基于200多位地理空间专家的洞察，探讨人工智能、云原生工具以及技能演变如何重塑2026年空间分析领域。

Explore how AI, cloud-native tools, and evolving skills are reshaping spatial analytics in 2026, based on insights from 200+ geospatial experts.

探索人工智能（AI）、云原生工具以及不断演进的技能如何在2026年重塑空间分析，基于200多位地理空间专家的洞察。空间分析行业正处于一个关键转折点。如果您从事位置数据相关工作，或刚刚开始探索空间分析如何融入您的组织，您可能已经感受到这一转变。工具正在快速演进，AI已无处不在，而“从事地理空间工作”的定义也在不断扩展。为更深入理解地理空间从业者所面临的变化，我们对超过200名地理空间分析、数据科学及GIS专业人士进行了调研，发布了《2026年空间分析现状报告》。研究结果揭示了当前空间分析的发展态势——以及行业未来的走向。请继续阅读下方内容，并下载完整报告，查看您是否走在正确方向上。如需更深入的分析，包括实用操作手册及我们对2028年地理空间领域的十大预测，请下载完整报告。 人工智能在空间分析中已不再是未知数，但其角色的演变速度远比早期炒作所预示的更为缓慢和审慎。根据调查，31%的组织已投资于AI工具，该比例自2024年以来趋于稳定。与此同时，表示没有计划采用AI的组织占比已上升至近18%，凸显出早期采纳者与滞后者之间的差距日益扩大。我们观察到的趋势是自下而上的采纳模式：近45%的受访者表示将AI作为个人生产力工具使用，而仅有18.3%的人表示AI已嵌入组织流程。分析师正通过协作者（copilots）、大语言模型（LLMs）和机器学习技术加速日常任务处理，但企业级AI的全面应用仍主要集中在早期采纳者中。这一点至关重要，因为它重新定义了AI的角色：AI正逐渐被定位为一种能力倍增器——承担重复性的运营工作，使专家能够专注于真正重要的事项：提出正确的问题、验证结果，并将空间洞察应用于实际决策。当AI被恰当嵌入时，它能放大专家能力，并实现组织知识的系统化落地。然而，当前面临的挑战已不再是探索AI能为空间分析带来什么，而是构建合适的条件——包括数据安全、数据访问权限和数据完整性——以确保AI能够安全、负责任地规模化应用。 报告显示，目前已有68.5%的组织在云端运行空间分析，这一比例自2024年以来保持稳定，此前经历了多年快速增长。在2026年，云原生空间分析已不再是一种差异化优势，而是基础架构。然而，“云原生”在2026年的含义已超越简单的GIS工具在线托管，它意味着直接在云数据仓库和湖仓（lakehouses）内部完成空间数据的处理、分析与可视化，无需跨系统复制或同步数据。这使得组织能够更高效、更敏捷地利用集中化且易于访问的空间数据。采用这种模式的组织通常能获得更好的成果。调查显示，在云端运行空间分析的组织表现出以下特征： 尽管具备这些优势，报告也指出一个显著挑战：工具碎片化。这一分散的生态系统已成为组织集成过程中最大的瓶颈。近30%的受访者表示，数据访问与集成延迟是阻碍团队前进的主要障碍，远超处理速度慢或应对非技术性请求等其他挑战。简而言之，云已成为可扩展、受控且可访问的空间分析的基础。但互操作性已不再是“锦上添花”，而是不可或缺的关键要素——唯有如此，组织的空间分析技术栈才能持续输出企业级的一致性洞察。 结果是，组织越来越依赖少数高度专业化的专家，常被称为“空间独角兽”（spatial unicorns），来满足全组织的需求。报告明确指出：仅靠招聘无法解决这一问题。相反，领先组织正转向在人员编制之外同步扩展专业知识。这意味着赋予非专家使用自助式工具的能力，将空间分析嵌入日常业务流程，并借助AI驱动的系统将专家知识延伸至整个团队。在此模式下，GIS专业人士不再仅提供一次性或临时性的分析服务，而是有机会设计系统、制定标准与工作流程，推动空间思维的规模化发展，从而使其影响力远远超越单一地图或仪表板。 总而言之，这些趋势预示着一个未来：空间分析将比以往任何时候都更加深入嵌入、更具战略意义且更加强大。贯穿本年度调查的清晰主线是：在快速变化的环境中，必须明确定义并实现“良好”的标准。在CARTO，我们致力于帮助组织与地理空间专家应对这一不断演变的格局。本报告是我们对这场对话的贡献，旨在为团队提供清晰认知与信心，推动前行。下载完整报告，发现其中最重要的洞见。您猜对了吗？深入探索数据、操作手册以及塑造空间分析未来的关键预测，展望2028年。了解2025年最佳空间分析实践。学习行业领袖如何利用CARTO分析历史风险、管理实时运营并规划未来发展。了解GIS分析师如何从地图制作者转型为依托云原生工具、驱动AI战略决策的企业级专家。学习CARTO与Google Earth AI如何通过基础模型与代理型GIS（Agentic GIS），让全球尺度的洞察触手可及。

