number of vari-ables to a set of uncorrelated variables, called principalcomponents (PC), each of which contains a linear combi-nation of all original variables. The first few PCs accountfor most of the variance of the variables. Regional povertylevels are determined by a number of socio-economic vari-ables. Cavatassi et al. (2004) revealed that the first PC, alinear combination that captures the greatest variationamongst the set of socio-economic variables, can be con-verted into factor scores that can serve as weights for thecreation of the marginality index or poverty index. In thisstudy we used the following 17 socio-economic variablesto extract an integrated poverty index (IPI) that can beused as a multidimensional community-level povertyindicator:

(1) per capita GDP;

imageries of China (2007–2009).

W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264 1257

(2) per capita labour compensation;(3) consumption level of residents;(4) urbanization rate;(5) life expectancy;(6) sex ratio;(7) dependency ratio;(8) illiteracy rate;(9) employment rate;

(10) regional finance income per capita;(11) the net income per peasant;(12) per capita consumption expenditure of farmers;(13) the living space per capita;(14) electricity consumption per capita;(15) the original value of productive fixed assets per rural

family;(16) beds in health care institutions;(17) tax income per capita.

Both KMO and Bartlett’s tests were applied to the 17variables using SPSS 18.0. The test results show that theP value is approximately 0, indicating strong relationshipsamongst the variables. The KMO sampling adequacy is0.753, showing that the PCA method is suitable for thisstudy.

Table 1 demonstrates the PCA results of the 17 socio-economic variables, where the first component accountedfor about 54.73%. The first four components togetheraccounted for about 85.56% of the total variance of thedataset, therefore are used in this study.

The component score coefficient matrix of the PCAresults are given in Table 2. The eigenvectors of the 17 vari-ables in the matrix can be used to express each of the com-ponents. The first PC Z1 can be expressed by the followingEq. (3):

Z1 ¼ 0:110x1 þ 0:110x2 þ 0:105x3 þ 0:103x4 þ 0:057x5

� 0:058x6 � 0:093x7 � 0:010x8 � 0:014x9

� 0:034x10 þ 0:096x11 þ 0:093x12 þ 0:032x13

þ 0:102x14 þ 0:044x15 þ 0:114x16 þ 0:110x17 ð3Þ

where xi are variables (see Table 2) used in this study andthe coefficients are the eigenvectors of the PCA result.

Similarly, the second, third and fourth components canbe expressed as follows:

Table 1Total variance of the 17 socio-economic variables explained by the first 4 PCs

Components Extraction sums of squared loadings

Total % of variance Cumulati

1 9.303 54.725 54.7252 2.139 12.581 67.3063 1.783 10.491 77.7964 1.319 7.759 85.555

Z2 ¼ �0:032x1 � 0:058x2 þ 0:003x3 þ 0:070x4

þ 0:195x5 þ 0:308x6 þ 0:040x7 � 0:413x8

� 0:067x9 þ 0:015x10 � 0:004x11 � 0:015x12

þ 0:043x13 � 0:103x14 � 0:417x15 � 0:082x16

� 0:037x17 ð4Þ

Z3 ¼ �0:003x1 þ 0:012x2 þ 0:082x3 � 0:091x4

� 0:050x5 þ 0:028x6 þ 0:260x7 þ 0:238x8

þ 0:498x9 � 0:044x10 þ 0:090x11 þ 0:156x12

þ 0:320x13 � 0:024x14 � 0:088x15 � 0:132x16

þ 0:072x17 ð5Þ

Z4 ¼ 0:012x1 � 0:001x2 � 0:068x3 � 0:025x4 þ 0:144x5

� 0:309x6 � 0:129x7 � 0:087x8 � 0:062x9

þ 0:612x10 þ 0:044x11 � 0:012x12 þ 0:097x13

� 0:378x14 � 0:010x15 � 0:112x16 � 0:107x17 ð6Þ

Combining the above 4 PCs, an integrated poverty index(IPI) that can best represent the 17 socio-economic vari-ables can be created using the following Eq. (7):

IPI ¼ k1 � Z1=ðk1 þ k2 þ k3 þ k4Þ þ k2 � Z2=ðk1 þ k2

þ k3 þ k4Þ þ k3 � Z3=ðk1 þ k2 þ k3 þ k4Þ þ k4

� Z4=ðk1 þ k2 þ k3 þ k4Þ ð7Þ

where k1, k2, k3 and k4 are eigenvectors for the first 4 PCs,and Z1, Z2, Z3 and Z4 are the values calculated by Eqs. (3)–(6).

