The closer the better? Examining support for a large urban redevelopment project in Atlanta

• Published in: Journal of urban affairs Vol. 40; no. 2; pp. 246 - 260
• Main Authors: Chiang Hsieh, Lin-Han, Noonan, Douglas
• Format: Journal Article
• Language: English
• Published: Newark Routledge 17.02.2018; Taylor & Francis Ltd
• Subjects: Affordable housing; General public; Housing; Location analysis; Mass transit; Parks; Parks & recreation areas; Pedestrian safety; Pedestrians; Public opinion; Public transportation; Railroads; Redevelopment; Spatial data; Tax increment financing; Taxation; Trails; Transit; Transportation corridors; Urban areas; Urban development; Atlanta Georgia; United States > US
• ISSN: 0735-2166; 1467-9906
• DOI: 10.1080/07352166.2017.1319232

The Atlanta BeltLine (BeltLine) is a large urban redevelopment project that is transforming 22 miles of historical railroad corridors into parks, trails, pedestrian-friendly transit, and affordable housing in the center of Atlanta, Georgia. This study examines how proximity to the BeltLine and other factors relate to public support for it, with data from a general public survey conducted in the summer of 2009. The result shows that support significantly declines as distance to the BeltLine increases. However, after controlling for expected use of the BeltLine parks and transit, the role of distance fades. Further, the results show that being a parent within the city limits is associated with the support for the BeltLine, which implies that the concern over tax increment financing (TIF) affecting future school quality hampers the support of the project. The findings point to individual tastes and family circumstances as driving support for the redevelopment project, rather than strictly property-specific attributes (as the homevoter hypothesis would predict). Another contribution of this study is to address the technical problem of missing precise spatial location values. Several imputation techniques are used to demonstrate the risks and remedies to missing spatial data.