Youthification across the metropolitan system: Intra-urban residential geographies of young adults in North American metropolitan areas

Cities 93 (2019) 224–237

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Youthification across the metropolitan system: Intra-urban residential geographies of young adults in North American metropolitan areas

T

Markus Moos , Pierre Filion, Matthew Quick, Robert Walter-Joseph ⁎

School of Planning, University of Waterloo, Ontario, Canada

ARTICLE INFO

ABSTRACT

Keywords: Young adults Residential geography Millennials Generations Youthification

The youthification hypothesis posits that young adult geographies are highly centralized, particularly in metropolitan regions with gentrified, amenity-rich downtowns successful in the knowledge economy. While prior studies have empirically substantiated centralized young adult geographies, none have considered intra-urban variations and linked these empirically to metropolitan-specific characteristics. Focusing on young adults aged 25 to 34 across 57 metropolitan regions in the United States and Canada with populations over one million, this study investigates how the residential geographies of young adults vary within and between metropolitan regions. Young adult geographies are analyzed via generalized additive models with cubic spline smoothing. Economic, housing, urban form, and demographic characteristics are compared between regions with different types of young adult geographies. Results show youthification to be widespread; young adult clusters exist in the downtowns of 56 metropolitan regions, with 31 regions having one downtown-focused young adult cluster and 25 regions having a multi-cluster profile. Only one region had a scattered profile with no clusters. Regions with a single centrally-located young adult profile had greater employment in the quaternary sector, higher public transit mode shares, fewer single-detached homes, and lower employment in manufacturing than those with multiple clusters. The study contributes to understanding the ways in which the residential geographies of specific age groups are shaped by aggregate characteristics of cohorts and the existing urban structures.

1. Introduction The latest cohort of young adults in North America, commonly referred to as the Millennials, has received significant attention in the academic and popular literature, where it has been argued that this generation possesses attributes, such as high educational attainment, smaller household sizes, and lifestyle values that align with centralized and higher density living in cities (Fry, 2013a; McDonald, 2015; Smith, 2005; Taylor & Keeter, 2010). The present cohort of young adults has also been disproportionately exposed to an increasingly precarious job market, uncertain economic conditions, and, in many metropolitan regions, issues of housing affordability that are closely associated with neoliberal governance, globalization, and the Great Recession of 2008–2010 (Myers, 2016; Waters, Carr, Kefalas, & Holdaway, 2011). Combined, the characteristics of the current young adult cohort and the contemporary economic and political contexts in North America have produced a particular residential geography with larger concentrations of young adults living centrally, referred to as an outcome of the “youthification” of higher density neighbourhoods (Moos, 2016). Originally explored in select Canadian metropolitan areas (Moos,

⁎

2014), there is now evidence of youthification from across the US and Canadian metropolitan systems in that young adults today have a higher tendency to reside in central neighbourhoods than previous cohorts (Lee, 2018; Moos, 2016; Moos, Pfeiffer, & Vinodrai, 2017; Patterson, Saddier, Rezaei, et al., 2014; Siedentop, Zakrzewski, & Stroms, 2018; Townshend, Miller, & Evans, 2018). However, gentrification, or the continued upscaling of central neighbourhoods, has not been replaced by youthification (Moos, 2016; Revington, 2018). Rather, the two occur both concurrently and independently as young adults are indeed part of the on-going gentrification of inner-city neighbourhoods but young adults of various socio-economic backgrounds also continue to live in central, higher density neighbourhoods, and gentrification is not exclusive to young adult cohorts (see Moos, Revington, Wilkin, & Andrey, 2018). Importantly, the youthification hypothesis proposes that young adult residential geographies have become more centralized particularly in metropolitan areas with amenity-rich, often already highly gentrified, downtowns ‘successful’ in the knowledge economy (Moos, 2016). This is because the gentrified areas continue to attract young gentrifiers (also see Hochstenbach, 2018). But also, it is because some

Corresponding author. E-mail address: [email protected] (M. Moos).

https://doi.org/10.1016/j.cities.2019.05.017 Received 14 August 2018; Received in revised form 29 April 2019; Accepted 9 May 2019 0264-2751/ © 2019 Elsevier Ltd. All rights reserved.

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young adults, due to their smaller household size and lower incomes, share apartments with roommates, whether permanently or temporarily through vacation rentals (Jiao & Wegmann, 2017). Although individual incomes may be lower, young adults living with roommates may be able to outbid larger single- or dual-earner households with children. Somewhat perversely, in some cases the young adult renters may provide a rental stream to first-time homebuyers who could be contributing to the very gentrification process that is leading to affordability concerns in the first place. While the centralization of young adults has been previously measured, past studies have thus far overlooked the variation in the residential geographies of young adults across metropolitan areas and the connections between the centralization of young adults and broader metropolitan characteristics, as the youthification hypothesis posits. Focusing on young adults aged 25 to 34 across the 57 metropolitan regions in the United States and Canada with populations over one million (in 2016), this study asks two questions regarding the residential geographies of young adults: One, do young adults adopt a centralized, multi-cluster, or scattered residential pattern within large metropolitan regions? And two, what metropolitan-level economic, housing, urban form, and demographic characteristics impact the different residential patterns exhibited by young adults? That is to say, our research considers whether it is indeed those metropolitan areas with amenity-rich downtowns, successful in the knowledge economy where young adults are most centralized. The results of this study show that most regions exhibit a monocentric profile with one downtown-focused cluster of young adults or a multi-cluster profile with two or more young adult clusters at various distances from the central-business district. This study also finds that regions with monocentric profiles have greater quaternary or knowledge-sector employment, higher public transit mode share, fewer single-detached dwellings, and less manufacturing employment than regions with multi-cluster profiles. The study thus confirms other recent analyses that observe a highly centralized residential pattern of young adults in most large North American metropolitan areas (Lee, 2018). However, our work also adds contextual nuance by applying a method that detects secondary clusters of young adults at some distance from the central business district in several metropolitan areas, and by showing that young adult location patterns are most centralized in regions with existing urban structures already tending toward such centralization. Following this introduction, we review literature characterizing the current residential geographies of young adults. We then discuss the analytical approaches used to model, classify, and characterize the young adult profiles, including their limitations. Next, we visualize and classify the young adult profiles. This is followed by an exploratory multivariate analysis that links the economic and urban form characteristics of metropolitan regions to the residential geographies of young adults. In conclusion, we discuss the implications of this study and detail directions for future research.

