Places and preferences: a longitudinal analysis of self-selection and contextual effects

Contextual theories of political behaviour assert that the contexts in which people live influence their political beliefs and vote choices. Most studies of political assimilation, however, rely on cross-sectional data and fail to distinguish contextual influence from self-selection of individuals into areas. This paper advances understanding of this longstanding controversy by tracking thousands of individuals over an 18-year period in England. We observe individual-level left-right position and party identification before and after residential moves across areas with different political orientations. We find evidence of both non-random selection into areas and assimilation of new entrants to the majority political orientation. However, these effects are contingent on the type of area an individual moves to and, moreover, contextual effects are weak and dominated by the larger effect of self-selection into areas.

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.