城市双元创新驱动因素的时空异质性——以生物医药多元创新要素耦合分区为例

构建有向的耦合协调度模型,分析生物医药技术、资金、人才在城市间流动的协同水平,依此划分耦合分区。利用均方差-突变级数法和地理加权回归模型,探究2000—2018年间双元创新冷热点演化格局、双元创新驱动因素的时空异质性及其在不同耦合分区内的主导效应。结果发现：① 城市集聚多元创新要素时“脱钩”现象突出,对外配置多元要素时“雁阵效应”明显。根据多元要素协同流动情况,将地域划分为以依附式耦合为主的保守区、以互惠式耦合为主的平衡区和以吸收式耦合为主的明星区;② 渐进式创新具有时空惰性,突破式创新具有时空间歇性,两者相互反哺,存在时空连锁效应。保守区是主要的双元创新冷点区,平衡区内渐进式创新热点呈现出点-轴扩散的空间增长趋势,突破式创新热点在重要节点城市间跳跃分布,明星区内长三角和珠三角城市群的双元创新热点呈圈层式分布;③ 双元创新的驱动因素具有时序不稳定性和空间异质性,不同耦合分区内城市资产对双元创新的时空主导效应差异明显。     

东北地区城镇化质量测度及其时空格局特征

采用熵值法确定权重,运用探索性空间数据分析方法对东北地区34个地级及以上城市2008—2018年城镇化质量进行测度并分析其时空格局特征。结果发现：从整体变化趋势来看,东北地区整体的城镇化质量呈现显著提升趋势,但2015—2018年增长曲线呈现出“U”字型波动特征,各子系统中“城市活力”得分呈现显著下降趋势。从时空格局特征来看,东北地区城镇化质量分布的时空分异明显,整体显著提升趋势下部分城市出现“阶段性”下降,通过LISA集聚图分析发现,东北地区城镇化质量的“高值”集聚区域主要分布在“哈长”城市群和辽中南城市群,“低值”集聚区域主要分布在黑龙江省北部地区。     

贝叶斯方法在犯罪时空格局研究上的应用——以长春市为例

对比分析了2008年和2018年长春市犯罪空间格局的变化特征,进而构建了贝叶斯时空分析模型,整合了犯罪时空格局演化中的固定效应、空间随机效应和时间随机效应,基于R环境中的INLA程序包对模型的各个参数进行了拟合,结合GIS制图,识别出异于总体趋势的犯罪相对风险高值区,并进一步解析了犯罪格局形成和演化的过程和规律。研究发现,犯罪总量在10 a间显著下降,犯罪数量较高的警区数量明显减少。长春市周边地区犯罪率有所提高,而城市中心区域的多数警区则明显下降。贝叶斯时空模型表明,虽然城市犯罪相对风险的平均水平较低,但其总体上却呈现出显著的增加趋势。空间效应的高值区主要集中在城市中心核心区域,特别是传统的商业网点或经济活动较为集中的警区。时间效应的高值区主要集中在城市外围地区,尤其是国家级开发区所在的警区。综合空间效应和时间效应,城市中心区域存在既是空间效应的高值区也是时间效应高值区的时空共同高风险区。贝叶斯方法在数据整合、区域异质性识别以及灵活性方面具有明显的优势,对于犯罪时空格局形成和演化规律的理解和把握上均有所助益。     

Non-current data and geographical information systems A case for data retention

Inter-urban income disparities reflect differences between individual urban localities in the average incomes of their residents. The present paper discusses different ways of visualizing such disparities on thematic maps. The approach we propose is based on the transformation of distances between individual localities and a reference city (e.g. a major population centre of a country) in proportion to the actual differences in the income levels. The general principle of such a transformation is to bring closer to the reference city places with higher incomes, while moving away localities with low income levels. Three alternative approaches to the implementation of this transformation technique are discussed. According to the ‘actual distance’ method, the spatial ‘shift’ of a locality on the map is set proportional to both the relative difference in incomes and the aerial distance between a locality and the reference city. In the ‘proportional increment’ transformation, the distance between a locality and the reference city is adjusted by a parameter whose values are proportional to income disparities between the two. Lastly, according to the ‘concentric circle’ transformation, localities with identical levels of incomes are positioned at a certain distance from the reference city, forming concentric circles around it. Both advantages and disadvantages of these transformation techniques are discussed, and the ‘proportional increment’ method is chosen as the best-performing visualization technique. The performance of this technique is demonstrated using income data for urban localities in Israel in 1991 and 1999. As analysis indicates, the proposed method helps to illustrate both the existing patterns of inter-urban income disparities and their dynamics over time.     