By introducing the k1, k2, k3 and k4 values from Table 1(k1 = 9.303, k2 = 2.139, k3 = 1.783, k4 = 1.319), IPI canthen be simplified to the following Eq. (8):

IPI ¼ 0:6396Z1 þ 0:1471Z2 þ 0:1226Z3 þ 0:0907Z4 ð8Þ

3. Results

3.1. IPIs of 31 regions in China

The IPIs of the 31 regions in China are shown in Table 3.The lower the IPI value is, the poorer the region is. All richprovinces and municipalities with positive poverty indexvalues are located in eastern China. The poorest 5 prov-inces with poverty index less than –0.50, including Qinghai,Yunnan, Gansu, Guizhou and Xizang, are all located in

.

Rotation sums of squared loadings

ve% Total % of variance Cumulative%

9.099 53.521 53.5212.079 12.229 65.7501.875 11.027 76.7761.492 8.779 85.555

Table 2Component score coefficient matrix.

Variables Variable name PCs

1 2 3 4

X1 Per capita GDP 0.110 �0.032 �0.003 0.012X2 Per capita labour

compensation0.110 �0.058 0.012 �0.001

X3 Consumption level ofresidents

0.105 0.003 0.082 �0.068

X4 Urbanization rate 0.103 0.070 �0.091 �0.025X5 Life expectancy 0.057 0.195 �0.050 0.144X6 Sex ratio �0.058 0.308 0.028 �0.309X7 Dependency ratio �0.093 0.040 0.260 �0.129X8 Illiteracy rate �0.010 �0.413 0.238 �0.087X9 Employment rate �0.014 �0.067 0.498 �0.062X10 Regional finance

income per capita�0.034 0.015 �0.044 0.612

X11 The net income perpeasant

0.096 �0.004 0.090 0.044

X12 Per capita consumptionexpenditure of farmers

0.093 �0.015 0.156 �0.012

X13 The living space percapita

0.032 0.043 0.320 0.097

X14 Electricityconsumption per capita

0.102 �0.103 �0.024 �0.378

X15 The original value ofproductive fixed assetsper rural family

0.044 �0.417 �0.088 �0.010

X16 Beds in health careinstitutions

0.114 �0.082 �0.132 �0.112

X17 Tax income per capita 0.110 �0.037 0.072 �0.107

Table 3the poverty indexes at provincial scale.

Province Poverty index Rank Province Poverty index Rank

Shanghai 2.13 1 Shanxi �0.16 17Beijing 1.73 2 Henan �0.22 18Zhejiang 0.93 3 Sichuan �0.23 19Jiangsu 0.72 4 Shaanxi �0.29 20Tianjin 0.69 5 Jiangxi �0.32 21Guangdong 0.46 6 Hainan �0.33 22Fujian 0.30 7 Guangxi �0.33 23Liaoning 0.24 8 Anhui �0.35 24Shandong 0.19 9 Ningxia �0.37 25Hebei �0.07 10 Xinjiang �0.46 26Chongqing �0.07 11 Qinghai �0.55 27Hunan �0.08 12 Yunnan �0.62 28Nei Mongol �0.08 13 Gansu �0.69 29Hubei �0.08 14 Guizhou �0.77 30Jilin �0.11 15 Xizang �1.02 31Heilongjiang �0.15 16

1258 W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264

western China, where regional economy is mainly based onagriculture with less industry and poor transportation andother public utilities. A large population in these provincesis suffering from poverty.

