Implications for sustainable land use in high-density cities: Evidence from Hong Kong

Sustainable land use policies are concerned with the kind of world we want to live in now, and in future, and therefore inevitably involve some form of community involvement or consultation process. Hong Kong's sustainable land use planning system is well developed, involving considerable community participation and therefore serves as a good model for similarly situated cities. However, although there are several recent studies involving aspects of its land use planning system, none has yet examined the system as a whole from the perspective of sustainability. To correct this, this paper describes the land use conditions of Hong Kong from both demand and supply perspectives, reviewing its statutory and administrative procedures of land development and allocation together with the sustainable urban renewal practices involved. Problems in current sustainable land use planning and management, such as difficulties in urban renewal, the inherent shortage of land and the lengthy time involved due to need for coordination and responsiveness to multiple stakeholders, and outdated and overcomplicated administrative processes were also analyzed.

A study of governance arrangements for land use and natural resource management planning in Cape York Peninsula

Complex intersecting social, economic and environmental dilemmas in Australia's Cape York Peninsula present a number of challenges for planners seeking to develop and implement land use and natural resource management (NRM) plans. There have been five different attempts at land use and NRM planning in Cape York Peninsula over the last 20 years. These processes have (to greater or lesser extents) failed to deliver community-owned and government-supported plans to guide development and/or the management of the region's natural resources. The region is remote, sparsely populated, and home to a significant Indigenous population. Much of the contestation within the region surrounds the access, use and ownership of the region's internationally valuable natural resources. This paper reviews, from the literature, the relevancy and applicability of criteria for best practice planning and governance. A range of identified best practice governance and planning principles are applied to assess the governance arrangements for planning in the Peninsula. The paper finds that decision-making arrangements for land use and NRM planning in the Peninsula are still in their infancy and are inadequate to support effective outcomes. The paper concludes that broader attention to best practice principles in governance for planning is needed to improve planning outcomes.

Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459-464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Aggressive surgical interventions for severe stroke: Impact on quality of life, caregiver burden and family outcomes

Introduction: Decompressive hemicraniectomy, clot evacuation, and aneurysmal interventions are considered aggressive surgical therapeutic options for treatment of massive cerebral artery infarction (MCA), intracerebral hemorrhage (ICH), and severe subarachnoid hemorrhage (SAH) respectively. Although these procedures are saving lives, little is actually known about the impact on outcomes other than short-term survival and functional status. The purpose of this study was to gain a better understanding of personal and social consequences of surviving these aggressive surgical interventions in order to aid acute care clinicians in helping family members make difficult decisions about undertaking such interventions. Methods: An exploratory mixed method study using a convergent parallel design was conducted to examine functional recovery (NIHSS, mRS & BI), cognitive status (Montreal Cognitive Assessment Scale, MoCA), quality of life (Euroqol 5-D), and caregiver outcomes (Bakas Caregiver Outcome Scale, BCOS) in a cohort of patients and families who had undergone aggressive surgical intervention for severe stroke between the years 2000–2007. Data were analyzed using descriptive statistics, univariate and multivariate analysis of variance, and multivariate logistic regression. Content analysis was used to analyze the qualitative interviews conducted with stroke survivors and family members. Results: Twenty-seven patients and 13 spouses participated in this study. Based on patient MOCA scores, overall cognitive status was 25.18 (range 23.4-26.9); current functional outcomes scores: NIHSS 2.22, mRS 1.74, and BI 88.5. EQ-5D scores revealed no significant differences between patients and caregivers (p=0.585) and caregiver outcomes revealed no significant differences between male/female caregivers or patient diagnostic group (MCA, SAH, ICH; p=""0.103).<"/span><"/span> Discussion: Overall, patients and families were satisfied with quality of life and decisions made at the time of the initial stroke. There was consensus among study participants that formal community-based support (e.g., handibus, caregiving relief, rehabilitation assessments) should be continued for extended periods (e.g., years) post-stroke. Ongoing contact with health care professionals is valuable to help them navigate in the community as needs change over time.

Land use regression model (LUR) for ultrafine particles in Brisbane

Traffic-related air pollution has been associated with a wide range of adverse health effects. One component of traffic emissions that has been receiving increasing attention is ultrafine particles(UFP, < 100 nm), which are of concern to human health due to their small diameters. Vehicles are the dominant source of UFP in urban environments. Small-scale variation in ultrafine particle number concentration (PNC) can be attributed to local changes in land use and road abundance. UFPs are also formed as a result of particle formation events. Modelling the spatial patterns in PNC is integral to understanding human UFP exposure and also provides insight into particle formation mechanisms that contribute to air pollution in urban environments. Land-use regression (LUR) is a technique that can use to improve the prediction of air pollution.