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Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA

Tenneson, Karis; Patterson, Matthew S.; Mellin, Thomas; Nigrelli, Mark; Joria, Peter; Mitchell, Brent. (2018). Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Remote Sensing, 10(3).

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Abstract

Historical forest management practices in the southwestern US have left forests prone to high-severity, stand-replacement fires. Reducing the cost of forest-fire management and reintroducing fire to the landscape without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to increase ability of managers to monitor AGB and other forest metrics at reduced cost. We developed a regional lidar-based statistical model to estimate AGB for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. Model selection was performed using Bayesian model averaging (BMA) to reduce researcher bias, fully explore the model space, and avoid overfitting. The selected model includes measures of canopy height, canopy density, and height distribution. The model selected with BMA explains 71% of the variability in field-estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model is structured in accordance with previously described local models, and performs equivalently to these smaller scale models. We have demonstrated the effectiveness of lidar for developing cost-effective, robust regional AGB models for monitoring and planning adaptively at the landscape scale.

Keywords

Laser Scanner Data; Landscape Restoration Program; Canopy Fuel Parameters; Discrete-return Lidar; Western United-states; Wave-form Lidar; Airborne Laser; Tropical Forest; Climate-change; Adaptive Management; Forest Biomass; Aboveground Biomass; Airborne Lidar; Monitoring; Regional Forest Inventory; Variable Selection; Bayesian Model Averaging; Multiple Linear Regression

Beyond the Bus Stop: Where Transit Users Walk

Eisenberg-Guyot, Jerzy; Moudon, Anne V.; Hurvitz, Philip M.; Mooney, Stephen J.; Whitlock, Kathryn B.; Saelens, Brian E. (2019). Beyond the Bus Stop: Where Transit Users Walk. Journal Of Transport & Health, 14.

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Abstract

Objectives: Extending the health benefits of public-transit investment requires understanding how transit use affects pedestrian activity, including pedestrian activity not directly temporally or spatially related to transit use. In this study, we identified where transit users walked on transit days compared with non-transit days within and beyond 400 m and 800 m buffers surrounding their home and work addresses. Methods: We used data collected from 2008 to 2013 in King County, Washington, from 221 non-physically-disabled adult transit users, who were equipped with an accelerometer, global positioning system (GPS), and travel diary. We assigned walking activity to the following buffer locations: less than and at least 400 m or 800 m from home, work, or home/work (the home and work buffers comprised the latter buffer). We used Poisson generalized estimating equations to estimate differences in minutes per day of total walking and minutes per day of non-transit-related walking on transit days compared with non-transit days in each location. Results: We found that durations of total walking and non-transit-related walking were greater on transit days than on non-transit days in all locations studied. When considering the home neighborhood in isolation, most of the greater duration of walking occurred beyond the home neighborhood at both 400 m and 800 m; results were similar when considering the work neighborhood in isolation. When considering the neighborhoods jointly (i.e., by using the home/work buffer), at 400 m, most of the greater duration of walking occurred beyond the home/work neighborhood. However, at 800 m, most of the greater duration of walking occurred within the home/work neighborhood. Conclusions: Transit days were associated with greater durations of total walking and non-transit related walking within and beyond the home and work neighborhoods. Accordingly, research, design, and policy strategies focused on transit use and pedestrian activity should consider locations outside the home and work neighborhoods, in addition to locations within them.

Keywords

Physical-activity; Public-transit; Accelerometer Data; Combining Gps; United-states; Travel; Transportation; Health; Time; Neighborhood

Introducing Supergrids, Superblocks, Areas, Networks, and Levels to Urban Morphological Analyses

Moudon, Anne Vernez. (2019). Introducing Supergrids, Superblocks, Areas, Networks, and Levels to Urban Morphological Analyses. Iconarp International Journal Of Architecture And Planning, 7, 1 – 14.

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Abstract

Urban morphological analyses have identified the parcel (plot), the building type, or the plan unit (tessuto in Italian) as the basic elements of urban form. As cities have grown in geographic size disproportionately to their growth in population over the past seven decades, new elements have been introduced that structure their form. This essay describes these new elements and proposes that they be formally recognized in urban morphology. It introduces a conceptual framework for a multilevel structure of urban space using areas and networks and including supergrids and superblocks to guide morphological analyses.

Keywords

Morphological Elements; A Posteriori Approach; A Priori Approach

Pan Coefficient Sensitivity to Environment Variables across China

Wang, Kaiwen; Liu, Xiaomang; Tian, Wei; Li, Yanzhong; Liang, Kang; Liu, Changming; Li, Yuqi; Yang, Xiaohua. (2019). Pan Coefficient Sensitivity to Environment Variables across China. Journal Of Hydrology, 572, 582 – 591.

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Abstract

Data of open water evaporation (E-ow), such as evaporation of lake and reservoir, have been widely used in hydraulic and hydrological engineering projects, and water resources planning and management in agriculture, forestry and ecology. Because of the low-cost and maneuverability, measuring the evaporation of a pan has been widely regarded as a reliable approach to estimate E-ow through multiplying an appropriate pan coefficient (K-p). K-p is affected by geometry and materials of a pan, and complex surrounding environment variables. However, the relationship between K-p and different environment variables is unknown. Thus, this study chose China D20 pan as an example, used meteorological observations from 767 stations and introduced the latest PenPan model to analyze the sensitivity of K-p to different environment variables. The results show that, the distribution of annual K-p had a strong spatial gradient. For all the stations, annual K-p ranged from 0.31 to 0.89, and decreased gradually from southeast to northwest. The sensitivity analysis shows that for China as a whole, K-p was most sensitive to relative humidity, followed by air temperature, wind speed and sunshine duration. For 767 stations in China, K-p was most sensitive to relative humidity for almost all the stations. For stations north of Yellow River, wind speed and sunshine duration were the next sensitive variables; while for stations south of Yellow River, air temperature was the next sensitive variable. The method introduced in this study could benefit estimating and predicting K-p under future changing environment.

Keywords

Atmospheric Temperature; Hydraulic Engineering; Meteorological Observations; Humidity; Water Supply; Evaporation (meteorology); Sunshine; Lake Management; China; Kp Most Sensitive To Relative Humidity; Open Water Evaporation; Pan Coefficient (kp); Pan Evaporation; Sensitivity Analysis; Reference Evapotranspiration; Reference Crop; Evaporation; Water; Model; Pan Coefficient (k-p); K-p Most Sensitive To Relative Humidity; Air Temperature; Ecology; Forestry; Geometry; Hydrologic Engineering; Lakes; Maneuverability; Meteorological Data; Models; Planning; Prediction; Relative Humidity; Solar Radiation; Wind Speed; Yellow River

Korean Apartment Complexes and Social Relationships of the Residents

Gu, Naeun. (2020). Korean Apartment Complexes and Social Relationships of the Residents. Housing Studies, 35(8), 1362 – 1389.

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Abstract

Korean apartment housing, where more than half of the population lives, has drawn attention with its spatial, historical, and cultural uniqueness. Among many questions on Korean apartments, this article explains how the socio-spatial characteristics of apartment housing have impacts on the social relationships among the residents. This article first analyses the historical, socio-cultural, and spatial characteristics of Korean apartments, and then synthesizes up-to-date empirical study results to examine how the diverse characteristics can be associated with the residents' social relations. The empirical evidence clarifies the effects of Korean apartments' characteristics on residents' social relations-the exclusive complex design, spatial configurations, shared spaces including community facilities, heights of the units, public/private housing types, social homogeneity, and community programs are all associated with social relations of the residents. Key methodological problems in current studies as well as implications for future apartment planning are highlighted.

Keywords

Housing; Homogeneity; Shared Space (traffic Engineering); Empirical Research; Sociocultural Theory; High-rise High-density; Korean Apartments; Residents; Social Relationships; Socio-spatial Characteristics; Built Environment; South-korea; Neighborhood; Community; Health; City; Place; Density; Seoul; Configuration Management; Apartments; Uniqueness; Social Relations; Empirical Analysis; Characteristics

Evidence-Driven Sound Detection for Prenotification and Identification Of Construction Safety Hazards and Accidents

Lee, Yong-Cheol; Shariatfar, Moeid; Rashidi, Abbas; Lee, Hyun Woo. (2020). Evidence-Driven Sound Detection for Prenotification and Identification Of Construction Safety Hazards and Accidents. Automation In Construction, 113.

