通过2024年巴黎奥运会探索时空分析！基于人类移动性数据发掘洞见，并了解如何利用空间分析工具。

Explore space-time analytics through the 2024 Paris Olympics! Discover insights from human mobility data & learn how to leverage spatial analysis tools.

通过2024年巴黎奥运会探索时空分析！了解人类移动数据带来的洞察，并学习如何利用空间分析工具。预计到2024年，全球将产生并消费120泽字节（zettabytes）的数据，相较于十年前增长了1,076%！推动这一增长的主要因素之一是数据速度的提升，数据在空间和时间维度上的粒度正不断细化。与此同时，组织机构对分析细节的要求也日益提高，以支持更明智的决策。但问题在于：如何有效将如此庞大的时空数据转化为真正有价值的洞察？这正是本文将深入探讨的内容——包括一个实际应用案例，我们将分析2024年奥运会期间的人类移动趋势！ 奥运会等大型活动会产生海量的人类移动数据。借助可扩展的时空数据分析技术，我们可以从中提炼出关于大规模事件如何影响城市动态与公共空间的宝贵见解，涵盖人群行为、场馆拥堵情况以及交通模式等方面。让我们从2024年日程中最重要的活动之一——巴黎奥运会开始，探索其对现有旅游景点、公共交通系统及体育场馆的意外影响，以及不同赛事之间的交叉访问模式。 本次分析所使用的数据由我们的数据合作伙伴Unacast提供，包含经匿名化处理的移动数据，并聚合至H3空间索引网格。您可在下方地图中进行探索（点击此处全屏查看），该图展示了2024年夏季期间每个网格单元内每日唯一设备数量。以下是我们在分析这些时空模式时发现的五个关键点——继续阅读，了解如何复现此类分析！ 该数据的时空粒度极为精细，使我们能够提取高度详细的洞察。例如，下图显示了男子篮球金牌赛前，贝尔西竞技场周边每小时的人流数据。地图清晰地显示出比赛开始前数小时内，附近桥梁及路口人流显著增加；同时，也揭示了观众在比赛即将开始前大量聚集于场馆入口处。这类信息对于交通规划人员和人群管理团队在筹备类似大型活动时极具价值。 结合完整的赛事日程表，我们能够识别出参与奥运会与未参与奥运会人群之间移动模式的差异。识别这些模式有助于城市规划者提前制定人群管控策略，确保为参与者提供充足的接待设施，并评估对本地商业和社区的广泛影响。 一些巴黎标志性旅游景点在奥运会前夕经历了令人意外的变化。您可在下方地图中探索其具体含义（点击此处全屏查看）。红色区域表示该地区在奥运会期间被认定为热点的频率上升，而青绿色区域则表示频率下降。该分析表明，像奥运会这样的大规模活动能够改变城市旅游格局，吸引注意力从传统景点转向新兴地点。 如上图所示，即便在通常具有高旅游吸引力的城区部分，成为热点的频率也有所上升，表明这些区域在2024年的拥挤程度甚至超过了2023年。由于可以追踪单个设备在不同体育赛事间的移动轨迹，我们还能分析其跨赛事访问模式，并推断赛事之间的关联规则。数据分析显示，不同赛事之间的出席率存在强烈相关性，多个项目吸引了相似规模的观众群体。主要趋势包括： 这些出席模式凸显了相关运动项目共享粉丝基础的特点，为赛事组织者优化赛程安排与场馆布局提供了机会，从而提升观众体验。 随着奥运会临近，移动数据揭示了巴黎主要火车站及公共交通枢纽周边活动量的上升。下图显示，火车站（绿色标记）成为关键连接节点，吸引大量观众在各赛事之间及城市各区域间流动。粉红色区域为“强化热点”——即人类活动水平持续上升的高密度集群。更多关于这些工具的信息，请参阅数据仓库Analytics Toolbox中的统计模块。 正如我们通过对2024年巴黎奥运会期间时空分析的深入研究所示，理解大型活动中的人员移动模式，可为城市规划者、活动组织者及企业带来至关重要的洞察。从优化交通资源配置到合理部署安保力量，时空数据揭示的关键趋势能够支持更智能、更高效的决策制定。 如果您希望利用这些强大工具，将自身拥有的时空数据转化为可操作的洞察，何不亲身体验一下？立即申请CARTO演示，了解我们的无代码工作流如何彻底革新您的空间分析方式！探索地理空间基础模型的最新进展，涵盖表征学习到人口动态等主题，获取来自CARTO与BSC联合研讨会的深度见解。现在，CARTO已支持直接在地理空间基础模型嵌入向量上运行分析。可视化、聚类、检测变化，将空间数据转化为决策依据。