中国COVID-19疫情扩散的时空模式及影响因素

在2020年全球暴发新型冠状病毒肺炎（COVID-19）疫情的背景下,揭示中国疫情扩散时空模式及影响因素对于科学制定防疫策略具有重要作用。针对2020年1月24日—3月18日期间中国COVID-19疫情从快速扩散到逐步控制的完整过程,基于累计确诊病例数据,以317个地级市为对象,建立疫情扩散时空模式判别模型,结合峰位置、半峰间距、峰度、偏度等参数,解析时空模式的基本特征;基于交通可达性、城市关联程度和人口流动构建多元Logistic回归模型,揭示时空模式的关键影响因素。结果显示：① 距武汉市直线距离588 km为判别疫情扩散4种空间模式的有效边界,综合同一空间模式下的时间过程类别,得到13类疫情扩散时空模式。② 蛙跳型的疫情扩散相对严重;除近距离蛙跳型以外,其余空间模式的疫情扩散时间过程差异明显;各种时空模式的新增确诊病例峰值大多为2020年2月3日;所有普通类城市的平均半峰间距约为14 d,与COVID-19病毒的潜伏期一致。③ 与武汉市的人口关联度主要影响蔓延型和近距离蛙跳型空间模式,与武汉市的通航状况对远距离蛙跳型空间模式具有正向影响,迁出人口数量对蛙跳型空间模式有显著作用,综合型空间模式受初级和次级疫情暴发地的双重影响。不同城市应根据自身的疫情扩散时空模式,在疫情期间高度重视交通管控,从关键环节遏制疫情扩散。     

Satellite Passive Sensing of the Marine Atmosphere Associated With Cold‐Air Mesoscale Cyclones

Contemporary research into extratropical cloud systems optimizes the increase in resolution of visible (VIS) and thermal infra‐red (IR) sensors, and the ability to retrieve wind and atmospheric moisture variables at mesoscales using microwave radiometry. These passively‐acquired remote sensing data are used to develop synoptic climatological (conceptual and simple statistical) ‘models’ of mesoscale cyclones in cold‐air outbreaks (mesocyclones, ‘polar lows’) occurring over the otherwise data‐void southern oceans. Mesocyclones present a limitation to successful weather forecasting for New Zealand and coastal Chile, southern Australia and South Africa, during the cold season. The synoptic climatological analyses show that: 1) the patterns of mesocyclone cloud vortex origins, movement and dissipation (‘mesocyclone regimes’), exhibit spatial dependence and have associations with upper‐ocean conditions; 2) mesocyclone ‘outbreaks’ are embedded within characteristic larger‐scale anomaly fields of tropospheric pressure, height, and layer thickness (mean temperature); and 3) composite (statistical average) models of cloud system structure based on the microwave retrievals of marine weather reveal mesocyclones to be relatively dry in comparison with synoptic cyclones, yet very windy. These analyses should permit the development of methods to better predict these important cold‐season storms over southern middle latitudes, and a fuller assessment of their significance for the larger hydroclimatic system.     

A Regional Scale Investigation of Climatological Tropical Convection and Precipitation in the Amazon Basin

This study constructs a regional scale climatology of tropical convection and precipitation from more than 15 years of monthly outgoing longwave radiation (OLR) and precipitation data on 2.5°× 2.5° latitude‐longitude grid to examine the spatial and temporal patterns and variability of convection and precipitation in the Amazon Basin. A linear regression analysis also detects if any trends exist in the two datasets. The region of study extends from 15°N to 25°S and 30° to 80°W that encompass the Amazon Basin and surrounding fringe areas for the period from January 1979 through December 1995 for the OLR data and up to 1996 for the precipitation dataset. The basin‐average mean monthly and seasonal climatology serve as a ‘baseline’ reference for comparison with the full time series of basin‐average monthly OLR and precipitation to illustrate the interannual variability and identify anomalous periods of wet and dry conditions. A linear trend analysis of OLR data found small negative values across the Amazon Basin indicating a slight increase in convective activity over the period of study. The analysis of the precipitation time series, however, shows no coincidental increase in precipitation as would be expected with an increase in convective activity. Portions of Rondônia and Mato Grosso, areas that have undergone extensive deforestation, illustrate no trend in precipitation as suggested by GCM simulation results. The only area featuring any large change in precipitation occurs in a small area in the northwestern region of South America where a large positive trend in precipitation exists.     

