Incorporating spatial interaction patterns in classifying and understanding urban land use

Land use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai.     

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.     

An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACT

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.     

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。     

Flow Cross K-function: a bivariate flow analytical method

Spatial flow data represent meaningful interaction activities between pairs of corresponding locations, such as daily commuting, animal migration, and merchandise shipping. Despite recent advances in flow data analytics, there is a lack of literature on detecting bivariate or multivariate spatial flow patterns. In this paper we introduce a new spatial statistical method called Flow Cross K-function, which combines the Cross K-function that detects marked point patterns and the Flow K-function that detects univariate flow clustering patterns. Flow Cross K-function specifically assesses spatial dependence of two types of flow events, in other words, whether one type of flows is spatially associated with the other, and if so, whether this is according to a clustering or dispersion trend. Both a global version and a local version of Flow Cross K-function are developed. The former measures the overall bivariate flow patterns in the study area, while the latter can identify anomalies at local scales that may not follow the global trend. We test our method with carefully designed synthetic data that simulate the extreme situations. We exemplify the usefulness of this method with an empirical study that examines the distributions of taxi trip flows in New York City.     

Combining spatial transition probabilities for stochastic simulation of categorical fields

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation.     

基于空间滤波方法的中国省际人口迁移驱动因素

人口迁移数据中往往存在较强的网络自相关性,以往基于最小二乘估计的重力模型与迁移数据的拟合度较低,而改进后的泊松重力模型仍存在过度离散的缺陷,以上问题均导致既有人口迁移模型中的估计偏差。本文构建了特征向量空间滤波（ESF）负二项重力模型,基于2015年全国1%人口抽样调查数据,研究2010-2015年中国省际人口迁移的驱动因素。结果表明：① 省际人口迁移流间存在显著的空间溢出效应,ESF能有效地提取数据中的网络自相关性以降低模型的估计偏差,排序在前1.4%的特征向量即可提取较强的网络自相关信息。② 省际人口迁移流之间存在明显的过度离散现象,考虑到数据离散的负二项重力模型更适用于人口迁移驱动因素的估计。③ 网络自相关性会导致模型对距离相关变量估计的上偏与大部分非距离变量估计的下偏,修正后的模型揭示出以下驱动因素：区域人口特征、社会网络、经济发展、教育水平等因素是引发省际人口迁移的重要原因,而居住环境与公路网络等因素也逐渐成为影响人口迁移重要的“拉力”因素。④ 与既有研究相比,社会网络因素(迁移存量、流动链指数)对人口迁移的影响日益增强,而空间距离对人口迁移的影响进一步呈现弱化趋势。     

大数据时代的空间交互分析方法和应用再论

空间交互是理解地表人文过程的重要基础,与空间依赖一起共同体现了地理空间的独特性、关联性以及对嵌入该空间的地理分布格局的影响,具有鲜明的时空属性,因此对于地理学研究具有重要意义。大数据为空间交互研究带来了新的机遇,能够使我们在不同时空尺度感知和观察空间交互模式并对其动态演化特征进行模拟和预测,从而为揭示人类活动规律及区域空间结构提供有力支持。本文在探讨空间交互与地理空间模式关系的基础上,描述了利用地理大数据感知空间交互的方式和定量模型,介绍了空间交互分析方法的研究进展及其在空间规划与交通、公共卫生、旅游等领域的应用情况,并就一些基本问题进行了讨论,以期为大数据支持下空间交互相关研究提供指导。     

UK-based terrorists' antecedent behavior: A spatial and temporal analysis

Background and purposeTerrorism is a real and present danger. The build-up to an attack includes planning, travel, and reconnaissance which necessarily require the offender to move through their environment. Whilst research has examined patterns of terrorist attack locations, with a few exceptions (e.g. Rossmo & Harries, 2011), it has not examined the spatial behavior of the terrorists themselves. In this paper, we investigate whether the spatial mobility patterns of terrorists resemble those of criminals (and the wider population) and if these change in the run up to their attacks.MethodUsing mobile phone data records for the ringleaders of four different UK-based terrorist plots in the months leading up to their attacks, we examine the frequency with which terrorists visit different locations, how far they travel from key anchor points such as their home, the distance between sequential cell-site hits and how their range of movement varies as the planned time to attack approaches.ConclusionsLike the wider population (and criminals), the sample of terrorists examined exhibited predictable patterns of spatial behavior. Most movements were close to their home location or safe house, and they visited a relatively small number of locations most of the time. Disaggregating these patterns over time provided mixed evidence regarding the way in which their spatial activity changed as the time to the planned attack approached. The findings are interpreted in terms of how they inform criminological understanding of the spatial behavior of terrorists, and the implications for law enforcement.     

