DECONSTRUCTING PLACES:
A DATA-DRIVEN ETHNOGRAPHY OF
NEIGHBORHOOD CHANGE FOR
ONE NEW YORK CITY BLOCK
By
Elisabeth Phoebe Kamine Holtzman BA in Anthropology
University of Chicago, 2010
Submitted to the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degree of
Master in City Planning at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2018
C Elisabeth Phoebe Kamine Holtzman. All Rights Reserved.
The author hereby grants to MIT the permission to reproduce and to distribute publicly paper and electronic copies of the thesis document in whole or in part in any medium
now known or hereafter created.
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AuthorDepartment of Urban Studies and Planning May 24th, 2018
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Certified by
Dr. Andrea Chegut Center for Real Estate
Accepte~j
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Thesis
Supervisor
Professor of the Practice,
Ceasar
McDowellMASSACHUSETTS INSTITUTE Department of Urban Studies and Planning
OF TECHNOLOGY
--
1Chair,
MCP CommitteeLJN
18 2018
LIBRARIES
DECONSTRUCTING PLACES:
A DATA-DRIVEN ETHNOGRAPHY OF
NEIGHBORHOOD CHANGE FOR
ONE NEW YORK CITY BLOCK
By
Elisabeth Phoebe Kamine Holtzman
Submitted to the Department of Urban Studies and Planning on May 24"', 2018 in partial fulfillment of the requirements for the degree of
Master in City Planning
ABSTRACT
Neighborhoods are complex and dynamic. An attempt to tease out how and why neighborhoods change requires interdisciplinary study that reflects the layers of interrelated people, places and things that make up an urban neighborhood. Urban data science aims to measure neighborhood change, yet it is challenging to quantify how a place changes over time, space and people. Moreover, these measures are important because planning and economic development policy that relies on these measures impacts future place-making and community development. To understand neighborhood change at a granular scale that can be useful to decision makers, I conduct a data-driven ethnography in which I assemble, analyze, and integrate over 45 urban planning and real estate datasets to develop quantitative metrics that measure the rate of change for the 1817 to 2017 period for Block 800 in New York City.
Quantitatively, long-run metrics on rates of neighborhood change were previously unable to identify. In this way, I was able to document that change is always happening to a building, property, person or price, but its positive and or negative trends are often very slow to articulate in datasets or statistical models. The quantitative results suggest that, on average, buildings move slowly by netting 0.01 buildings per annum over the 1817 to 2017 period, properties more rapidly at 0.45 per annum and people even more rapidly at a projected rate of 1400 people per annum. In addition, not all changes are equal in speed or impact, where change can accelerate at so-called inflection points where technological progress in society is meeting the built environment and the people operating within. At these points, the speed of a neighborhood can increase rapidly causing displacement and gentrification and at other times, progress is absent with long periods of decay.
Importantly, calculating rates of change could not be done without data-driven ethnographic methods that allows for integrating and not aggregating data. Integrated place data are intricately linked to retell a long, wide, and big data neighborhood story. These methods can now be replicated at a larger scale with the proliferation of city science to drive decision-making in cities at new scales.
Thesis Supervisor: Dr. Andrea Chegut
ACKNOWLEDGMENTS
This project embodies the curiosity, knowledge, generosity, support, and energy of many incredible people.
Andrea Chegut, my advisor, gave me the partnership of a collaborator, the expertise of a mentor, and the confidence to build something on my own. We shared in the enthusiasm of each discovery, and she always kept me afloat in my growing sea of Post-its® and footnotes. She created a space for me to take risks, ask questions, and explore the uncomfortable task of deconstruction. Andrea taught me to fall in love with my research.
Sarah Williams, my reader, taught me so many of the skills I used to conceive of, hack at, break, and rebuild this thesis. Sarah taught me how to explore data with Python and GIS, and she remains the voice in my head reminding me of the power and politics in every step of a data-driven project: from
data collecting and aggregating to gathering cleaning, slicing, dicing, and of course, visualizing. She taught me to respect the power of data and to search for the holes in it.
This project would also not have been possible without the generosity of the data providers, including Real Capital Analytics, Compstak, CrediFl, Live, CBInsights, Carto, LiveRamp, MasterCard, and the City of New York. When I approached the great librarians and clerks at the MIT Libraries, the New York Public Library, and the New York City Register's Office with my goal of 'finding all the data on who lived, worked, and invested in one block in Chelsea for as far back as possible,' they each provided essential guidance with patience and encyclopedic knowledge.
Brent Ryan, Garnette Cadogan, Marcela Escobari, and Ingrid Gould Ellen all provided intellectual and personal guidance at essential points throughout this project, and each inspired me to look at neighborhoods in a different way. Larry Susskind challenged me to find my 'theory of practice,' which was an underlying motivation behind this project's questions. Members of TWG (Thesis Working Group) gave early and often feedback, helping me structure this big, wide, long endeavor. Researchers in the Real Estate Innovation Lab patiently introduced me to the Wide Data Project, taught me how to 'close the mesh,' and put up with all my sidebars. Eric Huntley helped me hack through each data bug with enthusiasm and care. My oldest friend, Susan Widdicombe, is the genius behind any of the visual beauty in the pages ahead, helping me minimize my formatting faux pas. My friends at DUSP and beyond are always a constant source of humor, inspiration, and warmth. I'm especially grateful for every time they encouraged me to get out of CRON and take a break from Block 800.
My parents and my brothers taught me the importance of asking questions and not being satisfied with one discipline, method, or approach. Above all, they remind me every day that the true joy in learning something new comes from being able to share it with those you love.
Massimo, my husband, now knows more about Block 800 (and the birds!) than he ever thought he would. He gave me energy when I was exhausted, helped me to continue when I was discouraged,
celebrated with me when I made progress, and asked me questions when I felt stuck. His partnership and love makes me grateful every day.
