New Report Generating a Buzz
New study with Urban Institute and Institute of Transportation Studies looks at links between transportation, housing and economic opportunity for voucher recipients.More »
Partnership for Action Learning in Sustainability (PALS)
PALS is a new, campus-wide initiative designed to provide high-quality, low-cost assistance to local governments while creating valuable real-world problem solving experience for UMD students.More »
Purple Line Corridor Coalition
The NCSG has formed a coalition to stimulate sustainable and equitable economic development throughout the Purple Line corridor without displacing affordable housing or small businesses.More »
Makeover Montgomery 2
Along with the Montgomery County Planning Department and the UMD Planning Program, the NCSG hosted Makeover Montgomery 2 on May 8-10, 2014.More »
The NCSG will host Glen Weisbrod for a brown bag webinar on October 12. Mr. Weisbrod will provide insight on how to best navigate debates over transportation projects and their impacts on economic development.
The University of Maryland Environmental Finance Center, with support from the Waterfront Partnership of Baltimore, Inc. and the Abell Foundation, recently completed a study on a prospective beverage container deposit program in Maryland. The study looked at potential impacts on recycling rates, employment, beverage sales, and greenhouse gas emissions.
The Maryland Port Administration (MPA)/Maryland Department of Transportation (MDOT) announced today that it is providing funding to enhance the Mid-Atlantic Dray Truck Replacement Program, which is funded by a grant from the U.S. Environmental Protection Agency (EPA) and administered by the Mid-Atlantic Regional Air Management Association (MARAMA) and the University of Maryland Environmental Finance Center.
It is an expensive and time consuming task to develop a new model. Besides, a single model often cannot provide answers required for integrated decision making. Therefore, coupling existing models is often used for model integration. The paper provides an overview of possible model integration approaches, briefly explains the models of a particular application and focuses on the integration methods applied in this research. While the initial attempt was to integrate all models as tightly as possible, the authors developed a much more agile integration approach. Python wrappers were developed to loosely couple land-use, transportation and emission models developed in different environments. ArcGIS Model Builder was used to provide a graphical user interface and to present the models’ workflow. The suggested approach is efficient when the models are developed in different programming languages, their source codes are not available or the licen!
sing restrictions make other coupling approaches infeasible.
The “Sustainable neighborhoods” has become widely proposed objective of urban planners, scholars, and local government agencies. However, after decades of discussion, there is still no consensus on the definition of sustainable neighborhoods (Sawicki and Flynn, 1996; Dluhy and Swartz 2006; Song and Knaap,2007; Galster 2010). To gain new information on this issue, this paper develops a quantitative method for classifying neighborhood types. It starts by measuring a set of more than 100 neighborhood sustainable indicators. The initial set of indicators includes education, housing, neighborhood quality and social capital, neighborhood environment and health, employment and transportation. Data are gathered from various sources, including the National Center for Smart Growth (NCSG) data inventory, U.S. Census, Bureau of Economic Analysis (BEA), Environmental Protection Agency (EPA), many government agencies and private vendors. GIS mapping is used to visualize and identify variations in neighborhood attributes at the most detailed level (e.g census tracts). Factor analysis is then used to reduce the number of indicators to a small set of dimensions that capture essential differences in neighborhood types in terms of social, economic, and environmental dimensions. These factors loadings are used as inputs to a cluster analysis to identify unique neighborhood types. Finally, different types of neighborhoods are visualized using a GIS tool for further evaluation.
The proposed quantitative analysis will help illustrate variations in neighborhood types and their spatial patterns in the Baltimore metropolitan region. This framework offers new insights on what is a sustainable neighborhood.
New firms tend to arise in locations with a large number of jobs nearby.MORE »