Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AICC. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.

Geographic variation in allometry in the guppy (Poecilia reticulata)

Variation in static allometry, the power relationship between character size and body size among individuals at similar developmental stages, remains poorly understood. We tested whether predation or other ecological factors could affect static allometry by comparing the allometry between the caudal fin length and the body length in adult male guppies (Poecilia reticulata) among populations from different geographical areas, exposed to different predation pressures. Neither the allometric slopes nor the allometric elevations (intercept at constant slope) changed with predation pressure. However, populations from the Northern Range in Trinidad showed allometry with similar slopes but lower intercepts than populations from the Caroni and the Oropouche drainages. Because most of these populations are exposed to predation by the prawn Macrobrachium crenulatum, we speculated that the specific selection pressures exerted by this predator generated this change in relative caudal fin size, although effects of other environmental factors could not be ruled out. This study further suggests that the allometric elevation is more variable than the allometric slope.

A panel regression method for quantifying the convergence of house prices

The convergence among house prices has attracted much attention from researchers. Previous research mainly utilised a time-series regression method to investigate convergences of house prices, which may ignore the heterogeneity of houses across cities. This research developed a panel regression method, by which the heterogeneity of house prices can be captured. Seemingly unrelated regression estimators were also adapted to deal with the contemporary correlations across cities. Investigation of the convergence among house prices in the Australian capital cities was carried out by using the developed panel regression method. Results suggested that house prices converge in Sydney, Adelaide and Hobart but diverge in Darwin.

Robust-diagnostic regression : a prelude for inducing reliable knowledge from regression

Regression lies heart in statistics, it is the one of the most important branch of multivariate techniques available for extracting knowledge in almost every field of study and research. Nowadays, it has drawn a huge interest to perform the tasks with different fields like machine learning, pattern recognition and data mining. Investigating outlier (exceptional) is a century long problem to the data analyst and researchers. Blind application of data could have dangerous consequences and leading to discovery of meaningless patterns and carrying to the imperfect knowledge. As a result of digital revolution and the growth of the Internet and Intranet data continues to be accumulated at an exponential rate and thereby importance of detecting outliers and study their costs and benefits as a tool for reliable knowledge discovery claims perfect attention. Investigating outliers in regression has been paid great value for the last few decades within two frames of thoughts in the name of robust regression and regression diagnostics. Robust regression first wants to fit a regression to the majority of the data and then to discover outliers as those points that possess large residuals from the robust output whereas in regression diagnostics one first finds the outliers, delete/correct them and then fit the regular data by classical (usual) methods. At the beginning there seems to be much confusion but now the researchers reach to the consensus, robustness and diagnostics are two complementary approaches to the analysis of data and any one is not good enough. In this chapter, we discuss both of them under the unique spectrum of regression diagnostics. Chapter expresses the necessity and views of regression diagnostics as well as presents several contemporary methods through numerical examples in linear regression within each aforesaid category together with current challenges and possible future research directions. Our aim is to make the chapter self-explained maintaining its general accessibility.

Using geographic information systems (GIS) to assess the role of the built environment in influencing obesity : a glossary

Features of the built environment are increasingly being recognised as potentially important determinants of obesity. This has come about, in part, because of advances in methodological tools such as Geographic Information Systems (GIS). GIS has made the procurement of data related to the built environment easier and given researchers the flexibility to create a new generation of environmental exposure measures such as the travel time to the nearest supermarket or calculations of the amount of neighbourhood greenspace. Given the rapid advances in the availability of GIS data and the relative ease of use of GIS software, a glossary on the use of GIS to assess the built environment is timely. As a case study, we draw on aspects the food and physical activity environments as they might apply to obesity, to define key GIS terms related to data collection, concepts, and the measurement of environmental features.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

Face recognition using kernel ridge regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

Mortality disparities between geographic regions : results from a risk percentiles model for coronary heart disease

Background: Planning of disease prevention strategies requires information regarding the distribution of absolute risk in the population to allow targeting of people at high disease risk. It is well known that death rates from coronary heart disease (CHD) are higher in remote areas of Australia compared with major cities. Less well understood is the distribution of the absolute risk of CHD death within the different geographic regions. We present a mathematical model of CHD which projects the lifetime risk of death among individuals in different percentiles of CHD risk. We apply this to model the distribution of CHD risk within different geographic regions.

Methods: Using information from the Framingham1, MRFIT2 and AusDiab3 studies, the Australian population was divided into percentiles of CHD risk within age and gender groups by geographic location. Absolute mortality risk was determined at each percentile using current Australian mortality data. Survival curves were generated for each percentile using these risk estimates. Approximate confidence intervals were derived using bootstrap methods.

Conclusions: The difference in life expectancy at age 25 between those in the lowest decile of CHD risk compared to the highest was 5.8 years (95%CI:4.7,6.7) in major cities compared to 8.5 years (95%CI:7.6,9.7) in remote areas. The difference in risk of premature death (before age 75) was 12% (95%CI:10%,14%) in major cities compared to 33% (95%CI:28%,38%) in remote areas.

Cancer survival in Australia 1992–1997 : geographic categories and socioeconomic status

Cancer Survival in Australia 1992-1997 is the first national analysis of how cancer survival varies by socioeconomic status and geographic region. It presents an analysis of five-year relative survival proportions by geographic category and socioeconomic status for persons diagnosed with cancer during the years 1992-1997.This analysis is presented by age and sex for all cancers (Excluding non-melanocytic skin cancers) combined and for the following National Health Priority Area cancers - colorectal cancer, cancer of the lung, melanoma, cancer of the breast (females only), cancer of the cervix, cancer of the prostate, and non-Hodgkin's lymphoma.This report is the third in a series of three reports on relative survival after being diagnosed with cancer. It is an important reference for all those interested in the health of Australians.