出租车轨迹数据的南京人群出行模式挖掘

针对现有出租车轨迹数据挖掘中时间序列邻近度量方法存在的问题,提出一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,进而研究城市人群出行行为的时空差异。以南京市为例,结合电子地图对出行模式的空间分布特征进行分析,证明了本文所提出的方法的有效性。实验结果表明:在空间分布上,工作日出租车出行模式按照平均出行频次由高到低排序,从城市中心向四周扩散,呈中心环状分布,出行模式区域界限较为明显,同类出行模式分布区域对应相似的功能。提出了一种基于DBSCAN算法和改进的DTW距离的时间序列聚类算法提取具有相似性出行特征的时空模式,有效地分析城市人群出行行为的时空差异。     

Urban event detection with big data of taxi OD trips: A time series decomposition approach

Big urban mobility data, such as taxi trips, cell phone records, and geo‐social media check‐ins, offer great opportunities for analyzing the dynamics, events, and spatiotemporal trends of the urban social landscape. In this article, we present a new approach to the detection of urban events based on location‐specific time series decomposition and outlier detection. The approach first extracts long‐term temporal trends and seasonal periodicity patterns. Events are defined as anomalies that deviate significantly from the prediction with the discovered temporal patterns, i.e., trend and periodicity. Specifically, we adopt the STL approach, i.e., seasonal and trend decomposition using LOESS (locally weighted scatterplot smoothing), to decompose the time series for each location into three components: long‐term trend, seasonal periodicity, and the remainder. Events are extracted from the remainder component for each location with an outlier detection method. We analyze over a billion taxi trips for over seven years in Manhattan (New York City) to detect and map urban events at different temporal resolutions. Results show that the approach is effective and robust in detecting events and revealing urban dynamics with both holistic understandings and location‐specific interpretations.     

城市交通热点区域识别及空间交互分析

出租车载客数据可以用于研究居民的出行特征,提取城市的交通热点区域,但对城市交通热点区域的交互关系研究相对较少。本文以纽约市的出租车行程记录数据为数据源,利用交通小区划分结合出租车载客数据提取城市交通热点区域,基于复杂网络的方法对不同日期类型和天气情况的城市交通热点区域空间交互网络进行研究并进行社区发现。结果表明,热点区域受城市核心区的影响而聚集在核心区域周围,城市内社区的形成可以克服地形和行政区域等因素的影响。研究结论有望为城市规划、城市交通管理、出租车调度、以及人们的出行等提供信息参考。     

利用城市公共摄像头分析夜间灯光变化规律

与卫星影像相比,摄像机影像数据具有更高的时间和空间分辨率。互联网共享的摄像头视频提供了丰富的夜间灯光信息,但数据的获取难度较大。针对实时视频数据设计特定爬虫程序进行数据获取,再基于获取的视频进行数据挖掘。通过分析日本东京某实验区的影像,发现城市夜间灯光表现复杂且存在一定的随机性,但是整夜的夜间灯光随着时间呈现明显减弱的趋势,并且部分区域呈现出先增加后减少等更为复杂的变化规律,此外不同建筑物侧面灯光趋势也存在差异。     

Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data

Abstract

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas. We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.     

A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

ABSTRACT

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use decomposition in terms of human activities differs from traditional land use research, and it is more reasonable to infer the actual urban function of land. The framework consists of four steps, namely temporal activity signature extraction, urban function base curve extraction, mixed-use decomposition, and result validation. First, the temporal activity signatures (TASs) of each zone are extracted as the proxy of human activity patterns. Second, the diurnal TASs of routine activities are extracted as urban function base curves (i.e. endmembers). Third, a linear decomposition model is used to decompose the mixed use and obtain multiple results (urban function composition, dynamic activity proportions, and the mixing index). Finally, result validation strategies are concluded. This framework offers method extensibility and has few requirements for the input data. It is validated by means of a case study of Beijing, based on a social media check-in dataset.     

基于社交媒体的城市污染信息探测

了解城市污染状况是治理城市污染的首要环节。社交媒体中包含了能够反映人们对周围环境的感受的数据资料,可以帮助人们更直观地了解城市污染现状。本文以新浪微博为例,提出了从社交媒体中获取城市污染相关数据的方案,设计了基于社交媒体的城市污染信息分类法和可视化方法,在实验部分以北京市为例对本方法的结果进行了分析与讨论。     

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。     

Mapping collective human activity in an urban environment based on mobile phone data

Identifying and characterizing variations of human activity – specifically changes in intensity and similarity – in urban environments provide insights into the social component of those eminently complex systems. Using large volumes of user-generated mobile phone data, we derive mobile communication profiles that we use as a proxy for the collective human activity. In this article, geocomputational methods and geovisual analytics such as self-organizing maps (SOM) are used to explore the variations of these profiles, and its implications for collective human activity. We evaluate the merits of SOM as a cross-dimensional clustering technique and derived temporal trajectories of variations within the mobile communication profiles. The trajectories’ characteristics such as length are discussed, suggesting spatial variations in intensity and similarity in collective human activity. Trajectories are linked back to the geographic space to map the spatial and temporal variation of trajectory characteristics. Different trajectory lengths suggest that mobile phone activity is correlated with the spatial configuration of the city, and so at different times of the day. Our approach contributes to the understanding of the space-time social dynamics within urban environments.     

