awesome-repositories.com
博客
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目关于排名机制媒体报道MCP 服务器
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

6 个仓库

Awesome GitHub RepositoriesSpatial Aggregation Functions

Operations for combining geometry objects using convex hulls or point set unions.

Distinct from Spatial Data Extensions: Distinct from Spatial Data Extensions: focuses on aggregation operations rather than storage types.

Explore 6 awesome GitHub repositories matching data & databases · Spatial Aggregation Functions. Refine with filters or upvote what's useful.

Awesome Spatial Aggregation Functions GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • prestodb/prestoprestodb 的头像

    prestodb/presto

    16,711在 GitHub 上查看↗

    Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

    Combines multiple geometry objects into single results using spatial operations.

    Javabig-datadatahadoop
    在 GitHub 上查看↗16,711
  • visgl/deck.glvisgl 的头像

    visgl/deck.gl

    13,875在 GitHub 上查看↗

    This project is a declarative visualization library and geospatial framework designed for rendering large-scale data sets within web browsers. It functions as a high-performance graphics engine that leverages hardware acceleration to display complex 2D and 3D visual layers, enabling the visualization of millions of data points through a structured, component-based syntax. The framework distinguishes itself through its ability to synchronize custom data visualizations with third-party mapping platforms. By managing camera states and coordinate systems, it allows developers to overlay high-perf

    Provides hardware-accelerated spatial aggregation functions to summarize large datasets into grids or hexagons for visualization.

    TypeScriptdata-visualizationgeospatial-analysisjavascript
    在 GitHub 上查看↗13,875
  • keplergl/kepler.glkeplergl 的头像

    keplergl/kepler.gl

    11,871在 GitHub 上查看↗

    Kepler.gl is a web-based geospatial visualization framework designed for rendering large-scale location datasets. It functions as a modular React mapping component that enables developers to embed interactive, high-performance geographic visualizations into web applications, serving as a comprehensive engine for building browser-based GIS dashboards. The library distinguishes itself through a highly extensible architecture that centers on centralized state management. By utilizing a predictable state-driven model, it allows for the programmatic control of map layers, filters, and viewport set

    Groups point-based data into grids, hexagons, or clusters to visualize density, distribution, and statistical trends.

    TypeScriptdata-visualizationgeospatialkepler
    在 GitHub 上查看↗11,871
  • uber/h3uber 的头像

    uber/h3

    6,015在 GitHub 上查看↗

    H3 is an open-source library that provides a hierarchical hexagonal grid system for geospatial indexing. It projects the Earth onto an icosahedron and tiles each face with hexagons to minimize distortion, then encodes each hexagon as a 64-bit integer that stores its resolution and position in the hierarchy. This integer encoding enables fast bitwise operations for grid navigation and spatial analysis. The library offers a comprehensive set of grid topology algorithms for computing neighbor relationships, distances, and paths between cells directly on the hexagonal grid without geographic coor

    Groups geographic data into hexagonal cells for multi-resolution spatial aggregation and analysis.

    Cgeospatialh3hexagon
    在 GitHub 上查看↗6,015
  • geopandas/geopandasgeopandas 的头像

    geopandas/geopandas

    5,049在 GitHub 上查看↗

    GeoPandas 是一个 Python 库,通过对地理空间数据的原生支持扩展了 pandas。它将地理几何图形(点、线和多边形)视为 DataFrame 中的一等列类型,使用户能够将矢量空间数据与传统的表格属性一起存储、操作和分析。该库构建在成熟的地理空间组件之上:它使用 Shapely 进行所有几何运算,使用 Fiona 和 GDAL 读取和写入标准空间文件格式,使用 PyProj 进行坐标重投影,并使用 R-tree 空间索引(来自 Shapely)来加速空间查询。 GeoPandas 的独特之处在于它将完整的空间分析工作流无缝集成到了 pandas 生态系统中。用户可以执行坐标参考系统转换以对齐不同投影的数据,计算面积和长度等几何属性,生成缓冲区和质心,并进行交集和并集等集合运算。该库还支持基于位置的过滤、基于几何关系合并数据集的空间连接,以及产生聚合结果的叠加分析。在探索方面,它提供了地图可视化功能,可直接从空间表生成静态图表和交互式地图。 除了这些核心差异外,GeoPandas 还处理地理数据的全生命周期:从 Shapefile、GeoJSON 和 GeoPackage 等常见格式导入和导出;管理将几何图形与属性列链接的空间表;以及按位置、属性条件或空间谓词查询或过滤要素。其文档涵盖了安装、全面的 API 参考以及引导用户完成常见地理空间任务的用户指南。

    Combines geographic datasets based on spatial relationships to enrich attributes or aggregate results.

    Pythongeoparquetgeospatialpandas
    在 GitHub 上查看↗5,049
  • h2database/h2databaseh2database 的头像

    h2database/h2database

    4,607在 GitHub 上查看↗

    H2 是一个用 Java 编写的 JDBC 兼容关系型数据库管理系统。它作为一个可嵌入的 SQL 数据库,可以直接在应用程序进程内运行以消除网络延迟,或者作为内存数据库用于高性能的易失性存储。它还包含一个基于 Web 的控制台,用于执行 SQL 命令和管理模式。 该系统的特点是其灵活的部署模式,包括用于远程 TCP/IP 访问的独立服务器模式,以及用于同时进行本地和远程连接的混合模式。它具有方言模拟层和兼容模式,允许其模仿其他数据库系统的行为和语法。 该引擎提供了一套广泛的功能,涵盖具有多版本并发控制(MVCC)的 ACID 事务、地理空间和 JSON 数据支持,以及高级分析窗口函数。它包括通过压缩备份、SQL 脚本恢复和堆外内存管理来处理大数据集的数据保护工具。 该数据库使用标准的 Java 数据库连接驱动程序和连接 URL 与应用程序集成。

    Computes the minimum bounding box that encloses a collection of geometry values using spatial aggregation.

    Javadatabasejavajdbc
    在 GitHub 上查看↗4,607
  1. Home
  2. Data & Databases
  3. Spatial Data Extensions
  4. Spatial Aggregation Functions

探索子标签

  • Multi-Resolution Spatial AggregationsGrouping and analyzing geographic data by grid cells at user-chosen levels, from coarse to fine scales. **Distinct from Spatial Aggregation Functions:** Distinct from Spatial Aggregation Functions: focuses on multi-resolution hexagonal grid aggregation, not general geometry combination operations.