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.     

Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data

Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions.     

Local interpolation using a distributed parallel supercomputer

Abstract

Large spatial interpolation problems present significant computational challenges even for the fastest workstations. In this paper we demonstrate how parallel processing can be used to reduce computation times to levels that are suitable for interactive interpolation analyses of large spatial databases. Though the approach developed in this paper can be used with a wide variety of interpolation algorithms, we specifically contrast the results obtained from a global ‘brute force’ inverse–distance weighted interpolation algorithm with those obtained using a much more efficient local approach. The parallel versions of both implementations are superior to their sequential counterparts. However, the local version of the parallel algorithm provides the best overall performance.     

Evolution of clusters in dynamic point patterns: with a case study of Ants' simulation

Recent developments in sensing and tracking technologies have enabled large geographical databases to be established that represent spatial dynamics of ‘behavioral entities’. Within this type of dynamics there are several levels and modes of organization that need to be revealed. Clusters are high‐level groupings of entities, where change in their location and form, including split and merge events, represents self‐organization and functioning patterns. Such information may contribute for better understanding spatially complex dynamic patterns. The main objective of this article is to develop an adaptable methodology that facilitates exploration of spatial order and processes in point pattern dynamics. The approach presented here utilizes data‐clustering at each snapshot of the moving pattern, and then involves pairwise linking between the clusters identified at each snapshot and those identified in the following snapshot. Such linking is based on a new methodology that defines well globally optimized solutions for numerous possible linking combinations based on Linear Programming. A preliminary assessment of the approach was conducted with an existing Ants' simulation tool, capable of creating data sets covering in detail a substantial portion of the nest's life cycle.     

中国电子信息产业创新的集群网络模式与演化路径

流空间背景下,多空间尺度网络耦合对创新至关重要已达成学界共识。综合运用区位熵与社会网络分析,识别32个电子信息产业集群,进而基于1985—2015年合作申请发明专利数据,运用卡方检验和Infomap社区发现算法,借助Pajek和ArcGIS等软件,揭示中国电子信息产业创新合作空间模式,探讨创新网络空间组织演化规律。结果表明：① 中国电子信息产业创新合作呈现集群互联的模式,集群区域的创新主体更倾向于同本地和其他集群区域的创新主体合作,这与非集群区域的创新主体结网方式明显不同,印证了关系经济地理学者提出的全球集群网络论断。② 集群创新网络社区的空间组织从位于集群地理边界内,本地化特征显著,演化为超越集群地理边界,呈现等级差异明显的核心-边缘层级式特征,反映出地理邻近对集群主体创新结网影响程度的动态变化。     

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.     

Trapped in Jobless Household Areas: the spatio-temporal dynamics of children in jobless households in metropolitan Australia

Child disadvantage is an important issue in Australia today and is one of the key policy target areas of the Federal government. Child disadvantage not only affects child wellbeing in the present time but can also inhibit their future achievements. As well as individual- and family-level effects, there is growing evidence that child wellbeing and achievement is highly affected by the environment in which children grow up, and what resources and role models are available to them in their surrounding areas. In this paper, we examine the spatial clustering of children in jobless households through the computation of the Global and Local Moran's statistics, for the Sydney and Melbourne metropolitan regions, and observe how these clusters have changed between the 2001 and 2006 census years. We find that there are significant spatial clusters of children in jobless households in both periods, suggesting that ‘place’ is a driver of this particular phenomenon. Further, we observe an expansion of ‘hot spots’, in both Sydney and Melbourne, suggesting that areas with high proportions of children in jobless households have both persisted and increased despite the sizeable economic growth in Australia over the same period.     

A partition‐based serial algorithm for generating viewshed on massive DEMs

As increasingly large‐scale and higher‐resolution terrain data have become available, for example air‐form and space‐borne sensors, the volume of these datasets reveals scalability problems with existing GIS algorithms. To address this problem, a kind of serial algorithm was developed to generate viewshed on large grid‐based digital elevation models (DEMs). We first divided the whole DEM into rectangular blocks in row and column directions (called block partitioning), then processed these blocks with four axes followed by four sectors sequentially. When processing the particular block, we adopted the ‘reference plane’ algorithm to calculate the visibility of the target point on the block, and adjusted the calculation sequence according to the different spatial relationships between the block and the viewpoint since the viewpoint is not always inside the DEM. By adopting the ‘Reference Plane’ algorithm and using a block partitioning method to segment and load the DEM dynamically, it is possible to generate viewshed efficiently in PC‐based environments. Experiments showed that the divided block should be dynamically loaded whole into computer main memory when partitioning, and the suggested approach retains the accuracy of the reference plane algorithm and has near linear compute complexity.     