TABLE OF CONTENTS
1. UNDERSTANDING CHANGE
9
2. A REVIEW OF NEIGHBORHOOD CHANGE
12
Quantitative Studies of Change Over Long Periods of Time 12
Studies of How People Change 14
Studies that Unite the People and the Place 17
Studies of One Block 17
Studies That Look at Many Variables in a Place 19
3. A DATA-DRIVEN ETHNOGRAPHY
21
An Ethnographic Approach 21
Taking A Long and Wide View 23
Introducing Block 800 23
A Data-Driven Approach 24
Ethnographic Applications to the Built Environment 30
Deconstructing Change in Buildings 31
Deconstructing Change in Uses and Zoning 31
Deconstructing Change in People 32
Deconstructing Change in Transaction Prices 35
4. A DESCRIPTIVE STORY OF CHANGE
36
Setting the Stage 36
Block 800's First 150 Years 36
Transition to the 21st Century, A Digital Era 52
Changing Lots and Zoning 53
Changing Buildings and Properties 56
Changing Uses 59
Changing People 62
5. RESULTS 71
Lot Change Results 71
Building Change Results 72
Property Change Results 73
People Change Results 74
Price Change Results 77
Change Across the Block 80
6. TOWARDS A BETTER UNDERSTANDING OF CHANGE 83
Change in a Place 83
The Data and Methods for Understanding Change in a Place 86
7. FUTURE RESEARCH 91
8. CONCLUSION 92
9. APPENDIX 94
The Phases of Evolution 94
Zoning Types and Reflections on Zoning 98
A Closer Look at Changes in Condo Values 100
Tabular and Geospatial Data Used in This Project 102
LIST OF FIGURES
Figure 1: Reproduction of Chapple and Zuk 2016 Table of Common Data Sources 15
Figure 2: Screenshot from 'One Block' website 18
Figure 3: Screenshot from 'Streetscore' website 19
Figure 4: Map of Potential Sites 25
Figure 5: Timeline of Datasets 26
Figure 6: Key Datasets and Variables 27
Figure 7:1854 Perris Engineer's Map 37
Figure 8: Retroactively Assigned Lot Numbers from the NYC City Register's Office 38
Figure 9:1857 Perris Engineer's Map 39
Figure 10: 1879 Bromley Engineer's Map 40
Figure 11: 1890 Sanborn Fire Insurance Map 41
Figure 12:1899 Sanborn Fire Insurance Map 43
Figure 13:1911 Sanborn Fire Insurance Map 46
Figure 14:1920 Bromley Fire Insurance Map 47
Figure 15:1955 Bromley Insurance Map 49
Figure 16: Original Lot Lines, Drawn Retroactively in 1917 53
Figure 17: Lot Lines in 1948 53
Figure 18: Lot Lines in 1968 53
Figure 19: Zoning Changes by Lot 55
Figure 21: Building Footprints 57
Figure 20: Year Built 57
Figure 22: Building Changes 58
Figure 23: Land Use Over Time 59
Figure 24: Land Use Transitions 60
Figure 25: Land Use Changes by Lot 61
Figure 26: Land Use Changes by GFA 61
Figure 27: Summary of Select Demographic Changes from 2000 to 2010 62
Figure 28: Population Age Distribution in 2000 and 2010 63
Figure 29: Changes in Population by Race From 2000 to 2010 63
Figure 30: Population by Proportion of Race in 2000 and 2010 64
Figure 32: Number of Employees Broken Down by Business Size 65
Figure 33: Breakdown of October 2017 Retail Venues by High-Level Categories 66
Figure 34: Number of Employees and Residents by Tenure in Each Building in 2017 67
Figure 35: Residential Condo Transaction Prices Over Time (Nominal Dollars) 68
Figure 36: 200 Years of Changing Lots 71
Figure 37: 200 Years of Changing Buildings 72
Figure 38: 200 Years of Changing Properties 73
Figure 39: Number of People at Three Snapshots in Time 74
Figure 40: Average Change in People Per Annum 74
Figure 41: Propensity for Person Turnover Scale 75
Figure 42: Propensity for Person Turnover by Building 76
Figure 43: Estimates of Potential Person Turnover (Projected) 77
Figure 44: Regression Results Table 79
Figure 45: Sales Transaction Index for Residential Condo Units 79
Figure 46: Consolidated Results 81
Figure 47:1815 'Blue Book' of Manhattan 96
Figure 48: A Summary of Recent Zoning Types from the City's Abbreviated Zoning Descripitons 98
Figure 49: Summary of Residential Rental Rents According to StreetEasy 101
1. UNDERSTANDING CHANGE
Neighborhoods are complex and dynamic. An attempt to tease out how and why neighborhoods change requires interdisciplinary study that reflects the layers of interrelated people, places and things that make up an urban neighborhood. Urban data science aims to measure neighborhood change, yet it is challenging to quantify how a place changes over time, space and people. Moreover, these measures are important because planning and economic development policy that relies on these measures impacts future place-making and community development. Whether one is trying to improve the economic opportunities in a place that is facing disinvestment and increases in crime, or trying to alleviate the pressures of rising demand for retail or housing in tight urban markets, being able to access reliable, up-to-date, comprehensive information on the neighborhood is useful in informing their strategies (Greene and Pettit 2016). But most data and methodologies used are not designed or used to capture the complexity of a specific neighborhood Jacobs 1961; Greene and Pettit 2016; Chapple and Zuk 2016), let alone how the components of a neighborhood changing over time.
In the era of big data and the contemporary approach to city science, neighborhoods are often studied as complex systems and incorporate machine learning algorithms to help make sense of how cities change. Recent quantitative studies of neighborhood change focus on identifying specific types of change at a generalizable scale using machine learning and AI tools. As the complexity and non-randomness is further dissected and analyzed, a challenge remains in how to use these findings in a given place. With the proliferation of city science to drive decision-making in cities at all scales, the many specific components of a place are increasingly abstracted from each other and from the place in which they each occupy together. This makes it difficult for city decision-makers and neighborhood stakeholders to interpret and apply learnings to a specific place.
