Norma: A Framework for Finding Threshold Associations Between Continuous
Variables Using Point-wise Functions
Abstract
This study introduces Norma, a novel association-mining framework
tailored for continuous spatial variables analysis. Norma introduces the
unique Continuous Variable Threshold (CVT) pattern, aiming to identify a
pair of thresholds within the value domain of two continuous variables,
revealing strong associations within a specified geographic area. For
example, it may unveil a strong association between COVID-19 infection
rates above 2% and poverty rates above 15% in New Mexico. Norma
associates pointwise functions with each variable-e.g., a function that
returns poverty rates for each location in New Mexico. It employs a
novel interestingness function, which measures agreement with respect to
hotspots where variable pointwise functions exceed associated
thresholds. Norma also employs a grid-based spatial hotspot-growing
algorithm to discover high-interestingness regions and pairs of
thresholds that generate interestingness surpassing a predefined
threshold. Furthermore, the framework introduces measures for assessing
variable relatedness based on CVT associations. A comparative case study
against traditional correlation methods are presented using county-level
COVID-19 infection rates and nineteen other socio-economic variables
from the continuous United States, and demonstrate how Norma can be used
to explore association among subset of values related to spatial
continuous variables.