Agent-based simulation is a versatile and potent computational modeling technique employed to analyze intricate systems and phenomena spanning diverse fields. However, due to their computational intensity, agent-based models become more resource-demanding when geographic considerations are introduced. This study delves into diverse strategies for crafting a series of Agent-Based Models, named "agent-in-the-cell," which emulate a city. These models, incorporating geographical attributes of the city and employing real-world open-source mobility data from Safegraph's publicly available dataset, simulate the dynamics of COVID spread under varying scenarios. The "agent-in-the-cell" concept designates that our representative agents, called meta-agents, are linked to specific home cells in the city's tessellation. We scrutinize tessellations of the mobility map with varying complexities and experiment with the agent density, ranging from matching the actual population to reducing the number of (meta-) agents for computational efficiency. Our findings demonstrate that tessellations constructed according to the Voronoi Diagram of specific location types on the street network better preserve dynamics compared to Census Block Group tessellations and better than Euclidean-based tessellations. Furthermore, the Voronoi Diagram tessellation and also a hybrid -- Voronoi Diagram - and Census Block Group - based -- tessellation require fewer meta-agents to adequately approximate full-scale dynamics. Our analysis spans a range of city sizes in the United States, encompassing small (Santa Fe, NM), medium (Seattle, WA), and large (Chicago, IL) urban areas. This examination also provides valuable insights into the effects of agent count reduction, varying sensitivity metrics, and the influence of city-specific factors.

翻译：基于智能体的仿真是一种通用且强大的计算建模技术，用于分析跨多个领域的复杂系统与现象。然而，由于计算强度高，当引入地理考量时，基于智能体的模型变得更为资源密集。本研究深入探讨了构建一系列名为“单元内智能体”的基于智能体模型的不同策略，这些模型模拟了一个城市。这些模型融入了城市的地理属性，并采用来自Safegraph公开数据集的真实世界开源移动数据，模拟了不同情景下COVID的传播动态。“单元内智能体”概念设计指出，我们的代表性智能体（称为元智能体）与城市剖分中的特定家庭单元相关联。我们考察了不同复杂度的移动地图剖分，并实验了智能体密度，范围从匹配实际人口到为计算效率减少（元）智能体数量。我们的研究结果表明，根据街道网络上特定位置类型的Voronoi图构建的剖分，与人口普查区组群剖分及基于欧几里得的剖分相比，能更好地保持动态特性。此外，Voronoi图剖分以及一种混合型——基于Voronoi图和人口普查区组群——的剖分，只需较少的元智能体即可充分近似全规模动态。我们的分析涵盖了美国一系列城市规模，包括小（新墨西哥州圣塔菲）、中（华盛顿州西雅图）和大（伊利诺伊州芝加哥）城市区域。这一考察也为智能体数量减少、不同敏感性指标以及城市特定因素影响的效果提供了宝贵见解。