Process and Contextual Factors Supporting Action-Oriented Learning: A Thematic Synthesis of Empirical Literature in Natural Resource Management

Despite a long-term focus on learning in natural resource management (NRM), it is still debated how learning supports sustainable real-world NRM practices. We offer a qualitative in-depth synthesis of selected scientific empirical literature (N?=?53), which explores factors affecting action-oriented learning. We inductively identify eight key process-based and contextual factors discussed in this literature. Three patterns emerge from our results. First, the literature discusses both facilitated participation and self-organized collaboration as dialogical spaces, which bridge interests and support constructive conflict management. Second, the literature suggests practice-based dialogs as those best able to facilitate action and puts a strong emphasis on experimentation. Finally, not emphasized in existing reviews and syntheses, we found multiple evidence about certain contextual factors affecting learning, including social-ecological crises, complexity, and power structures. Our review also points at important knowledge gaps, which can be used to advance the current research agenda about learning and NRM.     

人口普查地理区域自动划分技术研究

针对中国现阶段人口普查模式和基础地理数据的拥有现状,该文总结归纳了国际上现有人口普查地理区域划分的技术和方法,比较了它们各自的优缺点,提出了基于Delaunay三角网对人口普查区域自动划分的技术。介绍了Delaunay三角网的特性及其空间划分的理论基础,论述了基于Delaunay三角网的人口普查区域划分技术、方法、过程和步骤,最后给出了该技术实验和应用实例。     

Device-free human micro-activity recognition method using WiFi signals

ABSTRACT

Human activity tracking plays a vital role in human–computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro- and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based micro-activity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.     

A regression model-based method for indoor positioning with compound location fingerprints

ABSTRACT

This paper proposed and evaluated an estimation method for indoor positioning. The method combines location fingerprinting and dead reckoning differently from the conventional combinations. It uses compound location fingerprints, which are composed of radio fingerprints at multiple points of time, that is, at multiple positions, and displacements between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning, the method uses short-range dead reckoning. The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at 30 measuring points, which were points at the intersections on a 1 × 1 m grid with no obstacles. A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them. Random Forests (RF) was used to build regression models to estimate positions from location fingerprints. The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons. This error is lower than that received with a single-point baseline model, where a feature vector is composed of only RSSI values at one location. The results suggest that the proposed method is effective for indoor positioning.     

数字新会地理空间框架建设与应用

为整合各类数据资源,建设新会区基础地理信息数据库,构建数字新会地理信息公共服务平台,建立健全政府部门间的数据共享机制,本文采用集中式数字县区建设模式建设数字新会地理空间框架,选取5个应用示范来检验数字新会地理空间框架的建设成效。平台在国土资源管理、民政地名服务、政务信息共享等政府管理上得到广泛应用,对实现科学化、精细化、智能化管理和优质服务等起到了很好的推动作用。     

技术机会、行业异质性与大城市创新集聚——以北京市制造业为例

引入技术机会理论,解释大城市内部不同行业创新活动的空间分布差异,以北京市为例进行实证检验。研究发现：① 不同技术层次产业的创新活动在城市内部表现出不同的区位选择。除汽车制造业外的大多数高技术产业的创新活动高度集聚在中心城区;中技术产业的创新活动开始由中心城区向近郊区扩散,低技术产业的创新活动主要分布在远郊区。② 行业间创新活动空间分布的差异主要由技术机会来源差异造成。大部分高技术产业的技术源于基础研究、产业间知识溢出、高技术劳动力,往往靠近大学或中心城区;对中技术产业来说,基础研究的影响并不像对高技术产业那样显著;对低技术产业来说,技术机会主要源于本产业的技术反馈,创新部门更倾向于分布在郊区的制造业集群中。     

