Discussed on point patterns:
Point Patterns
These refer to sets of points that are distributed within a defined space — essentially, an observed dataset of points in that space. Based on their spatial arrangement, point patterns are generally categorized into:
Poisson Patterns:
Points are randomly and independently scattered across the space, following a uniform distribution.
Clustered Patterns:
Points appear to group or cluster together, often due to some underlying factor or mutual influence.
Point Processes
A point process is a mathematical framework used to model the random distribution of points in space and/or time. It helps us understand how observed point patterns might be generated probabilistically.
We also explored an idea that we’d like to dive deeper into — using the Armed Conflict Location and Event Data (ACLED). The core thought is to select a specific location and try to predict the likelihood of a violent political demonstration occurring there in the future. The prediction would be based on analyzing past events at that location and its nearby neighbors, using historical patterns to estimate the probability of future conflict.