Yet, what if we had a way of talking about a place's evolution that pushed us towards a deeper understanding of how change happens in a neighborhood and cut across the silos or agendas of each stakeholder? What if we recognized that we actually often don't know when change starts or what change means, and that these fundamental questions deserve our attention? There is no framework in planning or any other academic or professional discipline that systematically conceptualizes what change means for a specific place over a long period of time. The complexity and dynamism of neighborhoods and cities necessitates interdisciplinary study and a blend of methodologies. Thus, there is an opportunity to conduct fundamental research on what does change mean in a specifc
place: what is changing and how? This is a question that people with a stake in a particular neighborhood
seek to understand, and answering it will help contextualize what they see and hear. To understand neighborhood change at a granular scale that can be useful to decision makers, I conduct a data-driven ethnography in which I assemble, analyze, and integrate over 45 datasets on people, places, and things to develop quantitative metrics that measure the rate of change for the 1817 to 2017 period on Block 800 in New York City. The objective is to deconstruct the components of a neighborhood across disciplines and reconstruct the data to tell the story of change in a place over time. However, and most importantly, from an inter-disciplinary perspective of planning, real estate, geography, anthropology, economics, and urban design.
Jane Jacobs advises that studies of the city be rooted in 'three habits'. To start to deconstruct how neighborhoods change, I use her three habits: "1. To think about processes; 2. To work inductively,
reasoning from particulars to the general, rather than the reverse; 3. To seek for 'unaverage' clues involving very small quantities, which reveal the way larger and more 'average' quantities are operating" Jacobs 1961). In this way, there is a strong role for quantitative, data-driven methods in understanding the complexity of cities, but the work to understand the kinds of urban systems in which cities are must be done first. This fundamental research is what I am setting out to do here.
1. What does change mean for a neighborhood and how can interacting with that change create new opportunities for learning, and ultimately help facilitate more inclusive growth?
2. What does a more granular approach to gathering, analyzing and visualizing data about a place over time reveal to city decision-makers, community leaders, and other stakeholders that would not be seen otherwise?
3. What kinds of change can be seen at a block-scale? How does a block change over time? At what scale (geographically and temporally) do the changes occur? At what pace do they occur? And how do these compare across types of people, places, and things within a block?
4. What kinds of data exist that can be measured at this granular level and what are the methods for gathering, consolidating, analyzing, and prioritizing these data?
In answering my questions, I developed some new approaches using the methods of a data-driven ethnography to understanding the "organized complexity" of a neighborhood by taking a "microscopic or detailed view" of a place Jacobs 1961). Below, I do the following: analyze change while also using quantitative, scalable methods of analysis; depict a neighborhood's change that is not captured elsewhere by parsing out what is changing, over what scale (time and space), and at what pace; and, incorporate theories, data, and methods from academic literature, to inform discussion on what new understandings of change might mean for
neighborhood decision-makers. Lastly, I will assess what it means ethically to execute this type of analysis in a data enabled age of city science.
Quantitatively, long-run metrics on rates of neighborhood change were previously unable to identify. Here, I was able to document that change is always happening to a building, property, person or price, but its
positive and or negative trends are often very slow to articulate in datasets or statistical models. The quantitative results suggest that, on average, buildings move slowly by netting 0.01 buildings per annum over the 1817 to 2017 period, properties more rapidly at 0.45 per annum and people even more rapidly at a projected rate of 1400 people per annum. In addition,
not all changes are equal in speed or impact, where change can accelerate at so-called inflection points where technological progress in society is meeting the built environment and the people operating within. At these
What does
change mean for a neighborhood? What kinds of change can be seen at a block-scale?
At what scale do the changes occur? At what pace do changes occur?
What kinds of data exist that can be measured at this granular level? What are the methods for gathering,
integrating, and analyzing these data?
points, the speed of a neighborhood can increase rapidly causing displacement and gentrification and at other times, progress is absent with long periods of decay.
Importantly, calculating rates of change could not be done without data-driven ethnographic methods that allows for integrating and not aggregating data. Integrated place data are intricately linked to retell a long, wide, and big data neighborhood story. These methods can now be replicated at a larger scale with the proliferation of city science to inform decision-making in cities at these new scales.
The remainder of my thesis aims to look at the organized complexity of one city block through the following analysis. In section 2, I assess how other scholars have looked at neighborhood change and seek out any clues in what I should adopt in my data-driven ethnography. In section 3, I document the background for using a data-driven ethnographic approach, introduce the block used for the analysis, and outline the data and methods used for the ethnographic analysis of the block. In section 4, I provide the descriptive patterns of the data-driven ethnography. In section 5, I present quantitative measures of neighborhood change. In section 6, I discuss my findings. In section 7, I present some opportunities for future research, and in section 8, I conclude with a discussion of the ethics of doing a project like this.
2. A REVIEW OF NEIGHBORHOOD CHANGE
Since the early days of urban planning and urban sociology, scholars have been trying to understand how places and the people within them came to be. This is a story of change. Some of these scholars focused on the place, taking a birds-eye view of the city as an ecosystem. Others took a very long view, looking at one particular aspect of the place, such as transaction prices as a proxy for changes in buildings and context, over a very long period of time. Others look at the people, using income from the census as the primary proxy for changes in people. And many interested in people, particularly in the last few decades are interested in a specific kind of change, gentrification, and the relationship between neighborhood change and displacement of people. There are other studies that use a qualitative, ethnographic approach to understand how a specific place and the people in it have changed, but these studies tend to exist in a different sphere than the quantitative 'change' studies. There is an emerging group of studies that look at one small place over a long period of time and try to weave together methods to look at how the people and the place have changed over time. Lastly, there is a growing group of studies that take a 'big' or 'wide' approach to studying change, by looking at the many variables on one place, or few variables on many places to identify patterns of change. Early models of neighborhood change originated with a group of geographers, sociologists and 'urban ecologists' part of 'The Chicago School' in the 1920s and 1930s, and they primarily of looked at change with a strong focus on land uses and values, and how these are spatially distributed and change over time.' Two influential models came from this research, including a model of concentric growth and a model of wedges. The first was that city grows and as buildings depreciate, the zones shift outward, and the working class people shift into the upper class residential zone and so on (Park, Burgess, and McKenzie 1967). The second model of change was sector-based, in which wedges of a city radiate out from its central business district, each with a different land use depending on the adjacency to other land uses and externalities such as pollution (Hoyt 1933). The Chicago School has broadly influenced how change has been understood and studied in the past century, in particular, the focus on land (or property) values and uses as a representation of change, as well as the scale to which they modeled change as a city-wide or regional approach.