Why did a vehicle stop? A methodology for detection and classification of stops in vehicle trajectories

ABSTRACT

Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems.     

Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.     

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.     

Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin,Paris, and Tallinn

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.     

Finding moving flock patterns among pedestrians through collective coherence

Tracking technologies are able to provide high-resolution movement data that can advance research in different fields, such as tourism management. In this specific field, developing methods to extract moving flock patterns from such data are particularly relevant to enable us to improve our knowledge of the nature of recreational use interactions, which is crucial for a good management of attractions and for designing sustainable development policies. However, ‘flocking’ has been usually associated with the form of collective movement of a large group of birds, fish, insects and certain mammals as well. Very few research efforts have been devoted in finding flock patterns associated with pedestrian movement. In this work, we propose a moving flock pattern definition and a corresponding extraction algorithm based on the notion of collective coherence. We use the term collective coherence to refer to the spatial closeness over some time duration with a minimum number of members. Furthermore, we evaluate the proposed algorithm by applying it to two different pedestrian movement datasets, which have been gathered from visitors of two recreational parks. The results show that the algorithm is capable of extracting moving flock patterns, disqualifying the patterns with flock members that remain stationary in a common place during the considered time interval.     

复杂网络视角下时空行为轨迹模式挖掘研究

针对时空行为轨迹大数据的序列性、时空交互性、多维度性等复杂特性,构建结合时间地理学与复杂网络的分析框架,建立时空行为路径与时空行为网络之间的转换关系,利用复杂网络社群发现算法对时空行为轨迹进行社群聚类、模式挖掘与可视化。基于北京郊区居民一周内活动出行GPS轨迹数据的案例分析发现:(1)复杂网络分析方法可以有效挖掘具有相似行为的群体特征和识别出典型的行为模式。(2)可以灵活处理多元异构与多维度的行为轨迹大数据以及满足不同叙事、不同空间相互作用、不同时序的应用需求。(3)北京郊区被调查居民的行为模式存在日间差异与空间分异。     

Visualising 30 Years of Population Density Change in Australia’s Major Capital Cities

ABSTRACT

This study uses a novel spatial approach to compare population density change across cities and over time. It examines spatio-temporal change in Australia’s five most populated capital cities from 1981 to 2011, and documents the established and emerging patterns of population distribution. The settlement patterns of Australian cities have changed substantially in the last 30 years. From the doughnut cities of the 1980s, programs of consolidation, renewal and densification have changed and concentrated population in our cities. Australian cities in the 1980s were characterised by sparsely populated, low density centres with growth concentrated to the suburban fringes. ‘Smart Growth’ and the ‘New Urbanism’ movements in the 1990s advocated higher dwelling density living and the inner cities re-emerged, inner areas were redeveloped, and the population distribution shifted towards increased inner city population densities. Policies aimed at re-populating the inner city dominated and the resultant changes are now visible in Australia’s five most populated capital cities. While this pattern has been reported in a number of studies, questions remain regarding the extent of these changes and how to analyse and visualise them across urban space. This paper reports on a spatial method which addresses the limitations of changing statistical boundaries to identify the changing patterns in Australian cities over time and space.     

基于众源地理数据的个性化路线服务框架及应用实验

随着移动通信与LBS的蓬勃发展,能够描述个体行为的众源时空大数据大量涌现,为感知群体时空行为模式与探究个性化路线提供了新视角。该文将众源时空信息与出行者的个人意愿映射到实际路网空间,融合大众偏好和定制趋势,构建包含主题序列生成、POI推荐、历史路线推荐的局部路网模型,进而实现一种利用众源时空数据改进的HMM路线规划方法,为用户提供合适且个性化的出行方案;以长沙市岳麓区为研究案例,利用真实路网数据与相关兴趣点作为实验数据,基于该方法可在短时间内提供满足用户需求的不同月份的最优路线。     

