Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China

Journal of Cleaner Production 255 (2020) 120245

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Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China Kaifang Shi a, b, c, Zhijian Chang a, b, c, Zuoqi Chen d, e, Jianping Wu d, e, Bailang Yu d, e, * a School of Geographical Sciences, State Cultivation Base of Eco-agriculture for Southwest Mountainous Land, Southwest University, Chongqing, 400715, China b Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, School of Geographical Sciences, Southwest University, Chongqing, 400715, China c Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China d Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China e School of Geographical Sciences, East China Normal University, Shanghai, 200241, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 June 2019 Received in revised form 27 December 2019 Accepted 23 January 2020 Available online 24 January 2020

Poverty is a chronic worldwide dilemma that can seriously hamper human sustainable development, which is closely related to economic growth, environmental protection, ecological restoration, and sustainable utilization of resources. Accurately and effectively identifying and evaluating poverty has become an important prerequisite for allowing Chinese governments to make reasonable poverty reduction and alleviation policies. Thus, using Chongqing as a study area, the purpose of this study was to analyze poverty from multiple viewpoints based on multiple data sources. First, a comprehensive poverty index (CPI) was developed by combining nighttime light data, the digital elevation model (DEM), the normalized differential vegetation index (NDVI), and point of interest (POI) data to map poverty at a 500-m spatial resolution. Then, the performance of the CPI was validated with poverty-stricken villages, Google Earth images, and the multidimensional poverty index (MPI). Finally, spatial autocorrelation analysis was used to explore the spatial distribution of poverty across county and town levels. The results revealed that the CPI could provide an effective way of identifying the spatial distribution of poverty when compared with three validated indexes. Most of the rich counties were in the center of Chongqing, whereas the poor counties were located in the northeast and southeast of Chongqing. The Global Moran’s I index showed that there were significantly positive spatial autocorrelations of poverty, and that the spatial autocorrelation of poverty was more significant at the town level compared to the county level. Among the selected factors, the POI cost distance was the most import factor for assessing poverty. Our study will be valuable for providing scientific references for the government to implement precise poverty alleviation methods with differentiated policies in China. © 2020 Elsevier Ltd. All rights reserved.

^ as de Handling editor: Cecilia Maria Villas Bo Almeida Keywords: Poverty Nighttime light data POI Multiscale analysis Chongqing

1. Introduction Poverty is a chronic worldwide dilemma that has been present throughout the entire historical process of human development (Steele et al., 2017). According to the 2018 World Development

* Corresponding author. Key Lab. of Geographical Information Science, Ministry of Education East China Normal University 500 Dongchuan Rd Shanghai, 200241, China. E-mail addresses: [email protected], [email protected] (K. Shi), 915164317@ qq.com (Z. Chang), [email protected] (Z. Chen), [email protected] (J. Wu), [email protected] (B. Yu). https://doi.org/10.1016/j.jclepro.2020.120245 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

report, approximately 10% of the global population still lives in poverty (World Bank, 2018). Currently, the elimination of all forms of poverty is one of the 17 Sustainable Development Goals proposed by the United Nations (United Nations, 2015). Hence, poverty reduction has become an important mission of all countries in the world, especially in less developed or developing countries (Zhao et al., 2019). As the largest developing country in the world, China’s economy has made remarkable advances, which greatly promotes the rapid development of anti-poverty practices (Liu et al., 2017). However, because of the imbalance of China’s socioeconomic development and the large scale of poverty-stricken population, poverty is still a major issue related to China’s

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K. Shi et al. / Journal of Cleaner Production 255 (2020) 120245

sustainable development (Wu et al., 2018). Reducing and eliminating poverty is still a long and arduous process in China (Wang et al., 2018). Therefore, accurate and reliable information on the poverty level is essential for the Chinese government to make reasonable poverty reduction and alleviation policies. Traditionally, poverty was the general term for what was considered to be an economic phenomenon, describing the condition in which the economic situation of individuals or families could not meet the basic standard of living (Johannes and Ernst, 2014). Thus, gross domestic product (GDP), per capita income, or per capita financial expenditure have become the most popular socioeconomic indicators that were used to identify and evaluate poverty (Gouveia et al., 2018; Labar and Bresson, 2011; Ren et al., 2018a). However, the collection and processing of varying quality and quantity of censuses or surveys requires substantial time and monetary costs. A lack of adequate and reliable data remains a major obstacle to poverty identification, especially in some poor and backward areas in China (Ren et al., 2018b). With socioeconomic development, poverty has received different meanings in different development periods. Poverty identification and evaluation are no longer defined along purely economic dimensions of poverty (e.g., GDP or per capita income) (Ren et al., 2018b). The definition of poverty has changed from a single economic dimension to a multidimensional definition that includes natural, human and ecological factors (Bossert et al., 2013). The 2000/2001 World Development report also pointed out that poverty is a multidimensional phenomenon that involves income, health, education, resource endowment, housing, and geographical location. Many studies have attempted to identify and evaluate poverty from a multidimensional assessment based on statistical data and have achieved very good results (Cheng et al., 2019; David et al., 2013; Wu et al., 2018). However, it would take this multidimensional evaluation for a long time to update the existing statistical data through the economic census (Li et al., 2015; Padda and Hameed, 2018). In addition, due to the absence of spatial distributions, the internal spatial patterns of poverty have not been clarified within an administrative unit, leading to a difficult mode for targeted poverty alleviation (Elvidge et al., 2009). Given the difficulties in traditional data acquisition and rough display, it is necessary to provide leveraged novel data sources to identify and evaluate poverty in China. Compared to the traditional statistical or survey data, remote sensing data are unique, objective, and valuable data resources, and they have the advantage of providing efficient and accurate spatial data for observing socioeconomic and physical phenomena from a multiple-scale perspective. Nighttime light (NTL) data, which are a common source of remote sensing data, have great potential to monitor human-related socioeconomic activities (Ghosh et al., 2013), such as GDP (Shi et al., 2014b), population (Amaral et al., 2005), carbon dioxide emissions (Shi et al., 2016a), electric power consumption (Shi et al., 2016b), housing vacancy rate (Chen et al., 2015), and others (Shi et al., 2014a). Because NTL data can indirectly reflect human society development, which is closely related to economic activity intensity, population density, natural environmental conditions, and geographical location, the data can provide an accurate and effective indication of poverty in detail over larger areas (Elvidge et al., 2009; Noor et al., 2008). Many studies have been developed to evaluate poverty based on NTL data. For example, Yu et al. (2015) explored the spatial overlap between NTL data and national poor counties, and demonstrated that the NTL data had great potential for identifying poverty at the county level in China. Li et al. (2019d) and Li et al. (2019c) demonstrated that high-poverty counties could be identified using a machine learning method based on NTL data. Pan and Hu (2018) further proved that NTL data could effectively reveal

