Environment and Planning B: Urban Analytics and City Science最新文献

This study evaluates the impact of introducing the Bus Rapid Transit (BRT) system on housing market dynamics across three diverse urban corridors in El Paso, Texas, utilizing spatial hedonic pricing and AITS-DID models. The research aims to understand how the BRT system influences property values, hypothesizing that the effects vary between developed and underdeveloped areas. Initial findings reveal an increase in property values in the affluent Mesa area until the BRT opening in 2014, followed by a decline, suggesting a shift in market perceptions post-implementation. Conversely, less affluent areas along the Alameda and Dyer corridors exhibit a reduction in the negative impact on property values over time, indicating growing appreciation for the BRT and associated urban improvements. The study contributes to urban planning and policy by highlighting the mixed impacts of BRT systems on property values across different urban fabrics and underscores the importance of context-sensitive transit planning. It also points to the necessity for comprehensive communication with stakeholders to ensure transit solutions meet community needs and support equitable urban development.

Agent-based transport models are gaining popularity due to their ability to model features such as heterogenous individual behaviour, household dependencies, and new dynamic modes of travel. Such models require as input disaggregate population datasets with detailed daily activity diaries (activity-based travel demand). While there is extensive literature on activity-based travel demand generation, few open-source tools are available for producing such datasets, and those that do exist are often difficult to adapt to different study areas. In this work, we present an open-source modular pipeline for generating activity-based travel demand for any region in England, producing individuals with household structures and geographically and temporally explicit daily activity plans. The framework includes activity scheduling and location assignment for a synthetic population, as well as self-consistency and validation frameworks to help fine-tune parameters.

This paper presents a survey of homeowners and renters who moved within or into the Greater Toronto Area (GTA), Canada, since January 2016. This timeline covers pre- and during-COVID movers. The survey investigates households' residential preferences and the potential dissonance between their preferred and actual residential location choices. The respondents are asked to answer questions about their previous and current residences, their housing search, and hypothetical residential location choices given the option of telecommuting. Empirical analysis of the actual and hypothetical residential location choices is conducted through a joint revealed preference and stated preference (RP-SP) error component mixed logit model. The model estimations identify discrepancies between RP and SP choices, indicating the presence of residential dissonance. The positive effect of transit accessibility variables shows that people are more likely to choose residential locations with better accessibility. However, dissonance in transit accessibility is found in both models, suggesting homeowners and renters may settle for lower accessibility in real life. Moreover, the model results show that homeowners and renters are more likely to relocate when working remotely, indicating the significant influence of telecommuting on residential dissonance.

Like many countries globally, the private rental sector in England and Wales contains some of the lowest quality and energy inefficient properties, despite being home to some of the most vulnerable households. We present a new data product that classifies small areas based on the energy (in)efficiency characteristics of private rental properties. Newly available Energy Performance Certificate (EPC) data enables us to analyse detailed energy and housing characteristics for 3.9 million private rentals (∼78.8% of total sector), the most comprehensive dataset of its kind, using k-means clustering. Demographic datasets allow us to explore wider socio-spatial inequalities, and uncertainties associated with granular - but at-times incomplete - EPC data. The classification can be used to evidence how inefficiency is spatially concentrated and fragmented, with a diverse range of energy and housing conditions shaping the everyday lives of tenants.

This paper describes {ActiveCA}, an open data product with Canadian time use data. {ActiveCA} is an R data package that contains analysis-ready data related to active travel spanning almost 40 years, extracted from Cycles 2 (1986), 7 (1992), 12 (1998), 19 (2005), 24 (2010), 29 (2015), and 34 (2022) of the Time Use Survey (TUS) from the General Social Survey (GSS). Active travel episodes are characterized by mode, with walking being part of every cycle and bicycling starting in 1992. The attributes of active trips are the types of locations of origins and destinations, the duration of trips, and episode weights for expanding the trips to population-wide estimates. Based on the year of the survey, a variety of locations are coded. In earlier cycles, these include home, work or school, and other's home, whereas in later cycles these are augmented with locations such as grocery stores, restaurants, outdoor destinations, and others. The geographical resolution includes the province and whether the episode was in an urban or rural setting.

