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

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.

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

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