Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.     

移动VR洪水灾害场景构建优化与交互方法

当前洪水灾害场景三维可视化存在沉浸感差、移动性不足等问题。本文将移动虚拟现实（virtual reality,VR）与洪水灾害场景三维可视化相结合,提出了移动VR洪水灾害场景构建优化与交互方法。该方法首先提出了一种免插件B/S架构下的移动VR场景构建方案,然后从多样化场景数据组织、场景数据量优化控制及自适应调度对移动VR场景构建进行了优化,最后设计了基于凝视的洪水灾害场景漫游探索、信息交互查询等交互分析模式。通过构建试验平台并选择案例进行试验,证明本文方法能够有效支持在智能手机上进行洪水灾害场景流畅地沉浸式展示与交互探索分析。     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

Enabling Spatially Aware Mobile Applications

Recently, increasing numbers of mobile phones are appearing on the market that feature advanced navigation capabilities: embedded GPS receivers for global positioning, integrated digital compasses for detecting the heading of the device, or accelerometer‐based tilt sensors will potentially enable upcoming and future mobile phones to measure their location and orientation in 3D space. In this paper, we present an application framework for building spatially aware mobile applications – applications that visualize, process or exchange geospatial information – on mobile phones equipped with such features. The core component of the framework is a novel, platform‐independent XML data exchange format for the interface between application server and mobile device that describes the geographic vicinity of the user. The format enables a variety of new mobile interaction styles and user interface types – from traditional text‐based local search and information interfaces to innovative real‐time user interfaces like Geo‐Wands and Smart Compasses.     

地理空间元数据可视化设计及关键技术研究

依据信息可视化技术的特点,研究地理空间元数据单要素和多要素可视化方法;构建地理空间元数据可视化的设计流程和逻辑模型,分析影响可视化设计的主要因素及可视化技术的应用条件,并设计两个方面的交互方式;研究可视化实现的关键技术,包括数据的多层次组织和显示,基于空间特征的关联属性获取以及元数据的可视化映射。通过原型系统的实验,证明可视化模式的有效性。     

基于空间网格的多尺度人文地理特征分析

随着手机等移动互联网设备的广泛使用,海量的人类社会活动的地理数据为地理学研究提供了新的数据基础,基于移动互联网的社会研究成为当前地理学研究的热点。本文以空间网格为基础,选取了2004年到2014年5月之间在中国境内上传至Flickr的包含位置信息的数据作为研究对象,以Alexander分形理论为依据探讨了地理空间中的尺度特征与人文地理特征的关系。选取了HT-指数测量地理对象分化程度的表征指标,并用信息熵理论对其进行检验。研究结果表明,尺度的特征一定程度上与人文地理特征是有正相关性的。可以用网格作为控制尺度的工具从不同尺度来认知人类社会活动的空间分布模式。     

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract

The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC).     

3-D Geovisualization of satellite images on smart devices by the integration of spatial DBMS,RESTful API and WebGL

Recent technological advancements in web-based geographic information systems have enabled access to satellite images on smart devices. The Representational State Transfer (REST) architecture overcomes difficulties that are associated with conventional data communications on the web, and the Web Graphics Library (WebGL) can be used as an alternative to web-based three-dimensional geographic visualization (3-D geovisualization) due to its efficient image processing capabilities. This paper describes a 3-D geovisualization system that was developed for satellite images on smart devices by integrating a spatial database management system (DBMS), a RESTful application programming interface (API), and WebGL. Spatiotemporal objects were constructed for time-series satellite images within a DBMS and a RESTful API was built for spatiotemporal queries to the time-series database so that the requested satellite data could be represented in 3-D on smart devices using WebGL. Satellite images that are represented in WebGL give a more realistic 3-D experience when they are combined with terrain data and provide for intuitive observations of the relationships between pixel values and associated geospatial conditions. This paper shows that a creative combination of existing technologies can be used to enhance and display satellite images on smart devices for 3-D geovisualization.     

一种带节点移动的手机蓝牙病毒传播模型

研究了手机节点的空间特性和移动特性,提出了一种新的手机蓝牙病毒传播模型。结合人类运动的行为模式,给出了移动环境下手机节点平均度的计算方法。结合渗流理论分析了蓝牙病毒大规模爆发的可能性,讨论了节点分布密度、蓝牙连接概率等参数对手机蓝牙病毒传播的影响。仿真结果表明,该模型较好地模拟了手机蓝牙病毒的传播过程,符合手机蓝牙病毒的传播规律。     

A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images

A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a “Dimension–Attributes” visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework.     