(McDaniel, 2004; Moos, 2014). This does not render other aspects such as class, race, or gender less important but rather highlights the importance of also studying geographies of different age groups in greater detail. Past research exploring young adult geographies has focused on specific themes: life cycle transitions in the housing market (Estiri, Krause, & Heris, 2015); the changing tendency of young adults to reside in the inner city versus suburbs over time (Lee, 2018); and the neighbourhood contexts where high concentrations of young adults are found, for instance resulting in the “youthification” of higher density neighbourhoods (Moos, 2014; Moos, 2016). We build on this research by looking at intra-urban residential geographies of young adults across a large sample of metropolitan areas. There are at least three overarching factors that would shape the intra-urban residential geographies of young adults: The characteristics of contemporary young adults, the social and economic contexts under which young adults are entering the workforce and the housing market, and the existing urban structure and characteristics of metropolitan regions in North America. Young adults today are, on average, more educated than previous generations (see Moos et al., 2017). This could help explain the interest many of them show for central locations and their urban amenities, as the literature on gentrification has long underscored the correlation between educational status and processes of neighbourhood upgrading (Glaeser, Scheinkman, & Shleifer, 1995; Naik, Kominars, Raskar, Glaeser, & Hidalgo, 2017; Smith, 2005). Young adults have also been shown to be more environmentally minded, less automobile-oriented, and less acquisitive of durable goods than earlier generations, indicating that a substantial proportion of this cohort may choose to live in central areas with high public transit accessibility, although the degree to which environmentalism factors into young adults' decisions remains understudied (Delbosc & Currie, 2013; Kalita & Whelan, 2011; Kuhnimhof et al., 2012; McDonald, 2015; Schoettle & Sivak, 2014). And, changes in driving behaviour may also be due to economic hardship rather than preferences per se (Delbosc & Ralph, 2017). Young adults have always endured financial hardship as they transition from education to the workplace and navigate the early stages of their careers, but conditions are generally considered to be worse for the present cohort of young adults than for previous young adults (Moos et al., 2017). Young adults today are confronted with the consequences of neoliberalism, which take the form of escalating university fees and long-lasting student debt (for part of this cohort), a more speculative housing market that has made housing unaffordable in more globalized and prosperous urban regions, and deregulated and increasingly polarized employment (Giroux, 2014; Mettler, 2014). The joint effects of neoliberalism and globalization produce socially fragmented metropolitan regions, which reverberate on the housing options available to the young adult cohort, which is, itself, feeling the polarization effects of neoliberalism and globalization (Hulchanski, 2010; Sassen, 1991). Furthermore, current young adults have been, and continue to be, disproportionately affected by the Great Recession because its effects were most acute as they entered the employment market (Fry, 2013a, 2013b; Lee & Painter, 2013; Mykyta, 2012; Rogers & Winkler, 2014). These economic difficulties, combined with gender equalization and an extension of the age range under the influence of youth culture, contribute to delay family formation and child bearing, making small centrally-located units appealing to a relatively larger segment of the current young adult cohort than was the case for previous generations (Martin, Astone, & Peters, 2014). Turning to urban structure, the residential patterns of young adults may follow or depart from the existing characteristics of metropolitan regions. The main impact of urban structure on young adult residential location has to do with the degree to which a region is already centralized or dispersed. For instance, the Los Angeles dispersed urban structure, whereby any activity and socioeconomic group can be juxtaposed within the super grid carved by expressways and arterials, was

2. Intra-urban young adult geographies Intra-urban residential geographies matter because they reflect broader processes of societal restructuring that channel people into specific neighbourhoods (Sampson, 2019). Intra-urban residential geographies shape access to job opportunities and social networks and may be indicative of larger exclusionary practices. Research exploring the intra-urban residential geographies of urban areas has traditionally focused on characteristics related to social class, ethnicity, race, and gender, but less attention has been paid to how intra-urban residential geographies vary by age (Gory, Ward, & Juravich, 1980; Hochstenbach, 2018; Moos, 2014). Yet, it has been argued that age has become an increasingly important factor shaping residential geographies, and that urban space has become increasingly “generationed” (i.e., delineated by age) 225

