Long-term glacier variations and the response to climate fluctuation in Qilian Mountains,China

Glaciers are considered to be'climate-sensitive indicators'and'solid reservoirs',and their changes significantly impact regional water security.The mass balance(MB)from 2011 to 2020 of the Qiyi Glacier in the northeast Tibetan Plateau is presented based on field observations.The glacier showed a persistent negative balance over 9 years of in-situ ob-servations,with a mean MB of-0.51 m w.e.yr-1.The distributed energy-mass balance model was used for glacier MB reconstruction from 1980 to 2020.The daily meteorological data used in the model were from HAR v2 reanalysis data,with automatic weather stations located in the middle and upper parts of the glacier used for deviation correction.The average MB over the past 40 years of the Qiyi Glacier was-0.36 m w.e.yr-1 with the mass losses since the beginning of the 21st century,being greater than those in the past.The glacier runoff shows a significant increasing trend,contributing-81％of the downstream river runoff.The albedo disparity indicates that the net shortwave radiation is much higher in the ablation zone than in the accumulation zone,accelerating ablation-area expansion and glacier mass de-pletion.The MB of the Qiyi Glacier is more sensitive to temperature and incoming shortwave radiation variation than precipitation.The MB presented a non-linear reaction to the temper-ature and incoming shortwave radiation.Under future climate warming,the Qiyi Glacier will be increasingly likely to deviate from the equilibrium state,thereby exacerbating regional water balance risks.It is found that the mass losses of eastern glaciers are higher than those of western glaciers,indicating significant spatial heterogeneity that may be attributable to the lower altitude and smaller area distribution of the eastern glaciers.     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.     

基于氢氧同位素和水化学的祁连山老虎沟冰川区径流过程分析

基于2012-2013年两个消融期在祁连山老虎沟冰川区连续2 a采集的冰川融水径流、雪冰以及降水样品,分析探讨了冰川区水体介质中氢氧同位素和水化学要素(主要化学离子、pH值、TDS和电导率等)在消融期的变化过程及特征。结果表明:祁连山老虎沟雪冰融水中的氢氧同位素值(δD和δ¹⁸O)表现出明显的消融期随月份波动,先升高再降低的趋势,在7月份表现出高值,反映了冰川消融强弱程度的变化过程。冰川径流中同位素含量与冰雪融水接近,且处于当地降水线上,其主要来自冰雪融水和降雨补给。老虎沟冰川融水径流水化学主要表现为Ca-Na-HCO₃-SO₄和Ca-Mg-HCO₃-SO₄型,其组成特征也表现出随消融过程而变化。对氢氧同位素和化学要素组成在消融期(6~9月)随时间的变化过程进行了分析,表明结合冰川区氢氧同位素和化学要素(包括化学离子、TDS、pH值和电导率等)的组成可以区分雪坑和新雪、河水的组分变化,可以反映冰川融水径流在消融期的变化过程。     

Scale-dependent analysis of satellite imagery for characterization of glacier surfaces in the Karakoram Himalaya

Information regarding process-structure relationships and change in the Karakoram Himalaya is of great importance in studying glacier hydrollogy, mass balance, and dynamic environmental change. Such information is not readily available. Detailed spatio-temporal assessment requires field investigation coupled with quantitative remote sensing studies. We conducted an investigation of the large Batura Glacier in Pakistan to determine if spectral variability can be quantified and used to characterize glacier surfaces. Specifically, SPOT Panchromatic satellite data were evaluated for differentiating features of glacier structure resulting from ice movement, ablation, and supraglacial fluvial action. Image semivariogram analysis was conducted. for assessing spectral variability patterns and fractal analysis was used to examine scale-dependent variation in the data. Results indicate that spectral variability from fields of ice seracs can exhibit fractal characteristics, although most surface features on the glacier exhibit a change in the fractal dimension over different ranges in scale. The fractal dimension was found to be useful for differentiating between glacier surfaces such as white ice and debris-covered ice. Characteristics of the debris-load and the scale-dependent nature of calculating the fractal dimension ultimately determined the potential of class separability.     

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.     

Metadata and provenance for spatial analysis: the case of spatial weights

Within a CyberGIS environment, the development of effective mechanisms to encode metadata for spatial analytical methods and to track the provenance of operations is a key requirement. Spatial weights are a fundamental element in a wide range of spatial analysis methods that deal with testing for and estimating models with spatial autocorrelation. They form the link between the data structure in a GIS and the spatial analysis methods. Over time, the number of formats for spatial weights implemented in software has proliferated, without any standard or easy interoperability. In this paper, we propose a flexible format that provides a way to ensure interoperability within a cyberinfrastructure environment. We illustrate the format with an application of a spatial weights web service, which is part of an evolving spatial analytical workbench. We describe an approach to embed provenance in spatial weights structures and illustrate the performance of the web service by means of a number of small experiments.     