county-level poverty in China. In addition, Wang et al. (2012) indicated that NTL data could assist in identifying poverty at the provincial level in China. However, most of the studies only considered NTL data as the sole evaluation data source. Considering the complexity of poverty characteristics, poverty evaluation is difficult to effectively identify in theory and practice with a single type of NTL data. For example, we could not tell the differences between wealthy, sparsely populated areas and poor, densely populated areas within NTL data (Jean et al., 2016; Zhao et al., 2019). To fill in this gap, some studies have attempted to identify and evaluate poverty using multisource data. For example, Zhao et al. (2019) accurately estimated poverty in Bangladesh by combining multiple data sources, including NTL data, road maps, land cover data, Google satellite images, and division headquarter location data. Elvidge et al. (2009) first produced a global poverty map based on the LandScan product and NTL data. However, on the one hand, a limitation of coarse spatial resolution (e.g., 10-km spatial resolution) made it difficult to accurately identify poverty in specific places, such as households and villages. On the other hand, the above studies focused on poverty in individual regions at an individual level (e.g., regional level or county level). As is well known, China’s administrative structure is a multisystem model where a county unit is directly composed of many town units. A single spatial level with varying geographical and socioeconomic contexts might produce different operation functions (Ma et al., 2016; Shi et al., 2019b). Hence, the findings of spatiotemporal change of poverty cannot be applied from one level (e.g., 10-km spatial resolution) to another level (e.g., county level). China’s precision poverty alleviation, a poverty alleviation policy in China, was to achieve comprehensive poverty alleviation at the national, provincial, county and township levels (Feng et al., 2018; Lo and Wang, 2018). The future poverty alleviation work mechanism must combine the precise targeting of villages and households with regional targeting. Therefore, it’s necessary to perform comparable work to identify and evaluate poverty within different spatial levels (e.g., grid, county and town levels) in China. Against these backdrops, the three study objectives are to 1) identify poverty at a fine spatial resolution from a poverty index using multiple-source data; 2) evaluate poverty at multiple spatial levels; 3) examine what are the major impact factors for poverty. To attain the above objectives, we selected Chongqing of China as a case study and identified poverty at the 500-m spatial resolution, and then we conducted experiments at two administrative levels (e.g., county and town levels) to test our multiple-spatial evaluation in this study. First, a comprehensive poverty index (CPI) was quantified to produce a poverty map at 500-m spatial resolution using NTL data, digital elevation model (DEM), normalized differential vegetation index (NDVI), and point of interest (POI) data. Second, a spatial autocorrelation model was employed to evaluate the spatial distribution of poverty in Chongqing across county and town levels. Finally, we examined the correlations between the CPI and several poverty factors (e.g., NTL data, DEM, NDVI, and POI). The study will offer a scientific and effective way to deepen our understanding of the spatial distribution of poverty, and it will provide auxiliary technical support for China’s comprehensive precise poverty alleviation strategy. 2. Study area and data sources 2.1. Study area Chongqing, which is located between 105
110 d110
110 E and N, was selected as the study area and consists of 38 counties (or districts) with a total area of 82,402 km2 (Fig. 1). Since 1997, Chongqing has experienced rapid growth of gross domestic 28
100 d32
130

K. Shi et al. / Journal of Cleaner Production 255 (2020) 120245

3

Chongqing China Shapingba Yuzhong

Nanan

Rongchang

0

730

1460 Km

Dadukou Jiangbei

Jiulongpo 0

60

120Km Km

Fig. 1. Spatial location of Chongqing in China.

product (GDP) and population. In 2015, the GDP and population in Chongqing were 1571.73 billion Yuan and 30.16 million persons, respectively (National Bureau of statistics of the People’s Republic of China, 2016). Although the per capita GDP in Chongqing was closer to the national average, socioeconomic development showed a significant spatial imbalance, causing an apparent socioeconomic inequality between the central regions and other regions. Regional inequality and poverty have become a huge obstacle to sustainable socioeconomic development in Chongqing (Yu et al., 2015), which can be regarded as a microcosm of China. Of the 38 counties in Chongqing, there are 14 national poverty counties and 4 city poverty counties, which are mainly distributed in hilly and mountainous areas within northeast and southeast Chongqing. In addition, there are 855 townships with poor villages, accounting for 81.9% of the total number of townships in Chongqing. Chongqing has become one of the main battlefields of poverty alleviation in China. Due to this, targeting and identifying the poverty in Chongqing is the most representative issue in China and is important for evaluating and understanding the distribution of poverty on multiple levels.

2.2. Data sources Seven kinds of data were used in this study, including NTL data, DEM, NDVI, POI data, survey data, socioeconomic data, and administrative boundary data (Fig. 2). Currently, there are a variety of NTL data, such as the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light data and the Luojia 1-01 nighttime light data (Zhang et al., 2018). In this study, the 2015 annual nighttime light composite data acquired by the Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) were used to reflect the degree of human development and were collected from the National Oceanic and Atmospheric

Administration’s National Geophysical Data Centre (NOAA/NGDC). The data included all of the lights emitted by human beings at night and were produced in 15 arc-second segments (approximately 500-m). The DEM was used to represent topographic change and was obtained from the Shuttle Radar Topography Mission (SRTM)Consortium for Spatial Information (CGIAR-CSI) with a 250-m spatial resolution. The 2015,500-m monthly NDVI data were downloaded from the Geospatial Data Cloud. It is noteworthy that the annual NDVI data were generated by the average monthly NDVI data. The POI data contain a large number of point locations with detailed information about human activities. In this study, there were 18 types of POI data with more than 250 thousand points in Chongqing, such as car services, hotels, medical services, shopping places, train stations, repasts, financial services, parking lots. POI data were obtained from online mapping services provider including AutoNavi. The survey data of 1919 poverty-stricken villages were collected from the field survey in 2016 and the 2015/ 2016 survey database of precision poverty alleviation farmers in Chongqing. The 2015 socioeconomic census data (e.g., per capita GDP, per capita disposable income, per capita cost of living, per capita beds in health, and urbanization rate) were obtained from the 2016 Chongqing Statistical Yearbook. Finally, the vector data of administrative boundaries were downloaded from the National Geomatics Centre of China.

3. Methods Previous studies have proved that poverty is a complex concept, involving multidimensional dimensions, such as natural, socioeconomic, and geographical dimensions. First, referencing the World Development report and other studies (Wang et al., 2012; Wu et al., 2019; Zhao et al., 2019), an integrated index (e.g., CPI) was developed to identify poverty when considering the typical natural-economic environment of Chongqing based on slope,

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K. Shi et al. / Journal of Cleaner Production 255 (2020) 120245

Fig. 2. Spatial distribution of data used in this study: (a) DEM, (b) NDVI, (c) NPP-VIIIR, and (d) POI.

Human Settlement Index (HSI), POI density, and POI cost distance. Data interpretation and preprocessing can be seen in section 3.1. Then, the CPI was validated by simple visual comparison analysis and socioeconomic data. Finally, we used the CPI to evaluate the spatial distribution of poverty in Chongqing from a multiscale view.

