Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach

Jung, Paul H.; Song, Jun

doi:10.1111/gean.12298

Cited by 6 publications

(3 citation statements)

References 41 publications

(62 reference statements)

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“…Likewise, the Neighborhood Trajectories capture the endpoints of historic redlining and current socioeconomic conditions in neighborhoods but do not explain what occurred during the intervening decades. Others have used U.S. Decennial Census data from 1970 to 2010 to categorize the temporal changes in neighborhoods 33 , 34 , which allow for a more nuanced analysis, albeit over a shorter time period. Additionally, residential redlining does not capture the full extent of structural racism in the U.S. as there are varying degrees of additional oppressive or segregated pressures including restrictive covenants or sundown towns that shape the present landscape and health.…”

Section: Discussionmentioning

confidence: 99%

Development of Neighborhood Trajectories employing Historic Redlining and the Area Deprivation Index

Carlos,

Weiss,

Carter

et al. 2023

Preprint

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The role of historic residential redlining on health disparities is intertwined with policy changes made before and after the 1930s that influence current neighborhood characteristics and shape ongoing structural racism in the United States. We developed Neighborhood Trajectories which combine historic redlining data and the current neighborhood socioeconomic characteristics as a novel approach to studying structural racism. Home Owners Loan Corporation (HOLC) neighborhoods for the entire U.S. were used to map the HOLC grades to the 2020 U.S. Census block group polygons based on the percentage of HOLC areas in each block group. Each block group was also assigned an Area Deprivation Index (ADI) from the Neighborhood Atlas®. To evaluate changes in neighborhoods from historic HOLC grades to present degree of deprivation, we aggregated block groups into “Neighborhood Trajectories” using historic HOLC grades and current ADI. The Neighborhood Trajectories are “Advantage Stable”; “Advantage Reduced”; “Disadvantage Reduced”; and “Disadvantage Stable.” Neighborhood Trajectories were established for 13.3% (32,152) of the block groups in the U.S., encompassing 38,005,799 people. Overall, the Disadvantage-Reduced trajectory had the largest population (16,307,217 people). However, the largest percentage of Non-Hispanic/Latino Black residents (34%) fell in the Advantage-Reduced trajectory, while the largest percentage of Non-Hispanic/Latino White residents (60%) fell in the Advantage-Stable trajectory. The development of the Neighborhood Trajectories affords a more nuanced mechanism to investigate dynamic processes from historic policy, socioeconomic development, and ongoing marginalization. This adaptable methodology may enable investigation of ongoing sociopolitical processes including gentrification of neighborhoods (Disadvantage-Reduced trajectory) and “White flight” (Advantage Reduced trajectory).

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Section: Discussionmentioning

confidence: 99%

Development of Neighborhood Trajectories employing Historic Redlining and the Area Deprivation Index

Carlos,

Weiss,

Carter

et al. 2023

Preprint

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“…This allowed for the identification of the areas benefiting from railway line projects, leveraging the unique attributes of FDA. Jung and Song [31] applied the FDA approach to analyze and cluster the time dynamics of neighborhood changes. Furthermore, an extended multivariate FDA was employed, allowing for the simultaneous consideration of multiple socioeconomic variables.…”

Section: B Applications Of Fda In Transportation Studiesmentioning

confidence: 99%

Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis

Choi,

Ryu,

Jung

et al. 2024

IEEE Access

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This study delves into the nuanced patterns of shock and recovery in transit ridership during and after the COVID-19 pandemic, aiming to illuminate the resilience exhibited by various geographic areas. This resilience is measured by the ability of transportation systems to withstand, adapt to, and bounce back from unforeseen shocks. In this research, smart card big data were exploited to track real-time mobility dynamics and economic activity within the city of Seoul, Korea. The approach employed multivariate twodimensional functional data analysis and a hierarchical clustering method to examine both boarding and alighting patterns, taking into account multi-scalar temporal units, monthly and hourly demand fluctuations. The findings present distinct varied shock-and-recovery patterns across areas in transit ridership based on the socioeconomic characteristics of specific areas. These characteristics encompass factors such as industry and land-use composition, income levels, population density, and proximity to points of interest. Additionally, this methodology proves effective in identifying abnormal surges in demand linked to local large-scale development projects.

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“…Only in the last decade, with the increasing availability of longitudinal spatial data and advances in statistical techniques, have scholars begun to fully embrace biographical approaches to understand population changes within cities (Delmelle, 2016(Delmelle, , 2017Li & Xie, 2018). There is now increasing recognition that structural population shifts in one domain (age composition) tend to co-occur with changes in other domains (employment status) (Jung & Song, 2021). Conceptualising local population changes within a trajectory framework can thus enhance our understanding of path dependence patterns; that is, how past conditions contribute to shape present outcomes.…”

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confidence: 99%

Trajectories of neighbourhood inequality in Britain: Unpacking inter‐regional socioeconomic imbalances, 1971−2011

Patias

Rowe

Arribas‐Bel

2021

Geographical Journal

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Inequality within nations remains a significant challenge for socioeconomic development despite significant progress over the last 200 years (Lakner & Milanovic, 2016). Inequality harms poverty reduction (Birdsall & Londono, 1997;McKnight et al., 2017) and diminishes people's sense of fulfilment and self-worth (Friedli, 2009). It can breed crime (Kelly, 2000), mental health illnesses (Friedli, 2009), and environmental degradation (Boyce, 1994). Inequality has a strong spatial perspective (McCann, 2016) and is linked to numerous factors expanding from income to socioeconomic, health, and ethnicity (Lloyd, 2015).Geodemographic classification is a method that helps condense and understand large complex datasets (Voas & Williamson, 2001). Geodemographics cluster areal units into homogeneous groups that are similar to one another across their social, economic, and demographic conditions (Singleton & Longley, 2009). By employing geodemographic classification on spatio-temporal data, changes in socioeconomic and population structures can be studied (e.g., McLachlan & Norman, 2021). This approach effectively captures aggregate changes in local population structures. However, by comparing changes between two points in time, such an approach neglects the broader context within which these changes

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Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach

Cited by 6 publications

References 41 publications

Development of Neighborhood Trajectories employing Historic Redlining and the Area Deprivation Index

Development of Neighborhood Trajectories employing Historic Redlining and the Area Deprivation Index

Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis

Trajectories of neighbourhood inequality in Britain: Unpacking inter‐regional socioeconomic imbalances, 1971−2011

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