How can my solution help support the needs of Indianapolis?
The purpose of this analysis was to help inform potential mobility solutions providers as they identify who the users of the solutions will be and where they will be deployed. In addition, we worked with a steering committee of research and planning professionals from the City of Indianapolis, IndyGo, IUPUI, the Indianapolis Metropolitan Planning Organization, and others to provide their input as we built out this model to ensure it wasn’t just statistically-accurate, but contextually-relevant as well.
Understanding cities built environment
First, to understand the different built environment types across Indianapolis, we compiled and analyzed data about transit coverage and frequency, non-motorized infrastructure, land use diversity, street network connectivity, density of jobs and people, and the business presence/mix in each neighborhood. In total, we compiled 20 indicators related to the built environment in each neighborhood. Using this data and a machine learning clustering algorithm, we then identified 7 distinct neighborhood ‘segments’ within Indianapolis.
These neighborhood types illuminate the vast differences in the built environment across Indianapolis and the need for different mobility solutions that are dependent of the context of the place in which they are operating.
Focusing on people
This over-indexing of technology access and social mobility organization in combination could be seen as indicating ability and willingness to adopt new, non-traditional mobility options.
To better understand the differences in who lives in these different neighborhood types, at the direction of the city of Indianapolis, we looked at population characteristics for Indianapolis residents living within each type. We compared income, household types (e.g., families with children v. nonfamily households), age distribution, vehicle ownership, veteran status, disability status, commute patterns, and more for each type. The result was a summary snapshot of the population within each Segment (example for Segment 1 below).
This gave insight to the people who live in different neighborhood types. For example, we learned that Segment 1 (above) has the highest share of population with disabilities, at over 16%. Segments 4 and 5 had the highest share of veteran population (8%). Segment 2 has the highest share of households with computer access and individuals who carpool to work, at 88% and 11%, respectively. This over-indexing of technology access and social mobility organization in combination could be seen as indicating ability and willingness to adopt new, non-traditional mobility options. We found this population crosstab useful, because developing mobility solutions isn’t just about understanding the person, but also understanding the environment in which that person resides.
Your mobility experience is tied to where you live
Finally, we sought to better understand how individuals experience mobility in Indianapolis depending on which neighborhood type they live in. This is important because location and proximity to services like transit can directly impact quality of life and the ability to access to important aspects of life like jobs, education and health services. To accomplish this, we worked together with Indianapolis to create a travel simulation approach using randomly generated home locations and real-world destinations for major points-of-interest (e.g., grocery stores, childcare facilities, and hospitals). We used a routing engine to help understand average trip length/distance if you attempted to accomplish these simulated trips by car, transit, or walking. The approach is summarized below.
The data revealed that car travel was often the fastest option for accomplishing job, childcare, education, hospital, grocery, convenience, and parks trips in terms of time spent traveling. However, there was significant nuance in these findings. For example, Segments 1 and 3 (those including downtown and many neighborhoods immediately surrounding) had much more competitive trip times by transit and walking when compared to driving than other Segments. A significant number of trips in those Segments could be accomplished in 30-minutes or less by transit and walking.
Those other trips (e.g., grocery, education, etc.) often fell in an awkward middle-ground range of 1-3 miles. This often meant they were too far to comfortably walk and too short to accomplish efficiently via transit…
Identifying the Missing Middle
Interestingly, the data indicated that job-related trips were often much longer on average compared to other trip types. Those other trips (e.g., grocery, education, etc.) often fell in an awkward middle-ground range of 1-3 miles. This often meant they were too far to comfortably walk and too short to accomplish efficiently via transit (especially where transit was less frequent/non-existent). This indicated a potential for ‘missing-middle’ mobility options that help individuals without a car get around easier or those with a car decide to leave it at home for more trips.
This analysis represents just one input of many into the Explore Phase research process. We also held several community working sessions to listen directly to Indianapolis community members and have received significant input via the online forum on this website as well. If you want to learn more about the overall process and findings from the Explore Phase, please check out the Challenge Brief Supplemental Resources. Identifying opportunity areas is only the first part of the process, now we want to hear from you on ideas to create a seamless mobility experience for Indianapolis residents, workers, and visitors as they move around Marion County.
If you want to get involved, add your proposal in the City:One Indianapolis Challenge Propose Phase.