About

Jackelyn Hwang is an Associate Professor in the Department of Sociology and Director of the Changing Cities Research Lab. She is also a faculty affiliate at the Center for Comparative Studies in Race and Ethnicity and the Urban Studies Program at Stanford University. Jackelyn’s main research focuses on the social and economic dynamics of urban change, particularly gentrification, residential mobility, and segregation.

Research topics

• Sociology
• Political Science
• Geography
• Economic geography
• Computer Science
• Demographic economics
• Demography
• Social Science
• Economics
• Economic growth
• Medicine
• Archaeology
• Cartography
• Socioeconomics
• Gender studies

Selected publications

• Cleaning Up the Neighborhood: White Influx and Differential Requests for Services

  Socius Sociological Research for a Dynamic World · 2024-01-01 · 2 citations

  articleOpen access

  Visible signs of disorder serve as markers of difference across urban space. Sociological theory suggests that variation in collective social control efforts contributes to variation in physical disorder. However, how structural characteristics shape differences in informal social control remains underexplored because of limited data on disorder and social control. Using city service request data and a novel dataset drawing on Google Street View imagery and computer vision methods, the authors examine the neighborhood characteristics associated with propensities to request trash-related services across five large U.S. cities. The authors find that socioeconomically advantaged neighborhoods and those with fewer minority and foreign-born residents have higher propensities. However, an increase in White residents, but not necessarily an increase in high–socioeconomic status residents, is strongly associated with greater propensities. The authors argue that incoming White residents introduce unique dynamics of social control that are not necessarily collective, thereby affecting spatial inequality and power relations within their new neighborhoods.

  PublisherOA PDFDOI

• CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series

  Proceedings of the AAAI Conference on Artificial Intelligence · 2024-03-24 · 8 citations

  articleOpen access

  Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, failing to capture real-time physical changes. While street view imagery, capturing the heartbeat of urban spaces in a pedestrian point of view, can add as a high-definition, up-to-date, and on-the-ground visual proxy of urban change. We curate the largest street view time series dataset to date, and propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. We demonstrate the effectiveness of our proposed method by benchmark comparisons with previous literature and implementing it at the city-wide level. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.

  PublisherOA PDFDOI

• Who Owns the Neighborhood? Ethnoracial Composition of Property Ownership and Neighborhood Trajectories in San Francisco

  City and Community · 2024-06-24

  articleSenior author

  Property owners play pivotal roles in the trajectories of neighborhoods with discretion over upkeep, residential turnover, and affordability. Yet, little is known about how and why the racial composition of ownership changes over time relative to residents within a neighborhood and, in turn, how this relates to the neighborhood’s change and stability. With a self-constructed dataset of all residential transactions in San Francisco from 1990 to 2017, we consider how the ethnoracial composition of ownership differs from that of residents and how this difference relates to neighborhood change. We find that neighborhoods with more non-White residents have greater differences between the ethnoracial compositions of owners and residents, with the largest differences in neighborhoods with more Black residents. An increase in the divergence between these distributions is related to future increases in White and Asian residents and higher socioeconomic status residents and decreases in Black and Hispanic residents, illustrating that neighborhoods where owners are more ethnoracially distinct from the residents are more prone to neighborhood change and residential turnover. Our findings contribute to understandings of inequalities in property ownership and illuminate the role of ownership in neighborhood change in the contemporary city.

  PublisherDOI

• CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series

  arXiv (Cornell University) · 2024-01-02

  preprintOpen access

  Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, failing to capture real-time physical changes. While street view imagery, capturing the heartbeat of urban spaces from a pedestrian point of view, can add as a high-definition, up-to-date, and on-the-ground visual proxy of urban change. We curate the largest street view time series dataset to date, and propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. We demonstrate the effectiveness of our proposed method by benchmark comparisons with previous literature and implementing it at the city-wide level. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.

  PublisherOA PDFDOI

• Psycho-behavioral responses to urban scenes: An exploration through eye-tracking

  Cities · 2024-11-05 · 8 citations

  article
  PublisherDOI

• The Reign of Racialized Residential Sorting: Gentrification and Residential Mobility in the Twenty-First Century

  City and Community · 2024-10-13 · 3 citations

  article1st author

  Despite recent theoretical advances in explaining the persistence of segregation in the United States through structural sorting processes, how the spread and intensification of gentrification intersects with these processes is less clear. Drawing on the 2000–2017 waves of the Panel Study of Income Dynamics (PSID) and fixed effects logistic and linear regression models, we examine gentrification’s relationship with residential mobility patterns and whether this relationship is racially stratified. We do not find evidence that living in gentrifying neighborhoods is associated with higher rates of moving for lower-income respondents compared to those living in nongentrifying neighborhoods nor does this differ by race. However, we find starkly different locational outcomes between Black and White movers across the income spectrum, rather than the gentrification status of one’s neighborhood. We argue that processes of racial stratification in the housing market largely govern residential outcomes, regardless of gentrification.

