Mapping and Quantifying Spatial and Temporal Dynamics and Bundles of Travel Flows of Residents Visiting Urban Parks

Abstract

Abstract:Understanding the spatial and temporal movement patterns of residents visiting urban parks is essential to link park use with human well-being. There has been increasing interest recently in quantifying travel behaviors, but most studies have focused on flows of commuters within cities or tourists flows between cities. Actual, specific spatial and temporal connections between urban parks and residents within cities are still poorly understood. Here, we present an approach to take different types of travel flows into account when mapping and quantifying the spatial delivery and temporal dynamics of specific travel flows. This approach is based on empirical investigation of individuals' movements from their homes to parks and online map navigation technology. The flow maps we generated capture key travel flow patterns, addressing where, which, when, and how much they were associated with the use of recreational service provided by urban parks in Wuhan, China, during the summers of 2015 and 2016. Such maps may aid efforts to restore or improve the efficiency of park access and use. We also examined bundles of these spatial and temporal flows, and show that similar transport conditions were important for bundling spatial flows, while temporal flow bundles were significantly related to spatial features. These findings could help to reveal potential trade-offs and synergies of travel flows associated with space and time constraints and facilitate exploration of policy alternatives for equitable and efficient use of urban park systems.

A comprehensive framework for quantifying ecosystem service flow focusing on social-ecological processes

“…The spatial flow of information-based ES mainly depends on the flow of humans or other species (e.g., pollinating insects). For human-related information (e.g., beautiful scenery) delivery, we can use models (e.g., the cumulative viewshed model, InVEST recreation model) (Sharp et al, 2014; Vigl et al, 2017), or analyze the flow mode of travelers (Liu et al, 2017), or the road network and crowdsourced data from social media and outdoor activity-sharing platforms (e.g., Flickr, Weibo of China) (Havinga et al, 2020). For example, Schirpke et al (2018) used the geo-tagged photos from Flickr’s public Application Programming Interface to depict the spatial flow of the outdoor recreation service (Schirpke et al, 2018); Liu et al, (2015) used information about pandas in newspapers and books to describe the flow of information (Liu et al, 2015).…”

State-of-the-Art Review on Destination Marketing and Destination Management

“…These data show that visitors are more likely to be at landmarks, attractions, and tourist sites than at other 'ordinary' places. Visitors create a dense web of individual journeys as they connect tourist attractions or sites by moving through other non-tourist spaces [46,47] where they may experience chance encounters [48]. Furthermore, recent work has started to classify large volumes of such journeys based on mobile phone data [49] to provide large large-scale evidence of spatial behavioral patterns [50] and visitor flow typologies [51].…”

Identification Of The Characteristics Of Park Visitors To Nostalgia Urban Park, Kupang City, East Nusa Tenggara Province

“…The results of this study differ from other research that indicates a relationship between distance from home and park visits. The park visits increased when the distance from home to the park decreased (Rossi et al, 2015;Liu et al, 2017). This might occur because the Nostalgia Park operates at a city-level service scale and is the only public park accessible to the community.…”