Cultural and economic residential sorting of Auckland’s population, 1991–2013: an entropy approach

Journal of Geographical Systems - Auckland, the largest city of New Zealand, is one of the most diverse cities in the world, with more than 40% of its population born abroad, more than 200...     

Controls on biochemical oxygen demand in the upper Klamath River,Oregon

A series of 30-day biochemical oxygen demand (BOD) experiments were conducted on water column samples from a reach of the upper Klamath River that experiences hypoxia and anoxia in summer. Samples were incubated with added nitrification inhibitor to measure carbonaceous BOD (CBOD), untreated to measure total BOD, which included demand from nitrogenous BOD (NBOD), and coarse-filtered to examine the effect of removing large particulate matter. All BOD data were fit well with a two-group model, so named because it considered contributions from both labile and refractory pools of carbon: BOD_t = a₁(1 ? e^? a₀t) + a₂t. Site-average labile first-order decay rates a₀ ranged from 0.15 to 0.22/day for CBOD and 0.11 to 0.29/day for BOD. Site-average values of refractory zero-order decay rates a₂ ranged from 0.13 to 0.25 mg/L/day for CBOD and 0.01 to 0.45 mg/L/day for BOD; the zero-order CBOD decay rate increased from early- to mid-summer. Values of ultimate CBOD for the labile component a₁ ranged from 5.5 to 28.8 mg/L for CBOD, and 7.6 to 30.8 mg/L for BOD. Two upstream sites had higher CBOD compared to those downstream. Maximum measured total BOD₅ and BOD₃₀ during the study were 26.5 and 55.4 mg/L; minimums were 4.2 and 13.6 mg/L. For most samples, the oxygen demand from the three components considered here were: labile CBOD > NBOD > refractory CBOD, though the relative importance of refractory CBOD to oxygen demand increased over time. Coarse-filtering reduced CBOD for samples with high particulate carbon and high biovolumes of Aphanizomenon flos-aquae. There was a strong positive correlation between BOD, CBOD, and the labile component of CBOD to particulate C and N, with weaker positive correlation to field pH, field dissolved oxygen, and total N. The refractory component of CBOD was not correlated to particulate matter, instead showing weak but statistically significant correlation to dissolved organic carbon, UV absorbance at 254 nm, and total N. Particulate organic matter, especially the alga A.flos-aquae, is an important component of oxygen demand in this reach of the Klamath River, though refractory dissolved organic matter would continue to exert an oxygen demand over longer time periods and as water travels downstream.     

Hydrostatic pore water pressure revisited

Hydrostatic or “normal” pressure can be easily visualized as a water column with pressure given by ρgh and any departures classified as abnormal pressure. This is the basis for commonly used hydrostatic pressure depth trends in sedimentary basins that are constructed on assumptions of constant gradients and are datumed at mean sea level or ground level. But the straightforward water column concept does not upscale in a simple way to sedimentary basins where the zones of interest are several thousands of metres below the land or sea surface. Sedimentary basins are heterogeneous, including stacked, confined reservoirs and variations in pore water composition. It is possible to construct pressure-depth profiles that honour the geology and hydrostratigraphy of a basin and these give different hydrostatic baselines from simple constant gradients hung from familiar local datums such as ground level. Key steps are using a reservoir-specific datums such as the water table or potentiometric surface relevant to that unit, then building a pressure-depth trend that represents the pore fluid salinity variation and density profile throughout the reservoir unit. At a given depth, this version of hydrostatic may predict pressures several hundred psi different from a single density gradient hung from a datum local to the well, and exhibit a notched profile reflecting the geological and hydrological stratigraphy. This construct redefines normal and abnormal pore fluid pressures in sedimentary basins. The impacts of this alternative approach to sedimentary basin hydrostatics, even if data are limited and pressure profiles have to be framed probabilistically, extend to many aspects of studying and interacting with fluid systems in sedimentary basins including basin modelling, petroleum systems analysis, well planning and well operations.     

General sky models for illuminating terrains

Sky models are quantitative representations of natural luminance of the sky under various atmospheric conditions. They have been used extensively in studies of architectural design for nearly a century, and more recently for rendering objects in the field of computer graphics. The objectives of this paper are to (1) describe sky models, (2) demonstrate how map designers can render terrain under various sky models in a typical geographic information system (GIS), (3) illustrate potential enhancements to terrain renderings using sky models, and (4) discuss how sky models, with their well-established standards from a different discipline, might contribute to a virtual geographic environment (VGE).

Current GIS hill-shading tools use the Lambertian assumption which can be related to a simple point light source at an infinite distance to render terrain. General sky models allow the map designer to choose from a gamut of sky models standardized by the International Commission on Illumination (CIE). We present a computer application that allows the map designer to select a general sky model and to use existing GIS tools to illuminate any terrain under that model. The application determines the orientations and weights of many discrete point light sources that, in the aggregate, approximate the illumination provided by the chosen sky model. We discuss specific enhancements to terrains that are shaded and shadowed with these general sky models, including additional detail of secondary landforms with soft shadows and more realistic shading contrasts. We also illustrate how non-directional illumination models result in renderings that lack the perceptual relief effect. Additionally, we argue that this process of creating hill-shaded visualizations of terrain with sky models shows parallels to other geo-simulations, and that basing such work on standards from the computer graphics industry shows potential for its use in VGE.     

A scoping review on the multiplicity of scale in spatial analysis

Journal of Geographical Systems - Scale is a central concept in the geographical sciences and is an intrinsic property of many spatial systems. It also serves as an essential thread in the fabric...     

Cloud computing and virtualization within the regional climate model and evaluation system

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.     

A neural network approach to classification of sidescan sonarimagery from a midocean ridge area

A neural-network approach to classification of sidescan-sonar imagery is tested on data from three distinct geoacoustic provinces of a midocean-ridge spreading center: axial valley, ridge flank, and sediment pond. The extraction of representative features from the sidescan imagery is analyzed, and the performance of several commonly used texture measures are compared in terms of classification accuracy using a backpropagation neural network. A suite of experiments compares the effectiveness of different feature vectors, the selection of training patterns, the configuration of the neural network, and two widely used statistical methods: Fisher-pairwise classifier and nearest-mean algorithm with Mahalanobis distance measure. The feature vectors compared here comprise spectral estimates, gray-level run length, spatial gray-level dependence matrix, and gray-level differences. The overall accurate classification rates using the best feature set for the three seafloor types are: sediment ponds, 85.9%; ridge flanks, 91.2%; and valleys, 80.1%. While most current approaches are statistical, the significant finding in this study is that high performance for seafloor classification in terms of accuracy and computation can be achieved using a neural network with the proper combination of texture features. These are preliminary results of our program toward the automated segmentation and classification of undersea terrain