3.1. Data preprocessing The slope, HSI, POI density, and POI cost distance should be preprocessed before they are made available. First, the slope represents the status of topographic relief, which plays a very important role in human survival and production (Shi et al., 2019a). Due to the complex topography of Chongqing, the flat terrain regions have often experienced rapid social and economic development. This again suggests that topography becomes an important factor for poverty evaluation in Chongqing. Thus, the DEM was used to calculate the slope for evaluating poverty in this study (Fig. 3(a)). Second, POI density and POI cost distance are composite indexes that reflect the convenience of human survival and production and the degree of regional socioeconomic development which are closely related to poverty. The higher the index values, the more prosperous the human development. POI density and POI cost distance were modeled with the kernel density estimation and cost distance analysis based on POI data, respectively (Fig. 3(c)e(d)). It is noted that cost distance analysis could evaluate the minimum cumulative cost distance from each unit to the nearest source on the cost surface. The kernel density estimation and cost distance analysis were calculated by the software of ArcGIS10.5 in this study. Third, previous studies have demonstrated that HSI is highly correlated with human settlements and population (Hu et al., 2017; Lu et al., 2015), which are also closely related to poverty (Fig. 3(b)). Thus, by combining the NPP-VIIRS and NDVI data, the HSI at 500-m resolution was developed with the following formula:

HSI ¼

ð1  NDVImax Þ þ NPPnor 1  NPPnor þ NDVImax þ NPPnor  NDVImax

(1)

where NPPnor is the normalized value of the NPP-VIIRS data, whereas NDVImax and NDVImin are the maximum and minimum NDVI data, respectively. The normalized method is introduced in section 3.2. It is noteworthy that the NPP-VIIRS data should be corrected before data processing because the original data have not been filtered to remove light detections, such as gas flares, volcanoes, fires, and other background noises (Li et al., 2013; Shi et al., 2014a). Following the study of Shi et al., 2014b, 2015, an eightdomain algorithm was calculated to remove the outliers when considering the maximum value of Chongqing Jiangbei Airport as the optimal threshold value (146.502). The details of the HIS could refer to the study of Lu et al. (2015). Additionally, all of the remote sensing data were projected into an Albers Conic Equal Area Projection and resampled to a spatial resolution of 500-m before data preprocessing.

3.2. Developing the CPI As mentioned above, poverty is a multidimensional system that refers to many aspects of human development and the natural environment. Thus, the slope, HSI, POI density, and POI cost distance were used to develop the CPI, which could then accurately identify and evaluate different themes of poverty in Chongqing. Because these poverty factors are related to different aspects, how to effectively integrate these factors into the multidimensional system of poverty has become an important issue for mapping poverty in Chongqing. To avoid the uncertainty of modeled results affected by weight setting (He et al., 2017; Shi et al., 2019a), the CPI was calculated by the geometric average method in this study:

Fig. 3. The data preprocessing results for (a) slope, (b) HSI, (c) POI density, and (d) POI cost distance.

K. Shi et al. / Journal of Cleaner Production 255 (2020) 120245

CPIi ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Y4   4 Xij þ 1 j¼1

(2)

where CPIi is the value of poverty in the ith pixel (500-m spatial resolution). The degree of poverty increases with increasing CPI value. Xij represents the normalized value of the j factor in the ith pixel. Because of the inconsistency of dimensions, the above poverty factors should be normalized with the following formula:

Xi ¼

X  Xmin Xmax  Xmin

(3)

where Xi is a normalized value for the ith pixel, X represents the value of the HSI, POI density, or POI cost distance, and Xmin and Xmax are the minimum and maximum values of these factors, respectively. Because the steeper the slope, the more unfavorable for human development, the slope was normalized by formula 4:

Xi ¼

Xmax  X Xmax  Xmin

(4)

where X represents the value of the slope. 3.3. Accuracy assessment Accuracy assessment is an essential part of evaluating the poverty mapping results. Due to the lack of data on poverty at fine scales, it is impossible to evaluate the mapping results pixel by pixel. Thus, two alternative approaches were used to analyze the performance of CPI on identifying poverty in Chongqing. First, a visual comparison was used to validate the accuracy of the spatial distribution of the CPI. Several studies have shown that the modeled results could be clearly and effectively validated by survey data, poverty county and high-resolution remote sensing images (Estel et al., 2015). For example, Shi et al. (2019a) and Xie and Weng (2016) visually validated cultivated land, fallow land and electricity consumption using Google earth images, and they demonstrated that the validated method could ensure the accuracy of results. Therefore, the survey of poverty-stricken villages, poverty county and Google earth images were selected to visually validate the CPI accuracy in this study. Second, the comparison and regression analysis were employed to evaluate the similarity between CPI and MPI. The multidimensional evaluation of poverty by MPI based on statistical data has been widely accepted by academic circles (Feng et al., 2018; Pan and Hu, 2018). Currently, a very popular and wellknown conceptual framework of sustainable livelihood proposed by the Department for International Development (DFID) in the UK has been used to evaluate multidimensional poverty (DFID, 1999e2005). This framework defined the livelihood capital from five aspects, including livelihood outcome, livelihood strategy, livelihood asset, vulnerability context, and transforming structure and process. Thus, following the DFID framework (DFID, 1999e2005) and other studies (Yu et al., 2015), 13 socioeconomic and natural variables, which were recognized as well-established indicators related to poverty, have been employed to develop the MPI for validating the CPI (Table 1). A multicollinearity test has been investigated for these variables. The MPI was calculated using the following formula:

MPIi ¼

n X

Wj  Pij

(5)

j¼1

where MPIi represents the value in the ith unit, Wj represents the weight of the jth variable, and Pij represents the standard value of

5

the jth variable in ith unit. Based on data availability, we calculated the MPI in Chongqing at the county level. It is noted that since weight was directly related to the quality of evaluation results, the entropy method was used to determine the weight setting (Table 1). The specific process can be referred to the study of Yu et al. (2015). 3.4. Spatial autocorrelation analysis Spatial autocorrelation analysis, including global Moran’s I index and local Moran’s I index, was used to evaluate how the spatial distributions of poverty varied substantially in Chongqing across county and town levels. Global Moran’s I index measures the spatial autocorrelation in a specific range by using a specific value (Shi et al., 2019b). The purpose of this index is to reflect the similarity of the attribute values of the unit adjacent to or near the space and to judge the spatial distribution pattern of the whole area. The specific formula is as follows:

  P P n ni ¼ 1 nj ¼ 1 wij ðxi  xÞ xj  x I ¼ P Pn Pn 2 n i¼1 j ¼ 1 wij i ¼ 1 ðxi  xÞ

(6)

where I represents global Moran’s I index, n represents the number of administrative units, xi and xj are the CPI value of the ith and jth administrative units, respectively, x is the average CPI value for all administrative units, and w represents the binary weight that was determined using the Queen-based adjacency method. Global Moran’s I index ranges from 1 to 1. Values greater than 0 indicate that there is a positive spatial correlation, and values less than 0 indicate that there is a negative spatial correlation. The larger the value, the higher the spatial correlation degree (Anselin, 2010). Local spatial autocorrelation analysis could reveal the heterogeneity of local spatial differences and fully reflect the spatial correlation and variation trend of poverty (Bone et al., 2013). Thus, local Moran’s I index was employed to measure the spatial difference of poverty in this study:

