MIT researchers are working on a potentially massive public-access database of metrics like retail density and delivery frequency.

Picture this chain of events: people living in small homes aren't able to store as much food as people in larger homes, so they might tend to purchase groceries more often. That sort of shopping behavior then leads them to prefer buying their food from close-by neighborhood stores, which might also be smaller and unable to stock as much product as huge supermarkets. And that, in turn, means delivery trucks will need to come more often. 

This scenario is a classic example of the complex urban systems that the MIT Megacities Logistics Lab is trying to study. According to Logistics Lab director Edgar Blanco, the question of supply chains has historically only focused on highly industrialized cities like New York, London, or Tokyo, even though the fastest-paced urbanization happening today is unfolding in cities in emerging markets like China or Brazil. That’s why the Lab is developing Km2, a public-access database that maps logistics systems (including retail space, parking areas, deliveries, and traffic disruptions) in rapidly developing cities around the world.


The representative square kilometers of each city studied
Logistics factors studied in each city 

The Lab launched its pilot data collection program this summer, sending a team of student researchers from MIT and partner universities to collect data in Mexico City, Rio de Janeiro, Beijing, Santiago, Sao Paulo, Kuala Lumpur and Madrid, with three to four researchers in each city. The Lab’s data collection method takes a page from urban planning field research techniques, using one square kilometer of a representative neighborhood in each city as the basic metric. Over the course of three weeks, the researchers recorded the retail density in the whole square kilometer, as well as data on traffic and deliveries on one specific street. The researchers also developed and used a web platform to upload and plot the data on a map as it’s being collected. 


Retail density in Beijing, mapped and color-coded by type (Click to enlarge)

Retail density in San Paulo, mapped and color-coded by type (Click to enlarge)

The hope is that such data can help planners identify cities with comparable densities, businesses, and transportation links. “Maybe what works in one area of Mumbai could also work in Mexico City,” as Blanco told MIT News


Delivery patterns in Kuala Lumpur (left) and Mexico City (right). (Click to enlarge)

       


Traffic disruption data for Madrid includes vehicle type, duration,level of impact on other vehicles. (Click to enlarge)

Detailed information on delivery patterns, traffic disruptions, and parking areas can help planners think about how best to allocate space. For example, data in Km2 can help planners evaluate the impact of putting in bike lanes. In a phone interview, Blanco says putting in bike lanes often means less space for trucks, so planners would need to consider consequences like whether to allow certain deliveries within bike lanes or whether affected roads might need more parking elsewhere. The data can also help businesses understand real conditions in each city and work to streamline logistics and lower costs. 

Applications of the data will grow as the database grows. Currently, data collection is still controlled, limited to trained researchers at MIT. But moving forward, the goal is incorporating more cities (Lagos, Karachi, and Mumbai are next on the list) by opening up data collection to the public. Creating a sturdy mobile application for open-access data collection is proving difficult, however. One of the biggest questions the Lab has been debating is how to deal with "overlap" — i.e., what happens when multiple people upload information about the same street? Although there’s no exact solution yet, Blanco says the Lab has generally decided to encourage overlapping data in the system, betting that there's a way to extract patterns from the redundancy. 

All images courtesy of the MIT Megacities Logistics Lab and its Km2 tool.

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