Environmental characteristics affect how individuals perceive an environment, and the emotions created by environmental characteristics originate from subjective feelings. Despite cities being crucial human living spaces, few studies have used geospatial technology to determine the relationship between urban environments and emotions. Therefore, this study explored the effects of urban environmental characteristics on emotions by surveying 50 sampling areas in Taipei City. Deep learning was performed with the DeepLab V3 architecture in combination with the LaDeco tool to identify environmental characteristics in over 200,000 Google Street View (GSV) images. These characteristics were divided into five major types, namely, vegetationscapes, waterscapes, streetscapes, landformscapes, and archiscapes, then further classified into 53 categories. To identify the emotions related to urban environments, 2090 participants who were asked to view GSV videos and report their emotions. Subsequent multiple regression analyses revealed that in vegetationscapes and waterscapes, grass and fountains induced positive emotions, whereas trees reduced negative emotions. Meanwhile, dense, old, and disorganized buildings, such as hovels, reduced positive emotions. The results of this study may serve as a reference to help designers create an urban environment that fosters positive emotions.

Urban green space disparities persist amid rapid urbanization, widening the supply-demand gap between parks and developed area. Population density is a critical determinant in estimating park visitors, defining suitable park locations, and allocating facilities for park accessibility. Conventionally, population density data were used as a foundational basis for urban green space planning decisions, often derived from sources like the US Census Bureau, primarily reflecting "nighttime residential" distribution. However, this approach fails to capture the dynamic urban life where daily routines and mobility significantly shape park usage. This study bridges this gap by exploring the relationship between daytime and nighttime mobility patterns and their influence on park visitations across diverse park types during weekdays, using Austin, TX as study area. Methodologically, we employ a fixed effects regression analysis integrating longitudinal data from SafeGraph for park visitation and LandScan USA for daytime-to-nighttime population density ratios, within 1 km buffers around each park. Control variables encompass socio-economic factors at the block group scale, park attributes, and weather conditions. Findings suggest that neighborhood and pocket parks demonstrate positive associations with daytime population density, while district and metropolitan parks exhibit stronger ties with nighttime population density. Further, median age, unemployment rate, and higher education attainment exhibit positive correlations with park visitation, especially during daytime. Park amenities, especially playgrounds and water features, significantly contribute to increased visitation across all park types. The findings offer valuable guidance for policymakers and urban planners, informing the reimagining of park distribution strategies, optimizing facilities, and fostering inclusive park spaces accessibility.

This study uses building footprint data from the Ordnance Survey MasterMap to analyze construction and demolition activities across England from 2017 to 2023. By comparing the Topographic Object Identifiers (TOIDs) of each building between years, we identified newly constructed and demolished buildings, quantified changes, and used the bivariate color maps to visualize spatial patterns across England and within its five major cities. The study highlights the effectiveness of building footprint data in providing insights into urban changes and development trajectories, which are vital for urban planners and policymakers to understand dynamic urban processes and inform decision-making toward sustainable urban development.

This study explores the integration of text-to-image generative AI, particularly Stable Diffusion, in conjunction with ControlNet and LoRA models in conceptual landscape design. Traditional methods in landscape design are often time-consuming and limited by the designer's individual creativity, also often lacking efficiency in the exploration of diverse design solutions. By leveraging AI tools, we demonstrate a workflow that efficiently generates detailed and visually coherent landscape designs, including natural parks, city plazas, and courtyard gardens. Through both qualitative and quantitative evaluations, our results indicate that fine-tuned models produce superior designs compared to non-fine-tuned models, maintaining spatial consistency, control over scale, and relevant landscape elements. This research advances the efficiency of conceptual design processes and underscores the potential of AI in enhancing creativity and innovation in landscape architecture.

Systems of street networks form a backbone for many aspects of human life and, once laid down, urban streets represent a nearly immutable influence on future urban form and concomitant travel, energy, and social outcomes. Moreover, as humanity is currently passing through its peak urbanization rate, decisions about how to design such networks at the local scale are being made faster than ever before. In this work, we quantify local street connectivity and provide a global, high-resolution time series of our Street Network Disconnectedness Index (SNDi) as an open data set. We derive a stylized version of the actual geographic road network from the 2023 vintage of OpenStreetMap by simplifying complex intersections, divided roads, and offset intersections. Using this functional representation of the network corrects systematic biases in derived properties of the network. We couple this simplified network with a newly available time series of urbanization in order to compute SNDi and provide a dynamic analysis to the year 2019 and a cross-sectional analysis for 2023. We release our data as the raw network of edges and nodes and as aggregates to a 1 km grid, to countries, and to five subnational administrative levels. We also provide interactive visualizations at sprawlmap.org. Overall, our findings present a picture of rapidly worsening street-network connectivity in many regions of the world.