DMSP/OLS数据支持的贫困地区测度方法研究
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摘要
消除贫困是全世界面临的重大问题，如何识别贫困并界定贫困区域是一个科学问题，至今仍然给政府和学者带来挑战。尤其改革开放以后，由国家和政府在我国农村开始实施大规模的扶贫开发战略，传统的社会经济统计数据缺乏空间信息，获取耗时费力，且客观性不易保证，无法满足大范围、长期、动态的区域贫困研究需求。
本研究主要引入了DMSP/OLS数据进行区域贫困程度的估测，但与传统的直接利用区域总强度、像元灰度值等统计量不同，研究提出了“单位灯光强度”的概念，并将DMSP/OLS数据信息与区域人口、建设用地信息耦合，构建了“单位建设用地灯光强度”和“单位人口灯光强度”两类共13个指标用于描述区域灯光特征，通过市辖区、县域、县域内城乡区域，3种不同尺度的单位灯光强度指标与地区生产总值、县区居民平均收入的相关分析，明确了不同尺度的单位灯光强度指标在估测县区经济状况时的尺度效应，在此基础上，以农村居民纯收入作为区域贫困程度的表征，通过逐步回归的方法，筛选不同尺度的“单位建设用地灯光强度”和“单位人口灯光强度”指标，作为变量构建了测算区域贫困程度的2个回归模型，两个模型的拟合优度分别为0.912和0.915。
研究搜集整理了全国各县区的人口和建设用地数据，利用2个区域贫困测度模型对县区农民收入水平进行了测算，将测算得到的相对贫困区域与现有的集中连片特困地区的空间范围进行了比对，同时按照省区分析了目前划定的国家级贫困县的农民收入在全国的相对水平，通过对比发现目前划定的集中连片特困地区基本覆盖了绝大部分的相对贫困区，而不同省区国家级贫困县的实际农民收入水平则有较大差异。研究进一步选择全局和局部空间自相关统计量对贫困县区的空间分布模式进行了分析，分析结果表明县域尺度上，贫困区的分布仍然表现出明显的空间聚集状态，京津冀环渤海经济区、长三角经济区、珠三角经济区是相对富裕的区县聚集区域，四川、云南、贵州、湖南、湖北、西藏等中西部的几个省区处于明显的相对贫困区域，并且区域内一致性很高，属于普遍贫困地区。
Elimination of poverty is an important matter of the world. Identification of povertyand analysis of spatial changes is the subject of science and still challenging governmentsand scholars. After opening and reform of China, nation and government starts large-scalepoverty relief and development strategy in rural areas. Without locating the real poorareas and groups in poverty, various poverty relief measures will lose their due effects.Also, it will be very challenging to make efficient development strategies and measures ofpoverty relief without understanding the characteristic of spatial distribution. Astraditional statistical data of social economy is lack of the information of space,time-consuming to be collected, and objectivity hard to be guaranteed, they cannot meetthe demand of large-scale, long-term and dynamic research in areas in poverty. Nightlight is closely associated with the development level of regional economy, populationand technology. DMSP/OLS night-light remote sensing data has been widely applied as akind of new remote sensing data source to the studies of urban expansion, regionaleconomy, population, energy consumption and so on.
The study mainly introduces DMSP/OLS data to evaluate the poverty degree ofareas. Different from the direct adoption of total regional strength, pixel gray value andother types of remote sensing data in traditional application, the study defines the conceptof “unit light intensity” and associates DMSP/OLS data with the information of regionalpopulation and construction land.13indexes, classified into two types, are designed todescribe the characteristic of regional light, like “unit light intensity of construction land”and “unit light intensity of population”. Through related analysis on3different types ofunit light intensity indexes in municipal district, country and rural-urban areas in country,as well as gross regional production and average resident income in country, authoridentified the scale effect of different unit light intensity indexes on country economy. Onthis basis and with net income of rural residents as indication of regional poverty degree,author screened out different scales of “unit light intensity of construction land” and “unitpopulation light intensity” as variables with the method of stepwise regression to construct2regression models to measure the poverty degree of areas. Goodness of fits of2models is0.912and0.915respectively.
The study sorted out and collected the data of population and construction land incountry, and then evaluated the income level of farmers with2regional poverty models.And then, author compared the spatial scale of areas in relative poverty and centralizedextremely poor areas. Meanwhile, author analyzed the objective poverty status ofidentified national-level poor countries. By comparison, author found that the identifiedconnected areas in extreme poverty basically covered most relatively poor areas.However, actual farmer income levels in national-level poor countries in differentprovinces vary greatly. Further, author analyzed the spatial distribution mode of poorcountries with self-correlation statistics of overall and regional space and the result showsthat the distribution of poor areas still represents obvious spatial assembly in the scale ofcountry. There are comparatively rich assembled country areas in Circum-Bohai SeaEconomic Zone of Beijing-Tianjin-Hebei, Yangtze River Delta Economic Zone and PearlRiver Delta Economic Zone, while the provinces in mid-west of China, such as Sichuan,Yunnan, Guizhou, Hunan, Hubei and Tibet, are obviously involved in relatively poorareas and regional consistency is very high, indicating that they are the areas inwidespread poverty.
