Call for Papers: Special Issue - Geospatial Artificial Intelligence (GeoAI) for Parks and Recreation

2026-05-27

 CALL FOR PAPERS

Special Issue: Geospatial Artificial Intelligence (GeoAI) for Parks and Recreation

Overview

Geospatial Artificial Intelligence (GeoAI) represents the convergence of advances in artificial intelligence (AI), geospatial data, and geographic science to enhance our ability to understand, model, and solve complex spatial problems. GeoAI extends beyond traditional geographic information systems (GIS) by integrating machine learning, deep learning, and data-driven algorithms with spatial thinking, enabling the automated extraction, classification, and interpretation of information from large volumes of structured and unstructured spatial data. These approaches facilitate advanced pattern recognition, spatial prediction, and spatiotemporal forecasting, offering powerful tools for analyzing dynamic human-environment interactions.

The growing availability of high-resolution spatial data, from remote sensing imagery and GPS-enabled devices to social media and sensor networks, has further accelerated the relevance of GeoAI. As parks and recreation systems become increasingly complex and data-rich, conventional analytical approaches often struggle to fully capture nonlinear relationships, spatial heterogeneity, and temporal dynamics. GeoAI addresses these limitations by enabling scalable, adaptive, and data-driven solutions that can uncover hidden patterns and support evidence-based decision—making.

In the context of parks and recreation, GeoAI is particularly important for advancing sustainable planning, equitable access, and efficient management. For example, GeoAI can be used to model visitor flows and crowd dynamics, assess accessibility for underserved populations, monitor environmental conditions and ecosystem health, and predict demand for recreational services under changing climate and socio-demographic conditions. These capabilities are critical for addressing contemporary challenges such as overcrowding, environmental degradation, climate change impacts, and social inequities in access to recreational resources. By providing actional insights, GeoAI empowers planners, managers, and policymakers to design more resilient, inclusive, and adaptive park and recreation systems.

We invite original, high-quality submissions for this special issue on GeoAI for Parks and Recreation. This special issue seeks contributions that examine how GeoAI can be applied to parks, recreation systems, trails, greenways, open spaces, and related environments. We welcome conceptual, methodological, and applied research that applies spatially explicit AI approaches to the analysis and management of park resources and recreation use.

Topics may include, but are not limited to:

  • Machine learning and deep learning applications for parks and recreation.
  • Spatial analysis, clustering, prediction, and forecasting of park and recreation use.
  • Remote sensing, imagery, Lidar, and point cloud analysis for park mapping and monitoring.
  • Object detection, feature extraction, image segmentation, and change detection in parks and open spaces.
  • GeoAI for trail systems, greenways, recreation facilities, and natural resource management.
  • Extraction of geospatial information from text, social media, and other unstructured data.
  • Spatiotemporal modeling of visitation, demand, and recreation behavior.
  • GeoAI for resource allocation, planning, and spatial decision-making.
  • Responsible, transparent, and effective use of GeoAI in parks and recreation research and practice.

Manuscripts should be original and not under review elsewhere. All submissions will undergo peer review and should follow the guidelines of Journal of Park and Recreation Administration.

Key Dates and Deadlines:

•    July 15: Abstracts due (300-500 words).
•    August 15: Authors notified of abstract acceptance.
•    January 15, 2027: full manuscript due.
•    Online publication – Summer 2027.

Please submit abstracts by July 15th HERE

Guest editors:

Jinyang Deng, Associate Professor, Arch H. Aplin III ’80 Department of Hospitality, Hotel Management and Tourism, Texas A&M University. Email: jinyang.deng@ag.tamu.edu.

Jinwon Kim, Associate Professor, Department of Tourism, Hospitality and Event Management, University of Florida. Email: jinwonkim@ufl.edu.