In the world of mapping and location intelligence, a revolution has been quietly building for decades. It’s a movement that challenges the dominance of expensive, proprietary software and empowers developers, analysts, and organizations to take full control of their spatial data. This movement is FOSS4G. If you are a GIS professional, a developer, or a…
For decades, Google has been at the forefront of studying the geospatial world, covering everything from maps and trends to weather, floods, and wildfires. This extensive information has been made accessible through AI models and real-time services. However, a significant challenge has always been synthesizing information across these diverse models and combining a user’s own data with…
Introduction In this analysis, we explore employee work arrival patterns using geospatial data to understand delays and their relationship with distance from the workplace. The dataset includes employee IDs, arrival times, expected arrival times, and geographic locations. Key Findings 1. Data Preparation and Merging We started by merging two datasets: 2. Calculating Delays We converted…
Introduction Tracking real-time location data is essential for applications like fleet management, asset tracking, and live location sharing. In this tutorial, we will build a real-time GPS tracking system that receives location data from a moving device and displays it dynamically on a web map. What You’ll Learn By the end of this tutorial, you’ll…
Understanding the spatial distribution of facilities like schools is crucial for urban planning and decision-making. In this post, we’ll walk you through how to use Python to perform a Kernel Density Estimation (KDE) analysis and visualize the results interactively. Why Kernel Density Estimation? KDE is a statistical method used to estimate the probability density function…
Introduction:With the increasing need for efficient urban planning and accessibility analysis, spatial data processing has become more vital than ever. In this blog post, I’ll guide you through analyzing walking paths between metro stations and shopping centers in Tehran using Python’s robust geospatial libraries like OSMnx, GeoPandas, Folium, Shapely, and NetworkX. By the end, you’ll…
GeoParquet vs Shapefile vs GeoJSON When it comes to handling geospatial data, choosing the right format is crucial for performance, compatibility, and usability. In this blog post, we will compare three popular geospatial data formats: GeoParquet, Shapefile, and GeoJSON. Each format has its strengths and weaknesses, making them suitable for different use cases. Below is…
Introduction In today’s interconnected world, geofencing has emerged as a groundbreaking technology that leverages geographic information systems (GIS) to redefine how businesses and organizations interact with the world around them. For GIS developers, understanding geofencing is not just an added skill but a vital component in creating innovative and impactful solutions. What is Geofencing? Geofencing…
This tutorial provides a step-by-step guide to extracting and visualizing driving paths in a specified location using Python’s OSMnx and NetworkX libraries. Learn how to compute the shortest paths between points, convert graph data into geospatial formats, and export the results as GeoJSON files for further analysis. Tutorial Content: Introduction Working with geospatial data is…
Easily Process Building Data for GIS Projects! In this video, I introduce you to the Building Footprint Processor, a powerful Python library that makes downloading and processing building footprint data simple and efficient. Whether you’re a GIS developer, urban planner, or researcher, this tool is perfect for extracting geospatial data for any area of interest…