Higher education Investment is not only the basic protection of daily operation of higher education, but also promotes regional economic development and enhances the strength of national science and technology. On the basis of analyzing spatial distribution differences of expenditure on higher education in all provinces of China during 1995~2011 by Cartogram map, this paper used Wolfson Polarization Index and Circular Cumulative Causation model to study on the mechanism of spatial differentiation higher education investment. We came to the following conclusions:1) There are distributive laws of zonality that higher education investment reduces from east to west, with spatial non-equilibrium and spatial polarization being increasingly significant; funding per student is relatively equal, although it’s still higher in both East China and West China than in Middle China; the non-equilibrium in higher education funding of central subordinate university is more prominent than that of local subordinate university.2) Complex circular cumulative causation which was leaded by national policy and influenced by population density and level of economic development, investing differently in accordance with the return rate of capital, has formed the investment differentiation on higher education.
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Debin Du et al
. Spatial Pattern and Formation Mechanism of Higher Education Investment of China[J]. SCIENTIA GEOGRAPHICA SINICA,
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Abstract: Map makers have long searched for a way to construct cartograms -- maps in which the sizes of geographic regions such as countries or provinces appear in proportion to their population or some other analogous property. Such maps are invaluable for the representation of census results, election returns, disease incidence, and many other kinds of human data. Unfortunately, in order to scale regions and still have them fit together, one is normally forced to distort the regions' shapes, potentially resulting in maps that are difficult to read. Many methods for making cartograms have been proposed, some of them extremely complex, but all suffer either from this lack of readability or from other pathologies, like overlapping regions or strong dependence on the choice of coordinate axes. Here we present a new technique based on ideas borrowed from elementary physics that suffers none of these drawbacks. Our method is conceptually simple and produces useful, elegant, and easily readable maps. We illustrate the method with applications to the results of the 2000 US presidential election, lung cancer cases in the State of New York, and the geographical distribution of stories appearing in the news.