A new insight into land use classification based on aggregated mobile phone data

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.     

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.     

人类活动研究的新支点：Wi-Fi数据的特点、研究现状与应用前景

把握人类活动的时空特征是地理学研究中探究人地关系、提升人类福祉的重要基础和核心内容,日益普及的Wi-Fi网络能够为此提供可靠的数据支持。为明确Wi-Fi数据融入地理学研究的切入点和发展方向,论文通过与GPS、手机信令、蓝牙等位置感应数据的比较,认为Wi-Fi数据具有更高的采样精度和更强的采样代表性,能够获取个体在室内外各类城市空间的连续活动轨迹,支撑精细尺度下的人类活动研究。通过系统梳理人群活动状态监测、个体间的社会关系识别、建筑物的功能识别和降低隐私泄露风险等方面的研究进展,认为Wi-Fi数据将会在基于实时动态人口数据的城市功能设施规划、融合多源数据的人地关系探究、以居民福祉为导向的宜居城市建设等方面具有应用前景,有望成为地理学研究人类活动的新支点。     

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.     

基于微博数据的北京市热点区域意象感知

“城市意象”研究对城市文化感知、城市管理与规划、旅游资源开发等具有重要意义。近年来,随着智能移动终端和社交媒体的普及,产生了大量城市内包含有文本和地理位置等信息的社交媒体数据,涉及城市的各个区域,为开展城市意象的综合感知研究提供了新的途径。本文以2016年北京市带位置签到的新浪微博数据为例,在空间聚类发现热点区域的基础上,采用词频—逆文件频率(TF-IDF)与文档主题生成模型LDA两类典型的文本分析的方法,挖掘城市不同热点区域的主题,以感知北京市不同热点区域的社会文化功能和人群行为,并在此基础上通过对热点区域高频主题词进行共词聚类分析,深度挖掘北京市的总体意象。研究表明,运用文本挖掘及地理大数据分析的城市意象研究方法,能及时感知人群在城市不同场所的活动、态度、偏好,从而揭示城市的社会文化及功能特征,是对刻画城市物质形态的城市意象五要素模型的重要补充。此外,以北京市热点区域为例的实证研究结果对现实中的城市特色传承与空间品质塑造等有一定的启发意义。     

社会感知视角下的若干人文地理学基本问题再思考

近年来,不同类型大数据在地理研究中得到了越来越多的重视,许多学者基于手机、社交媒体、出租车等数据开展了大量实证研究。社会感知概念刻画了地理空间大数据基于大量人的行为时空模式获取地理环境特征的的技术手段,该手段有助于重新审视地理学研究中的一些基本问题,因而本文选择了空间分布和空间交互这两个基本地理概念以及定性方法和定量方法这两个人文地理基本研究方法展开讨论。大数据从微观个体和宏观群体两个层面同时感知空间分布和空间交互,可以定量分析其中的距离以及尺度效应。进而,由于小样本访谈人群和场所是定性研究的基础,而大数据可以通过定量方法识别特定人群和场所并进行刻画,因此,社会感知手段为集成定性和定量研究方法,构建混合地理学奠定了基础。     

Understanding the bias of call detail records in human mobility research

ABSTRACT

In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.     

Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns

Understanding the stability of urban flows is critical for urban transportation, urban planning and public health. However, few studies have measured the stability of aggregate human convergence or divergence patterns. We propose a spatiotemporal model for assessing the stability of human convergence and divergence patterns. A mobile phone location data set obtained from Shenzhen, China, was used to assess the stability of daily human convergence and divergence patterns at three different spatial scales, i.e. points (cell phone towers), lines (bus lines) and areas (traffic analysis zones [TAZs]). Our analysis results demonstrated that the proposed model can identify points and bus lines with time-dependent variations in stability, which is useful for delineating TAZs for transportation planning, or adjusting bus timetables and routes to meet the needs of bus riders. Comparisons of the results obtained from the proposed model and the widely used entropy measure indicated that the proposed model is suitable for assessing the differences in stability for various types of spatial analysis units, e.g. cell phone towers. Therefore, the proposed model is a useful alternative approach of measuring spatiotemporal stability of aggregate human convergence and divergence patterns, which can be derived from the space–time trajectories of moving objects.     

人类活动轨迹的分类、模式和应用研究综述

各种传感器的应用与发展,如车载GPS、手机、公交卡、银行卡等,记录了人类的活动轨迹。这些海量的人类活动轨迹数据中蕴含着人类行为的时空分布模式。通过对这些轨迹的研究可以挖掘个体轨迹模式,理解人类动力学特征,进而为对轨迹预测、城市规划、交通监测等提供支持。因此,研究各类传感器记录的人类活动轨迹数据成为当前的研究热点。本文对人类活动轨迹的获取与表达方式进行剖析,并将人类的活动轨迹按照采样方式和驱动因素的不同分为基于时间间隔采样、基于位置采样和基于事件触发采样等3类轨迹数据。由于各类轨迹数据均由起始点、锚点和一般节点等构成,因而将轨迹模式挖掘的研究按照锚点、出行范围、形状模式、OD流模式、时间模式等进行组织,研究成果揭示人类活动轨迹在时间、空间的从聚模式、周期性等特点。在此基础上,将人类活动轨迹在城市研究中的应用,按照用户轨迹预测、城市动态景观、城市交通模拟与监控、城市功能单元识别以及城市中其他方面的研究应用进行系统综述,认为人类活动模式挖掘是城市规划、城市交通、公共安全等方面应用的基础。     

Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.     

