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Generating synthetic baseline populations from register data

机译：从寄存器数据生成合成基准种群

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The paper presents a population synthesiser based on the method of Iterative Proportional Fitting (IPF) algorithm developed for the new Danish national transport model system. The synthesiser is designed for large population matrices and allows target variables to be rep resented in several target constraints. As a result, constraints for the IPF are cross-linked, which makes it difficult to ensure consistency of targets in a forecast perspective. The paper proposes a new solution strategy to ensure internal consistency of the population targets in order to guarantee proper convergence of the IPF algorithm. The solution strategy consists in establishing a harmonisation process for the population targets, which com bined with a linear programming approach, is applied to generate a consistent target rep resentation. The model approach is implemented and tested on Danish administrative register data. A test on historical census data shows that a 2006 population could be pre dicted by a 1994 population with an overall percentage deviation of 5-6% given that tar gets were known. It is also indicated that the deviation is approximately a linear function of the length of the forecast period.
机译：本文针对新的丹麦国家交通模型系统，提出了一种基于迭代比例拟合（IPF）算法的人口综合器。该合成器专为大型总体矩阵而设计，并允许在几个目标约束条件下表示目标变量。结果，对IPF的约束是相互关联的，这使得很难在预测角度上确保目标的一致性。本文提出了一种新的解决方案策略，以确保人口目标的内部一致性，以确保IPF算法的正确收敛。解决方案策略是为人口目标建立统一的流程，并与线性规划方法相结合，以产生一致的目标表示。该模型方法是在丹麦行政登记数据上实施和测试的。对历史人口普查数据的测试表明，鉴于已知焦油的获得，2006年的人口可能由1994年的人口预测，总体百分比偏差为5-6％。还表明该偏差大约是预测周期长度的线性函数。

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