空间微观模拟方法及在城市研究中的应用

基于活动主体的城市系统微观模拟可能在未来城市研究中发挥重要作用,但其通常受到微观个体数据稀缺的限制。空间微观模拟方法（spatial microsimulation）主要基于家庭、个人等微观分析单元,通过整合不同层面的数据源,如宏观汇总层面的人口普查统计表以及微观层面的家庭活动日志调查等,合成大样本微观个体数据集,可以在精细化空间尺度上对微观个体行为进行模拟研究。该方法在城市系统微观模拟、空间分析以及政策评估等方面具有一定优势,在西方国家城市研究中的应用逐渐增多,但在国内较为缺乏。本文尝试对空间微观模拟方法的起源、三种核心算法,包括条件概率（conditional probability） 、确定性加权（deterministic reweighting）以及模拟退火（simulated annealing）进行介绍,并从国际层面综述该方法在城市研究,如收入与贫困、交通出行、健康等领域中的应用,为我国相关研究的开展提供借鉴。

A review of spatial microsimulation approach and its application in urban research

Individual agent-based microsimulation might be an important research direction for future urban modeling. However, possibly due to confidentiality issues, the spatially detailed microdata sets with a wide range of individual or household characteristics are usually not publicly accessible in many countries. There is a strong demand for the development of small area estimates of socio-demographics and the potential effects of policy changes, which could help the government acquire detailed information on population’s attributes at a fine geographic scale, better allocate the limited resources to the most needed places, and evaluate the potential impacts of policy decisions. Using individuals or households as the basic analytical unit, spatial microsimulation can synthesize much individual-level spatial microdata for large populations through combining different data sources, such as household activity diary survey and aggregate population census tabulates. Spatial microsimulation can simulate the virtual populations in a spatial setting, and it involves three major procedures, including the construction of small area microdata, static what-if simulations for one time point and dynamic microsimulation over a period. This approach can simulate the synthetic population’s behavior at fine geographic resolution, and perform different what-if simulations to explore the impacts of policy scenarios. In general, spatial microsimulation has multiple advantages for urban research, spatial analysis and policy evaluation, and thus has been increasingly applied in the fields of geography, transport, and social sciences, particularly in developed countries. However, in China, microsimulation studies has been very scarce to date, possibly due to the fact that the microsimulation development is challenging requiring a high level of programming skills, there is little publicly available software suited to microsimulation models, and there is a lack of data at an appropriate scale. This paper aims to first provide a comprehensive review of spatial microsimulation techniques, including conditional probability, deterministic reweighting, and simulated annealing, which have been widely used for creating synthetic populations in microsimulation studies. Further, this paper also reviews the recent applications of spatial microsimulation approach in urban research worldwide, focusing on income distribution and deprivation evaluation, travel behavior and transport carbon emission, and health behavior and outcomes. The paper ends with the discussion and conclusion.