QUANTITATIVE STUDIES OF CHANGE OVER LONG
PERIODS OF TIME
Studies that look at places over very long periods of time are rare, but provide valuable insight into change dynamics. These studies tend to look at changes in transaction prices and the rates of change
1 In The City, Park, Burgess, and McKenzie lay out a model of change, in which the city evolves in concentric
rings: the central business district is surrounded by a factory zone, which is surrounded by a working class zone of residences, which is surrounded by upper class residential homes, and then this is surrounded by a 'commuters zone.' In 1933, Homer Hoyt conducted a study of changes in land values over a hundred years in Chicago, and he created an alternative model of change in land values and uses. Hoyt found that while there were increases and decreases in prices during periods of growth and expansion, land values from the 1840s to the 1930s were essentially the same, when adjusting for the population and inflation. Land values would change as industries, populations, and externalities changed, but then they would stabilize overall.
in prices indices,2
and are often limited to cities that have extensive record-keeping and old cities that have remained relatively unchanged so the same properties can be tracked over time. The most remarkable example of this is the Herengracht Price Index, developed by Piet Eicholtz. Eicholtz collected transaction data from sales transactions of buildings between 1628 to 1973 along one canal, the Herengracht, in Amsterdam (Eichholtz 1997). The Herengracht has always remained relatively unchanged and uniform since its development in the seventeenth century. Most buildings are residential, with some becoming commercial, containing small offices. Eicholtz constructs a hedonic repeat measure index in real (inflation-adjusted) terms, in which he creates a repeat sales index but uses a hedonic model to control for changes in use from residential to office (Eichholtz 1997). The houses on the Herengracht remains mostly residential in 1973, and their real value is only double what it was in 1628 (Eichholtz 1997). Besides some changes in use, other changes to properties that he notes over time include the consolidation of buildings (from over six hundred to under five hundred) and an increase in the number of transactions over time (Eichholtz 1997). This may be due to better information, or actually more trades. Most of the increases in real values happened after World War II, where increases around 3.2% were seen per year (Eichholtz 1997). During the three and a half centuries, there were peaks and valleys, primarily during times of broader national or global changes, including Tulipmania, outbreak of disease or environmental disasters, wars and trade-wars. Housing prices decreased by more than 13% during World War I, then they shot up afterwards, and then down again during the Great Depression (Eichholtz 1997). "Despite no long-term trend, there are sustained swings in real prices over particular periods ranging from as short as a decade to as long as 50 years" (Wheaton, Baranski, and Templeton 2009).
Expanding on this study, Ambrose, Eicholtz, and Lindenthal look at rents as well as housing prices in the Herengracht and expand the length of the study to 2005 (Ambrose, Eichholtz, and Lindenthal
2013). They find that rents can be more stable than transaction prices over many periods in the Herengracht's history, but that the last century has been the most volatile for both rents and prices. They also find that contrary to popular belief, changes can be for long and sustained periods of time, and not
just
short booms and busts (Ambrose, Eichholtz, and Lindenthal 2013). Over three hundred and fifty years, they show long downward trends, like a thirty-three year downward trend around the turn of the 19th century, and also a long upward trend, like the one we are in now that started in the 1950s and is reaching peak levels today (Ambrose, Eichholtz, and Lindenthal 2013). Real house prices appear more volatile than rents, with periods of large fluctuations when rents are stabilized.The Herengracht study provides essential context for conceptualizing change over a very long time in a place. However, there is change that is important in a place that is not as easily tracked over this long period since many aspects of change are not recorded with such diligence as sales transactions. Furthermore, the Herengracht is a special kind of place, making it difficult to extrapolate the kinds of change seen in the Herengracht directly to a place where the buildings are not uniform and they are demolished and reconstructed over time with new uses and new people.
Another long-term study, by Wheaton et al. in New York City, looked at "86 repeat-sales transactions for office properties in lower and midtown Manhattan spanning the years from 1899 to 1999" (Wheaton, Baranski, and Templeton 2009). They examined a decade-interval changes in real property prices, looking only at buildings that were survived until 1999. Using Emporis data, sales
data, and CoStar data, they find that over the hundred year period, the "commercial office property values were 30% lower in 1999 than they were in 1899" (Wheaton, Baranski, and Templeton 2009).
However, they find that there can be peaks and troughs within a decade of 20-50% in real terms. 2 Price is used in this sense as a proxy for change.
This tells us about what change can look like over a long time for large, office buildings spread across many different neighborhoods. Again, it is helpful for contextualizing what we might be seeing in property changes, but each building is shaped so much by its context that to understand change, how the other aspects of the context change are important to track with the building
STUDIES OF HOW PEOPLE CHANGE
Influenced by the Chicago School, Stuart Rosenthal studies shifts in residents as properties age, taking a metropolitan scale to neighborhood change, and a longer view of change over time. Using decennial census data on census tracts in 3 metropolitan statistical areas (MSAs) from 1950 to 2000, he examines whether neighborhoods experience change in economic status (i.e. incomes of residents), whether these changes move in cycles, and the nature of these cycles (Rosenthal 2008). He finds that over this period, most lower-income neighborhoods get higher income and most higher income neighborhoods become lower income. "In each decade, the average change in neighborhood relative income status is roughly 12 to 13 percent" (Rosenthal 2008). But he says that most people don't realize this because they don't stay in their neighborhoods long enough, with the median renters moving "roughly every one to two years, while the median homeowner moves in six to seven years" (Rosenthal 2008). He concludes that neighborhood cycles in income are not random, and that change in incomes can take a long time, meaning that for a neighborhood to go through a full cycle may take many decades to a century (Rosenthal 2008). However, he is looking at ten year intervals and he is using census data at the census tract level as a neighborhood. While these findings are helpful for interpreting the change we see in a place, a census tract's scale and boundaries are not related to any physical aspects of a place. Furthermore, there is so much variation across each census tract and each MSA that the findings are not necessarily going to much explanatory value for a city like New York. Most importantly, this method cannot capture any acceleration in the rate of change that might be occurring in a neighborhood either within a decade or beyond five decades. This research presents a helpful model for understanding one cycle of change that a neighborhood might go through, but now we are left considering what the other cycles are that make up change in a place and how the lengths of these cycles increase or decrease over time.