Knowing into oblivion: Clearing wastelands and imagining emptiness in Vietnamese New Urban Zones

Lands cleared for Vietnamese New Urban Zones are commonly described as ‘wastelands’ (??t hoang) in need of development. But clearing these lands in Vietnam's densely populated urban areas requires evicting large numbers of urban residents. How is it possible to render populated spaces as empty wastelands? This paper juxtaposes new ethnographic research in Ho Chi Minh City against historical scholarship on Vietnamese concepts of ‘clearing the wasteland’ and colonial civilizing projects of mise en valeur. In doing so, the article describes these concepts as parallel legitimizing strategies that effectively enable developers and planners to render previously occupied and utilized lands as empty, uninhabited wastelands. The article shows that the same narrative structure used in these earlier contexts is used to justify current urban development projects.     

Spatial aspects of MRSA epidemiology: a case study using stochastic simulation,kernel estimation and SaTScan

The identification of disease clusters in space or space–time is of vital importance for public health policy and action. In the case of methicillin‐resistant Staphylococcus aureus (MRSA), it is particularly important to distinguish between community and health care‐associated infections, and to identify reservoirs of infection. 832 cases of MRSA in the West Midlands (UK) were tested for clustering and evidence of community transmission, after being geo‐located to the centroids of UK unit postcodes (postal areas roughly equivalent to Zip+4 zip code areas). An age‐stratified analysis was also carried out at the coarser spatial resolution of UK Census Output Areas. Stochastic simulation and kernel density estimation were combined to identify significant local clusters of MRSA (p<0.025), which were supported by SaTScan spatial and spatio‐temporal scan. In order to investigate local sampling effort, a spatial ‘random labelling’ approach was used, with MRSA as cases and MSSA (methicillin‐sensitive S. aureus) as controls. Heavy sampling in general was a response to MRSA outbreaks, which in turn appeared to be associated with medical care environments. The significance of clusters identified by kernel estimation was independently supported by information on the locations and client groups of nursing homes, and by preliminary molecular typing of isolates. In the absence of occupational/lifestyle data on patients, the assumption was made that an individual's location and consequent risk is adequately represented by their residential postcode. The problems of this assumption are discussed, with recommendations for future data collection.     

Deferred decentralized movement pattern mining for geosensor networks

This article presents an algorithm for decentralized (in-network) data mining of the movement pattern flock among mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to ‘graze’ over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge.     

Forthcoming events

Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail.     

空间认同:城市空间研究转向中的知识前沿、趋势与启发

中国城市化进程的快速发展,促使人们不断解构、调整和重构对城市空间的认同,这是城市化发展中不容忽视的新问题。以Web of Science（WoS）为数据源,对国外城市空间认同研究进行了分析和知识再现。研究发现：① 城市空间认同是人们对城市发展中社会经济认同、文化认同、集体认同、身份认同与情感认同的集合。作为重要的国民意识,城市空间认同主题是国外学者关注的重要领域,对该领域研究的本质上是对城市空间实践中人文主义空间诉求的知识表征;② 在2008~2017年的国外城市空间认同研究中,美国、英国、澳大利亚等国家具有明显优势,国际合作研究网络主要在美洲-欧洲、欧洲-澳洲,以及澳洲-欧美之间;国外城市空间认同研究高被引文献注重对“空间多中心性”“地方与连续性”“城市社区”“城市公民身份”“空间绅士化”“地方主义”等方面的讨论。③ 未来研究更加倾向在“认同”“空间”“城市”“地理”“政治”“地方”,以及“社区”等新主题方面。相比较而言,国内对城市空间认同的研究较为欠缺,随着中国城市空间实践不断推向纵深阶段,需要强化城市空间认同研究的“理论自觉”。     

Planning with complexity—how do we deal with stakeholder and spatial heterogeneity in land use planning?

Geografisk Tidsskrift, Danish Journal of Geography 105(2):23–37, 2005

As a reaction to the 'top-down 'approach to rural development during the 1970's and 1980's, and in line with the increased focus on ‘putting farmers first’ (bottom-up) through local participation, new and integrated approaches to natural resource management (NRM) have been launched to generate sustainable development. This paradigmatic change has greatly influenced land use planning. Some of the implications involved in this change are discussed in this paper and based on empirical data from Tanzania and Botswana. In this paper we will discuss three critical issues related to land use planning for sustainable NRM: the complexity of livelihood strategies at the local level, the role of different stakeholder interests and the importance of spatial scale considerations. The discussion emphasizes the importance of understanding that the different and often erratic resource endowment of the rural households influences the strategies these households follow in order to meet their overall needs, rather than ‘simply’ maximizing agricultural yields. Furthermore, the importance of spatial scale, in relation to both different stakeholder interests and their needs will be discussed. The paper highlights the need to take community heterogeneity into consideration in land use planning if sustainable NRM is to be achieved in the future.     

