了解如何在 Snowflake 中集成 MovingPandas 与 CARTO，以提升城市交通分析能力，识别交通热点并优化城市交通运输。

Learn how integrating MovingPandas with CARTO in Snowflake boosts urban mobility analysis by uncovering traffic hotspots and optimizing city transportation.

了解如何将MovingPandas与Snowflake中的CARTO集成，通过识别交通热点并优化城市交通，提升城市出行分析能力。城市出行是一个复杂且不断变化的领域。理解人流与车流在城市街道中的动态对于城市规划者、交通工程师和研究人员至关重要。随着城市人口持续增长，优化交通系统并提升城市宜居性的需求从未如此紧迫。此前的方法存在一些局限性，例如运行时间和并行化限制，这促使我们探索与Snowflake更无缝的集成方案。该分析结果在CARTO中进行可视化，用户可交互式地探索分析成果。中间步骤的示例也一并展示。最后，分析结果如下所示：H3/小时时长数据与SpaceTime Getis Ord分析输出并列呈现，清晰揭示了高流量区域与特定时间段之间的相关性。从识别高需求区域到优化交通服务路线，这种集成方法为构建更智能、高效的都市交通系统铺平了道路。随着我们持续完善和扩展这一框架，其在变革城市出行分析方面的潜力无限。本工作是欧洲地平线（Horizon Europe）研发计划（R&I）资助项目EMERALDS的一部分，项目编号为GA No. 101093051。Dr. Argyrios Kyrgiazos 是CARTO的数据科学家，研究方向聚焦于空间分析领域的优化技术与算法，致力于将复杂的地理空间数据转化为可操作的洞察。Dr. Argyrios Kyrgiazos 是CARTO的数据科学家，研究方向聚焦于空间分析领域的优化技术与算法，致力于将复杂的地理空间数据转化为可操作的洞察。Dr. Argyrios Kyrgiazos 是CARTO的数据科学家，研究方向聚焦于空间分析领域的优化技术与算法，致力于将复杂的地理空间数据转化为可操作的洞察。了解领先户外广告（OOH）企业如何利用云原生空间分析与人工智能，突破单纯依赖曝光量统计的局限，实现广告活动投资回报率（ROI）的验证。探索五种利用云原生GIS、自动化与实时洞察，推动空间分析在环境韧性建设中转型的方式。

Learn how integrating MovingPandas with CARTO in Snowflake boosts urban mobility analysis by uncovering traffic hotspots and optimizing city transportation. Urban mobility is a complex and ever-changing landscape. Understanding the flow of people and vehicles through city streets is crucial for city planners, transportation engineers, and researchers. With urban populations growing, the need to optimize transportation systems and enhance urban livability has never been more pressing. There were some limitations to this approach , such as running time and parallelization constraints, which led us to explore a more seamless integration with Snowflake. The outcome of this analysis is visualized in CARTO, where users can interactively explore the results. Examples of the intermediate steps are shown as well. Lastly, the outcome of this analysis is shown below. The H3/hour duration data is shown alongside the output of the SpaceTime Getis Ord analysis, revealing clear correlations between high-traffic areas and specific time windows. From identifying high-demand areas to optimizing routes for transportation services, this integrated approach paves the way for smarter, more efficient urban transportation systems. As we continue to refine and expand this framework, its potential to transform urban mobility analysis is boundless. This work was developed as part of the EMERALDS project, funded by the Horizon Europe R&I program under GA No. 101093051. Dr. Argyrios Kyrgiazos is a Data Scientist at CARTO. His research interest are optimisation techniques and algorithms that specialize in spatial analysis, transforming complex geospatial data into actionable insights. Dr. Argyrios Kyrgiazos is a Data Scientist at CARTO. His research interest are optimisation techniques and algorithms that specialize in spatial analysis, transforming complex geospatial data into actionable insights. Dr. Argyrios Kyrgiazos is a Data Scientist at CARTO. His research interest are optimisation techniques and algorithms that specialize in spatial analysis, transforming complex geospatial data into actionable insights. See how leading OOH companies use cloud-native spatial analytics and AI to move beyond impression counts and prove campaign ROI. Discover 5 ways to transform spatial analysis for environmental resilience using cloud-native GIS, automation, and real-time insights.