Understanding spatial structures and organizational patterns of city networks in China: A highway passenger flow perspective

The use of multi-perspective and multi-scalar city networks has gradually developed into a range of critical approaches to understand spatial interactions and linkages. In particular, road linkages represent key characteristics of spatial dependence and distance decay, and are of great significance in depicting spatial relationships at the regional scale. Therefore, based on highway passenger flow data between prefecture-level administrative units, this paper attempted to identify the functional structures and regional impacts of city networks in China, and to further explore the spatial organization patterns of the existing functional regions, aiming to deepen our understanding of city network structures and to provide new cognitive perspectives for ongoing research. The research results lead to four key conclusions. First, city networks that are based on highway flows exhibit strong spatial dependence and hierarchical characteristics, to a large extent spatially coupled with the distributions of major megaregions in China. These phenomena are a reflection of spatial relationships at regional scales as well as core-periphery structure. Second, 19 communities that belong to an important type of spatial configuration are identified through community detection algorithm, and we suggest they are correspondingly urban economic regions within urban China. Their spatial metaphors include the administrative region economy, spatial spillover effects of megaregions, and core-periphery structure. Third, each community possesses a specific city network system and exhibits strong spatial dependence and various spatial organization patterns. Regional patterns have emerged as the result of multi-level, dynamic, and networked characteristics. Fourth, adopting a morphology-based perspective, the regional city network systems can be basically divided into monocentric, dual-nuclei, polycentric, and low-level equilibration spatial structures, while most are developing monocentrically.     

中国省域人口空间结构特征及其对经济绩效的影响——基于实体城市的空间识别

基于LandScan全球人口和ESA全球土地利用数据,构建2000—2015年中国省域人口空间结构数据库,刻画省域人口空间结构的时空格局,进而运用面板固定效应法、两阶段最小二乘法和差分GMM法探索空间结构对省域经济绩效的影响及这种影响对不同规模省域的差异,最后从省会城市集聚（不）经济的角度分析其内在机制。结果发现：① 2000—2015年,中国所有省会城市的人口规模都得到了显著增加,省域人口空间结构存在单中心化的演变趋势,且中西部地区省域人口空间结构相对偏单中心化;② 对所有省份尤其是人口规模相对较小的省份而言,人口空间结构单中心化倾向于提高省域经济效率,而对人口规模相对大的省份,人口空间结构单中心化很可能不利于省域经济效率的提高;③ 省会城市自身已经初显集聚不经济的迹象。     

Crime concentrations and similarities in spatial crime patterns in a Brazilian context

Research within the geography of crime and spatial criminology literature most often show that crime is highly concentrated in particular places. Moreover, a subset of this literature has shown that the spatial patterns of these concentrations are different across crime types. This raises questions regarding the appropriateness of aggregating crime types (property and violent crime, for example) when the underlying spatial pattern is of interest. In this paper, using crime data from Campinas, Brazil, we investigate the crime concentrations and the similarities among different crime types across space. Similar to some recent research in another context, we find that crime is highly concentrated in Campinas but the ability to aggregate similar crime types at the street segment level is not generalizability when compared to a North American context.     

和田绿洲景观破碎化特征及驱动因素

景观破碎化改变着区域景观结构的完整性和系统的连通性,对景观的稳定性和可持续发展具有重要影响.选取新疆和田绿洲为研究区,以1970年新疆考察数据与MSS影像,1990、2000、2013、2018年4期Landsat TM/ETM+同月相数据为主要数据源,采用景观指数法、移动窗口法、梯度分析及归因分析等方法开展绿洲景观破...     