我国医学生死亡教育实施及研究述评

The degree-day model is one important method to estimate glacier melt, which is based on the specific relationship between glacial melting and the sum of daily mean temperatures above the melting point. According to the observation data on the Koxkar Glacier(KG) from 2005 to 2010, we analyzed the temporal and spatial variation of degree-day factors(DDF) and its influential factors. The results indicate that the average value of DDF was 7.2~10.4 mm/(°C·d) on the KG from 2005 to 2010. It showed a decreasing trend between 3,700 m and 4,200 m, and the deceasing trend was more obvious in the upper part of the KG. On a spatial scale, the DDF increased evidently with increasing altitude. The DDF ranged from3.6 to 9.3 mm/(°C·d) at 3,700 m a.s.l., with the average value of 9.3 mm/(°C·d). It varied from 6.9 to 13.0 mm/(°C·d) at4,000 m a.s.l., with the average value of 10.2 mm/(°C·d). During the period of ablation, the fluctuation of DDF was not significant at the lower altitude(3,700 m a.s.l.), but it decreased at the higher altitudes(4,000 m a.s.l. and 4,200 m a.s.l.).The debris changes the transmission of heat, which accelerates the melting of a glacier; and the DDF showed high value.This paper will provide the reference for temporal–spatial parameterization schemes of DDF on Tuomuer glaciers of the Tianshan Mountains.     

Contextual neural gas for spatial clustering and analysis

This study aims to introduce contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance. Thus, spatial dependency is incorporated independently from the attribute values of the data. To discuss and compare the performance of the CNG and GeoSOM, this study draws from a series of experiments, which are based on two artificial and one real-world dataset. The experimental results of the artificial datasets show that the CNG produces more homogenous clusters, a better ratio of positional accuracy, and a lower quantization error than the GeoSOM. The results of the real-world dataset illustrate that the resulting patterns of the CNG are theoretically more sound and coherent than that of the GeoSOM, which emphasizes its applicability for geographic analysis tasks.     

上海及周边主要城镇城市用地扩展空间特征及其比较

基于多时段TM遥感影像资料,运用缓冲分析法对上海市区及其周边主要城镇城市用地扩展的时空特征进行了分析和比较。研究结果表明:1)区域城市土地利用扩展过程主要受距中心市区(CBD)的距离的影响;2)城市用地扩展在距CBD10km范围内主要表现为上海市区扩展的空间异向性,距CBD10km范围之外城市扩展的异向性则源于不同时期、不同方向上周边城镇的异速扩展过程,并受河流、渠系等小尺度地貌格局差异的影响;3)上海主要郊区城镇扩展可分为标准型、被动扩展型、平缓扩展型和不规则扩展型。     

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.     

空间分析方法在房地产市场研究中的应用--以北京市为例

基于北京市2003年普通住宅数据,利用空间分析中点模式分析、空间自相关分析和空间插值方法等,对北京市房地产,尤其是普通住宅的空间格局进行分析。研究表明,北京市房地产发展在空间上具有强烈的集聚特点,而房价的空间自相关特性也非常明显。空间分析方法提供了准确认识、评价和综合理解空间位置和空间相互作用的手段,为定量研究空间格局提供了支持。在房地产发展等社会经济现象研究中,空间分析方法强调了“位置”因素的重要性,是刻画房地产空间格局的理想工具。     

Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk

Vulnerability refers to the degree of an individual subject to the damage arising from a catastrophic disaster. It is affected by multiple indicators that include hazard intensity, environment, and individual characteristics. The traditional area aggregate approach does not differentiate the individuals exposed to the disaster. In this article, we propose a new solution of modeling vulnerability. Our strategy is to use spatial analysis and Bayesian network (BN) to model vulnerability and make insurance pricing in a spatially explicit manner. Spatial analysis is employed to preprocess the data, for example kernel density analysis (KDA) is employed to quantify the influence of geo-features on catastrophic risk and relate such influence to spatial distance. BN provides a consistent platform to integrate a variety of indicators including those extracted by spatial analysis techniques to model uncertainty of vulnerability. Our approach can differentiate attributes of different individuals at a finer scale, integrate quantitative indicators from multiple-sources, and evaluate the vulnerability even with missing data. In the pilot study case of seismic risk, our approach obtains a spatially located result of vulnerability and makes an insurance price at a finer scale for the insured buildings. The result obtained with our method is informative for decision-makers to make a spatially located planning of buildings and allocation of resources before, during, and after the disasters.     