基于手机信令数据的县域城镇体系识别——以江西省鄱阳县为例

城镇是社会经济发展的重要载体,科学识别城镇体系对于推动县域经济发展具有重要意义。基于常住手机活跃用户识别、跨城镇出行起讫点模型和城镇中心性测度,利用手机信令数据识别江西省鄱阳县城镇体系,并与现行总体规划的城镇体系进行对比以揭示鄱阳县域城镇体系实际发展与规划目标之间的偏差。结果发现:鄱阳县各乡镇常住手机活跃用户数与其总人口之间的相关系数为0.762,表明两者之间存在强相关性;鄱阳县当前实际的城镇体系分为三级,其中县城为第一级,田畈街镇、油墩街镇和芦田乡为第二级,其余乡镇为第三级。发展至今,仅有县城、油墩街镇与芦田乡达到预期规划目标,其他乡镇均与规划目标存在一定差距,需要在新一版的规划中予以调整,从而引导要素资源优化配置,推动县域社会经济高质量发展。     

Metropolitan dynamics in Israel: an emerging “metropolitan island state”?

This study analyzes metropolitan dynamics in a small country with an “island state” context of closed boundaries, using commuting data and mobile phone tracking data. We examine whether the Israeli context encourages the formation of a monocentric “metropolitan state,” characterized by increasing links between localities throughout the country and its principal metropolitan node (Tel Aviv)—rather than with secondary metropolitan areas—and by fuzzy, overlapping metropolitan boundaries. Commuting data from the 1995 and 2008 censuses show that metropolitan expansion processes in Israel are gradual. Mobile phone tracking data for 2013 reveal similar patterns, confirming the urban structure’s stability and the reliability of tracking data as a means of assessing metropolitan processes. The “island state” context supports growing monocentricity, but, when it comes to commuting and travel for other purposes, Israel is not yet a metropolitan state; metropolitan boundaries are not as fuzzy and rapidly changing as expected.     

上海都市区边界划分——基于手机信令数据的探索

都市区是中心城市及其主要影响范围,在区域经济发展中有重要的引领作用。国外都市区范围根据中心城市与外围地区的通勤联系划分,本质上是与中心城市具有密切经济联系的统一劳动市场区。国内由于统计数据的缺乏,通过通勤联系划分都市区的相关研究难以开展,且划分结果在内涵上与国外都市区差异较大。移动电话是目前普及率最高的移动通讯终端设备,其包含的地理位置标签可反映用户的职住地信息。利用上海移动2014年上半年某两周2G手机信令数据,识别用户的居住地和工作地,参照国外标准,根据中心与外围地区的通勤联系划分上海市都市区。在通勤联系的基础上,尝试采用消费联系作为都市区划分的辅助标准,并对划分结果进行了调整。结果表明：① 基于通勤联系的上海都市区主要包括中心城周边地区、松江新城及外围大型工业区,基于消费联系调整的上海都市区包括中心城周边地区、松江、嘉定、青浦等新城及浦东惠南地区、长兴岛,整体上呈现东西略长、南北略窄的形态;② 通勤联系和消费联系可以相互结合划分中国的都市区边界,通勤联系作为主要指标,消费联系作为辅助指标;③ 上海与国外城市都市区差异较大,国外都市区范围扩展极广,上海都市区范围未超过行政边界,局限在在中心城周围相对较小的范围内。     

基于POI数据的广州零售商业中心热点识别与业态集聚特征分析

商业中心是城市零售活动的重要载体,优化商业资源在城市内部空间的合理配置,摸清不同零售经营形态的区位选择,显得尤为重要。以面向公众服务的商业机构兴趣点（POI）数据为研究对象,提出一种城市商业中心与零售业态集聚区识别的方法;以广州市为例,分析商业活动的热点地区以及零售业态集聚区的空间分布特征。研究表明：① 根据核密度估计法提取的商业中心在等级上表现出由城市中心圈层向外围圈层扩散的趋势,结果符合客观事实。② 以街区为单元,商业网点密度符合局域Getis-Ord G^*指数统计特征的热点区域主要分布在越秀区和天河区,广州市零售业发展的双核心空间格局已经形成。③ 不同的零售业态对商业集聚的区位选择具有显著差异性,百货商店、超市、便利店等零售经营形态的空间集聚特征与该业态的市场定位、经营模式及选址策略基本吻合。总体来看,基于POI数据的广州零售业集聚空间分析结果能够反映实体零售企业行为与广州商业经济分布的相关性,有助于提高政府部门商业规划和零售商选址前期研究的客观性和科学性。     

