SAGAI v1.1 introduces Module 3 v2.0, a stable and reproducible vision–language inference engine for streetscape analysis. Built exclusively on Hugging Face LLaVA models, it enables robust multimodal processing of street-level images for large-scale urban and geospatial analysis.
Calculating Nearest Neighbor Distance (NND) and comparing it with Complete Spatial Randomness (CSR) can be useful in various fields. In this tutorial, we will see together how to calculate a nearest neighbor distance from a given point pattern and compare it to a random distribution (CSR).
In this guide you'll find clear instructions on setting up Python with Anaconda for spatial analysis. Then, we'll cover installing Python alongside Anaconda and adding essential dependencies like GeoPandas via the Anaconda Prompt. Lastly, we'll explore using the Jupyter Notebook for practical application.
