1. Introduction
Poverty is a global social problem, and poverty eradication is the first of the 17 sustainable development goals proposed by the United Nations [
1,
2,
3,
4,
5]. The issue of poverty is particularly prominent in China. Although all previous national-level impoverished counties in China were lifted out of poverty in 2020, China is still facing a serious problem of relative poverty and a huge disparity between the rich and the poor [
6,
7,
8,
9]. Unlike absolute poverty (e.g., food poverty), relative poverty refers to individuals whose income can ensure food supplies only and cannot meet other basic living needs [
10,
11,
12,
13]. Therefore, during the relative poverty stage with multidimensional poverty as the main feature, it is of great importance to conduct poverty assessments to ensure the effectiveness and sustainability of targeted poverty alleviation.
Traditional poverty assessments rely mainly on official statistics or field surveys [
14,
15,
16,
17,
18]. Since such methods cannot comprehensively reflect poverty status, many studies have gradually constructed different poverty assessment systems by considering various factors (e.g., personal income and consumption level) [
19,
20,
21,
22,
23,
24]. These attempts include the human poverty index, multidimensional poverty index (MPI), and multiple deprivation index established by the United Nations Development Programme considering three dimensions of health, education, and living standards. For example, Li et al. [
25] used multidimensional statistical data to analyze the poverty status at county scales in China from 2000 to 2010 and revealed the spatial distribution characteristics of poverty over this period. Previous studies have demonstrated the effectiveness of various poverty indices from the perspectives of multidimensional poverty and relative poverty. Although the traditional statistical data at the county level in China are complete and accessible, the coarse statistical scales and poor timeliness have limited their applications. For example, the complete statistical data of 2023 will be available only one or two years later. In addition, there exist some inconsistencies caused by the change in administrative units [
26,
27,
28,
29,
30,
31].
To overcome these limitations, remote sensing data have been increasingly used to assess socioeconomic conditions and poverty status [
32,
33,
34,
35,
36,
37,
38,
39,
40]. For example, Yong et al. [
41] combined DMSP-OLS and NPP-VIIRS data to assess poverty in Southwest China from 2000 to 2019. Liu et al. [
42] used NPP-VIIRS data to identify relative poverty in the surrounding areas of Beijing and Tianjin during the period of 2012–2020. Yu et al. [
43] constructed the average light index using NPP-VIIRS data and then analyzed the correlation between the average light index and poverty status. Pan et al. [
44] used nighttime light data to build a multidimensional poverty model and verified the validity of the average light index using linear regression. However, as mentioned above, poverty is reflected in not only the economic dimension but also other aspects, such as health, education, and environment. Since nighttime light data reflect only nighttime economic activities, it is difficult to comprehensively measure poverty based on nighttime light data alone [
45,
46,
47,
48,
49,
50,
51,
52,
53]. For example, Pokhriyal et al. [
54] pointed out that it is necessary to integrate multidimensional data so that poverty status can be reflected more accurately.
To this end, spatial big data have been gradually considered to overcome the limitations of nighttime light data. In particular, a number of previous studies have shown that POIs can effectively reflect socio-economic activities and urban vitality [
51,
55], while housing price is also an important indicator of economic and residential conditions [
56,
57]. Therefore, it is feasible to combine these various different data sources for fine-scale poverty estimation. For example, Ni et al. [
58] indicated that combining nighttime lights with daytime remote sensing data can improve the accuracy of poverty prediction. Shi et al. [
59] combined topography, vegetation index, points of interest (POIs), and nighttime light data to identify poverty in Chongqing, China. Lin et al. [
60] found that POI data can reflect urban poverty to a certain extent, and thus, a combined use of POI and nighttime light data can improve the accuracy of poverty assessment. Although spatial big data have been increasingly used in poverty assessments, previous studies have relied greatly on the coarse-resolution DMSP-OLS and NPP-VIIRS data, and most of them have focused on regions with absolute poverty [
61,
62,
63,
64,
65,
66]. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, it is still necessary to analyze the performance of multisource spatial big data in assessing regions with relative poverty [
67,
68,
69,
70,
71].
In fact, housing price is an important indicator for measuring the level of regional economy and living conditions [
56,
57]. Housing expenditure varies greatly among different income groups [
72]. For example, Delang and Ho [
73] analyzed the influence of public housing policy on the phenomenon of poverty concentration and found that poor people tend to concentrate in public housing areas that do not require much expenditure. Nevertheless, traditional housing price data were usually collected based on official statistics, censuses, and surveys, which cannot provide accurate fine-scale information [
72,
74]. For example, Yi and Huang [
75] stressed the lack of housing price data in China. In this regard, online house price data can offer the possibility to measure poverty degrees at the micro scale [
74,
76]. Previous studies have also demonstrated that advanced machine learning techniques can be used to estimate fine-scale housing prices based on proxy variables in regions where offline housing transaction data are not available [
76,
77]. Therefore, this research employed online housing price data as one of the poverty indicators, which can compensate for the deficiency of nighttime light data and measure relative poverty more comprehensively.
In summary, this study aims to apply multisource spatial big data to relative poverty assessment. A big data poverty index (BDPI) was constructed by integrating high-resolution Luojia 1-01 data, housing prices, and POIs. In addition, we also constructed the traditional MPI by selecting 17 indicators from six dimensions (human capital, natural capital, financial capital, physical capital, social capital, and environmental vulnerability) according to the vulnerability–sustainable livelihood analysis framework established by the Department for International Development (DFID). We compared the poverty assessment results of the BDPI and MPI to analyze the performance of these two methods. We took the Pearl River Delta in China, where the disparity between the rich and the poor is huge, as a case study area. The conclusions could provide support for local governments to formulate poverty-alleviation policies and implement development plans for underdeveloped areas.
