Locality Attributes and Networks Serving to Reveal Amazonian Urbanization beyond the Cities

Beyond the Amazonian cities, distinct spatial units arranged in networks compose an extended urban space, usually invisible to government agencies and public polities. This article empirically reveals this space by studying the localities’ characteristics and connections. In southwestern Pará, Brazil, 236 communities were sampled using field expeditions. Variables related to the locality organization, history, health and education services, urban infrastructure, and land use were explored in a fuzzy cluster analysis. Local production and consumption flows were considered in the locality network analysis. The fuzzy analysis allowed identifying a hierarchy of localities for which the geographical location influences the spatial distribution patterns of five groups. On the other hand, consumption and production networks identified different locality's articulations, with persistent dependence on cities. These results characterize extensive urbanization at the local level and highlights the participation of the local populations configuring the Amazonian territory.     

中国城市网络关联格局的影响因素分析——基于电子信息企业网络的视角

城市网络关联格局影响因素的测度及其作用机理的解析是建立城市网络理论模型的关键环节。基于2017年中国电子信息100强企业网络视角构建城市网络,采用指数随机图模型定量测度了中国城市网络的影响因素,解析了城市网络生长发育的微观过程,并探索性的提出了理解中国城市网络发育机理的概念框架。研究发现：偏好依附效应和接收者（GDP）效应构成了中国城市网络中心性格局的微观基础,中国城市网络生长发育表现为择优选择的地理过程;互惠性链接深刻影响着城市间的关系格局,网络闭合机制逐步成为城市间链接关系的重要影响因素;空间距离对基于电子信息企业网络的城市网络约束作用并不明显,城市网络表现出在“流动空间”中生长发育的特征。     

1985年以来中国大陆沿海集装箱港口体系位序-规模分布及其网络联系

21世纪以来,中国大陆众多沿海城市纷纷转型为港口城市,港口区域化态势明显,港口发展呈现新的竞合关系。运用位序-规模法则与复杂网络模型,从规模结构、航运网络两方面刻画1985年以来中国大陆沿海集装箱港口体系的区域化特征。结果表明：① 港口体系日趋完善,位序-规模分布更加明显;发展演化呈低级均衡,明显集中化及相对分散化三个阶段,围绕世界级枢纽港形成区域集聚和群内扩散的空间格局。② 港口航运网络小世界特征得到强化,不同类型港口航线网络特性变化差异明显。枢纽港以广度上升深度下降、中转功能上升为主;大型港口变化较为复杂,超过一半的大型港口腹地引致特征明显,中转功能较弱;地方港呈现两极分化态势,大部分港口在航线网络深度、广度与辐射能力等多方面得到提升,少部分港口则明显萎缩。③ 以班轮航线为载体的物流网络推动港口区域化内在联系更加紧密,轴-辐特征得到强化,整个网络效率大为提升。从社会网络分析软件（UCINET）呈现的最强联系航线看,沿海集装箱航运网络已由局部网络转变为统一的双层轴-辐网络,全国性的港口网络联系水平大幅提升,区域航运网络与港口群存在空间错位而非一一对应。     

中国旅游城市生态安全系统评估与类型划分研究 ——基于节点权重约束网络DEA模型

采用 DPSIR概念模型作为旅游生态安全系统的基本形态,引入网络 DEA模型测度系统运行效率,从“投入-产出”的研究视角探究旅游生态安全系统内部作用机理。采用结构方程模型对网络 DEA模型的结构及权重加以限制,构建具有节点权重约束的网络 DEA评价模型,对主要旅游城市生态安全系统实例进行实证研究。研究结果认为：将生态安全系统分为自然运行阶段、管理反馈阶段2阶段进行效率评价,加权综合得出各旅游城市生态安全系统的综合评价值,以此为依据将样本城市划分为按系统效率的评价结果将其分划为绿色发展型、稳步发展型、高效发展型、双向提升型和管理能力缺失型,针对各类型旅游城生态安全系统特点提出改善其生态安全系统状况的建议。     

Location choices of Chinese enterprises in Southeast Asia: The role of overseas Chinese networks

Journal of Geographical Sciences - With the implementation of the “Going out” strategy and the Belt and Road Initiative, China’s investments have become increasingly influential...     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

Network analysis to inform invasive species spread among lakes

Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts.     

Network analysis to inform invasive species spread among lakes

Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts.     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.     

CORS网区域重力场变化与地面稳定性跟踪监测方法

为了实现地面稳定性降低时间与地点、持续作用时间与空间影响分布的全面跟踪监测,该文基于卫星定位连续运行站(CORS)站网观测数据,结合地表水、大气及海平面变化资料,提出了CORS站网时变重力场及负荷形变场精化的已知负荷移去恢复法,建立了基于时变重力场的确定性地面稳定性变化定量辨识准则。以丽水温州地区为例,利用2015-2017年CORS网及有关水文观测数据进行计算分析,根据40起已发生的历史地质灾害(险情)事件对结果进行验证:丽水温州地区的CORS网具备区域重力场变化与地面稳定性跟踪监测能力,具备地质灾害灾变过程追踪与前兆捕获能力,CORS站网的地质灾害前兆提前捕获率可达92.5%。