Google Street View in 2026
Hacker News
February 26, 2026
AI-Generated Deep Dive Summary
Google Street View continues to evolve, offering new insights into global coverage patterns through advanced data analysis. In a recent study, an author leveraged DuckDB, a lightweight database, to process a massive dataset of Google’s Street View captures. The dataset spans over 20 years, with records detailing the year and month each location was last imaged. By converting this data into Parquet format—a columnar storage format optimized for big data analytics—the author created a highly efficient and scalable resource for geospatial analysis.
The process involved downloading 131 JSON files totaling 647 MB, which were then parsed using DuckDB with various extensions including H3, JSON, Lindel, Parquet, and Spatial. These tools enabled the conversion of raw data into structured tables, revealing patterns in Street View coverage. The resulting Parquet file, weighing in at 85 MB, contains nearly 7.2 million rows of geospatial data. This file was exported with ZStandard compression and spatial sorting, ensuring efficient storage and retrieval.
Key findings include uneven global coverage, with notable gaps in regions like Bosnia and Herzegovina and Namibia. The dataset also highlights a surge in Street View captures starting in 2007, coinciding with Google’s broader expansion of the service. However, some countries remain underrepresented, likely due to technical or logistical challenges. This study underscores the importance of open-source tools like DuckDB and Parquet in handling large-scale geospatial data, offering a scalable solution for researchers and developers.
For tech enthusiasts and big data professionals, this work demonstrates how modern analytics tools can transform complex datasets into actionable insights. The use of H3 integration with QGIS adds another layer of spatial analysis capability, making it easier to visualize and understand coverage patterns. This approach not only enhances efficiency but also opens new possibilities for geospatial research, enabling better decision-making in urban planning, environmental monitoring, and more.
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Originally published on Hacker News on 2/26/2026