STUDIES OF GENTRIFICATION AS CHANGE
Gentrification - a specific kind of neighborhood change - is often studied in the context or
anticipation of displacement, or to test whether and how there is a link between gentrification and displacement. While each study of gentrification tends to have its own variation on how this type of change is defined, gentrification almost always involves a shift in the demographics, primarily the incomes and sometimes education and racial makeup, of the residents in a place. This shift is defined in different degrees - some define it as binary, and some on a scale of degrees, but the direction is always from lower-incomes (and less educational attainment, and more racial and ethnic minorities) to higher-incomes (greater educational attainment levels and more white residents) (Zuk et al. 2017; Chapple and Zuk 2016).'
3 For in-depth literature reviews of gentrification and displacement, see (Zuk et al. 2017). For in-depth literature reviews of gentrification, see (Royall 2016; Castagnola 2015).
Gentrification studies today often define a gentrifying neighborhood as a neighborhood that
undergoes a few types of changes (i.e. increases in private invest, increases in public invest, increases in educational attainment, etc.) over a set period and then within neighborhoods displaying those changes (often greater than the mean for the entire city), the studies will determine whether the average household incomes in the neighborhood changed or whether the average rents in the neighborhood changed (Zuk et al. 2017; Chapple and Zuk 2016; Freeman 2005). Neighborhoods in these studies are almost always at the zip code or census tract level and most define the periods of study by the availability of data (i.e. census years) or by a time in which the authors determined to be "pre-gentrification."
Gentrification studies tend to look at the following variables and use these data sources. In a 2016 paper, Karen Chapple and Miriam Zuk put together a table outlining common 'Indicators and Data Sources for Analyzing Gentrification and Displacement' and it is partially reproduced here for reference (Chapple and Zuk 2016).
Figure 1: Reproduction of Clhapple and
luk
2016 Table of Common Data SourcesIndicators Data Sources
- Sales value, property value - County tax assessors' offices, finance departments,
- Rent data aggregators
- Changes in availability of restricted affordable - Data aggregators, apartment operating licenses,
housing craigslist
- HUD, housing departments
- Building permits, housing starts, renovation - Jurisdictions' building or planning departments permits, absentee ownership - HMDA and assessors' data
- Mortgage lending and characteristics - County assessors' offices, data aggregators - Sales (volume and price) - Assessors' offices, housing departments, public - Condominium conversions works departments
- Change in community and business organizations - Chambers of Commerce, Dun & Bradstreet, (for example, number, membership, nature of neighborhood or local business associations, and so
activities) on
- Public investments (for example, transit, streets, - Public works departments, transit agencies, parks parks) and recreation departments, and so on.
- Building conditions, tenant complaints, vacancies, - Surveys, censuses, maps, building departments, fires, building condemnation utility shut-off data, fire departments
- School quality, crime, employment rates, - Departments of education, police departments/ neighborhood opportunity crime maps, censuses, Bureau of Labor Statistics - Neighborhood quality - Local surveys
- Tenure type, change in tenancy - Building departments, assessors' offices, censuses - Evictions - Rent boards, superior courts
- Foreclosure - HUD, proprietary data sources
- Demographics data on in- vs. out-movers (for - Censuses, voter registration data, real estate example, race, ethnicity, age, income, employment, directories, surveys, American Housing Survey, educational achievement, marital status) departments of motor vehicles
- Neighborhood and building characteristics (for - Tax assessors, censuses, deeds, and so on example, age and square footage, improvement-to- - Surveys of residents, realtors, lenders,
land ratio) neighborhood businesses, newspapers, television, - Neighborhood perceptions blogs, and so on
- Reason for move - Surveys of in-movers and out-movers, state housing discrimination complaints database
- Crowding or doubling up - Censuses, utility bills, building footprints - Increased travel distance and time - Censuses
Gentrification studies each look at one kind of change. These studies cannot tell us how these changes are different from other changes in the neighborhood in the neighborhood's past or present, the nature of any other kinds of change, when changes begin, and how they evolve over time. We are also limited in how much we can know about the causes and consequences of gentrification in a specific place because the studies tend to use data aggregated at the census tract or MSA-level.
There is disagreement among scholars as to the circumstances under which people are displaced in cities and what the various causes of displacement are, including but not limited to gentrification. Displacement studies primarily try to measure the displacement of people in neighborhoods that are undergoing other changes, like a decline in public and private investment or an increase in average incomes to tease out the causes. Zuk et al. in 2017 outline changes during which displacement (the movement of a household out of their residence due to force or in response to something out of their control). These include: changes in policies, changes in housing quality, neighborhood violence, natural disasters, condo conversions, among many more (Zuk et al. 2017). Displacement can essentially happen when there are changes in almost anything in the neighborhood, and each scholar tends to define displacement by the presumed cause. This presents two problems in studying displacement: 1) the proxies used for measuring displacement of people are different across studies because they are informed by a 'category of displacement', and 2) the context in which displacement is measured is defined by the displacement event and not the complex context of the place.
Proxies used for people being displaced include, loss of units (Marcuse 1985), numbers of eviction notices, numbers of landlord foreclosures, numbers of building condemnations, changes in credit scores (Ding, Hwang, and Divringi 2016), and decline in low income households while overall population stays stable (Zuk et al. 2017; Chapple and Zuk 2016). These proxies for people are very difficult to link to a specific place and to other attributes of the person to better understand the context of this displacement, and thus effectively tease out the causes and consequences of displacement in that particular place, at that particular time.
With access to special datasets, some studies are able to track individual people. Ellen and O'Reagan have internal access to the American Housing Survey (AHS) that is a sample survey conducted every two years of around 55,000 households (and units) across the U.S. They look at a panel dataset of individual households every two years from 1991 to 1999. While they cannot see where a household is coming from or where a household is going to, they can calculate exit rates and entrance rates of specific households (Ellen and O'Regan 2011). They note that most studies of displacement do not account for growth in housing units or changes in the stock of housing and density of housing specifically (Ellen and O'Regan 2011). This means that growth and turnover can be conflated. While they try to incorporate this by looking at construction, they recognize that this is not a perfect substitute.
