Urban Studies & Planning Research Works

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    Should We Leave? Attitudes towards Relocation in Response to Sea Level Rise
    (MDPI, 2017-12-04) Song, Jie; Peng, Binbin
    The participation of individuals contributes significantly to the success of sea level rise adaptation. This study therefore addresses what influences people’s likelihood of relocating away from low-lying areas in response to rising sea levels. The analysis was based on a survey conducted in the City of Panama Beach in Florida (USA). Survey items relate to people’s risk perception, hazard experience, threat appraisal, and coping appraisal, whose theoretical background is Protection Motivation Theory. Descriptive and correlation analysis was first performed to highlight critical factors which were then examined by a multinomial Logit model. Results show that sea level rise awareness is the major explanatory variable. Coping appraisal is qualitatively viewed as a strong predictor for action, while threat appraisal is statistically significant in driving relocation intention. These factors should be integrated in current risk communication regarding sea level rise.
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    Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks
    (MDPI, 2021-01-29) Ye, Xinyue; Duan, Lian; Peng, Qiong
    Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As crime incidents are distributed sparsely across space and time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in prediction of crime density. This paper proposes the use of deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related crime prediction based on non-emergency service request data (311 events). Specifically, it outlines the employment of inception units comprising asymmetrical convolution layers to draw low-level spatiotemporal dependencies hidden in crime events and complaint records in the 311 dataset. Afterward, this paper details how residual units can be applied to capture high-level spatiotemporal features from low-level spatiotemporal dependencies for the final prediction. The effectiveness of the proposed DIRNet is evaluated based on theft-related crime data and 311 data in New York City from 2010 to 2015. The results confirm that the DIRNet obtains an average F1 of 71%, which is better than other prediction models.
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    Bicycle Accessibility GIS Analysis for Bike Master Planning with a Consideration of Level of Traffic Stress (LTS) and Energy Consumption
    (MDPI, 2022-12-20) McNally, Devin; Tillinghast, Rachel; Iseki, Hiroyuki
    Measuring the impact of bicycle infrastructure and other mobility improvements has been a challenge in the practice of transportation planning. Transportation planners are increasingly required to conduct complex analyses to provide supporting evidence for proposed plans and communicate well with both decision makers and the public. Cyclists experience two important factors on roads: (a) travel stress related to the built environment along with the traffic conditions and (b) changes in physical burden due to topography. This study develops a method that integrates an energy consumption calculation and “bicycling stress” score to take into account external conditions that influence cyclists substantially. In this method, the level of traffic stress (LTS) is used to select street segments appropriate for different comfort levels among cyclists and is combined with biking energy consumption, in addition to distance, which is used as travel impedance to consider the effects of slopes and street intersections. The integrated Geographic Information System (GIS) analysis methods are used to evaluate bicycle infrastructure improvements in the coming years in Montgomery County, MD, USA. The analysis results demonstrated that the infrastructure improvements in the county’s bike master plan are well-targeted to improve bicycling accessibility. Furthermore, the use of energy as opposed to distance to generate bikeshed areas results in smaller bikesheds compared to distance-generated bikesheds. The method presented herein allows planners to characterize and quantify the impact of bicycle infrastructure and prioritize locations for improvements.
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    Come hybrid or high water: Making the case for a Green–Gray approach toward resilient urban stormwater management
    (Wiley, 2023-02-07) Hendricks, Marccus D.; Dowtin, Asia L.
    120 years or more of unsustainable urban development has damaged the natural environment and disrupted essential ways to stabilize water body overflow and even mitigate pluvial flooding. In light of catastrophic flooding that has occurred globally, a renewed commitment to transforming built surfaces and incorporating more green infrastructures (GIs) has emerged. In fact, one could argue that an overcommitment to GI is being touted in the literature, but largely disconnected from more real-world possibilities, considering all things. In this commentary, we make the case that as cities transition from development patterns of the past and even considering climate-induced storm characteristics of the future, a hybridized solution (e.g., Green–Gray) should be considered. Smaller approaches to urban greening have been implemented in areas that need larger-scale restorations, thus proving to be insufficient. Likewise, the uncertainty surrounding rainfall and storm events has forced us to be more strategically balanced in our efforts to achieve resilience in our stormwater infrastructure. Hybridized solutions that include a diverse set of systems, anchored in local conditions, position us best for effective urban stormwater management. In the absence of such solutions, runoff volumes will continue to rise, flooding will prevail, and disenfranchised communities will remain disproportionately impacted by these impacts of urbanization.
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    Recent intra-metropolitan patterns of spatial mismatch: Implications for black suburbanization and the changing geography of mismatch
    (Wiley, 2022-09-01) Eom, Hyunjoo
    Kain's spatial mismatch hypothesis (SMH) (1968) highlights the segregation of Black population in the inner city as well as the decentralization of jobs, both of which played a role in the poor labor market outcomes for Black residents in the inner city. Demographic and economic changes in U.S. metropolitan areas since the late 20th century have transformed the urban spatial structure. This paper aims to revisit the SMH and investigate whether the spatial pattern of mismatch has changed as a result of geographic shifts in the Black population. This paper specifically examines how the suburbanization of the Black population has affected the geographic patterns of mismatch and whether the mismatch is disappearing in the major U.S. metropolitan areas. Using spatial measures of mismatch, this paper presents intra-metropolitan spatial mismatch patterns that capture the clustering of jobs and the Black population based on their relative distributions, showing that the overall level of spatial mismatch declined in major U.S. metropolitan areas between 2000 and 2015. However, geographical evidence reveals that the spatial mismatch has shifted to the outer suburbs, replicating city-suburb spatial inequality, implying that although mismatch may have declined in the inner city due to Black suburbanization, spatial mismatch continue to persist in U.S. metropolitan areas in Black suburbs. The findings also demonstrate that although spatial mismatch generally declined in the inner city, it increased in cities with high inner city polarization, particularly New York, Chicago, San Francisco, and Seattle.