Tax Limits, Houses, and Schools: Seemingly Unrelated and Offsetting Effects

Property tax limitations, as well as other tax and expenditure restrictions on state and local governments in the United States, date back to the late nineteenth century. A surge in property tax limitation legislation occurred in the late 1970s and early 1980s, and its effects on government revenue, school financing, and educational quality have been studied extensively. However, there is surprisingly little literature on how property tax limits affect housing markets. For the first time, we examine the impacts of property tax limitations on housing growth, in addition to their impacts on housing prices. Using state-level data over twenty-three years, we find that property tax limits increase housing prices (indexes) by approximately 1.6%. These limits appear to have little impact on the growth in the housing stock, as measured by the number of permits. Our evidence suggests that this is because while property tax limits reduce property taxes they also increase the price of housing. These two counteracting effects lead to ambiguous impacts on the gross price of housing.

Effects of plot size, stand density, and scan density on the relationship between airborne laser scanning metrics and the gini coefficient of tree size inequality

© 2017, Canadian Science Publishing. All rights reserved. Estimation of the Gini coefficient (GC) of tree sizes using airborne laser scanning (ALS) can provide maps of forest structure across the landscape, which can support sustainable forest management. A challenge arise s in determining the optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450m 2 , which thus was considered the optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced that at least 3 points·m −2 were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing estimation of any forest attribute

PEGASUS: A multi-megawatt nuclear electric propulsion system

A propulsion system (PEGASUS) consisting of an electric thruster driven by a multimegawatt nuclear power system is proposed for a manned Mars mission. Magnetoplasmadynamic and mercury-ion thrusters are considered, based on a mission profile containing a 510-day burn time (for a mission time of approximately 1000 days). Both thrusters are capable of meeting the mission parameters. Electric propulsion systems have significant advantages over chemical systems, because of high specific impulse, lower propellant requirements, and lower system mass. The power for the PEGASUS system is supplied by a boiling liquid-metal fast reactor. The power system consists of the reactor, reactor shielding, power conditioning subsystems, and heat rejection subsystems. It is capable of providing a maximum of 8.5 megawatts of electrical power of which 6 megawatts is needed for the thruster system, leaving 1.5 megawatts available for inflight mission applications

Unconditional Transfers and Tropical Forest Conservation: Evidence from a Randomized Control Trial in Sierra Leone

Unconditional conservation payments are increasingly used by conservation non-governmental organizations to further their environmental objectives. One key objective in many conservation projects that use such unconditional payments schemes is the protection of tropical forest ecosystems in buffer zone areas around protected parks where the scope of instating mandatory restrictions is more limited. We use a randomized controlled trial to evaluate the impact of unconditional livelihood payments to local communities on land use outside a protected area – the Gola Rainforest National Park – which is a biodiversity hotspot on the border of Sierra Leone and Liberia. High resolution RapidEye satellite imagery from before and after the intervention was used to determine land use changes in treated and control villages. We find support for the hypothesis that unconditional payments, in this setting, increase land clearance in the short run. The study constitutes one of the first attempts to use evidence from a randomized control trial to evaluate the efficacy of conservation payments and provides insights for further research.Cambridge Conservation Initiative International Initiative for Impact Evaluation (grant # TW1.1042) NWO (#45-14-001 and #451-14-001

Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Abstract Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind. </jats:sec

Post-Simulation Structured Debriefing on Clinical Reasoning Skills Among Associate Degree Nursing Students: A Randomized Controlled Trial

Title from PDF of title page viewed June 5, 2019Dissertation advisor: Carol SchmerVitaIncludes bibliographical references (pages 79-85)Thesis (Ph.D.)--School of Nursing and Health Studies. University of Missouri--Kansas City, 2019Background: Debriefing is considered the most important aspect of simulation. As nursing programs utilize simulation as a substitution for traditional clinical experiences, it is necessary to compare different types of debriefing and their impact to student learning. The purpose of this randomized-controlled trial was to compare the effects of a structured debriefing method, Debriefing for Meaningful Learning©, (DML), and an unstructured debriefing method following a simulation activity on clinical reasoning skills among associate degree nursing students. Methods: Participants from one Midwest associate degree nursing program were randomized to the intervention group or the attention-control group following a simulation activity. The intervention group received the DML method and the attention-control group received an unstructured debriefing. Demographics and the Nurses Clinical Reasoning Scale pretest and post-test were collected and analyzed. Results: In this study, 67 associate degree nursing students participated with 33 in the intervention group and 34 in the attention-control group. The average age of participants was 28 and 61 participants were female. On average, participants who received the DML intervention scored 0.29076 higher on the clinical reasoning post-test than the participants who received the unstructured debriefing. This was statistically significant (p=.032) between the intervention group and the attention-control group on the pre-test and post-test clinical reasoning scores. Conclusion: The results suggest that using the DML structured debriefing following a simulation activity may increase the clinical reasoning skills of associate degree nursing students. Future studies are needed utilizing multiple research sites. It is recommended to utilize an instrument that is more objective in nature and that the debriefing facilitator be evaluated on the implementation of the intervention after receiving training and prior to data collection.Introduction -- Literature review -- Methods -- Results -- Appendix A. Letter of support - Labette Community College -- Appendix B. Institutional Review Board approval - UMKC -- Appendix C. Exempt research study information sheet -- Appendix D. Nurses Clinical Reasoning Scale- Permission email -- Appendix E. Debriefing for meaningful learning - permission letter -- Appendix F. Nurses Clinical Reasoning Scale - pretest -- Appendix G. Demographic information sheet -- Appendix H. Prebriefing simulation scenario -- Appendix I. Nurses Clinical Reasoning Scale - post-tes