Mapping impervious surface distribution in China using multi-source remotely sensed data

ABSTRACT

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling.     

Understanding geographical patterns of a city’s diurnal rhythm from aggregate data of location‐aware services

Do collective behaviors of the daily routine of a city's inhabitants form the periodical cycling of human activity at the city level (here termed the “city's diurnal rhythm”)? If the answer is yes, do there exist geographical patterns in the city's diurnal rhythm? Using a nationwide dataset of observed uses of location‐aware services in the largest Chinese social media platform, we first confirm the significant periodicity in city‐level human activity from the perspective of the aggregate degree of social media uses over a day. We then investigate geographical changes in the diurnal rhythm of human activity and its local variations in different parts of the city, and between weekdays and weekend days, over 340 Chinese cities. Our results show that a city's diurnal rhythm across the whole country exhibits both regular, nationally conspicuous shifts along geographical gradients and locally distinct spatiotemporal changes within the city. Our findings could provide insights into the characterization of the daily routine of city‐level human activity and its geographical patterns, and have potential for several issues in terms of planning, management, and decision‐making related to human population dynamics.     

New data sources for social indicators: the case study of contacting politicians by Twitter

ABSTRACT

Data availability is a persistent constraint in social policy analysis. Web 2.0 technologies could provide valuable new data sources, but first, their potentials and limitations need to be investigated. This paper reports on a method using Twitter data for deriving indications of active citizenship, taken as an example of social indicators. Active citizenship is a dimension of social capital, empowering communities and reducing possibilities of social exclusion. However, classical measurements of active citizenship are generally costly and time-consuming. This paper looks at one of such classic indicators, namely, responses to the survey question ‘contacts to politicians’. It compares official survey results in Spain with findings from an analysis of Twitter data. Each method presents its own strengths and weakness, thus best results may be achieved by the combination of both. Official surveys have the clear advantage of being statistically robust and representative of a total population. Instead, Twitter data offer more timely and less costly information, with higher spatial and temporal resolution. This paper presents our full methodological workflow for analysing and comparing these two data sources. The research results advance the debate on how social media data could be mined for policy analysis.     

利用行人轨迹挖掘城市区域功能属性

城市土地利用功能区是城市规划中的一个重要概念,遥感技术手段在城市土地利用类型识别和动态监测中取得了很大进展。然而,由于城市实际功能的复杂,往往很难从遥感影像中获得城市各个区域的社会、经济或文化等功能属性。互联网技术的发展和移动定位设备的普及,极大地便利了行人移动轨迹数据的获取。本文从行人移动规律和模式与城市功能分区之间高度相关的角度出发,通过机器学习的方法,从大量行人轨迹数据中挖掘隐含的城市功能属性与强度。该方法首先利用矢量栅格化和数学形态学方法,将城市不同等级的路网分割为互不相同的空间单元;其次,根据行人轨迹数据的时空分布特点,定义9个变量并构建高斯混合模型(Gaussian mixture model,GMM),对上述空间单元进行非监督分类,得到7种城市用地类型;随后,结合选定的60个样本区以及人为标识的6种功能区(教育用地、绿地休闲区、一般商业区、政府设施、中心商业区、住宅区),依据样本功能区GPS轨迹时间分布特征,最终对7种城市用地类型进行功能配对;最后,利用核密度估计方法进行功能区强度的可视化。该框架结合机器学习的优势,结果具有较高的准确度。     

Space,time, and situational awareness in natural hazards: a case study of Hurricane Sandy with social media data

ABSTRACT

Various methods have been developed to investigate the geospatial information, temporal component, and message content in disaster-related social media data to enrich human-centric information for situational awareness. However, few studies have simultaneously analyzed these three dimensions (i.e. space, time, and content). With an attempt to bring a space–time perspective into situational awareness, this study develops a novel approach to integrate space, time, and content dimensions in social media data and enable a space–time analysis of detailed social responses to a natural disaster. Using Markov transition probability matrix and location quotient, we analyzed the Hurricane Sandy tweets in New York City and explored how people’s conversational topics changed across space and over time. Our approach offers potential to facilitate efficient policy/decision-making and rapid response in mitigations of damages caused by natural disasters.     

