广州大都市登革热时空传播混合模式

以广州市2014年登革热疫情早期11周内679个登革热病例数据为例,提出了一种基于扩展Knox检验的疾病时空传播网络模型,研究登革热疫情早期时空传播特征。首先,应用扩展Knox检验方法发现2周1 km内、1周5~7 km两个时空标度下,病例之间具有显著的时空交互特征;其次,提取两个时空标度下的病例点对,分别构造时空近邻传播（STAT）网络和日常移动传播（HDMT）网络;最后,对STAT和HDMT传播网络的同配性、空间特征、网络中心位置进行对比分析。结果显示,广州市登革热传播是空间近邻交叉传播和空间扩散传播的混合传播模式;利用STAT网络亲近中心度标识出的“爆发中心”与病例空间核密度中心基本重合,HDMT网络中介中心度标识出的“扩散中枢”多位于城市快速交通干线附近。     

Incorporating the human-Aedes mosquito interactions into measuring the spatial risk of urban dengue fever

To block human–mosquito interactions by eliminating dengue vectors, Aedes aegypti L. and Aedes albopictus Skuse have been considered the main strategies for dengue prevention. Spatial targeting of dengue risk areas is the highest priority for implementing control measures. However, the frequency of human-Aedes mosquito contacts as human factors for assessing the risk of dengue has not been taken into account by past studies. The objective of this study is to clarify the geographic effects of crowd-gathering places on the frequency of dengue vector for assessing the spatial risk of exposure to dengue in Kaohsiung City, Taiwan. A geographic information system (GIS) was used to integrate crowd-gathering places and ovitrap locations for investigating potential human–mosquito contacts. A negative binominal regression was used to estimate the spatial risk of dengue by integrating vector mosquitoes from the ovitrap survey, urban environmental risk factors and human crowd-gathering places as the surrogate of human activities. We also compared the estimated spatial risk of exposure to dengue with traditional approaches. Our results indicated that the spatial distribution of the residential populations is not consistent with the locations of social activities. Additionally, people closer to crowd-gathering places have a higher frequency of contact with Ae. aegypti than with Ae. albopictus larvae. The dengue risk is caused by the human-Aedes aegypti contacts concentrated around city centers, while the risk caused by the human-Aedes albopictus contacts is distributed around the city boundary. Our study concluded that only relying on infected human cases or the abundance of vector mosquitoes is not enough for assessing the spatial risk of dengue. This reliance could ignore the areas with frequent vector existence and; therefore, result in the underestimated risk of dengue transmission. This study demonstrates the methodological framework for estimating the potential dengue risk and identifies the crowd-gathering places that facilitate dengue transmission.     

广州市登革热时空传播特征及影响因素

以广州市主城区为研究区,着眼于街道等微观尺度,首先通过实地调查分析法、核密度分析法、标准差椭圆法,探究登革热时空传播特征;其次结合交叉相关性分析法与地理探测器,分析温度、湿度、气压、用地类型对登革热传播的影响,讨论了不同用地类型交互作用与登革热扩散的关系。研究表明：1）登革热时空传播具明显的阶段性特征,多发生于居住环境较差、人口密度较高的区域,并快速向外扩散;2)登革热发展初期,以输入型病例为主;3）环境较差、老年人口众多、人员构成复杂、交通便捷的老城区是登革热高爆发风险区域;4）温度、湿度、气压对登革热传播存在显著滞后性影响关系,温度、湿度与登革热传播呈正相关关系,气压与登革热传播呈负相关关系;5）居民人口分布与登革热传播关系最为密切,池塘、农田、草地、公共绿地交互地区会增加登革热传播风险。     