基于空间面板计量模型的雾霾对中国城市旅游流影响的空间效应

以中国342个市域单元为研究对象,借助双变量LISA模型、空间面板杜宾模型等方法,探究了1998-2016年雾霾与中国城市旅游流的空间关联特征,分析了雾霾对旅游流的影响及其空间溢出效应。结果表明,在中国雾霾PM_2.5与城市旅游流有东高西低的分布特点,在胡焕庸线两侧的空间分布呈现出与地形和城市发展等因素的空间耦合性;雾霾与城市旅游流（含国内和入境旅游流）均表现出显著的空间集聚和空间依赖特征,雾霾污染对旅游流产生明显的影响并形成相应的空间效应;雾霾对旅游流的抑制区域在不断扩大,H-L型城市数量的增加、L-H型集聚区的片状扩张和华北、华中地区的L-H型集聚的“空心化”现象均表明旅游流具有低雾霾指向性;雾霾污染与旅游流的倒“U”型曲线关系表明经典的EKC假说对中国城市旅游流同样适用,且雾霾污染的显著负向影响主要存在于入境旅游方面;雾霾和旅游流均具有明显的正向空间溢出效应,将雾霾治理同经济发展、对外联系、旅游开发、生态保护和交通建设等因素结合起来进行综合治理,才能为旅游发展创造美好的环境,实现国际、国内旅游健康、协调、可持续的高质量发展。     

基于空间自相关的中国省际人口迁移模式与机制分析

人口迁移具有空间指向性,表现为迁入地和迁出地在地域上呈现一定的空间集聚特征。然而,大部分针对我国人口迁移进行分析和建模的研究忽视了这一空间指向性及其影响。该文利用全国第五次人口普查省际人口迁移数据和相关资料,以空间自相关分析为基础,对1995-2000年我国省际人口迁移的空间模式与动力机制进行了初步分析。首先,运用全局空间自相关统计量(Moran′s I)对人口迁移流中的空间自相关程度进行了测度,发现研究期间我国省际人口迁移的空间指向性明显:从一个区域出发(或抵达一个区域)的人口迁移流均受到周边地区人口迁移的影响。为了进一步研究这种空间指向性对人口迁移规模的影响,分别采用重力模型(仅用距离变量捕捉人口迁移过程中的距离衰减效应)和空间OD模型(采用因变量空间滞后的不同形式对迁移流的空间指向性加以考虑)研究中国省际人口迁移的动力机制,对比两种模型的估计结果发现:1)空间OD模型在参数估计和模型拟合等方面均优于传统的重力模型;在选取相同解释变量的情况下,空间OD模型的残差平方和仅为传统重力模型的47%,模型拟合指标AIC值也大大缩小。2)在对中国人口迁移动力机制的定量分析中,如果不考虑人口迁移流之间的空间自相关(空间指向性)现象,会导致对社会、经济等变量作用和距离衰减效应的过高估计。     

我国“淘宝村”的空间组织与地理根植性

以2015年淘宝村为对象,首先通过空间分布特征与空间组织模式推断出它们应具有地理根植性,之后以经营商品的特殊性为切入点,经由成本-价格分析确定其地理根植性,最后则通过产业根植性给出根源性解释。研究发现：淘宝村在国家、地区、省、市、县和乡镇六个层面都呈非均衡分布,且具有显著集聚化特征;淘宝村符合由两种空间形态、两种流态和五种主体构建的网络购物空间组织模式框架,且可划分为自产自销、村内生产、一般村外生产、村外生产与专业市场结合等4种类型;淘宝村经营商品的特殊性,即本地绝对优势产品和成本优势产品是其在全国竞争中胜出和存在的关键;淘宝村经营商品的自然根植性、社会根植性和复合根植性是其地理根植性的根源。     

