Aviation-Contrail Impacts on Climate and Climate Change: A Ready-to-Wear Research Mantle for Geographers

Although geographers are highly visible in research into contemporary climate changes accompanying greenhouse gas increases (“global warming”), a relatively little explored component by them is the impact of aviation. This contrasts with the atmospheric science community of cloud physicists, chemists, and numerical modelers, who use geographic tools of trade. Geographers are well positioned to investigate aviation-climate impacts, especially related to contrail clouds, given geographic traditions of environment, spatial variation, regionalization, generalization, and the integrative approach to studying physical and human systems (here, climate, transportation, and energy). We highlight the role played by geographers in contrail-climate studies and advocate for a broader involvement in aviation policy formulation.     

Multi‐Scale Qualitative Location: A Topology‐Based Model

Qualitative locations describe spatial objects by relating the spatial objects to a frame of reference (e.g. a regional partition in this study) with qualitative relations. Existing models only formalize spatial objects, frames of reference, and their relations at one scale, thus limiting their applicability in representing location changes of spatial objects across scales. A topology‐based, multi‐scale qualitative location model is proposed to represent the associations of multiple representations of the same objects with respect to the frames of reference at different levels. Multi‐scale regional partitions are first presented to be the frames of reference at multiple levels of scale. Multi‐scale locations are then formalized to relate multiple representations of the same objects to the multiple frames of reference by topological relations. Since spatial objects, frames of reference, and topological relations in qualitative locations are scale dependent, scale transformation approaches are presented to derive possible coarse locations from detailed locations by incorporating polygon merging, polygon‐to‐line and polygon‐to‐point operators.     

Simulating Spatial Dynamics and Processes in a Retail Gasoline Market: An Agent‐Based Modeling Approach

Simulating the dynamics and processes within a spatially influenced retail market, such as the retail gasoline market, is a highly challenging research area. Current approaches are limited through their inability to model the impact of supplier or consumer behavior over both time and space. Agent‐based models (ABMs) provide an alternative approach that overcomes these problems. We demonstrate how knowledge of retail pricing is extended by using a ‘hybrid’ model approach: an agent model for retailers and a spatial interaction model for consumers. This allows the issue of spatial competition between individual retailers to be examined in a way only accessible to agent‐based models, allowing each model retailer autonomous control over optimizing their price. The hybrid model is shown to be successful at recreating spatial pricing dynamics at a national scale, simulating the effects of a rise in crude oil prices as well as accurately predicting which retailers were most susceptible to closure over a 10‐year period.     

Total evaporation estimation for accurate water accounting purposes: an appraisal of various available estimation methods

The estimation of total evaporation is fundamental for water accounting, considering its influence on water availability. Moreover, the current increase in water consumption (e.g. in sub-Saharan Africa and the world over), land cover/use changes, deteriorating water quality and the climate change projections in most regions of the world underscore the need to understand water loss. So far, different approaches have been developed and implemented in estimating the variations of total evaporation, with varying accuracies. The aim of this work was therefore, to provide a review of these different approaches for estimating total evaporation, as well as a detailed discussion of their strengths and weaknesses. Findings from this review have shown that total evaporation estimates derived, using ground-based meteorological and micro-meteorological methods are inadequate for representing its large-scale spatial variations. On the other hand, remote sensing technology, which acquires data at different resolutions (i.e. radiometric, spectral, spatial and temporal), provides timely, up-to-date and relatively accurate spatial estimates of total evaporation over large geographic coverage, for sustainable and effective water accounting, which is key for well-informed and improved management of water resources at both catchment and regional scales. In this regard, more details on the remote sensing-based methods of estimating total evaporation are provided, especially considering the robust technological advancements and its potential in characterizing earth features over time and space. This work has also managed to identify research gaps and challenges in the accurate estimation of total evaporation, using remote sensing, especially with the emergence of more advanced sensors and the characteristics of the landscape.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

High-Resolution Forecast Models of Water Vapor Over Mountains: Comparison With MERIS and Meteosat Data

Propagation delay due to variable tropospheric water vapor (WV) is one of the most intractable problems for radar interferometry, particularly over mountains. The WV field can be simulated by an atmospheric model, and the difference between the two fields is used to correct the radar interferogram. Here, we report our use of the U.K. Met Office Unified Model in a nested mode to produce high-resolution forecast fields for the 3-km-high Mount Etna volcano. The simulated precipitable-water field is validated against that retrieved from the Medium-Resolution Imaging Spectrometer (MERIS) radiometer on the Envisat satellite, which has a resolution of 300 m. Two case studies, one from winter (November 24, 2004) and one from summer (June 25, 2005), show that the mismatch between the model and the MERIS fields ( rms = 1.1 and 1.6 mm, respectively) is small. One of the main potential sources of error in the models is the timing of the WV field simulation. We show that long-wavelength upper tropospheric troughs of low WV could be identified in both the model output and Meteosat WV imagery for the November 24, 2004 case and used to choose the best time of model output.