基于个体“移动—接触”的空间交互网络理论构建与疫情风险评估

个体在空间移动过程中不可避免地产生“人与人”之间的接触,使疫情传播具有复杂性和空间不确定性。但现有学术研究较少在理论上综合考虑个体的空间移动及移动过程中近距离接触分析疫情的空间扩散。本文综合考虑个体移动及移动过程中接触,提出基于个体“移动—接触”的空间交互网络的理论构建框架。鉴于公交刷卡数据能够有效地反映个体的移动路径及车厢内接触的群体,以北京市公交系统为例开展实证研究,构建基于个体“移动—接触”的空间交互网络,采用加权度中心性和K-shell分解方法识别疫情高风险区域,提出管控措施,并通过模拟管控措施前后网络社团结构变化来评价管控措施实施效果。结果发现疫情高风险区域集中分布在城际交通枢纽、商务中心、居住区周边区域。本文提出的理论框架对基于各类交通系统的疫情风险评估具有普适性,研究结果可为突发公共卫生事件中及时启动有效的疫情防控应急响应政策具有借鉴作用。     

A proportional odds model of human mobility and migration patterns

The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.     

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.     

新冠肺炎疫情在广东省的扩散特征

基于广东省各地市卫健委官网披露的新型冠状病毒肺炎确诊病例的详细信息，对新冠病例在广东省的时空扩散特征以及各地级市疫情传播和变动的空间差异进行分析。研究结果表明：1）广东省确诊病例具有中年人最多、青年人其次、老年人第三的年龄结构特点，与全国统计特征较为相似。2）广东省的疫情扩散比率为0.198，表明其防疫措施卓有成效，有效地抑制了疫情的扩散。3）广东省的疫情扩散格局和综合风险格局存在显著差异。三四线中型城市的扩散风险较高但综合风险处于中等水平，发达城市的扩散风险较低但综合风险最高，粤东西部的欠发达地区较为安全。文章建议可积极宣传广东抗疫防疫成果，适度缓解民众焦虑，但仍需重点防范因返工返学带来的二次冲击。在防疫的中后期，应根据各地市的疫情风险等级，在省内科学划分风险防控区域，实施分级管控，有序恢复社会生产生活，避免过度防疫对社会民生造成的负面冲击，并需特别关注原本经济韧性较为脆弱的欠发达地区。     

新冠肺炎疫情的空间扩散过程与模式研究

研究新冠肺炎疫情的空间扩散过程与模式对于防疫抗疫资源的合理配置、突发公共卫生事件的管理与应对以及未来公共卫生体系的完善具有重要意义。本文综合时间和空间尺度,从地理学视角研究了新冠肺炎疫情的城际空间扩散过程,归纳总结扩散模式,并揭示了其影响因素。研究发现：新冠肺炎疫情在中国的发展主要经历了6个阶段,并在空间上表现出邻近扩散、迁移扩散、等级扩散和廊道扩散等地理模式;地理邻近性、人口流动、人口规模、交通网络、疫情防控管理等因素对疫情的空间扩散具有显著影响。新冠肺炎疫情的空间扩散过程和模式一定程度上是“流空间”网络下人类社会经济活动空间组织模式的一种反映,与地理邻近性、社会经济联系的跨区域性及人类活动的时空规律等密切相关。本研究以期为世界各国的疫情防控措施制定提供参考,也为中国未来应对公共卫生应急风险提供经验借鉴。     

An approach for understanding human activity patterns with the motivations behind

A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations behind behaviors. The aim of this study is to model and understand human activity patterns within urban areas using both spatiotemporal and cognitive psychology methods to measure both human behavior patterns and the underlying motivations . We first propose a framework that enables us to analyze the spatiotemporal patterns of urban human activities, infer the associated semantic patterns that represent the motivations driving human mobility choices and behaviors, and measure the similarity between human activities. We then construct a human activity network based on the similarity to depict human activity patterns. The framework is applied to a case study of Toronto, Canada, where geotagged tweets are used as a proxy for human activities to explore activity patterns. The analysis of the human activity network shows that 61% of tweeter users follow similar activity patterns. Our work provides a new tool for better understanding the way individuals interact with urban environments that could be applied\ to a variety of urban applications.     

