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

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

空间数据分析与空间模型

虽然GIS已在各个领域得到广泛应用,但它相对较弱的空间分析功能正在限制其应用的广度和深度。文中系统论述了空间数据的基本特征,GIS中空间数据分析的基本需求,空间统计分析,空间数据探索分析以及空间模型,探讨了GIS与空间模型结合的必要性,结合途径以及结合中的问题与对策。     

探索性空间分析及其与GIS集成模式探讨

探索性空间分析基于让数据说话的理念,可以避免野值或非典型观测值的误导。在对探索性空间分析的基本原理和概念界定的基础上,探讨交互式和动态空间数据分析、地学可视化及可视化空间分布、确认性空间分析、空间数据挖掘等探索性空间分析的主要技术。由于统计分析软件和GIS的数据格式差异很大,直接将二者简单集成存在一定困难,因此切实可行的集成方式是采用对象连接和嵌入(OLE)技术,分别调用统计分析软件的探索性分析功能(或者函数)以及GIS的地图显示和空间分析功能,并进行必要开发,实现二者的集成。最后对探索性空间分析的发展方向进行展望。     

Identification of GIS Attribute Error Using Exploratory Data Analysis*

Exploratory data analysis (EDA), a data-centered, inductive approach to statistical analysis, provides effective tools for assessing the quality and integrity of GIS attribute data. This research presents selected examples demonstrating EDA distribution analyses, correlational statistics, and proximity analysis. An integrated modular software prototype to operationalize these techniques combines the mapping and display capabilities of ArcView with the statistical functionality of STATA in a MS-Windows multi-tasking, multiple-windowed environment.     

A Method for the Exploratory Analysis of Airline Networks

The airline industry in the United States has experienced significant changes in the spatial configuration of networks since the Airline Deregulation Act of 1978. Attempts have been made to study the structure of the hub-and-spoke network in the airline industry, but no efficient methodology has been available. This paper describes an exploratory approach to analyzing the spatial configuration of airline networks. Flight frequencies and numbers of passengers from published schedules of six U.S. domestic airlines (American, Continental, Delta, Northwest, United, and USAir) are used in this study. The analytic components in the exploratory system include the examination of network connectivity and network autocorrelation in a dynamic mapping environment. The results of the analyses show both the overall spatial patterns of airline networks and the hierarchical hubbing structures.     

Modal Choice in the Pittsburgh Metropolitan Statistical Area: An Exploratory Data Analysis

Journey-to-work mode choice is intertwined with ideological and pragmatic issues. This article reexamines such issues using socioeconomic data from the decennial census and American Community Survey (ACS). It investigates the structure of variables with exploratory data analysis (EDA) because this technique advises the formation of hypotheses and the specification of cause and effect. Traditional EDA reveals the nonnormal structure of raw data, mapping illustrates associations between transit and income, and both methods suggest the presence of a transit-by-choice population among affluent metropolitan residents. The results yield three hypotheses concerning propensity to use transit that have previously received little attention.     

Variability of diatom-inferred phosphorus profiles in a small lake basin and its implications for histories of lake eutrophication

Diatom analyses were undertaken of sediment cores covering a range of water depths in a small eutrophic lake (Lough Augher, Co. Tyrone, N. Ireland). The significance of between-core variability in diatom relative frequency stratigraphy was assessed by Canonical Correspondence Analysis (CCA) where the ordination axes were constrained to external environmental variables (sediment depth, core location coordinates, water depth, effective fetch, distance-from-shore and distance-from-inflow). After the removal of the effect of sediment age by partialling it out, the resultant first two axes from the partial-CCA were significantly correlated with water depth and distance-from-shore, indicating non-uniform diatom stratigraphies across the lake. Despite this variability, all cores show the same succession of species and, therefore, record the eutrophication of the lake. Diatom-inferred total phosphorus (DI-TP) was inferred for six cores using weighted averaging regression and calibration. Apart from considerable differences of DI-TP in surficial sediment samples, there was good between-core repeatability of DI-TP profiles. These data support the use of DI-TP for establishing background nutrient concentrations for lakes, and associated implications for lake restoration schemes using single cores. Comparisons of DI-TP profiles and total diatom accumulation rate data for the individual cores indicate that diatom production peaked prior to the maximum TP concentrations in the lake.     

