discussion on spatial Analysis for point patterns

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.

 

Correlation Heatmap of crime rates and Police shootings

UrbanPop & Police Shootings (0.39): Moderate correlation; urban areas may experience more
crime, leading to greater police presence and use of force, though rural areas also see notable
police shootings.
UrbanPop & Murder (Negative Correlation): Indicates that murder rates aren’t necessarily
higher in urban areas, suggesting other socioeconomic factors may play a role.