Multi-Scale Analysis of Regional Inequality based on Spatial Field Model: A Case Study of China from 2000 to 2012

Abstract

A large body of recent studies-from both inside and outside of China-are devoted to the understanding of China's regional inequality. The current study introduces "the spatial field model" to achieve comprehensive evaluation and multi-scale analysis of regional inequality. The model is based on the growth pole theory, regional interaction theory, and energy space theory. The spatial field is an abstract concept that defines the potential energy difference that is formed in the process of a regional growth pole driving the economic development of peripheral areas through transportation and communication corridors. The model is able to provide potentially more precise regional inequality estimates and generates isarithmic maps that will provide highly intuitive and visualized presentations. The model is applied to evaluate the spatiotemporal pattern of economic inequality in China from 2000 to 2012 amongst internal eastern-central-western regions as well as north-south regions at three geographical scales-i.e., inter-province, inter-city, and inter-county. The results indicate that the spatial field model could comprehensively evaluate regional inequality, provide aesthetically pleasing and highly adaptable presentations based on a pixel-based raster, and realise the multi-scale analyses of the regional inequality. The paper

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

“…Over the past decade, central and provincial governments have sought to foster urban agglomerations as regional growth poles and as strategic core areas with the potential to drive continuous economic growth [77]. In this context, the study of urbanization in the MYRB is necessary.…”

“…The change of the long axis is more obvious than that of the short one, meaning that the forces driving the spatial evolution mainly come from urban expansion in the "northwest-southeast" direction. Specifically, in 2000-2008, the short axis varied more than the long axis, that is, the "southwest-northeast" direction had rapid urban expansion during 2000-2008. In 2008, the long axis varied more than the short axis, indicating that the "northwest-southeast" had rapid urbanization during 2008-2016.…”

Using Areal Interpolation to Deal with Differing Regional Structures in International Research

“…We express this with the example of spatial autocorrelation (hot spot analysis), which helps to reveal spatial patterns. Yet, for better data comparability across different systems (such as countries) or across time at different geographical levels [6], it would be best to work with a regular grid. To do this, we first have to recalculate the values from source units to this regular grid.…”

Regional Inequality in China Based on NPP-VIIRS Night-Time Light Imagery

“…The GDP per capita of Tianjin, the wealthiest province, was nearly 4.1 times more than that of the poorest province. Regional inequality in China is much higher than in some developed countries [4]. For instance, in 2015, Alaska's GDP per capita (73,474 dollars) was the highest in America, while the lowest was in Mississippi, where it was 35,717 dollars.…”

“…Since the late 1990s, numerous studies have indicated that the NTL satellite imagery has been shown to be highly related to socioeconomic parameters [15][16][17][18][19][20]. Due to its evident advantages, associated with easy access, low cost, and relative objectivity, some social economists use night light data to study regional inequality [4,[21][22][23][24][25]. For instance, Lessmann and Seidel [21] provided a new dataset of regional income inequalities within countries, based on DMSP-OLS satellite NTL data.…”