本研究主要引入了DMSP/OLS数据进行区域贫困程度的估测，但与传统的直接利用区域总强度、像元灰度值等统计量不同，研究提出了“单位灯光强度”的概念，并将DMSP/OLS数据信息与区域人口、建设用地信息耦合，构建了“单位建设用地灯光强度”和“单位人口灯光强度”两类共13个指标用于描述区域灯光特征，通过市辖区、县域、县域内城乡区域，3种不同尺度的单位灯光强度指标与地区生产总值、县区居民平均收入的相关分析，明确了不同尺度的单位灯光强度指标在估测县区经济状况时的尺度效应，在此基础上，以农村居民纯收入作为区域贫困程度的表征，通过逐步回归的方法，筛选不同尺度的“单位建设用地灯光强度”和“单位人口灯光强度”指标，作为变量构建了测算区域贫困程度的2个回归模型，两个模型的拟合优度分别为0.912和0.915。
研究搜集整理了全国各县区的人口和建设用地数据，利用2个区域贫困测度模型对县区农民收入水平进行了测算，将测算得到的相对贫困区域与现有的集中连片特困地区的空间范围进行了比对，同时按照省区分析了目前划定的国家级贫困县的农民收入在全国的相对水平，通过对比发现目前划定的集中连片特困地区基本覆盖了绝大部分的相对贫困区，而不同省区国家级贫困县的实际农民收入水平则有较大差异。研究进一步选择全局和局部空间自相关统计量对贫困县区的空间分布模式进行了分析，分析结果表明县域尺度上，贫困区的分布仍然表现出明显的空间聚集状态，京津冀环渤海经济区、长三角经济区、珠三角经济区是相对富裕的区县聚集区域，四川、云南、贵州、湖南、湖北、西藏等中西部的几个省区处于明显的相对贫困区域，并且区域内一致性很高，属于普遍贫困地区。
Elimination of poverty is an important matter of the world. Identification of povertyand analysis of spatial changes is the subject of science and still challenging governmentsand scholars. After opening and reform of China, nation and government starts large-scalepoverty relief and development strategy in rural areas. Without locating the real poorareas and groups in poverty, various poverty relief measures will lose their due effects.Also, it will be very challenging to make efficient development strategies and measures ofpoverty relief without understanding the characteristic of spatial distribution. Astraditional statistical data of social economy is lack of the information of space,time-consuming to be collected, and objectivity hard to be guaranteed, they cannot meetthe demand of large-scale, long-term and dynamic research in areas in poverty. Nightlight is closely associated with the development level of regional economy, populationand technology. DMSP/OLS night-light remote sensing data has been widely applied as akind of new remote sensing data source to the studies of urban expansion, regionaleconomy, population, energy consumption and so on.
The study mainly introduces DMSP/OLS data to evaluate the poverty degree ofareas. Different from the direct adoption of total regional strength, pixel gray value andother types of remote sensing data in traditional application, the study defines the conceptof “unit light intensity” and associates DMSP/OLS data with the information of regionalpopulation and construction land.13indexes, classified into two types, are designed todescribe the characteristic of regional light, like “unit light intensity of construction land”and “unit light intensity of population”. Through related analysis on3different types ofunit light intensity indexes in municipal district, country and rural-urban areas in country,as well as gross regional production and average resident income in country, authoridentified the scale effect of different unit light intensity indexes on country economy. Onthis basis and with net income of rural residents as indication of regional poverty degree,author screened out different scales of “unit light intensity of construction land” and “unitpopulation light intensity” as variables with the method of stepwise regression to construct2regression models to measure the poverty degree of areas. Goodness of fits of2models is0.912and0.915respectively.
The study sorted out and collected the data of population and construction land incountry, and then evaluated the income level of farmers with2regional poverty models.And then, author compared the spatial scale of areas in relative poverty and centralizedextremely poor areas. Meanwhile, author analyzed the objective poverty status ofidentified national-level poor countries. By comparison, author found that the identifiedconnected areas in extreme poverty basically covered most relatively poor areas.However, actual farmer income levels in national-level poor countries in differentprovinces vary greatly. Further, author analyzed the spatial distribution mode of poorcountries with self-correlation statistics of overall and regional space and the result showsthat the distribution of poor areas still represents obvious spatial assembly in the scale ofcountry. There are comparatively rich assembled country areas in Circum-Bohai SeaEconomic Zone of Beijing-Tianjin-Hebei, Yangtze River Delta Economic Zone and PearlRiver Delta Economic Zone, while the provinces in mid-west of China, such as Sichuan,Yunnan, Guizhou, Hunan, Hubei and Tibet, are obviously involved in relatively poorareas and regional consistency is very high, indicating that they are the areas inwidespread poverty.
引文
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