Water chemistry of some karst environments in Norway

In August 1983, 18 samples were collected in Norwegian karst environments: seepage waters, surface flow upstream sinkholes or caves, allogenic and authigenie karst springs. Hydrochemical data (ion contents and computed variables for CO-CaCO system) are described by means of a factor analysis, the principal components analysis. Four factors, responsible for about 90 % of the total variance, are extracted and interpreted. Five clusters of samples are then identified:

– Authigenic karst waters (seepage and only carbonate aquifer);

– Waters of very short residence time (surface or karst flow);

– CO-rich waters (seepage and possibly organic-rich surface waters), CO2 part is discussed in more detail;

– CO-poor waters (surface waters above tree-line);

– a central group, combining 2 or more of these characters.     

内蒙古城市现代化水平综合评价与分析

城市现代化是城市素质的综合反映,城市现代化水平评价是衡量一个城市综合竞争力的最有效方法。本文根据城市现代化的概念与内涵,构建了内蒙古城市现代化评价指标体系与测度模型,运用层次分析法对自治区20个城市的现代化水平进行了综合评价与分析。结果表明:(1)内蒙古城市现代化的整体水平偏低,尚处于现代化的准备阶段,距离完全实现城市现代化还有很大差距;(2)各个城市的现代化水平差别较大,且各个层面的现代化水平发展亦不平衡;(3)地级市的现代化程度高于县级市,西部地区城市的现代化水平高于中东部地区。     

An assessment of land use and other factors affecting sediment loads in the Rio Puerco watershed, New Mexico

Rapid channel erosion in the Rio Puerco watershed of northwest New Mexico has been attributed to land use, climate changes, and internal channel adjustments. The objectives of this study were to assess (1) the impacts of land uses on sediment load, (2) the quantitative relationships between land use and sediment load, and (3) the effectiveness of different erosion control methods. The impacts of land uses on sediment load were assessed via hypotheses that, holding other erosion-related variables constant, sediment load correlates positively with grazing intensity and with density of unpaved roads, and correlates negatively with the number of erosion control treatments. We calculated the average annual sediment load for 17 subbasins of 0.67–17.97 km² by comparing sediment accumulation at two points in time (mid-1960s and 1999) behind intact sediment retention structures. We assessed land use via grazing records and measurements of unpaved roads generated from aerial photographs. Soil characteristics, vegetation, and physical factors were quantified for each subbasin. Using 18 variables for each subbasin, we employed Mallow's C_p as a selection criterion. We used six statistical models, including multiple regression and principal components analysis, to determine inherent mathematical relationships between significant independent variables and sediment load. The results indicate that sediment load does not correlate with grazing intensity except in small, relatively low-relief basins with fewer bedrock exposures. However, this interpretation may be compromised by the low quality of data available to quantify grazing. Sediment load is highly sensitive to the presence of unpaved roads, which serve as high gradient, channelized conduits of water and sediment during storms. Sediment load does not correlate with erosion control except in the subset of small, relatively low-relief subbasins that also proved sensitive to grazing intensity. Overall, the statistical analyses suggest that the impact of land uses on sediment load can be assessed using the data available although physical factors appear more significant than land use in producing high sediment loads in the Rio Puerco subbasins. Historical erosion control techniques, as implemented in the Rio Puerco watershed, prove largely ineffective against accelerated sediment loads because they have not been appropriately implemented or maintained.     

Climate variability and ecosystem dynamics of remote alpine and arctic lakes: the MOLAR project

This paper introduces the results of an integrated project designed to compare high resolution analysis of proxy records of climate change in the sediments of seven mountain lakes across Europe with reconstructed instrumental records of climate change over the last 200 years. Palaeolimnological methods used include radiometric dating (²¹⁰Pb, ¹³⁷Cs), mineral magnetics, dry weight, loss-on-ignition, carbon, nitrogen, sulphur, pigments, diatoms, chrysophyte cysts, cladocera and chironomids. Changes in fossil assemblages were summarised using principal components analysis. The stratigraphic data were compared with the instrumental record using linear regression techniques. The dated sediment records for each proxy from each site were treated as the response variables and the various attributes of the instrumental climate record as the predictor variables. The predictor variables were generated for each site for the period 1781 to 1997 using temperature reconstructions based on meteorological records. To harmonise the climatic predictors and the response variables, the climatic variables were smoothed along time with a LOESS regression. The results of the various analyses at the seven sites are presented in the following papers. A synthesis of the project and the relative performance of the different proxy methods are discussed in the final paper.     

Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.

     

Determinants of agricultural commercialization and mechanization in the hinterland of a city in Nepal

In view of the significance of agricultural commercialization for rural development, this study analyzed factors determining agricultural commercialization and mechanization in the hinterland of an urban centre in Morang district, Nepal. Information needed for the study was collected through a questionnaire survey, covering 120 farm households, and group discussion and key informant interviews. The regression analysis of determinants of agricultural commercialization revealed four significant variables, namely, the amount of inorganic fertilizer used, area under tractor-ploughing, area under pump-set irrigation and landholding size. The predicted R value of 0.87, R square of 0.75, and adjusted R square of 0.75 indicate the high explanatory power of the model as a whole. The regression model related to the area under pump-set irrigation predicted the degree of agricultural commercialization and the distance from the city as significantly influencing factors, with a predicted R value of 0.79, R square of 0.63, and adjusted R square of 0.62. The analysis of determinants of the area under tractor-ploughing found only the degree of commercialization as a significantly influencing factor, with a predicted R value of 0.77, R square of 0.59, and adjusted R square of 0.58. In both instances of farm mechanization, the degree of commercialization is the most influential factor, indicating the significant role of mechanization in agricultural commercialization. The major policy implications of the findings of the study are outlined.     

青藏高原农业现代化时空分异及其驱动机制

农业现代化是当代农村建设的重要内容和社会经济文明发展的重要衡量标准。本文基于1990—2017年青藏高原统计数据,构建农业现代化综合指标体系,运用熵值法计算各指标权重,进而获得农业现代化指数和各项指标贡献度,以此评估高原农业现代化发展时空特征和驱动因子。研究结果表明：① 1990—2017年青藏高原农业现代化水平普遍偏低（全区平均农业现代化指数为0.15）,呈增加趋势;② 从空间上看,农业现代化水平相对较高的地区分散在高原北部、西南部、南部边缘和一江两河的德格县、湟中县、白朗县、浪卡子县等部分县域;③ 农业现代化指标中,单位耕地面积用电量、有效灌溉率和土地生产率对高原农业现代化水平影响最大（对农业现代化指数的贡献度分别为13%、12%和11%）,其中单位耕地面积用电量和土地生产率增加促进了农业投入水平和农业产出水平,是农业现代化的主要正向驱动因子;单位耕地面积农药使用量的增加不利于环境可持续发展,是农业现代化的主要负向驱动因子。     

广州市社会空间的因子生态再分析

运用因子分析、聚类分析方法对广州市中心区1990年人口普查数据进行了研究.从9类47个变量中抽取出5个形成广州市社会区类型的主因子,根据各主因子的得分,把广州市中心区划分为七类社会区,并把形成广州市社会空间分异现象的原因归结为五类.通过与1985年类似研究结果的比较,发现广州市社会空间结构的分异现象更趋明显,具体反映在人口“外溢”、居住条件改善及新开发区的形成等方面.最后就如何引导广州市社会空间结构演变进行了讨论.     

Economic Notes

Abstract

This article introduces the development and validation of the spatial thinking ability test (STAT). The STAT consists of sixteen multiple-choice questions of eight types. The STAT was validated by administering it to a sample of 532 junior high, high school, and university students. Factor analysis using principal components extraction was applied to identify underlying spatial thinking components and to evaluate the construct validity of the STAT. Spatial components identified through factor analysis only partly coincided with spatial concepts used to develop the questions that compose the STAT and with the components of spatial thinking hypothesized by other researchers.     

