Revisiting spatial optimization in the era of geospatial big data and GeoAI

Abstract

Spatial optimization is an interdisciplinary field dedicated to the scientific and rational allocation of resources spatially, which has received tremendous attention across various disciplines including geography, operations research, management science, and computer science. Spatial optimization provides important theoretical foundations and solutions for determining optimal spatial arrangements or configurations of entities, resources, or goods. However, the complexity of spatial optimization problems poses critical challenges in spatial optimization problems modeling, and efficiently solving. Recently, the surge of multi-source geospatial big data, the emerging technologies such as geospatial artificial intelligence (GeoAI), and the advancements of computing technologies along with the ever-expanding capabilities of computer and data storage resources, have created significant opportunities to the effective and efficient addressing of spatial optimization issues, even though numerous challenges still exist. Therefore, this paper aims to revisit the existing literature of spatial optimization quantitatively and qualitatively, as well as reflect on the opportunities and challenges, especially posed by geospatial big data and GeoAI. Through these efforts, we seek to stimulate greater engagement in spatial optimization research and practices, accelerate the integration of novel technologies and methods, as well as collectively advance the development of the field.