AR技术在GIS可视化中的应用及方法研究

地理数据的增强现实可视化技术,是地理信息科学可视化的一个重要的发展方向之一。首先,笔者详细地综述了国内外AR技术在GIS可视化领域的应用。针对基于真实视频影像表达地理数据的问题,笔者构建了该可视化系统的概念模型;并以户外山区地形数据和影像数据为基础,详细地阐述了AR技术可视化地理数据的原理和方法;最后,实现了以视频影像为载体的地理数据AR可视化方法。与传统地理数据的可视化方式相比,AR可视化表达方式更简单直观,真实感更强。     

DBSTC: an effective method for discovering cluster features with different spatiotemporal densities

Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining. It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set. This paper presents an effective density-based spatiotemporal clustering algorithm (DBSTC). First, we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood, which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods. Second, we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities. The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets. In addition, practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.     

面向"人"的地学可视化探讨

面向"人"地学可视化中的"人".主要从地学可视化系统的使用者(简称"应用人"),从地学可视化要表达的地球表层系统地理环境中的社会人(简称"社会人").以及从作为能地理认知与思维、能表达/传递地理知识及知识创新的"人"(简称"知识人")3个方面定义.从上述3个方面建立了面向"人"的地学可视化概念框架.在面向"应用人"方面,主要介绍了协同可视化以及自我参照可视化;在面向"社会人"方面,主要阐述个体日常行为时空路径可视化、群体行为模拟可视化,以及社会关系网络可视化;在面向"知识人"方面,主要讨论知识可视化.本文最后从本体框架、信息采集与获取技术、数据组织与表达模型、可视化表达方法、主体概念方面,对于面向"人"GIS以及可视化的关键问题与技术进行了探讨.     

Quality of hybrid location data drawn from GPS‐enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo‐spatial quality of GPS‐enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter “active mobile phone data”). We focus on two key issues in active mobile phone data—systematic gaps in tracking records and positioning uncertainty—and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants’ online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals’ frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.     

Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.     

GeoGraphViz: Geographically constrained 3D force-directed graph for knowledge graph visualization

Knowledge graphs are a key technique for linking and integrating cross-domain data, concepts, tools, and knowledge to enable data-driven analytics. As much of the world's data have become massive in size, visualizing graph entities and their interrelationships intuitively and interactively has become a crucial task for ingesting and better utilizing graph content to support semantic reasoning, discovering hidden knowledge discovering, and better scientific understanding of geophysical and social phenomena. Despite the fact that many such phenomena (e.g., disasters) have clear spatial footprints and geographic properties, their location information is considered only as a textual label in existing graph visualization tools, limiting their capability to reveal the geospatial distribution patterns of the graph nodes. In addition, most graph visualization techniques rely on 2D graph visualization, which constrains the dimensions of information that can be presented and lacks support for graph structure examination from multiple angles. To tackle the above challenges, we developed a novel 3D map-based graph visualization algorithm to enable interactive exploration of graph content and patterns in a spatially explicit manner. The algorithm extends a 3D force directed graph by integrating a web map, an additional geolocational force, and a force balancing variable that allows for the dynamic adjustment of the 3D graph structure and layout. This mechanism helps create a balanced graph view between the semantic forces among the graph nodes and the attractive force from a geolocation to a graph node. Our solution offers a new perspective in visualizing and understanding spatial entities and events in a knowledge graph.     

Geovisualization illustrated

In recent years, the visualization of geospatial data has undergone dramatic and important developments. Next to static maps, nowadays, immersive and highly interactive virtual environments can be used to explore and present dynamic geospatial data. Additionally, the World Wide Web has developed into a prominent medium to disseminate geospatial data and maps. In visualizing geospatial data, methods and techniques from fields, such as scientific visualization and information visualization, are applied because of the large volumes of data at hand. This has accumulated in what is known as geovisualization—the use of visual geospatial displays to explore data and through that exploration to generate hypotheses, develop problem solutions and construct knowledge. Maps and other linked graphics play a key role in this process. The objective of this paper is to demonstrate the usefulness of geovisualization and, in particular, how alternative graphic representations can stimulate the visual thought process. This is demonstrated by applying geovisualization techniques to Minard's well-known map of Napoleon's 1812 campaign into Russia, the “Carte figurative des pertes successives en hommes de l'Armee Française dans la campagne de Russie 1812–1813” (http://www.itc.nl/personal/kraak/1812/).     

一种基于地形轮廓匹配的地理数据AR可视化新方法

地理数据的增强现实可视化技术,是地理信息科学可视化的一个重要的发展方向.作者针对结构特征明显的区域,初探了增强现实技术在地学可视化领域的应用.首先,分别提取视频影像数据的可视轮廓和DEM地形数据的水平轮廓,将两种地形轮廓匹配,实现地理数据的地理坐标系与视频影像的图像坐标系之间的配准,最后模拟了地理数据基于视频影像的AR...