Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data

Quantitative research of urban geography has benefited greatly from the rapid development of big geo-data. Spatial assembly is an essential analytical step to summarize and perceive geographical environment from individual behaviours. Most research focuses on the methodology of how to utilize the big data, while the adopted spatial units for data aggregation remain areal in nature. This article conceptually proposes an idea of sensing cities from a street perspective, emphasizes the significance of street units in quantitative urban studies. Using a three-month taxi trajectory dataset and the major streets in Beijing, we explore the spatio-temporal patterns of urban mobility on streets, cluster streets into nine types based on their dynamic functions and capacities. Additionally, we discuss the differences and connections between the linear street unit and traditional areal units, investigate the possibility of uncovering urban communities using streets, and point out the complexity of streets. We conclude that street unit as a supplement to areal units, is able to effectively minify the modifiable areal unit problem (MAUP), sense urban dynamics, depict urban functions, and understand urban structures.     

Scaling of foreign attractiveness for countries and states

People’s behavior on online social networks, which store geo-tagged information showing where people were or are at the moment, can provide information about their offline life as well. In this paper we present one possible research direction that can be taken using Flickr dataset of publicly available geo-tagged media objects (e.g., photographs, videos). Namely, our focus is on investigating attractiveness of countries or smaller large-scale composite regions (e.g., US states) for foreign visitors where attractiveness is defined as the absolute number of media objects taken in a certain state or country by its foreign visitors compared to its population size. We also consider it together with attractiveness of the destination for the international migration, measured through publicly available dataset provided by United Nations. By having those two datasets, we are able to look at attractiveness from two different perspectives: short-term and long-term one. As our previous study showed that city attractiveness for Spanish cities follows a superlinear trend, here we want to see if the same law is also applicable to country/state (i.e., composite regions) attractiveness. Finally, we provide one possible explanation for the obtained results.     

Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study

Travel activities are embodied as people’s needs to be physically present at certain locations. The development of Information and Communication Technologies (ICTs, such as mobile phones) has introduced new data sources for modeling human activities. Based on the scattered spatiotemporal points provided in mobile phone datasets, it is feasible to study the patterns (e.g., the scale, shape, and regularity) of human activities. In this paper, we propose methods for analyzing the distribution of human activity space from both individual and urban perspectives based on mobile phone data. The Weibull distribution is utilized to model three predefined measurements of activity space (radius, shape index, and entropy). The correlation between demographic factors (age and gender) and the usage of urban space is also tested to reveal underlying patterns. The results of this research will enhance the understanding of human activities in different urban systems and demographic groups, as well as providing novel methods to expand the important and widely applicable area of geographic knowledge discovery in the age of instant access.     

大科学思维与现代地理学的发展问题

本文结合钱学森先生关于“建立地理科学体系”的倡议,主张从当代大科学思维出发,研究现代地理学的发展问题,并以大科学为参照框架,提出了发展“大地理学”的思想。并探讨了围绕“大地理学”的若干理论问题和现代地理学向“大地理学”转变的途径。     

The “von Karman vortex street” to the west of Big Island

Satellite-tracked drifting buoy data and altimetry data are used to study the active vortex field to the west of Big Island. A pair of vortexes were observed at the trajectory of buoy in 1995. The westward propagation of the vortex pair is studied in detail by reproducing the loops of each vortex. The orbital period and radius of the pair of vortex are determined to be 10-11 d and 58-68 km. Two arrays of contra-rotating vortices are displayed in the average sea surface height anomaly (SSHA) field to the west of Big Island. Based on the calculation of the fluid dynamical parameter, the “von Karman vortex street” is proved to be generated to the west of Big Island as the North Equatorial Current impinges upon Big Island from the east. Finally, the analysis of the buoy trajectories in a decade contributes to the conclusion of the pattern of VKVS in a statistical view.     

