Location analytics and decision support: Reflections on recent advancements, a research framework, and the path ahead

The expansion in analytics and big data over the past decade has included a rapid growth in locational analytics, spatial analysis, and geographic information systems and science. Although research in Decision Support Systems (DSS) has typically tackled spatial decision problems through connections to geographic information systems (GISs), recent research has focused on the benefits from combining the two bodies of knowledge and research streams in addressing important challenges in delivering quality decisions in settings with locational/spatial components. Consequently, research in spatial decision support now seeks to take advantage of the advances in analytics, big data and cloud based decision support. This work incorporates spatiotemporal big data, mobile location-based services, 3-D, location in the sharing economy, space-time, and location-based social media. The goal of this special issue is to present explorations and knowledge enhancement on the cutting edges of decision making involving location and place. The work presented includes new problem areas, data sources, methodologies, and applications in today's more complex and data-rich decision-making environments. To provide a context for the ideas and findings in the special issue articles, this editorial reviews and extracts broad themes and categorizations from a selection of over two dozen past articles published in DSS that combine location analytics (LA), non-location analytics (NLA), and decision support (DS). We then propose a generic framework for LA/NLA/DS research, briefly summarize the eight articles in the special issue, and then outline the directions the field of location analytics and decision support is moving towards. Finally we discuss what gaps in the LA/NLA/DS research landscape need to be addressed by future research.