Li ¼ zi

X wij zj

(7)

j

where L represents the local Moran’s I index and zi and zj represent the standardization values of the CPI values in the ith and jth administrative units, respectively. All of the CPI values could be classified into four classes in the local Moran’s I index across county and town levels: High-High (HeH), representing high CPI values surrounded by high CPI values; Low-Low (L-L), representing low CPI values surrounded by low CPI values; and High-Low (H-L) and Low-High (L-H), representing high CPI values surrounded by low CPI values, and low CPI values surrounded by high CPI values, respectively. 4. Results 4.1. Accuracy of poverty identification Many studies have demonstrated that the evaluation results can be clearly identified by visual comparison from high-resolution remote sensing images. For example, Varshney et al. (2015) estimated the proportion of metal roofs and thatched using Google earth images and considered the villages with large percentages of thatched roofs as poor villages. With reference to former studies, we randomly selected six typical points to validate the accuracy of poverty identification. The spatial distribution of points was relatively balanced in Chongqing. Every point with a high (or low) CPI

6

K. Shi et al. / Journal of Cleaner Production 255 (2020) 120245 Table 1 Poverty evaluation index system and their corresponding weights. ID

Index

Index attribute

Weight

1 2 3 4 5 6 7 8 9 10 11 12 13

Per capita GDP Per capita disposable income Per capita financial expenditure Per capita cost of living Per capita deposit balance of financial institutions Per capita investment in fixed assets Poverty filing number Per student educational institutions Per capita medical institutions Per capita beds in health Urbanization rate Average altitude Proportion of slope area above 15


þ þ þ þ þ þ e þ þ þ þ e e

0.037 0.013 0.077 0.013 0.263 0.037 0.153 0.034 0.010 0.039 0.020 0.037 0.267

value corresponded to a Google earth image. Fig. 4 shows the comparison results. Visually, the points with high CPI values were found in some developed areas with flat terrains, convenient transportation, and density of buildings (Fig. 4(a)e(c)). Generally, the flat terrain is conducive to social and economic development (Lin et al., 2018). Many studies have proved that the developed areas tend to be concentrated in some flat valleys in Chongqing (Liu et al., 2016, 2018). In contrast, it was evident that the points with low CPI values were located in some undeveloped areas with inconvenient transportation and low density of buildings (Fig. 4(d)e(f)). In addition, we visually compared pixel-level CPI with 1919 poverty-stricken villages and found that most of the poverty-stricken villages were distributed in some areas with relatively low CPI values (Fig. 4). A comparison between average CPI values and poor counties defined by the government was also performed in Fig. 5(a). We found that the low CPI values were mainly concentrated in some poor counties but were less distributed in rich counties in Chongqing. These findings proved the

effectiveness of CPI in identifying and evaluating poverty (at a 500m spatial resolution) in Chongqing. To further evaluate the accuracy of CPI, a comparison between average CPI values and MPI values has been developed in Table 2. The class rank of these two indexes shows a relative similarity in Chongqing at the county level. Eleven counties had CPI class rank in accordance with MPI class rank, accounting for 28.94% of the total counties. Only seven counties, Wulong, Qianjiang, Tongnan, Rongchang, Banan, Yongchuan and Dadukou, showed relatively large differences in class ranks (with the absolute value greater than or equal to 3), accounting for 18.42% of the total counties. Moreover, we used a regression model to explore the correlation between CPI and MPI in Chongqing at the county level. From Fig. 6(a), the R2 value of 0.931 indicated a strong correlation between the CPI and MPI, showing good performance of the poverty map. In summary, both the county visual comparison and correlation analysis have proved that the CPI could be used to accurately identify and evaluate poverty in Chongqing.

Fig. 4. Spatial verification of the CPI using poverty-stricken village data and Google earth images in Chongqing.

K. Shi et al. / Journal of Cleaner Production 255 (2020) 120245

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Fig. 5. The spatial distributions of MPI, CPI, ANTL, and per capita GDP in Chongqing at the county level. Note: ANTL represents the average nighttime light index.

Table 2 Comparisons of MPI, CPI, and ANTL in Chongqing at the county level. County

MPI

Rank

CPI

Rank

Difference with MPI

ANTL

Rank

Difference with MPI

Per capita GDP

Rank

Difference with MPI

Wuxi Chengkou Wushan Fengjie Yunyang Pengshui Kaixian Youyang Wulong Shizhu Fengdu Xiushan Nanchuan Qianjiang Qijiang Wanzhou Fuling Zhongxian Dianjiang Jiangjin Liangping Tongnan Hechuan Changshou Dazu Rongchang Banan Tongliang Yongchuan Beibei Bishan Yubei Shapingba Dadukou Jiulongpo Nanan Jiangbei Yuzhong

0.048 0.086 0.190 0.270 0.249 0.256 0.256 0.267 0.297 0.327 0.341 0.347 0.364 0.372 0.386 0.388 0.390 0.392 0.406 0.407 0.407 0.407 0.426 0.429 0.430 0.433 0.445 0.452 0.453 0.508 0.510 0.576 0.597 0.606 0.611 0.636 0.750 0.906

38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

1.270 1.262 1.313 1.336 1.354 1.347 1.355 1.358 1.348 1.364 1.370 1.386 1.373 1.369 1.388 1.387 1.402 1.404 1.422 1.403 1.413 1.434 1.426 1.425 1.428 1.436 1.427 1.435 1.429 1.442 1.441 1.461 1.520 1.553 1.511 1.542 1.526 1.729

37 38 36 35 32 34 31 30 33 29 27 25 26 28 23 24 22 20 18 21 19 12 16 17 14 10 15 11 13 8 9 7 5 2 6 3 4 1

1 1 0 0 2 1 1 1 3 0 1 2 0 3 1 1 0 1 2 2 1 5 0 2 0 3 3 0 3 1 1 0 1 3 2 0 2 0

0.037 0.018 0.054 0.069 0.091 0.034 0.168 0.046 0.078 0.127 0.080 0.262 0.156 0.212 0.333 0.284 0.390 0.094 0.203 0.534 0.213 0.278 0.537 0.982 0.721 0.560 0.954 0.851 0.683 1.475 1.488 4.226 8.082 8.564 5.613 9.279 10.693 29.258

36 38 34 33 30 37 26 35 32 28 31 22 27 24 19 20 18 29 25 17 23 21 16 10 13 15 11 12 14 9 8 7 5 4 6 3 2 1

