THESIS - Young adults in the city: a spatial assessment of their residential differentiation

Young adults in the city: a spatial assessment of their residential differentiation Charlotte Chebassier - cmc959@nyu.edu - SID: N12700344 Thesis - Applied Quantitative Research - Summer 2014 Department of Sociology - New York University Pr. Mike Hout - Director

Pr. Eric Klinenberg - Advisor

Abstract. This paper takes recent theories of the conceptualization of young adults to present their level of segregation and residential condition in the metropolitan area of New York City. Estimates from the American Community Survey (2008-2012) and spatial data were combined to produce a descriptive, statistical and spatial analysis of 2,270 census tracts in the New York City area. Segregation was assessed through five indices of measurement.

Several maps illustrate the central and clustered pattern of young adults: 57 percent of them living in only 5.4 percent of the whole land area. After describing young adults’ housing environment, 3 discriminant factors were taken into account to identify 4 clusters of young adults: the proportion of young adults in each census tract, the median rent and the density of venues serving alcohol. While additional research is needed to identify causal factors in young adults segregation, these results suggest that it should not be treated as a linear process. Ecological and cultural factors affect young adults’ residential differentiation, with a balance of constrained economical choice and active isolation processes. (15,000 words) Keywords. Young adult, residential differentiation, age segregation, urban sociology, metropolitan study, New York City, spatial analysis, spatial statistics.

1 Introduction No longer adolescents, and not yet adults: young adults embody an emerging period of life that did not exist a few decades ago. The rise of this new age category has been discussed and described largely either as a shift in demographic trends or a generational phenomenon (Mary 2014). So far the body of literature has focused on social and developmental definitions of young adults in our contemporary society (Arnett 2000; Arnett et al. 2011; Carroll et al. 2007; Crites 1976; Hughes 2003). Some research has more explicitly clarified housing choices and living arrangements of young adults (Di and Liu 2006; Klinenberg 2013; Wu 2010). This paper aims to give a comprehensive overview of the residential condition of young adults in a specific metropolitan context.

Young adults tend to be especially attracted by metropolitan areas because they concentrate education, jobs and people. In the New York Metropolitan Statistical Area1 (NYMSA), the American Community Survey (ACS 2008-2012) indicates that 3 of 5 adults between 18 and 34 years are living in a central area of 32 square miles, representing only 5.2 percent of the whole land area. Central areas tend to be more expensive, and young adults pay a median gross rent of $1,280 while the median for the entire population is $1,130. This suggests that young adults are following a distinct housing pattern. Residential condition is a complex topic that is very informative about individuals and groups. It is at the same time the reason of individual arrangements and the result of societal trends.

Urban sociology and economy have demonstrated that housing patterns are not even or random, and are the result of social, economical and political forces (Alonso 1960; Glaeser 2007; Park, Burgess, and McKenzie 1925). Residential differentiation by age has been depicted within segregation studies since the 1970’s, but has focused mostly on the elderly (Chevan 1982; Cowgill 1978; Lagory, Ward, and Juravich 1980; Lagory et al. 1980). Age segregation distinguishes individuals or groups as included or excluded, enhancing the difference between the self and others, ‘us’ and ‘them’ (Bytheway 2005). Hagestad and Uhlenberg (2006) explains that it produces ageism and encourages isolation.

As was demonstrated for the elderly, the residential differentiation of young adults could potentially bound them to stereotyped life styles, with limited housing strategies. While 1 The New York Metropolitan area is a statistical area (#35620) defined by the US Office of Management and Budget. It comprises New York, Newark and Jersey City over 620 sq miles of natural land. More definition is provided in the methodology section.

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research described a more complex and less standardized life path (Arnett et al. 2011; Mary 2014) we hypothesize that they might follow a more uniform residential path. Their mobility and priorities lead them to mostly live in central areas and thus access a specific housing market.

Therefore, this analysis first addresses the level of segregation of young adults in the metropolitan area of New York City. The American Community Survey (2008-2012) provides the proportion of young adults in each census tract to implement this measurement with the recommendation of Massey and Denton (Massey and Denton 1988). In a second phase this analysis defines the residential condition of young adults in the city. Spatial and survey data support this assessment with thematic maps. To foster further research, a grouping analysis is suggested using spatial and quantitative statistical methods. These groups of young adults aim to reveal the underlying structure of their differentiation.

2 Literature Review This study on young adult residential patterns envisions the relationship between sociology, developmental psychology, urban sociology and urban economy. This literature review intends to enlighten the necessity of intra-disciplinary thinking. Residential patterning is not a linear phenomenon and is the result of several factors interacting together. First, the literature review provides a contemporary definition of young adults. Then it reports existing theories on residential differentiation due to social ordering. A third section discusses how the housing market structures the city spatially. This literature review ends with a section outlying reasons for choosing the New York City area.

2.1

Young adults definition

The body of literature does not propose a clear-cut consensus to define the term of young adult. However, many specialists (e.g., Arnett et al. 2011; Mary 2014) agree that the concept was introduced during the 1970’s. Young adults are caught in an in-between period of life: not adolescent anymore but not yet feeling themselves to be adults. Different disciplines have introduced competing definitions of the term from legal, physical, developmental and generational points of view.

The Law, education system, welfare organizations and the Government classify individuals by age. From of legal point of view, most of the world nations consider 18 years 3

as the threshold of childhood, called the age of majority. Young adults are then often described from 18 years to the mid-twenties. Census data is the main source of information used by U.S. institutions to picture demography. It often groups ages in increments of five years (like ‘15-19’, ‘35-39’ years) or open-ended categories (like ‘under 18 years’, ‘more than 65 years’). This type of classification assumes that individuals have similarities because of their age.

Young adult are also characterized by their physical condition. Zastrow and KirstAshman (2009) depict this moment of life as “the healthiest time of life”. They describe adolescence as a process of physical growth and transformation. Young men and women are in their physical prime between twenty and thirty, with maximum muscular strength and performance speed. For woman, young adulthood is also the biological stage of fertility.

While their peak fertility age is between 20 and 27 years, the age of 35 marks an accelerated biological decline (Lampic 2006).

In the past century, many theories have attempted to explain human development.

Some of the most prominent are from Freud and Piaget (Arnett et al. 2011). With the rise of life expectancy and the destandardization of life course in modern era, psychoanalysts tried to encompass a larger life span in their theories. Erikson (1993) considered the chronology of age less important than the stages of individual life. He described eight stages from infancy to adulthood where each person is negotiating with both biological and socio-cultural forces.

Erikson calls the 6th stage the “intimacy vs. isolation” conflict, when people from 20 to 39 years are entering in young adulthood. In this stage, individuals are defining their identity toward their friends and partners, while preparing themselves for long-term commitment.

Young adults are experiencing important life course changes with relationships, marriage, education, employment. These transitions lead to a greater residential mobility than their older counterparts.

These life-cycle transitions have evolved in the past 40 years. The 3 decades following World War II, also called ‘The Golden Age of Capitalism’ (1945-1975), generated unprecedented socio-economical security for the generation of Baby-Boomers (Mary 2014).

During this period, young people could rapidly reach adulthood in a chronological order.

Societal markers were associated with this process: complete education, reach financial independence with a job and depart from parental home to get married (Blatterer 2007; Mary 2014). Since the 1970’s, the shift in socio-economical context changed the financial resources of young adults. The workplace evolved with more non-standard hours, less stable

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employment, more unemployment and more education to respond to these constraints (Wyn and Woodman 2006).

Another shift happened with the spectacular rise in the average age of first marriage and first childbearing (Arnett et al. 2011). In the U.S., the age of first childbirth rose from 21 to 25 years between 1970 and 2006. Other trends are evolving simultaneously with the rise of cohabitation, sexual intercourse out of marriage and risky behaviors like binge drinking, drug use or car driving (Carroll et al. 2007). Arnett (2000) redefined this life span for people between 18 and 29 years as ‘emerging adulthood’. He argues (2011) that emerging adults are not following a uniform pathway.

Some recent research has criticized the psychosocial approach toward youth while proposing a generational definition. Mary (2014) argues that we should not examine young adults today through the lens of the Baby-Boomer generation because theirs is an outdated model. Young adults are not extending their youth or less responsible than their parents as some researchers have described them. They are adjusting their strategies to the socioeconomical framework (Mary 2014). Today, young adults have an economical and social self-consciousness as a group. They define themselves in a participative web dictionary as “the age group most royally screwed over by economy” (Urbandictionary.com). Several words have been used to name this generation: Generation Y, Generation Why, Millennial Generation. But none of them have agreed so far on a year range.

This study is interested in describing residential conditions at a particular stage of life and in a particular socio-economical context. On one boundary, 18 years is a convenient cutoff age recognized by the legal context and research. In the U.S. education system, students enter college around 18 years and often moving away from family and hometown for geographical reason. On the other boundary, we recognize that young adults do not follow identical and comparable pathways to reach adulthood, which makes it hard to choose a cutoff age to become ‘adult’. However it is necessary to define a countable range for quantitative purposes. For this study, we are interested in the accumulation of experiences like education, employment, relationships, family, marriage, childbearing and financial independence. To comprise them as much as possible, we decided to extend Arnett’s emerging adulthood range to 34 years. This age is also consistent with the decline of fertility for women, and their knowledge of it (Lampic 2006). The age range of this study is then 18 to 34 years old, representing 23.3 percent of the population in the United States. Within the New York Metropolitan Statistical Area (NYMSA), almost one in four individuals are

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between 18 and 34 years old (23.5 percent). Because we are not debating developmental or generational concepts, we decided to employ the neutral expression ‘young adults’.

To give a broader understanding of this young adult population, this research will compare young adults with older age categories. Two categories of older adults are distinguished with a threshold at 65 years, a standard age for retirement: middle-aged adults (35 to 64 years) and elderly (65 years and more). Younger individuals are in the children category (less than 18).

2.2

Residential differentiation

Because the cityscape is not distributed evenly, many theories tried to conceptualize the urban process in a spatial and socio-economical dimension. Researchers (Park, Burgess, and McKenzie, 1925; Duncan and Duncan, 1955; Taeuber and Taeuber, 1965; Pampel and Choldin, 1978; Massey and Denton, 1988) have studied residential differentiation extensively since the 1950’s. The processes accounting for urban segregation were explained with the ecological, political and cultural models (Lagory, Ward, and Juravich, 1980).

Residential differentiation indicates the degree to which two or more groups live separately from one another in the urban environment (Massey and Denton 1988). Similar populations can cluster together and characterize their area (Timms 1971). Urban segregation can happen according to many criteria. Individuals or subpopulations are physically separated because of socio-economic status, occupation, race, religion or life-cycle stage. This is a multidimensional phenomenon that can be measured along five distinct axes: evenness, exposure, concentration, centralization, and clustering. Massey and Denton (1988) contributed to the topic by comparing and recommending the use of five standard indices for future segregation studies.

The ecological model explains segregation as a spatial competition for valuable land.

The Chicago School (or ecological school) extensively studied this model. Burgess (1925, 55) proposed a general concentric urban model to explain market trends in relation with urban growth where human communities are competing for the most advantageous sites in the city. These sites are characterized by 3 essential proxies: the house, the road and water (Brunhes and Zeydel 1925). The economically weaker communities are forced to live in less valuable land.

By contrast, the political model rejects the ecological one. Here, discrimination explains population differentiation. Selection and regulation occur according to racial, social,

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cultural, economical and political criteria (Lagory et al. 1980; Taeuber and Taeuber 1965).

As they are trying to obtain access to housing, specific populations are disadvantaged because of their stigmatization. For instance, the elderly can struggle to fill an online application for rental and young adult are suspected to be noisy and risk-taking.

Where as previous models consider segregation as involuntary, it can also be a chosen process. The cultural model demonstrates that segregation can be voluntary, as it “decreases the chance of undesirable and potentially embarrassing contact” (Timms 1971). People with similar lifestyles and values concentrate in same areas to reduce “the likelihood of experiencing undesirable or difficult situations” (Lagory, Ward, and Juravich, 1980). Simmel (1903) theorized the need of physical isolation as a response to the urban overload. The withdrawal helps to preserve individual “psychic energy”. Indeed, the urban overload can reach a peak in the city center compared to the suburban or rural areas. In Midtown Manhattan, individuals can share the urban space with 220,000 persons compared to 20,000 persons in Newark (Milgram 1970).

