Elsevier

Advances in Space Research

Volume 49, Issue 8, 15 April 2012, Pages 1253-1264
Advances in Space Research

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

https://doi.org/10.1016/j.asr.2012.01.025Get rights and content

Abstract

All countries around the world and many international bodies, including the United Nations Development Program (UNDP), United Nations Food and Agricultural Organization (FAO), the International Fund for Agricultural Development (IFAD) and the International Labor Organization (ILO), have to eliminate rural poverty. Estimation of regional poverty level is a key issue for making strategies to eradicate poverty. Most of previous studies on regional poverty evaluations are based on statistics collected typically in administrative units. This paper has discussed the deficiencies of traditional studies, and attempted to research regional poverty evaluation issues using 3-year DMSP/OLS night-time light satellite imagery. In this study, we adopted 17 socio-economic indexes to establish an integrated poverty index (IPI) using principal component analysis (PCA), which was proven to provide a good descriptor of poverty levels in 31 regions at a provincial scale in China. We also explored the relationship between DMSP/OLS night-time average light index and the poverty index using regression analysis in SPSS and a good positive linear correlation was modelled, with R2 equal to 0.854. We then looked at provincial poverty problems in China based on this correlation. The research results indicated that the DMSP/OLS night-time light data can assist analysing provincial poverty evaluation issues.

Introduction

Poverty is a general term describing living conditions that are detrimental to health, comfort, and economic development (Elvidge et al., 2009). After 30 years of economic transformation, China has now become the second largest economy and the second largest trading nation in the world according to recent statistics of the World Bank and the World Trade Organisation. China’s Gross Domestic Product (GDP) has increased from 268.3 billion dollars to 5.3 trillion dollars since 1978, meanwhile the gap between Western China and other regions has been increasing (Li et al., 2008). Not everyone has equally shared the fruits of Chinese economic reform. Poverty is still a significant problem in China and it needs a long time and great efforts to be solved. So accurate assessments of regional poverty levels are essential for the central government and local policy makers to obtain reliable up-to-date data of the socio-economic situation and tackle regional inequality problems.

Traditionally, regional socio-economic development assessment is based on statistics collected by local governments. GDP is the most popular indicator of economic performance (Sutton and Costanza, 2002) and has been used in a wide range of socio-economic development studies in China. For example Jian et al. (1996) adopted GDP data to analyse the regional inequality trends. Li et al. (2004) applied it to evaluate economic standards of 31 provinces (or municipalities). Jin (2007) used GDP as one of the urban economic vitality indexes for quantitative economic analysis of 50 Chinese cities. However, there are limits to this type of data, as economic census is usually collected once every five years in China and it takes substantial manpower and generates huge amount of economic costs. It also needs a long period to update existing data and sometimes may become impossible because of various reasons, e.g. change of local administrative units. It cannot meet special demands either due to the lack of spatial information.

In comparison to traditional methods, satellite remote sensing has an advantage to provide efficient and accuracy spatial data for various physical and social science research purposes due to its high temporal resolution and extensive spatial coverage. Satellite imagery has been recognised to be capable of mapping and analyzing socio-economic related issues with high accuracy since the late 1960s (e.g. Tobler, 1969, Welch, 1980, Foster, 1983) and the night-time radiance data has been proven to be capable of providing strong estimation of population, GDP and electricity consumption based on the strong correlation between lights and human activities (Elvidge et al., 1997a, Elvidge et al., 1997b). It shows a good potential in regional poverty analysis. 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 Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) data. NGDC combines the cloud-free portions of night-time orbital segments over a full year to generate annual night-time lights products (Elvidge et al., 1997a, Elvidge et al., 1997b Elvidge et al., 2001) that have been used in a range of studies, such as GDP estimation and energy consumption analysis (Elvidge et al., 1997a, Elvidge et al., 1997b, Elvidge et al., 2001), mineral in-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 diameter of a city and its economy. Elvidge et al., 1997b, Elvidge et al., 1999 then found a close relationship between night-time light and human activity such as energy consumption and the important economic activity indicator GDP. The strong relationship between economic activities and CO2 emissions with the total lit area were also revealed and mapped by (Doll et al., 2000). Later, Doll (2003) used the cumulative radiance value in the radiance-calibrated night-time image to develop an area-GDP relationship at a national scale for the United Kingdom. Sutton et al. (2007) made a similar attempt to estimate sub-national GDP for the United States, China, India and Turkey. Elvidge et al. (2009) produced a global poverty map using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP/OLS night-time lights). The main socio-economic factors considered by most of these studies were population, energy consumption, greenhouse gas emissions, urban sprawl, forest fires monitoring and light pollution. There are no studies on poverty issues of China at a provincial scale using remote sensing data so far.

This study combines the 3-year DMSP/OLS night-time light data with other socio-economic statistical indicators to establish DMSP/OLS night-time average light indexes at a provincial scale in China and analyse the relationship between them and an integrated poverty index to explore the spatially irregular distribution of social wealth of China. It may contribute to the effort of a more balanced regional development in China.

Section snippets

Study area

31 provinces and municipalities in mainland China (Fig. 1) have been selected to carry out this study. The rapid economical growth of these 31 regions in the last 30 years has drawn worldwide attention and made China the world’s second largest economy according to the World Bank. Meanwhile, the uneven economic growth rate has caused apparent economical inequality amongst different regions and built up a big gap between the west and the east (Li et al., 2008). The inequality is now recognised as

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 rich provinces and municipalities with positive poverty index values are located in eastern China. The poorest 5 provinces with poverty index less than –0.50, including Qinghai, Yunnan, Gansu, Guizhou and Xizang, are all located in western China, where regional economy is mainly based on agriculture with less industry and poor transportation and other public utilities. A large

Comparison of IPI to GDP at a provincial scale

GDP refers to the market value of all final goods and services produced in a given period within a country (Goossens et al., 2007). It is a standard indicator used to measure a country’s economic performance and is often seen as an indicator of well-being. However, GDP was never intended to be used for measuring social well-being. Its key flaw is that it fails to differentiate costs from benefits, identify productive activities from destructive ones, and distinguish sustainable practices from

Conclusion

It is an important goal for governments and local policy makers to eradicate poverty in China and other countries. In order to tackle the excessively wide gap of socio-economic development levels in different regions, the measurement of the overall poverty situation at a regional scale is the primary task. To estimate poverty levels of different regions and analyse their spatial and temporal characteristics is the first step to research the regional disparity of social wealth.

GDP as an

Acknowledgements

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

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