Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

An origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit ‘residential-area to work-area’ patterns.     

Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

Hierarchical community detection and functional area identification with OSM roads and complex graph theory

An in-depth analysis of the urban road network structure plays an essential role in understanding the distribution of urban functional area. To concentrate topologically densely connected road segments, communities of urban roads provide a new perspective to study the structure of the network. In this study, based on OpenStreetMap (OSM) roads and points-of-interest (POI) data, we employ the Infomap community detection algorithm to identify the hierarchical community in city roads and explore the shaping role roads play in urban space and their relation with the distribution of urban functional areas. The results demonstrate that the distribution of communities at different levels in Guangzhou, China reflects the urban spatial relation between the suburbs and urban centers and within urban centers. Moreover, the study explored the functional area characteristics at the community scale and identified the distribution of various functional areas. Owing to the structure information contained in the identification process, the detected community can be used as a basic unit in other urban studies. In general, with the community-based network, this study proposes a novel method of combining city roads with urban space and functional zones, providing necessary data support and academic guidance for government and urban planners.     

Same space – different perspectives: comparative analysis of geographic context through sketch maps and spatial video geonarratives

ABSTRACT

The importance of including a contextual underpinning to the spatial analysis of social data is gaining traction in the spatial science community. The challenge, though, is how to capture these data in a rigorous manner that is translational. One method that has shown promise in achieving this aim is the spatial video geonarrative (SVG), and in this paper we pose questions that advance the science of geonarratives through a case study of criminal ex-offenders. Eleven ex-offenders provided sketch maps and SVGs identifying high-crime areas of their community. Wordmapper software was used to map and classify the SVG content; its spatial filter extension was used for hot spot mapping with statistical significance tested using Monte Carlo simulations. Then, each subject’s sketch map and SVG were compared. Results reveal that SVGs consistently produce finer spatial-scale data and more locations of relevance than the sketch maps. SVGs also provide explanation of spatial-temporal processes and causal mechanisms linked to specific places, which are not evident in the sketch maps. SVG can be a rigorous translational method for collecting data on the geographic context of many phenomena. Therefore, this paper makes an important advance in understanding how environmentally immersive methods contribute to the understanding of geographic context.     

Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed,Blue Nile Basin,Ethiopia

Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored the spatio-temporal distribution of land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Andassa watershed from 1986 to 2016 periods using Landsat data. Monthly average air temperature data of three meteorological sites were used for validating the results. The findings of the study showed that the LST of the Andassa watershed has increased during the study periods. Overall, average LST has been rising with an increasing rate of 0.081°C per year. Other results of this study also showed that there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in all the studied years. From this study we can understand that there has been degradation of vegetation and intensification of LST from 1986 to 2016.     

空间互相关方法在分析海底沙波迁移规律中的应用

海底沙波具有显著的活动性并能够对海底工程设施造成潜在威胁,因此对海底沙波活动性的评估一直以来受到广泛关注。目前大多数研究者仍利用平面剖面对比来分析海底沙波的迁移特征。然而,该方法难以全面高效地获取海底沙波二维平面迁移矢量。本文基于2007年和2009年的高分辨率多波束测深数据,详细阐述了空间互相关方法在分析北部湾东南海域海底沙波迁移规律中的应用过程。利用实测数据进行对比分析,获得了研究区海底沙波的活动特征,并进一步讨论了空间互相关算法中不同参数的选取对结果的影响。结果显示,利用空间互相关分析方法能够有效获取海底沙波二维迁移矢量,获得的海底沙波迁移速率和迁移方向与前人研究成果吻合,表明了该方法的可行性和可靠性。但在对实际DTM数据进行空间互相关分析时,需根据海底沙波形态、数据质量等因素选取合适的参数及矢量获取方法。本研究实验了一种确定海底沙波迁移规律的新方法,该方法将有效提升获得海底地形变化规律的效率和准确度,可获得更加精细的海底地形动力过程。     

