Measuring the Effects of a Land Value Tax on Land Development

The objective of this research is to evaluate a land value tax as a potential policy tool to moderate sprawling development in Nashville, TN, the nation’s most sprawling metropolitan community with a population of one million or more. To achieve this objective, the hypothesis is empirically tested that a land value tax encourages more development closer to preexisting development than farther from preexisting development. Specifically, the marginal effects of a land value tax on the probability of land development is hypothesized to be greater in areas around preexisting development than in areas more distant from preexisting development. The findings show that the marginal effects of a land value tax on the probability of developing parcels that neighbored previously developed parcels was greater than the probability of developing parcels that did not neighbor previously developed parcels. This finding suggests that land value taxation could be used to design compact development strategies that address sprawling development.Land value tax, Land development model, Urban sprawl, Land Economics/Use, Community/Rural/Urban Development,

Negative Externalities on Property Values Resulting from Water Impairment: The Case of the Pigeon River Watershed

The following hypothesis was tested: Willingness to bear a negative water impairment externality differs between those who do and those who do not receive economic benefit from the impairment source, e.g., a paper mill. The hypothesis was tested using a hedonic analysis of ambient water quality in two discrete housing markets in the Pigeon River Watershed, which have been polluted by the operation of a paper mill. The results suggest that North Carolina residents of the subwatersheds with impaired river, who experience economic benefits from the paper mill in addition to harmful effects, do perceive the pollution as a negative externality, whereas they may have a willingness to bear a similar type of negative externality associated with impaired streams. In contrast, the effects of both degraded river and streams on property values is perceived as a negative externality by residents in the Tennessee side, who experience only harmful effects from the pollution. North Carolina residents may hold greater willingness to bear the harmful effects of pollution as a given condition in their decision-making process because they receive economic benefits from the paper mill, while this internalization of the negative externality is weaker for residents in the Tennessee side.negative Externalities, water quality, spatial hedonic model, Environmental Economics and Policy,

Extreme coefficients in Geographically Weighted Regression and their effects on mapping

This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.extreme coefficient, fixed and adaptive calibrations, geographically weighted regression, Mapping, Research Methods/ Statistical Methods,

Impacts of the Boom-Bust Cycle on the Effectiveness of Policies for Moderating the Consequences of Sprawl on Residential Development

urban sprawl, spatial discrete-choice model, Land Economics/Use,

Spatial Reasoning for Few-Shot Object Detection

Although modern object detectors rely heavily on a significant amount of training data, humans can easily detect novel objects using a few training examples. The mechanism of the human visual system is to interpret spatial relationships among various objects and this process enables us to exploit contextual information by considering the co-occurrence of objects. Thus, we propose a spatial reasoning framework that detects novel objects with only a few training examples in a context. We infer geometric relatedness between novel and base RoIs (Region-of-Interests) to enhance the feature representation of novel categories using an object detector well trained on base categories. We employ a graph convolutional network as the RoIs and their relatedness are defined as nodes and edges, respectively. Furthermore, we present spatial data augmentation to overcome the few-shot environment where all objects and bounding boxes in an image are resized randomly. Using the PASCAL VOC and MS COCO datasets, we demonstrate that the proposed method significantly outperforms the state-of-the-art methods and verify its efficacy through extensive ablation studies.Comment: Pattern Recognition, Vol.120, 202

Effects of Forestland Ownership Conversion on Greenhouse Gas Emissions: The Case of South Korea

This research analyzed the effects of forestland conversion from private to public ownership on greenhouse gas emissions by quantifying the relationship between forestland ownership conversion and deforestation, and then examining the effects of the change in deforestation on greenhouse gas emissions in South Korea. Ex ante simulations forecast greenhouse gas emissions resulting from deforestation rates under the current level of national forestland and three scenarios of increased percentages of national forestland. The findings suggest that increasing the percentage of national forestland would mitigate the increase in the deforestation rate, which in turn would moderate the increase in greenhouse gas emissions.greenhouse gas emissions, Forestland Ownership, Environmental Economics and Policy, Q15, Q23, Q24, Q54,

Spatial Analysis of Rural Economic Development Using a Locally Weighted Regression Model

This study uses locally weighted regression to identify county-level characteristics that serve as drivers of creative employment throughout the southern United States. We found that higher per capita income, greater infrastructure investments, and the rural nature of a county tended to promote creative employment density, while higher scores on a natural amenity index had the opposite effect. We were also able to identify and map clusters of rural counties where the marginal effects of these variables on creative employment density were greatest. These findings should help rural communities to promote creative employment growth as a means of furthering rural economic development.creative class, locally weighted regression, natural amenities, rural economic development, Community/Rural/Urban Development,