2 1 2 2 4 4 6 4 2 1 3 5 1 1 5 3 4 8 5 2 5 4 0 5 1 2 1 1 4 0 0 0 1 1 2 0 0 0

18,741 22,713 19,317 25,855 20,934 22,687 27,882 20,908 37,824 33,199 24,876 28,145 33,159 44,099 34,821 51,570 71,430 31,115 35,372 46,150 36,542 39,573 35,267 52,665 46,120 47,577 57,423 45,626 52,317 55,275 52,821 78,139 63,768 48,173 85,156 80,011 81,411 147,423

38 33 37 31 35 34 30 36 21 26 32 29 27 19 25 13 6 28 23 16 22 20 24 11 17 15 8 18 12 9 10 5 7 14 2 4 3 1

0 4 1 4 1 1 2 5 9 3 4 2 1 6 1 10 16 7 3 3 4 3 8 4 3 2 4 7 2 0 2 2 1 9 2 1 1 0

4.2. Evaluation of the spatial distribution of poverty The spatial distribution of poverty was identified in Chongqing in 2015 (Fig. 4). The lower the CPI value, the poorer the area. We found that most of the areas with high CPI values were located in the center of Chongqing (CC). The poor areas with low CPI values were concentrated in the northeast region of Chongqing (NC) and the southeast region of Chongqing (SC), where regional socioeconomic development mainly relied on agriculture with an unfavorable natural environment. Although the grid-level poverty map could easily macroscopically, objectively, and intuitively present the spatial distribution of

poverty, it was difficult to successfully carry out poverty alleviation policies (Pan and Hu, 2018; Ren et al., 2018a). Most poverty alleviation funds or preferential policies were implemented based on administrative units. As we know, county-level and town-level administrative units are the basic units for poverty evaluation in China. Thus, we also evaluated the spatial distribution of poverty in Chongqing across county and town levels. From Fig. 5(a), the average CPI values of 38 counties were grouped into five classes based on the natural break method: 1) very low CPI (1.461e1.729, six counties), 2) low CPI (1.413e1.461, twelve counties), 3) medium CPI (1.373e1.413, seven counties), 4) high CPI (1.313e1.373, ten counties), and 5) very high CPI (1.262e1.313, three counties). The

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y = 0.487x + 1.216 R² = 0.931 P < 0.001

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Fig. 6. Correlations between MPI and CPI, ANTL, per capita GDP in Chongqing at the county level.

three counties, including Chengkou, Wuxi, and Wushan, were the poorest counties in Chongqing. In contrast, six counties (Yuzhong, Shapingba, Jiangbei, Nanan, Dadukou, and Jiulongpo) with the highest CPI values were the richest counties, and they are the cultural and economic centers of Chongqing. To reveal the geographic patterns of poverty at the different levels, the global Moran’s I index was calculated as shown in Fig. 7. The results showed that there were significant positive spatial autocorrelations of CPI values at the county and town levels. The Moran’s I statistics were 0.663 at the county level and 0.897 at the town level, and all Z-statistics were greater than 2.58 at a 99% significance level, indicating a significant spatial autocorrelation of poverty in Chongqing. To determine the local spatial autocorrelation of poverty, we further examined the local Moran’s I index across different levels (Fig. 8). We found that the CPI values showed a distinct spatial agglomeration. At the county level, the HH spatial clusters (6) accounted for 15.79% of the total counties in Chongqing and were mainly clustered in the CC. Compared to the HH spatial clusters, the LL clusters (7) accounted for 18.42% of the total counties in Chongqing and were clearly identified in the NC, including Wushan, Fengjie, and Wushan (Fig. 8(a)). At the town level, a number of towns showed positive spatial autocorrelations, exhibiting HH and LL spatial clusters (Fig. 8(b)). There were 163 HH and 194 LL towns, accounting for 16.03% and 19.08% of significantly correlated towns, respectively. The HH clusters were concentrated in the CC and surrounding towns, but the LL clusters were mainly located in the NC and SC.

4.5

5. Discussion 5.1. CPI provides a reliable identification of poverty in Chongqing In this study, the CPI was developed as a quantitative index for identifying and evaluating poverty in Chongqing. Compared with census data and survey data, which were often extremely expensive and required time-consuming acquisition without spatially explicit details, the CPI can provide an effective way for identifying the spatial distribution of poverty (Feng et al., 2018). Although some studies have attempted to assess poverty based on highmedium spatial resolution remote sensing data from a microscopic view (Jean et al., 2016), the CPI, which combined coarse spatial resolution remote sensing data and socioeconomic big data, could directly and quickly map poverty at multiple scales, ultimately causing lower costs in time and money for the government and poverty alleviation workers. In addition, the advantage of the CPI was that it was an integrated poverty index that considered multiple dimensions, including infrastructure and natural environment, as well as the geographical location that reflected sustainable development. In contrast, many indexes, such as per capita GDP, were only used for measuring social well-being. Generally, the results indicated that the CPI can provide better performance for mapping poverty from multiple perspectives. From Fig. 4, we found that the CPI provided a good proxy for mapping poverty at a finer spatial resolution. Compared with Google earth images, pixels with low CPI values were found in some

Moran’s I: 0.663 P-value: 0.001 Z-value: 6.511

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Fig. 7. Global Moran’s I index of poverty in Chongqing at the (a) county level and (b) town level.

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Fig. 8. Spatial autocorrelation of poverty in Chongqing at the (a) county level and (b) town level.

remote and uninhabited areas. However, some natural features, such as small water bodies, parks, and scenic spots within an urban area, were all related to high CPI values. These further demonstrated that because the CPI simultaneously considered various factors, it could identify poverty from multiple perspectives, not just from a single natural or humanistic perspective. To fully evaluate the accuracy of the CPI, a parallel comparison of CPI, ANTL, and per capita GDP was conducted in this study. From Fig. 5, we found that the low CPI values showed a clustered distribution in poor counties, but the high values related to ANTL and per capita GDP were randomly distributed in some rich counties in Chongqing. A comparison between the MPI and CPI, ANTL, and per capita GDP was also conducted in Table 2. The class ranks of the MPI and CPI showed relative closeness, but relatively different ranks for MPI, ANTL, and per capita GDP. Moreover, the R2 value between the MPI and CPI was 0.931, which was relatively high compared to the results for the ANTL and per capita GDP (Fig. 6). These further demonstrated that the CPI had a higher ability to identify poverty than those of other indexes. It is noted that a remarkable phenomenon was found in Figs. 4e5: the CPI could not identify Tongnan as a poor county. By comparison, the per capita GDP in Tongnan (39,573 Yuan) was far higher than those poor counties such as Wuxi (18,741 Yuan), Chengkou (22,713 Yuan) (Table 2). The reason might be that the delimitation of poor counties often took into account the government’s preferential policy towards the old and young border areas in China (Ye et al., 2018). The evaluation results are basically consistent with findings from previous studies. For example, Chen et al. (2017) and Pan and Hu (2018) indicated that there was a higher possibility of poverty clusters in the western regions of China, and they found that all of the richer counties were located in the One-Hour Economic Sphere of Chongqing and the poorer counties were widely distributed in the SC and NC. Similar conclusions have also been drawn in our accuracy assessment. Additionally, Yu et al. (2015) realized that some rich areas had good infrastructure and rich resources with dim nighttime lights. Some counties with dry riverbeds, snowy mountains, and deserts related to high nighttime lights should be identified as rich counties. In our study, social big data could make up for the defects of the NPP-VIIRS data, which could not accurately identify some confusing light areas, such as the Greater Khingan Range of China.

