Understanding the Spatial Pattern and Driving Factors Associated with Timberland Ownership Change in the Northern United States

This study analyzes changes in timberland ownership from 2003 to 2012 across the northern United States based on Forest Inventory and Analysis data identified according to five ownership categories. A total of 26,940 FIA plots that were remeasured between selected years were used for the analysis. Publicly available corporate ownership data were investigated and used to differentiate industrial and institutional (timber investment management organizations [TIMO] and real estate investment trusts [REIT]) ownership. Kernel density, Ripley’s K-function, and multinomial logistic regression (MLR) methods were used to study spatial patterns of timberland ownership and to explore statistical relationships. Among FIA plots showing ownership changes, the largest observed shift was from industrial to institutional ownership, with a 45% increase in the number of plots, equivalent to almost 1.4 million acres of timberland area. Bivariate Ripley’s K-function showed significant clustering for shifts between industrial and institutional ownership. A MLR model identified forest type as a significant factor associated with the transition of industrial timberlands to either institutional or family forest ownership. In addition, shifts from industrial to institutional ownership were related to road access and population density. Study Implications For the past few decades, we have seen an unprecedented trend of change in timberland ownership in the United States, particularly the divesture and change of traditional vertically integrated forest product companies into institutional ownership, i.e., timber investment management organizations (TIMOs) and real estate investment trusts (REITs). In this situation, key research questions to ask are where are these changes taking place in spatial terms and what are their possible linkages with different socio-economic and forest related factors. Such knowledge will help devise policy strategies to monitor and understand the affects of changing timberland ownership on future forest resources.

Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

EVALUATION AND QUANTIFICATION OF HEAT HOTSPOTS IN THE CHAPADA DOS GUIMARÃES-MT NATIONAL PARK

The study aimed to analyze the spatial distribution of heat sources inside and outside the Chapada dos Guimarães National Park (PNCG) in the State of Mato Grosso. The analyzes were performed through the estimate of kernel density (KDE) and Ripley's K function from 2005 to 2014. The data related to the number of hot spots were obtained from the National Institute for Space Research (INPE) from 2005 to 2014, and the vector files were acquired from the cartographic base of the Brazilian Institute of Geography and Statistics (IBGE). In the 10 years of analysis, 579 hot spots were detected in the PNCG, where it was found that the months of August and September had the highest incidence of hot spots in the park. The kernel maps demonstrated that most hotspots were observed in the years 2007, 2010 and 2012. The years 2005 and 2013 presented average densities and the years 2006, 2008, 2009, 2011 and 2014 indicated low density of the hot spots. Ripley's K function, calculated to observe the spatial distribution of the hot spots, rejected the hypothesis of complete spatial randomness (CSR), indicating that they showed a tendency to cluster during the study time series at the PNCG.

Generalized Fractional Calculus Operators Associated with K-function

The aim of this paper is to study some properties of K-function introduced by Sharma. Here we establish two theorems which give the image of this K-function under the generalized fractional integral operators involving Fox’s H-function as kernel. Corresponding assertions in term of Euler, Whittaker and K-transforms are also presented. On account of general nature of H-function and K-function a number of results involving special functions can be obtained merely by giving particular values for the parameters.