Any space left? Homeless resistance by place-type in Los Angeles County

This study develops a more nuanced concept of homeless resistance, incorporating a range of resistance behaviors (exit, adaptation, persistence, and voice) that bridge the gap between current frameworks that either romanticize or ignore it. We also consider the possibility that different kinds of space may theoretically allow for different kinds of resistance. To this end, we employ an ecological approach to homeless space by classifying Los Angeles County into three place-types (prime, transitional, and marginal). We empirically consider the issue of resistance within the hardening context among a group of 25 homeless informants, focusing on whether and how some of them have exercised their voices and sought to ameliorate one or more aspects of their situation, as well as how resistance may vary by place-type

Towards a contextual approach to the place–homeless survival nexus: An exploratory case study of Los Angeles County

The characteristics of the immediate locale greatly affect the ability of homeless people to adapt to life on the street and in shelters, with different types of places nurturing different circumstances for survival. Current conceptualizations of the place–survival nexus are too narrow, relying on small-scale, intensive studies of particular places that are known to sustain homeless survival while ignoring more suburban and exurban locales, as well as failing to set these places of survival within the larger socio-economic spaces of the metropolitan area. Further, the literature is heavily qualitative, lacking any kind of ‘‘big picture” quantitative assessment of the nexus. In response, we contribute to the place–survival nexus literature by developing a typology of space for homeless survival and then use interview data to examine the variation in survival strategies across three types of urban space in Los Angeles County. Our results speak to how our innovative and exploratory approach enabled a broader, more extensive and variegated understanding of place–survival among homeless people than previous studie

How Will Tobacco Farmers Respond to the Quota Buyout? Findings from a Survey of North Carolina Tobacco Farmers

The tobacco quota buyout is expected to have significant impacts on U.S. tobacco markets, farmers, tobacco-dependent communities, and public health. Using data from four surveys of a panel of North Carolina tobacco farmers conducted between 1997 and 2004, we investigate changing farmer attitudes towards and intentions following a quota buyout.Crop Production/Industries,

Invasive Wild pigs as primary nest predators for Wild turkeys

Depredation of wild turkey (Meleagris gallopavo) nests is a leading cause of reduced recruitment for the recovering and iconic game species. invasive wild pigs (Sus scrofa) are known to depredate nests, and have been expanding throughout the distributed range of wild turkeys in north America. We sought to gain better insight on the magnitude of wild pigs depredating wild turkey nests. We constructed simulated wild turkey nests throughout the home ranges of 20 GPS-collared wild pigs to evaluate nest depredation relative to three periods within the nesting season (i.e., early, peak, and late) and two nest densities (moderate = 12.5-25 nests/km2, high = 25-50 nests/km2) in south-central Texas, USA during March–June 2016. Overall, the estimated probability of nest depredation by wild pigs was 0.3, equivalent to native species of nest predators in the study area (e.g., gray fox [Urocyon cinereoargenteus], raccoon [Procyon lotor], and coyote [Canis latrans]). female wild pigs exhibited a constant rate of depredation regardless of nesting period or density of nests. However, male wild pigs increased their rate of depredation in areas with higher nest densities. Management efforts should remove wild pigs to reduce nest failure in wild turkey populations especially where recruitment is low

Victory Celebrations As Theater: A Dramaturgical Approach To Crowd Behavior *

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89522/1/si.1981.4.1.21.pd

Automated Matching of Segmented Point Clouds to As-built Plans

Terrestrial laser scanning (TLS) is seeing an increase use for surveying and engineering applications. As such, there is much on-going research into automating the process for segmentation and feature extraction. This paper presents a simple method for segmenting the interior of a building and comparing it to as-built plans. The method is based on analysing the local point attributes such as curvature, surface normal direction and underlying geometric structure. Random sampling consensus (RANSAC), region growing and voting techniques are applied to identify the predominant salient surface feature to extract wall and vertical segments. This information is used to generate a 2D plan of the interior space. A distance weighted method then automatically locates the corresponding vertices between the different datasets to transform them into a common coordinate system.A traditional survey was performed alongside the 3D point cloudcapture to compare and validate the generated 2D plans and the comparison to the existingdrawings. The accuracy of such generated plans from 3D point clouds will be explored

Underreporting of wildlife-vehicle collisions does not hinder predictive models for large ungulates

Conflicts from wildlife–vehicle collisions (WVCs) pose serious challenges for managing and conserving large ungulates throughout the world. However, underreporting of large proportions of WVCs (i.e., two-thirds of WVCs in some cases) creates concern for relying on governmental databases to inform management strategies of WVCs. Our objective was to test the sensitivity of WVC studies to underreporting using 2 species of large ungulates that experience substantial incidences of collisions but exist in different environmental settings: white-tailed deer (Odocoileus virginianus) in agricultural-dominated central Illinois and moose (Alces alces) in forest-dominated western Maine, USA. We estimated baseline relationships between the landscape, traffic, and abundance of wildlife on the probabilities of WVCs using the total number of reported WVCs. Then, we simulated underreporting by randomly excluding reports of WVCs and evaluated for relative changes in precision, parameter estimates, and prediction. Point estimates of the relationships between environmental influences and WVCs for both species were reliable until high rates of underreporting occurred (≥70%). When underreporting occurred with spatial bias, shifts in point estimates were detected only for variables that spatially-corresponded with the rate of reporting. Prediction estimates for both species were also reliable until high rates of underreporting occurred (≥75%). These findings suggest that predictive models generate reliable estimates about WVCs with large ungulates unless underreporting is severe; possibly because they occur in non-random patterns (i.e., hotspots) and variability in their environment influences is low. We recommend that concern about underreporting not impede research with existing databases, such as those in this study, for analyzing predictive models and developing management strategies for reducing WVCs

A landscape-based approach for delineating hotspots of wildlife-vehicle collisions

Imposing human perceptions about the scales of ecological processes can produce unreliable scientific inferences in wildlife research and possibly misinform mitigation strategies. An example of this disconnect occurs in studies of wildlife-vehicle collisions (WVCs). Subjective procedures are often used to delineate hotspots of WVCs, resulting in hotspots that are not spatially independent. We developed a new approach that identifies independent hotspots using attributes of the landscape to inform delineations instead of subjective measures. First, we generated a candidate set of grouping scenarios using unique combinations of kernel-density estimation parameterization (i.e., bandwidth and isopleth values). Next, we associated the groups of WVCs with attributes of the surrounding landscape. Finally, we identified the grouping scenario with the highest amount of variation in the landscape among the groups. The highest variation corresponded to hotspots that were most distinguishable from each other (i.e., most independent) based on the surrounding landscape. We tested our approach on 3 species of wildlife [island foxes (Urocyon littoralis) on San Clemente Island, CA; white-tailed deer (Odocoileus virginianus) in Onondaga County, NY; and moose (Alces alces) in western Maine] that exemplified varying degrees of space-use in different landscapes. We found that the landscape based approach was able to effectively delineate independent hotspots for each species without using subjective measures. The landscape-based approach delineated fewer or larger hotspots than currently used methods, suggesting a reduction in spatial dependency among hotspots. Variation in the landscape indicated that hotspots may be larger than previously identified; therefore current mitigation strategies should be adjusted to include larger areas of high risk