The above IPIs have been grouped into the followingfive classes by cluster analysis, as shown in Fig. 4: very highIPIs (>=1.7), high IPIs (0–1.7), medium IPIs (�0.3–0), lowIPIs (�0.6-�0.3), very low IPIs (<�0.6). Provinces withhigh IPIs are all located in eastern China. The two munic-ipalities, Shanghai and Beijing have much higher valuesthan any other regions. Shanghai is a global city, having

influences over finance, commerce, fashion, technologyand culture in both China and the world. Beijing is thepolitical, educational, and cultural centre of China. Otherregions with high IPIs are all east-coast developed prov-inces of China. Most of the provinces with medium IPIsare in central China, and the ones with lower IPIs aremainly western regions.

3.2. DMSP/OLS night-time ALIs of 31 regions in China

The DMSP/OLS night-time ALIs of 31 regions in Chinawere calculated using Eq. (2) as shown in Table 4. As mostof socio-economic activities during night time are centredin developed areas, associated with bright patterns in theDMSP/OLS night-time imagery, a higher DMSP/OLSnight-time ALI indicates more prosperous socio-economicvigour in a region. The 3 municipalities, namely Shanghai,Beijing and Tianjin, major cities in China since early 20thcentury, occupying the top ranks of the table, have thehighest ALI values, on the contrary, Guizhou, a provincein the southwest mountainous area of China is at the bot-tom of the table.

The ALIs have been separated into 5 classes (Fig. 5.),they are: very high ALIs (>=20), high ALIs (10–20), med-ium ALIs (8–10), low ALIs (6.5–8), very low ALIs (<6.5),indicating different levels of socio-economic vigour. Similarto the poverty index results, Shanghai, Beijing and Tianjinare the most developed regions and other east-coast devel-oped regions including Jiangsu, Guangdong, Zhejiang,Shandong, and Fujian still share the second class. Prov-inces with medium ALIs are mainly those in central China,and the regions with low or very low ALIs are mainly dis-tributed in Western China.

3.3. Relationship between ALI and IPI at a provincial scale

A regression analysis was carried out in SPSS to explorethe relationship between DMSP/OLS night-time ALI andthe statistical IPI. A positive linear relationship (Fig. 6.)was found with coefficient of determination R2 = 0.854.The linear regression model can be expressed as Eq. (9):

Y ¼ 0:091X � 0:975 ð9Þwhere Y is the regional IPI; X is the regional DMSP/OLSnight-time ALI.

The above linear relationship indicates that DMSP/OLSnight-time average light data can provide a good estimateof regional economic situation and poverty levels with bet-ter efficiency than the expensive and time-consuming socio-economic statistic data that traditional methods rely on.

4. Discussion

4.1. Comparison of IPI to GDP at a provincial scale

GDP refers to the market value of all final goods andservices produced in a given period within a country

Fig. 4. Poverty classification map for 31 regions in China.

W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264 1259

(Goossens et al., 2007). It is a standard indicator used tomeasure a country’s economic performance and is oftenseen as an indicator of well-being. However, GDP wasnever intended to be used for measuring social well-being.Its key flaw is that it fails to differentiate costs from bene-fits, identify productive activities from destructive ones,and distinguish sustainable practices from unsustainableactions. For example, GDP regards pollution and naturalresource depletion as an economic gain, whilst social activ-ities such as care for the elderly and children gain just azero rating. Natural and “man-made” disasters, crimeand accidents, are seen as positive contributors to GDPas they generate production, but they do not contributeto social well-being. GDP does not account for harm

resulting from industrial, household and vehicle emissions,or water disposal. Instead, it assumes that all monetarytransactions would add points to social well-being. It isobvious that we cannot assume things are improving justbecause more money has been spent. GDP is a total eco-nomic indicator; it only expresses the economical develop-ment for a region and is not capable of illustratinginequalities in well-being.

As described in Section 2.4.2, the IPI is an integratedpoverty index established using a comprehensive evalua-tion method that embraces many aspects of socio-eco-nomic situation including per capita GDP as well asfactors that reflect people’s living standards. The followingTable 5 and Fig. 7 show IPIs and GDPs of the 31 regions in

Table 4The DMSP/OLS night-time ALIs at a provincial scale.

