| 摘要: |
| 手机信令数据记录了城市个体的社会
经济属性信息，在城市贫困空间识别中可以更
精准地定位相对贫困群体。以广州市为例，尝
试结合手机信令数据与问卷调查数据，构建了
个体手机使用指标与经济水平间的多元线性回
归模型，再利用手机信令数据识别城市内部相
对贫困空间。研究发现，个体层面上“手机通讯
费用”“APP使用偏好”与其经济水平具有关联
性；空间层面上呈“中心高、四周低”的空间分布
规律，中心城区内相对贫困空间集中于老城区、
城中村和旧工业区。对比传统人口普查数据识别结果，发现基于手机信令数据的相对贫困识别具有较高的可行性。研究丰富了城市相对贫困空
间识别的方法体系，对有针对性推进贫困治理工作具有实践意义。 |
| 关键词: 相对贫困 手机信令数据 多元线性回归 贫困识别 贫困空间 |
| DOI：10.13791/j.cnki.hsfwest.20240409 |
| 分类号: |
| 基金项目:国家自然科学基金项目（41871161，52278085，
42301182）；广东省基础与应用基础研究基金项目（202
2A1515110331） |
|
| The identification of urban relative poverty based on mobile phone metadata: A case studyof Guangzhou |
|
YUAN Yuan,WU Qingyu,LI Shan,CHEN Xi,LIANG Xuchu
|
| Abstract: |
| China won the battle against poverty in 2020, signifying the resolution of absolute poverty
in rural areas, but the relative poverty phenomenon in urban areas persists. How to solve the intricate
problem of relative poverty within cities, as well as to alleviate the problem of unbalanced and inadequate
socio-economic development? It will become an important topic in the next stage of poverty research.
The prerequisite for eliminating urban relative poverty is the accurate identification of urban relatively
poor individuals as well as their concentration spaces. With the rapid development of Internet technology,
multi-scale and multi-type open big data, such as remote sensing images and street view images,
contain integrated information reflecting the built environment, improving the identification accuracy of
research in poverty space. However, there are some limitations in accurately identifying relatively poor
individuals, such as the difficulty of image data in revealing the socio-economic characteristics of poor
individuals. Mobile phone data record information on the socio-economic attributes of urban residents,
which can identify the urban poverty space more precisely by locating relatively poor groups. Therefore,
taking Guangzhou as an example, this paper combined mobile phone metadata with questionnaire data to
identify the relative poverty space within the city.
Starting from individual poverty, this paper collected information on mobile phone usage, income
and consumption of urban residents through a questionnaire survey. Then, this paper analyzed the
correlation between individual mobile phone usage and economic poverty, establishing a poverty index as
the dependent variable based on income and consumption data, and using mobile phone usage indicators
as the independent variables. After that, a multiple linear regression model between mobile phone
indicators and economic status was constructed. Finally, based on the mobile phone metadata, the poverty
index of each town or sub-district was calculated to identify the relative poverty space within the city. In
addition, based on the analysis of the model, this paper explored the differences in mobile phone usage
behavior across urban spaces as well as the reasons behind them.
A total of 233 valid questionnaires were collected in this study, and the representativeness of the
sample was verified by analyzing the income structure of the questionnaires. In the multiple linear
regression model constructed based on the questionnaire data, six significant mobile phone indicatorsmobile
phone cost, Pinduoduo usage time, online shopping time, office and study APP usage time,
takeaway APP usage time, and mobile game duration were selected through the stepwise regression
analysis. The model indicated that the mobile phone variable group explained 21.9% of the dependent
variable. After obtaining the poverty index, the dependent variable of the model, for each town and subdistrict
in Guangzhou based on the model and mobile phone metadata, it was divided into five classeslowest
income, lower income, middle income, higher income and highest income for further analysis. The
spatial distribution of relative poverty reveals that Tianhe District and the eastern part of Yuexiu Districtare the areas with relatively high economic levels, while the northern part of Nansha District, the eastern part of Zengcheng District and the southern part of Conghua
District are relatively poor areas. Moreover, it is found that the relatively poor space in the central city is concentrated on the border of the Yuexiu, Liwan and Haizhu
districts.
Identifying poverty through mobile phone usage helps generate new insight into relative poverty. At the individual level, higher income groups have higher
mobile phone costs and more diverse digital consumption, while lower income groups have a lower frequency of mobile phone use. The differences in mobile phone
usage reveal the existence of information deprivation, which comes from differences in information-seeking abilities and habitual preferences between groups. The
digital divide is one of the many barriers faced by relatively poor groups in the mobile Internet era due to the different development levels of information technology.
At the spatial level, for our study area, there is a spatial distribution pattern of high center and low periphery, and the relative poverty spaces in the central city
are concentrated in the old residential areas, urban villages and the old industrial zones. In general, the distribution of poverty inferred by our model is generally
consistent with that based on traditional census data, which also reflects the feasibility of identifying relative poverty through mobile phone usage.
This study verified the feasibility and applicability of mobile phone metadata in the field of poverty identification, and enriched the methodological
system of spatial identification for urban relative poverty, which is of practical significance for the targeted promotion of poverty governance. However, there
are certain shortcomings, such as the low accuracy of mobile phone metadata in identifying sub-districts with complex residential structures and widely
varying internal environments. In future research, it can be tried to improve the model by combining mobile phone metadata with other types of big data. |
| Key words: relative poverty mobile phone metadata multiple linear regression poverty identification poverty space |