青海共和盆地土地沙漠化影响因子的定量分析

通过对青海共和盆地土地沙漠化影响因子:农牧业人口、牲畜总数、耕地面积、降水量和大风日数42 a资料的主成分分析,结果表明,作为最主要综合指标的第一主成分中的人口、牲畜总数及耕地面积的影响,贡献率是46.5%;第二主成分中的降水量和大风日数的作用,贡献率为24.6%;第三主成分中的自然、人为因素,贡献率为14.9%。土地沙漠化是众多因子综合作用下生态与经济不相协调的产物,其中人口压力下生态不合理的土地利用是其主要原因。     

A Proposed Method for Spatial Data Disaggregation and Interpolation

The Model for External Reliance of Localities in (MERLIN) Coastal Management Zones is a proposed solution to allow scaling of variables to smaller, nested geographies. Using a principal components analysis and data normalization techniques, smaller scale data relationships are linked to data relationships within larger geographies and scaling factors are developed for each. In using these relationships, MERLIN will derive a set of variables scaled to a desired geography representing the attributes of the population present. The concept driving MERLIN's development is that correlations between variables at a specific location and level of geography can be tested and applied to other scales for interpolations. This flexibility and relative simplicity of calculation make MERLIN a valuable tool in many types of social research and for data interpolation. In a pilot study, the MERLIN model is applied to counties along the Gulf Coast of Florida to create a well-being index at the census tract level. Results reveal a landscape that better accounts for the localized patterns of values situated within a larger geographic unit.     

Groundwater Quality Assessment in a Hyper-arid Region of Rajasthan,India

Groundwater is an important source of livelihood in regions where rainfall is scanty, surface water sources are absent, and all domestic and agricultural needs are fulfilled with groundwater. This study deals with groundwater quality assessment in a hyper-arid region using multivariate statistical analysis. A total of 43 samples were collected and analyzed using principal component analysis and hierarchical cluster analysis to model the relationship and interdependence among the various physicochemical variables contributing to groundwater quality in the study area. The results of the statistical techniques showed that the variables are in strong correlation with each other. Cluster analysis proved to be a good tool to ascertain the spatial similarity between the contributing variables. The methodology adopted in the present study has been found to be effective and can be utilized to establish strong water quality monitoring network in similar areas.

     

Green and Sustainable Commercial Property Supply in Malaysia and Nigeria

The aim of this study is to identify the predictive factors and variables that motivate decisions to supply sustainable or green commercial properties, and to apply discriminant analysis technique to assess if there are significant differences in perception between real estate developers in Malaysia and Nigeria based on the identified variables. The result revealed a significant discriminant function differentiating the two countries based on their perception of the variables. The motivational components and attributes were found to be in favor of Malaysia. The Wilks' lambda F‐test and the standardized discriminant function coefficients, showed that there were significant differences between developers in both countries as assessed by the life‐cycle cost motivations, green policies and certification, market strategy, developers expected rate of return, green tax incentive, and available green skills. However, the variables with the most predictive power in accounting for the differences were found to be within the measures of life‐cycle, cost‐saving motivations.     

RELATIONSHIPS BETWEEN THE MORPHOMETRY OF ALPINE AND SUBALPINE BASINS AND REMOTELY SENSED ESTIMATES OF LAKE TURBIDITY,GLACIER NATIONAL PARK,MONTANA

Relationships between characteristics of alpine and subalpine drainage basin form and estimates of lake turbidity were examined. The study was designed to provide insight into sediment removal and delivery systems in an environment where direct process studies are difficult because of logistical constraints. Remote sensing techniques were developed to assess lake turbidity levels through the acquisition of spectral responses by the Landsat Thematic Mapper. Topographic maps were interpreted to derive measurements of morphometric characteristics of 25 study basins within Glacier National Park, Montana. The morphometric variables were chosen to describe basin characteristics related to sediment removal and transport processes. Correlation analysis, principal components analysis (PCA), hierarchical clustering, and analysis of variance (ANOVA) were used to assess characteristics of basin form and the relationship to levels of lake turbidity. A significant correlation between the first principal component, which accounted for the general morphometric character of basins, and estimated lake turbidity was discovered. The areal extent of glacial ice and total stream length within the basins were the most significant variables related to estimated levels of lake turbidity.     