Province Light index Rank Province Light index Rank

Shanghai 37.52 1 Shaanxi 8.46 17Beijing 26.75 2 Hubei 8.19 18Tianjin 20.35 3 Xinjiang 7.92 19Jiangsu 16.14 4 Sichuan 7.78 20Guangdong 15.18 5 Qinghai 7.43 21Zhejiang 15 6 Hainan 7.28 22Shandong 12.57 7 Gansu 6.95 23Fujian 10.1 8 Jiangxi 6.93 24Henan 10.06 9 Heilongjiang 6.43 25Shanxi 9.79 10 Xizang 6.34 26Anhui 9.71 11 Yunnan 6.09 27Hebei 9.6 12 Hunan 6.08 28Liaoning 9.48 13 Guangxi 5.88 29Chongqing 9.29 14 Jilin 5.86 30Ningxia 9.22 15 Guizhou 4.42 31Nei Mongol 8.72 16

Fig. 5. DMSP/OLS night-time average light

1260 W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264

China and demonstrate the class rank differences betweenthe two values in each region. As demonstrated in Sec-tion 3.1, the IPI values were grouped into 5 classes (rankingfrom 1 to 5, where lower class rank indicates poorer eco-nomic state) using cluster analysis. Correspondently theGDP values of these regions were also grouped into 5 clas-ses using the same method with higher rank associated withhigher GDP value. Obvious differences were shownbetween GDP and IPI class ranks in majority of the regionsdue to different evaluation criterions. Only a third of themhave GDP class rank in accordance with IPI class rank.High GDP does not necessarily produce better social wellbeing.

From Table 5 and Fig. 7, we can see that the povertyindexes (IPIs) display different trends from GDP. Largeclass rank differences (larger than 1 or smaller than �1)

index classification map for 31 regions.

Fig. 6. Relationship between DMSP/OLS night-time average light indexand poverty index.

Table 5IPI class rank versus GDP class rank.

Province IPI IPI class IPIclassrank

GDP(billionyuan)

GDPclass

GDPclassrank

Difference

Shanghai 2.13 Veryhigh

5 1364.45 Medium 3 2

Beijing 1.73 Veryhigh

5 1066.48 Medium 3 2

Zhejiang 0.93 High 4 2108.59 High 4 0Jiangsu 0.72 High 4 3017.04 Very

high5 �1

Tianjin 0.69 High 4 630.89 Low 2 2Guangdong 0.46 High 4 3542.11 Very

high5 �1

Fujian 0.3 High 4 1076.96 Medium 3 1Liaoning 0.24 High 4 1323.25 Medium 3 1Shandong 0.19 High 4 3031.15 Very

high5 �1

Hebei �0.07 Medium 3 1571.12 High 4 �1Chongqing �0.07 Medium 3 524.97 Low 2 1Hunan �0.08 Medium 3 1113.88 Medium 3 0Nei Mongol �0.08 Medium 3 786.44 Low 2 1Hubei �0.08 Medium 3 1117.41 Medium 3 0Jilin �0.11 Medium 3 632.92 Low 2 1Heilongjiang �0.15 Medium 3 798.73 Low 2 1Shanxi �0.16 Medium 3 667.68 Low 2 1Henan �0.22 Medium 3 1763.36 High 4 �1Sichuan �0.23 Medium 3 1238.76 Medium 3 0Shaanxi �0.29 Medium 3 682.9 Low 2 1Jiangxi �0.32 Low 2 654.53 Low 2 0Hainan �0.33 Low 2 144.56 Very

Low1 1

Guangxi �0.33 Low 2 696.21 Low 2 0Anhui �0.35 Low 2 876.71 Low 2 0Ningxia �0.37 Low 2 111.37 Very

low1 1

Xinjiang �0.46 Low 2 400.12 Verylow

1 1

Qinghai �0.55 Low 2 94.21 Verylow

1 1

Yunnan �0.62 Verylow

1 553.71 Low 2 �1

Gansu �0.69 Verylow

1 308.87 Verylow

1 0

Guizhou �0.77 Verylow

1 332.93 Verylow

1 0

Xizang �1.02 Verylow

1 39.32 Verylow

1 0

W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264 1261

between GDP and IPIs are seen in 3 municipalities, i.e.