5.2. The spatial distribution of poverty presents a diverse pattern in Chongqing Fig. 5(a) and Table 2 show a diversified pattern of poverty in Chongqing at the county level. Most of the rich counties were centered in the CC, whereas the poor counties were all located in the NC and SC. Specifically, because Yuzhong, Shapingba, Jiangbei, Nanan, Dadukou, and Jiulongpo are the political, cultural, and financial centers of Chongqing that provide favorable employment services and perfect living and production conditions, these six counties are related to the highest CPI values in Chongqing. Wanzhou, Jiangjin, and Nanchuan presented relatively high CPI values because they are regional political and economic centers in Chongqing. In contrast, several counties, such as Chengkou, Wuxi, Wushan, Youyang, and Yunyang, are all poor regions in Chongqing due to their unfavorable natural environment and weak infrastructure. For example, the serious rocky desertification (accounting for approximately 86.81% of the total area) severely limited agricultural production and transportation development in Youyang (Yu et al., 2015). Severe soil erosion has become an obstacle to economic development in Yunyang. In total, the CPI has effectively revealed the spatial distribution of poverty, which was essentially matched with the actual conditions. From the spatial autocorrelation analysis, the spatial distribution of poverty in Chongqing varied from the county to town levels. The Global Moran’s I indexes increase from the county level to town levels, indicating that there are significantly positive spatial autocorrelations of poverty in Chongqing (Fig. 8). Specifically, the county Global Moran’s I is 0.663, while the value is 0.897 at the town level. This further indicates that the spatial patterns of poverty in Chongqing are partly affected by spatial level change. From the local Moran’s I analysis, the spatial autocorrelation clusters mainly belong to HH and LL at the different levels (Fig. 9). Although the spatial autocorrelation distribution of poverty changes with the change across levels, the global pattern remains relatively stable. This means that the dual spatial structure of poverty has not changed in Chongqing across the county and town levels. Moreover, it is noted that the county LL clusters are mainly concentrated in NC, but the town LL clusters are widely distributed in NC, SC, and other regions of Chongqing. This indicates that the spatial autocorrelation of poverty is much cleaner at the town level

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Fig. 9. Correlations between the CPI and poverty factors.

compared to the county level. Identifying and comparing the spatial distribution of poverty from different levels would provide scientific references for the government to implement precise poverty alleviation with differentiated policies. 5.3. POI cost distance was one of the major factors for poverty The CPI combining multisource remote sensing and POI data provides a comprehensive evaluation of poverty from multiple dimensions. To quantify which factors contribute more to poverty, a regression analysis was conducted in this study. NDVI, DEM, nighttime light, POI cost distance, and POI density were thus selected to explore the correlations of the CPI at the pixel level in Chongqing. As shown in Fig. 9, although the R2 values varied respectively, all of the factors passed the significance test, indicating that they were all closely related to poverty. Specifically, the R2 of NDVI, DEM, nighttime light, and POI density were 0.371, 0.497, 0.140, and 0.346, respectively. The R2 of POI cost distance was 0.759, the largest among the five factors. In summary, POI cost distance was more correlated with the CPI than other factors, from which it could be inferred that POI cost distance closely related to socioeconomic prosperity was among the major contributions to poverty in Chongqing. Ren et al. (2018a) showed that regional infrastructure had a significant impact on poverty in China. Pan and Hu (2018) and Liu et al. (2017) indicated that the availabilities of education, financial services, and transportation related to poverty have become important factors impacting poverty. Through regression analysis, we showed that poverty could not be accurately identified from a single data source (e.g., the NPP-VIIRS data). A combination of multiple source data could be a more effective way of identifying poverty. From Fig. 9, we found that DEM and NDVI were negatively correlated to the CPI in Chongqing. This can be explained by the impact of the natural environment in poverty. For example, the more complex the topography, the more inconvenient human

production (or living). The POI cost distance was significantly negatively correlated with the CPI. This was attributed to the availabilities of infrastructure. The positive correlations between the NPP-VIIRS data and POI density were also shown in Fig. 9. This also implies that these factors have become bright lights for human prosperity. Our finds were consistent with the conclusions of previous studies (Liu et al., 2017; Wang et al., 2012; Yu et al., 2015). 5.4. Limitations and future perspectives Although the CPI has shown its efficiency and accuracy for poverty identification and evaluation in Chongqing, there are some limitations that will be further examined in future study. First, poverty is a complex and diverse system that involves many aspects, which not only include natural and socioeconomic development but are also related to peasants’ struggled willingness and the government’s organizational and management abilities. It is difficult for the CPI to determine all of the poverty causes in this study. Since only a few data sources were selected to develop the CPI in this study, future studies will adopt more diversified spatial data and family poverty surveys (e.g., rural labor migration, family disability) to quantify poverty. Second, we have to admit that the CPI-poverty relationship is an empirical relationship that cannot be seen as an absolute law. Although the poverty issue in each region was different, the CPI could represent a regional difference of poverty at different levels because the different factors, such as POI cost distance and DEM, have revealed the different states of socioeconomic and natural conditions within different regions. Thus, the CPI still can be used as an indirect indicator for modeling poverty. Third, according to data availability, there were only three measures, including poverty-stricken villages, Google earth images, and MPI, that were employed to validate the accuracy of the CPI. The weight setting of the MPI played an important role in accuracy assessment. Fourth, since the temporal coverage of data sources was limited, this study just analyzed the spatial distribution of

