Discovering relative motion patterns in groups of moving point objects

Technological advances in position‐aware devices are leading to a wealth of data documenting motion. The integration of spatio‐temporal data‐mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data‐mining algorithms has been implemented and tested with two case studies: tracked soccer players and data points representing political entities moving in an abstract ideological space. In both case studies, a set of non‐trivial and meaningful motion patterns could be identified, for instance highlighting the characteristic ‘offside trap’ behaviour in the first case and identifying trendsetting districts anticipating a political transformation in the latter case.     

A data‐mining approach for assessing consistency between multiple representations in spatial databases

When different spatial databases are combined, an important issue is the identification of inconsistencies between data. Quite often, representations of the same geographical entities in databases are different and reflect different points of view. In order to fully take advantage of these differences when object instances are associated, a key issue is to determine whether the differences are normal, i.e. explained by the database specifications, or if they are due to erroneous or outdated data in one database. In this paper, we propose a knowledge‐based approach to partially automate the consistency assessment between multiple representations of data. The inconsistency detection is viewed as a knowledge‐acquisition problem, the source of knowledge being the data. The consistency assessment is carried out by applying a proposed method called MECO. This method is itself parameterized by some domain knowledge obtained from a second method called MACO. MACO supports two approaches (direct or indirect) to perform the knowledge acquisition using data‐mining techniques. In particular, a supervised learning approach is defined to automate the knowledge acquisition so as to drastically reduce the human‐domain expert's work. Thanks to this approach, the knowledge‐acquisition process is sped up and less expert‐dependent. Training examples are obtained automatically upon completion of the spatial data matching. Knowledge extraction from data following this bottom‐up approach is particularly useful, since the database specifications are generally complex, difficult to analyse, and manually encoded. Such a data‐driven process also sheds some light on the gap between textual specifications and those actually used to produce the data. The methodology is illustrated and experimentally validated by comparing geometrical representations and attribute values of different vector spatial databases. The advantages and limits of such partially automatic approaches are discussed, and some future works are suggested.     

Environmental zonation across the Australian arid region based on long-term vegetation dynamics

Zonation of landscapes is generally based on broad scale biophysical data, field surveys, imagery and expert knowledge. Such zonation represents a static view of the environment and does not reflect dynamics and function. Arid environments are however often highly dynamic, and spatial and temporal patterns may be expressed over long periods of time. These dynamics need to be understood for management. Our aim is to understand the dynamics and functional response of vegetation in the Australian arid zone, and use this to inform and potentially improve the currently employed stratification. Principal component analysis of 25 years of satellite imagery identified underlying factors influencing patterns of arid vegetation growth, and regions of similar long-term response. Dominant factors of variation were identified as the spatial distribution of total vegetation growth, seasonality of growth, magnitude of seasonal variability in growth, and regularity of variation in growth. Additional variation resulted from episodic vegetation growth of limited spatial extent and duration. Classes expressing these functional components were compared with the existing biogeographical regions, revealing agreement in some instances, and in other cases adding information previously not available. The study demonstrates a new approach to Australian landscape zonation that has potential for much wider application.     

Scale variance analysis coupled with Moran's I scalogram to identify hierarchy and characteristic scale

Scale variance is highly sensitive to multi-scale patterns of variables, which is advantageous in identifying spatial hierarchy and characteristic scale(s). However, the significance of peak(s) in scale variance cannot be statistically tested, and different spatial patterns may be obtained when different zoning systems are used to calculate scale variance. To address these two problems, this study compared the scale levels with peaks in scale variance and the scale levels at which there were breaks in the nature of spatial autocorrelation as identified by shifts in Moran's I scalogram. The estimates for three simulated landscapes showed that accordance between scale levels identified employing the two methods can be used to evaluate the significance of peaks in scale variance and choose a more reasonable zoning system. The approach of scale variance analysis coupled with Moran's I scalogram was also applied to the Xilin River Basin of Inner Mongolia, China. The most vital characteristic scale (64 × 32 km) identified for the growing-season net ecosystem productivity (NEP) of the basin was validated by other spatial pattern analysis methods such as semi-variogram, Moran's I correlogram, and wavelet variance analyses, and the directionality of the chosen zoning systems was found to be similar to the orientation of actual dominant vegetation type patches. The results demonstrate that Moran's I scalogram can be used to improve the interpretation of the results of scale variance analysis and increase the reliability of scale variance analysis for landscapes having a repetitive patch pattern or gradient variation and that the proposed approach is suitable for identifying the hierarchy and the characteristic scales of patterns or processes. In summary, this study used a simple approach to solve two problems in scale variance analysis, thereby improving the methodology and enhancing the theoretical basis of multi-scale analysis.     