珠海市新型冠状病毒肺炎聚集发生的时空特征及传播路径

利用珠海市卫生健康局公开发布的98例新型冠状病毒肺炎病例流行病学调查数据,通过26个疫情聚集单元的时空特征分析和4类典型传播路径的案例剖析,深入探讨了珠海市新型冠状病毒肺炎聚集发生的时空规律和传播路径。结果表明：1）珠海的疫情发展与武汉的人口流动与管控密切相关,由于管控及时,目前仍以疫源区直接输入和家庭聚集扩散为主要传播方式,在有限范围内扩散。2）存在输入一代家庭聚集传播、输入一代非家庭聚集传播、输入二代家庭聚集传播、输入二代非家庭聚集传播4种不同的传播路径,且以第一种为主要方式,但非家庭聚集传播的路径复杂,应加强流行病学调查和防控。3）对输入性病例从输入到发病的滞后周期以及聚集单元最长发病时间间隔来看,有少部分病例或携带者可以突破14 d限制,应警惕目前普遍采用的14 d隔离措施可能存在的风险。     

COVID-19疫情时空聚集性特征及影响因素分析——以重庆市为例

新型冠状病毒肺炎(COVID-19)在城市内部的病例时空聚集性特征及影响因素对于疫情防控具有重要参考价值。论文以重庆市为例,收集2020年1月21日—2月24日的458例COVID-19确诊病例数据,结合手机信令数据与空间环境数据,采用空间聚集性分析、因子分析与回归分析等方法,探究重庆市新冠疫情在街道尺度上的时空聚集性特征,并分析其影响因素。结果表明：① 时间上,确诊病例前期增长较快,以外地输入为主,后期逐渐趋缓,以本地传播(包括街道间传播、街道内传播、家庭内传播)为主,其中家庭内传播占比最大(23%);② 空间上,病例呈现显著的聚集特征,且聚集性逐渐增强,热点街道分布于重庆市西部、东北部;分类型来看,本地病例热点街道集中于人口密度较大、经济发展更好的西北部、西南部,外地输入病例热点街道集中于地理邻近湖北省的中部、东北部;③ 所有病例密度、本地病例密度、外地输入病例密度与因子分析所得4个因子(即城市交通因子、街道间活动因子、生活服务因子、居民分布因子)的回归结果显示,交通设施水平与病例密度存在密切关联,商店超市餐饮等生活服务地点与病毒传播显著相关,街道间活动是发生聚集性疫情的重要因素,而外地输入型病例多出现于人口密集区域。疫情防控中应根据不同区域疫情传播模式差异实施针对性措施,例如在重庆市中部、东北部关注外地输入防控,西北部、西南部以本地传播防控为重点;同时,应加强城市内部交通与街道居民接触密集区域的防控措施,有效防止疫情反弹。     

老年人公交移动性的季节时空分异特征研究——以安徽省芜湖市为例

移动性是老年人生活质量的重要影响因素,提高老年人的移动性是延长老年人独立生活时间,从而减小社会养老成本的重要手段。公共交通是中国老年人较长距离出行的最主要交通方式,但已有研究对公交移动性的关注较少。论文从季节时空分异的角度出发,利用安徽省芜湖市智能公交卡数据,分析不同季节老年人公交移动性静态空间集聚特征以及季节变化条件下移动性变化值的空间集聚特征。结果表明：① 老年人公交移动性空间集聚明显,呈现圈层式分布的特征。不同季节、不同移动性指标在城市中心、城市中心外围和城市边缘的集聚现象存在较大差异。② 老年人公交移动性的时空分异现象是复合的,不同城市空间对季节变化的敏感程度存在差异,主要呈现出从城市中心向外递增的趋势。研究公交移动性的季节时空分异现象可以为老年友好型城市的规划建设提供更加适当的规划和灵活的政策建议。     

Regional variability in relationships between climate and dengue/DHF in Indonesia

