Effects of Aggregation Methodology on Uncertain Spatiotemporal Data

TL;DR

This paper compares different methods of aggregating uncertain spatiotemporal data, analyzing their errors and effectiveness in modeling search and rescue demands in the Pacific Ocean.

Contribution

It introduces a comprehensive framework for evaluating deterministic and stochastic aggregation methods with new error metrics that account for demand non-homogeneity.

Findings

Higher fidelity aggregations reduce distance-based error

Zonal approaches further decrease error with fewer zones

Stochastic aggregation effectively simulates future demand patterns

Abstract

Large spatiotemporal demand datasets can prove intractable for location optimization problems, motivating the need to aggregate such data. However, demand aggregation introduces error which impacts the results of the location study. We introduce and apply a framework for comparing both deterministic and stochastic aggregation methods using distance-based and volume-based aggregation error metrics. In addition we introduce and apply weighted versions of these metrics to account for the reality that demand events are non-homogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand dataset of search and rescue events in the Pacific ocean. Comparisons with these metrics between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering is conducted. We show that as quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while utilizing fewer zones. However, the higher fidelity aggregations have a detrimental effect on volume error. In addition, by splitting the search and rescue dataset into a training and test set we show that stochastic aggregation of this highly variable spatiotemporal demand appears to be effective at simulating actual future demands.