Explore how AI, cloud-native tools, and evolving skills are reshaping spatial analytics in 2026, based on insights from 200+ geospatial experts. The spatial analytics industry is at a pivotal moment. If you work with location data, or are just beginning to explore how spatial analytics fits into your organization, you’ve likely felt the shift. Tools are evolving. AI is everywhere. And the definition of what it means to “do geospatial work” is expanding. To better understand what’s changing for geospatial practitioners, we surveyed more than 200 geospatial analytics, data science, and GIS professionals for the 2026 State of Spatial Analytics Report. The findings highlight where spatial analytics stands today - and where the industry is headed next. Read on below and download the full report to see if you’re right. For a deeper dive, including practical playbooks and our top predictions for geospatial in 2028, download the full report. AI is no longer a question mark in spatial analytics, but its role is evolving more slowly and deliberately than the hype suggested. According to the survey, 31% of organizations have invested in AI tools, a figure that has stabilized since 2024. At the same time, the share of organizations with no plans to adopt AI has grown to nearly 18%, highlighting a widening gap between early and late adopters. What we’re seeing happening is a bottom-up adoption pattern. Nearly 45% of respondents report using AI as an individual productivity tool, while just 18.3% say AI is embedded into organizational processes. Analysts are experimenting with copilots, LLMs, and machine learning to speed up everyday tasks - yet enterprise-wide adoption of AI is primarily among early adopters. This matters because it reframes AI’s role. AI is increasingly positioned as a force multiplier- taking on repetitive operational work so experts can focus on what matters most: defining the right questions, validating results, and applying spatial insight to real decisions. When properly embedded, AI amplifies experts and operationalizes organizational knowledge. Yet the challenge ahead now isn’t discovering what AI can do for spatial analytics. It’s building the right conditions— data security, data access, and data integrity — that allows AI to scale safely and responsibly. The report shows that 68.5% of organizations now run spatial analysis in the cloud, a figure that has stabilized since 2024 after years of rapid growth. Cloud-native spatial analytics is no longer a differentiator—it’s foundational. However, cloud-native in 2026 means more than hosting GIS tools online. It means processing, analyzing, and visualizing spatial data directly inside cloud data warehouses and lakehouses, without copying or syncing data across systems. This enables organizations to be more effective and agile with spatial data centralized and easily accessible. Organizations that operate this way consistently see better outcomes. Survey results show organizations running spatial analysis on the cloud are: Yet despite these benefits, the report highlights a significant challenge: tool fragmentation. This fragmented ecosystem makes integration the single biggest bottleneck organizations face. Nearly 30% of respondents report that data access and integration delays are the primary obstacle holding their teams back, far outpacing challenges like slow processing or handling non-technical requests. In short, the cloud has become the foundation for scalable, governed, and accessible spatial analytics. But interoperability is no longer a “nice-to-have.” It’s essential and the only way an organization’s spatial stack can deliver consistent, enterprise-grade insights. The result is a growing reliance on a small number of highly specialized experts, often described as “spatial unicorns”, to support organization-wide needs. The report makes one thing clear: hiring alone won’t solve the problem. Instead, leading organizations are shifting toward scaling expertise alongside headcount. That means empowering non-experts with self-service tools, embedding spatial analytics into everyday workflows, and using AI-driven systems to extend expert knowledge across teams. In this model, GIS professionals don’t just deliver one-off or ad-hoc analyses. They have the opportunity to design the systems, standards, and workflows that allow spatial thinking to scale- amplifying their impact far beyond a single map or dashboard. In conclusion, these trends point to a future where spatial analytics is more deeply embedded, strategic, and powerful than ever. Across this year’s survey, a clear thread connects every challenge: the need to define and operationalize what “good” looks like in a rapidly changing environment. At CARTO, we’re here to help organizations and spatial experts navigate this changing landscape. This report is our contribution to the conversation, giving teams the clarity and confidence to move forward. Download the full report to discover the biggest insight. Did you guess it? Explore the data, playbooks, and top predictions for 2028 that will shape the future of spatial analytics. Discover the best spatial analytics of 2025. Learn how industry leaders use CARTO to analyze historical risk, manage real-time operations, and plan for growth. Discover how GIS Analysts are evolving from map makers to strategic AI-driven experts shaping enterprise decision-making with cloud-native tools. Learn how CARTO & Google Earth AI make planetary-scale insights accessible with foundation models & Agentic GIS.