geospatial | BestHub

Rare Earth Juejin Tech Community

Jan 8, 2025 · Databases

ByteHouse GIS: High‑Performance Geospatial Analytics and Benchmark Comparison with ClickHouse, StarRocks, PostGIS, and DuckDB

The article explains ByteHouse's GIS capabilities, describing its R‑Tree and Google S2 spatial index implementation, OGC‑compatible data types and functions, and presents benchmark results that show ByteHouse outperforming ClickHouse, StarRocks, PostGIS, and DuckDB on key geospatial queries.

BenchmarkByteHouseGIS

0 likes · 13 min read

ByteHouse GIS: High‑Performance Geospatial Analytics and Benchmark Comparison with ClickHouse, StarRocks, PostGIS, and DuckDB

Python Programming Learning Circle

Jul 10, 2024 · Big Data

Using the TransBigData Python Library for Mobile Signaling Data Processing, Analysis, and Visualization

This article introduces the open‑source Python package TransBigData, explains how to install it, and demonstrates step‑by‑step methods for reading mobile signaling data, preprocessing, identifying stays and moves, extracting home and work locations, and visualizing individual activity patterns using Jupyter notebooks.

Big DataMobile DataPython

0 likes · 8 min read

Using the TransBigData Python Library for Mobile Signaling Data Processing, Analysis, and Visualization

Full-Stack Internet Architecture

Mar 4, 2024 · Databases

Redis Extended Data Types: Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog

This article introduces Redis's five extended data types—Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog—explaining their concepts, common commands, and practical code examples, and highlights how they address specific internet‑scale scenarios more efficiently than traditional relational solutions.

BitMapBitfieldData Types

0 likes · 6 min read

Redis Extended Data Types: Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog

Python Programming Learning Circle

Feb 27, 2024 · Fundamentals

Drawing Terrain Maps with Python Using rasterio, geopandas, and cartopy

This article demonstrates how to create detailed terrain maps in Python by installing and importing rasterio, geopandas, cartopy, and related libraries, configuring map fonts, and executing a comprehensive script that reads vector and raster data, applies custom color maps, and renders the final visualization.

CartopyData Visualizationgeopandas

0 likes · 4 min read

Drawing Terrain Maps with Python Using rasterio, geopandas, and cartopy

Architecture Digest

Nov 3, 2023 · Databases

In‑Depth Analysis of Redis GEOADD and GEORADIUS Commands and Their Algorithmic Complexity

This article explains how Redis implements geospatial indexing with GEOADD and GEORADIUS commands, walks through their source‑code implementations, describes the underlying geohash calculations and nine‑cell grid search strategy, and derives the O(N + log M) time complexity for nearby‑people queries.

AlgorithmDatabasePerformance

0 likes · 15 min read

In‑Depth Analysis of Redis GEOADD and GEORADIUS Commands and Their Algorithmic Complexity

Rare Earth Juejin Tech Community

Nov 29, 2022 · Frontend Development

Comprehensive Guide to Using Entity and Primitive Methods for 3D Earth Visualization with Cesium

This article provides an in‑depth tutorial on creating and customizing Cesium entities and primitives—including boxes, points, and various geometric shapes—explaining their properties, visual attributes, performance considerations, and offering complete JavaScript code examples for interactive 3D earth visualizations.

3D visualizationCesiumEntity

0 likes · 18 min read

Comprehensive Guide to Using Entity and Primitive Methods for 3D Earth Visualization with Cesium

Python Programming Learning Circle

Dec 4, 2021 · Big Data

Visualizing US Census Data with Datashader: A Step‑by‑Step Python Guide

This tutorial demonstrates how to load a large US census Parquet dataset, convert WebMercator coordinates, filter by geographic regions, and generate high‑resolution visualizations of population density and racial distribution using Python's Datashader library with various colormaps and export options.

Big DataData VisualizationPython

0 likes · 8 min read

Visualizing US Census Data with Datashader: A Step‑by‑Step Python Guide

Amap Tech

Oct 16, 2019 · Artificial Intelligence

Visual Intelligence Connecting the Real World – Amap’s Mapping and AR Navigation Technologies

Amap leverages large‑scale visual intelligence—camera‑captured imagery, AI‑driven road‑sign and POI recognition, and compressed AR navigation overlays—to automate map creation, enhance real‑time positioning, and deliver richer travel experiences for its billion‑scale user base.

AIAR navigationcomputer vision

0 likes · 13 min read

Visual Intelligence Connecting the Real World – Amap’s Mapping and AR Navigation Technologies

Xianyu Technology

Dec 12, 2018 · Big Data

Community Data Normalization Using Prefix Matching and Text Similarity

The study presents a four‑step pipeline that normalizes community data for rental platforms by clustering records using longest‑common‑prefix patterns, geographic filtering, Levenshtein similarity, and pattern‑based parent‑child assignment, achieving under 8 % false positives and 5 % false negatives.

ClusteringReal Estatedata normalization

0 likes · 10 min read

Community Data Normalization Using Prefix Matching and Text Similarity

Manbang Technology Team

Nov 15, 2018 · Fundamentals

Introduction to GIS and Its Application in Highway Logistics Platforms

This article explains the fundamentals of Geographic Information Science (GIS), recounts John Snow's historic cholera map as an early GIS example, and details how GIS technology can be leveraged to build a road‑logistics platform that enables real‑time monitoring, dispatch, path planning, and data‑driven decision making for transportation companies.

Data VisualizationGISRoad Transport

0 likes · 10 min read

Introduction to GIS and Its Application in Highway Logistics Platforms

Architect

Feb 20, 2016 · Databases

Understanding GeoHash: Spatial Indexing and Its Application in Proximity Queries

This article explains the GeoHash algorithm, how it converts latitude‑longitude coordinates into hierarchical string codes, the precision trade‑offs of different code lengths, the binary encoding process, the use of space‑filling curves, and practical considerations when applying GeoHash for nearby point‑of‑interest searches.

Database IndexGeoHashGeolocation

0 likes · 9 min read

Understanding GeoHash: Spatial Indexing and Its Application in Proximity Queries