摘要翻译：
新房地产开发的影响与其人口分布（家庭类型和组成、收入、社会人口统计）密切相关，这些分布取决于住宅类型、价格、位置和楼层等方面。本文提出了一种基于机器学习的方法来建模在较大的社区/公寓设置中即将开发的新建筑的人口分布。我们使用来自越南河内房地产开发项目Ecopark Township的真实数据集，在那里我们研究了深度生成模型文献中的两种机器学习算法来创建合成代理群体：条件变分自动编码器(CVAE)和条件生成对抗网络(CGAN)。大量的实验研究表明，CVAE模型在估计新房地产开发项目人口分布方面优于经验分布模型和CGAN模型。
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英文标题：
《Population synthesis for urban resident modeling using deep generative
models》
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作者：
Martin Johnsen, Oliver Brandt, Sergio Garrido, Francisco C. Pereira
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最新提交年份：
2020
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: Conditional Variational Auto-Encoder (CVAE) and Conditional Generative Adversarial Networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.
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