Two Types of Planning in Neighborhoods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68338/2/10.1177_0739456X8400300209.pd

Modern American populism: Analyzing the economics behind the Silent Majority, the Tea Party and Trumpism

This article researches populism, more specifically, Modern American Populism (MAP), constructed of white, rural, and economically oppressed reactionarianism, which was borne out of the political upheaval of the 1960’s Civil Rights movement. The research looks to explain the causes of populism and what leads voters to support populist movements and politicians. The research focuses on economic anxiety as the main cause but also examines an alternative theory of racial resentment. In an effort to answer the question, what causes populist movements and motivations, I apply a research approach that utilizes qualitative and quantitative methods. There is an examination of literature that defines populism, its causes and a detailed discussion of the case studies, including the 1972 election of Richard Nixon; the Tea Party election of 2010; and the 2016 election of Donald Trump. In addition, statistical data analysis was run using American National Election Studies (ANES) surveys associated with each specific case study. These case studies were chosen because they most represent forms of populist movements in modern American history. While ample qualitative evidence suggested support for the hypothesis that economic anxiety is a necessary condition for populist voting patterns that elected Nixon, the Tea Party and Trump, the statistical data only supported the hypothesis in two cases, 2010 and 2016, with 1972 coming back inconclusive. The data also suggested that both economic anxiety and racial resentment played a role in 2010 and 2016, while having no significant effect in 1972 in either case. This suggests that further research needs to be conducted into additional populist case studies, as well as an examination into the role economic anxiety and economic crises play on racial resentment and racially motivated voting behavior

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Neoliberalism with a community face?:A critical analysis of asset-based community development in Scotland

In this article, we trace the ideological and social policy roots of asset-based community development (ABCD) in the United States and the United Kingdom, and explore how this approach has been legitimized in Scotland. We argue that ABCD is a capitulation to neoliberal values of individualization and privatization. Drawing on findings from our empirical work, we discuss how ABCD generates dilemmas for community development. Although some practitioners are able to adapt ABCD to focus on renewing Scottish democracy, several practitioners are using ABCD to privatize public issues such as inequality and justify dramatic cuts to the Scottish welfare state

Mapping and monitoring cheatgrass dieoff in rangelands of the Northern Great Basin, USA

Understanding cheatgrass (Bromus tectorum) dynamics in the Northern Great Basin rangelands, USA, is necessary to effectively manage the region's lands. This study's goal was to map and monitor cheatgrass performance to identify where and when cheatgrass dieoff occurred in the Northern Great Basin and to discover how this phenomenon was affected by climatic, topographic, and edaphic variables. We also examined how fire affected cheatgrass performance. Land managers and scientists are concerned by cheatgrass dieoff because it can increase land degradation, and its causes and effects are not fully known. To better understand the scope of cheatgrass dieoff, we developed multiple ecological models that integrated remote sensing data with geophysical and biophysical data. The models' R2 ranged from 0.71 to 0.88, and their root mean squared errors (RMSEs) ranged from 3.07 to 6.95. Validation of dieoff data showed that 41% of pixels within independently developed dieoff polygons were accurately classified as dieoff, whereas 2% of pixels outside of dieoff polygons were classified as dieoff. Site potential, a long-term spatial average of cheatgrass cover, dominated the development of the cheatgrass performance model. Fire negatively affected cheatgrass performance 1 year postfire, but by the second year postfire performance exceeded prefire levels. The landscape-scale monitoring study presented in this paper helps increase knowledge about recent rangeland dynamics, including where cheatgrass dieoffs occurred and how cheatgrass responded to fire. This knowledge can help direct further investigation and/or guide land management activities that can capitalize on, or mitigate the effects of, cheatgrass dieoff. © 2015 Society for Range Management. Published by Elsevier Inc. All rights reserved.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information