Conjoint modeling of residential group preferences: A comparison of the internal validity of hierarchical information integration approaches

 In this paper, two approaches for measuring residential group preferences, based on the method of Hierarchical Information Integration (HII), are compared. In particular, the hypothesis that group-based preference models estimated from integrated HII experiments better predict group preferences than part individual-based group models estimated from classical HII experiments is tested. To that effect, the models' ability to predict group preferences for new residential alternatives is compared in a study of residential preferences of co-ops. Results indicate that integrated HII group experiments indeed result in better predictions of residential preferences.     

Bertin’s matrix concepts reconsidered: transformations of semantics and semiotics to support geovisualization use

A system for managing and visualizing archaeological finds and their spatio-temporal properties is presented. The system is an attempt to combine archaeologists' interest to keep track of time with interests related to the representation and visualization of the spatio-temporal component of archaeological excavations, with the help of cartographic and GIS methods and techniques. Several parameters of a prehistoric excavation such as finds, their thematic characteristics, and their spatio-temporal distributions are examined. A three-dimensional GIS/cartographic environment and its user interface to the excavation database have been developed as a tool for enhancing archaeological interpretation in an exploratory environment. The development of the system is an ongoing task; the article describes its current use as a prototype.     

Using GPS multipath to measure soil moisture fluctuations: initial results

Measurements of soil moisture are important for studies of climate and weather forecasting, flood prediction, and aquifer recharge studies. Although soil moisture measurement networks exist, most are sparsely distributed and lack standardized instrumentation. Measurements of soil moisture from satellites have extremely large spatial footprints (40–60 km). A methodology is described here that uses existing networks of continuously-operating GPS receivers to measure soil moisture fluctuations. In this technique, incoming signals are reflected off and attenuated by the ground before reception by the GPS receiver. These multipath reflections directly affect signal-to-noise ratio (SNR) data routinely collected by GPS receivers, creating amplitude variations that are a function of ground reflectivity and therefore soil moisture content. After describing this technique, multipath reflection amplitudes at a GPS site in Tashkent, Uzbekistan are compared to estimates of soil moisture from the Noah land surface model. Although the GPS multipath amplitudes and the land surface model are uncalibrated, over the 70-day period studied, they both rise sharply following each rainfall event and slowly decrease over a period of ∼10 days.     

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

ABSTRACT

Urban areas presently consume around 75% of global primary energy supply, which is expected to significantly increase in the future due to urban growth. Having sustainable, universal energy access is a pressing challenge for most parts of the globe. Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security. However, urban energy analyses are severely limited by the lack of urban energy data. Such datasets are virtually non-existent for the developing countries. As per current projections, most of the new urban growth is bound to occur in these data-starved regions. Hence, there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns. Here, we apply a data-driven approach to characterize urban settlements based on their formality, which is then used to assess intra-urban urban energy consumption in Johannesburg, South Africa; Sana’a, Yemen; and Ndola, Zambia. Electricity is the fastest growing energy fuel. By analyzing the relationship between the settlement types and the corresponding nighttime light emission, a proxy of electricity consumption, we assess the differential electricity consumption patterns. Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.     

Turbulent kinetic energy budget in a gravel-bed channel flow

The present experimental investigation focuses on the characteristics of near bed turbulence in a fully rough, uniform open-channel flow over a gravel-type bed. Due to bed topography small scale heterogeneity, the flow is not uniform locally in the near bed region and a double averaging methodology is applied over a length scale much larger than the gravel size. The double-averaged Turbulent Kinetic Energy (TKE) budget derived in the context of the present flow over a gravel bed differs from the TKE budget written for flow over a vegetation canopy. The non-constant shape of the roughness function measured in our gravel bed leads to an additional bed-induced production term which is null for vertical roughness elements, such as simplified vegetation elements. The experimental estimation of the terms of the TKE budget reveals that the maximum turbulent activity takes place away from the reference plane, near the roughness crests. However, within the interface sublayer the work of the bed induced velocity fluctuations against the Reynolds stress is of the same magnitude as the main turbulence production term. Consequently, the characteristics of the TKE budget have similarities with uniform flows over canopies and strongly differ from uniform flows over smooth and transitionally rough flows over sedimentlike beds.     

Contrasting origins of Cenozoic silicic volcanic rocks from the western Cordillera of the United States

Two fundamentally different types of silicic volcanic rocks formed during the Cenozoic of the western Cordillera of the United States. Large volumes of dacite and rhyolite, mostly ignimbrites, erupted in the Oligocene in what is now the Great Basin and contrast with rhyolites erupted along the Snake River Plain during the Late Cenozoic. The Great Basin dacites and rhyolites are generally calc-alkaline, magnesian, oxidized, wet, cool (<850°C), Sr-and Al-rich, and Fe-poor. These silicic rocks are interpreted to have been derived from mafic parent magmas generated by dehydration of oceanic lithosphere and melting in the mantle wedge above a subduction zone. Plagioclase fractionation was minimized by the high water fugacity and oxide precipitation was enhanced by high oxygen fugacity. This resulted in the formation of Si-, Al-, and Sr-rich differentiates with low Fe/Mg ratios, relatively low temperatures, and declining densities. Magma mixing, large proportions of crustal assimilation, and polybaric crystal fractionation were all important processes in generating this Oligocene suite. In contrast, most of the rhyolites of the Snake River Plain are alkaline to calc-alkaline, ferroan, reduced, dry, hot (830–1,050°C), Sr-and Al-poor, and Nb-and Fe-rich. They are part of a distinctly bimodal sequence with tholeiitic basalt. These characteristics were largely imposed by their derivation from parental basalt (with low fH₂O and low fO₂) which formed by partial melting in or above a mantle plume. The differences in intensive parameters caused early precipitation of plagioclase and retarded crystallization of Fe–Ti oxides. Fractionation led to higher density magmas and mid-crustal entrapment. Renewed intrusion of mafic magma caused partial melting of the intrusive complex. Varying degrees of partial melting, fractionation, and minor assimilation of older crust led to the array of rhyolite compositions. Only very small volumes of distinctive rhyolite were derived by fractional crystallization of Fe-rich intermediate magmas like those of the Craters of the Moon-Cedar Butte trend. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Unique isoprene oxidation products demonstrate chlorine atom chemistry occurs in the Houston,Texas urban area

As part of the 2000 Texas Air Quality Study (TexAQS), we studied the isoprene oxidation process under ambient conditions to discern the presence of chlorine atom (Cl) chemistry in the Houston, Texas urban area. By measuring chloromethylbutenone (CMBO) and an isomer of chloromethylbutenal (CMBA), we clearly observed sixteen episodes of active Cl chemistry during the 24-day experiment. Estimated median Cl concentration during each of these episodes was between the detection limit of ~10² atoms cm⁻³ and 50 _{- 30} ^{+ 70} ×10⁴ 50_{ - 30}^{ + 70} \times {10^4} atoms cm⁻³. Cl concentration during all the episodes averaged 7.6 _{- 2.0} ^{+ 4.7} ×10⁴ 7.6_{ - 2.0}^{ + 4.7} \times {10^4} atoms cm⁻³ and thus amounted to less than 3% of the OH concentration during the same periods. During the episodes, the fraction of oxidation chemistry initiated by Cl ranged from 3–43% and was strongly dependent on the quantity and type of hydrocarbons present in the atmosphere. Because of its intermittent presence and low concentration, Cl is not a broadly influential oxidant in the Houston, Texas urban area.     

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities

ABSTRACT

Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.