Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques
Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques
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DOI：
10.18564/jasss.1909
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发表时间：
2012-01
期刊：
影响因子：
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通讯作者：
K. Harland;A. Heppenstall;Dianna M. Smith;M. Birkin
中科院分区：
文献类型：
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作者：
K. Harland;A. Heppenstall;Dianna M. Smith;M. Birkin
产生合成种群有几种既定的方法。这些方法包括确定性加权、条件概率（蒙特卡罗模拟）和模拟退火。然而，这些方法中的每一种都受到限制，例如，可以应用的地理水平，或者可以复制的实际人口的特征的数量。该研究考察和批评了这些方法在不同空间尺度上的表现。结果表明，模拟退火算法在所有空间尺度上产生了最一致和最准确的种群。在讨论中对每种方法的相对优点和局限性进行了评价。
There are several established methodologies for generating synthetic populations. These include deterministic reweighting, conditional probability (Monte Carlo simulation) and simulated annealing. However, each of these approaches is limited by, for example, the level of geography to which it can be applied, or number of characteristics of the real population that can be replicated. The research examines and critiques the performance of each of these methods over varying spatial scales. Results show that the most consistent and accurate populations generated over all the spatial scales are produced from the simulated annealing algorithm. The relative merits and limitations of each method are evaluated in the discussion.