Shanghai, Beijing and Tianjin. Although their GDP classranks are only medium high or low due to their smalladministrative areas, their good socio-economic environ-ment and high social welfare result in the highest class rankof IPIs. There are 4 major municipalities that are adminis-tratively at the same level as provinces in China, includingShanghai, Beijing, Tianjin and Chongqing. Unlike theother three municipalities, Chongqing is much larger thanany other cities in China and even larger than some smallprovinces. It is divided into 40 county-level subdivisions,consisting of 19 districts, 17 counties and 4 autonomouscounties, and a large portion of its administrative area(over 80,000 km2), is rural. It is more like a province otherthan a municipality economically. Similar to most prov-inces in western China, the overall economic performanceof Chongqing is lagging behind eastern coastal regions.For instance, its per capita GDP was 22,909 yuan in2009, below the national average. The surplus labour forceof Chongqing has to migrate to the east-coast areas to seekemployment opportunities. A third of the regions present agood agreement between the GDP and IPI class ranks.They are Zhejiang, Hunan, Hubei, Sichuan, Jiangxi, Guan-gxi, Anhui, Gansu, Guizhou and Xizang. Amongst theseregions, Zhejiang is the most developed province at theeast-coast of China. Its strong economy provides a solidbasis to improve its socio-economic environment and pro-duce adequate employment service, therefore, its IPI classrank is as high as its GDP rank. Sichuan, Guangxi, Gansu,Guizhou and Xizang are all underdeveloped landlockedareas in Western China. Their transport facilities are notfacilitative and their secondary and tertiary industries can’twell support local employment demands. Consequently,these regions fail to establish a good foundation for solving

various poverty problems. Provinces in central Chinaincluding Hunan, Hubei, Jiangxi and Anhui are influencedby east-coast developed regions. With their socio-economicenvironment superior to western regions, they exert greatefforts in developing economy and ensure livelihood issues.

Some previous studies considered GDP as the main fac-tor to analyse the regional poverty and inequality prob-lems. For example Jian et al. (1996) have adopted GDPas the evaluating indicator to analyse the regional inequal-ity trends in China. In their study, they grouped provincesinto 3 regions: North Coastal, South Coastal and Interior.The paper deals macroscopically with the overall econ-omy’s performance at regional level and has not consideredindicators that are closely related to livelihood issues. Some

Fig. 7. Comparison of the class ranks between regional poverty indexes (IPI) and GDP in China.

Table 6IPI class rank versus ALI class rank.

Province IPI IPI class IPIclassrank

ALI ALIclass

ALIclassrank

Difference

Shanghai 2.13 Veryhigh

5 37.52 Veryhigh

5 0

Beijing 1.73 Veryhigh

5 26.75 Veryhigh

5 0

Zhejiang 0.93 High 4 15.00 High 4 0Jiangsu 0.72 High 4 16.14 High 4 0Tianjin 0.69 High 4 20.35 Very

high5 �1

Guangdong 0.46 High 4 15.18 High 4 0Fujian 0.3 High 4 10.10 High 4 0Liaoning 0.24 High 4 9.48 Medium 3 1Shandong 0.19 High 4 12.57 High 4 0Hebei �0.07 Medium 3 9.60 Medium 3 0Chongqing �0.07 Medium 3 9.29 Medium 3 0Hunan �0.08 Medium 3 6.08 Very

low1 2

Nei Mongol �0.08 Medium 3 8.72 Medium 3 0Hubei �0.08 Medium 3 8.19 Medium 3 0Jilin �0.11 Medium 3 5.86 Very

low1 2

Heilongjiang �0.15 Medium 3 6.43 Verylow

1 2

Shanxi �0.16 Medium 3 9.79 Medium 3 0Henan �0.22 Medium 3 10.06 High 4 �1Sichuan �0.23 Medium 3 7.78 Low 2 1Shaanxi �0.29 Medium 3 8.46 Medium 3 0Jiangxi �0.32 Low 2 6.93 Low 2 0Hainan �0.33 Low 2 7.28 Low 2 0Guangxi �0.33 Low 2 5.88 Very

