Developing automated visitor monitoring tools for better management of urban nature

城市规划者、交通工程师和公园管理者缺乏系统的工具来监测和预测城市公园和绿道的使用情况。衡量城市自然的使用模式是理解哪些因素（如设计、程序、邻里条件）与这个地方的成功相关的先决条件。这种监测数据有助于更好地了解如何改善城市居民与自然的接触和福祉。随着大多数人都拥有移动设备，WiFi传感技术在工程研究中变得很有前景。研究人员在购物中心、校园或公共建筑中使用了这项技术，但尚未在公园和绿道等大型户外场所进行过测试。研究人员还将无人驾驶飞行器（uav，或无人机）应用于交通监控，因为它们成本低、部署方便、视野开阔。我之前的作品展示了携带摄像机的无人机结合了人类观察和视频记录的优点。我的长期研究愿景是为城市林业规划和设计提供新颖的监测工具、模型和数据，这些工具、模型和数据信息丰富、成本效益高，并且可供所有人使用。该计划有三个短期目标：1)使用WiFi传感器在公园和绿道中开发可靠有效的游客监控工具；2)通过WiFi传感器和无人机的结合，自动化公园和绿道的行为映射过程；3)使用时空模型估计游客数量和运动。该项目将在未来5年内培养2名博士生、3名硕士生和3-6名本科生。我将营造一个学习环境，鼓励成长心态、分析思维和同伴对同伴的培训。认识到城市林业缺乏种族、民族和性别多样性，我将通过招募未被充分代表的HQP来促进公平、多样化和包容性的学习环境，扩大对未被充分代表的群体的培训学员的知识，并开展有利于未被充分代表的社区的研究（关于Obj.3）。来自该研究项目的自动访客监控工具和可视化可以为城市官员和市民提供数据，以改进他们的社区规划和设计。这些工具将负担得起、易于实施和可靠，供预算有限的小城镇和低收入社区使用，从而增加新工具和方法的潜在影响。了解谁参观了城市自然，他们参与了哪些社会和体育活动，可以产生关于公共空间中人与地互动的知识。这些工具也可以更广泛地应用于使用后评估（例如，衡量城市自然（重新）设计的成功程度）、搜索和救援行动（例如，确定城市地区灾害受害者的位置）以及长期交通和娱乐规划工作。

Urban planners, transportation engineers, and park managers lack systematic tools to monitor and predict the usage of urban parks and greenways. Measuring the usage patterns of urban nature is a prerequisite to understanding which factors (e.g., design, program, neighbourhood conditions) are associated with the success of the place. Such monitoring data can contribute to a better understanding of how to improve urban residents' exposure to nature and well-being. WiFi sensing technology became promising in engineering research as most people possess mobile devices. Researchers used the technology in shopping malls, campuses, or public buildings, but it has not been tested at large-scale outdoor sites such as parks and greenways. Researchers have also applied unmanned aerial vehicles (UAVs, or drones) to traffic monitoring because of their low cost, easy deployment, and broad view scope. My previous works show that UAVs carrying a video camera combine the advantages of human observation and video recording. My long-term research vision is to provide urban forestry planning and design with novel monitoring tools, models, and data, which are informative, cost-effective, and accessible to all. The proposed program has three short-term objectives: 1) Develop reliable and valid visitor monitoring tools in parks and greenways using WiFi sensors, 2) Automate the behaviour mapping process in parks and greenways through the combination of WiFi sensors and UAVs, and 3) Estimate visitor volume and movements using spatio-temporal models. This DG program will train 2 PhD students, 3 MSc students, and 3-6 undergraduate students over the next five years. I will foster a learning environment that encourages a growth mindset, analytical thinking, and peer-to-peer training. Acknowledging that racial, ethnic, and gender diversity is lacking in urban forestry, I will promote an equitable, diverse, and inclusive learning environment by recruiting underrepresented HQP, extend knowledge on mentoring trainees from underrepresented groups, and pursue research that benefits underrepresented communities (regarding Obj.3). Automated visitor monitoring tools and visualizations from this research program can provide city officials and citizens with data to improve their community planning and design. The tools will be affordable, easy-to-implement, and reliable to be used by smaller towns and low-income neighbourhoods with a limited budget, thus increasing the potential impact of the new tools and methods manifold. Understanding who visits urban nature and which social and physical activities they engage in generates knowledge on people-place interaction in public spaces. The tools can also be applied more widely: in post-occupancy evaluations (e.g., measuring the success of the (re)design of urban nature), search-and-rescue operations (e.g., locating victims of disasters in an urban area), and long-term transportation and recreational planning efforts.