FDE index for goodness‐of‐fit between measured and calculated response signals

A method is presented for determining the goodness‐of‐fit between measured and calculated structural response signals. The method is termed as the frequency domain error (FDE) index, which is a value between 0 and 1 where zero indicates a perfect correlation. The FDE is calculated from the Fourier spectra of the measured and calculated waveforms, using a relatively simple calculation procedure, readily implemented using standard fast Fourier transform (FFT) libraries. The FDE is demonstrated herein using three different types of problems: simple harmonic waves, a large number of earthquake response simulations, and a set of limited simulations. Along with the FDE concept, effective methods for displaying the results are presented. Copyright © 2009 John Wiley & Sons, Ltd.     

海南省区域交通优势度与经济发展关系

以海南省为例,选取县级及以上城市为研究节点,采用交通网络密度、邻近度、通达性等指标,构建区域交通优势度综合评价的数理模型,运用GIS网络分析技术及IDW空间插值法定量分析海南省交通网络的地域空间特征.选取了经济总量和产业结构、人口与城市化、人民生活水平、交通与城市建设等4方面共20项主要的经济指标,运用SPSS软件用主...     

Toward space-time buffering for spatiotemporal proximity analysis of movement data

Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.     

Land use and land cover change on slope in Qiandongnan prefecture of Southwest China

This study uses DEM(Digital Elevation Model) data and remote sensing maps of the study area in 1993, 1999, and 2009 to analyze the slope gradient change of land use patterns in Qiandongnan Prefecture, Guizhou province, China. The land use data were classified into five types, forest, farmland, grassland, water and built-up, the slope gradients were divided into four grades. Indices for analyzing land use features were defined by their proportions, transformation matrixes, land use degree and changes.The results showed that all land use types can be found at every gradient. Generally, with the slope degree increased, the area of forest being augmented as well, while the area of the other land use types(farmland, grassland and build-up) declined. Moreover, a mass of farmland were shifted from other land use types from 0° to 25°, while a quantity of forest were transformed from the other land use types on 25° from 1993 to 2009. In terms of land use degree and changes, the area of farmland and buildup land use types decreased when slope degree increased. Finally, we calculated the five landscape pattern metrics: Patch Density(PD) value, Largest Patch Index(LPI), Shannon's Diversity Index(SHDI), Area-Weighted Mean Shape Index(AWMSI) and Contagion Index(CONTAG). The results of metricsanalysis showed that PD values, SHDI values and CONTA values had a similar variation trend, that is, they decreased when slope degree increased. There was no obvious variation trend on LPI value.     

Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.     

机载激光雷达数据分析与反演青海云杉林结构信息

利用机载激光雷达点云数据,计算了9种度量指标,并将其分为冠层的高度指标、结构复杂度指标和覆盖度指标。利用高度指标和结构复杂度指标,结合大量实测单木结构与年龄估测数据,从样点和区域尺度分别分析了青海云杉林冠层垂直结构分布,分析得知实验区内主要以中龄林和成熟林为主,冠层垂直分布复杂程度偏低,高度分化程度一般。通过回归分析发现首次回波覆盖度指标FCI与实测的有效植被面积指数PAI_e有良好的相关性（R²=0.66）,在此基础上基于辐射传输模型反演了实验区内PAI_e的水平分布,且用实测数据验证发现反演的PAI_e略高于实测值（R²=0.67）,绝对平均误差为0.65。分析结果很好地反映了激光雷达在森林空间结构信息提取方面的应用潜力。     

Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay,China

Analyzing the spatial determinants of urban growth is helpful for urban planning and management. In a case study of urban agglomeration around Hangzhou Bay (China), four landscape metrics (total area, total edge, landscape shape index and aggregation index) were used to describe the landscape characteristics of the urban growth at two block scales (4 km and 7 km) during two temporal intervals (1994–2003 and 2003–2009). Spatial autocorrelation regression was employed to identify the geographic determinants of the urban landscape changes. The results indicated that the urban landscapes became more dominant, unstable, irregular and compact, especially in the centers of cities. These changes exhibited notable spatial variations and spatial autocorrelation at the two block scales. The distances to national and provincial roads influenced the urban pattern changes. The impacts of the urban centers on urban expansion gradually declined with the urbanization progress. The slope factor was the most influential determinant of urban growth. Our study emphasized the importance of considering the autocorrelation and scale effects when analyzing the determinants of urban growth. These findings may help land planners create policies and strategies for future urban development.     

Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States

We parameterized neural net‐based models for the Detroit and Twin Cities metropolitan areas in the US and attempted to test whether they were transferable across both metropolitan areas. Three different types of models were developed. First, we trained and tested the neural nets within each region and compared them against observed change. Second, we used the training weights from one area and applied them to the other. Third, we selected a small subset (～1%) of the Twin Cities area where a lot of urban change occurred. Four model performance metrics are reported: (1) Kappa; (2) the scale which correct and paired omission/commission errors exceed 50%; (3) landscape pattern metrics; and (4) percentage of cells in agreement between model simulations. We found that the neural net model in most cases performed well on pattern but not location using Kappa. The model performed well only in one case where the neural net weights from one area were used to simulate the other. We suggest that landscape metrics are good to judge model performance of land use change models but that Kappa might not be reliable for situations where a small percentage of urban areas change.     

Calibrating cellular automata based on landscape metrics by using genetic algorithms

Landscape metrics have been widely used to characterize geographical patterns which are important for many geographical and ecological analyses. Cellular automata (CA) are attractive for simulating settlement development, landscape evolution, urban dynamics, and land-use changes. Although various methods have been developed to calibrate CA, landscape metrics have not been explicitly used to ensure the simulated pattern best fitted to the actual one. This article presents a pattern-calibrated method which is based on a number of landscape metrics for implementing CA by using genetic algorithms (GAs). A Pattern-calibrated GA–CA is proposed by incorporating percentage of landscape (PLAND), patch metric (LPI), and landscape division (D) into the fitness function of GA. The sensitivity analysis can allow the users to explore various combinations of weights and examine their effects. The comparison between Logistic- CA, Cell-calibrated GA–CA, and Pattern-calibrated GA–CA indicates that the last method can yield the best results for calibrating CA, according to both the training and validation data. For example, Logistic-CA has the average simulation error of 27.7%, but Pattern-calibrated GA–CA (the proposed method) can reduce this error to only 7.2% by using the training data set in 2003. The validation is further carried out by using new validation data in 2008 and additional landscape metrics (e.g., Landscape shape index, edge density, and aggregation index) which have not been incorporated for calibrating CA models. The comparison shows that this pattern-calibrated CA has better performance than the other two conventional models.     

How can a few streamflow measurements help to predict daily hydrographs at almost ungauged sites?

Abstract

Available data from nearby gauging stations can provide a great source of hydrometric information that is potentially transferable to an ungauged site. Furthermore, streamflow measurements may even be available for the ungauged site. This paper explores the potential of four distance-based regionalization methods to simulate daily hydrographs at almost ungauged pollution-control sites. Two methods use only the hydrological information provided by neighbouring catchments; the other two are new regionalization methods parameterized with a limited number of streamflow data available at the site of interest. Based on a network of 149 streamgauges and 21 pollution-control sites located in the Upper Rhine-Meuse area, the comparative assessment demonstrates the benefit of making available point streamflow measurements at the location of interest for improving quantitative streamflow prediction. The advantage is moderate for the prediction of flow types (stormflow, recession flow, baseflow) and pulse shape (duration of rising limb and falling limb).

Editor Z.W. Kundzewicz; Associate editor A. Viglione