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Research Product Types: Journal Article

peer-reviewed article in academic journal

Integration of Urban Science and Urban Climate Adaptation Research: Opportunities to Advance Climate Action

Lobo, J., Aggarwal, R. M., Alberti, M., Allen-Dumas, M., Bettencourt, L. M. A., Boone, C., Brelsford, C., Broto, V. C., Eakin, H., Bagchi-Sen, S., Meerow, S., D’Cruz, C., Revi, A., Roberts, D. C., Smith, M. E., York, A., Lin, T., Bai, X., Solecki, W., … Gauthier, N. (2023). Integration of urban science and urban climate adaptation research: opportunities to advance climate action. Npj Urban Sustainability, 3(1), 32–39. https://doi.org/10.1038/s42949-023-00113-0

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Abstract

There is a growing recognition that responding to climate change necessitates urban adaptation. We sketch a transdisciplinary research effort, arguing that actionable research on urban adaptation needs to recognize the nature of cities as social networks embedded in physical space. Given the pace, scale and socioeconomic outcomes of urbanization in the Global South, the specificities and history of its cities must be central to the study of how well-known agglomeration effects can facilitate adaptation. The proposed effort calls for the co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies.

Detecting Subpixel Human Settlements in Mountains Using Deep Learning: A Case of the Hindu Kush Himalaya 1990–2020

Chen, T.-H. K., Pandey, B., & Seto, K. C. (2023). Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020. Remote Sensing of Environment, 294, 113625–. https://doi.org/10.1016/j.rse.2023.113625

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Abstract

The majority of future population growth in mountains will occur in small- and medium-sized cities and towns and affect vulnerable ecosystems. However, mountain settlements are often omitted from global land cover analyses due to the low spatial resolution of satellite images, which cannot resolve the small scale of mountains settlements. This study demonstrates, for the first time, the potential of deep learning to detect human settlements in mountains at the sub-pixel level, based on Landsat satellite imagery. We hypothesized that adding spatial and temporal features could improve the detection of mountain settlements since spectral information alone led to inaccurate results. For spatial features, we compared a U-shaped neural network (U-Net), a deep learning algorithm that automatically learns spatial features, with a simple random forest (RF) algorithm. Then, we assessed whether temporal features would increase accuracy by comparing two input datasets, multispectral imagery and temporal features from the Continuous Change Detection and Classification (CCDC) algorithm. We evaluated each method by calculating the accuracies of (1) the binary settlement footprint, (2) the subpixel estimates of impervious surfaces, and (3) urban growth. We tested the accuracies using visually interpreted datasets from time-series Google Earth images across the Hindu Kush Himalaya that were not used for training to evaluate model transferability. The U-Net successfully improved mountain settlement mapping compared to the random forest, with a substantial discrepancy in small settlements. The time-series results from the U-Net successfully captured long-term urban growth but fewer short-term changes. Contrary to expectations, the CCDC temporal features reduced the accuracy of mountain settlement mapping due to frequent cloud cover in hilly areas. Our subpixel analysis reveals that the built-up area of the Hindu Kush Himalaya has expanded at a rate of 61 km2 per year from 1990 to 2020, which is about twice the estimate of the Global Human Settlement Layer using binary urban/non-urban classifications.

Keywords

Urban land cover; Land cover fraction; Peri-urban; Built-up area; Subpixel mapping; Machine learning; Time-series; Himalaya; CCDC

Higher Depression Risks in Medium- Than in High-Density Urban Form Across Denmark

Chen, T.-H. K., Horsdal, H. T., Samuelsson, K., Closter, A. M., Davies, M., Barthel, S., Pedersen, C. B., Prishchepov, A. V., & Sabel, C. E. (2023). Higher depression risks in medium- than in high-density urban form across Denmark. Science Advances, 9(21), eadf3760–eadf3760. https://doi.org/10.1126/sciadv.adf3760

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Abstract

Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensional (3D) urban form (i.e., building density and height) over time. Combining satellite-derived urban form data and individual-level residential addresses, health, and socioeconomic registers, we conduct a case-control study (n = 75,650 cases and 756,500 controls) to examine the association between 3D urban form and depression in the Danish population. We find that living in dense inner-city areas did not carry the highest depression risks. Rather, after adjusting for socioeconomic factors, the highest risk was among sprawling suburbs, and the lowest was among multistory buildings with open space in the vicinity. The finding suggests that spatial land-use planning should prioritize securing access to open space in densely built areas to mitigate depression risks.

