An Interactive Approach to Understanding Unsupervised Learning Algorithms

A software quality analysis tool based on hotspot prioritization and commits

Analysis of when and where New York City (NYC) vehicle collisions occur with a focus on collisions involving pedestrians and cyclists.

We will visualize the results of hotspot analysis and use kernel density estimation, which is the most popular algorithm for building distributions using a collection of observations. By the end of the course, you should be able to leverage Python libraries to build multi-dimensional density estimation models and work with geo-spatial data.

Contains Weekly Activites for the (ISTE 740) Geographic Information Sciences and Technologies Course @ RIT

Spatial Hotspot Analysis on Geo-Spatial Data using Apache Spark and Scala

Spatiotemporal analysis of the course of the COVID-19 pandemic in Germany

Hotspot analysis on Big Data of a major taxi company using Apache Spark and Scala

Using LiDAR to characterize urban forest structure and composition and locate hotspots based on derived individual tree attributes

A hotspot refers to an area with a higher-than-expected concentration of events relative to a random distribution. n examining point patterns, the density of points within a specific area is compared to a model of complete spatial randomness, which represents a scenario where point events occur entirely randomly.

Simple statistical functions that are useful for exploratory spatial data analysis (ESDA) on-the-fly in JavaScript