Nearby outdoor recreation modelling: An agent-based approach

Abstract

Modelling and simulating the movement of humans during outdoor nearby recreational activities can deliver important insight into the landscape services available to people in urbanized areas. Recreational activities such as walking, jogging and cycling are known to have a positive effect on people's mental and physical health, however in urban areas access to nearby recreation areas is sometimes lacking, underdeveloped or impeded by man-made infrastructures. In this context, understanding the spatial behaviour of humans during outdoor recreation is a crucial step towards implementing improvement measures. In this paper, we investigated the recreation strategies of multiple individuals and the flow in the movements of these individuals using a spatially-explicit agent-based model. The model combined information on the available infrastructures (roads, housing areas), the recreational potential of the landscape and rule-based movement strategies mimicking recreationists decision-making. At the individual level, we demonstrated that the shortest-path and weighted-path strategies were used the most during nearby recreation activities. At the population level, we successfully predicted recreationist flow patterns, highlighting gaps in path infrastructure and the most preferred path sections in a small town environment. We further illustrated the versatility of the model and its potential to support planning decisions to increase the accessibility of nearby recreation areas.

Landscape Preferences and Distance Decay Analysis for Mapping the Recreational Potential of an Urban Area

“…The "urban" recreation profile (D1, see above) is linked to elements such as historic buildings and developed infrastructure, characterizing mostly central areas where accessibility is higher and urban leisure activities are present, e.g., shopping or strolling. The strong effect of accessibility is also visible in the GLM and confirms other research suggesting that people's route choice for recreation is often based on a short path strategy inside the city and becomes a combined strategy with trade-offs between quick access and a high quality route when distance increases [49]. On the other hand, the nature recreation profile (D2, see above) in our study is in line with other studies focusing on the spatial locations of visited recreation areas [34,62].…”

“…Studies on outdoor recreation often focus on a user's landscape preferences and their attitudes [36,48,49], the latter being important factors that shape recreation patterns. People's preferences can also serve as a proxy in the process of mapping recreational potential [6].…”

What prevents people accessing urban bluespaces? A qualitative study

“…Research into attitudes to one urban waterway suggests varying perceptions, with not all residents identifying them as valuable environments (Miller 2016). It may be expected that those living nearest bluespaces are most likely to use them, as proximity strongly influences outdoor recreation (Gascon et al 2015;Morelle et al, 2018). But the amount of space perceived available may not be accurate (Aoshima et al 2018).…”

Exploring the Long‐Term Economic and Social Impact of Green Infrastructure in New York City

“…Prior models have explored natural resource policies and decision‐making, for example, modeling local water use (Becu et al, 2003; Berger et al, 2006; Kandiah et al, 2019) and overharvesting of community resources (Andersen et al, 2015; Jager & Mosler, 2007), and studied the links between human behavior and biophysical processes, for example, predicting the effect of household fuel use on deforestation and habitat (An et al, 2005; Matthews et al, 2007). More recently, researchers have used ABMs to study GI implementation rates (Castonguay et al, 2016; Montalto et al, 2013; Zidar et al, 2017) and to understand how policy decisions affect co‐benefits (Chen et al, 2012; Morelle et al, 2019). However, few researchers have used ABMs to examine GI policy, implementation, and co‐benefits simultaneously (Castonguay et al, 2016) as an integrated urban water system model (Bach et al, 2014).…”

“…They simulate complex physical and social dynamics concurrently (Grimm et al, 2010), with outcomes that emerge from interactions between agents and between agents and their environment (Macal & North, 2010; Railsback & Grimm, 2012). ABMs are also important planning tools that can forecast the effects of new policy decisions and compare alternative solutions to a problem (Berger et al, 2006; Ghaffarzadegan et al, 2010; Kandiah et al, 2019; Levy et al, 2016; Matthews et al, 2007; Montalto et al, 2013; Morelle et al, 2019; Zidar et al, 2017). Berger et al (2006) determined that ABMs can be particularly useful in studying small‐scale infrastructure decisions, including GI.…”