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poverty in 2015. In the future, we will identify and evaluate poverty at a large scale (e.g., the national scale) from multiyear scales. Fifth, a 500-m spatial resolution of poverty mapping from multiplesource data might be not precise enough. Some other remote sensing data with relatively high spatial resolution, such as Luojia-1 nighttime light data (Li, X. et al., 2019a) and Quick Bird images will be probably adopted to get more accurate findings in the following study. Finally, the correlations between the CPI and the selected factors have been explored, but the driving mechanisms were not discussed from different perspectives. An in-depth analysis of driving forces from multiple dimensions, such as government policy and peasants’ struggled willingness, will be investigated. 6. Conclusions and implications for sustainability practices This study has attempted to identify and evaluate poverty in Chongqing. First, the CPI index was developed to map poverty by integrating NTL data with DEM, NDVI, and POI data. Then, the CPI accuracy was validated by poverty-stricken village data, Google earth images, and MPI. Finally, we explored the spatial distribution of poverty in Chongqing at the county and town levels. The results clearly show that a remarkable visual fitness was found between the CPI results and the poverty-stricken village data and Google earth images. The R2 between the CPI and MPI is 0.931, further indicating a good modeling power of our index in poverty evaluation. The poverty showed diverse spatial patterns across different levels. The Global Moran’s I index increased from 0.663 at the county level to 0.897 at the town level. The local spatial autocorrelation analysis showed that the county LL clusters are mainly concentrated in NC, but the town LL clusters are widely distributed in NC and SC. In addition, the regression analysis showed that the POI cost distance was one of the important factors for poverty, with an R2 of 0.759. Our empirical findings could provide several implications for -vis viable poverty alleviation the government making vis-a reduction strategies. For example, because a diversified pattern of poverty was shown at the different levels, a “proceed in light of local conditions” poverty alleviation strategy should be adopted in Chongqing. Due to the effect of POI cost distance on poverty, it is necessary to perfect infrastructure construction and improve connectivity convenience in some poor regions. Eradicating poverty, improving ecological fragility, and reducing environmental pollution are essential ways to realize China’s sustainable development. Poverty has a close and complex relationship with other problems. The World Bank report (2015) indicated that climate change is a direct threat to poverty alleviation. Du et al. (2019) found that China’s clean development mechanism projects could contribute to social benefits. Li et al. (2019b) revealed that there was a close relationship between poverty and air quality and found that many poverty-stricken counties are exposed to higher PM2.5 concentrations. Yang and Liu (2018) evaluated the environmental pollution-health effect relationship and examined its implications for health inequality. The “environment-health” was a process closely integrated with poverty. For example, health inequality was largely concentrated in poor areas, especially in the rural and western areas of China. Thus, to effectively eradicate poverty and realize sustainable development, China’s government should consider multidimensional development goals, including economic growth, environmental protection, ecological restoration, and sustainable resource utilization. Declaration of competing interest The entire text of the manuscript is original. It has not been published and is not being considered for publication in other

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journals. In addition, we declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. CRediT authorship contribution statement Kaifang Shi: Writing - original draft, Conceptualization, Formal analysis. Zhijian Chang: Data curation, Formal analysis. Zuoqi Chen: Conceptualization, Methodology. Jianping Wu: Supervision. Bailang Yu: Supervision, Writing - review & editing. Acknowledgements This study has been supported by the National Natural Science Foundation of China (No. 41871331 and No. 41830648), and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 18XJC790011). The authors also want to express our gratitude to the three anonymous reviews for their valuable comments and suggestions to improve our paper’s quality. Data acquisition may be contacted at the first author ([email protected]. cn). References Amaral, S., C^ amara, G., Monteiro, A.M.V., Quintanilha, J.A., Elvidge, C.D., 2005. Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data. Comput. Environ. Urban Syst. 29 (2), 179e195. Anselin, L., 2010. Thirty years of spatial econometrics. Pap. Reg. Sci. 89 (1), 3e25. Bone, C., Wulder, M.A., White, J.C., Robertson, C., Nelson, T.A., 2013. A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran’s I statistic. Appl. Geogr. 40, 161e170. Bossert, W., Chakravarty, S.R., D’Ambrosio, C., 2013. Multidimensional poverty and material deprivation with discrete data. Rev. Income Wealth 59 (1), 29e43. Chen, Z., Yu, B., Hu, Y., Huang, C., Shi, K., Wu, J., 2015. Estimating house vacancy rate in metropolitan areas using NPP-VIIRS nighttime light composite data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (5), 2188e2197. Chen, Y., Wang, Y., Zhao, W., Hu, Z., Duan, F., 2017. Contributing factors and classification of poor villages in China (In Chinese). Acta Geograph. Sin. 72 (7), 1827e1844. Cheng, H., Dong, S., Li, F., Yang, Y., Li, Y., Li, Z., 2019. A circular economy system for breaking the development dilemma of ‘ecological FragilityeEconomic poverty’vicious circle: a CEEPS-SD analysis. J. Clean. Prod. 212, 381e392. David, G., Mark, S.S., Owen, G., Johan, R.M., Ohman, M.C., Priya, S., Will, S., Gisbert, G., Norichika, K., Ian, N., 2013. Policy: sustainable development goals for people and planet. Nature 495 (7441), 305e307. DFID, 1999e2005. Sustainable Livelihoods Guidance Sheets. Department for International Development (UK, London. http://www.eldis.org/go/home&id. Du, Y., Takeuchi, K., 2019. Can climate mitigation help the poor? Measuring impacts of the CDM in rural China. J. Environ. Econ. Manag. 95, 178e197. Elvidge, C.D., Sutton, P.C., Ghosh, T., Tuttle, B.T., Baugh, K.E., Bhaduri, B., Bright, E., 2009. A global poverty map derived from satellite data. Comput. Geosci. 35 (8), 1652e1660. Estel, S., Kuemmerle, T., Alc antara, C., Levers, C., Prishchepov, A., Hostert, P., 2015. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens. Environ. 163, 312e325. Feng, Y., Pan, J., Yang, L., 2018. Analysis on spatial simulation of rural poverty at county level in China (In Chinese). J. Geo Inf. Sci. 20 (3), 321e331. Ghosh, T., Anderson, S.J., Elvidge, C.D., Sutton, P.C., 2013. Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability 5 (12), 4988e5019. Gouveia, J.P., Seixas, J., Long, G., 2018. Mining households’ energy data to disclose fuel poverty: lessons for Southern Europe. J. Clean. Prod. 178, 534e550. He, C., Gao, B., Huang, Q., Ma, Q., Dou, Y., 2017. Environmental degradation in the urban areas of China: evidence from multi-source remote sensing data. Remote Sens. Environ. 193, 65e75. Hu, K., Yang, X., Zhong, J., Fei, F., Qi, J., 2017. Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data. Environ. Sci. Technol. 51 (3), 1498e1507. Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S., 2016. Combining satellite imagery and machine learning to predict poverty. Science 353 (6301), 790e794. Johannes, H., Ernst, F., 2014. On the psychology of poverty. Science 344 (6186), 862e867. Labar, K., Bresson, F., 2011. A multidimensional analysis of poverty in China from 1991 to 2006. China Econ. Rev. 22 (4), 646e668. Li, X., Xu, H., Chen, X., Li, C., 2013. Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China. Rem. Sens. 5 (6), 3057e3081.

