A name‐led approach to profile urban places based on geotagged Twitter data

Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.     

Reflections on Arno Peters (1916–2002)

Abstract

This paper describes a methodology for evaluating the planimetric accuracy of three US Civil War maps using GIS and spatial analytical techniques. The case-study is the Battle of Stones River in Tennessee and, in particular, maps depicting the events of December 31, 1862. An examination of the objectives, limitations and techniques employed by the topographer engineers who created the maps provides focus for the quantitative analysis and establishes the historical context needed to understand how and why the maps were constructed. The paper shows how GIS and spatial analysis can be utilized to document vanishing historic landscapes and reconstructing where certain historical events took place.     

Extracting human emotions at different places based on facial expressions and spatial clustering analysis

The emergence of big data enables us to evaluate the various human emotions at places from a statistical perspective by applying affective computing. In this study a novel framework for extracting human emotions from large‐scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user‐generated footprints collected from social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state‐of‐the‐art computer vision techniques. Two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected from greater than 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research offers insights into integrating human emotions to enrich the understanding of sense of place in geography and in place‐based GIS.     

Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space

Human activities and more generally the phenomena related to human behaviour take place in a network‐constrained subset of the geographical space. These phenomena can be expressed as locations having their positions configured by a road network, as address points with street numbers. Although these events are considered as points on a network, point pattern analysis and the techniques implemented in a GIS environment generally consider events as taking place in a uniform space, with distance expressed as Euclidean and over a homogeneous and isotropic space. Network‐spatial analysis has developed as a research agenda where the attention is drawn towards point pattern analytical techniques applied to a space constrained by a road network. Little attention has been put on first order properties of a point pattern (i.e. density) in a network space, while mainly second order analysis such as nearest neighbour and K‐functions have been implemented for network configurations of the geographical space. In this article, a method for examining clusters of human‐related events on a network, called Network Density Estimation (NDE), is implemented using spatial statistical tools and GIS packages. The method is presented and compared to conventional first order spatial analytical techniques such as Kernel Density Estimation (KDE). Network Density Estimation is tested using the locations of a sample of central, urban activities associated with bank and insurance company branches in the central areas of two midsize European cities, Trieste (Italy) and Swindon (UK).     

“All the World's a Stage”: A GIS Framework for Recreating Personal Time‐Space from Qualitative and Quantitative Sources

This article presents a methodological model for the study of the space‐time patterns of everyday life. The framework utilizes a wide range of qualitative and quantitative sources to create two environmental stages, social and built, which place and contextualize the daily mobilities of individuals as they traverse urban environments. Additionally, this study outlines a procedure to fully integrate narrative sources in a GIS. By placing qualitative sources, such as narratives, within a stage‐based GIS, researchers can begin to tell rich spatial stories about the lived experiences of segregation, social interaction, and environmental exposure. The article concludes with a case study utilizing the diary of a postal clerk to outline the wide applicability of this model for space‐time GIS research.     

客家聚落地名时空分布与演变方法研究

针对目前地名文化中客家聚落地名研究的不足,该文从客家迁徙历史的角度,提出了客家地名定性和定量的研究方法,以客家摇篮石城地名为研究对象,采用核密度分析法和多元logit回归模型方法,深入分析石城客家地名的时空分布特征和历史演变因素。结果显示:在时间上,客家地名数量整体呈现上升趋势,增长时间分为5个阶段,隋唐萌芽时期、宋朝大量增长期、元朝缓慢增长期、明朝平稳增长期和清朝快速增长期;在空间上,客家地名分布显现出聚集特征,但聚集程度各异,聚集点和聚集区域随着时间的推移而变化。根据logit模型结果显示,地形、河流、人口和经济等多种因素交融在一起,对客家地名的演化产生了重大而深远的影响。     

How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data

This study proposes a framework to investigate the roles of urban spaces in connecting social contacts (i.e., “friends”). The framework is applied to a Call Detail Record (CDR) dataset collected in Singapore. First, a comparative analysis is performed to understand how friends share urban space differently from random people. Then, we derive two metrics to quantify the “bonding” and “bridging” capabilities of places in the city. The two metrics reflect the potential of a place in connecting friends and random people (e.g., chance encounters), respectively. Finally, we examine the temporal signature of the places’ bonding capabilities, and associate the results with various types of Points of Interest (POIs). We find that: (1) friends are more likely to share urban space than random people, and they also share more locations; (2) a place could play different roles in connecting friends vs. random people, and the relationship (between bonding and bridging) varies depending on the time and type of a day (weekdays vs. weekends); (3) the temporal signature of bonding capability is strongly related to the semantics of a place; (4) certain POI types (e.g., shopping malls) tend to have a much higher impact on bonding capability than others (e.g., sports centers).     