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presented as a multi-cluster alternative to the monocentric forms observed in Chicago and older east coast cities (Crampton, 1991; Gordon, Richardson, & Wong, 1986). Absent the attraction effects of a strong urban core or central business district, and the attendant parsing out of different income groups forced to compromise between accessibility to the core and the consumption of residential space, socioeconomic concentrations take a more randomized, and often quite scattered, distribution than found in prior models (Dear, 2003; Dear & Flusty, 1998). While the dispersed Los Angeles-like form of development has predominated across the United States and Canada over the last seventy years, increasing redevelopment of core areas and revitalization of inner-city neighbourhoods has led to many North American metropolitan regions retaining, and in some cases consolidating, their centralized urban form (Warner & Whittemore, 2012, pp. 102–135). The revitalization, and often gentrification, of central cities is also specific to certain kinds of metropolitan areas, such as those that have originally developed in a centralized fashion, and is often believed to be particularly strong in those with higher shares of knowledge, or new, economy workers, more expensive housing markets, existing centralized public-transit infrastructures, and a younger demographic (Moos, 2016). For instance, Frey (2012, 2014) has observed that in the early 2010s, inner cities were growing faster than the suburbs for the first time in more than nine decades and that some of this growth was attributable to the attraction of highly skilled young adults. Predilection for inner-city and high-density living has also been shown by Estiri et al. (2015), who find that young adult households in 2010 were more likely than both middle-aged and elderly households to reside in central locations characterized by mixed land uses and the presence of restaurants and amenities. Therefore, overall, we expect that young adults as a cohort will be more likely to reside in smaller dwellings in higher density neighbourhoods, which continue to be mostly located in the centre of growing major metropolitan areas. This is the case for several different reasons. First, the smaller household size of many members of this cohort and the more difficult economic circumstances many of them face will contribute to an increased tendency to move into smaller dwellings. Second, the increased educational attainment means a larger share of this cohort may hold preferences that contribute to the continued gentrification of inner-city neighbourhoods. Third, young adults of different socio-economic backgrounds may reside in higher density neighbourhoods but for different reasons: Some to live out their desires for walkability, transit-availability, and access to urban lifestyle amenities, and others to access smaller and/or shared rental apartments due to economic hardship. Finally, the diversity of the Millennial cohort may have led more of them to central areas: For instance, Frey (2018) found in the US that “racial and ethnic minorities” contributed more to the “urban core growth” of Millennials than they did to Millennial growth in suburbs or exurbs between 2010 and 2015 (p. 23). However, our data do not permit consideration of how different categories of young adults are distributed within metropolitan regions. While there are characteristics that differentiate contemporary young adults from other age cohorts as well as from previous young adult cohorts, it is important to acknowledge the internal diversity of this age group (Moos et al., 2017). Some young adults are progressing in their chosen career paths while others are pursuing education, working in insecure low-paid jobs, or are unemployed. Young adults today are also the most racially and ethnically diverse in history (Frey, 2018). Furthermore, although there has been growth in smaller households, the living conditions of young adults continue to be varied: living on their own, with their parents, as couples, with or without children, or sharing accommodation with friends or strangers (Biggart & Walther, 2006; Mitchell, 2006; Qian, 2012). Moreover, not all members of this cohort adhere to environmentalism, are attracted by the urban amenities present in city centres, or display less interest than previous generations for suburban living. We must be careful not to generalize adherence to

specific values and preferences to the entire young adult cohort even if we find patterns that suggest overarching residential concentrations near the centre of metropolitan areas. 3. Data and methods We construct and classify residential profiles that visually capture how the geography of young adults, relative to the population as a whole, varies in relation to regional central business districts (also see Walker, 2016). Data were retrieved from the 2016 United States American Community Survey and the 2016 Canadian Census for 57 metropolitan regions with populations greater than one million (Fig. 1). Large metropolitan regions were selected for analysis because, compared to smaller regions, they attract larger proportions of young adults and are more likely to exhibit diverse residential geographies that are suitable for comparative research (Frey, 2012; Moos, 2016; Plane, Henrie, & Perry, 2005). In the United States, fifty-one Metropolitan Statistical Areas (MSA) were analyzed at the block group scale (BG). In Canada, six Census Metropolitan Areas (CMA) were analyzed at the dissemination area scale (DA). 3.1. Young adult data Following prior research, young adults were defined as individuals between 25 and 34 years of age to capture predominantly young people who are at the early stages of entering housing and labour markets and mostly completed their education (Moos, 2016; Moos et al., 2017). This age group corresponds to birth years between 1982 and 1991. This subset of young adults also captures a sizeable proportion of the Millennial generation. Across the fifty-seven metropolitan regions, young adults aged 25 to 34 constituted approximately 14.46% of the total population, ranging between a minimum of 12.19% in the ClevelandElyria metropolitan region and a maximum of 17.31% in the AustinRound Rock metropolitan region (Fig. 1). The concentration of young adults within each BG and DA is measured using the location quotient (LQ). The equation for the young adult LQ is shown in Model 1, where pi and ti are the number of young adults and the total population in BG or DA i, respectively; and P and T are the total number of young adults and the total population in the host metropolitan region, respectively. LQs were calculated separately for each metropolitan region: This means we are measuring young adult geographies relative to the population of each specific metropolitan region. LQs for all BGs and DAs are interpreted relative to one, where a value of one indicates that a BG or a DA had the same proportion of young adults as the regional average, a value greater than one indicates that there was a higher proportion of young adults in a BG or a DA than the regional average, and a value less than one indicates that there was a smaller proportion of young adults in a BG or DA than the metropolitan region average.

LQ i = (pi /ti)/(P/T)

(1)

3.2. Modeling young adult profiles Young adult profiles, or visual representations of how the concentrations of young adults vary relative to the city centre (Walker, 2016), were estimated using a set of generalized additive models with cubic spline basis functions. Generalized additive models provide a statistical framework for estimating smoothed non-linear relationships between dependent and independent variables (Hastie & Tibshirani, 1990; Wood, 2006). Briefly, generalized additive models specify nonlinear and non-parametric covariates as a set of smoothed functions, where one or more independent variables are partitioned into segments and a smoothed function is fit to each segment (Wood & Augustin, 2002). We fit one generalized additive model to each region, where the 226

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Fig. 1. The percent of young adults aged 25 to 34 in the 57 metropolitan regions with populations greater than one million.