天气因素对城市地铁客流时空分布的影响——基于智能交通卡数据的实证研究

城市地理空间、气候环境及交通系统间存在复杂的相互联系、相互制约的关系,交通及地理时空数据为理解三者间关系带来了新的机遇。城市轨道交通是居民绿色出行、缓解中国大城市交通拥堵的重要交通方式。深入研究影响城市地铁客流时间和空间分布变化的因素,有利于制定合理的土地利用及交通需求管理政策,也可为实时响应特定天气条件下旅客出行需求的变化和优化公交服务运营提供理论依据。论文使用智能交通卡数据,以南京市为例,通过建立一种季节性差分自回归移动平均(seasonal autoregressive integrated moving average with explanatory variables, SARIMAX)模型,解释不同种类的天气因素(如降雨、气温、相对湿度、风速等)对地铁客流量时空分布的影响程度。研究发现：降雨类因素在高峰和周末时段对地铁客流量的影响较大;各天气因素对各地铁站点客流量的影响大致呈现出从城市中心区域向外围区域逐渐变小的渐变式规律,且地铁无规律出行者比有规律出行者更易受恶劣天气因素的影响。     

基于公路客流的中国城市网络结构与空间组织模式

多视角和多尺度城市网络逐渐成为认识空间关系的主要途径。公路流数据具有显著的空间依赖性和距离衰减特性,对于刻画区域尺度空间关系具有重要意义。基于全国地级行政单元间的公路客运流,论文尝试刻画中国城市网络功能结构和区域效应,并对其空间组织模式进行特征提取和规律挖掘,以期能够为城市网络研究提供新的方法支撑和认知视角。研究结果表明：① 基于公路流的城市网络空间形态表现出强烈的空间依赖性和层级特征,与中国主要城市群分布存在较大程度的空间耦合,更多体现的是区域尺度的空间关系及核心—外围组合关系;② 通过社区发现算法识别出19个城市经济区,其空间内涵主要包括行政区经济、巨型区域溢出效应和核心—边缘结构等;③ 不同地域系统内城市网络自成体系,表现出显著的空间依赖性和多元的空间组织模式,多层次、流动性和网络化的地域系统格局凸显;④ 从空间形态上看,区域城市网络空间结构大致可划分为单中心结构、双核心结构、多中心结构和低水平均势结构等区域关联形态,并以单中心发育模式为主。     

Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs

Visual data mining of spatial data is a challenging task. As exploratory analysis is fundamental, it is beneficial to explore the data using different potential visualisations. In this article, we propose and analyse network graphs as a useful visualisation tool to mine spatial data. Due to their ability to represent complex systems of relationships in a visually insightful and intuitive way, network graphs offer a rich structure that has been recognised in many fields as a powerful visual representation. However, they have not been sufficiently exploited in spatial data mining, where they have principally been used on data that come with an explicit pre-specified network graph structure. This research presents a methodology with which to infer relationship network graphs for large collections of boolean spatial features. The methodology consists of four principal stages: (1) define a co-location model, (2) select the type of co-association of interest, (3) compute statistical diagnostics for these co-associations and (4) construct and visualise a network graph of the statistic from step (3). We illustrate the potential usefulness of the methodology using an example taken from an ecological setting. Specifically, we use network graphs to understand and analyse the potential interactions between potential vector and reservoir species that enable the propagation of leishmaniasis, a disease transmitted by the bite of sandflies.     

杭州下沙高教东区学生行为时空特征研究

以杭州下沙高教东区为例,尝试探讨空间要素对大学生生活、学习等行为的时空影响,从大学生的需求出发,为完善大学城规划发展提供案例数据和思路。通过访谈和问卷调查方法,以杭州下沙高教东区部分大学的大学生日常行为调查问卷为基础,对其行为的时空特征进行调查和分析,并与处于市区的老校区学生进行对比。调查表明,新生和高年级学生行为空间模式和空间选择存在差异,新老校区的学生之间同样也存在差异。进而,探讨了造成这些差异的原因,总结出目前高教东区空间布局对学生行为的影响主要有:(1)功能分区;(2)校园区位与对外交通;(3)树形空间结构;(4)公共空间及设施布局。由此提出建议:(1)东区有必要考虑优化管理体制提高配套设施的共享效率;(2)加强高校与城市社区的互动;(3)大学城规划建设进一步"以人为本"满足师生合理的空间需求。