Echoing major themes of American history, most research on segregation has focused on racial-ethnic and socio-economic cleavages. Residential age segregation is a more recent phenomenon that has significantly increased since the 1940’s. Cowgill (1978) found that this change was due to urban sprawl and population growth in the industrialized metropolis. The main argument against age segregation is that it reduces cross-age relationships while promoting ageism (Hagestad and Uhlenberg 2006).

Age segregation has been studied essentially for an aging American population (Cowgill, 1978; Pampel and Choldin, 1978; Lagory, Ward, and Juravich, 1980). A combination of growing factors explains this particular interest in the elderly: increasing life expectancy, unstable economical context, creation of institutional housing, political voice of older community and age-segregated activities (e.g., Cowgill 1978). However, newspapers (Bahrampour 2005; O’Leary 2012) are reporting difficult housing conditions for young adults today. We hypothesized that elderly are not the only population suffering due to residential differentiation: young adults are concerned by age segregation too.

2.3

Housing market

Several researchers (Cowgill 1978; Lagory et al. 1980; Pampel and Choldin 1978) have argued that ecological factors are able to explain age segregation. They are the results of competition for land where the individual ability to pay regulates the housing market (Hansen

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1959). Urban economics, a field that has expanded rapidly since the 1960's, examines the geographical dimension of this economic phenomenon. O’Sullivan (2003) distinguished six related themes in urban economy: market forces, land use, transportation, problems (like crime), housing, government expenditure and taxes. These theories help to illuminate the residential dimension of urban spaces.

The first model developed by urban economics is the monocentric city model (Alonso 1960), which describes cities built before the democratization of automobile transportation in U.S. In this model, cities are developing from a center point in a concentric way. They grow because they support economies of scale, produce common resources and foster innovation.

Conceptually, a spatial equilibrium exists between the distance from the Central Business District (CBD) and the price of land. Glaeser explains that “the theoretical centerpiece of urban economics is the concept of a spatial equilibrium which assumes that there are no free lunches to be gained by changing location” (2007, para. II). In the metropolis, high housing prices are offset by better access to jobs, culture and social amenities. The spatial equilibrium is captured in a linear utility function defining the location choice: Income + Amenities – Housing Costs – Transportation Costs

(1)

Housing cost is varying in the space where exogenous attributes are incrementing its value. The distance from the city center explains only 15 percent of the heterogeneity in prices in urban areas (Glaeser 2007). The three laws of real estate are famously known as “location, location and location”, which means that neighborhood is an essential attribute of housing. Kauko (2006) even noted that housing environment matters more than the housing quality. Huttman and van Vliet (1988) describe the neighborhood quality in four dimensions: “(1) the quality of the environment; (2) proximity and convenience to various activity nodes, such as shopping or work; (3) the quality of local services and facilities; and (4) the quality of the neighborhood’s socio-cultural environment”.

Neighborhood matters but there is not doubt that housing attributes affects the price.

Householders consider floor area, number of room, type of building and housing quality as primary factors of choice. Clark and Onaka demonstrate that housing type and size are strongly correlated with the composition of household. Evolution in life-cycle leads couple to adapt their housing as they will have more children or get older. The propensity to move is predicted by the floor area available per person and household type (Clark, Deurloo, and Dieleman 1984).

Housing-market indicators like price per area or sales index give a measure of housing and neighborhood quality over time. Since few decades, housing prices have risen sharply 8

(JCHSHU 2013). The first physical explanation is the inelasticity of housing stocks in metropolitan context. This inelasticity is due to density but also to land use regulation (O’Sullivan 2003). Demographical increase of population and immigrants is pressuring the market at the time. In addition, the decrease of income in the past decade has given less opportunity to households, and create more demand on a specific portion of the market (JCHSHU 2013). Eventually, societal changes are playing a role in the increase of price.

Americans need larger home and are more willing to live alone (Klinenberg 2013; Wilson and Boehland 2005).

To standardize housing cost, The National Housing Act of 1937 has established a convention. It recommends spending a share of incomes not larger than 30 percent on housing cost. When this share is higher than 30 percent, householders are ‘cost burden’, and when it is above 50 percent they are ‘severely cost-burdened’. Today in United States, one in two tenants are cost-burdened meanwhile it was only one in four renters in 1960 (JCHSHU 2013). Observing burden cost over time is a good indicator to observe housing affordability.

It also indicates the importance given to housing or neighborhood by households.

Understanding first concepts of housing market is helping us to conceive residential differentiation not only as a social process but also has an urban economical process. Young adults neighborhoods need to be explain in space. Several dimensions are represented spatially in this study with variables presented in the data section. From the six themes developed by O’Sullivan (2003), five of them are represented and quantified by chosen variables: market forces, land use, transportation, problems and housing.

2.4

New York City

One of the central ideas of urban economics is that people attract more people. Young adults are in a stage of life where they are particularly attracted by human density and urban lifestyle. They are looking for job opportunities, relationships and social life. Because of its thigh housing market, New York City is the context of our spatial study. Even if the scale of New York is remarkable, urban processes are ensuing theories. Thus, this context is comparable to other metropolitan context to a certain extend.

Since the first Census in 1790, New York City is the biggest metropolis in the United States. The population of New York City is estimated at 8.20 million residents over 305 square miles (ACS 2012). It has a density of more than 27,000 people per sq mi, compared to San Francisco with 17,000 people per sq mi and Chicago with 11,800 people per sq mi. The

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“ongoing ability to dominate America’s urban landscape” (Glaeser 2005) is explained by geographic determinism, cultural influence and resiliency to change. Since its creation, New York has always been able to attract immigrants from all religions and ethnicities. As well as highly skilled people, because the flow of ideas is amplified by the flow of people (Glaeser 2005). Like other populated cities in United States, crime and social problems used to skyrocket during the 1960’s. But New York had the capacity to change over time, and today has the lowest overall crime rate and second lowest murders rate among the largest US cities (Federal Bureau of Investigation 2012).

According to recent studies, New York is growing slower than expected but should grow by one million residents until 2040 (Keenan and Chakrabarti 2013). Population growth affects housing market. Since a decade, rental market in New York is increasing widely with a median gross rent in market rate units rising from $920 in 2002 to $1540 in 2011 (Furman Center 2012). This increase is following a larger trend where U.S. rents climbed by 6 percent between 2000 and 2012 (JCHSHU 2013). Moreover, rents are not expected to decrease. After the economical crisis of 2007, housing prices drop by 15.7 percent between 2007 and 2012 in New York City, meanwhile median rents increased by 8.5 percent between 2007 and 2011 (Furman Center 2012). In 2004, a political party was found for Mayor, Governor and Senator's elections to fight against rent. The party ‘The rent is too damn high!’ emphasizes struggles New Yorkers are facing with housing market and inflation.

As well as for largest American cities, more than half of all renter households were moderately or severely rent burdened in 2011 (Furman Center 2012). To deal with rent burden, many dwellers adapt their housing needs or spend less on other necessities like food and transportation (U.S. Bureau of Labor statistics 2012). Health care, retirement savings, apparel, entertainment and others expenses are also affected by burden costs (JCHSHU, 2013). "Our collective love affair with New York is commonly invoked as a justification for living in a closet" writes Tara Bahrampour (Bahrampour 2005).

New York City is an exceptional city and a particular fascination magnified the City in diverse ways over the decades. In social talks, moving or living in New York is today a sign of success in life. Its cultural and intellectual influence continues to attract young individuals.

Nonetheless the increase of price in housing market and the drop of affordability for households makes it comparable to other American cities today.

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3 Methodology The main body of research on young adults reports demographic and social measures.

This study presents the residential condition of young adults with multidimensional measures. The American Community Survey (ACS 2008-2012) is providing survey data.

Spatial data are obtained from various official sources. The mix of these data is illustrated in space, with a specified areal of unit and then examined with analytic methods. The geographical dimension of New York is presented in the first part. Next, a comprehensive data framework is build to combine demographic, economic, spatial and urban data. Without clear recommendations proposed by the contemporary body of researchers, the relevancy of our statistical methods is finally asserted.

3.1

Geography

In residential differentiation studies, the geographical area encompassed is determinant.

The metropolitan form is hard to conceptualize since it is an ecological process in constant evolution since several decades. Ecologists have discussed about the size and form of metropolitan areas and proposed spatial distribution models by social groups and areas (e.g., Park et al. 1925). Census data also intended to provide a geostatistical measure of cities and their populations. Geographical units like State, Counties, Census Tract or Block are used to define the size and precision of studied areas.

Geographical area Segregation indices are usually higher when computed for metropolitan area rather than central cities because they provide a better picture of the spatial distribution (Cowgill 1978).

Metropolitan areas are larger than cities because they comprise area under the economical and demographical influence of central cities. Central areas engender spillover in their surrounding areas with a concentric pattern. To have comparable indices, we will assess the level of segregation of young adults at the metropolitan level.

But spatial analysis requires a tremendous amount of very specific data. No institution is today providing uniform land use, transportation and facility data at the national or metropolitan level. Data are most of the time provided by local Department of City Planning with no homogeneous classification. Because the location of young adults is essentially central and the collection of data is laborious, empirical and statistical analyses are realized on a reduced geographical area called New York City Area.

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New York Metropolitan Statistical Area (NYMSA) New York metropolitan area is the most populous of the United States with 19.8 billion people. The U.S. Census Bureau has defined an area of 23 counties and almost 6,700 sq miles of land. It is called the New York-Northern New Jersey - Long Island Metropolitan Statistical Area (NY-NJ-PA MSA) and we will name it NYMSA for convenience. In this area, the proportion of young adults is 22.0 percent. It is relatively close to the U.S. average of 23.3 percent. As presented in the map 1, young adults are not evenly distributed in the metropolitan area. They live essentially around the city center.

Map 1

Young adults proportion in the New York Metropolitan Statistical Area (NYMSA)

New York City Area (NYCA) An evident smaller geography is New York City, composed of 5 counties that represent administrative, geographical and historical boundaries. But central theories of urban sociology and urban economy are thinking pre-automobile cities as a concentric model. They consider the factor of distance for center more influential than administrative boundaries.

Central Business Districts of Manhattan are not attracting only New Yorkers. Public

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transportation is facilitating commute and every day, workers are coming from New York, New Jersey, Pennsylvania or Connecticut. Housing costs incite households to live further and out of New York City.

Having this in mind, we envisioned our studied area as a concentric pattern. New Jersey City is facing Manhattan and proposing good transportation infrastructure to cross the Hudson River. After comparison, Hudson County has the highest proportion of people working out of their state of residence with 28 percent of its workers (Essex, NJ: 12.4 percent; Union, NJ: 9.9 percent). It is also the physically closest County from Manhattan.

Because administrative boundaries are not limiting individual residencies, we decided to include the Hudson County in our spatial study.

Map 2

Young adults proportion in the New York City area (NYCA)

The study area is composed of the 5 Boroughs of New York City and the Hudson County in New Jersey (the 6th Borough). We named it New York City area (NYCA) to avoid confusion with New York City. The proportion of young adults living within this area is 24.9 percent. This proportion falls at 19 percent in remaining Counties of NYMSA. Hence, 57 percent of young adults are living within the boundaries of NYCA.

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3.2

Data

This research reassembles data from different sources and institutions, with a combination of census data, spatial data, urban data and business data. Because of the variety of sources, the timeframe is not uniform. The reference year is 2012, but the variables time span goes from 2007 to 2012. While all demographic and spatial data are consistent in time (between 2008 and 2012), land use data can vary slightly because they are not updated every year.

Census Data are provided by the 2008-2012 American Community Survey (ACS), a cross-sectional population-based sample survey of the population of United States (U.S. Census Bureau 2014). The last 5-year estimate provides to most accurate and recent picture of populations. With a rolling sample design, it does not represent yearly specific trends.

Demographic, social, economic and housing information are provided. Our key variable is an estimate young adult population in each census tract.