Monitoring qualitative spatiotemporal change for geosensor networks

Recent technological advances in geosensor networks demand new models of distributed computation with dynamic spatial information. This paper presents a computational model of spatial change in dynamic regions (such as may be derived from discretizations of continuous fields) founded on embeddings of graphs in orientable surfaces. Continuous change, connectedness and regularity of dynamic regions are defined and local transition rules are used to constrain region evolution and enable more efficient inference of a region's state. The model provides a framework for the detection of global high‐level events based on local low‐level ‘snapshot’ spatiotemporal data. The approach has particular relevance to environmental monitoring with geosensor networks, where technological constraints make the detection of global behaviour from local conditions highly advantageous.     

A collective human mobility analysis method based on data usage detail records

Human mobility patterns have been widely investigated due to their application in a wide variety of fields, for example urban planning and epidemiology. Many studies have introduced spatial networks into human mobility analyses at the collective level. However, these studies merely analyzed spatial network structure, and the underlying collective mobility patterns were not further discussed. In this paper, we propose a collective mobility discovery method based on community differences (CMDCD). We constructed spatial networks where nodes represent geographical entities and edge weights denote collective mobility intensity between geographical entities. The differences between communities detected from the networks constructed in different periods were then identified. Since collective spatial movement has a large influence on network structure, we can discover groups with different mobility patterns based on community differences. By applying the method to data usage detail records collected from the cellular networks in a city of China, we analyzed different collective mobility patterns between the Spring Festival vacation and workdays. The experimental results show that our method can solve these two problems of identifying community differences and discovering users with different mobility patterns simultaneously. Moreover, the CMDCD method is an integrated approach to discover groups whose mobility patterns have changed in different periods at the large spatial scale and the small spatial scale. The discovered collective mobility patterns can be used to guide urban planning, traffic forecasting, urban resource allocation, providing new insights into human mobility patterns and spatial interaction analyses.     

Inferring spatial interaction patterns from sequential snapshots of spatial distributions

Spatial interactions underlying consecutive sequential snapshots of spatial distributions, such as the migration flows underlying temporal population snapshots, can reflect the details of spatial evolution processes. In the era of big data, we have access to individual-level data, but the acquisition of high-quality spatial interaction data remains a challenging problem. Most research has been focused on distributions of movable objects or the modelling of spatial interaction patterns, with few attempts to identify hidden spatial interaction patterns from temporal transitions of spatial distributions. In this article, we introduced an approach to infer spatial interaction patterns from sequential snapshots of spatial population distributions by incorporating linear programming and the spatial constraints of human movement. Experiments using synthetic data were conducted using four simple scenarios to explore the characteristics of our method. The proposed method was used to extract interurban flows of migrants during the Chinese Spring Festival in 2016. Our research demonstrated the feasibility of using discrete multi-temporal snapshots of population distributions in space to infer spatial interaction patterns and offered a general analytical framework from snapshot data to spatial interaction patterns.     

Interactive discovery of sequential patterns in time series of wind data

Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth’s surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time.     

Quantifying deforestation and secondary forest determinants for different spatial extents in an Amazonian colonization frontier (Rondonia)

Spatial patterns of deforested areas and secondary forest are analyzed in terms of the spatial variation in location factors at different spatial extents. The spatial extents considered are old and new agrarian colonization projects and the administrative units of two different municipalities in Rondonia: Vale do Anari and Machadinho d'Oeste. A grid database was constructed including land cover and potential location factors based on biophysical, accessibility, socioeconomic and policy data. Results of the spatial analyses confirmed the hypothesis that different extents yield different relationships between land use/cover patterns and their location factors, particularly between old and new agrarian colonization projects. It emphasizes that current patterns of forest, secondary forest and pasture/agriculture can only be understood with a combination of policy, accessibility, biophysical and socioeconomic factors while accounting for the historical pathways of change. Because we are dealing with different trajectories of land use/cover change, static analysis of the spatial pattern without acknowledging these trajectories will lead to erroneous interpretations of the current and future land use/cover dynamics.     

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.     