dependent variables were the young adult LQis, and the independent variables were the Euclidean distances between each DA, or BG, centroid and the corresponding central business district (CBD). For each region, one CBD was identified by the researchers at the intersection with the tallest buildings as a rough proxy for the peak value intersection (i.e., the location that has the highest land value). The tallest buildings were identified using a combination of web searches and Google Earth. The generalized additive model used to estimate the young adult profile for each metropolitan region is shown in Model 2, where the intercept is denoted by β0, the set of covariates capturing the non-linear relationships between young adult LQi and distance to the CBD (xi) are denoted by fn(xi) (n = 1, …, 10), and the set of error terms are represented by ei. For the covariates, ten segments of equal distance from the CBD were specified and a cubic spline basis function was fit to each segment. Conceptually, this divides the data into ten concentric circles focused on the CBD and fits a cubic spline basis function to the data located in each circle. This approach allows the young adult profiles to have a local minimum and a local maximum within each segment. The generalized additive modeling approach used here contrasts with the parametric functions often estimated in linear models, such as negative exponential or cubic polynomial functions, that allow for only one global minimum or global maximum (e.g., Anas, Arnott, & Small, 1998; Batty & Kim, 1992). Ten segments were chosen to standardize the analysis and interpretation across a range of metropolitan sizes. Cubic spline basis functions were chosen based on past research using cubic polynomials to explore the nonlinear relationships between the CBD and characteristics such as population density and employment density (Anderson, 1985; Skaburskis, 1989). To ensure continuity across the ten segments, generalized additive models impose constraints such that the

modeled LQi value and both the first and second derivatives of the cubic spline basis functions are equal at the location where segment n joins segment n ± 1.

LQ i =

0

+ fn (xi) + e i

(2)

3.3. Classifying young adult profiles The young adult profiles were classified based on the frequency and location of young adult clusters. At the CBD, young adult clusters were identified when the estimated concentration of young adults was greater than the regional average (i.e., modeled values of LQi were greater than one after accounting for the lower bound of the 95% confidence interval). Away from the CBD, young adult clusters were identified as locations on the profiles where a) the estimated concentration of young adults was greater than the regional average (also after accounting for the lower bound of the 95% confidence interval), and b) the slope of the young adult profile changed from positive to negative (i.e., at a local maximum). Regions with one cluster at the CBD were classified as exhibiting a monocentric residential geography of young adults, regions with one cluster at the CBD and one or more clusters away from the CBD were classified as exhibiting a multi-clustered geography, and regions with no clusters were classified as scattered. This classification approach makes use of the flexibility of the generalized additive modeling approach to capture a range of possible young adult profiles that can be interpreted in the context of existing urban forms (Champion, 2001; Estiri et al., 2015; Moos, 2014) and also accounts for uncertainty in the model results through the use of confidence intervals. Furthermore, it is important to note that our classification approach refers only to the residential geography of young 227

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adults and not population density, other age cohorts, or the overall spatial structure of the metropolitan regions. While modeling and constructing profiles of economic, social, and demographic characteristics using distance to the urban core is common in urban research (e.g., Anderson, 1982; Skaburskis, 1989; Walker, 2016; Walker, 2018), two limitations should be noted. First, the approach is non-directional and the young adult profiles summarize the range of young adult LQis located at a given distance from the CBD. For example, consider two areas that are located the same distance from the CBD in different directions, where one has a high concentration of young adults (LQi = 1.5) and the other has a low concentration of young adults (LQi = 0.5). If only these areas were analyzed, then the young adult profile at this distance would likely be close to one. However, if additional areas with high concentrations of young adults were located at the same distance away from the CBD in a third direction, then the profile would show a cluster of young adults (i.e., the modeled LQ value would likely be higher than one). The result of this limitation is that we may not detect some (likely quite small) clusters outside the CBD that may be ‘averaged’ out. Also, because the generalized additive models are non-directional, it is possible that young adult clusters are composed of areas located at the same distance from the CBD but not located geographically near to each other. The overall result of this limitation is that when our model does indicate the presence of clustering (i.e., a peak on the residential profiles), we can be quite certain that young adults are more concentrated at this distance from the CBD than the population as a whole (i.e., the region does not have a monocentric young adult residential profile); however, we cannot determine the exact location or size of this clustering. Second, we conduct our analysis of residential profiles relative to a pre-determined urban core, the historic CBD. Although a single-centre dominant form has recently still been found to remain the most common urban structure (despite growing polycentricism), this still means we cannot show how residential profiles vary in relation to secondary centres that do exist in some metropolitan areas (Arribas-Bel & Sanz-Gracia, 2014). Defining and including several centres is beyond the scope of this paper but would also introduce new technical limitations. For example, using three city centres and assigning areas the shortest distance to any one of these centres will overcount the number of areas with small distances to the CBD (i.e., areas close to the secondary/tertiary centres will be analyzed as though they are close to the primary centre) and overlook variations in the profile that may occur away from a primary urban core.

