Matthew Quick’s research while affiliated with Arizona State University and other places

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Publications (20)


Urban-rural variation in the association between social support availability and cognitive function in middle-aged and older adults: Results from the baseline Tracking Cohort of the Canadian Longitudinal Study on Aging
  • Article

September 2022

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19 Reads

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3 Citations

Health & Place

Matthew Quick

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Emily Rutter

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The purpose of this study was to investigate if and how the associations between social support availability (SSA) and cognitive function varied across urban, rural, and geographical regions in Canada. Data from a population-level sample of community-dwelling adults aged 45–85 years were obtained from the baseline Tracking Cohort of the Canadian Longitudinal Study on Aging. The associations between SSA and two domains of cognitive function, memory and executive function, were analyzed using multilevel regression models. SSA was positively and significantly associated with both executive function and memory. We found SSA had stronger positive associations with executive function among participants living in rural areas compared to urban areas in all geographical regions; however, geographical variation in the associations between SSA and memory were not supported by model results. Understanding how the associations between cognitive function and modifiable risk factors, including SSA, vary across geographical contexts is important for developing policies and programs to support healthy aging.


Exploring the global and local patterns of income segregation in Toronto, Canada: A multilevel multigroup modeling approach

June 2021

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45 Reads

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6 Citations

Environment and Planning B Urban Analytics and City Science

Residential income segregation is a spatial manifestation of social inequality and is an important factor that influences access to resources, services, and amenities. In general, past research analyzing income segregation has applied index-based methods to describe the separation of low-income households at one spatial scale; however, existing studies have not yet considered how income segregation varies across multiple income classes, spatial scales, and local contexts. This study applies a multilevel multigroup modeling approach to explore the global and local patterns of income segregation between dissemination areas (micro-scale), census tracts (meso-scale), and neighborhoods (macro-scale) in Toronto, Canada. A global model that estimates the overall multiscale segregation of five income classes finds that the most affluent families had the highest levels of segregation and that the segregation of all income classes was strongest at the macro- and micro-scales. A local model that allows the micro-scale segregation measures to vary geographically shows that higher-income families were less segregated in the city center than in the inner suburbs, that middle-income families were highly segregated in areas serviced by public transit, and that almost all income classes had high levels of segregation in disadvantaged neighborhoods prioritized for investment by local policymakers. The methodological and substantive contributions of this study for understanding the complex patterns of income segregation are discussed.


FIGURE 1. ER service areas for the province of Ontario
FIGURE 2. Data analysis approach used to quantify the association between ER wait time and marginalization
Examining the Association between Community-Level Marginalization and Emergency Room Wait Time in Ontario, Canada
  • Article
  • Full-text available

May 2020

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115 Reads

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6 Citations

Healthcare policy = Politiques de sante

Objective: This study examines the association between community-level marginalization and emergency room (ER) wait time in Ontario. Methods: Data sources included ER wait time data and Ontario Marginalization Index scores. Linear regression models were used to quantify the association. Results: A positive association between total marginalization and overall, high-acuity and low-acuity ER wait time was found. Considering specific marginalization dimensions, we found positive associations between residential instability and ER wait time and negative associations between dependency and ER wait time. Conclusions: Reductions in community-level marginalization may impact ER wait time. Future studies using individual-level data are necessary.

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The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis

May 2020

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47 Reads

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7 Citations

Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low-educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.


Figure 2. Crime-general (exp(θ i )) and crime-specific spatial patterns (exp(s ik þ u ik )).
A Bayesian spatial shared component model for identifying crime-general and crime-specific hotspots

January 2020

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194 Reads

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18 Citations

Annals of GIS

The spatial patterning of crime hotspots provides place-based information for the design, allocation, and implementation of crime prevention policies and programmes. However, most spatial hotspot identification methods are univariate, analyse a single crime type, and do not consider if hotspots are shared amongst multiple crime types. This study applies a Bayesian spatial shared component model to identify crime-general and crime-specific hotspots for violent crime and property crime at the small-area scale. The spatial shared component model jointly analyzes both violent crime and property crime and separates the area-specific risks of each crime type into one shared component, which captures the underlying crime-general spatial pattern common to both crime types, and one type-specific component, which captures the crime-specific spatial pattern that diverges from the shared pattern. Crime-general and crime-specific hotspots are classified based on the posterior probability estimates of the shared and type-specific components, respectively. Results show that the crime-general pattern explains approximately 81% of the total variation of violent crime and 70% of the total variation of property crime. Crime-general hotspots are found to be more frequent than crime-specific hotspots, and property crime-specific hotspots are more frequent than violent crime-specific hotspots. Crime-general and crime-specific hotspots are areas that may be targeted with comprehensive initiatives designed for multiple crime types or specialized initiatives designed for a single crime type, respectively.