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Li, Y., Long, H., Liu, Y., 2015. Spatio-temporal pattern of China’s rural development: a rurality index perspective. J. Rural Stud. 38, 12e26. Li, X., Li, X., Li, D., He, X., Jendryke, M., 2019a. A preliminary investigation of Luojia-1 night-time light imagery. Remote Sens. Lett. 10 (6), 526e535. Li, G., Cai, Z., Liu, J., Liu, X., Su, S., Huang, X., Li, B.J.S.I.R., 2019b. Multidimensional poverty in rural China: indicators, spatiotemporal patterns and applications. Soc. Indicat. Res. https://doi.org/10.1007/s11205-019-02072-5. Li, G., Cai, Z., Liu, X., Liu, J., Su, S., 2019c. A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery. Int. J. Rem. Sens. 1e21. Li, G., Chang, L., Liu, X., Su, S., Cai, Z., Huang, X., Li, B., 2019d. Monitoring the spatiotemporal dynamics of poor counties in China: implications for global sustainable development goals. J. Clean. Prod. https://doi.org/10.1016/ j.jclepro.2019.1004.1135. Lin, Y., Li, Y., Ma, Z., 2018. Exploring the interactive development between population urbanization and land urbanization: evidence from Chongqing, China (1998e2016). Sustainability 10 (6), 1741. Liu, W., Dunford, M., Song, Z., Chen, M., Policy, 2016. Urbanerural integration drives regional economic growth in Chongqing, Western China. Area Dev. Pol. 1 (1), 132e154. Liu, Y., Liu, J., Zhou, Y., 2017. Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. J. Rural Stud. 52, 66e75. Liu, Y., Fan, P., Yue, W., Song, Y., 2018. Impacts of land finance on urban sprawl in China: the case of Chongqing. Land Use Pol. 72, 420e432. Lo, K., Wang, M., 2018. How voluntary is poverty alleviation resettlement in China? Habitat Int. 73, 34e42. Lu, D., Tian, H., Zhou, G., Ge, H., 2015. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens. Environ. 112 (9), 3668e3679. Ma, Q., He, C., Wu, J., 2016. Behind the rapid expansion of urban impervious surfaces in China: major influencing factors revealed by a hierarchical multiscale analysis. Land Use Pol. 59, 434e445. National Bureau of statistics of the People’s Republic of China, 2016. China Statistical Yearbook. China Statistical Press, Beijing. Noor, A.M., Alegana, V.A., Gething, P.W., Tatem, A.J., Snow, R.W., 2008. Using remotely sensed night-time light as a proxy for poverty in Africa. Popul. Health Metrics 6 (1), 5. Padda, I.U.H., Hameed, A., 2018. Estimating multidimensional poverty levels in rural Pakistan: a contribution to sustainable development policies. J. Clean. Prod. 197, 435e442. Pan, J., Hu, Y., 2018. Spatial identification of multi-dimensional poverty in rural China: a perspective of nighttime-light remote sensing data. J. Indian Soc. Remote Sens. 46 (7), 1093e1111. Ren, Q., Huang, Q., He, C., Tu, M., Liang, X., 2018a. The poverty dynamics in rural China during 2000e2014: a multi-scale analysis based on the poverty gap index. J. Geogr. Sci. 28 (10), 1427e1443. Ren, Q., Huang, Q., He, C., Tu, M., Liang, X., 2018b. The poverty dynamics in rural China during 2000e2014: a multi-scale analysis based on the poverty gap index, 28 (10), 1427e1443. Shi, K., Huang, C., Yu, B., Yin, B., Huang, Y., Wu, J., 2014a. Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sens. Lett. 5 (4), 358e366. Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., Chen, L., Wu, J., 2014b. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: a comparison with DMSP-OLS data. Rem. Sens. 6 (2), 1705e1724. Shi, K., Yu, B., Hu, Y., Huang, C., Chen, Y., Huang, Y., Chen, Z., Wu, J., 2015. Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data. GIScience Remote Sens. 52 (3), 274e289.

Shi, K., Chen, Y., Yu, B., Xu, T., Chen, Z., Liu, R., Li, L., Wu, J., 2016a. Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSPOLS nighttime stable light data using panel data analysis. Appl. Energy 168, 523e533. Shi, K., Chen, Y., Yu, B., Xu, T., Yang, C., Li, L., Huang, C., Chen, Z., Liu, R., Wu, J., 2016b. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data. Appl. Energy 184, 450e463. Shi, K., Yang, Q., Li, Y., Sun, X., 2019a. Mapping and evaluating cultivated land fallow in Southwest China using multisource data. Sci. Total Environ. 654, 987e999. Shi, K., Yu, B., Zhou, Y., Chen, Y., Yang, C., Chen, Z., Wu, J., 2019b. Spatiotemporal variations of CO2 emissions and their impact factors in China: a comparative analysis between the provincial and prefectural levels. Appl. Energy 233, 170e181. Steele, J.E., Sundsøy, P.R., Pezzulo, C., Alegana, V.A., Bird, T.J., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.-A., Iqbal, A.M., 2017. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14 (127), 20160690. United Nations, 2015. About the Sustainable Development Goals. https://www.un. org/sustainabledevelopment/sustainable-development-goals/. Varshney, K.R., Chen, G.H., Abelson, B., Nowocin, K., Sakhrani, V., Xu, L., Spatocco, B.L., 2015. Targeting villages for rural development using satellite image analysis. Big Data 3 (1), 41e53. Wang, W., Cheng, H., Zhang, L., 2012. Poverty assessment using DMSP/OLS nighttime light satellite imagery at a provincial scale in China. Adv. Space Res. 49 (8), 1253e1264. Wang, Y., Chen, Y., Chi, Y., Zhao, W., Hu, Z., Duan, F., 2018. Village-level multidimensional poverty measurement in China: where and how. J. Geogr. Sci. 28 (10), 1444e1466. World Bank, 2015. Climate Change Complicates Efforts to End Poverty. the World Bank. http://www.worldbank.org/en/news/feature/2015/02/06/climatechange-complicates-efforts-end-poverty. World Bank, 2018. Decline of global extreme poverty continues but has slowed: world Bank. Available online. http://www.worldbank.org/en/news/pressrelease/2018/09/19/decline-of-global-extreme-povertycontinues-but-hasslowed-world-bank. Wu, R., Yang, D., Dong, J., Zhang, L., Xia, F., 2018. Regional inequality in China based on NPP-VIIRS night-time light imagery. Rem. Sens. 10 (2), 240. Wu, Y., Ke, Y., Wang, J., Li, L., Lin, X., 2019. Risk assessment in photovoltaic poverty alleviation projects in China under intuitionistic fuzzy environment. J. Clean. Prod. 219, 587e600. Xie, Y., Weng, Q., 2016. Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries. Energy 100, 177e189. Yang, T., Liu, W., 2018. Does air pollution affect public health and health inequality? Empir. Evid. China 203, 43e52. Ye, C., Tang, B., Liu, L., 2018. Is the poverty evaluation of national poverty county accurate? A evaluation based on the night-time light (In Chinese). China Agric. Univ. J. Soc. Sci. Ed. 35 (5), 44e57. Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z., Wu, J., 2015. Poverty evaluation using NPPVIIRS nighttime light composite data at the county level in China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (3), 1217e1229. Zhang, G., Li, L., Jiang, Y., Shen, X., Li, D., 2018. On-orbit relative radiometric calibration of the night-time sensor of the Luojia1-01 satellite. Sensors 18 (12), 4225. Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., Wu, J., 2019. Estimation of poverty using random forest regression with multi-source data: a case study in Bangladesh. Rem. Sens. 11 (4), 375.