基于Android的南极人员定位软件的设计与实现

为满足南极人员实时定位的需要,基于Android智能手机平台,利用Arc GIS API for Android,根据南极的地理环境,结合Arc GIS Server平台、GPS定位技术,设计并实现了具有地图浏览、定位、电子罗盘、导航、记录、计算距离等功能的南极人员定位软件。逐步阐述软件的设计与建立过程,重点论述底图的设计方案、软件的主要功能和成果分析。该手机软件以实用性为前提,将Arc GIS Server地图缓存技术融入其中,实现基于GIS底图服务的南极实时离线定位功能。该手机软件界面友好,操作便捷,经过在南极中山站测试,软件运行正常,实现了所有的设计功能,并且定位数据准确,为南极科学考察人员提供了便捷的手机定位服务。     

基于移动定位的行为数据采集与地理应用研究

微观个体行为的研究是人文地理学者解释人文地理现象的重要视角,而个体时空数据的获取是制约行为研究的重要瓶颈之一。随着手机的普及,移动定位服务为个体时空数据的获取提供了新的技术手段。通过与人口普查、交通调查等传统移动性数据获取方法的比较,基于移动定位的时空行为数据所具有特点包括:(1)采集个体位置和移动信息真实;(2)定位精度高、信号覆盖广;(3)可持续实时动态获取移动信息;(4)可减少行为数据编码过程的信息损失,适合大规模采集。详细介绍了基于移动通信网络和手机终端的移动定位技术原理,并通过微观个体行为分析和宏观城市动态空间规划的两个典型案例,实证基于移动定位的时空行为数据在城市规划等领域的可行性和适用性。移动定位方法显示出了巨大的技术优势和应用前景,但一些重要的基础性问题仍没有解决,特别是个人的隐私保护和移动定位的地理尺度问题。现有的研究成果表明,基于移动定位数据的地理应用仍处在试验和探索阶段,新的数据获取协议、数据分析方法、地理时空模式提取方法等研究亟待展开。     

中国时空间行为研究进展

自时间地理学和活动分析法引入中国以来的近20年间,时空间行为研究已经成为中国城市地理学的重要领域。中国时空间行为研究关注城市空间重构的描述与解释,试图从行为角度解释中国城市社会转型,强调转型期中国城市空间与居民个体行为之间的互动关系,重视日常生活、生活质量、社会公正、低碳社会、智慧城市等热点问题,探索在城市交通、旅游和城市规划等领域中的实践应用。中国时空间行为研究已经形成了以解读城市转型为目标、以规划应用为导向的鲜明特点,为理解中国城市制度与空间转型背景下人类行为模式的复杂性和多样性提供了一个全新的视角。但是,中国时空间行为研究依然面临着理论发展滞后、实践应用需要突破等挑战。本文是对时空行为研究近年来发展的综述性文章,从数据采集与分析方法演进、实证研究与规划应用进展等方面回顾了近20年来中国城市时空间行为研究的最新进展,致力于推动不同学科领域之间的交流和时空间行为研究自身的发展。     

STLP-GSM: a method to predict future locations of individuals based on geotagged social media data

ABSTRACT

An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users’ spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users’ mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations.     

A crowd sensing system identifying geotopics and community interests from user-generated content

This paper presents a crowd sensing system (CSS) that captures geospatial social media topics and allows the review of results. Using Web-resources derived from social media platforms, the CSS uses a spatially-situated social network graph to harvest user-generated content from selected organizations and members of the public. This allows ‘passively’ contributed social media-based opinions, along with different variables, such as time, location, social interaction, service usage, and human activities to be examined and used to identify trending views and influential citizens. The data model and CSS are used for demonstration purposes to identify geotopics and community interests relevant to municipal affairs in the City of Toronto, Canada.     

Crowdsourcing urban form and function

Urban form and function have been studied extensively in urban planning and geographical information science. However, gaining a greater understanding of how they merge to define the urban morphology remains a substantial scientific challenge. Toward this goal, this paper addresses the opportunities presented by the emergence of crowdsourced data to gain novel insights into form and function in urban spaces. We are focusing in particular on information harvested from social media and other open-source and volunteered datasets (e.g. trajectory and OpenStreetMap data). These data provide a first-hand account of form and function from the people who define urban space through their activities. This novel bottom-up approach to study these concepts complements traditional urban studies to provide a new lens for studying urban activity. By synthesizing recent advancements in the analysis of open-source data, we provide a new typology for characterizing the role of crowdsourcing in the study of urban morphology. We illustrate this new perspective by showing how social media, trajectory, and traffic data can be analyzed to capture the evolving nature of a city’s form and function. While these crowd contributions may be explicit or implicit in nature, they are giving rise to an emerging research agenda for monitoring, analyzing, and modeling form and function for urban design and analysis.