Geographical data is provided by the Census Bureau with 2012 Tiger/Line shapefiles.

Land use data are provided by different public institutions: the Department of City Planning of New York City with BYTES of the BIG APPLE (PLUTO and LION files) in New York City and the New Jersey Department of Environment Protection in Hudson County (NJDEP Landuse/Landcover and MODIV files). Land use classifications vary by administration and they were recoded to deliver the same information.

Other variables refer to businesses or quality of life. They are compiled through the Simply map interface. Businesses variables are provided by Dun&Bradstreet with U.S. Business and points of interest data. They are labeled with the North American Industry Classification System (NAICS) created by the U.S. Census. Quality of life data are obtained from Easy Analytic Software Inc., an independent developer of demographic data. All these data tables were retrieved through SimplyMap, a technology from geographical research.

Geographical unit This study is interested in neighborhood residential condition. Even if boundaries are difficult to define, the census tract level is mainly used to measure neighborhood characteristics (Cowgill 1978; Ellen and Turner 1997). Census tract are geographic divisions relatively small and created for the purpose of the Census. Their boundaries are following visible and identifiable features and coinciding with neighborhoods structure. The census tract optimal size is about 4,000 people, but can vary between 1,200 and 8,000 people. Thus, census tract area varies by density and become larger away from city center. In the NYCA, 14

the median size is 610,043 square feet for 2331 census tracts (compared to 3,800,000 square feet in U.S.).

The ACS is available publicly at various geographical scales. To preserve respondent identities, census tract data are provided as summary statistics with estimates and margin of error. This data can be interpreted only at the neighborhood level and for its group of residents. To avoid any ecological fallacy, ACS individual data are also available with Public Use Microdata Sample (PUMS) files (Census Bureau, 2009). The geographical scale is larger than census tracts with Public Use Microdata Areas (or PUMAs). PUMAs are partitioned by state and are sized with a population of a least 100,000 individuals (U.S. Census Bureau 2009). Their boundaries are contained within Counties, which enabled us to extract the same data for NYMSA and NYCA at PUMAs and census tracts level. PUMS files are not used to infer causality between individuals and neighborhood population but only to precise young adults characteristics.

Almost all our data are provided at the census tract level and merged with a unique identifier ‘geoid’. Some specific data could not fall into this geography because of their spatial properties. Points and lots are located with their longitude and latitude. A geographic coordinate system is referencing these values in three-dimension with degree and angular unit of measure. The North American Datum (NAD) of 1983 was used to represent and align all our shapefiles (ESRI 2014). For statistical analysis, all files are converted in two-dimension with constant lengths, angles and areas. NAD 1983 State Plane New York Long Island FIPS 3104 Feet is used as projected coordinate system.

Limitations ACS provides estimates with margins of error (MOE) for each census tract and variable. MOE are derived from standard error and indicating the reliability of estimates at 90% confidence level (U.S. Census Bureau 2014). ESRI indicates 3 reliability categories for MOE: high reliability (Coefficient of Variation (CV) ≤ 12%), medium reliability (12% < CV ≤ 40%) and low reliability (CV > 40%). To improve our accuracy, 61 census tracts with low reliability were removed of the dataset. Most of them were area with very low population.

Since 2006, the ACS sample includes group quarter population. Group quarters population are assigned to their location and not able to decide of their housing. As this study focuses on housing choice, they are not representative. But they are comprised in census tracts estimates. However, it represents a small proportion of the sample with 17,687 group quarter persons for a total of 11,635,383 individuals in NYCA (less than 0.002%).

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Table 1 Variables by type Variable

Description

Year

Unit

Source

A. Demographic variables Young adults (key variable) Population

Proportion of individuals between 18 and 34 years

2008-2012

Census tract

ACS estimate

2270

-

Quantity

2008-2012

Census tract

ACS estimate

2270

-

Children

Proportion of individuals under 18 years

2008-2012

Census tract

ACS estimate

2270

-

Middle-aged adults

Proportion of individuals between 35 and 64 years

2008-2012

Census tract

ACS estimate

2270

-

Elderly

Proportion of individuals older than 64 years

2008-2012

Census tract

ACS estimate

2270

-

Black people

Proportion

2008-2012

Census tract

ACS estimate

2270

-

U.S. citizen

Proportion

2008-2012

Census tract

ACS estimate

2270

-

Less than high-school degree With bachelor degree and more Fertility rate Married household with child under 18 years Employed people in civilian labor Mean travel time to work Median household income Below poverty level Median housing value Rentership rate Size of rental unit Median gross rent

Proportion Proportion Number of birth per 1,000 women over 15 years Proportion Proportion - with indiviuals over 16 years Count in minutes Total income of the household in the past 12 months Proportion - Poverty threshold defined by U.S. census Estimate of the respondent for sale in adjusted dollars Proportion of renter-occupied housing units Number of room without utilities rooms and bathroom Contract rent with average monthly cost of utilities in adjusted dollars Proportion - Percentage of gross rent per household income higher than 30% Proportion - Did not live in the same house one year ago Proportion Proportion Proportion - Structure with one unit (family-house type) Proportion - More than 1 occupant per room

2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012 2008-2012

Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract

ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate ACS estimate

2270 2270 2270 2270 2270 2270 2270 2270 2270 2270 2270 2270

101 -

2008-2012

Census tract

ACS estimate

2270

-

2008-2012

Census tract

ACS estimate

2270

-

2008-2012 2008-2012 2008-2012

Census tract Census tract Census tract

ACS estimate ACS estimate ACS estimate

2270 2270 2270

-

2008-2012

Census tract

ACS estimate

2270

-

Burden cost Residential mobility Moved in before 1990 Build before 1960 Single-unit Overcrowding

Obs. Missing

B. Spatial variables Census tracts boundaries Counties boundaries

Shoreline boundary - with unique identifier 'geoid' Shoreline boundary

2012 2012

Census tract County

Tiger/Line Tiger/Line

2330

-

6

-

Distance for city center

Distance from the centroid of each census tract to city center Population per area

2012

Miles

Tiger/Line

2270

-

2008-2012

# per sqkm

ACS estimate Tiger/Line

2270

-

2013

polygone

PLUTO NJGIN CUNY NJOIT CUNY NJOIT LION NJDOT EPA NYC DCP NJOIT NYC DCP NJOIT PLUTO NJ|MODIV PLUTO NJ|MODIV

Population density

C. Land use, infrastructure and facilities variables Open space Parks and open spaces for recreation Public railroad transportation Public railroad stop

MTA lines, PATH train New Jersey Transit Rail, Light Rail, and Subway MTA and PATH stops New Jersey Transit Rail, Light Rail, and Subway

Non-pedestrian road EPA Superfund site Hospital Post-secondary school

Facilities: hospitals HSIP New Jersey Hospitals point locations Facilities:post-secondary school HSIP New Jersey Hospitals point locations

Manufacturing land use Public housing properties

2010 NYC 2008 NJ 2010 NYC 2008 NJ 2013 NYC 2003 NJ 2014 XY coordinate 2013 NYC 2008 NJ 2013 NYC 2007 NJ polygone 2013 2014 polygone 2013 2014

D. Misceallenous variables Employees

Count

2011

ZIP code

Crime index

100 is the U.S. average

2012

Census tract

Theater

2013 XY coordinate

Women clothes retail

NAICS code 711110: Theater companies and dinner theaters NAICS code 722410: Drinking places (alcoholic beverages) NAICS code 713940: Fitness and recreational sports centers NAICS code 448120: Women's clothing stores

Place of worship

NAICS code 813110: Religious organizations

2013 XY coordinate

Retail serving alcoholic drinks Fitness and health center

2013 XY coordinate 2013 XY coordinate 2013 XY coordinate

Businesses Patterns EASI D&B U.S. Business D&B U.S. Business D&B U.S. Business D&B U.S. Business D&B U.S. Business

16

Also, the ACS collects data with the ‘two-month rule’. It defines an individual as resident only if he stayed in the same place for at least two months. This rule may miss a population that is moving very often. Young adults are more prone to live in a house less than 2 months because they will come for an internship in the city, or move from place to place because they cannot afford to sign a regular lease. We assume it still remains a limited population and do not affect our sample.

Imposed by confidentiality restrictions, several variables are transformed with topcoding. As extreme values could identify an individual, high responses exceeding a particular value are replaced by a predetermined value. Several variables in our study were top-coded: age, household income, housing unit value and median gross rent. This results in skewed data.

The final sample comprises 2270 census tract with 8,823,736 individuals in NYCA. Young adults are representing 27.5 percent of this population (most of the unreliable census tracts were open spaces or unpopulated areas).

Variables All our variables are described in Table 1. They are classified in 4 categories: Demography (A), Space (B), Land use – Transportation - Equipments (C) and Miscellaneous (D).

3.3

Method

Spatial statistics are a developing field since few decades, but only used recently by sociologists and demographers (Chi and Zhu 2008). The field is complex and has not resolved numerous issues: for instance geographical unit measure effect, spatial nonlinearity or clustering measures (ESRI 2014). Moreover, there is no ‘codebook’ reviewed by peers that recommend guidelines in spatial data analysis. Thus, we spend a fairly amount of time to explore several methods, understand them and compare their efficiency. The following section intends to present the methodological choices and their relevancy in our framework.

City center Theories of urban land use and economy are modeled with a city center as reference point (Alonso 1960). But New York City does not have one historical center. For ecologists and urban economists, the definition of city center is economical with the Central Business District (CBD) (Glaeser 2007). As this concept remains subjective and New York has 17

actually several CBD, we looked for the location of jobs. The ZIP code Business Patterns series indicate the number of employees per ZIP codes. The 2011 latest edition indicates that zip codes 10036 and 10017 are the two biggest ‘employers’ of the city, with more than 130,000 paid employees for each of them (U.S. Census Bureau 2011). These zip codes are located Midtown, between 42nd St and 49th St. An interesting fact is that they are also hosting the biggest subway hub in the area of Bryant Park. The New York Public Library is located at the intersection of these 2 ZIP codes and at the edge of Bryant Park. Therefore, we choose this recognizable and inspiring public building as city center.

Transformation of the data Our key variable is a proportion bonded by two physical limits 0 and 1. For statistical analysis, it is necessary to correct the distribution of the proportion with a transformation into a functional form. All proportions are converted into their logit to be bounded by [-∞,+∞].

Then our key variable is logitp = ln (p/(1-p)) where p is the proportion.

In spatial query, data are representing polygons, points or lines with a specific location.

To test these variables in our statistical analysis, we quantified them in the space. The conversion of the data was realized in ArcGIS with the Euclidean distance analysis tool and Kernel density tool. They measure the quantity or density of features in each census tract.

Euclidean distance measures the straight-line distance from a feature to its closest source (ESRI 2014). For each census tract, the number of points of interest was computed within a specified perimeter. This tool was used to measure the proximity to a single point of interest like post-secondary school or open space. The distance threshold was ½ mile from each census tract boundary, which represents about 8 to 20 minutes walk depending on how straight is the line.

Kernel density measures the magnitude of point or polyline features in a given unit area (ESRI 2014). The tool is creating a density raster with a kernel function. We used this tool to have a better idea of urban polarities where density has an attracting effect. The kernel density was computed for specific variable like density of subway stops, retails serving alcoholic drinks or fitness center. A smoothing distance was indicated at either ¼ mile (4 to 10 minutes walk) for local facilities like subway stop, and ½ mile (8 to 20 minutes walk) for local amenities like theaters2.

2

Transformation methods are in annex 1.

18

Exploratory analysis An exploratory spatial data analysis (ESDA) has to be done before engaging any spatial analysis (Chi and Zhu 2008). This stage of analysis helps to visualize the spatial patterns of our data and identify clusters or outliers. Tobler (1970) stated the First Law of geography as “all things are related, but nearby things are more related than distant things”. This expresses that spatial interactions can exists when nearby features present similarities or dissimilarities.

It can also be called spatial autocorrelation. It is important to look for spatial autocorrelation because it will then affect the statistical method used for analysis.

