Name
Abstract | ||
|---|---|---|
Spatial autocorrelation is a very general statistical property of economic variables, it indicates correlation of a variable with itself through space. Spatial association rule mining, discovery of interesting, meaningful rules in spatial databases, ignores autocorrelation of the spatial data, or just generalizes the spatial data into attribute data currently. In order to compare the results between spatial autocorrelation and spatial association rule mining, in this paper, the spatial association rules were mined by Apriori algorithm and it's development algorithm. Then, spatial autocorrelation analysis and spatial regression analysis were implemented on the same spatial data set. The experimental data is about the county-level revenue and population, education state, health state and social security state in China from 2000 to 2005. The results of the spatial association rules mining proves that economic level such as per capita revenue and social security have stronger correlation. The result of spatial autocorrelation is that from 2000 to 2005, national county-level per capita revenue, education, health and social security present positive spatial correlation. There is little interannual change in the spatial distribution of per capita revenue, and low economic level applies to almost all counties all over the nation. Education, the situation that low value areas are surrounded by high value areas universally exists, which shows that little significant positive influence from high level areas is exerted on low level areas. At the same time, the interprovincial education gap is gradually increasing. Health, in the year 2000 to 2005, there is a growing aggregation trend in China's county-level health spatial pattern, and there are more low value areas in health. Social security, in the research years, the aggregation trend is gradually decreasing. While spatial heterogeneity is increasing. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICSDM.2011.5969000 | ICSDM |
Keywords | Field | DocType |
apriori algorithm,spatial autocorrelation analysis,spatial regression analysis,apriori,spatial data mining,regression analysis,visual databases,spatial association rule mining,spatial regression,spatial databases,data mining,spatial autocorrelation,spatial correlation,association rules,algorithm design,spatial database,association rule mining,association rule,spatial pattern,correlation,spatial heterogeneity,spatial data,algorithm design and analysis | Spatial analysis,Common spatial pattern,Population,Data mining,Computer science,Apriori algorithm,Per capita,Association rule learning,Spatial heterogeneity,Statistics,Spatial distribution | Conference |
ISBN | Citations | PageRank |
978-1-4244-8352-5 | 1 | 0.35 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangping Chen | 1 | 4 | 1.44 |
| Yanan Chen | 2 | 1 | 1.36 |
| Jie Yu | 3 | 41 | 10.55 |
| Zhaohui Yang | 4 | 1 | 2.04 |