Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness
Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness
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DOI：
10.3390/land10060560
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发表时间：
2021-05
期刊：
影响因子：
3.9
通讯作者：
A. Gosal;Janine A. McMahon;K. Bowgen;Catherine H. Hoppe;G. Ziv
中科院分区：
文献类型：
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作者：
A. Gosal;Janine A. McMahon;K. Bowgen;Catherine H. Hoppe;G. Ziv
世界各地的保护区每年接待数十亿游客。大自然对健康和福祉的积极影响，除了提供娱乐和审美等文化活动的机会外，都有充分的记录。管理，以减少对生物多样性和保护目标的负面影响，同时提供设施和访问游客是很重要的。了解游客的环境意识和他们的现场空间模式，可以帮助在往往有限的资源范围内作出有效的管理决策。然而，目前缺乏战略，为特定地点的识别和预测映射的游客的环境意识。在这里，我们展示了一种方法来映射现场参观的潜在群体的游客根据他们的环境意识的现场问题。现场调查和参与性绘图被用来收集数据的环境意识的鸟类筑巢和空间访问模式在高地摩尔在北方英格兰。潜类分析（LCA），一种结构方程模型，被用来发现潜在的群体的环境意识，随机森林（RF）建模，机器学习技术，使用一系列的现场预测（生物气候，土地覆盖，海拔，视野，接近路径和淡水）来预测和映射每个组的访问整个网站。游客被分割成“意识”和“模糊”的群体和他们潜在的空间访问模式映射。我们的研究结果表明，有能力发现群体的用户的环境意识和映射他们的潜在访问在一个网站上使用各种现场预测。对不同环境意识的游客群体的运动模式的空间理解可以帮助有效地确定保护教育工作的目标（即，标志、工作人员的定位、监测方案等），从而最大化它们的功效。此外，我们预计这种方法将是重要的环境管理人员和教育工作者在部署有限的资源。
Protected areas worldwide receive billions of visitors annually. The positive impact of nature on health and wellbeing, in addition to providing opportunities for cultural activities such as recreation and aesthetic appreciation, is well documented. Management to reduce negative impacts to biodiversity and conservation aims whilst providing amenities and access to visitors is important. Understanding environmental awareness of visitors and their on-site spatial patterns can assist in making effective management decisions within often constrained resources. However, there is a lack of strategies for site-specific identification and predictive mapping of visitors by environmental awareness. Here, we demonstrate a method to map on-site visitation by latent groups of visitors based on their environmental awareness of on-site issues. On-site surveys and participatory mapping were used to collect data on environmental awareness on bird nesting and spatial visitation patterns in an upland moor in northern England. Latent class analysis (LCA), a structural equation model, was used to discover underlying groups of environmental awareness, with random forest (RF) modelling, a machine learning technique, using a range of on-site predictors (bioclimatic, land cover, elevation, viewshed, and proximity to paths and freshwater) to predict and map visitation across the site by each group. Visitors were segmented into ‘aware’ and ‘ambiguous’ groups and their potential spatial visitation patterns mapped. Our results demonstrate the ability to uncover groups of users by environmental awareness and map their potential visitation across a site using a variety of on-site predictors. Spatial understanding of the movement patterns of differently environmentally aware groups of visitors can assist in efficient targeting of conservation education endeavours (i.e., signage, positioning of staff, monitoring programmes, etc.), therefore maximising their efficacy. Furthermore, we anticipate this method will be of importance to environmental managers and educators when deploying limited resources.