The most well-know index is the Moran’s I statistic to measure this phenomena. It indicates the degree of linear association of a given feature in space and with neighboring locations. This index is used to measure the segregation of young adults at the metropolitan level in Section 4. It informs about the presence of statistically significant clusters or dispersions in the data.

To go further is this exploration, another helpful tool looks for statistically significant clusters and outliers at the local level. The Local Moran’s I value is computed with a Z-score and a p-value for each census tract. The results confirm that our data is significantly clustered in the city center and dispersed far from the city center3. Outliers are also revealed by this analysis. After empirical observation, it appears that several of them are census tract with post-secondary school like Columbia University (84 percent of young adults) or area with high percentages of young adults. As these census tracts are not a mistake, we decide to keep them but it would not be appropriate to run a linear regression for this study.

To deal with outliers, the strength of association between variables is measured with a nonparametric function: the Spearman’s rank correlation coefficient (rs). The sign of the correlation indicates the direction of association and is ranging between -1 and 1, when 0 indicates no correlation between the two variables.

Weights Because it is necessary to account for spatial autocorrelation, the data needs to be weighted. A spatial weights matrix quantifies the relationship between units and how they interact together with a ‘NxN’ table where N is the number of units. This matrix helps to treat clusters and spatial distribution with variance and covariance. The Generate Spatial Matrix tool in ArcGIS was used to find the most appropriate method to weight our data (ESRI 2014).

Row standardization was used to compute them. Following Voss and Chi recommendation 3

Annex 2 is the resulting map indicating high and low clusters or outliers in the NYMSA. 19

(2006), we choose the matrix providing the highest spatial autocorrelation of our response variable with a high level of significance4. The spatial weight matrix selected is the 3 nearest neighbors at metropolitan and city scale. These 2 matrices weight all census tracts with a minimum of 3 neighbors. It is a binary matrix where (1) indicates features that are neighbors and (0) in other case.

Survey data need also to be adjusted. The ACS is publishing estimates that are the result of a ratio estimation procedure with two sets of weights: one for each individual or household record and one for each housing unit record. These weights are used to increase the precision of the estimates and correct sampling non-interview bias (U.S. Census Bureau 2014). PUMS files are also provided with 80 replicate weights for each population and housing unit record (U.S. Census Bureau 2009).

Spatial Interaction To propose a better understanding of young adults residential differentiation, we intend to understand their spatial ordering. Since our data is not linear and interactions interfere, it appears necessary to classify them by groups. This procedure intends to find natural clusters with similar characteristics (ESRI 2014). To operate this classification, we used the grouping analysis tool of ArcGIS. This tool tests several variables to find the best combination of cluster algorithms. No spatial constraint was specified to find natural groups in the New York City area. Section 6 presents the optimal model to classify young adults per census tracts.

This method has drawback because hundred of cluster analysis algorithms exists.

Groups can vary by size, density, shape or measurement unit. There is no right answer. The optimal solution is the one that performs the best scenario with the best significance and fit.

Therefore, grouping census tracts with similar characteristics is helping to learn about the underlying structure of the data but cannot be used for causal inference.

4 Are young adults segregated?

There is little doubt that spatial ordering of young adults is uneven, either at the metropolitan scale than at the city scale (maps 1 and 2). In the New York Metropolitan Area, young adults tend to live closer to New York City center. A spatial ordering is recognizable but is there residential differentiation? To assess the residential differentiation of young adults, we measured segregation with five multidimensional indices: evenness, exposure, 4

Annex 3 provides the results of testing with all matrices.

20

concentration, centralization and clustering. As recommended by the literature (e.g., Cowgill 1978), they are quantified with at the census tract level in the New York Metropolitan statistical area. Except for the clustering index, indexes are computed following Massey and Denton recommendations (1988). Since age segregation has been essentially studied for the elderly, indices are computed for these 2 age categories.

4.1

Evenness

Evenness describes the differential distribution of a minority group relatively to other groups among areal units in the city. The index of dissimilarity measures the weighted mean absolute deviation of every unit’s minority proportion from the city’s minority proportion.

The formula is the following: n DI = ∑ [ti |pi – P| / 2TP(1-P)] i=1

(2)

where ti and pi are the total population and the minority population in the unit area i, T and P are the total population and the minority population in the whole city. n is the number of census tract in the metropolitan area. It varies between 0 and 1.

In NYMSA, the index of dissimilarity is 0.18 for young aduls. It means that 18 percent of young adults would have to change their residence to achieve an even distribution in the metropolitan area. For elderly people, this value is slightly higher at 0.20. Cowgill (1978) measured an average DI of 0.23 for aged population in 241 metropolitan areas (ranging between 0.11 and 0.44). These results are notably lower than racial segregation values, with a mean of 0.86 found by Taeuber and Taeuber (1965) for black people in 1960.

Young adults dissimilarity index presents a moderate degree of segregation. However, young adult residential differentiation is almost similar to the elderly segregation in NYMSA. Nonetheless it should be mentioned that these results could significantly increase with a smaller geographic unit. Cowgill (1978) found doubled values when he computed the index at the block level.

4.2

Exposure

Exposure indices inform about the interaction potential between minority and majority groups in a metropolitan area by sharing a residential area. This measure is dependent of the

21

size of the minority population: the smaller will be the community, the larger will be the probability to be in contact with the majority. Their equation is respectively: n xP*y = ∑ [xi/X] [yi/ti] i=1

(3)

n xP*x = ∑ [xi/X] [xi/ti] i=1

(4)

where xi, yi and ti are the number of X minority members city-wide, Y majority members citywide and the total population in the unit area i. It varies between 0 and 1.

Interaction index is xP*y=0.51, meaning that 51 of every 100 people that a young adult will meet randomly will be an older adult (more than 34 years). Isolation index is xP*x=0.27, indicating that 27 percent is the probability that a young adult shares a census tract with another young adult. Elderly people have 62 percent of chance to be exposed to young and middle-aged adults, and only 17 percent probability to share a spatial unit with their age category.

These values are not indicating a strong isolation of young adult. But the comparison with elderly population indicates that young adults are more isolated and less interacting with other ages than elderly.

4.3

Concentration

This measure is an indicator of concentration in the urban environment, informing about the physical space occupied by the minority relatively to the majority group. The relative concentration index is not necessarily following the trend of the index of dissimilarity. It observes if minorities are restricted to a smaller number of neighborhood than others. The formula is:

RCO =

n n { [∑ (xiai/X)] / [∑ (yiai/Y)]- 1 } i=1

i=1

(5)

n1 n { [∑ (tiai/T1)] / [∑ (tiai/T2)]- 1 } i=1

i=n2

where ai correspond to the land area of unit i. n is the number of census tract ordered by geographic size from smallest to largest. n1 and n2 are different points in the rank ordering, n1 is rank of the tract where the cumulative total population of areal units equals the total minority population of the city, and equal to 1295 over 4885 census tract. n2 is the rank of the

22

tract where the cumulative total population of units equals the minority population totaling from the largest unit down, and equal to 997. T1 equals the total population in tract for 1 to n1, and T2 for tracts from n2 to n. It varies between -1 and 1.

The concentration index is RCO=0.38 for young adults. This ratio means the concentration of young adults exceed by 38 percent that of other adults. They would be equally concentrated in the urban space with a zero ratio. Elderly people are far less concentrated than young adults with a ratio of -0.11. It means that the concentration of other adults exceed by 11 percent the concentration of the elderly. These results are indicating that young adults occupy less physical space than the elderly. They are concentrated on a smaller number of census tracts.

4.4

Centralization

Several studies have shown that minority populations tend to be located in the center of metropolitan area to be close to jobs and public transportation (Glaeser, Kahn, and Rappaport 2008). The absolute centralization index provides a measure of the tendency to live close to the business center. As mentioned previously, the CBD of New York City is located midtown, and the New York Public Library is the reference point. The index is computed with the formula: n n ACE = ( â&#x2C6;&#x2018; Xi-1 Ai) â&#x20AC;&#x201C; (â&#x2C6;&#x2018; Xi Ai-1) i=1 i=1

(6)

where n is the number of census tract ordered by increasing distance from the central business district. X1 and A1 are the respective cumulative proportions of X population and A land area in tract i. It varies between 0 and 1.

The index of absolute centralization is ACE=0.71 for young adults. A proportion of 71 percent young adults would need to move out from the central area to obtain an even distribution within NYMSA. The comparable value for the elderly is lower, with ACE=0.62.

The centralization of young adult is very high and confirms what map 1 illustrated: young adults are extremely concentrated around the center of the city. This result is also consistent with previous findings that the elderly tend to live in city center (Chevan 1982; Pampel and Choldin 1978).

23

4.5

Clustering

Associated with concentration and centralization, clustering indicates the degree of spatial clustering of one minority group. This index measures how a minority group is aggregated and contiguous in the urban area. High measure of clustering will highlight urban enclave(s). The Moran’s I statistic (1950) is used to measure this dimension, which varies from Massey and Denton recommendation. This choice is the result of recent improvements in spatial statistics that have facilitated the construction of spatial matrix. The Moran’s I statistic is described by the following formula:

Moran’s I =

n n n ∑ . ∑ wi,j zi zj i=1

j=1

(7)

n S 0 . ∑ z2 i i=1 where zi is the deviation of an attribute for feature i from its mean (xi – X), wi,j is the spatial weight between feature i and j, and S0 is the aggregate of all the spatial weights. It varies between -1 and +1 to indicate a dispersed or clustered pattern. A zero-value indicates a perfect random distribution. z-score is computed to determine the statistical significance of the results.

The Moran’s I statistic for the logit of young adults is 0.52 in NYMSA (p<0.001). In comparison the same value is 0.34 for the elderly (p<0.001). Since this index is recent and has few references in the literature, we computed other indices for adults population group (Moran’sIadults=0.30***) and black people (Moran’sIblack=0.69***). These complements are helpful to give a scale of clustering. From this comparison, we can infer that young adults are highly clustered in NYMSA. Their clustering is higher than for elderly, but still less than black people.

4.6

Summary

Table 2 summarizes the five measure of segregation of young adults compared to the elderly population. These results confirmed empirical observations from map 1: young adult are not evenly and randomly distributed in the metropolitan context. Surprisingly, they tend to be more isolated than aged people even if they are a larger group of individuals. Their degree of concentration is relatively high, which indicates that they are concentrated in smaller area that other adults. Their degree of centralization is even more striking, they live

24

around the city center. And finally, they live significantly clustered together. However, young adult segregation should not be compared to racial segregation.

Table 2

Comparison of five measures of segregation between young adults and elderly

Index Evenness (DI)

Young adults (18-34) 0.18

Elderly (+65) 0.20

Exposure: interaction (xP*y)

0.51

0.62

Exposure: isolation (xP*x)

0.27

0.17

Concentration (RCO)

0.38

-0.11

Centralization (ACE)

0.71

Clustering (Moranâ&#x20AC;&#x2122;s I)

0.52

0.62 ***

0.34***

5 Where do young adults live?

The segregation analysis revealed a multi-dimensional residential differentiation of young adult in the metropolitan context of New York. To help the understanding of this segregation, it is necessary to have a better picture of their urban environment. While most previous researches have focused on specific housing criteria like type of housing, shared living or mobility, we intend to propose a larger picture of living conditions in emerging adulthood. This section describes what characterize neighborhoods with more or less young adults in an economic, urban, spatial, social and demographic dimension5. Five thematic maps based on contemporary data are illustrating our arguments.

5.1

Cost

Ecological theories and urban economics are pointing the ability to pay as the main factor causing residential differentiation. Affordability is a compromise between income and market price. As we know how much emerging adulthood is affected by the economical contemporary context, it is important to understand the economic condition of young adult through their neighborhoods.

5

Demographic data are available in annex 4.

25

! ! ! ! ! !