5. Discussion and Conclusions
5.1. Advantages and Disadvantages of the BDPI
The above analysis shows that compared with the MPI, the BDPI proposed by this study has three major advantages. First, the BDPI was constructed by integrating multi-source spatial big data, including high-resolution Luojia 1-01 nighttime light, housing price, and POI data. Our experiments have suggested that the proposed BDPI can identify areas that are relatively lagging behind in emerging-development cities. Second, the BDPI can reduce the cost of poverty assessment and enhance the timeliness of results due to the use of easily accessible, low-cost, and rapidly updated data. Third, the BDPI can effectively reflect social, economic, living environment, and other conditions. Therefore, it provides a new approach to poverty assessment for developing countries lacking accurate socioeconomic statistics.
It should also be noted that the people in some less developed counties are more inclined to complete housing transactions offline, and these data cannot be acquired online. Fortunately, the difference between online and offline data is not significant in many regions. In addition, as mentioned above, previous studies have demonstrated that advanced machine learning techniques can be used to estimate fine-scale housing prices based on proxy variables in regions where offline data are not available. Although there are uncertainties and limitations in data preparation, BDPI still has distinct advantages in fine-scale poverty assessment. Compared with MPI, which relies on dated indicators and periodic data collection, BDPI can capture changes in poverty status instantly and provide more timely support for policy making. In addition, the use of spatial data can reflect regional differences at a grid scale.
Notwithstanding these advantages, the BDPI still has the following disadvantages. First, online housing price data may not be available in some underdeveloped areas. In that case, advanced machine learning techniques and offline data, such as local government statistics and survey data, should be used to supplement the BDPI’s data sources. Second, the indicators used to construct the BDPI also have some limitations. Poverty is a multidimensional phenomenon involving not only social, economic, and natural aspects but also humanities, policies, and other factors. Therefore, although the BDPI can be used as an indirect indicator, the associated assessment results may overestimate or underestimate the actual poverty status, and the BDPI needs to be further enhanced. Third, it is difficult for the BDPI to assess the poverty status from a long time ago due to the unavailability of historical data. The inconsistency of data years may affect the poverty assessment results to some degree. In future research, we will adopt long-term time-series data for poverty assessment to further test the applicability of the BDPI. Fourth, we will consider more spatial big data and higher-resolution nighttime light data, compare the poverty assessment results of the Pearl River Delta with other regions, and analyze the differences in poverty issues in those regions.
5.2. Policy Recommendations
In this research, the internal development of a city is unbalanced if this city contains both counties with positive ranking differences and counties with negative ranking differences, such as DingHu County and DuanZhou County in ZhaoQing City. DingHu County has an average living environment and a relatively remote location, but as a new developing zone of ZhaoQing City, this county has desirable social and economic resources. As the central urban area of ZhaoQing City, DuanZhou County has a satisfactory living environment and relatively complete supporting facilities. However, there is basically no more space for urban expansion since the amount of land available for development is decreasing gradually. Therefore, the urban development costs are relatively higher in DuanZhou County.
Overall, the poverty assessment results obtained in this study can offer the following policy recommendations for poverty alleviation. The huge disparity between the rich and the poor in the Pearl River Delta shows that poverty-alleviation operations require not only raising social income but also reducing inequality in regional development. For areas with a shortage of social resources, it is necessary to improve the diversity of their resources, for example, enhancing infrastructures such as those for medical care, public transportation, and education, to improve urban vitality. For the fringe areas of the Pearl River Delta, which are greatly affected by natural conditions, the development strategy should be carefully designed according to local characteristics. In relatively underdeveloped areas of rapidly developing cities, the government needs to offer more support, carry out detailed strategic planning, and allocate more socioeconomic resources to areas with unbalanced development to reduce the disparity between the rich and the poor. In addition, the government should further upgrade the poverty assessment method in the context of big data and improve the accuracies and pertinence of the assessment results. Taking the Pearl River Delta region as an example, this study has demonstrated that the proposed BDPI could provide technical support for poverty assessment in many other regions.
5.3. Main Conclusions
The significance and novelty of this research are twofold. Firstly, high-resolution nighttime light and spatial big data have been integrated to construct the novel big data poverty index (BDPI) for relative poverty assessment. Secondly, a poverty assessment has been conducted in the regions with relative poverty at a grid scale after the validation of the BDPI. Both the traditional MPI and BDPI were used to assess the poverty status of the Pearl River Delta, where there is a considerable disparity between the rich and the poor. We analyzed the differences in the poverty assessment results obtained by the two methods. These two methods generated similar assessment results, which verify the effectiveness of the BDPI. Overall, the results of the two poverty assessment methods show that the poverty index gradually decreases from the center to the fringe of the study area. The counties in the western Pearl River Delta were relatively impoverished, and even fast-growing cities also contained some impoverished counties.
The statistical-based MPI is more suitable for old urban areas with convenient but obsolete infrastructures, while the BDPI is more suitable for emerging-development areas that are rapidly developing but still lagging behind. Therefore, combining these two types of indices for poverty assessment can yield optimal results if the data are well-prepared. The BDPI can also successfully replace the traditional MPI if the statistical data are not updated in time. The BDPI proposed by this study can effectively assess regional poverty status and provide a new method of poverty assessment for developing countries lacking accurate socioeconomic statistics. The results of this study could help local governments to fully understand the multidimensional characteristics of poverty and could provide decision support for formulating targeted poverty-alleviation policies.