Taking the LEED? Analyzing spatial variations in market penetration rates of eco-labeled properties

This paper investigates the impact of policies to promote the adoption of LEED-certified buildings across CBSA in the United States. Drawing upon a unique database that combines data from a large number of sources and using a number of regression procedures, the determinants of the proportion LEED-certified space for more than 170 CBSA in the US is modeled. LEED-certified space still accounts for a relatively small proportion of commercial stock in all markets. The average proportion is less than 1%. There is no conclusive evidence of a positive impact of policy intervention on the levels of LEED-certified space. However, after accounting for bias introduced by non-random assignment of policies, we find preliminary evidence of a positive impact of city-level green building incentives. There is a significant positive association between market size and indicators of economic vitality on proportions of LEED-certified space

Structure of 311 service requests as a signature of urban location

© 2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions

A data-driven methodology for equitable value-capture financing of public transit operations and maintenance

Despite the importance of rail infrastructure to the effective and efficient functioning of dense urban areas and their commercial business districts, funding for operations and maintenance of transit systems is a common challenge for cities. Operational funds are derived from a range of sources, including fare and toll revenues, taxes, and fees. In cities with aging infrastructure, traditional funding mechanisms are falling short of actual need, even as many cities experience record ridership levels. Therefore, new funding streams are necessary to safely, efficiently, and equitably operate and maintain an aging rail infrastructure in the face of growing demand. This paper presents a socio-spatial model of rail transit ridership demand to develop a computational method for value-capture funding mechanisms that link existing commercial properties and transit infrastructure operations. Using a diverse range of large-scale data for New York City (NYC) and the surrounding region, our methodology provides a data-driven approach to address fundamental issues of horizontal and vertical equity in value-capture fees, including (1) the magnitude of the special assessment, (2) the property types to include, and (3) the boundaries of the special assessment district. We find that a marginal special assessment of $0.50 to$1.00 per square foot on commercial properties, proportionate to the lost wages and output associated with system delays, within 1/4-mile of a subway station in NYC\u27s core commercial district could yield between $332 and$664 million annually to support the Metropolitan Transit Authority\u27s operating budget. This is equivalent to the revenue generated by an average, system-wide per ride fare increase of $0.22, and significantly less than the estimated implicit transit subsidy for these buildings of$4.58 per square foot per year

Structure of 311 service requests as a signature of urban location

© 2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions