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Urban parks, pivotal in fostering physical activity, mental well-being, and environmental stewardship, are integral to green infrastructure planning. Despite advances in georeferenced data applications, existing park classifications often overlook actual visitation patterns. This study reclassifies urban parks using over 5.9 million records from approximately 330 thousand visitors across 300 Tokyo parks, comparing with size-based park categorizations. We employed a range of analytical tools, including principal component analysis, Isolation Forest algorithm, various clustering algorithms, and the Gini index. Our findings first revealed four key visitation indicators, activity intensity, utilization efficiency, temporal occupancy, and revisit volume. These indicators uncovered parks with atypical visitation patterns within each size category, leading to three novel park classifications, everyday leisure parks, social destination parks, and seasonal activity parks. Moreover, we discovered notable disparities in distances traveled to parks, particularly during nights, weekends, and holidays, with pronounced inequalities in seasonal activity parks and smaller parks. The findings advocate for a nuanced park management strategy, prioritizing maintenance and amenity development aligned with observed visitation patterns to enhance recreational potential, thereby contributing insights to urban park research that support the advancement of green infrastructure planning and policy aimed at improving park utility and enjoyment. • Unveiled four principal visitation indicators, capturing nuanced spatio-temporal visitation behaviors. • Detected atypical visitation patterns across all size-based park categories, highlighting disparities in park usage. • Identified three distinct park categories based on visitation data, each with unique visitation patterns. • Significant trip distance disparities were found in smaller and seasonal activity parks. [ABSTRACT FROM AUTHOR]

Polymerase chain reaction (PCR) testing programs have been crucial in combating infectious diseases, such as COVID-19. However, the widespread implementation of frequent nucleic acid testing requires the dense deployment of PCR sample collection booths (SCBs). In this study, we propose two-stage stochastic modeling frameworks to optimize the deployment and operation of SCBs, considering demand uncertainty and both regular and ad hoc services. The first stage optimizes the location and regular staff allocation to achieve a specified service reliability (SR). The second stage determines citizens' equilibrium SCB choice and allocates ad hoc staff when demand exceeds the capacity determined in the first stage. We propose an SR-based gradient solution approach to efficiently solve the model. Numerical experiments using real-world PCR data demonstrate the effectiveness and efficiency of our framework. Compared with the deployment plan implemented in Shanghai during the pandemic, the optimized deployment and operation plan results in 7% and 12% saving in total cost for the Laoximen subdistrict and the Huangpu district, respectively. The modeling framework and findings presented in this study can inform government decision-making regarding the deployment and operation plan of SCBs in combatting infectious diseases, which can specify the optimal number and location of SCBs to deploy and the regular and ad hoc staff allocation for each SCB to meet the demand at the lowest cost. • Location and labor allocation for PCR sample collection booths with uncertain demand. • Equilibrium considering citizens' choice behavior and the endogenous queuing cost. • A tailored Service Reliability (SR)-based gradient solution method. • Informed decisions ensuring both cost-effectiveness and equity. [ABSTRACT FROM AUTHOR]

This study examines the effects of subway extension on housing prices in affluent urban neighborhoods, focusing on the Q-line extension in Manhattan, New York City. Utilizing synthetic controls and treatment effects estimators, distinct pricing trends across property types are revealed, particularly condominiums. The observed pricing dynamics deviate from the assumption that increasing supplies are associated with price discounts. Moreover, the connection between price discounts and noise complaints is not observed, highlighting the significant role of demand-side factors, especially neighborhood characteristics, in shaping housing prices. An unexpected demographic shift is observed, suggesting that the Q-line extension may disproportionately benefit non-white and low-income groups, challenging the prevailing gentrification narrative. We use the term "inverse premiumization" to denote the phenomenon where anticipated price increases in affluent neighborhoods due to transit improvements fail to materialize. Furthermore, our analysis of speculative behavior reveals a spike in short-term growth during the public notice period, gradually slowing down during construction and operation phases. These findings offer the nuanced adverse effects of subway extension on housing prices, contributing to our understanding of short-to medium-term price premiums and discounts. These insights are key considerations for city planners and policymakers navigating urban development, housing market, and speculative behavior dynamics. • Examines the short-to medium-term impact of the Q-line extension on housing prices in affluent urban neighborhoods. • Reveals that increases in supply not necessarily lead to price discounts. • Uncovers unintended benefits of subway extension for non-whites and low-income groups. • Introduces the concept of "inverse premiumization" related to gentrification and speculative behavior. • Provides a comprehensive analysis of the relationship between subway extension and property values. [ABSTRACT FROM AUTHOR]