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Abstract

As the construction industry experiences a high rate of casualties and significant economic loss associated with accidents, safety has always been a primary concern. In response, several studies have attempted to develop new approaches and state-of-the-art technology for conducting autonomous safety surveillance of construction work zones such as vision-based monitoring. The current and proposed methods including human inspection, however, are limited to consistent and real-time monitoring and rapid event recognition of construction safety issues. In addition, the health and safety risks inherent in construction projects make it challenging for construction workers to be aware of possible safety risks and hazards according to daily planned work activities. To address the urgent demand of the industry to improve worker safety, this study involves the development of an audio-based event detection system to provide daily safety issues to laborers and through the rapid identification of construction accidents. As an evidence-driven approach, the proposed framework incorporates the occupational injury and illness manual data, consisting of historical construction accident data classified by types of sources and events, into an audio-based safety event detection framework. This evidence-driven framework integrated with a daily project schedule can automatically provide construction workers with prenotifications regarding safety hazards at a pertinent work zone as well as consistently contribute to enhanced construction safety monitoring by audio-based event detection. By using a machine learning algorithm, the framework can clearly categorize the narrowed-down sound training data according to a daily project schedule and dynamically restrict sound classification types in advance. The proposed framework is expected to contribute to an emerging knowledge base for integrating an automated safety surveillance system into occupational accident data, significantly improving the accuracy of audio-based event detection.

Keywords

Construction Projects; Occupational Hazards; Construction Workers; Construction; System Safety; Video Surveillance; Work-related Injuries; Audio-based Accident Recognition; Autonomous Safety Surveillance; Construction Safety; Evidence-driven Sound Event Detection; Accident Prevention; Accidents; Audio Acoustics; Classification (of Information); Construction Industry; Health Hazards; Health Risks; Knowledge Based Systems; Learning Algorithms; Losses; Machine Learning; Monitoring; Motion Compensation; Occupational Diseases; Steel Beams And Girders; Audio-based; Construction Accidents; Construction Work Zones; Historical Construction; Sound Event Detection; State-of-the-art Technology; Vision Based Monitoring; Algorithm; System

Impact of Energy Benchmarking and Disclosure Policy on Office Buildings

Shang, Luming; Lee, Hyun Woo; Dermisi, Sofia; Choe, Youngjun. (2020). Impact of Energy Benchmarking and Disclosure Policy on Office Buildings. Journal Of Cleaner Production, 250.

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Abstract

Building energy benchmarking policies require owners to publicly disclose their building's energy performance. In the US, the adoption of such policies is contributing to an increased awareness among tenants and buyers and is expected to motivate the owners of less efficient buildings to invest in energy efficiency improvements. However, there is a lack of studies specifically aimed at investigating the impact of such policies on office buildings among major cities through quantitative analyses. In response, this study evaluated the effectiveness of the benchmarking policy on energy efficiency improvements decision-making and on real estate performances, by applying two interrupted time series analyses to office buildings in downtown Chicago. The initial results indicate a lack of statistically strong evidence that the policy affected the annual vacancy trend of the energy efficient buildings (represented by ENERGY STAR labeled buildings). However, the use of interrupted time series in a more in-depth analysis shows that the policy is associated with a 6.7% decrease in vacancy among energy efficient buildings. The study proposed a method to quantitatively evaluate the impact of energy policies on the real estate performance of office buildings, and the result confirms the positive impact of energy-efficient retrofits on the real estate performance. The study findings support the reasoning behind the owners' decision in implementing energy efficiency improvements in their office buildings to remain competitive in the market. (C) 2019 Elsevier Ltd. All rights reserved.

Keywords

Office Buildings; Building Failures; Time Series Analysis; Real Property; Energy Consumption; Metropolis; Building Performance; Chicago (ill.); Building Energy Benchmarking And Disclosure Policies; Building Energy Efficiency; Time Series Modeling; Energy Star (program); Building Management Systems; Buildings (structures); Decision Making; Energy Conservation; Maintenance Engineering; Time Series; Disclosure Policy; Energy Benchmarking Policies; Building; Benchmarking Policy; Energy Efficiency Improvements Decision-making; Estate Performance; Energy Efficient Buildings; Energy Star; Energy Policies; Energy-efficient Retrofits; Interrupted Time-series; Regression; Behavior; Designs; Building Energy Benchmarking And; Disclosure Policies; Buildings; Cities; Energy Efficiency; Energy Policy; Markets; Quantitative Analysis; United States

Tsunami Preparedness and Resilience in the Cascadia Subduction Zone: A Multistage Model of Expected Evacuation Decisions and Mode Choice

Chen, Chen; Lindell, Michael K.; Wang, Haizhong. (2021). Tsunami Preparedness and Resilience in the Cascadia Subduction Zone: A Multistage Model of Expected Evacuation Decisions and Mode Choice. International Journal Of Disaster Risk Reduction, 59.