How landscapes change: integration of spatial patterns and human processes in temperate landscapes of southern Chile

A comprehensive understanding of the patterns that occur as human processes transform landscapes is necessary for sustainable development. We provide new evidence on how landscapes change by analysing the spatial patterns of human processes in three forest landscapes in southern Chile at different states of alteration (40%&#-90% of old-growth forest loss). Three phases of landscape alteration are distinguished. In Phase I (40%&amp;amp;-65% of old-growth forest loss), deforestation rates are < 1% yr&;8722#1, forests are increasingly degraded, and clearance for pastureland is concentrated on deeper soils. In Phase II (65%&#-80%), deforestation reaches its maximum rate of 1&amp;amp;-1.5% yr&;8722#1, with clearance for pastureland being the main human process, creating a landscape dominated by disturbed forest and shrubland. In this phase, clearance for pastureland is the primary driver of change, with pastures expanding onto poorer soils in more spatially aggregated patterns. In Phase III (80%&#-90%), deforestation rates are again relatively low (<1% yr&;8722#1) and forest regrowth is observed on marginal lands. During this phase, clearance is the dominant process and pastureland is the main land cover. As a forest landscape is transformed, the extent and intensity of human processes vary according to the existing state of landscape alteration, resulting in distinctive landscape patterns in each phase. A relationship between spatial patterns of land cover and human-related processes has been identified along the gradient of landscape alteration. This integrative framework can potentially provide insights into the patterns and processes of dynamic landscapes in other areas subjected to intensifying human use.European ComissionFONDECYT Chil

Individual Tree Species Classification from Airborne Multisensor Imagery Using Robust PCA

Remote sensing of individual tree species has many applications in resource management, biodiversity assessment, and conservation. Airborne remote sensing using light detection and ranging (LiDAR) and hyperspectral sensors has been used extensively to extract biophysical traits of vegetation and to detect species. However, its application for individual tree mapping remains limited due to the technical challenges of precise coalignment of images acquired from different sensors and accurately delineating individual tree crowns (ITCs). In this study, we developed a generic workflow to map tree species at ITC level from hyperspectral imagery and LiDAR data using a combination of well established and recently developed techniques. The workflow uses a nonparametric image registration approach to coalign images, a multiclass normalized graph cut method for ITC delineation, robust principal component analysis for feature extraction, and support vector machine for species classification. This workflow allows us to automatically map tree species at both pixel- and ITC-level. Experimental tests of the technique were conducted using ground data collected from a fully mapped temperate woodland in the UK. The overall accuracy of pixel-level classification was 91%, while that of ITC-level classification was 61%. The test results demonstrate the effectiveness of the approach, and in particular the use of robust principal component analysis to prune the hyperspectral dataset and reveal subtle difference among species.Department for Environment, Food and Rural AffairsThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/JSTARS.2016.256940

A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions

Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (QMD), Gini coefficient (GC), basal area larger than mean (BALM) and density of stems (N) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS – maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions

Aggressive shadowing of a low-dimensional model of atmospheric dynamics

Predictions of the future state of the Earth's atmosphere suffer from the consequences of chaos: numerical weather forecast models quickly diverge from observations as uncertainty in the initial state is amplified by nonlinearity. One measure of the utility of a forecast is its shadowing time, informally given by the period of time for which the forecast is a reasonable description of reality. The present work uses the Lorenz 096 coupled system, a simplified nonlinear model of atmospheric dynamics, to extend a recently developed technique for lengthening the shadowing time of a dynamical system. Ensemble forecasting is used to make forecasts with and without inflation, a method whereby the ensemble is regularly expanded artificially along dimensions whose uncertainty is contracting. The first goal of this work is to compare model forecasts, with and without inflation, to a true trajectory created by integrating a modified version of the same model. The second goal is to establish whether inflation can increase the maximum shadowing time for a single optimal member of the ensemble. In the second experiment the true trajectory is known a priori, and only the closest ensemble members are retained at each time step, a technique known as stalking. Finally, a targeted inflation is introduced to both techniques to reduce the number of instances in which inflation occurs in directions likely to be incommensurate with the true trajectory. Results varied for inflation, with success dependent upon the experimental design parameters (e.g. size of state space, inflation amount). However, a more targeted inflation successfully reduced the number of forecast degradations without significantly reducing the number of forecast improvements. Utilized appropriately, inflation has the potential to improve predictions of the future state of atmospheric phenomena, as well as other physical systems.Comment: 14 pages, 16 figure