
Draft Slide for LADOT Mobility Hubs
Animations made in After Effects

Data Visualization and Analysis from Data

Using K-Clustering methods to categorize and assess US inventory of over 90,000 dams. K-Means clustering is a popular algorithm for grouping data points based on minimizing the distance to the center (centroid) of a cluster. This model uses a mixture of quantitative and categorical variables like dam height, age, water storage, drainage, state regulation, hazard potential and more. The categorization can be use improve analysis times when researched are limited for inspections, maintenance, and analysis. It can also just be used to repair datasets easier using averages from the cluster rather than the population.
This project was for the URP 402 Urban AI taught by Xiaofan Liang.
In blue cycle
Raw Output (Limited Resolution)
Noise-Field from afar