‘I’ve got my Sat Nav,it’s alright’: Users’ Attitudes towards,and Engagements with,Technologies of Navigation

The use of qualitative research techniques in a largely quantitative cartographic domain is opening up myriad ways to explore users’ engagements technologies of navigation. This study draws on young UK-based students’ real words and life experiences as they engage with Satellite Navigation and other wayfinding technologies during first-time visits to new places to reflect on the nature of the changing relationships between self, navigational object, space and place.     

Big data analytics: six techniques

Abstract

Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data. The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. Fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. Furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.     

Approaches for Linking People,Place, and Environment for Human Dimensions Research

Abstract

Much of the human dimensions of environmental change research emphasize the mapping and modeling of land use and land cover patterns over space and time, and the linkages between people, place, and environment as proximate and distal forces of landscape dynamics. Spatial digital technologies, framed within a GIScience (GISc) context, figure prominently in the characterization of land use and land cover through remote sensing technologies, and in the assessment of social and demographic factors and local and regional site and situation considerations achieved through global positioning systems, data visualizations, and spatial and statistical analyses. Here, we describe some fundamental approaches for linking data across thematic domains, essential for the study of human‐environment interactions. The goal is to generate compatible data sets that extend across social, biophysical, and geographical domains so that the causes and consequences of land use and land cover dynamics might be explored within a spatially‐explicit context.     

Human dynamics in space and time: A brief history and a view forward

This article highlights the key intellectual development in human dynamics research, examines the modeling emphases in publications, and argues for research directions in need. Human dynamics research is discussed in two broad directions: spacing time and timing space, to model human activities and interactions. Time is essential to human dynamics research. Space, while often being overlooked, in complement with time is critical to understanding human dynamics because knowing where activities take place is essential to knowing how and why people act and interact. Some interactions allow remote or asynchronized participations, and others require movement to collocate individuals for participating in synchronized activities. A spacing time approach examines the temporal gaps between interactions. A timing space approach investigates the spatial pulses between interactions. Primary research in the spacing time of human dynamics established queueing theories to explain the bursts and heavy‐tailed distribution of human interactions. Although research on the timing space of human dynamics enjoys growing popularity with data from geo‐tagged social media and location‐aware social internet of things (SIoT), its publications remain mostly exploratory. This article suggests a hierarchical framework to systematically study human dynamics and relate findings to build the body of knowledge about human dynamics.     

基于地理事件的时空数据模型及其在土地利用中的应用

本文通过分析基于位置和基于对象的空间数据表达方法的特点,顾及与空间相关的属性与地理实体空间特征之间的相依关系,将“地理事件”引入模型,提出一种顾及“隐含”地理事件和“显式”地理事件的时空数据模型(GESTDM模型).本文提出的模型能够同时提供基于位置、基于对象和基于时间的时空表达和查询功能.将该模型应用于土地利用领域,...     

Representing Complex Geographic Phenomena in GIS

Conventionally, spatial data models have been designed according to object- or field-based conceptualizations of reality. Conceptualization of complex geographic phenomena that have both object- and field-like properties, such as wildfire and precipitation, has not yet been incorporated into GIS data models. To this end, a new conceptual framework is proposed in this research for organizing data about such complex geographic phenomena in a GIS as a hierarchy of events, processes, and states. In this framework, discrete objects are used to show how events and processes progress in space and time, and fields are used to model how states of geographic themes vary in a space-time frame. Precipitation is used to demonstrate the construction and application of the proposed framework with digital precipitation data from April 15 to May 22, 1998, for the state of Oklahoma, U.S.A. With the proposed framework, two sets of algorithms have been developed. One set automatically assembles precipitation events and processes from the data and stores the precipitation data in the hierarchy of events, processes, and states, so that attributes about events, processes, and states are readily available for information query. The other set of algorithms computes information about the spatio-temporal behavior and interaction of events and processes. The proposed approach greatly enhances support for complex spatio-temporal queries on the behavior and relationships of events and processes.     

