Human-centered Geospatial Data Science

2025-01-09

AI Summary

Conventional geographic data science often neglects human subjective experiences—such as emotion and cognition—and human–environment interactions, while exhibiting deficiencies in fairness, privacy protection, and algorithmic transparency, thereby limiting its societal impact. Method: This study proposes the first human-centered geographic data science framework, integrating geoinformatics, psychogeography, and humanities and social sciences. It systematically incorporates subjective experience modeling and technology-for-good objectives—namely fairness, privacy-preserving computation, and model interpretability—through synergistic coupling of GeoAI, multi-source spatiotemporal big data analytics, eXplainable AI (XAI), and ethics-aligned techniques. Contribution/Results: The framework establishes a human-perception–spatial-intelligence co-modeling pathway, offering a novel paradigm and methodological foundation for sustainable urban governance, inclusive spatial planning, and digital humanities research.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Humans and AI: Other Foundations of Human Computation & AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This entry provides an overview of Human-centered Geospatial Data Science, highlighting the gaps it aims to bridge, its significance, and its key topics and research. Geospatial Data Science, which derives geographic knowledge and insights from large volumes of geospatial big data using advanced Geospatial Artificial Intelligence (GeoAI), has been widely used to tackle a wide range of geographic problems. However, it often overlooks the subjective human experiences that fundamentally influence human-environment interactions, and few strategies have been developed to ensure that these technologies follow ethical guidelines and prioritize human values. Human-centered Geospatial Data Science advocates for two primary focuses. First, it advances our understanding of human-environment interactions by leveraging Geospatial Data Science to measure and analyze human subjective experiences at place including emotion, perception, cognition, and creativity. Second, it advocates for the development of responsible and ethical Geospatial Data Science methods that protect geoprivacy, enhance fairness and reduce bias, and improve the explainability and transparency of geospatial technologies. With these two missions, Human-centered Geospatial Data Sciences brings a fresh perspective to develop and utilize geospatial technologies that positively impact society and benefit human well-being and the humanities.

Problem

• Human-Centered Geospatial Analysis
• Ethical Technology Use
• Spatial Data Bias

Innovation

• Human-Centric Geographic Data Science
• Bias Reduction and Privacy Protection
• Enhanced Quality of Life