employment in the quaternary sector (e.g., education, law, arts, research and development), employment in the manufacturing sector, and the unemployment rate. Housing variables comprised the homeownership rate and the percent of single-detached homes. Urban form was operationalized via public transit mode share (i.e., the percent of workers who commute via public transit), where high public transit mode share was interpreted as a proxy for existing monocentric metropolitan forms (Muller, 2004). Demographic variables included the total population size, population growth since the previous census period, and the percent of young adults in the metropolitan region (see Appendix B for descriptive statistics). We chose these variables to get a broad picture of the types of metropolitan regions where a certain type of young adult residential profile may be more common than another. We were guided here by the initial hypothesis that young adults may be most centralized in metropolitan regions with existing amenity-rich downtowns ‘successful’ in the knowledge economy. Regions with high employment in quaternary sector occupations, often linked to gentrification, lower shares of singledetached housing, and higher public transit shares are expected to be more centralized in their overall urban structures. It is these kinds of regions that offer more opportunities for centralized, higher density living and where we may therefore expect young adults to be more centralized as well. 4. Results Of the 57 metropolitan regions, 31 exhibited a downtown-focused monocentric form with one young adult cluster located close to the CBD, 25 regions exhibited a multi-cluster form with one cluster located close to the CBD and one or more clusters located away from the CBD, and one region exhibited a scattered form with no distinguishable young adult clusters. The monocentric, multi-cluster, and scattered forms, as well as the grouping of the young adult profiles into these three forms, was justified based on the large number of young adult profiles with one cluster (monocentric) or more than one cluster (multicluster), and is consistent with prior analyses exploring the spatial and social structures of metropolitan regions in North America (e.g., Hackworth, 2006). Fig. 2 visualizes the downtown-focused monocentric young adult profiles. In general, downtown-focused monocentric profiles have one cluster of young adults located close to the CBD and decreasing concentrations of young adults as distance from the CBD increases. This profile type visually resembles the negative exponential function commonly applied in past research exploring the urban structure of population density, where population density is highest in the city centre and exponentially decreases toward the periphery of a region (Anderson, 1982; Batty & Kim, 1992). While this was the most common profile type amongst this sample of large metropolitan regions, there was considerable heterogeneity amongst the downtown-focused monocentric profiles based on the maximum concentration of young adults at the CBD as well as the variability of young adult location explained by proximity to the CBD. For example, the peak profile values ranged from more than twice the metropolitan region average in Washington-Arlington-Alexandria (2.62), Ottawa-Gatineau (2.61), and Chicago-Naperville-Elgin (2.57) to just above the metropolitan region average in Riverside-San Bernardino-Ontario (1.17), Birmingham-Hoover (1.25), and LouisvilleJefferson County (1.26). Of the monocentric profiles, proximity to the CBD was most important for explaining where young adults lived in Calgary, Ottawa-Gatineau, and Vancouver (43.14%, 41.63%, and 39.97% deviance explained, respectively), but was least important for young adults in Los Angeles-Long Beach-Anaheim, BirminghamHoover, and Oklahoma City (5.21%, 5.91%, and 6.12% deviance explained, respectively). Despite these differences amongst the monocentric profiles, young adults were more highly concentrated near the CBD than the total population in all but one of these regions; only

3.4. Young adult profiles and metropolitan characteristics Based on the classification approach described above, a set of unpaired two-sample t-tests and a set of Mann-Whitney U tests were applied to compare the economic, housing, urban form, and demographic characteristics between the regions exhibiting different types of young adult profiles (i.e., monocentric, multi-clustered, or scattered). The unpaired two-sample t-test and the Mann-Whitney U test both quantify if the means of measurement variables (i.e., metropolitan region characteristics) are statistically different for two groups (i.e., the different types of young adult profiles). The unpaired two-sample t-test assumes that the measurement variables are normally distributed whereas the Mann-Whitney U test allows for non-normality. Two tests were used to confirm that the results identified in one test were robust to alternative assumptions. We chose not to include a multivariate regression analysis because our intent is to detect associations between each variable and a specific young adult profile individually, not to control for the effects of other explanatory variables. Twelve variables representative of the different types of economies, housing markets, urban morphologies (or forms), and demographic characteristics exhibited by North American metropolitan regions were chosen for analysis. Economic variables included average income, employment in management, business, and engineering occupations, 228

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Toronto had a higher concentration of the total population located around the CBD. We know from prior research that this is the result of both young adults and other demographics moving into Toronto's inner city, not the absence of young adult clusters (Moos, 2016). The multi-cluster young adult profiles are shown in Fig. 3. All of the regions with multi-cluster profiles had one cluster located close to the CBD as well as one or more clusters located away from the CBD. Of these 25 regions, 19 had two clusters, five had three clusters, and one had four clusters (Dallas-Fort Worth-Arlington). Like the monocentric regions, there were also many differences amongst the regions with multi-cluster young adult profiles in the maximum young adult concentration as well as the variability of young adult concentration

explained by distance. For example, Rochester, Milwaukee-WaukeshaWest Allis, and Denver-Aurora-Lakewood had the strongest clustering of young adults around the CBD (modeled LQivalues of 3.26, 3.18, 2.93, respectively) whereas Jacksonville, Virginia Beach-Norfolk-Newport News, San Antonio-New Braunfels had the weakest clustering of young adults at the CBD (modeled LQi values of 1.18, 1.33, 1.47, respectively). Many of the multi-cluster regions with the strongest clustering around the CBD also had the highest proportion of variability of young adults explained by distance at 30.01% for Denver, 25.96% for Rochester, and 24.03% for Milwaukee. The importance of proximity to the CBD in explaining where young adults lived was much lower in the metropolitan regions with a more dispersed urban form, such as Detroit-

Fig. 2. Metropolitan regions with a downtown-focused monocentric young adult profile. The vertical axes are the modeled LQi values and the horizontal axes are the distances relative to the CBD. 229

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Fig. 2. (continued)

Warren-Livonia (4.79%), Memphis (6.89%), and Virginia Beach-Norfolk-Newport News (6.92%). As was the case for most of the regions with monocentric profiles, young adults in all multi-cluster regions were also more centralized than the total population. Of the 57 large metropolitan regions in the United States and Canada, only Las Vegas-Henderson-Paradise had a scattered profile with no clusters based on our classifications (Fig. 4). In Las VegasHenderson-Paradise, the concentration of young adults was approximately equal to the metropolitan region average at the CBD and decreased almost linearly as distance from the CBD increased. This scattered profile was attributable to a range of young adult concentration values in areas close to the CBD and a number of areas with low young adult concentrations located near to the regional boundary. In Las Vegas-Henderson-Paradise, in particular, distance explains only 3.30%

of the total variability of young adults, which suggests that, in this metropolitan area, other factors are relatively more important for explaining the residential geography of young adults. Before reflecting on the findings, it is important to be aware of the degree to which the young adult population within these regions directly contributes to the clustering observed in both monocentric and multi-cluster young adult profiles. We find that a majority of all young adults in this set of metropolitan regions (58.86%) lived in areas with LQi ≥ 1.0, and that about 24% of all young adults lived in areas with LQi ≥ 1.0 that were located within the central clusters. Focusing on young adults living in areas with higher concentrations than the metropolitan region averages, about 44% of young adults lived in areas with LQi ≥ 1.2, and 20% lived in central clusters that had LQi ≥ 1.2, and approximately 27% of young adults lived in areas with LQi ≥ 1.5, 230

