As urban landscapes evolve and tourism rises, understanding urban human mobility has become increasingly critical for sustainable development and urban resilience. While both locals and tourists contribute to urban dynamics, their mobility patterns frequently diverge. Current research falls short in differentiating these groups’ mobility patterns and often overlooks the influence of weather conditions on such patterns. This study employs complex network analysis techniques to dissect the urban mobility patterns of locals and tourists in Tokyo. The data is derived from geotagged photos uploaded to Flickr from July 2008 to December 2019. Utilizing a novel, non-linear approach, the time series of itinerary total times and travel distances of both groups are transformed into networks using the horizontal visibility graph algorithm. The resulting networks were analyzed to identify complex system characteristics and to detect shifts in tourists’ and locals’ mobility patterns over time. The analysis revealed a positive correlation between tourists’ and locals’ mobility patterns, although traveled distances showed more sporadic behavior. Weather conditions are found to significantly impact the predictability of these patterns, with core behaviors remaining largely resilient to changes in weather. This study uncovers the potentially chaotic nature of these networks and their implications for urban infrastructure and resident lifestyles. These findings underscore the potential for data-driven insights to inform adaptive and resilient urban planning and sustainable tourism management strategies, offering invaluable insights for city planners, tourism managers, and policymakers.

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