They find that exit rates in neighborhoods with no gain in average incomes greater than the citywide average are slightly higher than neighborhoods with large gains in average incomes greater than the citywide average. Exit rates across the board tend to be between 20-40% every two years, with renters being on the higher end of the spectrum (Ellen and O'Regan 2011). They find that "units located in large gain neighborhoods were slightly less likely to be vacated than units in non-gaining neighborhoods" and even after controlling for various characteristics of the households, there was still no evidence that those most vulnerable (according to demographic characteristics) exit at higher rates (Ellen and O'Regan 2011).
STUDIES THAT UNITE THE PEOPLE AND
THE PLACE
There have been many studies, particularly employing sociological and ethnographic methods, of a specific place, and the people in it. These vary in the degrees to which they utilize formal ethnographic or sociological methods, but they each aim to represent the character of a place and how it came to be. In The Philadelphia Negro: A Social Study, WE.B. DuBois conducted extensive mapping and thousands of surveys of the community in a neighborhood of Philadelphia at the end of the 19"' Century (Du Bois
1967). He roots his description of this community with a historical context going back two hundred years, and provides an incredible precedent for how a community could be studied in a place a specific point in time. With less of a statistical or academic method, in the 1950sJaneJacobs sets another precedent for how to study a place and its evolution, with her study of Boston's North End and of New York City's Greenwich Village Jacobs
1961). Sharon Zukin's Naked City, an analysis of changes in neighborhoods through the lens of "authenticity," is often considered a 21" Century
Death and Life of Great American Cities. She studies changes in six New York
neighborhoods, and how 'authenticity' changes as each generation tries to preserve the past and contribute in their own way, but that a desire for authenticity is preserving places and not the people who made those places 'authentic' (Zukin 2010). Each of these has influenced the way in which we think about how places and the communities within them are formed and continue to shape each other, and add great value in terms of trying to interpret the relationships seen in other less detailed studies of places and communities.
STUDIES OF ONE BLOCK
There are an increasing number of works devoted to long histories of single blocks in New York City. Two examples include an academic case study of Greene Street in Soho and a beautiful interactive journalism piece called, 'One Block' by New York Magazine. In 2015, a group of "reporters knocked on every door, crashed the block party, and hunted through public records to track down and interview sixty-two current and former block residents. They mined census data, deeds, crime reports, and other public records. The result shows not just how the block changed by the
numbers but also the psychic weight of those changes" (New York Magazine 2015). The interactive site takes you door-to-door, meeting the residents and learning about their experience of the changing place. It is the story of the experience of the people on one block in Bedford-Stuyvesant, Brooklyn "that's seen home prices nearly double in just the past five years." The block was 100% white at the turn of the 20"' Century, 6% white in
Here, the masterpiece by Richard McGuire,
is a variation on the 'block' story and was very influential to this project. He illustrates the history of his childhood home going back tens of thousands
of years, and overlays
those histories of place on top of each other to share parallel narratives. It is a beautiful
portrayal of the role of
the past in the present
and the subtle, myriad of ways in which time influences a place.
1950, 0.8% in 1980 and then in 2010 it's 5.7% white (New 16rk Magazine 2015). People there today remember the change of the last sixty years, and the non-demographic ones that went along with it. More of a history of the people and a collection of stories of those there today. It is trying to capture the experiences of the people, as they say, "By the numbers, the block is now richer and whiter. But what's harder to capture in a statistic is how those changes have reverberated through the life of the block itself, in the experiences of every kind of resident-how neighbors look out for each other and sometimes alienate each other; how their lives intertwine" (New Jork Magazine 2015). As they note on the website, it is a tale of epic change, in miniature.
Fgure 2: Screenshotfiom 'QneLBlxck' website
In 2016, Easterly, Freschi, and Pennings released a study (and accompanying interactive website) designed to observe the 'unintended consequences' of change in a place. They conduct a four-hundred-year history of Green Street in Soho, NYC and show how difficult it is for "prescriptive planners to anticipate changes in comparative advantage" (Easterly, Freschi, and Pennings 2016). They look at a block because then they "can see change initiated at the level of individual households of firms that in turn make up sectors of economic activity" (Easterly, Freschi, and Pennings 2016), which is very closely aligned with the rationale behind this thesis design. They use US Federal Census Records from
1830-1880,
NYC Residential and Commercial Directories from1834-present,
NYC Tax Assessment Records from1808-'t940s,
Sanborn Manhattan Land Book from1905-present,
NYS Factory Inspectors Reports from 1890-1913, and NYC Reverse Listing Telephone Books from 1930-1993 (Easterly, Freschi, and Pennings 2016).In this study, they see periods of rapid changes, and six changes they call 'surprises.' One surprise is that in forty years (1850-1890) the block transitions from high-end residential to the city's densest area of brothels. In only a few years, it then transitions to become the center of garment manufacturing in NYC (Easterly, Freschi, and Pennings 2016). The last surprise occurs towards the end of the 20th
Century, when the property values on Green Street double between 1970 and 1990. The surprises are not evenly spaced, they note, with around 150 years between first and second surprise, and then increased frequency in the last century. To find these changes, they look at turnover of people and buildings, which is something I try to replicate in a more detailed, systematic way.
STUDIES THAT LOOK AT MANY VARIABLES IN A PLACE
There are a growing number of quantitative change studies that take a broader approach to change, in an effort to capture some of the complexity and ambiguity that the qualitative studies have been able to capture much better. Using census data (the preferred data source of quantitative neighborhood change studies), Delmelle builds a machine learning algorithm to identify unique pathways of change in census tracts in the fifty largest metropolitan areas from 1980 to 2010 (Delmelle 2017). She finds thirty-five unique pathways or 'sequence clusters,' yet a majority of the neighborhoods in her study do not change (according to her metrics) over the thirty-year period (Delmelle 2017). This resonates with the other studies of change that demonstrate how slow change often is, but just relying on census data will likely hide much of the change that is happening on the ground. Delmelle is starting to build a way to look at the way change happens at a very big scale, but only looking at eighteen variables and only using census data, so the intervals are long.