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(caption on next page) 231

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Fig. 3. Metropolitan regions with a multi-cluster young adult profile. The vertical axes are the modeled LQi values and the horizontal axes are the distances relative to the CBD.

and 15% lived in central clusters that also had LQi ≥ 1.5. Combined, these statistics suggest that a considerable share of the total young adult population are driving the young adult clusters observed in large metropolitan regions in North America. The unpaired two-sample t-tests and the Mann-Whitney tests found that four characteristics were significantly different between the metropolitan regions with monocentric and multi-cluster young adult profiles: employment in the quaternary sector, employment in the manufacturing sector, the proportion of single-detached homes, and public transit mode share (Table 1). Regions with a monocentric profile had higher employment in the quaternary sector and higher transit mode share than multi-cluster regions. Regions with a multi-cluster profile had a higher proportion of single-detached homes and a higher level of manufacturing employment than monocentric regions. Furthermore, regardless of the monocentric or multi-clustered profile of young adults, correlation analysis between the percent of deviance explained by distance (relative to a null model with no covariates) and these metropolitan region characteristics shows that distance to the central business district was relatively more important for explaining where young adults lived in regions with high levels of quaternary employment and public transit mode share (not shown for brevity).

5. Discussion Our research has explored how the residential geographies of young adults vary within and between metropolitan regions, and has identified four characteristics that distinguish the regions exhibiting a monocentric and a multi-cluster young adult pattern. Visualizing and classifying the young adult profiles showed that young adults were relatively more concentrated at the CBD, both in monocentric or multicluster profiles, than the total population in 56 of 57 large metropolitan areas. This can be explained in part via traditional models of residential location that have generally placed a higher share of young adults in central areas due to their smaller household size and absence of, or fewer, children (e.g., Estiri et al., 2015; Shearmur & Charron, 2004). The results also found that the CBDs of most major metropolitan areas in North America are relatively younger as compared to their own internal population distributions. That is to say that youthification is probably more widespread than originally anticipated when it was linked primarily to those metropolitan areas with amenity-rich downtowns, successful in the knowledge economy. Metropolitan areas of all kinds display relative centralization of young adults. Yet, distance from the CBD is indeed more important in explaining young adult locations in regions with higher shares of quaternary sector occupations and higher public transit shares.

Fig. 4. The scattered young adult profile in Las Vegas-Henderson-Paradise. The vertical axis is the modeled LQi's and the horizontal axis is the distance relative to the CBD. Table 1 Results of unpaired two-sample t-tests (t) Mann-Whitney tests (U) comparing twelve economic, demographic, housing, and urban form characteristics between regions with monocentric and multi-cluster young adult profiles.

Average income (thousands $) Management employment (%) Quaternary employment (%) Manufacturing employment (%) Unemployment (%) Housing > 30% of income (%) Homeownership (%) Single-detached homes (%) Public transit mode share (%) Total population size (millions) Population growth (%) Young adult population (%)

Monocentric (mean)

Multi-cluster (mean)

33.59 16.42 11.97 3.96 7.16 33.12 62.54 56.36 8.10 3.84 5.40 14.72

33.24 16.13 10.65 4.41 7.12 32.16 63.56 64.90 3.05 2.84 4.10 14.12

p < 0.10. p < 0.05. ⁎⁎⁎ p < 0.004 (Bonferroni correction). ⁎

⁎⁎

232

t (df)

U

0.29 (49.95) 0.60 (52.77) 3.07 (42.77)⁎⁎⁎ −2.21 (49.87)⁎⁎ 0.11 (52.87) 0.69 (44.52) −0.83 (51.30) −3.44 (48.15)⁎⁎⁎ 3.19 (41.63)⁎⁎⁎ 1.25 (42.87) 1.46 (58.94) 1.94 (51.83)

401 430 547⁎⁎ 261⁎⁎ 390 411 354 214⁎⁎⁎ 561⁎⁎⁎ 403 458 492⁎

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The question arises as to why young adults centralize to different degrees, and why they concentrate in non-central locations in some metropolitan regions. From the variables found to be significantly different between regions with monocentric and multi-cluster profiles we develop a profile of metropolitan regions that are at least somewhat successful in the knowledge economy as well as having existing strong centres and transit systems that result in centralized and higher density developments attractive to the young adults. Generally, a large quaternary sector is often associated with a centralized configuration and the success of the metropolitan region in the knowledge economy but also with high levels of gentrification. Meanwhile, metropolitan regions with an important manufacturing component are generally more decentralized. Their weaker centres are thereby not suited to high concentrations of young adults in the core. Another factor accounting for centralized young adult patterns in metropolitan regions with strong downtowns is the appeal of alternatives to car use in the central parts of such regions to those young adults with less interest in driving than prior generations (Schoettle & Sivak, 2014). For example, New YorkNewark-Jersey City, Toronto, and Montreal, which all showed a monocentric young adult profile, were the metropolitan regions with the highest public transit mode share (31%, 24%, and 23%, respectively) and have strong downtowns that facilitate public transit use (Fig. 2). Young adult residential patterns show that metropolitan population size is not as strong a predictor of young adult locational patterning as expected. Rather than a close association between population size, which often parallels the strength of the CBD, and the concentration of young adults within and around this district, it is found that places like Rochester, Milwaukee-Waukesha-West Allis, and Denver-AuroraLakewood have relatively higher concentrations of young adults close to the CBD (although not higher numbers of young people) than in New York-Newark-Jersey City, Chicago-Naperville-Elgin, and Toronto (Figs. 2, 3). One reason may be that high housing costs in many of these large metropolitan regions may force many young adults to compromise when selecting their residential areas, balancing proximity to the CBD with the location of available and affordable rental housing. In larger metropolitan regions in particular, which often have polarized economies, high housing costs are a barrier preventing the entry of many young adults into the housing market, either as tenants or owners, and constraining young adults to specific areas with plentiful rental housing. For example, in the New York-Northern New Jersey-Long Island metropolitan region, high concentrations of young adults are located in Manhattan, as well as in nearby areas of Brooklyn and New Jersey (Jersey City) that have more affordable housing but are still close to the CBD. Another reason may be that high concentrations of young adults in slightly less expensive metropolitan regions have the capability to transform an urban area in a fashion that enhances its appeal to this age group. For example, the downtown-focused young adult concentration pattern in Denver is at least partly due to the presence of neighbourhoods that have been transformed by the strong presence of young adults and have consequently become more attractive to this age group, such as LoDo, LoHi, Auraria and Capitol Hill. The adjustment of the retail and hospitality scene can be driven by local young adults themselves or be the outcome of chains targeting the Millennials' market (Lantos, 2014; Yarrow & O'Donnell, 2009).