Explore space-time analytics through the 2024 Paris Olympics! Discover insights from human mobility data & learn how to leverage spatial analysis tools. In 2024, it’s estimated that the world will create and consume 120 zettabytes of data. That’s an increase of 1,076% in just 10 years! A big part of what is driving this is an increase in data velocity, with data being produced at increasing granularity, both in space and time. This is mirrored by organizations demanding ever greater detail in their analysis to drive more informed decision making. But the question is, how can you effectively turn such a huge amount of space-time data into actually useful insights? That’s exactly what we’ll explore in this blog - including an example of space-time analytics in action where we’ll explore trends in human mobility at the 2024 Olympics! Events like the Olympics generate an enormous volume of human mobility data. By leveraging scalable space-time data science techniques, we can turn this into valuable insights on how large-scale events influence urban dynamics and public spaces, with important insights on crowd behaviors, venue congestion, and transportation patterns. Let’s start by exploring space-time trends at one of the top events from the 2024 calendar, the Paris Olympics! We'll explore the unexpected impact of the Olympics on existing tourist attractions, public transportation, and sports venues, as well as cross-visitation patterns between different events. For this analysis, our data partners Unacast have provided us with a sample of anonymized mobility data, which have been aggregated to a H3 Spatial Index grid. You can explore this on the map below (open in full screen here), which shows the daily count of unique devices per cell in the 2024 summer period. Here are 5 things we learned from exploring the space-time patterns in this data - keep reading to learn more about how you can replicate this analysis! The spatio-temporal granularity of this data allows us to extract insights at an incredibly detailed level. For example, the map below displays hourly footfall data around the Bercy Arena just before the men's basketball gold medal game. The map clearly shows increased foot traffic on nearby bridges and at intersections in the hours leading up to the event. It also reveals a high concentration of people gathering at the venue's entrance right before the match begins. This type of information can be extremely valuable for transportation planners and crowd management teams when preparing for similar large-scale events. Using the complete sports events schedule, we could identify how mobility patterns differed depending on whether people attended the Games and people that did not. Identifying these patterns can guide city planners in preparing for crowd control, ensuring accommodations are in place for attendees, and considering the broader impact on local businesses and communities. Some of Paris’ iconic tourist destinations experienced a surprising shift in the period leading up to the games. You can explore what this means on the map below (open in full screen here). Red areas indicate that the frequency of an area being considered a hotspot has increased during the Olympics games, while turquoise areas indicate that the frequency has decreased. This analysis demonstrates how large-scale events like the Olympics can influence urban tourism, drawing attention away from typical tourist sites and toward novel locations. As we can see in the map above, even in some typically touristic parts of the city, the frequency of becoming a hot spot was increased, indicating that these parts of the city were even more crowded than in 2023. Since we can trace a single device through different sports events, we can also check its cross-visitation patterns and infer association rules between events. Analysis of this data has shown strong correlations between attendance at different events, with several sports drawing similar crowds. Some top trends include: These attendance patterns highlight how closely related sports attract shared fan bases, providing organizers with opportunities to optimize scheduling and venue layouts to enhance fan experiences. As the Olympic Games approached, mobility data has revealed an increase in activity around major train stations and public transportation hubs in Paris. The below map shows how train stations (green points) became vital connectors, drawing in large crowds traveling between events and across the city. The pink areas are “strengthening hotspots;” clusters of high levels of human activity, which is increasing over time. Learn more about these tools in the statistics module of your data warehouse’s Analytics Toolbox. As we’ve seen from this deep dive into space-time analytics during the 2024 Paris Olympics, understanding human mobility patterns at such large-scale events can provide critical insights for city planners, event organizers, and businesses alike. From optimizing transportation to planning security resources, space-time data reveals key trends that can inform smarter, more efficient decision-making. If you're interested in harnessing these powerful tools to turn your own space-time data into actionable insights, why not see it in action? Request a demo with CARTO today and discover how our no-code workflows can revolutionize your approach to spatial analysis! Explore the state of geospatial foundation models, from representation learning to population dynamics, with insights from the CARTO & BSC workshop. CARTO now lets you run analytics directly on geospatial foundation model embeddings. Visualize, cluster, and detect changes to turn spatial data into decisions.