SUDS tours Google: better cities through public-private data partnerships

Last Wednesday, SUDS visited Google’s Pittsburgh office, on the eve of its 10-year anniversary celebrations. We got to see their famous hammock room, Kennywood-themed hallways, and micro kitchens stocked according to behavioral science. But as jealous as we were of the nap pods, the best part of the visit was a talk by CMU Computer Science PhD alumna Sarah Loos on Google’s Better Cities project.

Cities face huge challenges in monitoring and managing their transportation infrastructure. In the US alone, $124 billion is wasted each year in traffic jams. The Better Cities team at Google has been piloting methodologies that match up cities’ transport data with aggregate, anonymized snapshots of historical traffic statistics in order to yield insight and solutions to nasty traffic problems.

For example, Google partnered with the City of Amsterdam to validate sensor readings on the A10 highway, which can tell when cars are slowing down (and thus, if a traffic jam might occur). The city can then analyze the data and change speed limits on its digital signs and take other measures to mitigate the jam’s impact. The physical sensors are really accurate, but also really expensive to install and maintain. Google found that by combining only some of the sensor data with representative models of aggregate data, it could detect the same traffic patterns with a high level of accuracy. By reducing the number of sensors needed in each stretch of road, Amsterdam’s government can save between 50,000-100,000 Euros per kilometer per year.


As cities are using more individual-level data from more sources, including public-private partnerships, Loos stressed the importance of keeping information anonymous and private. Her work is focusing on differential privacy algorithms, which add enough noise to the data to mask the influence of any one individual’s contribution to the set.

These pilot projects are an exciting example of how simple, but smart, data collaboration can improve city management. And Loos and her Google team are looking for new cities to partner with—we hope Pittsburgh will be one of them!




Energy for all in Nigeria

by Madeleine Gleave

Nigeria’s energy poverty crisis

Like many developing countries, Nigeria is facing an energy poverty crisis. The International Energy Agency (IEA) estimates that nearly 1.3 billion people globally lack access to electricity, and about half of these people live in Africa. Energy poverty has crippling side effects; no electricity also means no access to safer and healthier electric cooking and heating, powered health centers and refrigerated medicines, light to study at night, or electricity to run a business. In Nigeria, the average level of access is only 53%.

Despite being rich in natural resources required to produce energy, such as oil and gas, Nigeria’s energy infrastructure is lacking. Many people live near power plants and transmission lines, but aren’t yet connected to the grid. Others are in very remote areas where off-grid solutions, such as solar panels, may help them generate their own electricity long before a power line reaches them.

To explore this problem, I created a StoryMap in ArcGIS that shows the highly disparate levels of electricity access, energy demand, and infrastructure across Nigeria.

Check out the full StoryMap here:

screenshot storymap
[Click to view StoryMap]

Identifying the best electricity access solution

As Nigeria and its development partners look for energy access expansion solutions, how can they choose the best intervention for the best region? Where should they target grid connections, grid expansion, or off-grid solutions?

Selecting the best approach from this set of solutions depends on the context of the specific geographic area, and is influenced by existing levels of access, proximity to existing lines and power plants, level of urban development, demographic characteristics, and income levels. I developed a composite energy access index, mapped using a kernel density heat map, to evaluate an area’s suitability for each type of access intervention. The higher the index score (the red areas on the heat map seen here), the more suited the area is to grid supply. The lower the index score (pale yellow), the more suitable for off-grid power. Mid-range scores (the orange and dark yellow areas) are good candidates for grid expansion.


Madeleine Gleave is a Public Policy and Management student in the Heinz College at CMU. She is particularly passionate about using data to improve planning, management, and evaluation in international development policy and programs.