Scaling Down from the Neighborhood in Urban Planning Research and Practice: The Potential Benefits of a Micro-Scale Focus

Walter, R. J., Tillyer, M. S., Ramiller, A., & Acolin, A. (2023). Scaling Down from the Neighborhood in Urban Planning Research and Practice: The Potential Benefits of a Micro-Scale Focus. Journal of Planning Education and Research. https://doi.org/10.1177/0739456X231175593

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Abstract

The neighborhood has been the dominant spatial unit in urban planning since the early 20th century. Criticisms of the neighborhood unit include disagreements about defining boundaries, methodological challenges in capturing neighborhood effects, and negative impacts on communities. With advancements in data management, and public data available at smaller units (street block or property), quantitative analyses are possible at the micro-scale. This commentary draws on crime research and prevention to illustrate the benefits of micro-scale approaches to quantitative analyses in the field of urban planning, arguing that the devolution to smaller scales may be a vehicle for efficient resource allocation.

Utilizing Fractal Dimensions as Indicators to Detect Elements of Visual Attraction: A Case Study of the Greenway along Lake Taihu, China

Fan, R., Yocom, K. P., & Guo, Y. (2023). Utilizing Fractal Dimensions as Indicators to Detect Elements of Visual Attraction: A Case Study of the Greenway along Lake Taihu, China. Land (Basel), 12(4), 883–. https://doi.org/10.3390/land12040883

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Abstract

It is widely acknowledged that the quality of greenway landscape resources enhances the visual appeal of people. While most studies have evaluated visual perception and preference, few have considered the relationship between the distribution of greenways in relation to the proximity of water bodies such as lakes and rivers. Such an investigation requires an in-depth analysis of how to plan and design greenways in order to better enhance people's willingness to access and utilize them. In this research we propose specific color brightness and contour visual attraction elements to further discuss the quality of greenway landscape resources in the rapidly urbanizing Lake Taihu region of China. Specifically, we utilize a common method in fractal theory analysis called counting box dimension to calculate and analyze the sample images. The method generates data on fractal dimension (FD) values of two elements; the optimal fractal dimension threshold range; the characteristics exhibited by the maximum and minimum fractal dimension values in the greenway landscape; and the relationship between the two visual attraction elements allowing us to derive distribution of the greenway and water bodies. The results reveal that greenway segments with high values of the visual attraction element of color brightness fractal dimension (FD) are significantly closer to the lake than those subject to high values of the visual attraction element. Some segments are even close to the lake surface, which is because the glare from the direct sunlight and the reflection from the lake surface superimposed on each other, so that the greenway near the lake surface is also affected by the brightness and shows the result of high color brightness values. However, the greenway segments with high values of contour element FD are clearly more influenced by plants and other landscape elements. This is due to the rich self-similarity of the plants themselves. Most of the greenway segments dominated by contour elements are distant from the lake surface. Both color brightness and contour elements are important indicators of the quality of the visual resources of the Lake Taihu Greenway landscape. This reveals that the determination of the sub-dimensional values of color brightness (1.7608, 1.9337) and contour (1.7230, 1.9006) visual attraction elements and the optimal threshold range (1.7608, 1.9006) can provide theoretical implications for the landscape planning and design of lake-ring type greenways and practical implications for assessing the quality of visual resources in greenway landscapes.

