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Appearance has two meanings. On the one hand, appearance is about the physical qualities of a person being of certain height, weight, complexion and having a particular hair, eye and skin colour as well as choice and style of dress and attire. On the other hand, appearance has a social dimension, as those physical qualities of a person are interpreted, rated and judged, and attributed varied meanings and values across different settings. Appearances can influence the experiences of individuals in the workplace in both positive and negative ways: Positive, when they are mobilised as a resource that increases the influence and advantage of individuals on others; and negative, when individuals are discriminated or disadvantaged on the basis of their appearance. Drawing on a Bourdieusian conceptual repertoire, this chapter delves into this duality of appearance and frames appearance both as a resource (a form of carnal capital) and a source of symbolic violence. As appearance is an aspect of an individual's self-identity in the workplace, this chapter explores appearance and intersectionality across gender, ethnicity, class and sexual orientation at work. Appearance is examined as a cross cutting category of diversity as both privilege (carnal capital) and disadvantage (symbolic violence).

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.