Hannah M. Young, MRP and Daniel A. Rodríguez, Ph.D. Using Census Data to Examine the Pedestrian Friendliness of the Built Environment An Empirical Examination in Portland Department of City and Regional Planning University of North Carolina, Chapel Hill Hannah M. Young, MRP and Daniel A. Rodríguez, Ph.D. Abstract Comparison (cont) Results Results (cont) Comparison of three methods of calculating a BEI Very high agreement between PCA and cluster analysis (kappa = 0.88; % agreement = 0.93)*. Lower agreement between naïve method and other methods. Comparison of three methods with PEF PEF correlates best with PCA. However, the low kappa statistic and comparison scores indicate that the BEI is not a replacement for the PEF. * Significant at the p = 0.01 level. We summarize the development of a built environment index (BEI) based mostly on Census data, which we hypothesize measures attributes of the built environment believed to support non-motorized transportation activity. We examine the reliability of using three common ways of developing these indices and conclude that established statistical methods are reliable. Then, we compare the BEI with Portland’s Pedestrian Environment Factor (PEF), arguably the best-documented and most frequently cited indices of the built environment with respect to pedestrian friendliness. This comparison suggests that though there may be similar uses of the indices, they capture different concepts. We conclude that the BEI is a powerful tool to gain a broad overview of the built environment and can be supplemented with more fine-grained analyses where needed and available. Index Components BEI PEF Source: TransMilenio S.A. Disadvantages of BEI Analysis is removed from the actual site, raising questions of validity Lacks neighborhood scale factors Practical and theoretical advantages of BEI Easy to implement Readily available data sources (Census & planning agencies) Straightforward analytic approach Less time, lower costs Provides a broad scale overview of the built environment Uniform objective measure allows comparison between different geographic regions Introduction Transportation and land use planners have a growing interest in understanding the attributes of the environments that support walking activity. Uses of Built Environment Indices: In travel models, to enhance model accuracy and policy usefulness; For a community, to provide a better spatial understanding of the environment; For policy-makers, to target priority funding for cost-effective infrastructure investments. However, existing indices are labor- and data-intensive, and the reliability of the diverse methods used in calculating them has not been established. We provide a new alternative here. Conclusions The BEI facilitates the analysis of urban form by enabling planners to create a local index using GIS and Census data. Given its broad scale and ease of implementation, the BEI provides an overview of the built environment. PCA or cluster analysis are recommended in the development of environmental indices. BEI does not measure the same construct as PEF and is not a substitute for the index. When a more fine-grained analysis of the environment is required, other methods can be used to supplement the BEI. Methods Attribute 2 Attribute n Characteristics of the urban environment measured using GIS Index (BEI) Analytical tools Principal components analysis Naïve Ranking Raw scores High Low Cluster analysis Comparison of Two Indices Built Environment Index (BEI) Pedestrian Environment Factor (PEF) 4 factors in index Method of Analysis: Field survey of key intersections by staff. Simplified Delphi approach to achieve consensus. 11 factors in index GIS analysis followed by classification of data using one of three possible methods (principal components analysis, cluster analysis, or naïve ranking). Challenges Census data sources were supplemented with data from regional planning agency Historical data unavailable for some factors (bus routes, light rail station, and sidewalks) Validity of index needs to be determined (predictive validity and generalizability) Support for this project was provided in part by a grant from The Robert Wood Johnson Foundation®, Princeton, New Jersey. Special thanks to Robert Schneider (Toole Design) for initial feedback on the BEI, Michael Greenwald, Ph.D. for PEF shapefiles, Marc Schlossberg, Ph.D. and Mark Bosworth (Metro) for data.