广州大都市登革热时空传播混合模式

以广州市2014年登革热疫情早期11周内679个登革热病例数据为例,提出了一种基于扩展Knox检验的疾病时空传播网络模型,研究登革热疫情早期时空传播特征。首先,应用扩展Knox检验方法发现2周1 km内、1周5~7 km两个时空标度下,病例之间具有显著的时空交互特征;其次,提取两个时空标度下的病例点对,分别构造时空近邻传播（STAT）网络和日常移动传播（HDMT）网络;最后,对STAT和HDMT传播网络的同配性、空间特征、网络中心位置进行对比分析。结果显示,广州市登革热传播是空间近邻交叉传播和空间扩散传播的混合传播模式;利用STAT网络亲近中心度标识出的“爆发中心”与病例空间核密度中心基本重合,HDMT网络中介中心度标识出的“扩散中枢”多位于城市快速交通干线附近。     

An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACT

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.     

Toward space-time buffering for spatiotemporal proximity analysis of movement data

Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.     

The use of surface monitoring data for the interpretation of landslide movement patterns

The Tessina landslide is a large, seasonally active slope failure located on the southern slopes of Mt. Teverone, in the Alpago valley of NE Italy, consisting of a complex system that has developed in Tertiary Flysch deposits. The landslide, which first became active in 1960, threatens two villages and is hence subject to detailed monitoring, with high quality data being collected using piezometers, inclinometers, extensometers, and through the use of a highly innovative, automated Electronic Distance Measurement (EDM) system, which surveys the location of a large number of reflector targets once every 6 h. These systems form the basis of a warning system that protects the villages, but they also provide a very valuable insight into the patterns of movement of the landslide.In this paper, analysis is presented of the movement of the landslide, concentrating on the EDM dataset, which provides a remarkable record of surface displacement patterns. It is proposed that four distinct movement patterns can be established, which correspond closely to independently defined morphological assessments of the landslide complex. Any given block of material transitions through the four phases of movement as it progresses down the landslide, with the style of movement being controlled primarily by the groundwater conditions. The analysis is augmented with modelling of the landslide, undertaken using the Itasca FLAC code. The modelling suggests that different landslide patterns are observed for different parts of the landslide, primarily as a result of variations in the groundwater conditions. The model suggests that when a movement event occurs, displacements occur initially at the toe of the landslide, then retrogress upslope.     

A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data

Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%.     

A method for discovery and analysis of temporal patterns in complex event data

Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.     

基于高速公路流的区域城市网络空间组织模式——以江苏省为例

在“流空间”理论的影响下,强调要素连接性和空间关联性的城市网络成为指示区域空间关系的核心视角,探讨城市网络空间结构将能为区域发展政策制定提供科学依据。以江苏省2014年日平均的高速公路流数据为依托,刻画城市网络的宏观格局与层级特征,利用社区发现算法对城市网络进行空间剖分并挖掘潜在的关联结构。研究发现：① 江苏城市网络宏观格局的空间构造呈现出类似围棋的“金角、银边、草肚皮”的分散性特征,可大致辨别内部发育出多个不同的子网络系统;② 城市节点和联系轴线具有明显的层级结构,城市等级的重要性与节点本身的社会经济属性、地理区位等具有一定的空间耦合性,轴线层级与地理距离具有明显的空间交互叠加作用;③ 社区发现算法对网络的空间剖分共识别出包括苏锡常社区、宁镇扬泰社区、通盐社区、连云港社区、宿淮社区和徐州社区等6个在空间上相连且边界清晰的“城市社区”,具有一体化效应、邻域效应、行政区效应、袭夺效应和置换效应等五个具有典型性的区域效应;④ 依据社区间的空间交互作用,可凝练出双核—多核交叉型、双核—单核交叉型、单核—多核交叉型、单核—单核集中型、双核—多点分岔型、单核—多点分散型和多核—多点分散型等7类空间关联结构。     

“人地关系地域系统”是综合研究地理格局形成与演变规律的理论基石

同近年国外人文地理学呈现人文化趋势相比,中国人文与经济地理学秉承吴传钧先生关于人文与经济地理学是研究自然圈与人文圈相互作用下、人类活动分布格局形成和演变规律的一门交叉学科的定位,形成了以不同空间尺度的地域、重要的生产生活领域、以及典型的地域空间类型的可持续发展时空规律作为研究指向的中国人文与经济地理学主流学派。吴先生提出的“人地关系地域系统”理论不仅为人文与经济地理学,而且是为整个地理学的综合研究提供了重要的理论基石。地域功能性、系统结构化、时空变异有序过程、以及人地系统效应的差异性及可调控性,是该理论的精髓,这与“未来地球”研究计划的前沿思想完全契合。近10年来,以城镇化科学模式、主体功能区划、一带一路路线图、京津冀城市群、农村空心化和精准扶贫、东北振兴与资源型城市转型、行政区划优化等为研究对象,发展了人文与经济地理重要的可持续过程、地域功能形成和综合地理格局有序化规律、城市群形成演化机理及其资源环境效应、问题地区可持续生命周期与振兴路径、地缘政治地缘经济和区域间相互作用关系、人文界线对可持续发展的影响等理论方法。人文与经济地理学科建设取得重要进展,应用成果对近年来中国生态文明建设和可持续发展产生了重要影响。中国人文与经济地理学在全球范围内发展态势最佳、总体水平领先,以此告慰吴传钧先生,并以此纪念吴传钧先生百年诞辰。     

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.     

基于电子地图兴趣点的城市建成区边界识别方法

城市建成区边界是认识和研究城市的重要基础性信息,也是落实城市功能空间布局、实施界限管控的前提。但是,以往通过夜间灯光的强度、土地覆被或建筑覆盖率等信息获取城市空间范围的方法,由于受到数据精度和尺度限制,对城市社会经济活动的解释性不强,因而存在较大局限性。电子地图兴趣点（POI）作为城市空间分析的基础数据之一,直观且有效地反映了各类城市要素的集聚状况。本文基于POI与城市空间结构和城市要素空间分布的关联性,提出了一种新的通过POI密度分布来判别城市建成区边界的技术方法。为此,开发了Densi-Graph分析方法,用来分析POI密度等值线的变化趋势,在此基础上对城乡过渡地带的阈值识别的方法进行了理论分析,并讨论了单中心圆结构、双中心“鱼眼型”结构、双中心“子母型”结构等各类城市POI密度等值线的生长规律,证明了Densi-Graph分析方法的适用性。较之以往的城市建成区边界识别方法,这种方法的基础数据更加直观可信,分析结果也更加客观。运用这种方法,本文对全国地级以上城市的建成区边界进行了实证分析,探索了城市建成区边界的阈值及其与城市人口规模、城市所在区域之间的关系。     

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: A case of the threatened yellow-billed cuckoo in Arizona,USA

Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.