regions have CBD clusters, and there are exceptions to the overall findings that differentiate regions with these patterns. For instance, there are regions more characteristic of the knowledge economy in the multi-cluster category than in the monocentric one (for instance, Buffalo has a monocentric profile and San Francisco a multi-clustered one). Part of the explanation here is that secondary clusters of young adults are more common in regions with high housing costs that disperse young adults away from central areas. Yet, in other contexts, such as Buffalo, historic decline due to the loss of manufacturing may provide relatively greater opportunities for revitalization and centralization of young adults than in more expensive metropolitan regions. The reality is also that most of the metropolitan areas included in this analysis have seen at least some degree of growth in the knowledge economy: We are indeed comparing the largest regions in North America where, despite decline in some instances (and significant decline to be sure in certain cases), inner city revitalization, and particularly gentrification, have played an important role in profoundly (re) shaping residential geographies (Lees, Slater, & Wyly, 2013), which would contribute to the centralization of young adults (Moos, 2016). There are also several other limitations that are not unique to this paper but reflect the nature of aggregate analysis of the distribution of residential populations more generally (Shearmur & Charron, 2004; Wyly, 2009). First, the generalized additive models used to estimate young adult profiles only include distance to the CBD as the independent variable. Such an approach lends itself to a comparison of concentrations of young adults at different distances from the centre of the CBD, however it does not cast light on the full range of young adult concentrations at a given distance from the CBD. Future studies may look to explore maps of spatial young adult clusters identified using cluster detection techniques, such as local indicators of spatial association (Anselin, 1995), and explain the location of young adults as a function of social, economic, and urban form variables, as well as distance to the CBD, in generalized additive models. We also stress here the impact of two factors associated with the distribution of young adults, not accounted for in our study. First, the observed profiles are representative of the overall patterns of young adult residential geographies and do not necessarily reflect how the residential geographies of age intersect with other characteristics, such as ethnicity, family composition, education level or socioeconomic status, for example. Future research should investigate residential patterns of specific sub-populations of young adults, and investigate how the residential locations of these have changed over time. Second, this study only concentrates on proportions of young adults who lived in metropolitan regions in 2015 and does not consider how residential patterns have changed historically. Therefore, the three residential young adult profiles identified in this paper represent only a subset of the varied living circumstances experienced by young adults in North America and cannot speak to the varied pathways different kinds of young adults follow over time (Estiri et al., 2015). Further studies could focus on some of the findings in the context of specific types of, or of individual, metropolitan regions. 6. Conclusions Young adults have long been a vanguard of urban change. Each generation has a propensity to develop values of its own, reacting against those of the previous generation, while being shaped by the broader social, political, and economic conditions of the time. And it is in the young adult phase that these values congeal, and a generation musters the energy and wherewithal to translate them into lifestyle and social change (Moos, 2014). Millennials have been depicted as environmentally conscious, attracted to urban living and displaying less

5.1. Limitations and future research It is important to remember that our analysis is a relative and overarching comparison. Both the monocentric and multi-clustered

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enthusiasm for the automobile than precedent generations of young adults did. Some of the identified residential patterns, notably downtown-focused monocentric patterns and their association with high public transit modal shares, are consistent with these assumed values and preferences. However, equally important to remember is that a segment of this cohort is probably pushed rather than pulled downtown by the availability of smaller apartments that can be rented and/or shared with roommates. Regardless of whether it is by choice, housing market constraints, both, and/or other factors that bring young adults centrally, based on our young adult profiles we can foresee a North American urban future where downtowns will continue to expand, where central areas and some other higher density sectors will be revitalized, and where reliance on public transit will increase (also see Polzin, Chu, & Godfrey, 2014; Sakaria & Stehfest, 2013; Frey, 2018). Looking to the future, the answers to important questions will shape the degree to which this more ‘urban’ scenario is fully realized. First, will current young adults maintain their current distinctive values and preferences as they age, or will they blend with the general population as they age (Pendall, 2012)? Even if not living centrally completely by choice, will young adults have grown accustomed to the conveniences and amenities of centralized living? Second, how will residential

environments change over time, and how will processes including gentrification of central city areas, state-led and otherwise, influence the geographical patterns of both current and future young adults of different income levels or ethnic backgrounds? Third, will young adults continue to be confronted with economic challenges that prevent them from making choices consistent with their values and preferences, perhaps related to precarious employment, the impact of neoliberal policies, and an uncertain post-recession economy? How values and lifestyles of Millennials change, and how Millennials respond to changing urban contexts and economic obstacles will dramatically influence the residential geographies of metropolitan areas. Acknowledgements This research was supported by funding from a Province of Ontario Early Researcher Award and a Social Sciences and Humanities Research Council of Canada Insight Development Grant. The authors would like to thank the anonymous reviewers for their comments and detailed review that helped improve the paper. Any remaining errors or omissions are the authors' responsibility.