More than 14.3% individuals changed residence within the past year (upper quartile)

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

Open space

Unreliable data

More than 30.8%

X

X

X

X X X X X

XX

X X

X

X

X X

X X X

X X

X

X

XX X X

X

X

X

X

X

X

X

0

X X X X X X XX X XX X X X X XX X X XX X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X XX X X XX X X X X XX XX X X X X X X X X X X X X X X X X X XXX X X X X X X X X X X X XX X X XX X X X X X X XX X X X X X X X X X X X X X X X X X XX X X X X XX X X X X X X XX X X X X X X X X X XX X X X X X X X X X XX X XX X X X XX X X X X XX X XX XX X X X X X X X X X X X XX X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X XX X X X X X X X X X X XX X X X XX X X X X X X X X X X X X XX X X XX X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X XX X XX X X X X X XX X X X X X X X X X X X XX X XX X XX X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X XX XX X X X X XX X X X X X X X X X X XX X X X X XX X X X X X X X X X X X X X X XX X X X X X X X X X XX X XXX X X X X X XX X X X X X X X XX XX X X X X X X XX XX X X X XX XX X X X X X X X X X X X X X X X X X X X X X

Sources: 2012-2008 American Community Survey, 2012 Census Bureau TIGER/Line shapefiles

! ! ! ! ! !

Between 26.1% and 30.8%

Between 22.2 and 26.09%

Less than 22.2%

Young Adults proportion in NYC area

Highest quartile of median rents (above $1402)

Lowest quartile of median rents (under $1058)

Median Gross Rent

X

COST

X

X

X

X

X

X X

1

X

X

X

X X

X

2

X

X X

X

X

X

X

X X

X

X

X X

X

X

X

X

4

X

X

X

X

X

X

X

X

X

X

X

6

X

X

X X

X

8 Miles

¯

Table 3 Descriptive statistics and spearman intercorrelation among variables Measure

1

1. Young adults

-

2

3

4

-0.51

***

-

0.12

***

-0.28

***

-

4. Rentershiprate

0.47

***

-0.49

***

0.02

5. Median gross rent

0.11

***

-0.11

***

0.36

***

-0.06

***

0.21

***

-0.17

***

0.49

***

-0.41

***

0.10

***

-0.40

***

0.25

***

0.06

**

9. Population density

0.33

***

-0.47

***

0.14

***

Mean

0.28

7.59

520,571

0.63

SD

0.09

3.49

195,977

Min

0.00

0.13

10

Max

0.87

21.91 +1,000,000

2. Distance from CBD 3. Median housing value

6. Burden cost 7. Residential mobility 8. Moved in before 1990

***

5

6

7

8

9

-0.39

***

-

0.07

***

-0.16

***

-

0.36

***

0.18

***

-0.12

***

-

-0.39

***

-0.04

***

-0.11

***

-0.37

***

-

0.60

***

-0.19

***

0.20

***

-0.18

0.07

*

***

-

1234

0.54

0.11

0.22

19.05

0.25

344

0.14

0.07

0.10

13.28

0.00

0.00

0.00

0.00

0.00

0.04

1

+2,000

1

0.74

1

81.62

N=2270 census tracts, * Significant at p â&#x2030;¤ 0.05 ** Significant at p â&#x2030;¤ 0.01 *** Significant at p â&#x2030;¤ 0.001

Rentership Young adult neighborhoods are highly positively correlated with the number of rental units. In Metropolitan areas, renting is more prevalent because prices are high (JCHSHU, 2013). In New York City area, 63 percent of housing units are renter-occupied compared to 36 percent in U.S. Young adults are traditionally the largest group interested by rental market, and 67 percent of them are renting their home in NYCA. This trend reflects partly limited financial possibilities. The median yearly income per individual is estimated at $15,000 for a young adult when it is twice larger for middle-aged adults with $30,000 per year.

But surprisingly young adults are paying higher median rents than other adults. Their median rent is $1,280 whereas it is $1,100 for middle-aged adults and $830 for elderly people. The spatial query indicates that higher rents are located in either in central areas where young adults live, either in suburbs where more family-house are located.

Market increase Rentership is also motivated by mobility. Recent movers tend to rent rather than own their home. As shown on the map, census tract with the highest proportion of recent movers are essentially located in areas close to the city center, and also in the Bronx. Correlation coefficient confirms a very high relationship between young adults neighborhood and recent movers.

27

Young adult move more often than older population. 19.3 percent of young adults did not live in the city one year ago compared to 6.2 percent individuals for older adults. Facing lots of change in the course of their life, they are mobile because they can find a job in another area, change of roommate, raise their income, move in with a partner or need an extra room with the birth of a child. These changes make them more bouncy by living in a short or mid-term perspective. Thus, they could potentially be less exacting in their housing choice because it can be seen as a temporary situation.

This mobility has a very negative effect on their housing cost since the market is in a constant increase. To give an idea, the median rent in New York City increased by 8.5 percent from 2007 to 2011 (Furman Center 2012). Indeed, this increase is higher in central areas like Manhattan (13 percent) or Brooklyn (9 percent). Therefore, long-term residents tend to have smaller rents. The median rent of householders within the same place for more than 4 years is $330 lower ($1,010) than for people that moved in the last year ($1,340).

Landlords tend to raise rents more when the householder changes (Furman Center 2012).

Governments have tried to control housing market by law. New York City and New Jersey have regulated subsidized and rent-regulated housing units since the beginning of the nineteenth century. The median gross rent in rent-regulated units was $1,180 in 2011 compared to $1,540 for market-rate units (Furman Center 2012). More than 45 percent of rental units in New York City are ruled by rent-stabilization or rent controlled. These numerous controlled or subsidized units are leading to a fairly high percentage of tenants in New York City living in regulated units (62 percent). But these units are hosting a very specific demography of households. Ended in 1971, new rental units were decontrolled.

Thus, only old residents have access to these units today.

Burden cost In the New York City area, one in two young adult rental householder is cost-burdened.

22 percent of them are spending more than 30 percent of the recommended share of income, and 27 percent more are severely burdened by spending a share higher than 50 percent. But surprisingly, the relationship between young adults neighborhood and burden-cost is not significantly high.

The burden young adults are handling can affect their quality of life. With the same income, individuals will spend more on housing and less on other necessities. These economies can be done on food, transportation, health care, retirement savings, apparel, entertainment or others expenses (JCHSHU 2013; U.S. Bureau of Labor statistics 2012).

28

5.2

Housing

Land use influences residential differentiation. The market equilibrium is the result of bids for land between residential, business and industrial uses. As Alonso (1960) explained, businesses can bid higher than families and higher than agriculture to be close to the city center. Therefore, housing is ordered by price, type and density in the city area. The stage of life of individuals or households becomes a meaningful determinant of their residential location.

Table 4 Descriptive statistics and spearman intercorrelation among variables Measure 1. Young adults 2. Distance from CBD 3. Built before 1960 4. Single-unit structure 5. Married family household 6. Overcrowding 7. Size of rental unit Mean SD Min Max

1 -0.51 -0.02 -0.43 -0.33 0.20 -0.01 0.28 0.09 0.00 0.87

2

***

*** *** ***

***

-0.04 0.63 0.54 0.08 0.25 7.59 3.49 0.13 21.91

3

*** *** *** ***

0.11 0.09 0.08 0.08 1 0.00 0.22 0.68

4

** *** *** ***

0.60 -0.22 0.20 0.22 0.24 0.00 1

5

*** *** ***

0.24 0.71 0.65 0.16 0.00 1

6

*** ***

0.59 0.09 0.08 0.00 1

7

***

2.60 0.63 0 6.41

N=2270 census tracts, * Significant at p â&#x2030;¤ 0.05 ** Significant at p â&#x2030;¤ 0.01 *** Significant at p â&#x2030;¤ 0.001

Land use Ecological studies argue that differential distribution of social categories is the result of spatial competition (Park, Burgess, and McKenzie, 1925; Hawley, 1950). In consequence, there is a spatial ordering by housing type regarding the distance from the central business district. Burgess urban land use model (1925) describes six concentric zones where the central ones are denser, with industries and multi-family buildings, and single-family dwelling are located in the fifth residential loop in periphery.

New York City area reflects this model with more manufacturing and high-density housing in central area, and more single-family units away from the city center6. As a result

6 Single-family units are not represented in the map because they are not classified as a unique category in the Hudson County, NJ. Therefore, the representation would not have been homogenous in NYCA. The proportion of married families with children under 18 years is highly correlated with the proportion of single unit housing.

29

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

Open space

Unreliable data

More than 30.8%

Sources: 2012-2008 American Community Survey, 2012 Census Bureau TIGER/Line shapefiles, 2013 New York City Department of City Planning, 2013 New Jersey geographic information network, 2007 NJDEP Landuse/Landcover, 2010 NJ Department of Treasury -TaxList search

! ! ! ! ! !

Between 26.1% and 30.8%

Between 22.2 and 26.09%

Less than 22.2%

Young Adults proportion in NYC area

More than 34.9% (median) married household with children under 18 years

Public Housing

Manufacturing

HOUSING

0

1

2

4

6

8 Miles

ÂŻ

of spatial distribution, young adults neighborhood are significantly and negatively correlated with the location of single-family units. Table 5 shows that they tend to live in multi-unit structures rather than one-family house. Their financial limitations make them more willing to live in more various and cheaper type of housing.

Table 5

Number of units in the structure of residence for householders, per age category.

Units in structure

Young adults

Middle-aged

Elderly

Mobile home or alternative housing

0.31%

0.49%

0.74%

One-family house

18.22%

35.49%

35.99%

2 to 4 apartments

24.97%

21.06%

16.28%

5 to 9 apartments

9.74%

5.48%

3.88%

10 to 19 apartments

7.80%

4.85%

3.90%

20 to 49 apartments

14.22%

11.26%

10.44%

50 or more apartments

24.74%

21.37%

28.77%

Young adults also tend to live in manufacturing areas as the result of central city areas deindustrialization and shift to service economy (Curran 2007). This process was profusely described in gentrification literature since the 1980’s (Marcuse 1986; Smith 1996). Marcuse (1986) describes gentrification as a movement of upper and middle-income households into historically working-class neighborhood, causing the displacement of lower-income residents. For Smith (1996), it is happening when “the difference between the actual and the potential ground rent is great enough for major profit to be realized by landowners or speculators”.

Converting manufacturing areas are first attracting a population more willing to adopt a loft-living lifestyle (Zukin, 1989). Industrial apartments are not initially adapted for housing purpose, with large floor and high ceiling to receive machines. When they are transformed for residential use, it creates very deep apartment without bulk of windows or natural light and usually with low environmental performance. A 24 years-old waitress has a window in her bedroom for the first time since a long time, she says being “very grateful for things like a windows".

Because they search for central areas and are more resilient with their housing condition, young adults are more likely to live in gentrified neighborhood. They are more willing to accept lower housing conditions with lower quality of neighborhood. Industrial

31

areas offer less facilities and environment quality. By living there, they are often stereotyped as artists, hipsters or students.

As a result of ecological process, young adults could also live in older and more central housing units. Chevan (1982) showed that elderly tend to live in older dwelling units.

Surprisingly, it is true for elderly but not significant for young adults. 88 percent of elderly are living in buildings constructed before 1960 compared to 82 percent of young adults.

Life cycle With lower income and smaller households than middle-aged adults, young adults are less competitive to live in single-family home. Only 18 percent of young adults householders live in one-family units, compared to 36 percent for older adults. The composition of young adults households is definitely more heterogeneous than older adults: 29 percent of young adults are married or widowed, 10 percent of unmarried young adults live with their partner, 25 percent live with non relative people, and 10 percent live alone. Young adults live with younger children on average compared to middle-aged adults. They are more flexible with their housing size with young children.

Young adult live in smaller dwelling unit and with less space per person. There is one fewer room in young adults housing units than in older adults. On average, they live in households with 0.8 people per room, compared to 0.7 for middle-aged adults and 0.5 for elderly. Even for households with children, young adults have 0.6 people per room compared to 0.5 for middle-aged adults. There is a significant and positive relationship between young adults neighborhood and overcrowding.

Discrimination In tight housing market and economical context, the demand for low and median rents is higher than the offer. Therefore, owners and landlords have more power and flexibility in their decision. They are free to choose their renters or buyers on their own criteria. These criteria can be based on incomes, family type, number of children, job type, ethnicity and more. Segregation often happens because some populations are discarded from specific type of dwelling units.