Multi‐temporal transport network models for accessibility studies

Vehicle tracking is a spatio‐temporal source of high‐granularity travel time information that can be used for transportation planning. However, it is still a challenge to combine data from heterogeneous sources into a dynamic transport network, while allowing for network modifications over time. This article uses conceptual modeling to develop multi‐temporal transport networks in geographic information systems (GIS) for accessibility studies. The proposed multi‐temporal network enables accessibility studies with different temporal granularities and from any location inside the city, resulting in a flexible tool for transport and urban planning. The implemented network is tested in two case studies that focus on socially excluded people in a large global city, São Paulo, Brazil, including accessibility analyses from slum areas. It explores variations within a day and differences between transport modes across time. Case study results indicate how the accessibility is heterogeneous in low‐income regions.     

Geo-questionnaires in urban planning: recruitment methods,participant engagement,and data quality

ABSTRACT

Recent focus on sustainable urban development and livability has increased the demand for new data sourcing techniques to capture experiences and preferences of urban dwellers. At the same time, developments of geospatial technologies and social media have enabled new types of user-generated geographic information and spatially explicit online communication. As a result, new public participation GIS methods for engaging large groups of individuals have emerged. One such method is geo-questionnaire, an online questionnaire with mapping capabilities, which has been used to elicit geographic data in variety of topics and geographical contexts. This article presents two recent cases, in which geo-questionnaires have been used in Polish cities to obtain public input on quality of life and development preferences in local land use planning. The article evaluates participant recruitment methods focusing on sample representativeness, participant engagement, and data quality. Recruitment via social media was found to increase bias towards younger population. Paper questionnaires used along the online version provided for better representation of target population’s age structure, but did not reduce bias related to educational attainment. We discuss how these issues relate to data usability and generalizability in the context of digital divide, and suggest directions for future research.     

Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution

With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500 m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States.     

融合图像和时空信息的社交媒体用户活动分类方法

社交媒体签到数据中蕴含着大量的用户活动信息。理解社交媒体用户的活动和行为类型,对探索人类的移动性和行为模式等有着重要意义。提出了一种针对新浪微博（简称为微博）的用户活动分类方法,结合图像表达和时空数据分类技术,识别微博签到数据所代表的用户活动类型。首先,根据兴趣点属性信息将微博签到数据所代表的用户活动分为餐饮、生活服务、校园、户外、娱乐、出行6大类;然后,基于卷积神经网络和K近邻分类方法,融合签到数据中的图像场景信息与时空信息,对微博用户的活动行为进行分类。实验结果表明,所提方法能够显著提高微博用户活动类型识别的准确性,为精确探索人类行为活动提供更加有效的数据支持。     

多源卫星遥感影像时空融合研究的现状及展望

高空间分辨率的地表或者大气环境动态监测需要高时间-空间分辨率的卫星遥感影像作为数据支撑,但由于卫星传感器硬件技术及卫星发射成本等客观因素的限制,使得获取高时空分辨率遥感影像的较为便捷高效、低成本的可行手段就是将分别具有高时间和高空间分辨率的多源遥感影像进行时空融合,从而生成不同研究和应用所需的高时空分辨率卫星影像。现阶段,虽然国内外的学者进行了大量的时空融合算法研究,但是这些研究都局限于特定的数据类型、算法原理、应用目的等客观限制,而且其发展呈现出多样性。本文对现有主流的时空融合算法研究进行了归纳总结,将其分为4种:(1)基于地物组分的时空融合;(2)基于地表空间信息的时空融合;(3)基于地物时相变化的时空融合;(4)组合性的时空融合。同时,本文还对时空融合算法中存在的问题和面临的挑战进行了分析,并对其未来的发展方向进行了前瞻性的展望。     

A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data

Social media networks allow users to post what they are involved in with location information in a real‐time manner. It is therefore possible to collect large amounts of information related to local events from existing social networks. Mining this abundant information can feed users and organizations with situational awareness to make responsive plans for ongoing events. Despite the fact that a number of studies have been conducted to detect local events using social media data, the event content is not efficiently summarized and/or the correlation between abnormal neighboring regions is not investigated. This article presents a spatial‐temporal‐semantic approach to local event detection using geo‐social media data. Geographical regularities are first measured to extract spatio‐temporal outliers, of which the corresponding tweet content is automatically summarized using the topic modeling method. The correlation between outliers is subsequently examined by investigating their spatial adjacency and semantic similarity. A case study on the 2014 Toronto International Film Festival (TIFF) is conducted using Twitter data to evaluate our approach. This reveals that up to 87% of the events detected are correctly identified compared with the official TIFF schedule. This work is beneficial for authorities to keep track of urban dynamics and helps build smart cities by providing new ways of detecting what is happening in them.