中国物理海洋学研究70年:发展历程、学术成就概览

本文概略评述新中国成立70年来物理海洋学各分支研究领域的发展历程和若干学术成就。中国物理海洋学研究起步于海浪、潮汐、近海环流与水团,以及以风暴潮为主的海洋气象灾害的研究。随着国力的增强,研究领域不断拓展,涌现了大量具有广泛影响力的研究成果,其中包括:提出了被国际广泛采用的“普遍风浪谱”和“涌浪谱”,发展了第三代海浪数值模式;提出了“准调和分析方法”和“潮汐潮流永久预报”等潮汐潮流的分析和预报方法;发现并命名了“棉兰老潜流”,揭示了东海黑潮的多核结构及其多尺度变异机理等,系统描述了太平洋西边界流系;提出了印度尼西亚贯穿流的南海分支(或称南海贯穿流);不断完善了中国近海陆架环流系统,在南海环流、黑潮及其分支、台湾暖流、闽浙沿岸流、黄海冷水团环流、黄海暖流、渤海环流,以及陆架波方面均取得了深刻的认识;从大气桥和海洋桥两个方面对太平洋–印度洋–大西洋洋际相互作用进行了系统的总结;发展了浅海水团的研究方法,基本摸清了中国近海水团的分布和消长特征与机制,在大洋和极地水团分布及运动研究方面也做出了重要贡献;阐明了南海中尺度涡的宏观特征和生成机制,揭示了中尺度涡的三维结构,定量评估了其全球物质与能量输运能力;基本摸清了中国近海海洋锋的空间分布和季节变化特征,提出了地形、正压不稳定和斜压不稳定等锋面动力学机制;构建了“南海内波潜标观测网”,实现了对内波生成–演变–消亡全过程机理的系统认识;发展了湍流的剪切不稳定理论,提出了海流“边缘不稳定”的概念,开发了海洋湍流模式,提出了湍流混合参数化的新方法等;在海洋内部混合机制和能量来源方面取得了新的认识,并阐述了混合对海洋深层环流、营养物质输运等过程的影响;研发了全球浪–潮–流耦合模式,推出一系列海洋与气候模式;发展了可同化主要海洋观测数据的海洋数据同化系统和用于ENSO预报的耦合同化系统;建立了达到国际水准的非地转(水槽/水池)和地转(旋转平台)物理模 型实验平台;发展了ENSO预报的误差分析方法,建立了海洋和气候系统年代际变化的理论体系,揭示了中深层海洋对全球气候变化的响应;初步建成了中国近海海洋观测网;持续开展南北极调查研究;建立了台风、风暴潮、巨浪和海啸的业务化预报系统,为中国气象减灾提供保障;突破了国外的海洋技术封锁,研发了万米水深的深水水听器和海洋光学特性系列测量仪器;建立了溢油、危险化学品漂移扩散等预测模型,为伴随海洋资源开发所带来的风险事故的应急处理和预警预报提供科学支撑。文中引用的大量学术成果文献(每位第一作者优选不超过3篇)显示,经过70年的发展,中国物理海洋学研究培养了一支实力雄厚的科研队伍,这是最宝贵的成果。这支队伍必将成为中国物理海洋学研究攀登新高峰的主力军。     

渤、黄海生态环境模拟的参数敏感度空间差异分析

随着海洋生态系统模型的发展,生态变量增多,众多生物过程参数量值的确定成为制约生态环境模拟的瓶颈问题,生态系统结构区域性要求模型中的生态参数具有区域差异。为探究不同海区的关键参数及参数敏感度的空间差异,本研究在渤、黄海建立了ROMS-CoSiNE物理–生物耦合的高分辨率生态系统模型,并对13种生态参数的敏感度空间分布进行分析。结果表明:南黄海中部与渤海及近岸海域的敏感度差异较大。渤海敏感度最大的参数为决定光合速率的浮游植物P-I曲线初始斜率,其次为浮游动物捕食半饱和常数和浮游动物最大捕食率。而南黄海中部敏感度最大的参数为浮游动物最大捕食率,其次为浮游植物死亡率和浮游植物P-I曲线初始斜率。结合敏感度分布及浮游植物生物量收支得出,渤海水体透明度较南黄海偏低、浮游植物生长光限制较强,是引起浮游植物P-I曲线初始斜率敏感度在渤海高于黄海的主要原因。浮游动物最大捕食率及浮游植物死亡率的敏感度空间差异,受渤、黄海浮游植物生物量差异的影响,与生态系统中的高度非线性特征有关。