For young adult, discrimination can easily happen because of their level of incomes, cultural stereotypes and family status. Their level income is obviously constraining their range of choice. It is also limiting because of lease requirements. For many leases, candidates

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must justify a minimum payroll as a multiplier of their rent. A landlord can require a payroll three to five times the amount of rent.

Stereotypes are also happening. Young adults convey lots of stereotypes like being roisterers, noisy, thoughtless or penniless. Landlords can decide to avoid young tenants because they look for a quiet and responsible community of tenants, embodied by families and the elderly. Cooperative buildings are openly protecting this quiet lifestyle. The corporation of residents in the co-op decides of their future neighbors without any justifications for approval or refusal. Boards often ask for extensive financial resource, specific requirements and personal presentation. Only 11 percent of the census tracts with more than one in three young adult have co-ops buildings built in New York City7.

The access to housing can be conditioned by the family status. New York City has developed successive politics since several decades to promoted subsidized housing for lowincome households. But public housing is accessible on specific criteria that are essentially the level of income and the size of household. Secondary priority criteria are worker status, victims of domestic violence, threatened persons and homeless people (NYCHA 2014b).

As presented on the map, census tract with public housing tend to have less young adults living in. This fact is explained by the length of wait and the requirements asked by public offices. Today, 267,567 families are on the waiting lists of The New York City Housing Authority for Conventional Public Housing and Section 8 Housing, with a 3.1 percent annual turnover rate (NYCHA 2014a). In 1999, the U.S. Department of Housing and Urban Development (1999) was reporting an average waiting time of 8 years in New York.

So even if many young adults could respond to the economic criteria and have a large family at the same time, they can access to subsidized housing in a later stage of life.

5.3

Accessibility

The concept of proximity is an important determinant of choice in housing. On the following query, we assess the accessibility of young adults neighborhood to jobs and facilities. As mentioned previously, young adults are in a stage of life where they can either follow post-secondary education, start their professional life and give birth.

7 The data is not represented in the map because it was not available in the Hudson County, NJ. 33

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Table 6 Descriptive statistics and spearman intercorrelation among variables Measure

1

1. Young adults

-

2. Distance from CBD 3. Kernel density of subway stop 4. Mean travel time to work 5. Kernel density of hospital 6. Distance to post-secondary school Mean SD Min Max

2

3

4

-0.51 *** 0.33 ***

-0.36 ***

-0.25

***

0.53

***

-0.19 ***

0.17 -0.00

***

-0.23 -0.02

***

***

0.28 0.09 0.00 0.87

***

7.59 3.49 0.13 21.91

5

6

-

-

0.13 -0.09 ***

-0.17 *** 0.05 *

-0.07 ***

2.88 4.68 0.00 45.26

39.60 7.32 0.00 79.20

0.33 0.93 0.00 13.39

0.76 0.62 0.00 2.00

N=2270 census tracts, * Significant at p ≤ 0.05 ** Significant at p ≤ 0.01 *** Significant at p ≤ 0.001

Public transportation In old concentric cities, the role of public transportation is essential to explain spatial distribution (Glaeser et al. 2008). Theory of urban market has shown that central cities are attracting disproportionally poor populations because they want to save commuting time and cost, and be closer to jobs (Alonso 1960). New York City has one of the most complex and developed mass transit network in North America (MTA, 2012). The Metropolitan Transportation Authority covers 24-hours-per-day/7-days-a-week a 5000-square-miles area up to New Jersey and Connecticut. Almost 43 percent of workers in the NYCA commute with public transportation, compared to the national rate of 5 percent. Among young adults, one in two is using public transportation to go to work. They tend to be concentrated around subway stop and hubs: the more subway stops, the more young adults live around. The positive correlation is significant and moderated between young adults neighborhood and subway stops. Actually, individual young adults’ commute is shorter of 3.5 minutes than the average.

Job market Job clusters are located close to CBDs. In New York City, they are mostly located in Manhattan, from Downtown to Uptown (except in Upper West Side)8. All of these areas are well connected to public transportation, and not easily accessible by car because of traffic

8

To facilitate the lecture of the map, we did not represent these areas with the densest concentration of jobs (with more than 3 employees per square miles).

35

congestion and lack of parking. Indeed, Wu (2010) has shown that the proximity to workplace was the second most important criteria in young adults housing choice.

As they are starting their professional career, 83 percent of young adults work as employees. More than 10 percent of them search for a job, which is 4 percent point higher than middle-aged adults. The tendency to witch job frequently might increase the need of proximity with the CBD. To access jobs, it is necessary to be physically close to the market.

Job-hunting is challenging because it demands reactivity and proximity. Employers often advantage candidates living close to their office, expecting less retard and non-attendance (Green 2014).

Education In NYCA, 27 percent of young adults are currently enrolled in school. It represents a population of almost 740,000 individuals. This is larger than the whole population of Boston or Detroit. But the relationship between young adults neighborhood and post-secondary schools is surprisingly not significant. However, the map indicates some school surrounded by very young adults neighborhood, but not all of them. There are clusters in Manhattan with NYU, CUNY and Columbia University. In ESDA, they appear as outliers with very high percentage of young adults.

Health Young adults neighborhood present a positive relationship with the density of hospitals.

It might be explained by their centrality. Nonetheless, Wu (2010) has shown that the access to medical and health facilities was the fourth top determinant in housing consumption for young people in Guangzhou, China.

Between 18 and 34 years, individuals need a good access to health facilities. They go more often at the hospital than teenagers or adults because they give birth and have young children in need of particular health attention (Zastrow and Kirst-Ashman 2009). 8 percent of young adult women gave birth in the past 12 months, compared to 3 percent for middle-aged adults; and 24 percent of young adults are living with at least a child under 6 years old, compared to 8.5 percent later.

5.4

Environment

The quality of the environment is an ecological factor affecting housing values. To understand the quality of environment, the following map in also observing the urban 36

residuum. As a result of competition for valuable land, the best lands are characterized by a natural, healthy and safe neighborhood, with potential viewpoint toward the city or nature. In opposition, the worst lands host environment hazards, noisy speedways and unsafe streets.

Table 7 Descriptive statistics and spearman intercorrelation among variables Measure 1. Young adults 2. Distance from CBD 3. Distance from open spaces

1

2

-0.51 *** 0.09 *** ***

3

-0.00

4

5

0.07 ** -0.05 *

0.03

-

0.35 0.25 0.00 0.99

0.03 0.24 0.00 5.05

112.55 36.45 0.00 199.00

***

4. Kernel density of Superfund site 5. Crime EASI index (U.S. average=100)

0.17 0.29 ***

-0.16 -0.36 ***

Mean SD Min Max

0.28 0.09 0.00 0.87 ***

7.59 3.49 0.13 21.91

N=2270 census tracts, * Significant at p ≤ 0.05 ** Significant at p ≤ 0.01 *** Significant at p ≤ 0.001

Safety Young adult neighborhoods have a moderately positive relationship with the level of Crime in their neighborhood. This relationship is partly due to their location in central area (Shihadeh and Ousey 1996). The demographic distribution and level of poverty are also affecting the rate of crime (Ellen and O’Regan 2009). However, this relationship is not consistent with Wu’s findings (2010). He classified the ‘sense of safety’ as the 3rd most important factor in young adults housing choice.

Pollution The build environment can also be unsafe for its residents. As a result of years of uncontrolled industrial activities, some lands contain hazardous waste. The toxic waste can take liquid, solid or volatile form. Over the past two decades, the U.S. Environment Protection Agency (EPA) has designated Superfund sites to protect ecosystems and people and restore environment. Contaminated sites represent a real risk for surrounding residents and public health. It has been proved that they significantly increase the risk of adverse health effects like low birth weight, birth defects and certain types of cancers (Vrijheid 2000).

Residents living close to landfill site are reporting health symptoms such as fatigue, sleepiness, and headaches.

37

! ! ! ! ! !

Open space

Highest crime index (Upper quartile)

Non-Pedestrian Road

EPA Superfund Site

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

! ! ! ! ! !

Unreliable data

More than 30.8%

# I

# I

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# I

# I

# I # I

# I

# I

# I

# I

# I

# I # I

Sources: 2012-2008 American Community Survey, 2012 Census Bureau TIGER/Line shapefiles, 2013 New York State GIS Clearinghouse, 2012 Easy Analytic Software Crime Index, 2013 New York City Department of City Planning, 2013 New Jersey geographic information network, 2013 United States Environmental Protection Agency

! ! ! ! ! !

Between 26.1% and 30.8%

Between 22.2 and 26.09%

Less than 22.2%

Young Adults proportion in NYC area

# I

ENVIRONMENT

0

1

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8 Miles

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Young adults neighborhood have a positive significant relationship with Superfund sites. The river context and history of New York City (Glaeser 2005) enabled the location of manufacturing sites close the city center. With lower housing prices and central location, young adults tend to be more present in these areas. If they are recent movers, they also might have less knowledge of the neighborhood and its environmental hazard.

Another source of build pollution is the road. Roads infrastructures are generating gas, noise and visual pollution. There are usually generating obnoxious urban context, where the pedestrian do not have its place. It can be a source of danger and discomfort in the daily life of urban dwellers. No statistic quantifies the relationship between young adult neighborhood and non-pedestrian roads. However, the empirical observation shows that more young adults tend to live close to freeways and their interchange, particularly in central city area.

Green Young adult neighborhoods have a weak positive relationship with distance from open spaces. Ecological factors should be engaged in this process, since housing close to parks tend to have higher price. But the geographical unit might be too large to observe this process. The map demonstrates that young adults are living in area with smaller parks, not larger than a couple of blocks, while areas with large open spaces like Central Park, Park Slope or Cypress Hill are surrounded by slightly less young adults.

5.5

Lifestyle

The previous maps presented economical and political factors to determine to location of young adults. Lastly, this spatial query explores cultural factors. The current generation of young adults have different lifestyles than previous ones (Arnett et al. 2011; Miles 2000; Pew Research Center 2010). Miles (2000) describes them as hedonists. Emerging adults look for a balanced-life as a way to keep option open and live in the present moment (Wyn and Woodman 2006). This lifestyle can be achieved in particular in social, cultural, spiritual and physical activities.

39

! ! ! ! ! !

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More than 30.8%

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! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !#! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! # # # # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! # !! # ### ! ! # ! ! ! !# ! ! ! ! ! ! ! ! ! !#! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! # ! ! ! # # ! ! # ! ! # ! ! ! ! ! ! ! # # ! ! !# # # ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! # ! ! ! # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! # ! !! ! ! ! ! ! ! ! ! ! ! ! ! !# ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! # ## # ! # ! # # # !# ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! !# ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !# ! # !! ! ! # # ! # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !# ! !! ! # # # # ## # ! # # ! !! #! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !# # ## # ## # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! # # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! #! ! ! !# # # ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! # ! # !!! ! # ! # # # ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! # # ### #

#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !!! ! ! ! ! ! ! ! !! !!! ! !! ! ! !! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !!! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! !! ! ! ! !!! ! ! ! !!! ! ! ! ! !! ! ! !! !!! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !

Sources: 2012-2008 American Community Survey, 2012 Census Bureau TIGER/Line shapefiles, 2011 Zip code Businesses Patterns

! ! ! ! ! !

Between 26.1% and 30.8%

Between 22.2 and 26.09%

Less than 22.2%

Young Adults proportion in NYC area

Retail serving alcoholic drinks

!

LIFESTYLE

!

