Spatial dynamic microsimulations allow for the multivariate analysis of complex socio- economic systems with geographic segmentation. For this, a synthetic replica of the system as base population is stochastically projected into future periods. Thereby, the projection is based on micro-level transition probabilities. They need to accurately represent the characteristic dynamics of the system to allow for reliable simulation outcomes. In practice, transition probabilities are unknown and must be estimated from suitable survey data. This can be challenging when the characteristic dynamics vary locally. Survey data often lacks in regional detail due to confidentiality restrictions and limited sampling resources. In that case, transition probability estimates may misrepresent local dynamics as a result of insufficient local observations and coverage problems. The simulation process then fails to provide an authentic evolution. We present two transition probability estimation techniques that account for regional heterogeneity when the survey data lacks in regional detail. Using methods of constrained optimization and ex-post alignment, we show that local micro level transition dynamics can be accurately recovered from aggregated regional benchmarks. The techniques are compared in theory and subsequently tested in a simulation study.

中文翻译：

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从缺乏区域细节的调查数据估计空间动态微观模拟的区域转移概率

空间动态微观模拟允许对具有地理分割的复杂社会经济系统进行多变量分析。为此，系统的合成副本作为基础人口被随机预测到未来时期。因此，投影是基于微观级别的转换概率。他们需要准确地表示系统的特征动态，以实现可靠的模拟结果。在实践中，转移概率是未知的，必须根据合适的调查数据进行估计。当特征动态局部变化时，这可能具有挑战性。由于保密限制和有限的抽样资源，调查数据往往缺乏区域细节。在这种情况下，由于局部观察不足和覆盖问题，转换概率估计可能会歪曲局部动态。然后，模拟过程无法提供真实的演变。当调查数据缺乏区域细节时，我们提出了两种转换概率估计技术来解释区域异质性。使用约束优化和事后对齐的方法，我们表明可以从聚合的区域基准中准确地恢复局部微观水平过渡动态。这些技术在理论上进行了比较，随后在模拟研究中进行了测试。使用约束优化和事后对齐的方法，我们表明可以从聚合的区域基准中准确地恢复局部微观水平过渡动态。这些技术在理论上进行了比较，随后在模拟研究中进行了测试。使用约束优化和事后对齐的方法，我们表明可以从聚合的区域基准中准确地恢复局部微观水平过渡动态。这些技术在理论上进行了比较，随后在模拟研究中进行了测试。