{"id":12452,"date":"2020-04-21T16:57:12","date_gmt":"2020-04-21T20:57:12","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/replica-informing-urban-planning-with-synthetic-simulations\/"},"modified":"2020-04-21T16:58:29","modified_gmt":"2020-04-21T20:58:29","slug":"replica-informing-urban-planning-with-synthetic-simulations","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/replica-informing-urban-planning-with-synthetic-simulations\/","title":{"rendered":"Replica: Informing Urban Planning with Synthetic Simulations"},"content":{"rendered":"

Replica, an urban planning tool spin-off from Google\u2019s Sidewalk Labs, is leveraging data analytics to understand \u201chow, when, where, and why people move around a region.\u201d Having recently closed Series A funding of $11 million, Replica is working with cities Portland, Kansas City, Chicago, and Sacramento and is moving towards expansion.<\/p>\n

How does it work?<\/strong><\/p>\n

Replica is built into two parts: its database and its data analysis tool (called Explorer). The database consists of all trips and activities of people in a region; the data is gathered anonymously via cell phone location data and demographics are recreated into a statistically representative \u201csynthetic\u201d population. Location data is translated into a \u201ctravel behavior model\u201d which is then attached to a (synthetic) person. The data analysis tool Explorer is then used to simulate and generate a week of one person\u2019s trips and activities (and is updated every 3 months to stay current). The Explorer tool then further allows users to generate maps, charts, simulations with various filters and data queries to understand how the population of a given area moves over time.<\/p>\n

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Generating Synthetic Data<\/strong><\/p>\n

Replica was born out of a need for better, more inclusive urban planning and development. Urban planners rely on people\u2019s travel data to understand the link between transportation and land use; without relevant data, planners won\u2019t be able to make good decisions on how land in used in a city and what forms of (often public) transportation is needed. This results in inefficient bus routes, overused sidewalks, inadequate bike lanes, or transportation options in low-use areas, among others.<\/p>\n

Replica instead offers population data that is more representative, current and relevant. This allows city planners to add or subtract bus routes, bike lanes, and sidewalks where they are actually needed<\/em>. The most important use of AI here is in the machine learning algorithms that takes inputs from cell phone data and census data to create a \u201csynthetic\u201d population. Why the need for this? Because cities are often restricted by privacy concerns from capturing more precise data on its population. By using an algorithm to generate a representative<\/em> population, no one individual\u2019s data is traceable thus addressing privacy concerns. This allows planners to have access to much more nuanced data, since it is representative<\/em> and not actual<\/em>, data they were not allowed to access before. [It\u2019s worth noting that city planners did have ways of stripping personal identifiers from population data before, but the process was often time intensive and expensive. Here \u201csynthetic\u201d data offers a huge value add.]<\/p>\n

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Building Travel Behavior Models<\/strong><\/p>\n

The other value creation component is in the travel behavior models built for each \u201csynthetic\u201d person in the population. Individual cell phone location data is used to train<\/em> machine learning algorithms to generate representative <\/em>travel behavior models (think of it as \u201cmock\u201d cell phone location data for the \u201csynthetic\u201d person). This again addresses privacy concerns while allowing planners to gather data at the individual (albeit disguised) level. These individual <\/em>travel models outputted by the ML algorithm are necessary to then build the city-wide map of a population<\/em> travel model. In the end, planners end up with a detailed map of how the population of an area moves about.<\/p>\n

Running Travel Simulations <\/strong><\/p>\n

Finally, the greatest benefit Replica offers is the ability to run detailed simulations and generate findings in a matter of minutes. Typically, planning work can take months if not years; to understand if a new bike lane is effective, the bike lane must be installed, used, and analyzed over a period of time. But Replica offers the opportunity to run a simulation of the addition of a bike lane and<\/em> understand its impact on not just the whole population but various groups. The ability to filter data and model specific queries allows a level of nimbleness that city planners have never known before. Being able to \u201cplay around\u201d with ideas and alternatives online will inevitably lead to a better use of city funds and more inclusive city development.<\/p>\n

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Despite everything Replica has done to address privacy concerns, agencies are still hesitant to adopt the new technology. Critics claim that despite the efforts to strip personal data, re-identification <\/em>of individuals is still possible. Cities are also asking for more detailed information about the provenance of \u201csynthetic\u201d data and more clarity on what seems to be the \u201cblack box\u201d of algorithms. Still, Replica offers a lot of promise for the future of urban planning and development.<\/p>\n

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