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Abstract

Physical scientists have estimated that the Cascadia Subduction Zone (CSZ) has as much as a 25% chance to produce a M9.0 earthquake and tsunami in the next 50 years, but few studies have used survey data to assess household risk perceptions, emergency preparedness, and evacuation intentions. To understand these phenomena, this study conducted a mail-based household questionnaire using the Protective Action Decision Model (PADM) as a guide to collect 483 responses from two coastal communities in the CSZ: Crescent City, CA and Coos Bay, OR. We applied multistage regression models to assess the effects of critical PADM variables. The results showed that three psychological variables (risk perception, perceived hazard knowledge, and evacuation mode efficacy) were associated with some demographic variables and experience variables. Evacuation intention and evacuation mode choice are associated with those psychological variables but not with demographic variables. Contrary to previous studies, location and experience had no direct impact on evacuation intention or mode choice. We also analyzed expected evacuation mode compliance and the potential of using micro-mobility during tsunami response. This study provides empirical evidence of tsunami preparedness and intentions to support interdisciplinary evacuation modeling, tsunami hazard education, community disaster preparedness, and resilience plans.

Keywords

False Discovery Rate; American-samoa; Earthquake; Washington; Behavior; Oregon; Wellington; Responses; Disaster; Tsunami Evacuation; Cascadia Subduction Zone; Risk Perception

Measuring the Urban Forms of Shanghai’s City Center and Its New Districts: A Neighborhood-Level Comparative Analysis

Lin, Lin; Chen, Xueming (Jimmy); Moudon, Anne Vernez. (2021). Measuring the Urban Forms of Shanghai’s City Center and Its New Districts: A Neighborhood-Level Comparative Analysis. Sustainability, 13(15).

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Abstract

Rapid urban expansion has radically transformed the city centers and the new districts of Chinese cities. Both areas have undergone unique redevelopment and development over the past decades, generating unique urban forms worthy of study. To date, few studies have investigated development patterns and land use intensities at the neighborhood level. The present study aims to fill the gap and compare the densities of different types of developments and the spatial compositions of different commercial uses at the neighborhood level. We captured the attributes of their built environment that support instrumental activities of daily living of 710 neighborhoods centered on the public elementary schools of the entire Shanghai municipality using application programming interfaces provided in Baidu Map services. The 200 m neighborhood provided the best fit to capture the variations of the built environment. Overall, city center neighborhoods had significantly higher residential densities and housed more daily routine destinations than their counterparts in the new districts. Unexpectedly, however, the total length of streets was considerably smaller in city-center neighborhoods, likely reflecting the prominence of the wide multilane vehicular roads surrounding large center city redevelopment projects. The findings point to convergence between the city center's urban forms and that of the new districts.

Keywords

Quantifying Spatiotemporal Patterns; Fast-food Restaurants; Instrumental Activities; Physical-activity; Chinese Cities; Land; Schools; Redevelopment; Expansion; Transformation; Built Environment; Planning; Neighborhood; Urban Form; Shanghai

A Framework for Estimating Commute Accessibility and Adoption of Ridehailing Services Under Functional Improvements from Vehicle Automation

Zou, Tianqi; Aemmer, Zack; Mackenzie, Don; Laberteaux, Ken. (2022). A Framework for Estimating Commute Accessibility and Adoption of Ridehailing Services Under Functional Improvements from Vehicle Automation. Journal Of Transport Geography, 102.

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Abstract

This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find - unsurprisingly - that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation.

Keywords

Choice Of Transportation; Demand Forecasting; Poor People; Adoption; Price Cutting; Metropolis; Employment Statistics; Los Angeles (calif.); New York (state); Chicago (ill.); Accessibility; Autonomous Vehicles; New Mobility Services; Ridehailing; Travel Demand; Preferences