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Social science and popular media have described political polarization as a threat to democracy and effective policy. Scholars connect right/left political divides to macro‐level social divisions, such as those between rural and urban residents, environmentalists and farmers, and pro‐versus anti‐government sentiments. While previous scholars have complicated these dichotomies, political polarization scholarship often seeks out evidence of polarization without considering these complications. In addition, we know little about how polarization is affecting community responses to social problems. This paper explores the rhetoric of political and social polarization as it appears in community responses to a particular social problem, the decline of small and mid‐sized dairy farms in southern Wisconsin. During interviews, farmers, municipal leaders, and community members indeed used polarizing rhetoric and identified polarization as a problem in their communities. We argue, though, that the connections between commonly bifurcated identities, including a common attachment to the land and relationships across the rural–urban continuum, are equally important. We conclude by encouraging policy responses meant to address the fallout of the loss of mid‐sized dairy farms to draw on these connections to avoid inadvertently reinforcing political divisions. [ABSTRACT FROM AUTHOR]

Impervious surface distribution and its temporal changes are considered key urbanization indicators and are utilized for analysing urban growth and influences of urbanization on natural environments. Recently, urban impervious surface information was extracted from medium/coarse resolution remote sensing imagery (e.g. Landsat ETM+ and AVHRR) through spectral analytical methods (e.g. spectral mixture analysis (SMA), regression tree, etc.). Few studies, however, have attempted to generate impervious surface information from high resolution remotely sensed imagery (e.g. IKONOS and Quickbird). High resolution images provide detailed information about urban features and are, therefore, more valuable for urban analysis. The improved spatial resolution, however, also brings new challenges when existing spectral analytical methods are applied. In particular, a higher spatial resolution leads to reduced boundary effects and increased within-class variability. Taking Grafton, Wisconsin, USA as a study site, this paper analyses the spectral characteristics of IKONOS imagery and explores the applicability of SMA for impervious surface estimation. Results suggest that with improved spatial resolution, IKONOS imagery contains 40-50% of mixed urban pixels for the study area, and the within-class variability is a severe problem for spectral analysis. To address this problem, this paper proposes two approaches, interior end-member set selection and spectral normalization, for SMA. Analysis of results indicates that these approaches can reasonably reduce the problems associated with boundary effects and within-class variability, therefore generating better impervious surface estimates. [ABSTRACT FROM AUTHOR]