Keywords

color brightness; contour; visual attraction; fractal dimension (FD); boxplot; Lake Taihu

Statistical Analysis and Representation Models of Working-Days Liquidated Damages

Abdel Aziz, A. M. (2023). Statistical Analysis and Representation Models of Working-Days Liquidated Damages. Journal of Construction Engineering and Management, 149(7). https://doi.org/10.1061/JCEMD4.COENG-13330

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Abstract

Contractors tend to challenge the enforceability of liquidated damages (LDs), claiming they are unreasonable, excessive, penalty statements, or concurrently caused. States customarily assert that the LD rates are a genuine reflection of the expenses expected to be suffered when a project gets delayed due to noncompletion. While there are common practices among the states for articulating LD specifications, which generally follow the Federal Code of Regulations, there are no published studies that assist states in comparing their LD rates to those of other states so that the LD rates might be defended. Further, there are no studies that offer models that would uncover the relationship between the LD rates and the contract sizes so that the LD rates might be justified. This work addresses such gaps in the body of knowledge (BOK) in LDs. With emphasis on the working-days (WD) LD rate schedules, the objectives of this work are to characterize the LD rate schedules across the states and to model a formula(s) that would represent the relationship between the WD LD rates and the contract amounts. The data set for the work represents the LD schedules in the standard specifications of all departments of transportation in the United States. Descriptive and cluster statistical analyses were used for the LD rate characterization. For model development, several linear and nonlinear regression models were employed. The results highlighted considerable LDs variability in the smaller contract sizes and exceptional LD rates stability in the larger sizes. Despite the economic differences among the states, it is found that the LD rate is, on average, 0.02 ¢/$ for projects $20 million or above. Below that, the rate increases between 0.03 ¢/$ and 0.18 ¢/$ until the contracts reach $750,000. LD rates tend to decrease sharply with the increase in contract sizes, forming an L or reverse J shape. This pattern proved complex, and only nonlinear regression with transformed variables successfully modeled it. Credible models were obtained after satisfying the least-squares regression assumptions. The work contributes to the BOK by adding a new statistical dimension to understanding LDs and developing regression model(s) that explain the relationships between the LD rates and the contract sizes. The work should help SHAs create, evaluate, and justify their LD rates.

 

Time-Varying Food Retail and Incident Disease in the Cardiovascular Health Study

Lovasi, G. S., Boise, S., Jogi, S., Hurvitz, P. M., Rundle, A. G., Diez, J., Hirsch, J. A., Fitzpatrick, A., Biggs, M. L., & Siscovick, D. S. (2023). Time-Varying Food Retail and Incident Disease in the Cardiovascular Health Study. American Journal of Preventive Medicine, 64(6), 877–887. https://doi.org/10.1016/j.amepre.2023.02.001

Does Compact Development Mitigate Urban Thermal Environments? Influences of Smart Growth Principles on Land Surface Temperatures in Los Angeles and Portland

Won, Jongho, and Meen Chel Jung. 2023. Does Compact Development Mitigate Urban Thermal Environments? Influences of Smart Growth Principles on Land Surface Temperatures in Los Angeles and Portland. Sustainable Cities and Society 90.

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Abstract

The smart growth paradigm has emerged as a major planning framework to respond to the adverse outcomes of reckless development, but its influences on urban thermal environments are underexplored in the scholarly literature. Since elevated land surface temperature (LST) is closely related to the physical expansion of developed areas, it is necessary to identify the effects of smart growth strategies on LST. This study, therefore, investigated the relationships between LST, landscape variables, and smart growth variables at the census block group level in two distinct urban locales: the City of Los Angeles, California, and the City of Portland, Oregon, from 2010 to 2018. Through multivariate analyses—including the principal component analysis (PCA), K-means clustering, analysis of covariance (ANCOVA), and regression models—this study revealed the potential of urban forms promoted by the smart growth principles comprehensively to mitigate LST. Given the different features of built environments and planning systems between the two cities, the results of this study also indicate the necessity of considering local contexts rather than suggesting a “one-size-fits-all” policy.

Keywords

Smart growth; Compact development; Land surface temperature; Urban form; Landscape

Selection of Wearable Sensor Measurements for Monitoring and Managing Entry-Level Construction Worker Fatigue: a Logistic Regression Approach

Lee, Wonil; Lin, Ken-Yu; Johnson, Peter W.; Seto, Edmund Y.W. (2022). Selection of Wearable Sensor Measurements for Monitoring and Managing Entry-Level Construction Worker Fatigue: a Logistic Regression Approach. Engineering, Construction, and Architectural Management, 29(8), 2905–23.

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Abstract

The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors.

Keywords

Technology, management, construction safety, information and communication technology (ICT) applications