Since 1970, the worldwide distribution, frequency and intensity of epidemics of dengue and dengue haemorrhagic fever (DHF) have increased dramatically. In Indonesia, as elsewhere, the geographic distribution and behaviour of the two main vectors – Aedes aegypti and Aedes albopictus – and the consequent transmission dynamics of the disease are strongly influenced by climate. Monthly incidence data were examined in relation to monthly data for temperature, rainfall, rainfall anomalies, humidity and the Southern Oscillation Index for 1992–2001. Focusing on eight provinces, significant Pearson correlations were observed between dengue/DHF incidence and at least one climate variable ( r  = ±0.2 to ±0.43; P  < 0.05). Multiple regression analyses showed that 12.9–24.5 per cent of variance in incidence was explained by two or three climate variables in each province ( P  < 0.1–0.01). Rainfall appears to be the principal climatic agent affecting the geographic distribution and temporal pattern of incidence while temperature appears to play a critical role in outbreak intensity. Wide regional and temporal variations in the strength and nature of the observed associations led to the identification of three groups of provinces where increases in dengue/DHF incidence were variously associated with increased rainfall, decreased rainfall and/or high susceptibility to climate variability. Although climatic factors play an important role in explaining the timing and intensity of dengue/DHF outbreaks, a wide range of other factors specific to local environments also appear to be involved – information that may assist in the prediction and mitigation of regional dengue/DHF outbreaks.     

The variable impact of ENSO events on regional dengue/DHF in Indonesia

Although studies have demonstrated significant associations between ENSO events and dengue fever, few have explored regional impacts on dengue fever of separate events. This study explores the impacts of two ENSO events on regional patterns of dengue/ dengue haemorrhagic fever (DHF) incidence in Indonesia. Data consist of monthly cases of dengue/DHF from 1992 to 2001 for each of Indonesia's 27 provinces, and monthly figures for rainfall, rainfall anomalies, temperature, relative humidity and the Southern Oscillation Index (SOI). We conducted Pearson correlation analyses for each independent variable against dengue/DHF incidence, using a direct month‐by‐month correlation and applying a lag of between one and six months to each variable with respect to dengue/DHF incidence. Based on the SOI value, we identified two ENSO events between 1992 and 2001. To explore each event, we created two dummy variables and in regression analyses for eight provinces. The variance of between 12.9 per cent and 24.5 per cent in provincial dengue/DHF incidence is explained by two or three climate variables in each of the provinces (p < 0.01 to 0.1). During the 1997/98 event, the explained variance increased by between 7 per cent and 15 per cent in provinces whose climate regimes were most affected by this event. This study demonstrates that indicators of ENSO such as the SOI may assist in the forecast of potential dengue/DHF incidence and distribution in Indonesia.     

移动定位大数据视角下的人群移动模式及城市空间结构研究进展

了解城市人群移动行为和空间结构对城市规划、交通管理、应急响应等具有重要的意义。近年来,随着信息技术(ICT)的快速发展,采集大规模、长时间序列的人群移动定位大数据变得容易,为人群移动行为研究带来了新的机遇和挑战。本文首先介绍了目前用于城市人群移动行为和空间结构研究的主要数据源及其特征,并分别从人群移动行为、城市空间结构2个方面对近3年国内外相关研究进行归纳总结。目前的研究主要从移动定位大数据中挖掘人群移动模式,理解人群移动时空规律,进一步透视城市的空间结构特征;而对城市空间结构与人群移动行为影响的研究较少。未来可通过融合多源时空数据,综合研究人群移动行为与城市空间结构之间的相互作用,发展大规模群体移动行为时空分析理论和模型,进一步深入理解人群移动行为与城市空间结构的耦合关系。     

Spatial mining of migration patterns from web demographics

Volunteered Geographic Information, social media, and data from Information and Communication Technology are emerging sources of big data that contribute to the development and understanding of the spatiotemporal distribution of human population. However, the inherent anonymity of these crowd-sourced or crowd-harvested data sources lack the socioeconomic and demographic attributes to examine and explain human mobility and spatiotemporal patterns. In this paper, we investigate an Internet-based demographic data source, personal microdata databases publicly accessible on the World Wide Web (hereafter web demographics), as potential sources of aspatial and spatiotemporal information regarding the landscape of human dynamics. The objectives of this paper are twofold: (1) to develop an analytical framework to identify mobile population from web demographics as an individual-level residential history data, and (2) to explore their geographic and demographic patterns of migration. Using web demographics of Vietnamese–Americans in Texas collected in 2010 as a case study, this paper (1) addresses entity resolution and identifies mobile population through the application of a Cost-Sensitive Alternative Decision Tree (CS-ADT) algorithm, (2) investigates migration pathways and clusters to include both short- and long-distance patterns, and (3) analyze the demographic characteristics of mobile population and the functional relationship with travel distance. By linking the physical space at the individual level, this unique methodology attempts to enhance the understanding of human movement at multiple spatial scales.     