Appendix A. Additional results from the young adult profiles Table A1

Metropolitan regions with monocentric profiles. Cluster location (km from CBD) Austin-Round Rock Baltimore-Columbia-Towson Birmingham-Hoover Boston-Cambridge-Newton Buffalo-Cheektowaga-Niagara Falls Calgary Chicago-Naperville-Elgin Edmonton Los Angeles-Long Beach-Anaheim Louisville-Jefferson County Miami-Fort Lauderdale-West Palm Beach Minneapolis-St. Paul-Bloomington Montreal New Orleans-Metairie New York-Newark-Jersey City Oklahoma City Orlando-Kissimmee-Sanford Ottawa-Gatineau Phoenix-Mesa-Scottsdale Pittsburgh Portland-Vancouver-Hillsboro Providence-Warwick Raleigh Richmond Riverside-San Bernardino-Ontario San Diego-Carlsbad San Jose-Sunnyvale-Santa Clara Seattle-Tacoma-Bellevue Toronto Washington-Arlington-Alexandria Vancouver a

0.23 0.06 0.26 0.21 0.70 0.05 0.11 1.96 0.28 0.30 0.10 0.11 0.30 3.07 0.18 0.26 0.40 0.34 0.57 0.10 0.17 0.31 0.10 3.43 0.61 0.43 0.11 0.22 0.23 0.76 0.09

Deviance explained (%)a 21.78 15.91 5.91 29.91 10.60 43.14 25.79 28.90 5.21 6.40 9.29 20.84 38.55 15.77 23.81 6.12 15.31 41.63 12.03 20.59 22.36 9.74 13.82 12.38 6.51 17.03 7.88 15.52 33.40 23.91 39.97

The proportion of deviance explained by the model with smoothed distance covariates relative to a model with no covariates.

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Table A2

Metropolitan regions with multi-cluster profiles. Cluster locations (km from CBD) Atlanta-Sandy Springs-Roswell Charlotte-Concord-Gastonia Cincinnati Cleveland-Elyria Columbus Dallas-Fort Worth-Arlington Denver-Aurora-Lakewood Detroit-Warren-Dearborn Hartford-West Hartford-East Hartford Houston-The Woodlands-Sugar Land Indianapolis-Carmel-Anderson Jacksonville Kansas City Memphis Milwaukee-Waukesha-West Allis Nashville-Davidson-Murfreesboro-Franklin Philadelphia-Camden-Wilmington Rochester Sacramento-Arden Arcade-Roseville Salt Lake City San Antonio-New Braunfels San Francisco-Oakland-Hayward St. Louis Tampa-St. Petersburg-Clearwater Virginia Beach-Norfolk-Newport News a

Deviance explained (%)a

0.24, 14.97 0.07, 9.82 0.24, 8.75 0.11, 8.73 0.35, 12.33, 16.51 0.30, 16.81, 24.94, 53.12 0.42, 7.40 0.18, 15.01, 22.59 0.43, 12.86 0.43, 21.55 0.82, 10.22, 14.97 2.61, 14.94 0.16, 10.19 0.72, 12.24, 19.96 0.18, 6.69, 9.88 3.78, 14.71 0.15, 8.60 0.02, 5.13 0.50, 8.25 0.71, 6.72 0.62, 14.65 0.27, 13.53 0.38, 9.98 0.76, 12.67 0.37, 15.15

16.61 19.63 12.65 10.24 14.19 9.14 30.01 4.79 13.36 9.67 15.11 10.31 9.33 6.89 24.03 17.59 18.21 25.96 21.60 23.35 10.28 23.35 7.85 14.01 6.92

The proportion of deviance explained by the model with smoothed distance covariates relative to a model with no covariates.

Table A3

Metropolitan region with a scattered profile. Cluster locations (km from CBD) Las Vegas-Henderson-Paradise

Deviance explained (%)a

NA

3.30

a

The proportion of deviance explained by the model with smoothed distance covariates relative to a model with no covariates.

Appendix B. Economic, housing, urban form, and demographic characteristics Table B1

Descriptive statistics for the 56 metropolitan with monocentric and multi-cluster young adult forms. Mean X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12

Individual average income ($ in thousands) Employment in management sector (%) Employment in quaternary sector (%) Employment in manufacturing sector (%) Unemployment (%) Homeownership (%) Households spending > 30% on housing (%) Single-detached homes (%) Public transit mode share (%) Total population size (millions) Population growth (%) Young adult population (%)

32.56 16.28 11.38 4.16 7.14 63.00 32.70 60.17 5.84 3.39 4.82 14.46

235

SD 4.66 1.89 1.84 0.83 1.25 4.80 5.54 10.62 6.81 3.23 3.37 1.18

Min 24.15 11.58 9.09 1.11 1.92 48.41 21.86 29.38 0.42 1.08 −0.75 12.19

Max 46.92 22.04 19.13 6.04 11.08 72.97 46.89 73.87 30.98 20.03 13.93 17.31

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Table B2

Pairwise Pearson's correlation coefficients for the twelve economic, housing, urban form, and demographic characteristics. X1 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12

–– 0.63 −0.02 −0.31 −0.27 −0.12 0.14 −0.10 0.09 0.16 −0.06 0.10

X2 –– 0.37 −0.57 −0.41 −0.08 −0.09 −0.41 0.41 0.13 0.34 0.39

X3

–– −0.61 −0.12 −0.03 −0.19 −0.72 0.76 0.14 0.14 0.21

X4

–– 0.24 −0.08 0.25 0.44 −0.38 0.18 −0.43 −0.28

X5

–– −0.05 0.33 0.01 0.04 0.19 −0.17 −0.27

X6

X7

–– −0.72 0.35 −0.18 −0.52 −0.13 −0.31

–– −0.26 0.03 0.52 −0.16 0.04

X8

–– −0.83 −0.45 −0.12 −0.31

X9

–– 0.52 0.11 0.33

X10

–– −0.07 0.12

X11

–– 0.69

X12

––

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