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This paper presents an analysis of Seattle’s redevelopment under Washington State’s urban containment policy and the city’s own urban village plan, with a particular focus on outcomes that arise via a combination of urban planning and land market activity. By comparing the city’s parcel layer between 2010 and 2020, the analysis tracks changes in the form of land consolidation and subdivision, which indicate the intensity of redevelopment activities motivated by the market. It reveals that much redevelopment has happened in single- and multifamily areas, but multifamily areas are more likely to have changed. By implementing an exploratory discrete choice model, the analysis also reveals that urban village policy may reduce redevelopment within Seattle—but one subtype, so-called urban hubs, is more likely to accommodate redevelopment. This leads to further discussion of the goals and effectiveness of this urban village policy. Overall, the findings of this work form a picture of a happy, healthy, and sustainable city that sets a high bar for other cities seeking to achieve the same success.
Hanxue Wei; Lucien Wostenholme; John Carruthers. Planning and Markets at Work: Seattle under Growth Management and Economic Pressure. Sustainability 2021, 13, 7634 .
AMA StyleHanxue Wei, Lucien Wostenholme, John Carruthers. Planning and Markets at Work: Seattle under Growth Management and Economic Pressure. Sustainability. 2021; 13 (14):7634.
Chicago/Turabian StyleHanxue Wei; Lucien Wostenholme; John Carruthers. 2021. "Planning and Markets at Work: Seattle under Growth Management and Economic Pressure." Sustainability 13, no. 14: 7634.
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.
Xiao Huang; Zhenlong Li; Junyu Lu; Sicheng Wang; Hanxue Wei; Baixu Chen. Time-Series Clustering for Home Dwell Time During COVID-19: What Can We Learn From It? ISPRS International Journal of Geo-Information 2020, 9, 675 .
AMA StyleXiao Huang, Zhenlong Li, Junyu Lu, Sicheng Wang, Hanxue Wei, Baixu Chen. Time-Series Clustering for Home Dwell Time During COVID-19: What Can We Learn From It? ISPRS International Journal of Geo-Information. 2020; 9 (11):675.
Chicago/Turabian StyleXiao Huang; Zhenlong Li; Junyu Lu; Sicheng Wang; Hanxue Wei; Baixu Chen. 2020. "Time-Series Clustering for Home Dwell Time During COVID-19: What Can We Learn From It?" ISPRS International Journal of Geo-Information 9, no. 11: 675.
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph, and further assess the statistical significance of sixteen demographic/socioeconomic variables from five major categories. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures. Policymakers need to carefully evaluate the inevitable trade-off among different groups, making sure the outcomes of their policies reflect interests of the socially disadvantaged groups. Highlights We perform a trend-driven analysis by conducting Kmeans time-series clustering using fine- grained home dwell time records from SafeGraph. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order. The results suggest that socially disadvantaged groups are less likely to follow the order to stay at home, potentially leading to more exposures to the COVID-19. Policymakers need to make sure the outcomes of their policies reflect the interests of the disadvantaged groups.
Xiao Huang; Zhenlong Li; Junyu Lu; Sicheng Wang; Hanxue Wei; Baixu Chen. Time-series clustering for home dwell time during COVID-19: what can we learn from it? 2020, 1 .
AMA StyleXiao Huang, Zhenlong Li, Junyu Lu, Sicheng Wang, Hanxue Wei, Baixu Chen. Time-series clustering for home dwell time during COVID-19: what can we learn from it? . 2020; ():1.
Chicago/Turabian StyleXiao Huang; Zhenlong Li; Junyu Lu; Sicheng Wang; Hanxue Wei; Baixu Chen. 2020. "Time-series clustering for home dwell time during COVID-19: what can we learn from it?" , no. : 1.