Mining spatiotemporal association patterns from complex geographic phenomena

ABSTRACT

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.     

2020年湖南省COVID-19疫情的地理学分析

以发病数和发病率为依据,利用ArcGIS 10.3软件和地理探测器,探究湖南省2020-01-21—02-28间COVID-19疫情的时空变化及其影响因素,结果显示：1）疫情演变具有时序阶段性。湖南省COVID-19流行过程可分为扩散期—鼎盛期—衰减期—消亡期4个阶段;所有发病县区中84.47%的首发病例为武汉输入。2）疫情传播具有交通依赖性。确诊患者和高发病率地区集中在铁路干线附近,“地级市早发、主城区多发”特征明显。3）疫情程度具有距离衰减性。邻近武汉的湘中、湘北、湘东北地区发病数和发病率最高。4）疫情分布具有空间集聚性。发病数和发病率集聚水平先增大后保持高位,疫情热点区位于长株潭及岳阳市,冷点区位于湘西州。5）疫情流行具有因子交互性。无风、寒冷、干燥的天气更利于疫情传播;人口密度、路网密度和GDP是影响疫情空间分异的主要因素;自然因子与社会因子的交互作用最强。6）影响因子具有空间异质性。空间距离、人口密度对发病数和发病率两者的影响都有空间异质性,空间距离对湘东北地区疫情的影响最大,人口密度对湘西北、湘北地区疫情的影响最大;人口流动性、卫技人员数仅对发病数的影响有空间异质性,人口流动性的影响程度由北向南递减,卫技人员数的影响程度以长株潭城市群为核心向外围圈层递减。     

A framework for identifying activity groups from individual space-time profiles

ABSTRACT

Datasets collecting the ever-changing position of moving individuals are usually big and possess high spatial and temporal resolution to reveal activity patterns of individuals in greater detail. Information about human mobility, such as ‘when, where and why people travel’, is contained in these datasets and is necessary for urban planning and public policy making. Nevertheless, how to segregate the users into groups with different movement and behaviours and generalise the patterns of groups are still challenging. To address this, this article develops a theoretical framework for uncovering space-time activity patterns from individual’s movement trajectory data and segregating users into subgroups according to these patterns. In this framework, individuals’ activities are modelled as their visits to spatio-temporal region of interests (ST-ROIs) by incorporating both the time and places the activities take place. An individual’s behaviour is defined as his/her profile of time allocation on the ST-ROIs she/he visited. A hierarchical approach is adopted to segregate individuals into subgroups based upon the similarity of these individuals’ profiles. The proposed framework is tested in the analysis of the behaviours of London foot patrol police officers based on their GPS trajectories provided by the Metropolitan Police.     

基于社会网络的城市疫情空间扩散及治理研究

以深圳市卫生健康委员会发布的新冠肺炎确诊病例数据信息为依据,借助数据挖掘方法、空间分析方法和社会网络分析方法,对城市疫情空间扩散特征及治理对策进行了分析。研究表明：（1）从流行病学基本特征来看,疫情确诊病例人群结构复杂,来源地较广,延续时间较长,被传染病例发病滞后周期复杂。（2）从家庭内部及夫妻之间传播网络来看,疫情扩散具有强烈的空间聚集性特征。（3）从重点疫区和非疫区传播网络来看,疫情扩散具有明显的空间流动性特征。（4）从社交活动传播网络来看,疫情空间扩散具有私密性和不确定性特征。（5）从境外疫情空间蔓延的传播网络来看,疫情扩散具有国际性和不可抗拒性特征。（6）在城市管理中,应该优化城市空间效能,利用现代通信网络技术提高治理时效性和柔韧性。