Real Estate Development and Pedestrian Activity

Quantifying the impact of real estate development on pedestrian activity in Kendall Square.

This research began with the broad question asking how we could model the impact of development on pedestrian activity in Kendall Square (Cambridge, MA) – a neighborhood that is home to the nation’s largest cluster of biotech companies and MIT. Moving from this question, our team gathered data on planned and under construction real estate development, made assumptions based on the number residential units and square footage of office space, and narrowed our focus on morning and evening rush hours. Often times real estate impact analyses include items like parking demand, shadow, and traffic analyses – leaving positive impacts of development such as increased pedestrian activity out of the discussion. All work was completed by a cross disciplinary MIT-Harvard team consisting of: Firas Suqi, Marco Miotti, Carmelo Ignaccolo, and Adham Kalila.

New Real Estate Developments

Future Kendall Square development in black.

Observer Points of Pedestrian Footfall + Network

Points with observed pedestrian counts and sidewalk network highlighted.

Original ESRI Business Analyst Distribution

Originally the data at origin points wasn't properly distributing throughout MIT's campus.

Redistributing Employee Counts

Employees were redistributed proportionally by building ground floor area.

Prior to running any correlations to calibrate each origin weight, we had to clean ESRI Business Analyst data, which had assigned all MIT employees to a single building. By segmenting MIT buildings and redistributing employee counts, we were able to more accurately predict pedestrian flows from origin points.

Calibrating Origin Weights

Revised origin weights based on calibration with observed rush-hour values, simulating 42 origin and destination pairs.

Calibrating Destination Weights

Results after calibrating β-Beta, the destination attractiveness weight. We ended up using a value of .0012.

Absolute Changes in Pedestrian Counts

Total predicted pedestrian changes in Kendall Square due to redevelopment.

Relative Changes in Pedestrian Counts

Relative change in pedestrian counts due to new development.

Conclusion

By modeling different behaviors using origin and destination pairs, we were able to offer predictions to the change in pedestrian footfall for each new development in the Kendall Square area. It’s important to note that we used estimated resident and employee counts that were calculated based on existing data residents and employee counts per square-foot of GFA in Kendall Square.

This analysis uses the betweenness algorithm to model pedestrian counts at given observer points. The betweenness tool can be found in the UNA Toolbox for Rhinoceros 3d, which has been developed by the City Form Lab at Harvard. A full tutorial I developed on the betweenness function can be found here.