Table 8 Descriptive statistics and spearman intercorrelation among variables Measure 1. Young adults 2. Distance from CBD 3. Kernel density of theater and company 4. Kernel density of venues serving alcoholic drinks 5. Kernel density of fitness center 6. Kernel density of women clothes retail 7. Kernel density of worhip place Mean SD Min Max

1

2

-0.51 *** 0.26 *** 0.37 *** 0.22 *** 0.30 *** 0.17 ***

-0.44 *** -0.48 *** -0.40 *** -0.43 *** -0.25 ***

0.28 0.09 0.00 0.87

***

7.59 3.49 0.13 21.91

3

0.35 0.35 0.32 0.13 0.67 2.54 0.00 28.13

4

*** *** *** ***

0.42 0.44 0.19 11.51 25.21 0.00 280.64

5

*** *** ***

0.46 0.14

6

*** ***

0.32

12.56 24.43 25.44 84.23 0.00 0.00 264.82 2,358.19

7

***

8.60 13.58 0.00 245.11

N=2270 census tracts, * Significant at p ≤ 0.05 ** Significant at p ≤ 0.01 *** Significant at p ≤ 0.001

Social & Cultural life Young adult are in a stage of life where their social life is particularly important in their daily life. Erikson (1993) defines a stage where young adult are exploring friendships and affiliations. More recently, Arnett (1992) has described emerging adulthood has a peak period to engage in risky behaviors like drinking. Even if social places like restaurants, bars and nightclubs are settings for risky behavior, they are also serving people’s needs for sociability and relaxation (Oldenburg 1989).

The location of theaters and service retails serving alcoholic drinks present significant and moderate relationships with young adult location. Their relationship is strongly related to centrality also. However, the map presents interesting clusters of drinking places and young adults. New York City is well known for being a ‘never stopping city’. Jacobs (1961) has already magnified the freewheeling nightlife of the city several decades ago. Still today, it is referred as “one of the most expensive places to live on the planet, but presently the center of the Universe, much like Rome was during the Roman Empire” in the participative urbandictionary.com (2014).

Nightlife and gentrification processes are also intercorrelated as Hae (2011) explained it. Establishments choose to locate in abandoned manufacturing districts to benefit off cheap rents, large loft spaces and no complaining neighbors. They contribute in the same time to renew the atmosphere of these neighborhoods. Thus, the real estate is marketing nightlife as a real attribute of housing market. It is a symbol of urban vibrancy for middle-class professionals (Hae 2011). Landlords and renters enhance their proximity to social and

41

cultural life to charm potentials renters. The following rental post demonstrates common practices.

Greenpoint and Williamsburg are two of the hottest neighborhoods in New York City! There is a Metropolitan Swimming Pool & Fitness Center [...]. There are tons of artists, musicians, designers, bars, boutiques, bookstores and cafes to get to know and it's literally 15-20 minutes from the city...

Post extract on Craigslist.com, November 2013 The contemporary society value the role of young people as â&#x20AC;&#x2DC;consumers itemsâ&#x20AC;&#x2122; (Wyn and Woodman 2006). The generalization of marketing strategies has built a need for consumption where the last generation of young adults has grown up. In the process of constructing their identities, consumption and shopping are either a way to follow mainstream tendencies or express creativity. The location of women clothes retail is a good indicator of shopping area, mostly located in city center. Their relationship is positively significant to the presence of young adult neighborhood. Miles (2000) has demonstrated that young adults see shopping as a leisure activity, even with their economical limitations.

Spiritual life Over the past decade, two trends with religious beliefs have been observed simultaneously. First, new generations are distancing themselves from organized religion but not from spirituality (Hout and Fischer 2002). Secondly, emerging adulthood is less religious than older generations. One in four young adult under age 30 claims to be atheist, agnostic or nothing in particular compared to only 15 percent and less for adult after 40 years (Pew Research Center 2010). Young adults often leave home and break ties with the religious institutions of their parents (Arnett and Jensen, 2002).

These tendencies would suggest that young adult would be less likely to live close to worship places like churches, synagogues and shrine. Surprisingly, young adult neighborhood and place of worship are positively correlated. This is a weaker strength than other activity variables, but still significant and associated to centrality. It should be recalled that places of worship are actually highly correlated with poor neighborhood (rspovertylevel=0.33***).

Empirical findings indicates two trends toward religion: some of the highly young areas (darkest areas) have less places of worship places, and census tracts with more than average young adults (medium grey areas) tend to have more worship place than average. The two religious enclave of South Williamsburg and Borough Park in Brooklyn should be consider apart, well-know for being one of the largest Hasidic and Orthodox Jewish communities in the U.S. However, these findings do not indicate if young adults are attending religious

42

services or meetings. To extend this discussion, it is interesting to remind that religious participation is rising in the late 20â&#x20AC;&#x2122;s when young adult are evolving in their life-cycle: marriage, childbearing and settling down (Arnett and Jensen 2002; Erikson 1993).

Physical activity A growing trend toward physical exercise has been observed with the last generation of young adults. The Pew Research Center (2010) reported that 56 percent of them declare they had a vigorous exercise (like jogging, biking or working out) in the past 24 hours. The previous generation reports only 48 percent of them that had recent physical activity. With the delay of childbearing in age, this trend could be explained by their free time.

The location of fitness and health center with young adults neighborhood is positively correlated and significant. Again, the high concentration of fitness center is correlated with centrality, and especially with jobs location. But fitness centers tend to be more spread in the urban area, and are actually more correlated with median household income (rshhincome=0.17***). The higher presence of fitness center in Staten Island and Flushing in Queens confirms this relationship.

43

6 How young adults are clustered?

The past section have identified that young adults are living clustered. Thus, the assumption of spatial interaction is strong. The grouping analysis tool of ArcGIS is used to find natural clusters that have similar demographical and spatial characteristics. The creation of spatial groups aims to identify the underlying structure of the spatial distribution of young adults in the city.

In the perspective of further research, the number of groups is preferred small. We want to avoid the multiplication of interpretation. The number of discriminant variables should also be small to facilitate the analysis. To find the best model, our studied variables are tested. The method starts with a single variable and build step by step with other variables.

For each case, the optimal number of groups is reported by the highest pseudo F-statistic (ESRI 2014). Each variable has a R2 value indicating how much variation was retained from the original variable.

6.1

Results

The most consistent model combines 3 variables: the proportion of young adults in each census tract per median rent and by density of venues serving alcohol like restaurants, bar or nightclubs. The best fit breaks down 4 groups that are named A, B, C & D. These four groups are respectively distributed from the center to the periphery. Map 8 and table 10 are presenting the results9.

These clusters partly support previous research on age segregation. The level of median rents divides spatially different areas that are attracting more or less young adults. This confirms that residential differentiation is determined by ecological factors. Another finding is that young adults are sensitive to their social environment. The density of venues serving alcoholic drinks is differentiating their location. Cultural factors are thus contributing to the spatial ordering of young adults in the city. A high density of social venues is inducing more activity, more density of people, more encountering and more noise at night.

9

The parallel box plot of the variable is in annex 5.1. More detailed spatial statistics with mean center, median center and directional distribution of each group are in annex 5.2.

44

Sources: 2012-2008 American Community Survey, 2012 Census Bureau TIGER/Line shapefiles, 2011 Zip code Businesses Patterns.

0

1

2

Group D

Group C

Group B

Group A

4

Groups of young adults in NYC area

6

ÂŻ

8 Miles

Map 8 Grouping Analysis of proportion of young adults per census tract per median rent and by density of retails serving alcohol, in NYCA (2008-2012).

45

Table 10 Groups statistics Variables

Mean Std. dev

Percentage of young adults

Min

Max

R2

27

0.45

0

84

0.51

Median gross rent (in dollars)

1238

338

273

2000

0.51

Kernel density of retails serving alcohol

11.5

25.2

0

280.6

0.72

Mean Std. dev

Min

Max

Share

Variables GROUP A Count = 78; Median distance from center = 0.015 miles Percentage of young adults

42

0.44

23

70

0.28

Median gross rent (in dollars)

1742

342

697

2000

0.75

Kernel density of retails serving alcohol

123.0

49.0

67.7

280.6

0.76

41

0.41

27

84

0.37

Median gross rent (in dollars)

1581

282

826

2000

0.68

Kernel density of retails serving alcohol

18.4

16.0

0

69.2

0.25

26

0.25

9

43

0.44

1031

214

275

1384

0.64

6.6

8.7

0

124.3

0.44

GROUP B Count = 282; Median distance from center = 0.039 miles Percentage of young adults

GROUP C Count = 1268; Median distance from center = 0.060 miles Percentage of young adults Median gross rent (in dollars) Kernel density of retails serving alcohol GROUP D Count = 635; Std. Distance = 239.4; Median distance from center = 0.095 miles Percentage of young adults (sdt. deviation in logit) Median gross rent (in dollars) Kernel density of retails serving alcohol

6.2

20

0.35

0

30

0.65

1439

239

273

2000

1

4.8

8.3

0

71.8

0.26

Interpretation

Group A The first group lives in the most central areas, which are almost only in Downtown Manhattan. With an extremely reduced size compared to other groups (n=78), it represents only 3.4 percent of NYCA census tracts. It is also a small population with 4.4 percent of the entire population. But with 42 percent of young adults in the area, this group is very

46

concentrated. The median rent is very high at $1,742, which is a third higher than the whole area median rent. The density of social venues is also extremely high at 123 on average, and ranging between 68 and 281. This area of New York is well known to be a neighborhood hosting nightlife and social venues.

This central group indicates that some young adults have the ability to pay very high rents. They might have good incomes or good external financial resources from their relatives. The median household income is $95,635 compared to the NYCA average at $57,354. The lowest percentage of burden-cost renters (43%) confirms that people in this area are more willing to afford their living than any other group. But to live in the most central area of NYCA, people have to live in smaller housing units or with more roommates.

Rental dwellers have less space with only 1.8 room per person, while the city average is 2.6 room. The human density is also very high with 30 residents per meter square compared to 19 on average. But surprisingly overcrowding is the lowest in the city by only 5 percent.

In this group, young adults are willing to pay a high rent and live in less space to be located in the â&#x20AC;&#x2DC;Cityâ&#x20AC;&#x2122;. This area has a very high concentration of social venues and activities like theaters, shopping or fitness. Since they have high income and lot of housing opportunity, we can infer that these residents do not mind to leave in noisy and dense area.

They are close to many subways, which enable them to be very close to their work with only 26 minutes of average commute. However, the turnover is relatively high because there are twice more people than average who did not live in the same place one year ago.

The small size of this group reveals a stronger withdrawal from other groups. The demographical diversity is very poor and indicates more isolation of wealthy households. The level of education is very high with almost 3 in 4 adults with at least a Bachelorâ&#x20AC;&#x2122;s degree while the NYCA average is less than 1 in 3. Married families and children are underrepresented, as well as childbearing. 2 in 3 individuals are white people and only 4 percent are black people. From these statistics, we hypothesize that this wealthy population lives in this area at a particular stage of life before commitment or childbearing. These young adults appear very focused on professional, educational and social life.

Group B The second group is comparable to the previous one in a certain measure. group B is very central and represents a slightly larger area of the city with 282 census tracts (12.4 percent of the whole area). Census tracts are mainly located on the border of Manhattan and on the closest area to city center in Brooklyn, Queens and Jersey City. These areas comprise

47

41 percent of young adults, which is equivalent to group A. The median rent is fairly high at $1,580. The density of social venues is smaller but still relatively high with a mean of 18.4.

This group is very comparable to the previous one, but with less extreme values. Both areas present similar characteristics: centrality, high young population (42%), high rents ($1,580), less cost-burdened (47%), with high income ($75,807) and more education than average (52% with Bachelor and more). The density is however different and more representative of the NYCA average with 20 person per meter square. Thus, life is more comfortable because people have more room to live in. The residential mobility is still relatively high with 18.5 percent.

The differentiation resides more in the householdsâ&#x20AC;&#x2122; composition. These neighborhoods are hosting more families and children. The fertility rate is almost twice higher than in Group A, with 45.2 child for 1,000 women (average at 53.3). Half of the households are married couples when they were less than 1 in 3 in the previous group, and children represent almost 15 percent of the population. By contrast, these areas have the lowest proportion of middleaged and older population. However, we are not able to track the path of these individuals and tell if they moved here to have a family life or were already living in the neighborhood to have kids. Nonetheless, it suggests that young professional families are living in the neighborhoods. The high distance from post-secondary education corroborates this fact.