A Regional Scale Investigation of Climatological Tropical Convection and Precipitation in the Amazon Basin

This study constructs a regional scale climatology of tropical convection and precipitation from more than 15 years of monthly outgoing longwave radiation (OLR) and precipitation data on 2.5°× 2.5° latitude-longitude grid to examine the spatial and temporal patterns and variability of convection and precipitation in the Amazon Basin. A linear regression analysis also detects if any trends exist in the two datasets. The region of study extends from 15°N to 25°S and 30° to 80°W that encompass the Amazon Basin and surrounding fringe areas for the period from January 1979 through December 1995 for the OLR data and up to 1996 for the precipitation dataset. The basin-average mean monthly and seasonal climatology serve as a ‘baseline’ reference for comparison with the full time series of basin-average monthly OLR and precipitation to illustrate the interannual variability and identify anomalous periods of wet and dry conditions. A linear trend analysis of OLR data found small negative values across the Amazon Basin indicating a slight increase in convective activity over the period of study. The analysis of the precipitation time series, however, shows no coincidental increase in precipitation as would be expected with an increase in convective activity. Portions of Rondônia and Mato Grosso, areas that have undergone extensive deforestation, illustrate no trend in precipitation as suggested by GCM simulation results. The only area featuring any large change in precipitation occurs in a small area in the northwestern region of South America where a large positive trend in precipitation exists.     

Exploiting autoregressive properties to develop prospective urban arson forecasts by target

Municipal fire departments responded to approximately 53,000 intentionally-set fires annually from 2003 to 2007, according to National Fire Protection Association figures. A disproportionate amount of these fires occur in spatio-temporal clusters, making them predictable and, perhaps, preventable. The objective of this research is to evaluate how the aggregation of data across space and target types (residential, non-residential, vehicle, outdoor and other) affects daily arson forecast accuracy for several target types of arson, and the ability to leverage information quantifying the autoregressive nature of intentional firesetting. To do this, we estimate, for the city of Detroit, Michigan, competing statistical models that differ in their ability to recognize potential temporal autoregressivity in the daily count of arson fires. Spatial units vary from Census tracts, police precincts, to citywide. We find that (1) the out-of-sample performance of prospective hotspot models for arson cannot usefully exploit the autoregressive properties of arson at fine spatial scales, even though autoregression is significant in-sample, hinting at a possible bias-variance tradeoff; (2) aggregation of arson across reported targets can yield a model that differs from by-target models; (3) spatial aggregation of data tends to increase forecast accuracy of arson due partly to the ability to account for temporally dynamic firesetting; and (4) arson forecast models that recognize temporal autoregression can be used to forecast daily arson fire activity at the Citywide scale in Detroit. These results suggest a tradeoff between the collection of high resolution spatial data and the use of more sophisticated modeling techniques that explicitly account for temporal correlation.     

中国东部沿海城市旅游发展的时空演变

城市旅游时空演变是旅游地理学研究的热点领域。以中国东部沿海城市为研究区,分析城市旅游发展的时空演变格局。首先分析沿海城市旅游发展强度的时空演变,进而采用DEA数据包络分析法计算沿海城市旅游发展效率的时空演变。研究表明：① 在旅游发展强度方面,东部沿海城市之间的旅游发展强度差异较大,长三角、珠三角以及环渤海的辽东半岛和山东半岛地区的旅游发展强度较强,海峡西岸经济区和环北部湾地区的旅游发展强度较弱。② 10年间在旅游发展综合效率方面基本维持不变,而旅游发展纯技术效率显著增强,旅游发展的规模效率则显著减弱。说明沿海城市的旅游发展已经逐渐由规模效率向技术效率转变。③ 综合旅游发展强度和旅游发展效率两个方面看,将东部沿海城市旅游发展类型分为“高—有效型”、“低—有效型”、“高—无效型”和“低—无效型”四种类型。其中“高—有效型”城市旅游发展较为成熟,“低—有效型”和“高—无效型”城市旅游发展一般,“低—无效”城市旅游发展相对较差。整体来看,珠三角和海峡西岸地区旅游发展强度和旅游发展效率均出现一定程度下滑,长三角、环渤海和北部湾地区的旅游发展强度和旅游发展效率均呈现不同程度增加。     

An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data

Abstract

Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation.

In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the ‘snapshot’ model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.