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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]

In this paper, we look at differences in travel behavior and location characteristics across income in Chengdu, China at two points of time, 2005 and 2016, using household travel surveys. Specifically, we compare changes over time for different income groups for Chengdu in 2005 and 2016. We find that walking or biking remains the most common mode for all income groups but higher-income households appear to have more choices depending on the proximity of their neighborhood to downtown. We also find that both average local and average regional access have worsened since 2005. Furthermore, it appears that there is less economic diversity within neighborhoods in 2016 when compared to 2005, with more locations appearing to have 40% or more of low-, middle-, or high-income households than in the past. Finally, we find that low-income households and older trip makers are more likely to walk or bike and that high-income households are the most likely to own cars and use motorized modes. Built environment characteristics like mixed land use appear to significantly reduce travel time in 2016 but do not result in higher non-motorized transport mode share. We contribute to existing literature by evaluating changes in the relationship of built environment and travel behavior during a period of rapid urbanization and economic growth in a Chinese city. [ABSTRACT FROM AUTHOR]

Previous studies have frequently taken urban density as a surrogate for economic efficiency and applied composite scores such as urban intensity to assess the existing physical forms of towns. However, measures of intensity might nuance crude calculations of density while considering how such measures relate to future plans and the process of transformations. This paper used open source geospatial data and regulatory detailed planning to measure urban intensity of the existing and the planned. Spatial analytical techniques were applied to compute accessibility to destinations, building density, compactness of development, and diversity of land use function of sixteen specialty towns in China. The results showed that the regulatory detailed planning of these cases might not improve town planning measured by intensity in terms of spatial arrangement in the time horizon of 15–20 years. We found that well-integrated monocentric forms with the finer street network could potentially provide a more suitable spatial distribution for mid-sized towns measured by urban intensity, no matter whether the urban form is linear or center-spreading. Moreover, the comparison between the existing and the planned implies that the poly-centric urban form for mid-sized towns in China could end up with a lower score of urban intensity. We outline a framework of urban intensity measures that include analytical consideration of what is there now, planned to what is forthcoming, and conceptual in terms of time and space. • Differentiating urban intensity from density • Using urban block as the spatial unit of study is more accurate than a synthetic spatial grid. • Comparing existing and planned conditions of medium-sized towns • Evaluation and intervention of town planning policies [ABSTRACT FROM AUTHOR]

This study investigated the urban growth dynamics of urban regions. The study area was the Marmara Region, one of the most densely populated and ecologically diverse areas in Turkey. Using CORINE land cover data for 2006, 2012, and 2018, the study utilized multiple correspondence analyses and cluster analyses, to analyze land cover changes. The resulting maps, visualized in GIS, revealed the rapid urban transformation of the regional structure, formerly comprised of four distinct areas, into a more complex structure, in which densification and sprawl occur simultaneously. Our findings demonstrated a dissonance between the spatial dynamics of the Marmara Region during the study period, and the capacity and scope of the simultaneously initiated regional policies and mega-projects. This uncoordinated approach has endangered the region's sustainable development. The paper, therefore, discusses the importance of land use planning and transboundary collaboration for sustainable regional development. Beyond the local case, the results contribute to critical theories in regional planning by linking theory and practice. [ABSTRACT FROM AUTHOR]

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide. [ABSTRACT FROM AUTHOR]