基于PRISM和泰森多边形的地形要素日降水量空间插值研究

以黑河流域河西走廊中段地区为例,利用该研究区年、月降水与地形间较强的相关性特点,在PRISM方法的基础上对该地区日降水量进行了空间插值计算。文章提出了以月降水量的PRISM空间插值结果为该月逐日降水空间分布的参考本底,利用泰森多边形方法确定空间日降水的概率,从而实现黑河流域河西走廊中段地区日降水的空间制图方法,并对该方法得到的日降水时空数据集进行了误差分析和评估。分析结果表明,这一方法简单可靠,满足分布式水文模型或相关陆表过程分布式模拟对分布式日降水数据时空精度的要求。     

基于公路客流的中国城市网络结构与空间组织模式（英文）

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.     

Population landscape: a geometric approach to studying spatial patterns of the US urban hierarchy

We present a geometric and graphic approach to studying spatial patterns of urban hierarchy in the US. The multiplicatively weighted Voronoi diagram is found to be effective for visualizing theoretical regions delineated by socio‐economic variables. The population landscape of the continental US demonstrates overall and stepwise patterns reflecting population, neighborhood and distance, with overwhelming influence from huge metropolitan areas. Stepwise exploration and cluster analysis of the spatial pattern reveal an urban hierarchy. Attributes and arrangement are the two important factors of urban hierarchy, with attribute having a stronger local influence and arrangement having a stronger global influence. The study also presents a variation of Zipf's law to visualize the rank‐size distribution from tabular and statistical space to map space.     

Geomorphological limits to self-organization of alpine forest-tundra ecotone vegetation

Feedback in the establishment of vegetation has been shown to produce spatial patterns that differ from the geomorphological basis for resources. The dynamics of these spatial patterns have been characterized as self-organization because local processes produce them at landscape scales. Geomorphic patterns could, however, enhance or disrupt the processes that lead to patterns and the interpretation of self-organization. A simulation model that showed such indication of self-organization at alpine forest-tundra ecotones is modified to incorporate a geomorphic feature commonly seen in this environment – solifluction steps – as an exogenous condition in the model. Analyses linking spatial patterns and rates of advance of vegetation indicate that such geomorphic patterns do not alter the dynamics of vegetation until the size of the patterns is about double that of the dimension within which endogenous dynamics operate. The sizes of some geomorphic patterns incorporated in the model are probably larger than any realistic solifluction feature at such ecotones in western North America.     

沈阳市部门企业地理集聚与城市结构

基于产业空间聚集分布情况探寻城市结构特征,是当前大都市区实证研究中的聚焦点所在,但由于方法论的限制而无法真正揭示产业地理集聚之间的内在关联性。基于已有研究基础,试图通过完善潜力模型、设置距离参数、结合主成分分析法实现对产业地理集聚测度方法论的完善与发展,并选取极具代表性大都市区核心城市——沈阳市为样本单元,以2008年的经济普查部门企业数据开展实证检验。结果表明：沈阳市部门企业之间除了交通运输、仓储和邮政中心产业属于地方化经济外,其余的均为企业关联;水利、环境和公共设施管理业产业依附于制造业呈临街抑或隔街集聚,而与公共管理和组织产业之间同街道集聚;支配主角之间,存在中心CBD主宰制造业的布局,而制造业又在很大程度上影响着交通运输、仓储和邮政中心的布局;企业地理集聚形成的城市结构依然是一个明显的“单中心圈层”结构,没有表现出“去中心化”抑或多极化或分散化演变趋势。研究成果与现实情况基本吻合,侧面说明该模式对揭示城市产业地理集聚模式以及由此形成的城市结构特征具有一定的解释力。     

A bottom‐up approach to discover transition rules of cellular automata using ant intelligence

This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

Quality assessment of linear data

This paper presents methods to evaluate the geometric quality of spatial data. Firstly, a point‐based method is presented, adapting conventional assessment methods whereby common points between datasets are compared. In our approach, initial matches are established automatically and refined further through interactive editing. Second, a line‐based method which uses correspondences between line segments is proposed. Here, the geometry of line segments in vector is transformed into a set of rasterized values so that their combination at each pixel can restore their original vector geometry. Matching is performed on rasterized line segments and their matching lengths and displacements are measured. Experimental results show that the line‐based approach proposed is efficient to evaluate the geometric quality of spatial data without requirements of topological relationships among line features.     

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。