基于ESDA的西北地区公路网分布空间差异分析

利用探索式空间数据分析（ESDA）方法分析了2004-2010年西北地区市域尺度的公路网空间分布差异及其变化特征,并结合定性分析和空间计量模型探讨了公路分布差异的驱动机制。结果表明:（1）西北地区公路网空间分布具有显著的全局正相关关系,存在明显的集聚特征,公路网空间差异趋势愈加明显,公路网集聚模式发生明显变化的区域集中在陕甘宁交界地带,“陕南-关中-陇东-青东”地区形成公路网“高-高”集聚类型分布的连绵区域,“低-低”集聚类型区域分布在河西走廊、新疆、青海地区;（2）公路网密度水平高于全国平均水平的区域规模数量呈增长态势,其中“西安-兰州-西宁”地区的公路网密度水平高于全国平均水平,该地区已形成具备向外扩张能力的交通带;（3）自然环境状况是约束公路交通基础设施建设运营的基本条件,而区域经济的非均衡发展格局及其空间溢出效应是形成公路网分布差异的重要驱动力。     

Assessment of freshwater diatoms as quantitative indicators of past climatic change in the Yukon and Northwest Territories,Canada

We identified, enumerated, and interpreted the diatom assemblages preserved in the surface sediments of 59 lakes located between Whitehorse in the Yukon and Tuktoyaktuk in the Northwest Territories (Canada). The lakes are distributed along a latitudinal gradient that includes several ecoclimatic zones. It also spans large gradients in limnological variables. Thus, the study lakes are ideal for environmental calibration of modern diatom assemblages. Canonical correspondence analysis, with forward selection and Monte Carlo permutation tests, showed that maximum lake depth and summer surface-water temperature were the two environmental variables that accounted for most of the variance in the diatom data. The concentrations of sodium and calcium were also important explanatory variables. Using weighted-averaging regression and calibration techniques, we developed a predictive statistical model to infer lake surface-water temperature, and we evaluated the feasibility of using diatoms as paleoclimate proxies. This model may be used to derive paleotemperature inferences from fossil diatom assemblages at appropriate sites in the western Canadian Arctic.     

基于ESDA方法的黄土高原地区经济发展格局及其演化特征分析

探索式空间数据分析(ESDA)方法可以很好地揭示区域经济增长特征及其与空间环境的关系,是深入了解和把握区域经济的空间分布和演化规律的有效手段之一.本文基于ESDA方法,利用黄土高原地区284个县市级行政单元1990、2000和2007年3期的人均GDP数据,分析了黄土高原地区经济增长的空间分布格局和动态演变特征,验证了...     

Spatiotemporal data model for network time geographic analysis in the era of big data

There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.     

利用探索式空间数据解析北京城市空间经济发展模式

城市经济空间结构是城市研究和城市规划基本内容之一。利用其探索式空间数据分析方法,根据1949-2004年企业登记就业数据在邮政区尺度上对北京市1949年以来城市空间经济发展进行了探讨。基于空间邻近矩阵和不同距离矩阵的Global Moran'sI和Local Moran'sI都表明解放后市区并没有明显的经济集聚,50多年来空间经济发展模式几乎都是以市区为核心集聚式发展;尽管郊区一些重要乡镇呈现出发展势头,但几乎没有和周边地区形成互动关系,而是和市中心或其它地区保持较强的联系。分析还表明,北京地区经济的空间相互作用在计划经济时代大体在60km以内,市场经济时代(1983-2002年)提高到75km。北京经济空间模式经历了以下6个阶段:①1957-1982年,中心集中发展;②1983-1987年,沿京津廊道空间组织;③1988-1992年,北部为主的城市中心发展;④1993-1997年,城市中心集中扩张模式;⑤1998-2002年,城市中心填充发展;⑥2002-2004年,出现了城市空心化结构态势,空间经济组织面临新一轮重组。     

A parallel computing approach to viewshed analysis of large terrain data using graphics processing units

