Toward Ethical Spatial Analysis: Addressing Endogenous Bias Through Visual Analytics

TL;DR

This paper explores how visual analytics can help identify and mitigate endogenous biases in spatial analysis, addressing an overlooked ethical issue by developing visualization strategies and a detection framework.

Contribution

It introduces a novel framework using visual analytics to detect endogenous biases in spatial data, filling a gap in ethical spatial analysis methods.

Findings

Visual analytics can uncover hidden endogenous biases.

A framework for bias detection was synthesized from visualization strategies.

Visual tools can improve interpretation and reduce errors in spatial analysis.

Abstract

Spatial analysis can generate both exogenous and endogenous biases, which will lead to ethics issues. Exogenous biases arise from external factors or environments and are unrelated to internal operating mechanisms, while endogenous biases stem from internal processes or technologies. Although much attention has been given to exogenous biases, endogenous biases in spatial analysis have been largely overlooked, and a comprehensive methodology for addressing them is yet to be developed. To tackle this challenge, we propose that visual analytics can play a key role in understanding geographic data and improving the interpretation of analytical results. In this study, we conducted a preliminary investigation using various visualization techniques to explore endogenous biases. Our findings demonstrate the potentials of visual analytics to uncover hidden biases and identify associated issues. Additionally, we synthesized these visualization strategies into a framework that approximates a method for detecting endogenous biases. Through this work, we advocate for the integration of visualization at three critical stages of spatial analysis in order to minimize errors, address ethical concerns, and reduce misinterpretations associated with endogenous biases.