Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia

The technique of Geographically Weighted Regression (GWR) was used for estimation of Leaf Area Index (LAI) from remote sensing-based multi-spectral vegetation indices (VI) such as Normalized Difference Vegetation Index (NDVI), the mid-infrared corrected Normalized Difference Vegetation Index (NDVIc), Simple Ratio (SR), Soil-Adjusted Vegetation Index (SAVI) and Reduced Simple Ratio (RSR) in a region of equatorial rainforest in Central Sulavesi, Indonesia. The linear regressions between NDVI, NDVIc, SR, SAVI and RSR as explanatory variables and ground measurements of LAI at 166 plots as a dependent variable were produced using common modelling approach — Ordinary Least Squares (OLS) regression fitted to all data points, as well as GWR. Accuracy and precision statistics indicate that the GWR method made significantly better predictions of LAI in all simulations than OLS did. The relationships between LAI and the explanatory variables were found to be significantly spatially variable and scale-dependent. GWR has the potential to reveal local patterns in the spatial distribution of parameter estimates, it demonstrated sensitivity of the model's accuracy and performance to scale variation. The GWR approach enables finding the most appropriate scale for data analysis. This scale was different for each VI. The results suggest that spatial non-stationarity and scale-dependency in the relationship between LAI and remote sensing data has important implications for estimations of LAI based on empirical transfer functions.     

Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: a case study of Guizhou Karst Plateau,China

Knowing the spatial relationships between the normalized difference vegetation index (NDVI) and environmental variables is of great importance for monitoring rocky desertification. This article investigated the spatially non-stationary relationships between NDVI and environmental factors using geographically weighted regression (GWR) at multi-scales. The spatial scale-dependency of the relationships between NDVI and environmental factors was identified by scaling the bandwidth of the GWR model, and the appropriate bandwidth of the GWR model for each variable was determined. All GWR models represented significant improvements of model performance over their corresponding ordinary least squares (OLS) models. GWR models also successfully reduced the spatial autocorrelations of residuals. The spatial relationships between NDVI and environmental factors significantly varied over space, and clear spatial patterns of slope parameters and local coefficient of determination (R ²) were found from the results of the GWR models. The study revealed detailed site information on the different roles of related factors in different parts of the study area, and thus improved the model ability to explain the local situation of NDVI.     

The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data

Geographically weighted regression (GWR) extends the conventional ordinary least squares (OLS) regression technique by considering spatial nonstationarity in variable relationships and allowing the use of spatially varying coefficients in linear models. Previous forest studies have demonstrated the better performance of GWR compared to OLS when calibrated and validated at sampled locations where field measurements are collected. However, the use of GWR for remote-sensing applications requires generating estimates and evaluating the model performance for the large image scene, not just for sampled locations. In this study, we introduce GWR to estimate forest canopy height using high spatial resolution Quickbird (QB) imagery and evaluate the influence of sampling density on GWR. We also examine four commonly used spatial analysis techniques – OLS, inverse distance weighting (IDW), ordinary kriging (OK) and cokriging (COK) – and compare their performance with that using GWR. Results show that (i) GWR outperformed OLS at all sampling densities; however, they produced similar results at low sampling densities, suggesting that GWR may not produce significantly better results than OLS in remote-sensing operational applications where only a small number of field data are collected. (ii) The performance of GWR was better than those of IDW, OK and COK at most sampling densities. Among the spatial interpolation techniques we examined, IDW was the best to estimate the canopy height at most densities, while COK outperformed OK only marginally and produced larger canopy height estimation errors than both IDW and GWR. (iii) GWR had the advantage of generating canopy height estimation maps with more accurate estimates than OLS, and it preserved patterns of geographic features better than IDW, OK or COK.     

Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models

Formerly, tree height has been more difficult to measure accurately in the field than tree diameter at breast height. As a consequence, models to predict height from diameter measurements have been widely developed in the forestry literature. Through the use of airborne laser scanning technology (e.g., LiDAR), tree variables such as height and crown diameter can be measured accurately, a development which has spawned the need for models to predict diameter from airborne laser-derived measurements. Although some work has been done for fitting such models, none have incorporated spatial information to improve the accuracy of the predicted diameters. Using a simple linear model for predicting tree diameter from laser-derived tree height and crown diameter measurements, we compared the performance of ordinary least squares (OLS), generalized least squares with a non-null correlation structure (GLS), linear mixed-effects model (LME), and geographically weighted regression (GWR). Our data were obtained from 36 sample plots established in Norway. This is the first study to examine the use of spatial statistical models for tree-level LiDAR data. Root mean square prediction errors in tree diameter with LME are 3.5%, with GWR are 10%, and with OLS and GLS are 17%. LME also exhibited low variability in predicting performance across all the validation classes (based on laser-derived height). Giving the difficulties of using parametric statistical inference (such as maximum likelihood-based indices) for GWR, we used permutation tests as a way for detecting statistical differences. LME was significantly better than the other models, as well as GWR was to OLS and GLS. Our results indicate that the LME model produced the best predictions of tree diameter from LiDAR-based variables to a degree that has previously not been possible.     

Revisiting empirical ocean-colour algorithms for remote estimation of chlorophyll-a content on a global scale

Remote-sensing data can be useful for investigating the bio-optical properties of the ocean. Among these bio-optical properties, chlorophyll-a content is of great importance. The standard NASA empirical ocean-colour (OC) algorithms are used widely to estimate global chlorophyll-a content. Despite their simplicity and effectiveness, these regression-based models have two shortcomings that we investigate here: (1) the general form of the models is a fourth-order polynomial that results in multicollinearity, and (2) the models have the same parameters for all ocean regions (i.e. they use global approaches). To resolve the first issue, we use partial least squares (PLS), which allows for an orthogonal transformation such that the covariance between the transformed independent variables and the dependent variable is maximized. To investigate the second issue, we use geographically weighted regression (GWR) to reveal the spatial variation of estimated parameters, demonstrating how the global model underperforms in some locations. GWR results show that model coefficients vary substantially between eastern and western portions of the same ocean basin. By including sea-surface temperature (SST) as an additional independent variable in the PLS model, we also develop a new approach that provides additional explanatory power and makes the global estimation of chlorophyll-a content more valid.     

基于岭回归的RVPMCD滚动轴承故障诊断方法

针对多变量预测模型模式识别方法中的最小二乘拟合可能出现病态的问题,提出了基于岭回归的多变量预测模型（Ridge regression-Variable Predictive Model based Class Discriminate,RVPMCD）分类方法,该方法通过引入岭参数,降低其均方拟合误差,减小自变量间复共线性关系对参数估计的影响,改善了原方法中最小二乘回归拟合参数失真的现象,从而有望建立更加准确的预测模型。对滚动轴承的振动信号提取特征值,组成特征向量,采用RVPMCD方法对训练样本建立预测模型,利用RVPMCD所建立的预测模型进行模式识别。实验分析结果表明,基于岭回归的多变量预测模型分类方法可以更有效地对滚动轴承的工作状态和故障类型进行识别。     

Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology.     

Estimation of individual prediction reliability using the local sensitivity analysis

For a given prediction model, some predictions may be reliable while others may be unreliable. The average accuracy of the system cannot provide the reliability estimate for a single particular prediction. The measure of individual prediction reliability can be important information in risk-sensitive applications of machine learning (e.g. medicine, engineering, business). We define empirical measures for estimation of prediction accuracy in regression. Presented measures are based on sensitivity analysis of regression models. They estimate reliability for each individual regression prediction in contrast to the average prediction reliability of the given regression model. We study the empirical sensitivity properties of five regression models (linear regression, locally weighted regression, regression trees, neural networks, and support vector machines) and the relation between reliability measures and distribution of learning examples with prediction errors for all five regression models. We show that the suggested methodology is appropriate only for the three studied models: regression trees, neural networks, and support vector machines, and test the proposed estimates with these three models. The results of our experiments on 48 data sets indicate significant correlations of the proposed measures with the prediction error.     

Approximate Bayesian inference in spatial GLMM with skew normal latent variables

Spatial generalized linear mixed models are common in applied statistics. Most users are satisfied using a Gaussian distribution for the spatial latent variables in this model, but it is unclear whether the Gaussian assumption holds. Wrong Gaussian assumptions cause bias in the parameter estimates and affect the accuracy of spatial predictions. Thus, there is a need for more flexible priors for the latent variables, and to perform efficient inference and spatial prediction in the resulting models. In this paper we use a skew normal prior distribution for the spatial latent variables. We propose new approximate Bayesian methods for the inference and spatial prediction in this model. A key ingredient in our approximations is using the closed skew normal distribution to approximate the full conditional for the latent variables. Our approximate inference and spatial prediction methods are fast and deterministic, using no sampling based strategies. The results indicate that the skew normal prior model can give better predictions than the normal model, while avoiding overfitting.     

Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag,spatial error and GWR

Many methods can be used to construct geographical cellular automata (CA) models of urban land use, but most do not adequately capture spatial heterogeneity in urban dynamics. Spatial regression is particularly appropriate to address the problem to reproduce urban patterns. To examine the advantages and disadvantages of spatial regression, we compare a spatial lag CA model (SLM-CA), a spatial error CA model (SEM-CA) and a geographically-weighted regression CA model (GWR-CA) by simulating urban growth at Nanjing, China. Each CA model is calibrated from 1995 to 2005 and validated from 2005 to 2015. Among these, SLM and SEM are spatial autoregressive (SAR) models that consider spatial autocorrelation of urban growth and yield highly similar land transition probability maps. Both SAR-CA and GWR-CA accurately reproduce urban growth at Nanjing during the calibration and validation phases, yielding overall accuracies (OAs) exceeding 94% and 85%, respectively. SAR-CA is superior in simulating urban growth when measured by OA and figure-of-merit (FOM) while GWR-CA is superior regarding the ability to address spatial heterogeneity. A concentric ring buffer-based assessment shows OA valleys that correspond to FOM peaks, where the ranges of valleys and peaks indicate the areas with active urban development. By comparison, SAR-CA captures more newly-urbanized patches in highly-dense urban areas and shows better performance in terms of simulation accuracy; whereas, GWR-CA captures more in the suburbs and shows better ability to address spatial heterogeneity. Our results demonstrate that spatial regression can help produce accurate simulations of urban dynamics featured by spatial heterogeneity, either implicitly or explicitly. Our work should help select appropriate CA models of urban growth in different terrain and socioeconomic contexts.     

基于ARIMA模型的二手房价格预测

 摘要： 近年来，我国一二线城市房价持续上涨，房屋成了人们日常生活讨论的热门话题，大家纷纷对未来的房价走势做出猜测。本文爬取国内某知名大型房产网站自2013年以来广州和深圳的二手房均价数据，采用ARIMA模型对未来的房价进行滚动预测，并使用RMSE对预测精度进行判断。结果表明，该模型可以对二手房均价进行持续预测，且预测精度较高，可为房屋买卖者提供参考。     

物联网模式下的智能小区综述

提出了基于物联网模式下的智能小区设计思路与方案。"物联网"改变了传统的信息交互模式和管理模式,基于物联网模式下的住宅小区智能化是通过有效的传输网络,将多元信息服务与管理、物业管理与安防、住宅智能化系统集成,为住宅小区的服务与管理提供高技术的智能化手段,以期实现快捷高效的超值服务与管理。     

一种参数容错辨识法判别和剔除野值方法研究

对于空间目标(卫星、战略导弹)运动轨道的预测,有重要的军事价值,影响预测精度主要是运动模型的确定和观测数据的精度。本文提出一种参数容错辨识法判别和剔除野值方法,已提高观测数据的精度,尽而提高预测精度。     

Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process

We observe a surface roughness in end milling machining process which is influenced by machine parameters, namely radial rake angle, speed and feed rate cutting condition. In this machining, we need to minimize and to obtain as low as possible the surface roughness by determining the optimum values of the three parameters. In previous works, some researchers used a response surface methodology (RSM) and a soft-computing approach, which was based on ordinary linear regression and genetic algorithms (GAs), to estimate the minimum surface roughness and its corresponding values of the parameters. However, the construction of the ordinary regression models was conducted without considering the existence of multicollinearity which can lead to inappropriate prediction. Beside that it is known the relation between the surface roughness and the three parameters is nonlinear, which implies that a linear regression model can be inappropriate model to approximate it. In this paper, we present a technique developed using hybridization of kernel principal component analysis (KPCA) based nonlinear regression and GAs to estimate the optimum values of the three parameters such that the estimated surface roughness is as low as possible. We use KPCA based regression to construct a nonlinear regression and to avoid the effect of multicollinearity in its prediction model. We show that the proposed technique gives more accurate prediction model than the ordinary linear regression’s approach. Comparing with the experiment data and RSM, our technique reduces the minimum surface roughness by about 45.3% and 54.2%, respectively.     