Exploring diverse lived experiences in the Smart City through Creative Analytic Practice

January 2020

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48 Reads

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40 Citations

Cities

The “smart city” is a set of policies and programs that aim to increase the efficiency and effectiveness of municipal services, encourage urban (re)development, facilitate private investment, and improve quality of life through investing in information and communication technologies. Yet critics contend that the benefits of investment and (re)development programs are not shared equally and there is need for better understanding diverse lived experiences of community members. This paper highlights local voices and lived experiences amid physical and social change in downtown Kitchener, Ontario, Canada, a city that is actively pursuing a smart city agenda. Drawing on data collected from a community workshop that included representatives from local government, technology and start-up sectors, community service providers, and community activists, we adopt a Creative Analytic Practice (CAP) approach and present a set of hybrid vignettes centered on the experiences of these stakeholder groups. The vignettes highlight differing expectations for the role of technology in promoting quality of life and illustrate a struggle to translate aspirations for collaboration and equity into on-the-ground action. We argue that CAP is an effective tool for presenting and enhancing the inclusiveness and accessibility of smart city discussions and debates.


Total violent crime counts at the dissemination area scale (a) and the five-year violent crime trend (b). The central commercial corridor is highlighted in (a)
The geographical boundaries of the lower- and higher-level units. The central business districts in Cambridge, Kitchener, and Waterloo are highlighted in (a). The central commercial corridor is highlighted in (b), (c), and (d)
Posterior medians of the higher-level neighbourhood (exp(γ1)) (a), electoral ward expλωj2+γ2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {\exp \left( {\lambda \omega_{{j_{2} }} + \gamma_{2} } \right)} \right)$$\end{document} (b), and police patrol zone (exp(γ3)) terms (c)
Neighbourhood hot spots and coldspots (a) and total dissemination area violent crime within two hot spots and two coldspots (b). Dissemination areas with red boundaries had DA effects (posterior median of exp(ui)) that were greater than neighbourhood effects (posterior median of exp(γ1)). The insets of H1, H2, C1, and C2 are not to scale
Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach

September 2019

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129 Reads

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15 Citations

Journal of Geographical Systems

Characteristics of the urban environment influence where and when crime events occur; however, past studies often analyse cross-sectional data for one spatial scale and do not account for the processes and place-based policies that influence crime across multiple scales. This research applies a Bayesian cross-classified multilevel modelling approach to examine the spatiotemporal patterning of violent crime at the small-area, neighbourhood, electoral ward, and police patrol zone scales. Violent crime is measured at the small-area scale (lower-level units) and small areas are nested in neighbourhoods, electoral wards, and patrol zones (higher-level units). The cross-classified multilevel model accommodates multiple higher-level units that are non-hierarchical and have overlapping geographical boundaries. Results show that violent crime is positively associated with population size, residential instability, the central business district, and commercial, government-institutional, and recreational land uses within small areas and negatively associated with civic engagement within electoral wards. Combined, the three higher-level units explain approximately fifteen per cent of the total spatiotemporal variation of violent crime. Neighbourhoods are the most important source of variation among the higher-level units. This study advances understanding of the multiscale processes influencing spatiotemporal crime patterns and provides area-specific information within the geographical frameworks used by policymakers in urban planning, local government, and law enforcement.


Spatiotemporal Modeling of Correlated Small-Area Outcomes: Analyzing the Shared and Type-Specific Patterns of Crime and Disorder

October 2018

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75 Reads

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11 Citations

Geographical Analysis

This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small‐area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best‐fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type‐specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed.


Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale

September 2018

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89 Reads

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46 Citations

Journal of Criminal Justice

Purpose To examine if, and how, spatial crime patterns are explained by one or more underlying crime-general patterns. Methods A set of Bayesian multivariate spatial models are applied to analyze burglary, robbery, vehicle crime, and violent crime at the small-area scale. The residual variability of each crime type is partitioned into shared and type-specific components after controlling for the effects of population density, deprivation, residential instability, and ethnic heterogeneity. Shared components account for the correlations between crime types and identify the crime-general patterns shared amongst multiple crimes. Results Two shared components are estimated to capture the crime-general pattern for all four crime types and the crime-general pattern for theft-related crimes (burglary, robbery, and vehicle crime). Robbery and violent crime exhibit the strongest positive associations with deprivation, instability, and ethnic heterogeneity. Shared components explain the largest proportions of variability for all crime types. Burglary, robbery, and vehicle crime each exhibit type-specific patterns that diverge from the crime-general patterns. Conclusions Crime-general patterns are important for understanding the spatial patterning of many crime types at the small-area scale. Multivariate spatial models provide a framework to directly quantify the correlation structures between crimes and reveal the underlying crime-general patterns shared amongst multiple crime types.


Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends

December 2017

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94 Reads

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35 Citations

Environment and Planning B Urban Analytics and City Science

Neighborhood land use composition influences the geographical patterns of property crime. Few studies, however, have investigated if, and how, the relationships between land use and crime change over time. This research applies a Bayesian spatio-temporal regression model to analyze 12 seasons of property crime at the small-area scale. Time-varying regression coefficients estimate the seasonally varying relationships between land use and crime and distinguish both time-constant and season-specific effects. Seasonal property crime trends are commonly hypothesized to be associated with fluctuating routine activity patterns around specific land uses, but past studies do not quantify the time-varying effects of neighborhood characteristics on small-area crime risk. Results show that, accounting for sociodemographic contexts, parks are more positively associated with property crime during spring and summer seasons, and eating and drinking establishments are more positively associated during autumn and winter seasons. Land use is found to have a more substantial impact on spatial, rather than spatio-temporal, crime patterns. Proposed explanations for results focus on seasonal activity patterns and corresponding spatio-temporal interactions with the built environment. The theoretical and analytical implications of this modeling approach are discussed. This research advances past cross-sectional spatial analyses of crime by identifying built environment characteristics that simultaneously shape both where and when crime occurs.


Citations (19)


... To the best of our knowledge, no prior studies have explored cognitive function disparities based on residence and region in Indonesia. However, studies from other countries consistently showed that elderly individuals living in urban areas tend to have a lower prevalence of poor cognitive function compared to their rural counterparts; this pattern has been observed in China [27], Canada [28], and Chicago [29]. One plausible explanation for this discrepancy is that urban-dwelling older individuals have greater access to social activities and healthcare resources. ...

Reference:

Sleep quality and cognitive function on self-rated health status among the elderly: Findings from the Indonesian family life survey (IFLS-5)
Urban-rural variation in the association between social support availability and cognitive function in middle-aged and older adults: Results from the baseline Tracking Cohort of the Canadian Longitudinal Study on Aging
  • Citing Article
  • September 2022

Health & Place

... If, however, the conditions in which children are raised matters more than where they enter the labor market (e.g., Chetty et al., 2014), rural childhoods could confer long-lasting benefits. Small towns are, after all, noted for having high levels of community trust, social capital, accelerated paths through key life states, and, at least historically, more two-parent households (Heaton et al., 1989;Hofferth & Iceland, 1998;Miller & Edin, 2022;Putnam, 2016;Wirth, 1938). 2 Prior findings, including those from the Fragile Families and Child Wellbeing Study and Opportunity Insights, highlight the critical role of family and community conditions in shaping children's early cognitive development and later life attainment (Chetty et al., 2022a;Dupraz & Ferrara, 2023;Jackson et al., 2017;James et al., 2021;McLanahan & Sandefur, 2009). These findings are related to a longer and contested literature (Moynihan, 1965;Wilson, 1987), describing what Kearney (2023) refers to as the "Two-Parent Privilege". ...

Exploring the global and local patterns of income segregation in Toronto, Canada: A multilevel multigroup modeling approach
  • Citing Article
  • June 2021

Environment and Planning B Urban Analytics and City Science

... Few health-system issues command as much public attention as Emergency Department (ED) crowding, a highly visible source of prolonged suffering and risk to patients (McDonald et al., 2020). The problem is particularly acute in Canada, which persistently shows the highest ED utilization rates and longest waits among similar countries (Canadian Institute for Health Information, 2021). ...

