Examining longitudinal patterns of individual neighborhood deprivation trajectories in the province of Quebec: A sequence analysis application

GIScience and neighborhood change: Toward an understanding of processes of change

“…Greenlee (2019) further linked neighborhood sequences with disaggregate residential mobility data to apprehend a multi‐scalar approach toward understanding processes of change. The development of historical trajectories linked with health outcomes is helping to advance research on neighborhood effects by taking a longitudinal perspective on both the place and the individual (Letarte et al, 2021).…”

Association between Neighbourhood Deprivation Trajectories and Self-Perceived Health: Analysis of a Linked Survey and Health Administrative Data

“…This study is part of larger research interested in the creation of indicators of neighbourhood deprivation trajectories (NDTs) for the population of Quebec, Canada. NDTs constitute a measure of long-term exposure to deprivation; for example, it examines the sequence of exposure to greater neighbourhood-level deprivation [24,29]. Sequence analysis based on optimal matching and clustering around theoretical types has been used to construct an indicator of NDTs [29].…”

“…NDTs constitute a measure of long-term exposure to deprivation; for example, it examines the sequence of exposure to greater neighbourhood-level deprivation [24,29]. Sequence analysis based on optimal matching and clustering around theoretical types has been used to construct an indicator of NDTs [29]. This indicator should be useful in epidemiological surveillance in Quebec and can be replicated in different provinces of Canada [29].…”

“…Sequence analysis based on optimal matching and clustering around theoretical types has been used to construct an indicator of NDTs [29]. This indicator should be useful in epidemiological surveillance in Quebec and can be replicated in different provinces of Canada [29]. This study thus aimed to estimate the association between long-term NDTs and poor SPH status.…”

A manifesto for computational sociology: The Canadian perspective

“…Computational methods include a gamut of different techniques including machine learning (e.g., deep learning, statistical learning, reinforcement learning), social network analysis, text and data mining (e.g., sentiment analysis, topic modelling, named‐entity recognition), agent‐based modelling, more flexible regression/estimation models (e.g., regression shrinkage and selection, Bayesian statistics, spatial regression models), advances in survey methods (e.g., survey experiments, optimum design, respondent‐driven sampling), and so on. Some sociologists in Canada have contributed directly to the development of particular methods (Alexander & Alkema, 2021; Andersen, 2008; Bignami‐Van Assche et al., forthcoming; Fosse & Winship, 2019; Fox, 2015; Fox & Andersen, 2006; Fu et al., 2020, 2021; Hayduk, 1996; Li et al., forthcoming; Miles, 2016; Nelson, 2020; Stecklov et al., 2018; Wellman et al., 2003, 2020), but more often sociologists have embraced and adapted methods developed by computer scientists, statisticians, and econometricians (Abul‐Fottouh et al., 2020; Boase, 2016; Das, 2022; Gallupe et al., 2019; Gruzd & Mai, 2020; Gu et al., 2021; Hogan & Berry, 2011; Howe et al., forthcoming; Kudla & Parnaby, 2018; Letarte et al., 2021; Li & Luo, 2020; McLevey, 2022; McMahan & McFarland, 2021; Quan‐Haase et al., 2021; Richardson et al., 2021; Roth et al., forthcoming; Shor & Miltsov, 2020; Shor et al., 2013; Silver & Silva, 2021; Smith, 2020; Sytsma et al., 2021; Veenstra & Vanzella‐Yang, 2022; Yuan et al., 2022).…”