X-ray temperature-emission measure modeling of the solar corona

Several problems related to the calculation of an emission measure differential in temperature are resolved using a new calculation technique. The analysis of a flare on 20 April, 1976, is used to evaluate the usefulness of this new approach and to estimate its future value.Amelia Earhart Fellow.     

HOMICIDE AND ASSAULT: A COMPARATIVE ANALYSIS OF ATTRIBUTES IN DALLAS NEIGHBORHOODS, 1981–1985*

A geographic perspective is utilized to test the hypothesis that the legal labels homicide and assault represent essentially similar behaviors. The data base describes some 1200 homicide and 32 000 serious assault victimizations in Dallas, TX, for 1981–85. Detailed comparisons across urban neighborhoods (differentiated by socioeconomic status) examine the demographic, temporal, locational, and weapons characteristics of both types of serious violence. The analysis indicates strong congruence between the offenses with respect to most attributes, implying that violence data should be classified differently for behavioral analyses.     

Estimating the economic potential for agricultural soil carbon sequestration in the Central United States using an aggregate econometric-process simulation model

The purpose of this paper is to develop and apply a new method to assess economic potential for agricultural greenhouse gas mitigation. This method uses secondary economic data and conventional econometric production models, combined with estimates of soil carbon stocks derived from biophysical simulation models such as Century, to construct economic simulation models that estimate economic potential for carbon sequestration. Using this method, simulations for the central United States show that reduction in fallow and conservation tillage adoption in the wheat-pasture system could generate up to about 1.7 million MgC/yr, whereas increased adoption of conservation tillage in the corn–soy–feed system could generate up to about 6.2 million MgC/yr at a price of $200/MgC. About half of this potential could be achieved at relatively low carbon prices (in the range of $50 per ton). The model used in this analysis produced estimates of economic potential for soil carbon sequestration potential similar to results produced by much more data-intensive, field-scale models, suggesting that this simpler, aggregate modeling approach can produce credible estimates of soil carbon sequestration potential. Carbon rates were found to vary substantially over the region. Using average carbon rates for the region, the model produced carbon sequestration estimates within about 10% of those based on county-specific carbon rates, suggesting that effects of spatial heterogeneity in carbon rates may average out over a large region such as the central United States. However, the average carbon rates produced large prediction errors for individual counties, showing that estimates of carbon rates do need to be matched to the spatial scale of analysis. Transaction costs were found to have a potentially important impact on soil carbon supply at low carbon prices, particularly when carbon rates are low, but this effect diminishes as carbon prices increase. This research was supported in part by the Montana State Agricultural Experiment Station, by the EPA STAR Climate Change program and by the Consortium for the Agricultural Mitigation of Greenhouse Gases. Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through grant R-82874501-0 to Montana State University, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Capturing the heterogeneity of urban growth in South Korea using a latent class regression model

This study aims to analyze the spatial patterns of urban growth in South Korea between 2000 and 2010. Fourteen suspected causative independent variables were selected and latent class regression (LCR) was used to analyze the relationship between dependent (urban growth) and independent (causative) variables. The goodness‐of‐fit was assessed by comparison to logistic regression (LR) analysis. The LR analysis produced consistent coefficients for each independent variable across the study area. In contrast, an LCR analysis, with a three‐class assumption, resulted in a different magnitude and directional effects of the coefficients for each class. The LCR analysis enabled the identification of both spatially homogeneous and heterogeneous areas. In addition, the LCR analysis performed better than the LR analysis with a lower Akaike information criterion and Bayesian information criterion value, and a higher receiver operating characteristic value. We conclude that LCR analysis should be used to establish causative “driving” factors for efficient urban growth planning and urban spatial policy.     

Using expert knowledge to map the level of risk of shallow landslides in Brazil

Shallow landslides are common in Brazil's urban areas. Geomorphology and land use are contributing factors, and rainfall is the triggering one. In these urban areas, anthropogenic activities that increase the level of landslide risk are common, such as cutting and filling or discharging wastewater onto the slopes. The Brazilian Government has developed a methodology to map the risk level in landslide-prone areas. The methodology is based on field observation and divides the risk into four main categories: low, moderate, high, and very high. Technicians in the field decide the sector's landslide risk level based on their professional and personal experiences, but without mathematical calculations or without using specific weights for the contributing factors. This study proposes a method for automatically computing the risk level by involving many experts for deriving each classifier weight, thereby reducing the subjectivity in selecting the final risk level. The weights were calculated using the Analytical Hierarchical Process based on 23 experts on landslides, and the standard deviation was used to define the risk level threshold. We validated the study using a prior risk mapping of São Paulo city. Finally, an application (app) that can be used on a tablet, computer, or smartphone was created to facilitate data collection during fieldwork and to automatically compute the risk level. Risk areas in Brazil are frequently changing as new residents move to the area or changes in the buildings or terrain are made. In addition, mapping the risk areas is expensive and time-demanding for municipalities. Therefore, an application that gathers the data easily and automatically computes the risk level can help municipalities rapidly update their risk sectors, allowing them to use updated risk mapping during the rainy season and be less dependent on rarely available financial resources to hire a risk mapping service.

     

Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco

Sustainable solar energy is of the interest for the city of San Francisco to meet their renewable energy initiative. Buildings in the downtown area are expected to have great photovoltaic (PV) potential for future solar panel installation. This study presents a comprehensive method for estimating geographical PV potential using remote sensed LiDAR data for buildings in downtown San Francisco. LiDAR derived DSMs and DTMs were able to generate high quality building footprints using the object‐oriented classification method. The GRASS built‐in solar irradiation model (r.sun) was used to simulate and compute PV yields. Monthly and yearly maps, as well as an exquisite 3D city building model, were created to visualize the variability of solar irradiation across the study area. Results showed that monthly sum of solar irradiation followed a one‐year cycle with the peak in July and troughs in January and December. The mean yearly sum of solar irradiation for the buildings in the study area was estimated to be 1675 kWh/m². A multiple regression model was used to test the significance of building height, roof area and roof complexity against PV potential. Roof complexity was found to be the dominant determinant. Uncertainties of the research are mainly from the inherent r.sun limitations, boundary problems, and the LiDAR data accuracy in terms of both building footprint extraction and 3D modeling. Future work can focus on a more automated process and segment rooftops of buildings to achieve more accurate estimation of PV potential. The outcome of this research can assist decision makers in San Francisco to visualize building PV potential, and further select ideal places to install PV systems. The methodology presented and tested in this research can also be generalized to other cities in order to meet contemporary society's need for renewable energy.