Examining the Association between Community-Level Marginalization and Emergency Room Wait Time in Ontario, Canada

Healthcare policy = Politiques de sante

... Email: pablo.escobar@uv.es against woman (IPVAW) and child maltreatment , crimes against women (Vicente, Goicoa & Ugarte , 2021), vehicle theft, larceny, and burglary (Chung & Kim , 2019), disadvantage variables (Quick & Luan , 2021), police calls reporting street-level violence and behind-closed-doors crime (Marco, Gracia, López-Quílez & Lila , 2021) and substantiated and unsubstantiated child maltreatment referrals (Marco, Maguire-Jack, Gracia & López-Quílez , 2020). ...

The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis
  • Citing Article
  • May 2020

... In this regard, BSCS modeling, as a joint-modeling technique, allows adjusting for the multidimensionality associated with the main and higher-order interaction effects of the studied outcomes (YO and VC) and any confounders (Papageorgiou et al., 2015). Lastly, the use of BSCS modeling allows the realization of three major spatial processes within the model architecture (Cesaroni & Doob, 2020): first, the youth crime, which can be modeled as a function of the spatial processes occurring across different neighborhoods in the study area; second, the influence from the putative risk factors that affect the distribution of YO and VC (Law & Quick, 2013;Law et al., 2015Law et al., , 2020, and lastly, the influence of non-spatial protective measures, such as the youth justice system that responds to the spatially varying occurrence of violent youth crimes (Cesaroni & Doob, 2020). Hence, the outputs of BSCS models have an intuitive meaning that can be used for assessing crime risks, mapping shared and YO-or VC-specific hotspots, and understanding high-priority areas for crime management interventions that can simultaneously target to reduce risk from YO and VC. ...

A Bayesian spatial shared component model for identifying crime-general and crime-specific hotspots

Annals of GIS

... Other studies have focused on addressing local issues and benefiting communities, underlining the role of local governments and communities in the inclusive adoption of smart solutions (Edge et al. 2020;Harrison et al. 2020;Lockwood 2020;Nguyen, Marques, and Benneworth 2022). Urban planning involves consideration of different scales and dimensions, each with its own context, and technologies and data can support the application of customized solutions tailored to these specific contexts (Sidani, Veglianti, and Maroufkhani 2022). ...

Exploring diverse lived experiences in the Smart City through Creative Analytic Practice
  • Citing Article
  • January 2020

Cities

... The spatial frequentist techniques include the zero-inflated negative binomial model (Liu et al., 2018;Swartout et al., 2015), geographically weighted negative binomial regression (GWNBR) (Chen et al., 2020;Wang et al., 2017), spatial Durbin (R. P. Haining & Li, 2020), and spatial spline regression models (Sangalli et al., 2013). Additionally, with the advancement of computational power, Bayesian spatial techniques have gained considerable popularity, such as the Bayesian Poisson hierarchical regression (Law & Haining, 2004;Law & Quick, 2013;Persad, 2020;Quick et al., 2017), Bayesian semiparametric joint quantile regression (Bresson et al., 2021;Chen & Tokdar, 2021;Jang & Wang, 2015;Kottas & Krnjajić, 2009), Bayesian cross-classified multilevel spatial (and temporal) modeling (Quick, 2019) and Bayesian spatial network learning (Baumgartner et al., 2005;Mahmud et al., 2016). Each of these techniques is applied considering different aspects of crime and poses its own advantages and disadvantages. ...

Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach

Journal of Geographical Systems

... Based on the review of existing literature, the BSCS modeling technique has been predominantly used as a hotspot detection technique (Ancelet, Abellan, Del Rio Vilas, Birch, & Richardson, 2012;R. P. Haining & Li, 2020;Ibáñez-Beroiz et al., 2011;Knorr-Held & Best, 2001;Law & Perlman, 2018;Law et al., 2020;MacNab, 2010;Paradinas et al., 2017;Quick et al., 2019), and this is the first study that has extended the statistical principles of shared component modeling and applied the modeling technique for detecting the association between multiple outcomes of crime. ...

Spatiotemporal Modeling of Correlated Small-Area Outcomes: Analyzing the Shared and Type-Specific Patterns of Crime and Disorder
  • Citing Article
  • October 2018

Geographical Analysis

... Additionally, the patterns of crime distribution throughout the day and week can also vary. Quick et al. (2017) found the impact of landuse on crime patterns in more significant when looking at the physical location of crimes. This study also found that previous studies have discovered that there is a positive relationship between the spatial distribution of property crime and non-residential land uses, such as commercial areas and public transit stations. ...

Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends
  • Citing Article
  • December 2017

Environment and Planning B Urban Analytics and City Science