TIME-SPACE PATTERNS OF RISK CLUSTER OF POPULATION AGING IN CHINAAT MULTI-SCALES: THE APPLICATION OF FLEXIBLE SPATIAL SCAN STATISTICS
JI Xiao-mei1, ZENG Qun-zhou1, WANG Chao2
1. College of Tourism, Nanchang University, Nanchang 330031, China;
2. College of Business, Guizhou University of Finance and Economicas, Guizhou 550025, China
Abstract:Population aging is one of the most heated topics among domestic and foreign societies which has been attached numerous attention by scholars from variant research fields. On the one hand, under the context where the social and spatial reconfiguration take places, population aging in China tends to be increasingly complicated, the new normalcy characterized as getting old before being rich is throwing a great threaten to the sustainable development. On the other hand, due to the fact that regions vary in development stage and geographical condition, population aging in China displays its strikingly spatial and temporal imbalance. Using Flexible Spatial Scan Statistic, this paper scrutinized spatiotemporal disparity in aging risk clusters at provincial, prefectural and county scales. The authors found that, aging has become the main melody of Chinese population transformation, and low birth rate has been the new normal at nation-wide. The regional imbalance comes to be increasingly worsened and there exist diverse regional models. All three scales share such common features as followings:aging risk clusters are spatially concentrated on the southeast part of China, the risk degree and distributional area become larger, but the gap among clusters comes to be narrowed. Generally, the smaller spatial scale Flexible Spatial Scan Statistic adopts, the more scattered, precise and higher-risk clusters will be identified, and gaps between them become enlarged.
纪小美, 曾群洲, 王超. 多尺度下中国人口老龄化风险集群的时空格局——Flexible空间扫描计量方法的应用[J]. 人文地理, 2018, 33(2): 116-123.
JI Xiao-mei, ZENG Qun-zhou, WANG Chao. TIME-SPACE PATTERNS OF RISK CLUSTER OF POPULATION AGING IN CHINAAT MULTI-SCALES: THE APPLICATION OF FLEXIBLE SPATIAL SCAN STATISTICS. HUMAN GEOGRAPHY, 2018, 33(2): 116-123.
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[29]
Kulldorff M, Nagarwalla N. Spatial disease clusters:Detection and inference[J]. Statistics in Medicine, 1995,14(8):799-810.
[34]
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[28]
Besag J, Newell J. The detection of clusters in rare diseases[J]. Journal of the Royal Statistical Society Series A-Statistics in Society, 1991,154(1):143-155.
[29]
Kulldorff M, Nagarwalla N. Spatial disease clusters:Detection and inference[J]. Statistics in Medicine, 1995,14(8):799-810.
[30]
Naus J. The distribution of the size of maximum cluster of points on the line[J]. Journal of the American Statistical Association, 1965, 60(310):532-538.
[35]
Tango T. A spatial scan statistic with a restricted likelihood ratio[J]. Japanese Journal of Biometrics, 2008,29(2):75-95.
[29]
Kulldorff M, Nagarwalla N. Spatial disease clusters:Detection and inference[J]. Statistics in Medicine, 1995,14(8):799-810.
[30]
Naus J. The distribution of the size of maximum cluster of points on the line[J]. Journal of the American Statistical Association, 1965, 60(310):532-538.
[31]
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[31]
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[33]
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[32]
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[33]
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[34]
Kulldorff M, Huang L, Pickle L, et al. An elliptic spatial scan statistic[J]. Statistical of Medicine, 2006,25(22):3929-3943.
[33]
Takahashi K, Kulldorff M, Tango T, et al. A flexibly shaped spacetime scan statistic for disease outbreak detection and monitoring[J]. International Journal of Health Geography, 2008,7(7):14.
[34]
Kulldorff M, Huang L, Pickle L, et al. An elliptic spatial scan statistic[J]. Statistical of Medicine, 2006,25(22):3929-3943.
[35]
Tango T. A spatial scan statistic with a restricted likelihood ratio[J]. Japanese Journal of Biometrics, 2008,29(2):75-95.
[34]
Kulldorff M, Huang L, Pickle L, et al. An elliptic spatial scan statistic[J]. Statistical of Medicine, 2006,25(22):3929-3943.
[35]
Tango T. A spatial scan statistic with a restricted likelihood ratio[J]. Japanese Journal of Biometrics, 2008,29(2):75-95.
[35]
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