The density of social venues conveys another difference. It is significantly lower with 18.4, but still higher than average. It suggests that residents still care about social life and centrality but are looking for a more â&#x20AC;&#x2DC;quietâ&#x20AC;&#x2122; urban life. The crime index is lower than for group B. The small quantity of census tract and proportion of other ages indicates that this area is also a cultural cluster of young people. In social wisdom, several neighborhoods like Williamsburg, Astoria, Hoboken are described as hipsters location. Hipster designates a contemporary subculture of young people that reject the mainstream culture and enhance alternative lifestyle. They are often associated with gentrifying neighborhood and loft-living lifestyle. Manufacturing structure are actually well represented, and the density of Superfund sites at 0.11 is much higher than the average at 0.04.

Group C Representing the largest part of the census tract by 56 percent, this group represents also the largest share of whole population (59 percent). This group represents areas inbetween the city center and the suburbs. Even if there is a wide variation within the group, it

48

is fairly central. The average proportion of young adults is representative of the New York area with 26 percent of young adults. Thanks to this off-center position, the median rent is smaller than average at $1,031 and with the smallest standard deviation ($213). Social venues are less present that in previous groups, and their density is only 6.6, ranging from 0 to 124.

This group have the lowest rents of the city and yet 56 percent of the population is moderately or severely cost-burden. It is the highest proportion within the 4 groups. Indeed, the population of group C has lower incomes than other groups ($43,289) and 21.2 percent of people live below the poverty level. Thus, the proportion of renter is still relatively high with 72 percent. The size of rental unit is average but the density and overcrowding are higher.

The distribution of age is representative of NYCA with 24 percent of children, 26 percent of young adults, 38 percent of middle-aged adults and 12 percent of elderly. 2 in 3 households are married with a high fertility rate (58.0). Correlated with low incomes, the population is significantly less educated than in central areas. By far these neighborhoods are more mixed and potentially racially segregated. White people represent only 22 percent of the population with large black and Hispanic populations.

The average distance from CBD is reflected in the access to facilities. These areas have an average access to main activities like jobs, post-secondary education, hospitals or open space. Population relies on public transportation with fewer cars than average (58 percent without vehicle). Their commute is 41 minutes almost equivalent to the average. But cultural and social activities like social venues, theaters, shopping or fitness centers tend to be less present. The proximity to venues serving alcohol is less differentiating than in groups A and B. These findings tend to indicate less age segregation because of cultural factors. This group is essentially affected by ecological factors. People intend to live close to jobs and city center but in low rents areas.

Group D This last group represents almost a third of the census tract and 1 in 4 individual of NYCA population. It is essentially located in off-center areas that are considered as suburbs.

Some area of Upper West and East sides are also belonging to the group. It is a very interesting finding because they are extremely central and located in Manhattan. This group comprises less young adults than citywide with only 20 percent and a maximum at 30 percent. The median rent is higher than average at $1,438. There are very few social venues in these neighbors, with an average of 4.8 and a maximum value of 72.

49

As a results of the bid rental curve (Glaeser et al. 2008), housing prices are higher in periphery because families are looking for larger lots and larger house. Rents and housing values are slightly higher than average, but lower than in very central areas. However, homeownership is quite common with only 41 percent of renters, which is a least 30 points lower than other groups. The proportion of single-family house is twice higher than average and only 1 in 4 structure have more than 4 units. Thus the size of units is as high as in group C, but the density is lower with only 12 individuals per meter square and 6 percent of overcrowding.

A healthy environment and some cultural activities like theaters, fitness centers or education surround group D. The quality of life is good in these areas. With the lowest density of social venues and subway stops, neighborhoods are very calm and somewhat isolated. The crime index is significantly lower than in any other groups. Less connected to public transportation, these areas are not further to their job with 41 minutes of commute time. Vehicle transportation is more used with only 29 percent of the population without car.

Their ability to pay contrasts with group C. Demography is less even in this group, with less young adults than average and more middle-aged adults and older people in contrast. Married families compose 71 percent of the group. The diversity in education and race is more even than in expensive central areas. The inclusion of central areas like Upper West and East Manhattan suggests that the differentiation of group D is both ecological and cultural. Expensive rents associated with sparse social venues and older population chase away young adults. The low residential mobility of these neighborhoods might also generate some â&#x20AC;&#x2DC;not in my backyardâ&#x20AC;&#x2122; (NIMBY) attitude from residents. NIMBY characterizes the opposition of existing residents against new developments close to them, which could be for instance public housing, high-density structure, establishment serving alcohol or youth hostel (Schively 2007). The factor of centrality seems secondary in the housing choice of this group.

7 Discussion Similar to previous findings for the elderly (e.g., Cowgill 1978), we find a substantial level of segregation for young adults in New York City metropolitan context. We describe this complex phenomenon along three specific dimensions: centrality, concentration and clustering. Young adults are highly agglomerated around the city center. By living in central

50

area, they tend to have a homogeneous residential condition associated with density and centrality. Most of them rent in multi-unit structures and live close to public transportation, jobs, education, social and entertainment activities. Their rents tend to be higher on average while they have less space to live in. They also share their living space with more people.

Young adults are also very mobile and frequently change apartments, in keeping with their evolving life paths. As consequence, they do not have easy access to controlled and subsidized units or homeownership.

Moreover, the segregation of young adults is mostly remarkable with their level of clustering. Part of this clustering can been attributed to their age category (young adult), their ability to afford their housing (median gross rent) and the density of surrounding social venues (establishments serving alcohol). These findings are consistent with previous research on age segregation (Lagory et al. 1980). Ecological factors explain a large part of the residential differentiation of young adults. They are discriminated by a constraining factor: their â&#x20AC;&#x2DC;ability to payâ&#x20AC;&#x2122;. Some young professionals with high education are able to afford expensive rents in center while other young adults are constrained to live in cheaper and less central areas. Social venues are also a discriminating factor, which involves more active choice processes. Cultural segregation is playing a role in the spatial ordering of young adults. Literature on emerging adulthood has already enlightened the importance of social interactions and personal development in latest generations (Arnett 2000; Wyn and Woodman 2006).

The presence of clusters of young adults supports the latest theories about emerging adulthood (Mary 2014; Wyn and Woodman 2006). It is not a linear process and a generalization of their housing condition would not be appropriate. The causal role of ecological and cultural factors has to be explored with a nonparametric spatial model and individual data. Our study misses some important variables that could affect young adultsâ&#x20AC;&#x2122; housing choice: for instance their family background, the location of their acquaintances and family, their housing history and their lease conditions.

Balancing between ecological and cultural explanatory factors, several hypotheses are suggested. (1) Young adults could be segregated in the city center as a result of land use and economic forces. They would be limited to a specific type of housing stock of market-rate rental units in multi-unit structures. (2) They could be concentrated in central areas because of an association of ecological and cultural factors. Their life stage encourages them to focus on jobs, social interactions and relationships. (3) As a marker of this generation, young adults

51

could be segregated mostly for cultural reasons. The need to live in community would isolate them from other age groups.

Reducing age segregation should be a key objective. Youth has to be envisioned and defended as a social and economical capital in cities. While marketing, mass media and governments emphasize young adults’ distinctiveness (Hagestad and Uhlenberg 2006), residential patterns are following the same trend. Similar to an ageism phenomenon, we are concerned about a ‘young-eism’ phenomenon. The different causal forces implicated in young adult clustering would demand appropriate responses either it is undergone or chosen.

However, the effect of this process has shared drawbacks. It prevents individuals to discover and cultivate relationships that cross age lines. Intergenerational learning helps to transmit knowledge between generations, as older people share their wisdom and the young share their modern skills and philosophy (Mead 1970). Relationships between people in different phases of life enhance social support and understanding. Discriminating attitude adopted by NIMBY communities could also be reduced. Research has shown that when individuals are encouraged to take the perspective of ‘the other’, prejudices and stigma are reduced and new links are forged (Galinsky and Moskowitz 2000).

References ACS. 2012. “American Community Survey 5-Year Estimate.”

Alonso, William. 1960. “A Theory of the Urban Land Market.” Papers in Regional Science 6(1):149–57.

Arnett, J. J. 2000. “Emerging Adulthood - A Theory of Development from the Late Teens through the Twenties.” The American psychologist 55(5):469–80.

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Annex 1 Transformation method of spatial variables Variable

Method

Threshold distance

C. Land use, infrastructure and facilities variables Open space

Euclidean distance

1/4 mile

Public railroad stop

Kernel density

1/4 mile

EPA Superfund site

Kernel density

1/2 mile

Hospital

Kernel density

1/2 mile

Euclidean distance

1/2 mile

Theater and company

Kernel density

1/2 mile

Retail serving alcoholic drinks

Kernel density

1/4 mile

Fitness and health center

Kernel density

1/4 mile

Women clothes retail

Kernel density

1/2 mile

Place of worship

Kernel density

1/4 mile

Post-secondary school D. Misceallenous variables

Annex 2

Clusters and outliers analysis in NYMSA

Not Significant Significant high cluster Significant high outlier Significant low outlier Significant low cluster

56

Spatial autocorrelation of the response variable at the Metropolitan scale (NYMSA) Annex 3 Testing for Spatial autocorrelation of the response variable in NYMSA Spatial Weight Matrix

Moran's I (p-value)

Inverse distance WM, 15 mile, power 1 Inverse distance WM, 15 mile, power 2

0,2582 0,3893

***

General distance WM, 15 mile

0,1896

***

3 nearest neighbors 4 nearest neighbors

0,5658 0,5562

***

5 nearest neighbors

0,5370

***

6 nearest neighbors 7 nearest neighbors

0,5268 0,5136

***

8 nearest neighbors

0,5070

***

***

***

***

Notes: *** Significant at p â&#x2030;¤ 0.001. Only matrix including all census tracts were tested.

57

0.28 0.09 0.00 0.87

Mean SD Min Max ***

***

***

***

***

***

***

***

***

***

0.22 0.08 0.00 0.60

-0.34 -0.37 0.26 -0.03 0.44 -0.61 0.32 -0.34 -0.43 0.55

2

***

***

***

***

***

***

***

***

***

0.39 0.06 0.01 0.76

0.13 -0.02 0.04 -0.28 0.15 -0.23 0.27 0.29 -0.44

3

**

***

***

***

***

***

***

0.12 0.06 0.00 0.88

-0.12 0.30 -0.21 0.15 -0.05 -0.30 0.12 -0.29

4

***

***

***

*

***

***

***

***

0.24 0.30 0.00 0.99

0.13 0.04 -0.34 -0.06 -0.17 -0.22 0.18

5

***

***

***

**

***

*

***

0.82 0.10 0.40 1

-0.50 0.29 -0.03 -0.09 0.30 -0.23

6

N=2270 census tracts, * Significant at p ≤ 0.05 ** Significant at p ≤ 0.01 *** Significant at p ≤ 0.001

-0.36 -0.49 -0.49 -0.12 -0.22 -0.04 0.32 -0.08 0.33 0.08 0.04

1

1. Young adults 2. Children 3. Middle-aged adults 4. The elderly 5. Black people 6. U.S. Citizen 7. Less than High-school degree 8. With Bachelor degree and more 9. Fertility rate 10. Employed people in civilian labor 11. Median household income 12. Below poverty

Measure

***

***

***

***

***

0.21 0.13 0.00 0.73

-0.73 0.14 -0.46 -0.70 0.69

7

***

***

***

***

***

0.32 0.20 0.02 0.94

-0.13 0.56 0.75 -0.56

8

***

***

***

***

53.43 41.72 0.00 526

-0.12 -0.11 0.17

9

***

***

***

0.57 0.10 0.00 0.89

0.59 -0.49

10

-0.70 57,355 27,822 0 243,622

***

***

11

***

0.16 0.13 0.00 0.68

-

12

Annex 4 Demography, Descriptive statistics and spearman intercorrelation among variables

58

Annex 5.1 Grouping analysis, parallel box plot (A - red, B - green, C – blue, D – yellow)

59

! # (

#

! ! (

# *

Sources: 2012-2008 American Community Survey, 2012 Census Bureau TIGER/Line shapefiles, 2011 Zip code Businesses Patterns.

! ( #

0

1

Colored Median center

2

Group D

Group C

Group B

Group A

4

6

8 Miles

Colored directional distribution with 1 standard deviation

Colored Mean center

!

#

Descriptive statistics of groups of young adults in NYC area

ÂŻ

Annex 5.2 Grouping analysis, spatial statistics

60