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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]

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 rise of social networking platforms provides opportunities to examine the relationship between public emotion and housing price. This study investigates frequency and places of visits, population sentiment, and housing price using 8.7 million tweets retrieved from Manhattan, New York City in 2019. We implemented kernel density estimation, Getis-Ord G i hot spot analysis, and spatial lagged hedonic pricing models to identify the location variation of sentiment levels. The results show: (1) the spatial clustering of tweets frequency was highly related to land use types in places such as parks, financial districts, and train stations; (2) high sentiment levels coincided with high frequency clusters and higher positive sentiment is associated with higher housing price; and (3) sentiment level was significantly associated with housing price and building structure, amenities, and proximity to landmarks all had significant influences on housing price. The study indicates that a population with higher concentration of happiness correlates to higher property value and provides an innovative perspective to understand public sentiment in relation to housing price using social media data, supplemented by housing transaction data. We demonstrate a feasible framework for researchers and stakeholders to utilize in future urban and spatial geographical research. • Identify spatial clustering of tweets frequency and land use types. • Assess spatiotemporal characteristic of public sentiment using Twitter data. • Evaluate the relationship of public sentiment level to housing price at a neighborhood scale. [ABSTRACT FROM AUTHOR]