中国现代产业体系四位协同的地区差异及动态演进

构建实体经济-科技创新-现代金融-人力资源等四位协同指标体系,基于2001~2015年省级面板数据,对中国现代产业体系四位协同度进行系统测度。分析四位协同度的地区差距,对其动态演进过程进行科学预测。研究发现：① 中国现代产业体系四位协同度呈波动性上升态势,其中东部地区四位协同度最高、中部次之、西部最低。② 现代产业体系四位协同度区域相对差距与绝对差距均呈扩大趋势,东部地区内差异与东-西部地区间差异均最大,其中超变密度差距是区域间差距主要来源。③ 现代产业体系四位协同度演进方式表现为邻近演进,低水平阶段向更高水平阶段跃升概率增大,整体向更高阶段发展。     

工业用地转型下地理想象与地方意义构建 ——广州广钢新城案例

在城市化进程中工业用地转型背景下,选择广钢新城为案例进行实证分析,从地理想象的视角,探讨周边村民、旧广钢职工以及新进入者等主体对广钢新城地方意义和文化内涵进行解构和重构的过程,并进一步解析地方建构与身份认同的联系。研究发现：1）由于主观性和空间实践体验的差异,不同社会群体对于广钢的地方意义有着截然不同的想象;2）周边村民、广钢旧厂职工集体记忆的嬗变,反映了空间形态、社会结构变化与在地群体的想象构建以及身份认同的耦合关系;3）新进入者的地方身份认同建立在排除在地群体的地方文化想象之上,二者对于广钢新城的地方意义和身份认同是断裂的,2个社会群体对于地方意义的感知与认同在未来还需要进一步的交流与融合;4）在社会话语构建的文化冲击以及权力和资本的支配下,外界群体逐渐产生对广钢新城地方文化意义的多元想象。广钢片区的大规模空间重塑以及由此衍生的地方意义重构说明经济、社会和文化因素对地方建构具有直接而深刻的影响。     

地缘关系对中国出口增长的影响

本文结合现有文献中的方法和自身思考构建了地缘关系指标体系,利用Google的GDELT数据库,探索地缘关系对中国出口贸易增长的作用。本文发现地缘关系在短期和长期维度上对国家出口贸易带来的涉及政治、地理、制度和文化等方面的多重影响。一方面,政治关系反映了短期政府间关系的波动,对中国出口的影响十分明显。其中较高的政府交往频度不利于中国出口增长,而政府亲密程度对中国出口的影响则是积极的,中国与出口目的国亲密程度越高,则越有向该国出口的倾向。另一方面,地理关系、制度关系和文化关系同样对中国出口有重要影响。在地理关系中,较大的地理距离会减小中国对一地区出口的倾向,而邻接关系会增大中国对一地区出口的倾向。不同制度关系对中国出口的影响存在差别,没有证据表明经济制度距离对中国出口有额外的影响,而法律制度距离的增大则会导致中国不向该地区出口的倾向。文化关系同样对中国出口有重大影响,其中较大的文化距离会抑制中国对一地区的出口倾向,而如果中国对一地区文化影响力较大,中国对其出口则更加容易。此外,进一步分行业研究的结果表明,不同行业的产品出口对地缘关系的响应程度既存在共性,也存在差异。其中,地区间的亲密程度对全类别产品和服装类、机械设备类、交通工具类三类产品的出口都有显著的影响。     

基于IABC优化LSSVR的变形预测研究

针对最小二乘支持向量回归机(LSSVR)中惩罚参数c和核函数参数σ难以确定,以及标准人工蜂群算法(ABC)易陷入局部最优、收敛速度慢等问题,提出一种改进的人工蜂群算法(IABC)来优化LSSVR的参数并进行变形预测研究。首先,IABC算法利用反向学习策略生成正反2个种群来增加初始群体的多样性,一次迭代后对双种群的当前最优食物源进行信息交换以实现优中选优,并设计食物源自适应权重函数及适应度自适应选择函数平衡ABC的勘探和开发能力;其次,以LSSVR的预测精度为目标函数,并将其转化为IABC的适应度函数,以此构建出基于IABC优化LSSVR的预测模型;最后,以基坑监测数据为例,将IABC优化的LSSVR模型、ABC优化的LSSVR模型以及基于PSO的组合模型进行预测对比分析。结果表明,IABC增加了种群的多样性,提高了收敛精度,基于IABC优化的LSSVR模型预测的变形趋势更符合实际,预测精度高于对比模型。