low1 1

Anhui �0.35 Low 2 9.71 Medium 3 �1Ningxia �0.37 Low 2 9.22 Medium 3 �1Xinjiang �0.46 Low 2 7.92 Low 2 0Qinghai �0.55 Low 2 7.43 Low 2 0Yunnan �0.62 Very

low1 6.09 Very

low1 0

Gansu �0.69 Verylow

1 6.95 Low 2 �1

Guizhou �0.77 Verylow

1 4.42 Verylow

1 0

Xizang �1.02 Verylow

1 6.34 Verylow

1 0

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other studies carried out household surveys whilst consid-ering GDP. For example Yao et al. (2004) adopted theurban household survey data obtained in 1998 that con-tains 17,000 households in 31 provinces and regions.Although the household survey data are detailed enoughto identify the poverty lines for different regions, it takessubstantial manpower and casts huge economic costs. Itis also weak in terms of timely assessment. Apart from that,inconsistencies in the sampling structures, the nature andtiming of the surveys, and different definitions of povertymakes the assembly of a consistent spatially disaggregatedpoverty map impossible with the survey data alone(Elvidge et al., 2009). Our study tries to overcome theshortcomings of the above previous studies on socio-eco-nomic situation and poverty indices by using PCA method,only considering GDP as one important aspect of the inte-grated poverty index (IPIs) and introducing the DMSP/OLS night-time light data as a good measure of economicactivities.

4.2. Comparison of IPI to DMSP/OLS night-time ALI at a

provincial scale

Table 6 and Fig. 8 compare the IPI, i.e. the integratedpoverty indexes, and ALI, i.e. regional DMSP/OLSnight-time average light indexes class ranks. The classranks of the two indexes show similar trends in Fig. 8. Theyare identical in the majority of the regions (20 provincesand municipalities), in other words, different DMSP/OLSnight-time average light index levels can well reflect differ-ent poverty levels of these regions. Only 3 provinces displaylarge differences (larger than 1). They are Hunan, Jilin andHeilongjiang. Amongst them, 3 provinces, namely HunanJilin and Heilongjiang, have their secondary industriesaccount for 43.5%, 48.7% and 47.3% of their own regionalGDP in 2009, however, their tertiary industries are not veryactive which results in relatively low ALI class ranks inthese regions. Minor class rank differences (equal to 1 or�1) between IPIs and ALIs are seen in 8 regions, includingTianjin, Liaoning, Henan, Sichuan, Guangxi, Anhui, Ning-xia and Gansu. Amongst these regions, Tianjin has a very

Fig. 8. Comparison of the class ranks between regional poverty indexes (IPI) and ALI in China.

W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264 1263

high ALI class rank. Located at the west coast of BohaiGulf in north China, Tianjin is a dual-core modem interna-tional metropolitan, composed of the old city and BinhaiNew Area. The Binhai New Area is a new growth pole inChina, which maintains an annual growth rate of nearly30% of its GDP. It has been seen as a base of China’sadvanced industry, a base for financial reform, and a baseof innovation in China. By the end of 2010, 285 FortuneGlobal 500 companies have established their branch officesin this area. On the contrary, Sichuan, Guangxi, Ningxiaand Gansu are all underdeveloped provinces in westernChina. Due to the limit of natural conditions and transportfacilities, their economic performances are much less activethan the eastern coastal regions. Their lower ALIs can welldemonstrate the regional economic disparity. For example,Henan has its value-added tertiary industry accounts for570 billion yuan of its GDP in 2009 that achieved it theninth position among the 31 provinces and municipalities.Its growing economic activities in the tertiary industrieshave also contributed to its high ALI class rank. Mean-while, Henan is a very populous province with a popula-tion of 94.87 million, ranking the third place in China. Alot of surplus labour force has to go to the east-coast areasto seek employment opportunities. As a result, its ALIclass rank is not corresponding to its IPI class rank. Beingone of the central regions, Anhui has a lot in common withHenan. It is also a populous province with a population of61.31 million, of which the rural population accounts for57.9%. The main social and economic development targetfor Anhui is to provide adequate employment service forits population. Being influenced by east-coast developedregions, Anhui makes full use of its advantageous geo-graphical location to develop its commercial economy. Itseconomic performance is superior to western regions, andits ALI class rank is medium. Liaoning is an industrialprovince, with only 39.65% of its population living in therural area. It has the largest economy of Northeast China.Its secondary industry accounted for 52.0% of its GDP in2009 and its nominal GDP for 2010 was 1.83 trillion yuan,making it the 7th largest economy in China. Its good

