Abstract: The core driving force of urban development is the growth of population, while the spatial-temoral structure of the distribution of urban population is one of the theoretical frontier issues. This paper, taking Jiangsu Province as an example, describes the spatial pattern and evolving trend of the diverse population of Jiangsu with data at county level since the 1990s through the related analysis of spatial autocorrelation analysis, population gravity center model and shift-share analysis, as well as other tools such as population density and unbalanced index. Based on three time discontinuity surfaces, with software platform of ArcGIS and GeoDA, some conclusions are drawn as follows. (1) Population density of Jiangsu Province has“low-south and high-north” features, which are corresponding with its economic development level. (2) There is certain spatial relevance on population distribution in Jiangsu. The similar areas cluster in space. And this trend is receding as time goes by. Because of the higher economic development level, people gathered to the Yangtze River downstream counties, to form a high-low area. Counties between high-high area and low-low area become a low-high area due to less radiation from developed cities. Densely-populated area is distributed along the Yangtze River. The general pattern remains relatively stable, and the counties in the high-high area firstly increase and then decrease while counties in the low-low area continue to decrease. (3) The population distribution in Jiangsu is imbalanced and such situation is enhanced. The population gravity center slowly moves to the northwest of Jiangsu since 1990. (4) The population of Jiangsu is growing at a slow rate in recent 20 years. Population concentration ability is not alike in the three regions of Jiangsu Province. Central Jiangsu is short of clustering people and the population concentration ability of Southern Jiangsu constantly strengthens but that of Northern Jiangsu weakens.

Key words: evolvement of spatial pattern, Jiangsu, population density, Shift-Share Analysis, spatial autocorrelation