Others, like Naik, Glaeser, Hidalgo and others, are trying to use new kinds of data and computer learning techniques to measure changes in neighborhoods. They built a computer vision algorithm that measures changes in how places look by evaluating images from Google Street View, which they call a Streetscore (Naik et al. 2017). They generate a Streetscore for 2007 and another for 2014, and then run these Streetscores through regression models with more classical data on neighborhood change, such as economic and demographic data, to see whether there is predictive power in census data to determine whether a neighborhood will improve how a place looks. While they find that denser populations and higher education areas are more likely to have improved Streetscores (Naik et al. 2017), they are fundamentally relying on census data and matching census tract-level data with individual Streetscores and not a uniform point in time. The amount of variation that can exist at a given corner within an hour, day, month or year, is reduced to a single Google Street View screenshot, and the scoring of that screenshot is inherently biased by places that appear familiar, known, or comfortable, all highly objective assessments. This study an important contribution, but the gaps in data and study design are still big enough that drawing conclusions about neighborhood change is premature.
Fss
Kontokosta is developing a new 'laboratory' and framework for measuring change in New York City neighborhoods in his Quantified Communities project. Starting with three neighborhoods in NYC (Lower Manhattan, Hudson Yards, and Red Hook) and now expanding to five, Kontokosta's team is collecting and aggregating data, as well as generating new data with sensors and community input. This 'community-based approach to smart cities' is a long project still in the works,
but
the goal is to identify the 'pulse' of a place and then start to see deviations from those normal conditions (Kontokosta 2016). Among other things, they are collecting data on light levels, sound/noise, air quality, temperature/ humidity/pressure /wind speed, energy consumption, water consumption, waste production, 311 complaints, code and administrative violations, sentiment analysis, public space quality (through surveys), user-provided health, bike share usage, bus ridership, subway ridership, and taxi use (Kontokosta 2016). They aim to bring enough data together that they can create a 'comprehensive measurement of changes' at different scales of a neighborhood, to inform land use decisions and other location decisions (Kontokosta 2016).3. A DATA-DRIVEN ETHNOGRAPHY
Jacobs (1961) implores us to think about processes because the context and circumstance will inherently impact the effects that any object in a city will have on something else. Each thing is specific to its place and thus will not necessarily have "generalized effects or qualities." "City dwellings-either existing or potential-are specifc and particularized buildings always involved in
difering, specific processes..." (Jacobs 1961). We must think inductively, going from specifics to general
inferences because, "City processes in real life are too complex to be routine, too particularized for application as abstractions. They are always made up of interactions among unique combinations of particulars, and there is no substitute for knowing the particulars" Jacobs 1961). It's fine to generalize from particulars, she says, but don't use generalizations to explain what the particulars in a place should mean. Lastly, we must seek unaverage clues, which are not statistically significant and might not even be obviously detected, but they are essential to understanding how the broader place operates. They might be a kind of store or place, or a few "public characters" but they reveal things about how the rest of the population behaves or doesn't behave. These habits outlined by Jane Jacobs speak to what I call, a data-driven ethnography.
AN ETHNOGRAPHIC APPROACH
An ethnography, borrowing from anthropology, is the "descriptive study of a particular human society or the process of making such a study. Contemporary ethnography is based almost entirely on fieldwork and requires the complete immersion of the anthropologist in the culture and
everyday life of the people who are the subject of his study," according to Encyclopedia Britannica ("Ethnography" n.d.). Ethnography is both the study of a people or culture and the body of work produced from such a study. An ethnographic study is the primary method of anthropologists, and usually involves participant observation and interviews.
Ethnographic research is useful when there is not a spokesperson to answer questions on behalf of the subject of study, either because such a spokesperson does not exist or because the questions would not be received. When looking at neighborhood change in a specific place, one cannot simply ask the place how it changed, or ask even a series of people how it changed. A person may be asked how the place changed and what that means for them, but the place is the composition of all the people, and the spaces, and the things over time.
This kind of fundamental research to understand change itself, and not a particular kind of change or for any specific reason fits well within the ethnographic framework. The difference between the ethnographer and other stakeholders (e.g. the "educated traders and planters, medical men and officials" of Bronislaw Malinowski's day') is that the other stakeholders each have their own goals or vested interests in understanding the people, but the ethnographer's sole concern is developing 1 Malinowski, like many anthropologists of the first half of the 2 0"' Century, approach their studies of peoples and cultures in a way that is deeply rooted in the racism, prejudices, and biases of the day. While their writings and interpretations of these peoples and cultures had profound (and in many ways damaging) effects on how those people and cultures were viewed in the Western hemisphere, the methods documented in the pursuit of this research paved the way for contemporary anthropological research. The writings of Malinowski and his prot6g6s, including E.E. Evans-Pritchard, are still the foundation of ethnographic methods and at the same time reveal many of the flaws and biases inherent in any ethnography.
as thorough an understanding as possible (Malinowski 1961). In a neighborhood, there are many experts, each with their own interests in understanding how a place changes. The resident wants to understand change to ensure that their neighborhood remains (or becomes) one that fits their preferences, and the real estate developer wants to understand change to create the most value possible, and the store-owner wants to understand change to be able to adapt to the desires of their clientele. But for the ethnographer, understanding change is the goal itself. The goal is to map out the various components of change and then piece them together, to describe what this whole is.
As with any strong ethnography, this project begins with an in-depth exploration of the theory behind urban growth and change. Prior studies of neighborhood change were used to development precedent-driven hypotheses. While equipped with this training and preparation, it is also important
to let the work change the course of study and reflect on those changes. Everyone goes in with bias and preconceptions about the object of study, but an ethnographer's preconceptions are based on "a very considerable body of accumulated and sifted knowledge" and the ethnographer must, in the process of research, be willing to test those hypotheses and change those preconceptions (Malinowski
1961; Evans-Pritchard 1937).
As Malinowski lays out in his famous chapter on the 'Subject, Method, and Scope' of his research in New Guinea, one must immerse oneself, look at all the aspects, and use the process of putting the pieces together to further guide the research (Malinowski 1961). The importance of immersing oneself is to see and feel things that one didn't know to look for. This is part of the rationale behind picking a small area, such as a block, and looking at each part of the block to immerse myself in the place and the data.
