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Research Product Types: Journal Article

peer-reviewed article in academic journal

Association of Perceived Neighborhood Environments With Cognitive Function in Older Adults

Kim, B., Rosenberg, D. E., Dobra, A., Barrington, W. E., Hurvitz, P. M., & Belza, B. (2023). Association of Perceived Neighborhood Environments With Cognitive Function in Older Adults. Journal of Gerontological Nursing, 49(8), 35–41. https://doi.org/10.3928/00989134-20230707-04

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Abstract

The current study examined the associations between perceptions of the social and physical neighborhood environments and cognitive function in older adults. This cross-sectional study analyzed 821 adults aged & GE;65 years from the Adult Changes in Thought study. Perceived neighborhood attributes were measured by the Physical Activity Neighborhood Environment Scale. Cognitive function was assessed using the Cognitive Ability Screening Instrument. The associations were tested using multivariate linear regression. One point greater perceived access to public transit was associated with 0.56 points greater cognitive function score (95% confidence interval [CI] [0.25, 0.88]), and an additional one point of perceived sidewalk coverage was related to 0.22 points higher cognitive function score (95% CI [0.00, 0.45]) after controlling for sociodemographic factors. The perception of neighborhood attributes alongside physical infrastructure may play an important role in supporting older adults' cognitive function.

Keywords

Built environment; Physical-activity; Dementia; Reverse; Walking; Disease

Mortgage Loan Costs: Magnitude and Drivers of Variation

Arthur Acolin & Rebecca J. Walter (2023). Mortgage Loan Costs: Magnitude and Drivers of Variation. Housing Policy Debate, DOI: 10.1080/10511482.2023.2236984

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Abstract

This article uses national data disclosed as part of the Home Mortgage Disclosure Act (HMDA) to examine variations in loan costs based on type of loan, borrower, purpose (purchase, improvement, or refinance), and neighborhood characteristics. Loan costs are generally higher for nonconventional conforming loans with higher levels of credit risks (loans with higher combined loan-to-value, higher debt-to-income ratios, and for investment properties). This implies that product and borrower risk impact loan costs. However, borrower characteristics such as income and race/ethnicity are also associated with differences in loan costs even after controlling for loan characteristics, location, and lender fixed effects. Total loan costs are higher both in dollar terms and as a share of the loan amount for Black borrowers and Hispanic borrowers, and total loan costs represent a higher share of the loan amount for lower income borrowers. These disparities are larger in neighborhoods with higher levels of lender concentration and implicit racial bias. These findings suggest that in addition to access to mortgages and interest rates, loan costs can represent a barrier for access to homeownership with a disparate impact for Black and Hispanic borrowers, which contributes to perpetuate the homeownership gap.

Keywords

Mortgage loan costs; homeownership; borrowing constraints; homeownership gap

“Moving or not?”: Factors affecting community responses to environmental disruption

Depari, Catharina D.A., & Lindell, Michael K. (2023). “Moving or not?”: Factors affecting community responses to environmental disruption. International Journal of Disaster Risk Reduction, 95, 103898.

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Abstract

Disputes between government authorities and high-risk communities about community relocation following disasters are not new. Nevertheless, there remains a limited understanding of factors affecting people's decisions about whether to relocate from a hazard zone, particularly in the Indonesian context. Through the experience of the Pelemsari community, a culturally distinct community near Mt. Merapi that once was located less than five km from the volcano crater, this article attempts to explain why the Pelemsari community differed from neighboring communities by abandoning its previous resistance to relocation after an unprecedented eruption in 2010. To explain this behavior change, the study used hermeneutic phenomenology, a methodology rooted in the people-place relationship and specifically used to unfold the meaning structures of a lived experience. Data were collected using semi-structured interviews, field observations, and document reviews. The results showed that people's strong place attachment affected residents' decision to uphold unity with their neighbors, select a relocation site that is outside the hazard zone but close to the former location, and engage in collective action that pressured the government to issue legal certificates of their former homes. These results show how a deep understanding of people's place attachment can make it possible to achieve a successful community relocation.

Keywords

Post-disaster displacement; Community relocation; Place attachment; Cultural attachment; Risk perception

Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance

Kim, Minju & Lee, Dongmin. (2023). Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance. Automation in Construction, 154. https://doi.org/10.1016/j.autcon.2023.105024.

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Abstract

Despite the availability of 3D digital models, 2D floor plans remain extensively used for quality inspection and maintenance as they offer firsthand information. While laser scanners enable efficient capture and reconstruction of real-world scenes, challenges arise in accurately extracting building geometry from laser scanning data due to the loss of geometric features. This paper describes a method for accurately reconstructing 2D geometric drawings of built facilities using laser scanning data. These techniques involve transforming the dimension of 3D data into 2D and displaying the registered data as pixels to extract solid lines that represent wall structures. By employing dimensionality transformation and pixelation techniques, the method supports reliable quality inspection and maintenance processes, overcoming the challenges of extracting precise geometry from laser scanning data. This paper contributes to the automated extraction of geometric features from point clouds and inspires the future development of fully automated 2D CAD and 3D BIM in alignment with Scan-to-BIM.

Applicability of Smart Construction Technology: Prioritization and Future Research Directions

Ahn, H., Lee, C., Kim, M., Kim, T., Lee, D., Kwon, W., & Cho, H. (2023). Applicability of smart construction technology: Prioritization and future research directions. Automation in Construction., 153. https://doi.org/10.1016%2Fj.autcon.2023.104953

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Abstract

The potential for facilitating faster, safer, and more sustainable construction processes through the adoption of smart construction technologies is widely recognized. However, the limited adoption of these technologies in construction projects highlights the significance of identifying the technological needs of major stakeholders and the prioritization of research and development investment. In this study, the quality function deployment technique is employed to extract and prioritize the required technologies (RTs) from various stakeholders, while a thematic literature review is conducted to identify challenges and future research directions. The findings improve the efficiency of resource allocation, allowing policymakers to strategically address pressing issues. This can facilitate collaboration and communication among researchers, stakeholders, and the wider community, fostering a shared vision and understanding of future research goals and outcome. Prioritizing smart construction technologies can enhance their applicability. The top nine of technologies were prioritized by using quality function deployment. Thematic review was conducted for each of the top nine technologies. The challenges and future research directions were presented by review.

Keywords

Fourth industrial revolution (4IR); Prioritization; Quality function deployment (QFD); Smart construction technologies; Technology innovation

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