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The shortest path between two locations is crucial for location modeling, spatial analysis, and wayfinding in complex environments. When no transportation system or network exists, continuous space movement adds substantial complexity to identifying a best path as there are increased travel options as well as barriers inhibiting potential movement. To derive the shortest path, various methods have been developed. Recent work has attempted to exploit spatial knowledge and geographic information system functionality, representing significant advantages over existing methods. However, a high density of obstacles increases computational complexity making real‐time solution difficult in some situations. This article presents a spatial filtering method to enhance Euclidean shortest path derivation in complex environments. The new approach offers substantial computational improvement while still guaranteeing an optimal path is found. Application results demonstrate the effectiveness of the approach and its comparative superiority.

A sharing economy accommodation service like Airbnb, which provides trust between strangers to connect them for profiting from underutilized assets, was born and has thrived thanks to the innovations in the platform technology. Due to the unique structure of Airbnb, the pricing strategies of hosts are very different from the conventional hospitality industry. However, existing Airbnb pricing studies have limitations considering the varying scale of operation among hosts, spatial variances in pricing strategies, and crucial geographic information for estimating the influence of the pricing variables, as well as ignoring inter-city variances. In this research, we explored the spatially heterogeneous relationship between price and pricing variables using an innovative spatial approach, Multiscale Geographically Weighted Regression (MGWR). Analysis results for Airbnb listing in Log Angeles and New York in the US showed the effectiveness of MGWR regarding estimating the influence of pricing variables spatially. By revealing spatially heterogeneous and dependent relationships, this research fills gaps in Airbnb pricing research and deepens the understanding of the pricing strategies of the hosts.