A resampling approach for adjustment in prediction models for covariate measurement error

Recent works on covariate measurement errors focus on the possible biases in model coefficient estimates. Usually, measurement error in a covariate tends to attenuate the coefficient estimate for the covariate, i.e., a bias toward the null occurs. Measurement error in another confounding or interacting variable typically results in incomplete adjustment for that variable. Hence, the coefficient for the covariate of interest may be biased either toward or away from the null. This paper presents a new method based on a resampling technique to deal with covariate measurement errors in the context of prediction modeling. Prediction accuracy is our primary parameter of interest. Prediction accuracy of a model is defined as the success rate of prediction when the model predicts new response. We call our method bootstrap regression calibration (BRC). We study logistic regression with interacting covariates as our prediction model. We measure the prediction accuracy of a model by receiver operating characteristic (ROC) method. Results from simulations show that bootstrap regression calibration offers consistent enhancement over the commonly used regression calibration (RC) method in terms of improving prediction accuracy of the model and reducing bias in the estimated coefficients.     

An inferential modeling method using enumerative PLS based nonnegative garrote regression

In this paper a robust linear regression method with variable selection is proposed for predicting desirable end-of-line quality variables in complex industrial processes. The development of such prediction models is challenging because there is usually a large pool of candidate explanatory variables, limited sample data, and multicollinearity among explanatory variables. The proposed method is named as the enumerative partial least square based nonnegative garrote regression. It employs partial least square regression in enumerative manner to generate initial model coefficients and then uses a nonnegative garrote method to shrink original coefficients so that irrelevant variables can be eliminated implicitly. Analysis about the advantages of the proposed method is provided compared to existing state-of-art model construction methods. Two simulation examples as well as an industrial application in a local semiconductor factory unit are used to validate the proposed method. These examples witness substantial improvement in terms of accuracy and robustness in variable selection compared to existing methods. Specifically, for the industrial case the percentages of improvement in terms of root mean squared error is up to 24.3% compared with the previous work.     

Output-associative RVM regression for dimensional and continuous emotion prediction

Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spatial dependencies between the output vectors, as well as repeating output patterns and input–output associations, that can provide more robust and accurate predictors when modeled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions, and evaluate the proposed framework by focusing on the case of multiple nonverbal cues, namely facial expressions, shoulder movements and audio cues. We demonstrate the advantages of the proposed OA-RVM regression by performing subject-independent evaluation using the SAL database that constitutes naturalistic conversational interactions. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in terms of accuracy of the prediction (evaluated using the Root Mean Squared Error) and structure of the prediction (evaluated using the correlation coefficient), generating more accurate and robust prediction models.     

深圳市房地产信息系统框架研究

从深圳市房地产管理信息化建设的现状出发,提出建立全市基本楼盘表数据库和房屋基础空间数据库,并以此为基础建立空间数据与业务数据统一管理的三层架构的数据库。利用GIS、MIS、OA、WFS(工作流)等技术,实现房产测绘管理、房地产市场交易、房产产权登记、物业管理、租赁管理以及政策性住房管理等跨部门业务集成统一的房地产信息化平台,构建协同办公体系,促进相关业务之间信息畅通,形成房产资源信息共建、共享体系。     

Utility-Based Video Adaptation for Universal Multimedia Access (UMA) and Content-Based Utility Function Prediction for Real-Time Video Transcoding

Many techniques exist for adapting videos to satisfy heterogeneous resource conditions or user preferences, whereas selection of the best adaptation operation among various choices usually is either ad hoc or inefficient. To provide a systematic solution, we present a conceptual framework based on utility function (UF), which models video entity, adaptation, resource, utility, and the relations among them. In order to support real-time video adaptation, we present a content-based statistical paradigm to facilitate the prediction of UF for real-time transcoding of live videos. Instead of modelling the UF through analytical models, as in the conventional rate-distortion framework, the proposed approach formulates the prediction as a classification and regression problem. Each video clip is classified into one of distinctive categories and then local regression is used to accurately predict the utility value. Our extensive experiment results based on MPEG-4 transcoding demonstrate that the proposed method achieves very promising performance - up to 89% accuracy in choosing the optimal transcoding operation (in both spatial and temporal dimensions) with the highest quality over a diverse range of target bit rates