Using Big and Open Data to Analyze Transit-Oriented Development

Problem, research strategy, and findings: In this study, we investigate how to exploit big and open data (BOD) to quantitatively examine the relationships between transit-oriented development (TOD) attributes and TOD outcomes. Here, TOD attributes are measurable or perceivable attributes that TOD proponents cherish, and TOD outcomes are the targeted outcomes, such as increased ridership, associated at least partially with TOD attributes. Based on BOD from Shenzhen (China), we create indicators to measure both TOD attributes and outcomes. We explore the associations of TOD attributes, including centrality of a TOD site, travel time to the central business district, density, destination, diversity, and design, with TOD outcomes. We identify the TOD attribute that best predicts TOD outcomes such as metro ridership, frequent riders, people co-located in a station area, and ratios derived from these outcomes. We find that special neighborhoods, specific metro lines, and age of the district significantly influence TOD outcomes. Our study has a few limitations: a) the BOD we use do not directly measure TOD attributes, so proxies must be used; and b) the BOD we use contain little information about “why,” “who,” and “how,” such as why people rode transit, who they were, and how they perceived/appreciated various TOD attributes.

Takeway for practice: BOD-derived variables allow planners to revalidate existing planning guidelines and principles concerning TOD and adapt them to local contexts. BOD can also be used to formulate new metrics to evaluate different TOD plans or projects in ways not achievable with traditional data alone. In short, BOD can and should be used to refine TOD analytics and design and to implement corresponding theories in pursuit of TOD.