A general-purpose framework for parallel processing of large-scale LiDAR data

ABSTRACT

Light detection and ranging (LiDAR) data are essential for scientific discoveries such as Earth and ecological sciences, environmental applications, and responding to natural disasters. While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands. Efficiently storing, managing, and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications. However, handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity. To tackle such challenges, we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle ‘big’ LiDAR data collections. The contributions of this research were (1) a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, (2) two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks, and (3) by coupling existing LiDAR processing tools with Hadoop, a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application. A proof-of-concept prototype is presented here to demonstrate the feasibility, performance, and scalability of the proposed framework.     

Connected Geomatics in the big data era

The development of global informatization and its integration with industrialization symbolizes that human society has entered into the big data era. This article covers seven new characteristics of Geomatics (i.e. ubiquitous sensor, multi-dimensional dynamics, integration via networking, full automation in real time, from sensing to recognition, crowdsourcing and volunteered geographic information, and service-oriented science), and puts forward the corresponding critical technical challenges in the construction of integrated space–air–ground geospatial networks. Through the discussions outlined in this paper, we propose a new development stage of Geomatics entitled ‘Connected Geomatics,’ which is defined as a multi-disciplinary science and technology that uses systematic approaches and integrates methods of spatio-temporal data acquisition, information extraction, network management, knowledge discovery, and spatial sensing and recognition, as well as intelligent location-based services pertaining to any physical objects and human activities on the earth. It is envisioned that the advancement of Geomatics will make a great contribution to human sustainable development.     

基于大数据的强震临震前地震背景噪声异常分析——以2013年芦山7.0级地震为例

目前针对强震临震前震动信号的拾取和分析基本都是在有限的频带内开展,且分析的数据量时空范围有限。本文提出一种基于大数据的地震背景噪声计算和分析方法,对海量地震观测数据进行分布式噪声功率谱计算,数据采用2013年3—4月的四川地震台网记录的连续波形数据。通过对2013年四川芦山7.0级地震前四川地震台网记录的50Hz～200s范围内的多个频点时序变化进行分析,发现芦山地震距震中50km范围内的MDS台和BAX台在震前水平方向30～150s周期上噪声出现增强10～20dB且持续3～5天的情况,垂直向未发现明显变化,同时超出50km范围内的台站也未有明显变化。这种长周期异常变化很难通过传统的地震动信号拾取来发现,表明本文提出的强震临震异常分析方法可以用于发现和分析震前的长周期异常信号。     

大数据环境下卫星对地观测数据集成系统的关键技术

建立卫星对地观测数据集成系统是遥感卫星数据信息资源有效管理与应用的重要手段。从我国对地观测重大需求以及前沿科学问题入手,提出大数据环境下卫星对地观测数据集成系统建立中亟待解决的关键技术,包括大容量异构对地观测数据集成的语义技术、基于网格的遥感图像快速处理技术、遥感大数据深度分析技术、多数据中心协同处理及云平台技术,为实现集成卫星图像、地面观测数据和模拟模型的元数据管理、几何精度纠正和卫星数据质量评价、海量卫星图像数据的空间分析与知识发现、分布式高性能卫星图像数据管理和归档等基本功能,为解决海量卫星数据分布式存储与计算、数据集成与互操作、空间数据分析与地学知识发现提供新思路、新技术与新方法。     

Research challenges and opportunities in mapping social media and Big Data

Social media and Big Data have transformed our world into interconnected cyberspace and realspace. Cartographers can now trace, monitor, and map the spread of social movements, disease outbreaks, nature hazards, and popular events by digitally collecting social media and Big Data with locational contents, such as global positioning system tags and user location profiles. The dynamic characteristics of social media and Big Data provide a great research opportunity for cartographers to map and analyze human behaviors, communications, and movements. However, there are many challenges and pitfalls in cartographic research associated with spatiotemporal analysis of social media contents and Big Data. This short paper will address important research challenges and major opportunities for cartographers to process and visualize Big Data and social media.