Viewshed analysis, often supported by geographic information system, is widely used in many application domains. However, as terrain data continue to become increasingly large and available at high resolutions, data-intensive viewshed analysis poses significant computational challenges. General-purpose computation on graphics processing units (GPUs) provides a promising means to address such challenges. This article describes a parallel computing approach to data-intensive viewshed analysis of large terrain data using GPUs. Our approach exploits the high-bandwidth memory of GPUs and the parallelism of massive spatial data to enable memory-intensive and computation-intensive tasks while central processing units are used to achieve efficient input/output (I/O) management. Furthermore, a two-level spatial domain decomposition strategy has been developed to mitigate a performance bottleneck caused by data transfer in the memory hierarchy of GPU-based architecture. Computational experiments were designed to evaluate computational performance of the approach. The experiments demonstrate significant performance improvement over a well-known sequential computing method, and an enhanced ability of analyzing sizable datasets that the sequential computing method cannot handle.     

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association(LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China's economic growth and carbon emission load will continue for some time.     

空间数据统计分析的思想起源与应用演化

系统总结了空间数据统计分析的发展历程,并分为五个时期：① 早期孕育（计量革命之前）,其重要思想是19世纪初德国的区位论;② 计量革命（1950-1960年代）,主要是经典统计学的应用和理论探索;③ 空间统计学（1970-1980年代）,重点是空间点数据、面数据和空间连续性数据的分析;④ 成熟与扩散（1990-2000年代）,空间数据统计分析发展成熟并快速向其他领域扩散;⑤ 时空大数据（2010年以后）。换句话说,计量革命开始后的空间数据统计分析大约每20年有重要的新技术或方法出现,到现在已经具有成熟、系统化的方法和显著的社会效益。而在当前的时空大数据时期,其发展需要计算机科学家、统计学家和地理学家等不同学科领域人员的共同努力。     

An automated approach to the classification of the slope units using digital data

Digital elevation and remote sensing data sets contain different, yet complementary, information related to geomorphological features. Digital elevation models (DEMs) represent the topography, or land form, whereas remote sensing data record the reflectance/emittance, or spectral, characteristics of surfaces. Computer analysis of integrated digital data sets can be exploited for geomorphological classification using automated methods developed in the remote sensing community. In the present study, geomorphological classification in a moderate- to high-relief area dominated by slope processes in southwest Yukon Territory, Canada, is performed with a combined set of geomorphometric and spectral variables in a linear discriminant analysis. An automated method was developed to find the boundaries of geomorphological objects and to extract the objects as groups of aggregated pixels. The geomorphological objects selected are slope units, with the boundaries being breaks of slope on two-dimensional downslope profiles. Each slope unit is described by variables summarizing the shape, topographic, and spectral characteristics of the aggregated group of pixels. Overall discrimination accuracy of 90% is achieved for the aggregated slope units in ten classes.     

An analysis on movement patterns between zones using smart card data in subway networks

Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to as movement patterns between zones (MZPs), from historical boarding behaviours of passengers in subway networks by using an agglomerative clustering method. In addition, evaluation measures of MZPs are suggested in terms of coverage and accuracy. The effectiveness of the proposed approach is finally demonstrated through a real-world data set obtained from smart cards on a subway network in Seoul, Korea.     

Diatom assemblages from Adirondack lakes (New York,USA) and the development of inference models for retrospective environmental assessment

Detrended canonical coreespondence analysis (DCCA) was used to examine the relationships between diatom species distributions and environmental variables from 62 drainage lakes in the Adirondack region, New York (USA). The contribution of lakewater pH, Al_m (monomeric Al), NH₄, maximum depth, Mg, and DOC (dissolved organic carbon) were statistically significant in explaining the patterns of variation in the diatom species composition. Twenty-three and sixteen diatom taxa were identified as potential indicator species for pH and Al_m, respectively (i.e. a taxon with a strong statistical relationship to the environmental variable of interest, a well defined optimum, and a narrow tolerance to the variable of interest). Using weighted-averaging regression and calibration, predictive models were developed to infer lakewater pH (r ²=0.91), Al_m (r ²=0.83), DOC (dissolved organic carbon) (r ²=0.64), and ANC (acid neutralizing capacity; r ²=0.90). These variables are of key importance in understanding watershed acidification processes. These predictive models have been used in the PIRLA-II (Paleoecological Investigation of Recent Lake Acidification-II) project to answer policy-related questions concerning acidification, recovery, and fisheries loss.