Using social media data to understand the impact of the COVID-19 pandemic on urban green space use

Having access to and visiting urban green space (UGS) improves liveability and provides considerable benefits to residents. However, traditional methods of investigating UGS visitation, such as questionnaires and social surveys, are usually time- and resource-intensive, and frequently provide less transferable, site-specific outcomes. This study uses social media data (Twitter) to examine spatio-temporal changes in UGS use in London associated with COVID-19 related lockdowns. It compares georeferenced Tweets posted in a 3 month period from 23 March to 23 June for 3 years covering the first lockdown in the UK in 2020, with Tweets for the same period in 2019 and 2021. The results show that (1) the land-use type of Public Park and Garden was the most frequently visited type of UGS, which may be correlated with these UGS areas remaining opening during the lockdown period; (2) the usage of UGS decreased in central London and increased in other areas during lockdown, which may correlated with working from home restrictions; (3) activities were positively associated with Physical activities maybe as a result of allowing people to take a single daily exercise, and (4) people spent more time in UGS areas on weekdays than weekends compared to pre-lockdown. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic.     

基于CNN和SVM的无人机多光谱遥感草地植物识别

为选择最佳的物候期、飞行高度和识别模型提高植物识别的精度，本研究以伊犁绢蒿(Seriphidium transiliense)荒漠草地主要植物伊犁绢蒿、角果藜(Ceratocarpus arenarius)以及裸地为识别对象，选择4月、6月、9月3个飞行时期，15m，30m，60m 3个飞行高度，通过无人机搭载多光谱相机采集草地群落多光谱数据，在分析光谱反射率差异的基础上，利用最佳指数因子(Optimum index factor，OIF)筛选特征波段，通过卷积神经网络(Convolutional neural network,CNN)和支持向量机(Support vector machines,SVM)建立识别模型。结果表明：地物反射率4月＞6月＞9月，15m＞30m＞60m；不同飞行高度下OIF值一致，但在月份间具有差异，4月敏感波段为Green，Red和NIR，6月和9月敏感波段为Red，Red edge，NIR；在识别精度上SVM＞CNN，4月＞9月＞6月，15m＞30m＞60m，裸地＞伊犁绢蒿＞角果藜。综合来看，采用SVM在4月、15m飞行高度下进行识别的总体精度最高，达到86.23%。     

基于PLC技术的火龙果智能补光调控器设计与应用

目的 不同植物对光照强度、光质的需求程度不同，本文旨在通过LED灯实时照射实现对植物的智能补光。方法 针对火龙果Hylocereus undatus生长发育过程充足补光和调节果期的需要，应用新一代可编程控制器(Programmable logic controller, PLC)技术，选择波长570~590 nm的黄色LED灯，本文设计了一套种植火龙果专用的智能补光调控器和相应的数据传输协议，进行硬件选型、电路设计与软件设计， 建立了调控策略和远程通信协议，最后在广东省新兴县10 666 m²的火龙果农庄进行了实际布署与应用试验。结果 该调控器能够长时间稳定地通过手动、自动以及远程的方式实现对火龙果的补光，并且数据传输稳定，调控器的响应时间不超过3 s，每次补光4 h。利用该调控器进行火龙果补光调控可以让果实成熟期提前2~4 d，部分果树挂果可以多2~3个，补光后产量提升16.7%。结论 该装置实现了火龙果的智能补光，经济效益和社会效益提升明显，具有较好的推广前景。     

基于SSM的水质在线监测与数据分析

以巢湖水质为研究对象，设计和开发了基于SSM的轻量级水质在线监测系统，对巢湖水中的总磷、氨氮等9种成分进行采集、传输和预测分析。监测系统中的智能传感器通过现场总线协议Modbus 将采集信息发到监测站，监测站再将信息通过TCP/IP协议传送到系统主机；设计了基于Vue的系统主机的前端界面，结果表明，基于SSM框架构建的系统降低了开发难度，系统稳定可靠，实现了设计目标。     

Are social media data and survey data consistent in measuring park visitation,park satisfaction,and their influencing factors? A case study in Shanghai

Given the importance of urban parks for recreation, it is critical to understand how they are used and perceived. Currently, relatively few studies have examined the public’s activities and preferences at the same time. Social media data are increasingly recognized as a promising data source to study these two aspects. However, little is known regarding the utility and representativeness of social media data for urban parks. In particular, a lack of understanding exists on the comparability of research results measured by bottom-up social media data and top-down official survey data in urban park studies. This research used social media data from Ctrip and Dianping and survey data from the local government agency with park visits statistics and park satisfaction surveys to understand the public’s visitation and satisfaction of 102 urban parks in central Shanghai. We first assessed the similarities between the social media data and survey data by correlation analysis. We then used the negative binomial regression model and beta regression model to examine the matches and mismatches of the different data sources in investigating factors influencing park visitation and park satisfaction. Our correlation results showed that the social media data significantly correlated with the survey data and that social media data performed better in more frequently visited parks. Our regression results showed that the water bodies, recreational facilities, and surrounding commercial facilities were the common influencing factors of park visitation and park satisfaction for all parks. While significant correlations were noticed between these two data sources, we found that the surrounding population density was negatively associated with park visitation measured by social media data but positively associated with park visitation measured by survey data. This study provides a comparative perspective to study park visitation and park satisfaction by combining social media data and official survey data. Clarifying the consistencies and inconsistencies between the social media data and official survey data is important because it could help us to understand the representativeness and potential biases of the social media data when used in visitation monitoring and satisfaction monitoring.     

基于Xen的工业过程综合监控平台设计与实现

随着现代工业生产自动化水平的提高，监控平台已经成为自动控制系统中必不可少的部分；近年来信息化技术的发展要求工业企业破除各装置之间存在的“自动化孤岛”壁垒，实现统一的系统监控、身份认证以及软硬件平台的高度可靠。本文基于Xen实现统一监控虚拟化软硬件平台，并在其上完成主流监控软件（Ifix/PIMS/VXSCADA）的部署，进行冗余热备机制和高度可用性等技术的试验，实现异构监控平台的统一部署、统一接入和即插即用。     

常州市金坛区林草湿数据与第三次全国土地调查数据融合研究

本文基于2020年常州市金坛区森林资源管理“一张图”和第二次湿地资源调查数据，以GIS为平台，将上述数据与第三次全国国土调查数据进行融合研究，在厘清林地、草地、湿地与其他土地范围界线的基础上，结合遥感技术对林草湿图斑进行属性更新，形成金坛区林草资源“图数库”。