呼伦贝尔沙地沙丘砂来源的定量分析--逐步判别分析(SDA)在粒度分析方面的应用

Quantltatively determining the sources of dune sand uis one of the problems necessarily and urgently to be solved in aeolian landforms and desertification research. Based on the granulometric data of sand materials from the Hulun Buir Sandy Land, the paper employs the stepwise discriminant analysis technique (SDA) for two groups to select the principal factors determining the differences between surface loose sediments. The extent of similarity between two statistical populations can be described quantitatively by three factors such as the number of principal variables, Mahalanobis distance D^2 and confidence level α for F-test. Results reveal that: 1) Aeolian dune sand in the region mainly derives from Hailar Formation (Q3), while fluvial sand and palaeosol also supply partially source sand for dunes; and 2) in the vicinity of Cuogang Town and west of the broad valley of the lower reaches of Hailar River, fluvial sand can naturally become principal supplier for dune sand.     

Housing submarket classification: The role of spatial contiguity

Understanding housing submarket structure is of crucial importance to both public and private agencies. It can also help current and future homeowners make informed decisions on their residential choices. Current research on submarket focuses on comparative analyses of different classification techniques. Few studies, however, have examined the function of spatial contiguity on housing submarket classification. To address this issue, this paper developed a spatially constrained data-driven submarket classification methodology to obtain spatially integrated housing market segments. Specifically, a data-driven model based on principal component analysis and cluster analysis was developed for delineating housing submarkets. Within the model, a number of location attributes were used for principal component analysis, and the geographic locations of houses were also incorporated in the cluster analysis. The performance of this method was compared with other unconstrained data-driven techniques and a priori classifications using three measurements: substitutability, spatial integrity, and similarity. Results indicate that spatially contiguous submarkets can be obtained without compromising housing hedonic model accuracy and attribute homogeneity.     

湖南省城市现代化与生态化耦合协调发展研究

通过构建湖南省城市现代化与生态化耦合效应评价指标体系及其数学运算模型,计算得出湖南省14个地州市2010年和2015年城市（经济、社会、生活、空间）现代化与生态化的耦合度和耦合协调度。研究结果表明：（1）全省总体城市现代化与生态化耦合效应较为良好,且五年来基本实现稳中有进,但存在东部、北部地区耦合效应强于中部、南部和西部地区的地域分布差异;（2）湖南省城市现代化与生态化耦合程度受经济创新度、高层次人才结构、社会开放程度、生产单位能耗和生态产业结构等影响较大,受限于城市经济总量和建设规模等传统指标较小;（3）可按耦合协调效应将全省城市分为三类,即一类耦合城市现代化建设与生态发展处于紧密联系、良好共进的发展程度,二类耦合城市现代化建设和生态建设均在提速发展但发展程度不充分不协调,三类耦合城市现代化建设与生态化发展两大层面均有待提高。     

DISCRIMINANT PRINCIPAL COMPONENTS ANALYSIS

When the number of variables exceeds the number of samples, one method of multivariate discriminationis to use principal components analysis to reduce the dimensionality and then to perform canonicalvariates analysis (PC-CVA). This paper proposes an alternative approach in which discriminant analysisis carried out by a weighted principal component analysis of the group means (DPCA). This method doesnot require prior data reduction and produces discriminant factors that are orthogonal in the original dataspace. The theory and performance of the two methods are compared. Although the individual factors ofDPCA are found to be less discriminating than PC-CVA, the overall discrimination, calculated bymultivariate analysis of variance, and the predictive value, estimated by the leaving-one-out error rate,are broadly comparable.     

主成分分析在农田土壤环境评价中的应用

本文尝试利用主成分分析方法对农田土壤污染物进行识别,并对土壤环境质量进行分级。结果表明,利用主成分分析可以有效地识别土壤污染物中的主要成分,揭示土壤污染物的数据结构和相互间的关系。主成分分析方法可用于定量化的土壤复合污染研究或对历史数据较为缺乏的地区进行土壤环境质量评价。在污染物检测指标数量较大时,可以在一定程度上简化农田土壤重点污染物的定量化识别过程。