现实与赛博空间数据相结合的城市活动事件时空建模

城市活动事件(如文化、娱乐、体育等事件)的规模与影响力是城市经济文化发展的重要体现,其发生的全过程对城市现实空间与赛博空间都会产生巨大影响,从现实空间与赛博空间对城市活动事件的演化感知、动态建模与时空分析,具有重要的理论研究与应用价值。提出了一种结合现实空间交通数据与赛博空间社交媒体数据的城市活动事件时空建模分析方法,从事件进行中的交通轨迹,探测识别与事件显著相关的城市时空区域和交通流,分析现实空间事件热度的时空变化;从事件发生全过程的社交媒体数据中,探测分析赛博空间事件热度的时空变化;通过将现实空间和赛博空间的融合,建立城市活动事件时空模型,刻画事件全过程中城市地理空间与城市行为空间的时空演变特征。以2015年周杰伦"魔天伦2.0"世界巡回演唱会(武汉站)事件为例,采用武汉市出租车GPS轨迹数据和微博数据,对演唱会的事前、事中、事后实现城市地理空间与行为空间全过程建模与时空演变分析,并与单一数据源事件刻画模型进行比较,结果显示本方法能更合理地结合现实空间和赛博空间刻画城市活动事件。     

Event-driven flood management: design and computational modules

Flood management is a set of activities that have to be carried out in collaboration with multiple agencies. Advanced flood information with early warning generated using remote sensing satellite technologies can help the agencies to effectively manage the situation on ground. Various environmental parameters and forecasts provided by different agencies can be analyzed and compared with historical flood events for generating probable flood event alerts. The information (environmental parameters) provided by the agencies are heterogeneous and noncompliant to standards and distributed in nature. Synchronization of data from distributed resources and automation of data analysis process for flood management is a primary prerequisite for faster and efficient decision-making. Web 2.0-based web services enable data creation, sharing, communication, and collaboration on web. Spatial data sharing on web 2.0 for making quality of service using open-source software for efficient flood management is a challenge. Available software architectures proposed for risk and environmental crisis management are too generic in nature and needs lot of modification for flood management. An event-driven model coupled with data standardization procedures using service-oriented architecture provides an effective framework for flood management. In this paper, a framework capable of collecting heterogeneous distributed flood-related information for analyzing and alerting probable flood events is proposed. The framework has been implemented to generate automatic flood extent maps, by analyzing the distributed satellite data (as service). The automation of flood delineation process reduces the overall flood product generation time. Open-source web tools have been utilized in development of spatial information system to visualize and analyze the actual situation on ground facilitating overall decision-making process.     

顾及属性空间分布不均的空间聚类方法——以城市商业中心的提取为例

针对Delaunay三角网空间聚类存在的不足,提出一种顾及属性空间分布不均的空间聚类方法。首先将Delaunay三角网空间位置聚类作为约束条件,采用广度优先搜索方法,以局部参数"属性变化率"作为阈值识别非空间属性相似簇的聚类过程。以城市商业中心为例,验证了该方法能够更客观地识别非空间属性相似的簇,且自适应属性阈值可以满足不同聚类需求,为城市商业中心等空间实体的提取提供了一种有效方法。     

Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data

ABSTRACT

Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses. It is not clear how human activities collectively respond to a disaster. In this study, we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data. We proposed a Multilevel Abrupt Changes Detection (MACD) methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato. Results show that, at the grid level, most anomaly grids were located within a radius of 53?km around the typhoon trajectory. At the city level, there are significant spatial difference in terms of the human activity recovery duration (230?h on average). At the subnational level, the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected.     

Place cookies and setting spiders in dream cartography

This article contributes to understanding the difference between objective space and subjective place. New data models and visual methods, which make possible the comparison between dream settings, are necessary to an exploratory analysis of dreams. The subjective perception of settings is decomposed by studying dream reports, by applying a survey, and by considering related scientific literature. This leads to the construction of two data models, which are applied in dream cartography. The place cookie model features the dreamer's familiarity with the setting, being visualized in the form of concentric circles. The setting spider model is based on 26 variables, extensively characterizing the setting. These are grouped into eight factors, and visualized in a compact radar chart with eight “legs.” As a superordinate system of the setting spider, the event spider is developed, describing the whole dream scene. The proposed models and visualization methods can be transferred for real‐life events (settings).     

Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

ABSTRACT

It remains difficult to develop a clear understanding of geo-located events and their relationships to one another, particularly when it comes to identifying patterns of events in less-structured textual sources, such as news feeds and social media streams. Here we present a geovisualization tool that can leverage computational methods, such as T-pattern analysis, for extracting patterns of interest from event data streams. Our system, STempo, includes coordinated-view geovisualization components designed to support visual exploration and analysis of event data, and patterns extracted from those data, in terms of time, geography, and content. Through a user evaluation, we explore the usability and utility of STempo for understanding patterns of recent political, social, economic, and military events in Syria.