economic performance results in a high IPI class rank,however, the inadequate development of its tertiary indus-try only achieved it a rather lower ALI class rank.

The DMSP/OLS night-time light data have been used insome previous studies. Elvidge et al. (2009) produced a glo-bal poverty map using a poverty index calculated by divid-ing population count (LandScan 2004) by the brightness ofsatellite observed lighting (DMSP/OLS night-time lights).The study is based on global scale, not capable of reflectingthe regional poverty details. Moreover, as it only considersthe population factor other than considering a comprehen-sive mix of a few socio-economic factors that reflecting bet-ter social wellbeing, the accuracy of its evaluations towardspoverty is reduced. In our study, 17 main socio-economicindicators have been adopted to establish an integratedpoverty index for every region and a better comprehensiveevaluation of poverty situation for each region is produced.The good correlation between the ALI and IPI valuesrevealed in Section 3.3 has also proven that remote sensingtechnique can advance poverty evaluation at a regionalscale more efficiently and accurately.

5. Conclusion

It is an important goal for governments and local policymakers to eradicate poverty in China and other countries.In order to tackle the excessively wide gap of socio-eco-nomic development levels in different regions, the measure-ment of the overall poverty situation at a regional scale isthe primary task. To estimate poverty levels of differentregions and analyse their spatial and temporal characteris-tics is the first step to research the regional disparity ofsocial wealth.

GDP as an indicator on its own is not capable of reflect-ing regional poverty level. Household surveys containdetailed information for poverty evaluation, however, ittakes substantial manpower and huge economic costs andit is time consuming. Based on 17 socio-economic indica-tors (including GDP) the IPI in this study is capable ofdemonstrating the socio-economic situations of the 31

1264 W. Wang et al. / Advances in Space Research 49 (2012) 1253–1264

study regions in China. Satellite remote sensing has theadvantage to provide efficient and accuracy spatial datafor various physical and social science research purposesdue to its high temporal resolution and extensive spatialcoverage. DMSP/OLS night-time light satellite imageryas a new data source is proven in this study that it can pro-vide a practical, efficient and reliable approach to explorepoverty issues at a regional scale. A good correlationbetween IPI and ALI is revealed in this study with a coef-ficient of determination (R2) of 0.854. Therefore, we sug-gest that government administrators and policymakersmay refer DMSP/OLS night-time light data to a valid datasource for estimating regional poverty issues.

This study is currently preliminary. Some scholars havetested the combination of the LandScan population dataand the global DMSP/OLS night-time light data. Forexample Elvidge et al. (2009) produced a global povertymap using a poverty index calculated by dividing popula-tion count (LandScan 2004) by the brightness of satelliteobserved lighting (DMSP/OLS night-time lights). TheLandScan population data produced by the US Depart-ment of Energy, Oak Ridge National Laboratory is usedas it can help disaggregate estimated data from regionalscale to pixel scale. However, the demographic data changeover time and the LandScan population data from differentyears are not compatible, thus the use of the older versionsis not recommended (Oak ridge National Laboratory,2010). Due to this fact, we believe that the LandScan pop-ulation data has limitations for studying regional povertyproblems in China. The Chinese government is currentlydeveloping a native population grid data that is based onthe 2010 nationwide population census. This data will havemuch higher accuracy and better reliability in comparisonwith old data collected by traditional methods as GIS tech-nique has been introduced into census. We plan to bringthe native population grid data into our future studies onChinese poverty issues once it is publicly published.

Acknowledgements

This study was supported by the Fundamental ResearchFunds for the Central Universities, and the ResearchFunds of Renmin University of China (10XNI008).

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