  PublisherDOI

• Detecting Neighborhood Gentrification at Scale via Street-level Visual Data

  arXiv (Cornell University) · 2023-01-04 · 1 citations

  preprintOpen accessSenior author

  Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large. While some efforts have been made to detect gentrification in cities, existing approaches rely mainly on estimated measures from survey data, require substantial work of human labeling, and are limited in characterizing the neighborhood as a whole. We propose a novel approach to detecting neighborhood gentrification at a large-scale based on the physical appearance of neighborhoods by incorporating historical street-level visual data. We show the effectiveness of the proposed method by comparing results from our approach with gentrification measures from previous literature and case studies. Our approach has the potential to supplement existing indicators of gentrification and become a valid resource for urban researchers and policy makers.

  PublisherOA PDFDOI

• Who Moved and Where Did They Go? An analysis of residential moving patterns in King County, WA between 2002–2017

  2023-01-31

  articleOpen access1st authorCorresponding

  From 2002-2017, moderate and middle socioeconomic status (SES) King County residents exhibited greater rates of moving compared to the lowest and highest SES residents. Geographically, movement rates were highest in East King County and North Seattle. High-SES residents were the least likely to move during all years. The percentage of King County residents in the high-SES group increased by 8.9 percentage points between 2002 and 2017, while the percentages of middle-, moderate- and lower-SES residents decreased (by 3.3, 3.2, and 2.4 percentage points, respectively). Neighborhoods in Seattle and East King County have seen the biggest percentage increases in high-SES residents from 2002 to 2017. Low-SES residents in all regions were the most likely to move out of the Puget Sound area. High-SES residents were most likely to move within their neighborhood.

  PublisherOA PDFDOI

• Systematic Social Observation at Scale: Using Crowdsourcing and Computer Vision to Measure Visible Neighborhood Conditions

  Sociological Methodology · 2023-04-10 · 24 citations

  article1st authorCorresponding

  Analysis of neighborhood environments is important for understanding inequality. Few studies, however, use direct measures of the visible characteristics of neighborhood conditions, despite their theorized importance in shaping individual and community well-being, because collecting data on the physical conditions of places across neighborhoods and cities and over time has required extensive time and labor. The authors introduce systematic social observation at scale (SSO@S), a pipeline for using visual data, crowdsourcing, and computer vision to identify visible characteristics of neighborhoods at a large scale. The authors implement SSO@S on millions of street-level images across three physically distinct cities—Boston, Detroit, and Los Angeles—from 2007 to 2020 to identify trash across space and over time. The authors evaluate the extent to which this approach can be used to assist with systematic coding of street-level imagery through cross-validation and out-of-sample validation, class-activation mapping, and comparisons with other sources of observed neighborhood characteristics. The SSO@S approach produces estimates with high reliability that correlate with some expected demographic characteristics but not others, depending on the city. The authors conclude with an assessment of this approach for measuring visible characteristics of neighborhoods and the implications for methods and research.

  PublisherDOI

• Curating Training Data for Reliable Large-Scale Visual Data Analysis: Lessons from Identifying Trash in Street View Imagery

  Sociological Methods & Research · 2023-05-15 · 15 citations

  article1st authorCorresponding

  Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze these data with existing approaches limit their use. Computer vision methods hold promise but often require large and nonexistent training data to identify sociologically relevant variables. We present a cost-efficient method for curating training data that utilizes simple tasks and pairwise comparisons to interpret and analyze visual data at scale using computer vision. We apply our approach to the detection of trash levels across space and over time in millions of street-level images in three physically distinct US cities. By comparing to ratings produced in a controlled setting and utilizing computational methods, we demonstrate generally high reliability in the method and identify sources that limit it. Altogether, this approach expands how visual data can be used at a large scale in sociology.

  PublisherDOI

Recent grants

• Collaborative Research: Using Computer Vision to Measure Neighborhood Variables Affecting Health

  NSF · $139k · 2018–2020

Frequent coauthors

• Bina Patel Shrimali

  Stanford University

  32 shared

• Iris H. Zhang

  Stanford University

  27 shared

• Julia Greenberg

  Konkuk University Medical Center

  25 shared

• Karen Chapple

  25 shared

• Jae Sik Jeon

  Konkuk University

  25 shared

• Vasudha Kumar

  Stanford University

  13 shared

• Elizabeth Kneebone

  9 shared

• Becky Liang

  Harvard University Press

  8 shared

Awards & honors

• Jane Addams Award for Best Article in the Community and Urba…