Regularities in Spatial Information Processing: Implications for Modeling Destination Choice

This paper investigates the determinants of spatial knowledge and how our knowledge of space varies according to geographic location. By using data on U.S. city names recalled at 22 test locations, a multivariate model of the information surface specific to each test location is calibrated. This model links the probability of a city being recalled from memory to its distance from the test site, its population size, its location with respect to other cities, and whether or not it is a state capital. The paper then suggests that these recall data provide information on spatial knowledge surfaces from which large-scale spatial choices, such as migration destinations, are made. Results from the analysis lend further evidence to the idea that spatial knowledge is stored and processed hierarchically and that individuals underrepresent information in large clusters. Consequently, the results have important implications for modeling any spatial behavior based on individuals' spatial information surfaces. In particular, the results cast further doubt on the validity of traditional large-scale spatial choice frameworks and lend support to the competing destinations hypothesis.     

Regularities in Spatial Information Processing: Implications for Modeling Destination Choice

This paper investigates the determinants of spatial knowledge and how our knowledge of space varies according to geographic location. By using data on U.S. city names recalled at 22 test locations, a multivariate model of the information surface specific to each test location is calibrated. This model links the probability of a city being recalled from memory to its distance from the test site, its population size, its location with respect to other cities, and whether or not it is a state capital. The paper then suggests that these recall data provide information on spatial knowledge surfaces from which large-scale spatial choices, such as migration destinations, are made. Results from the analysis lend further evidence to the idea that spatial knowledge is stored and processed hierarchically and that individuals underrepresent information in large clusters. Consequently, the results have important implications for modeling any spatial behavior based on individuals' spatial information surfaces. In particular, the results cast further doubt on the validity of traditional large-scale spatial choice frameworks and lend support to the competing destinations hypothesis.     

Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

An origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit ‘residential-area to work-area’ patterns.     

Cross‐border rubber cultivation between China and Laos: Regionalization by Akha and Tai rubber farmers

This paper is a multi‐sited ethnography of cross‐border rubber cultivation between China and Laos. Smallholder minority rubber farmers from Xishuangbanna (China) have forged successful informal share‐cropping arrangements to grow rubber trees on the land of relatives and friends in neighbouring Laos. By becoming rich and entrepreneurial rural citizens, Akha and Tai farmers have also, in their own eyes, raised their own ‘quality’ (suzhi) and see themselves as ‘modern’. By examining various meanings of ‘modern’ in China, and contrasting the rubber farmers' experience with Jacob Eyferth's notion of rural ‘deskilling’, this paper shows how through learning to plant, cultivate and tap rubber, these farmers have taken on the discipline and technical knowledge of ‘modern’ workers and become ‘skilled’. By rising in ‘quality’, minority farmers on China's periphery challenge the entrenched binaries of urban/rural, modern/backward, prosperous/poor and Han/minority nationality. Xishuangbanna minority farmers acknowledge that they are also ‘backward’ in the Chinese social hierarchy, but their extension of rubber cultivation to kin and others in Laos has confirmed their modernity as dispensers of development, technical know‐how and ‘superior’ Chinese culture to Lao farmers who are ‘backward and poor’. In contrast to large state rubber farms that have failed to establish rubber plantations in northern Laos, minority farmers have created regionalization.     

基于R树的分布式并行空间索引机制研究

为提高分布式并行计算环境下海量空间数据管理与并行化处理的效率,基于并行空间索引机制的研究,设计一种多层并行R树空间索引结构。该索引结构以高效率的并行空间数据划分策略为基础,以经典的并行计算方法论为依据,使其结构设计在保证能够获得较好的负载平衡性能的前提下,更适合于海量空间数据的并行化处理。以空间范围查询并行处理的系统响应时间为性能评估指标,通过实验证明并行空间索引结构具有设计合理、性能高效的特点。     

Thematic land-cover map assimilation and synthesis: the case of locating potential bioenergy feedstock in eastern Ontario,Canada

Insufficient spatial coverage of existing land-cover data is a common limitation to timely and effective spatial analysis. Achieving spatial completeness of land-cover data is the most challenging for large study areas which straddle ecological or administrative boundaries, and where individuals and agencies lack access to, and the means to process, raw data from which to derive spatially complete land-cover maps. In many cases, various sources of secondary data are available, so that land-cover map assimilation and synthesis can resolve this research problem. The following paper develops a reliable and repeatable framework for assimilating and synthesizing pre-classified data sets. Assimilation is achieved through data reformatting and map legend reconciliation in the context of a specific application. Individual maps are assessed for accuracy at various geographic scales and levels of thematic precision, with an emphasis on the ‘area of overlap’, in order to extract information that guides the synthesis process. The quality of the synthesized land-cover data set is evaluated using advanced accuracy assessment methods, including a measure describing the ‘magnitude of disagreement’. This method is applied to derive a seamless thematic map of the land cover of eastern Ontario from two disparate map series. The importance of assessing data quality throughout the process using multiple reference data sets is highlighted, and limitations of the method are discussed.