In today’s data-driven world, understanding and mitigating risks through geospatial analysis has become crucial for industries ranging from insurance to urban planning. In this post, we’ll explore a Python implementation for geospatial risk assessment based on a GitHub repository that demonstrates these concepts. Understanding the Project Structure The repository contains a well-organized src directory with the following…
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…
Introduction In this tutorial, we’ll walk through creating a WebGIS application using FastAPI, PostGIS, and Leaflet.js. The goal is to create a map that displays points stored in a PostGIS database and allows users to add new points by clicking on the map. We’ll cover setting up the FastAPI backend, creating the PostGIS database, and developing the frontend with Leaflet.js…
Introduction Graph coloring is a fundamental problem in graph theory with applications in map coloring, scheduling, register allocation in compilers, and network design. The key question we explore in this tutorial is: What is the minimum number of colors needed to color a map (or network) so that no two adjacent regions (or connected nodes)…
Introduction Python is known for its simplicity and readability, but writing truly “Pythonic” code takes your skills to the next level. Pythonic code adheres to the language’s idioms and best practices, making it more readable, efficient, and maintainable. In this blog post, we’ll explore 10 practical examples of Pythonic code that will help you write cleaner and…
Have you ever needed realistic-looking data for testing or demo purposes? Whether you’re populating a database, creating mock APIs, or testing forms, the faker library in Python is your go-to tool. In this post, we’ll explore the faker library, covering installation, usage, and customization to supercharge your projects. What is Faker? faker is a Python…
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…