Next, it is crucial to look at all the aspects and not just the astonishing ones. One should make a map of the system and the rules, in this case data cleaning and organization is as much a part of the research process as it is preparing for one. Malinowski urges the ethnographer to make lots of maps, diagrams and plans as we go since those are "the more fundamental documents of ethnographic research" (Malinowski 1961). In this project, each data table is a piece of the map and the aggregated diagrams of change are the "kinship" maps of the place. The ethnographer's job is to try to
understand how things connect to each other, and then go through each document and write out how one interprets the data and what is learned. But unlike other sciences where the facts are clearer, one must clearly show what is observed, where it came from, and what was the context was, before outlining the inferences.
An ethnographer's job is to examine each individual piece and how each one fits into 'one coherent whole' (Malinowski's phrase). The ethnographer should look for examples and then use those to look for more, and document an understanding of what the ethnographer sees and finds as the ethnographer loops through this process. Malinowski says, "the comparison of such data, the attempt to piece them together, will often reveal rifts and gaps in the information which lead on to further investigations" (Malinowski 1961).
TAKING A LONG AND WIDE VIEW
This project examines one city block, attempting to assemble data on each component of the block for as far back in time as possible. To deconstruct how a neighborhood changes, we must zoom in and take all the pieces apart to examine the details. AsJaneJacobs, said understanding cities
"depend[s] on the microscopic or detailed view, so to speak, rather than on the less detailed, naked-eye view suitable for viewing problems of simplicity or the remote telescopic view suitable for viewing problems of disorganized complexity" (Jacobs 1961). This project will focus on a block, asJacobs would start with a park or a street. In this regard, I borrow from the qualitative social sciences, and take the scale of an ethnography. Site-specific changes might dramatically affect a block, but could go unseen if data is only gathered at zip code or census tract level. If data are aggregated at a census tract, but there is a lot of variation within the tract, flawed decisions can result from a lack of granularity. Since we don't know what it is that we are looking for, we are starting small at the scale of individual people and buildings, and asking what is here, what is not here, and what is happening? Any building, structure, sidewalk or street, can tell us something about the time during which it was built (Ryan 2017). Once built, these things start to age and change, and how something ages influences future change. Cities are a collage of historical physical elements and present ones, Brent Ryan observes in his book, The Largest Art. These historical elements influence today in the way they shape our spaces and our activities.Jane Jacobs describes the importance of old buildings in being able to house activities of the past in the West Village, and Sharon Zukin describes a similar concept about the East Village in that the past is present in "pre-grid" NYC and in programs and activities that resist change (Ryan 2017;Jacobs 1961; Zukin 2010). Kairos, as Zukin calls it, is the way in which the past situates itself into the present (Zukin 2010; Ryan 2017).
While cities might seem like artifacts at a given moment, they are not frozen in time. The role of
Kairos, accumulating and emerging from different points in the city's history, cities can change at
different paces. "For the most part cities are the opposite [not stable], temporally dynamic entities that can change at rates from gradual and calm to frantic" (Ryan 2017). Ryan describes howJane Jacobs and Kevin Lynch both sought ways to slow the pace of change in cities, primarily in response
to large-scale government programs. On the other hand, in 21" Century China we see nearly instant destruction of neighborhoods and construction of new ones in their place (Ryan 2017). The past shapes the present and lingers in the present, but we do not know how far back to begin our study of change, and so we will see how far back we can take it.
INTRODUCING BLOCK 800
Given that there is no methodological precedent for choosing a single block study over another, the following process was used to minimize bias as much as possible. To minimize bias, site selection process was guided by the following objectives: Find a block that is the shape and size of the
average NYC block (i.e not Battery Park City or any of the other larger blocks). A block is a unit defined spatially in the NYC PLUTO (Primary Land Use Tax Lot Output) data. Find a block that demonstrates diversity of people, places, and things so that more kinds of change are visible and datasets are usable. However, also select a block that is surrounded by blocks with fewer land uses, so that as to minimize the impacts of being in a location that is broadly diverse in land uses; since, a high
number of land uses today usually result from historic high numbers of land uses. Find a block in an area that has been noticeably changing over a longer period of time (in other words, not an area that is rapidly changing now after being stable for many decades). This will allow me to tease out any trends over a longer period of time.
Using PLUTO data, I calculated the number of distinct land uses on each block in Manhattan in 2016. Examining this map (Figure 4) and the City's
Community Districts website, I selected six potential sites based on the number of land uses per block and those surrounding them.
Most blocks in the city are the same size and reflect a diversity of land uses. The parts of the island with the highest number of land uses (i.e. a large cluster of the darkest blocks) are Washington Heights, East Harlem, Hudson Yards/Midtown South, Flatiron, Chelsea, the East Village, and Chinatown.2 The six blocks highlighted in orange make up the shortlist of potential
sites for this project. Each of these contains a large number of distinct land uses, including industrial & manufacturing and vacant land. They are also each surrounded by blocks with lower numbers of distinct land uses, thus decreasing some of the bias in the selection.
I selected Block 800 in Chelsea, between 24' Street and 25' Street, and
6" and 7 h Avenues, as the focus of this study. Of all the blocks visited, this
block felt the most mixed. It has a diversity of building ages, architecture, newly rehabbed buildings and others not. There are a mixture of high-end residential as well as older tenement-style walk-up rental buildings. There is also a mixture of chain stores on the street, such as T-Mobile and David's Bridal, as well as older independent shops, like a fabric store and mannequin shops. Additionally, the block has a large vacant lot on it and a new tower under construction, so visible changes related to construction and development could be part of the story of change.
A DATA-DRIVEN APPROACH
In the 2 Is Century, there is a lot of information on people, places and things, and entire sectors of the economy are dedicated to finding, aggregating, analyzing and selling these data. Historically, the data sources are fewer in number, but many are very rich in detail. There is also a lot of data that is not possible to access or does not exist, and so the exercise of going through what is collected and available and what is not is very important, and is partially the driving force behind this thesis and the data-driven ethnographic approach. A key part of deconstructing how neighborhoods change is examining what assumptions and conclusions we are drawing from data, whether we have the data to be making those assumptions and drawing 2 Battery Park City also has a high number of land uses but this is misleading because of its large area relative to other blocks.
Block 800 is in Chelsea, between 2 41h St & 25th
Street, and 6b* Avenue &
7th Avenue .1L -IL SF k
