Sacred natural sites can be defined as any place in nature possessing a unique spiritual significance to peoples and communities. Throughout the world, sacred natural sites support ecological biodiversity, provide habitat stepping stones, and play a crucial role in the continuation of traditional spiritual practices. These variables in combination create a unique biocultural landscape that warrants conservation and conversely, sacred sites offer a mode of conservation. This research examines land cover change within the Goviefe Todzi sacred forest, located in Ghana’s Volta region, using satellite imagery. We utilized techniques in digital image processing to generate land cover classification maps of the study area for 2012 and 2015. Land cover classifications of high resolution QuickBird-2 and WorldView-2 imagery informed the hypothesis that closed forest cover within the sacred site decreased at a lesser rate than neighboring non-sacred closed forest cover. The Global Forest Change dataset from the University of Maryland framed our understanding of how forest cover changes within the study area and how it fits into the global context of forest cover change. Results demonstrated that the Goviefe Todzi sacred forest exhibited less closed forest loss when compared to non-sacred forest and closed forest was most often converted to open forest in the event of a land cover change. We recognized that other factors such as accessibility to nearby settlements, pre-existing agricultural fields, the time span of the study, as well as the topography of the sacred forest may also contribute to its lack of closed forest loss.

The human and environmental impacts of urban heat islands (UHI) have become an increasingly relevant issue to city planners. This topic has spurred research into the relationships between land cover, ambient temperature, and the role of greenspace in emitting cooler air to its surrounding area, now known as the ‘park cool island’ effect (PCI). While ample research has been given to this phenomenon in dense urban areas, much less has been dedicated to semi-urban communities who may wish to inform their development practices as they expand their footprints. This research used satellite-derived Landsat Level-2 Provisional Surface Temperature data, MassGIS 2016 Land Cover / Land Use data, and MassGIS Standardized Assessors’ Parcels data to analyze parcels in Essex County, Massachusetts, for PCI intensity and the influence of land cover and parcel characteristics on PCI. LST data from July, 2016, were used to evaluate the mean temperature difference between parcels and their surrounding area to derive PCI. Replicating methods demonstrated by Cao et al. [Landscape and Urban Planning, 96(4):224-231 (2010)], linear regression analyses were undertaken to determine the relationships between PCI, parcel land cover and geometry. The 500 meter buffer distance used by Cao et al. to calculate PCI was also analyzed. Twenty iterations of the linear regression model were run based on a changing buffer value to calculate PCI. Two sensitivity analyses were performed based on these model iterations: 1) change in model performance, as expressed by its R2 value, across the range of PCI buffer distances and 2) the change in the coefficient strengths of the independent variables across the range of PCI buffer distances. The linear regression model underperformed as compared to Cao et al.’s study, however, it affirmed the 500 meter buffer distance as a parameter for calculating PCI, with that model iteration returning the highest R2 value (0.587). Buffer distances greater than 500 meters performed relatively well, however, smaller buffer values were associated with weak model performance. Among land cover coefficients, there were scale-sensitivities observed, with some variables changing in strength and polarity across the model iterations. It was determined that PCI could effectively evaluate cooling intensity in the study area, however, using it as a dependent variable within a linear regression model had only moderate performance. This was due to heterogeneity among the makeup of land cover within parcel buffer areas which inhibited the regression model’s ability to build consistent relationships between land cover and PCI.

Political redistricting plans often need to consider the compactness of the district’s shape. For states requiring districts to be compact, there is a need to quantify compactness. Existing measures of compactness unfairly penalize districts with coastlines and islands or whose geography itself is not compact. By incorporating information about the underlying geography into the calculation of a modified compactness score, it would be possible to use a compactness test more effectively and fairly across all districts. Several methods of incorporating such data were explored with test districts. A Python script was created to apply the calculations behind the selected method to any polygon shapefile. The script was run on the 436 districts of the 114th Congress of the United States to consider and analyze the modified compactness calculation and its usefulness. Scores for districts covering areas with a significant amount of water were improved when the modified compactness calculation was applied.