北京市长安街沿线的扒窃案件高发区分析及防控对策

本文利用空间分析方法,对北京市长安街沿线的扒窃类案件的空间分布特征进行了研究。首先分析了长安街沿线的犯罪分布状况,发现该长安街沿线共存在3个异常明显的犯罪高发区域,分别对应于西单商业圈(A)、建国门路段(B)和大望路—四惠路段(C),通过将长安街沿线的常住人口与兴趣点(Point of Interest, POI)分布进行比较,发现案件与POI密度较高的区域存在较高的相关性。随后,利用核密度估计方法和时空热点矩阵方法分析了3个异常明显的案件高发区域内的犯罪活动时空分布模式,结果表明：高发区域A的犯罪高发时段为上午10时至下午18时,其中峰值出现在正午12时,犯罪活动主要集中在区域内的一些大型购物场所内;高发区域B和C的犯罪高发时间均为上午6时和下午18时前后,与早晚交通高峰时间比较一致,犯罪活动主要集中在区域内比较重要的公共交通站点附近。最后,根据长安街沿线案件高发区域内犯罪活动的时空规律性特征,提出了犯罪防控措施与建议。     

A spatial analysis of the geographic distribution of musculoskeletal and general practice healthcare clinics in Auckland,New Zealand

Despite the high incidence of musculoskeletal health problems in New Zealand, the use of specialised musculoskeletal healthcare providers appears to be limited to a small subsection of society. Analysis of the spatial distribution of musculoskeletal and general practice clinics in Auckland, New Zealand was undertaken to understand the geographical component of the issue of musculoskeletal healthcare accessibility. The locations of all physiotherapy, chiropractic, osteopathic, podiatry, acupuncture and general practitioner (GP) clinics in urban Auckland were mapped and analysed using a combination of spatial statistical tools. Results showed a contrast between the distribution of the predominantly privately-funded musculoskeletal clinics, which appeared clustered in central areas of the city, and predominantly publicly-funded GP clinics, which appeared more evenly spread across the study area. Several physical and social environmental variables appeared to be correlated with clinic location, including clinic proximity to major roads and urban centres, and residential socioeconomic status and ethnicity. This study represents a first step in understanding the underlying causes of the demographic divide between users and non-users of musculoskeletal healthcare. These results may help to inform the development of strategies to improve the accessibility of musculoskeletal healthcare services for people living or working in areas with low provision of musculoskeletal clinics.     

鲁甸地震灾后重建的综合地理分析与对策研讨

地震灾区是地表各圈层相互作用最剧烈、人地关系最紧张的区域。深入开展综合地理研究对于协调这一区域的人地关系,促进区域可持续发展有着重大的意义。本文首先对鲁甸灾前的资源环境特点与区域发展特征进行了评价和分析,发现该地区生态环境脆弱、人口密度大、贫困面广、生产生活对资源的依赖程度高、资源环境长期处于超载状态。进而,针对性地提出了尽早启动资源环境承载能力评价,科学制定重建规划,体制机制创新,以及开展整个青藏高原边缘地带及近邻地区的防灾减灾系统研究和整体规划等对策建议。     

An algorithm for converting weather radar data into GIS polygons and its application in severe weather warning systems

An algorithm was developed for converting radar data from matrix format into polygons that can be easily visualized, processed, and analyzed using Geographic Information Systems. Spatial operators can be used to overlap radar polygons with land surface features represented by points, lines, and polygons to meet the demands of severe weather identification and tracking and risk recognition. Application and testing of the algorithm demonstrate that the converted radar polygons are suitable for use in weather modification, risk assessments of flash floods in urban areas, and the identification of lightning activity for lightning risk recognition, all of which are essential in real-time severe weather monitoring and warning.     

比例性偏离份额空间模型推演及应用

通过在模型分量结构中置换共享分量、嵌入区域分量和重组残差分量实现了区域分量和结构分量的初步分离,又通过增速标准化处理分别排除了产业结构和规模差异对区域分量和结构分量测算的影响,实现了偏离份额分析从区域考察期增量中分离出区域经济结构差异和规模差异对经济增长贡献的目标。新推演的结构分量真正实现了区域经济结构差异的横纵向比较,尤其是通过同时测算区域产业规模结构和增速结构差异揭示了区域规模和增速占优产业的集中分布情况。此外,为已有的20种分解结构分类补充了8种分解结构,并以环比式动态算法下的比例性偏离份额空间模型对安徽省各地级市2000-2010年经济增长进行了分析。     

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。