December 2025 Summaries

Spatial Analytics has evolved from a niche specialty to a critical component of business strategy, as highlighted in recent industry reports. Key findings show a significant shift towards cloud-based spatial analysis, with nearly 70% of respondents now using the cloud, up 14 percentage points from previous surveys, emphasizing the cloud's role in managing complex spatial data. While platforms like GeoPandas, GDAL, CARTO, Esri, QGIS, and Tableau remain widely used, there is an emerging trend of integrating AI and machine learning models to enhance spatial analysis capabilities. Despite the growing demand for spatial expertise, a skills shortage persists, with almost 69% of respondents reporting difficulty in hiring Spatial Data Scientists. Additionally, over 25% of organizations have yet to adopt AI in their spatial workflows due to concerns about data security and unclear use cases. The report underscores the increasing democratization and integration of Spatial Analytics, driven by the cloud, data governance, and AI, as the industry transitions from traditional GIS to more advanced, agent-driven models.

CARTO has introduced new capabilities that enable users to perform analytics directly on geospatial foundation model embeddings, facilitating the visualization, clustering, and change detection of spatial data to enhance decision-making processes. Foundation models, trained on diverse datasets such as satellite imagery and online behavior, generate geo-embeddings that encapsulate the context of geographic locations in compact vector forms. These geo-embeddings reveal spatial patterns and relationships, helping organizations to identify trends, optimize infrastructure, and assess risks. Through CARTO Workflows, these complex AI models become accessible to non-experts, allowing integration with existing business, environmental, or demographic datasets. The platform's tools, such as the Geospatial Foundation Models Extension Package, enable users to visualize and analyze embeddings directly in data warehouses like BigQuery, improving scalability and reproducibility without needing specialized machine learning expertise. This advancement in geospatial analysis provides valuable insights across various applications, from urban planning to disaster response, by uncovering patterns previously hidden using traditional methods.