The wildland-urban interface (WUI) represents landscapes where human settlements coexist with natural features. Trails within the WUI areas, valued for their ecological, recreational, and educational values, lack comprehensive research on landscape sensitivity influenced by both landscape and urban development. This paper addresses the gap by proposing a comprehensive landscape sensitivity index (CLSI) using multiple regression, cluster analysis, and correlation analysis. The Appalachian Trail (AT) serves as a case study to explore the characteristics of high sensitivity areas, considering various attributes and their connection with federal reserved land. Results show that eliminating covariance in landscape indices refines the landscape aggregation pattern, with Moran's I decreasing from 0.776 to 0.449, aligning with the observed fragmented landscape. In comparison to modified landscape indices (MLSI), the CLSI reveals that 85.6% of the area experiences changes in landscape sensitivity, with 42.5% of the AT region displaying significant landscape sensitivity, including 4.9% as having high landscape sensitivity (HLS), influenced by rock formations, wetlands, and biodiversity. A spatial mismatch is identified between HLS and current federal preservation efforts, with a correlation of only 0.011. The paper proposes tailored conservation strategies for HLS areas in urban, wilderness, and protected regions. Considering the combined impact of ecological and urbanization forces, this study assists in prioritizing land conservation objectives and finding a balance between wilderness protection and urban development. [Display omitted] • The comprehensive landscape sensitivity index is applicable in the WUI region. • Rock, wetlands, and biodiversity are the key factors of high landscape sensitivity. • Only 14.3% of the landscape sensitivity is agreed considering urban disturbances. • Elimination of co-linear landscape indices reveals fragmented landscape pattern. • Spatial mismatch exists between federal land and high landscape sensitivity areas. [ABSTRACT FROM AUTHOR]

The rapid adoption of on-demand mobility services has brought a challenge in accessing their equity impacts at a regional scale due to the absence of consistent trip data and efficient transportation models. This study shows the potential of integrating synthetic population data and a choice-based optimization model to support large-scale mobility service region design with equity concerns. We propose a decision support tool that specifies budget-constrained optimal service regions for new mobility services under one of several objectives such as minimizing consumer surplus insufficiency. We test using New York State synthetic data and illustrate its application by considering new ride-hailing and microtransit services. The "cost of equity" is measured: under budget level II, each vehicle requires a subsidy of $71.39/day to minimize disparity and $29.86/day to minimize insufficiency. Our findings contribute to the literature by quantifying trade-offs among different services, objectives, and budget levels, thereby supporting funding and resource allocation. [ABSTRACT FROM AUTHOR]

The growing recognition of the societal implications stemming from technological advancements highlights the need for innovative governance approaches, particularly in urban environments. Focused on connected and autonomous vehicles (CAVs), this study proposes an integrated analytical framework for a dedicated open data platform (ODP). By integrating thematic analysis with process landscape and stakeholder analysis methods, we present a holistic CAV-ODP model positioned to optimize benefits across industries, public sectors, and society as a whole. We argue that multiple stakeholders can enhance both supply and demand aspects of the CAV-ODP, catalyzing activities such as data access, management, transformation, incubation, coordination, education, and application. Importantly, it advocates for government-led integration of interests spanning private and public domains, fostering a collaborative social network for data application and innovation. This inquiry not only reveals the social benefits generated by open CAV data but also underlines the significance of the CAV-ODP platform as an innovative governance arrangement, serving as a catalyst for a culture of sharing, bolstering collaborative governance, and preserving public value amid disruptive technological changes. Overall, the concept of government leadership is universally applicable, with the tangible power dynamics among the government, private sectors, and society influencing the expeditious materialization of the proposed framework. • Pioneer in comprehensive CAV open data application. • Stakeholders should optimize both supply and demand sides through data access, management, and incubation. • Reveals social benefits of CAV open data and the significance of a CAV-ODP platform as a social network. • Efficient platform operation boosts sharing culture and fosters a robust social environment. • Government leadership is universally applicable, with tangible power dynamics among private sectors and society. [ABSTRACT FROM AUTHOR]