Even since the days of Athens and Sparta, cities have staked their claim to greatness. Kafka had Prague. Hemingway had Paris. The iconic New York poet Walt Whitman made his case to qualify a great city back in 1865. “A great city,” he wrote, “is that which has the greatest men and women.” In 2017 that quote might be effectively edited to include the greatest men and women and the greatest data.
Data is helping government agencies and their administrators fulfill the promise as the hubs of opportunity and improved quality of life. Part of that quality of life has moved beyond the simple “food, water and shelter,” concept of government. Cities are moving toward connectivity and collective intelligence. The concept of a “smart city” is now being driven at the speed of “smart data,” as city governments, and their business partners, boil a staggering scale of information down into actionable data sets that make planning and decision making more efficient and effective. Just as big companies have evolved with big data, big cities are doing the same. Data is making smart cities possible.
Using data to improve the quality of life in global cities has gone beyond the idea stage. It is already in practice. At Mastercard we are used for more than 160 million transactions every hour all over the world. We are constantly looking for ways to take the byproduct of those transactions, which is secure and aggregated data, to help governments and their citizens. That data shows how consumer behavior happens in real time. By combining data about how consumers shop, travel, and interact with services, governments can learn more about how to be more efficient and productive.
Perhaps most importantly, we look for ways in which data can help city governments plan for the future. The IQ of a “smart” city, from our perspective, will be measured on how it plans for the future.
Rohit Chauhan
Executive Vice President, Advanced Analytics
City Possible: Using Data to Create New Opportunities
Cities are no doubt the epicenters of social and economic activity of any country. That is why the notion of smart cities, not just cities, is so important.
This urban migration is an opportunity and a challenge for city governments and their private sector partners. As cities around the US and around the globe continue to face the dynamics of growing populations, transportation investments and changing economics, there is one common element that stands to make even the “smart” city smarter. It’s data.
Just as large companies have embraced data strategies and advanced analytics, cities of all sizes are following the lead. As cities continue to embrace technology to improve quality of life, embracing data will be the fuel for continuing sustainable growth.
For some major cities the problems inherently presented by growing populations are already starting to show. Many cities, most prominently around London, have creatively addressed problems caused by the shadow of growth in large cities. However, solutions stemming from data and payments innovations are already in progress in many cases. Evidence is mounting that members of government agencies as well as the companies who navigate them can help cities grow and improve the quality of life. Here are four ways to make it happen:
Data-Driven Growth Opportunities
Complex data sets for urban planning have quickly moved beyond subjective exercises. Data and advanced data analytics have achieved some true success stories for large scale projects and small scale adjustments. Cities need to move toward these advanced solutions. For example, the zoning board no longer has to guess at commercial real estate valuations or compare them to other cities. Advanced data sets and predictive algorithms including transaction data have made real estate pricing a data-driven discipline. Innovative data usage can help cities identify and measure their most attractive properties with an eye toward generating tax revenue, better environments for businesses and improved services for citizens. Cities can use innovative data approaches to benchmark vs. other population centers, maximize revenue, and guide city planning to identify future growth opportunities.
An example of smart data in action can be seen in Mastercard Retail Location Insights. It is a suite of data products that utilizes Mastercard anonymized and aggregated databases to measure the revenue performance to validate, evaluate and benchmark retail locations. Mastercard uses the location of the merchant and the date, time and amount of the transaction to create a “time series of data,” to build a retail scoring algorithm. Among the data: Growth, stability and ticket size. A composite score is then calculated from 0 to 1,000. A user can review each component score independently or focus on the weighted composite score.
Retail Location Insights, and databases like it, can be effective in presenting the valuation of a large block of urban real estate. A good example of the promise of geo-location insights like this can be found within the King’s Cross development in Central London. King’s Cross is a city within a city. A one-time economically deserted area, suffering for connections to London proper or any other urban center from which to attract visitors or workers. Begun in 1996 and finished in 2012 it is now a shared use area with office, retail, multifamily housing, hotel, parks and open space, parking and transport facilities.
But perhaps most importantly, King’s Cross planning and development was based on data. It was a completely planned project with no decisions left up to chance. When begun it was estimated by the UK government and Urban Land Institute to serve 63 million people via a new transit stop by 2020. Starting with that number, and the need to upgrade its transportation hub, the various agencies planned all elements of King’s Cross based on the amount of people that would move in and out of the area via better transportation, work in King’s Cross and live there. In 2006 a sophisticated government data initiative analyzed crime patterns in the area and suggested law enforcement changes1. Travel patterns for pedestrians and vehicles are constantly measured to produce more data including integration into existing data sources (for example pedestrian footfall counts, car park usage, automatic traffic counts).
Once a pariah to Londoners, King’s Cross has become a shining example of smart planning and transportation emphasis. In fact, it hosted the Aspen Institute’s CityLab conference in 2015.
Transportation
As noted in the King’s Cross example, urban planning suggestions can hinge on effectively getting people in and out on a daily basis. Cities can use analytics to predict what they will spend while within city limits. They can use new advancement in payments systems to make their passage more efficient.
The use of contactless payments systems, digital payments and cross-platform payments (open-loop ticketing through globally accepted bank cards) have been deployed successfully in many cities including London, Singapore and Sydney. Data from these systems has enabled some cities to manage increasing commuter load. The government of Singapore recently extended its policy of offering discounted early morning train rides to 18 transit stations. Commuters who exit those stations before 7:45 ride free and those who exit between 7:45 to 8:00 am will get 50 cents off their train fare. The scheme, started by the Land Transport Authority in 2013, has seen a sustained 7 per cent of commuters shifting travel from peak hours, according to a government report.
The next evolution and the innovative edge of transportation and data is in personalization. Personalization can increase revenue both for governments and business partners. Example: The New York region’s Metro-North Railroad has a smartphone app that allows users to buy tickets and alerts users about service delays depending on their location and travel patterns. In July, Governor Andrew Cuomo announced that mobile transport ticketing was coming to New York. MTA eTix, a customized version of Masabi’s JustRide platform, was launched across the city’s Long Island Rail Road and Metro North lines, meaning that the 174 million annual commuters no longer had to wait in line to buy a ticket. Masterpass is now embedded in the mobile ticketing app, allowing customers to purchase their tickets anywhere, anytime. Within the first 3 months, prior to full advertising and marketing campaign, 200,000 unique users had downloaded the app, with 8 percent of all tickets now sold through the solution.
For transit personalization the “what if” is not far off. By mid-2017, expect that truly “smart” transit systems will be able to alert a passenger that their train is ten minutes late and when it has been re-scheduled for (based on travel data patterns). It will also be able to serve a 10 percent discount on that passenger’s favorite nearby coffee shop (based on spending patterns).
Tourism and Travel
Total government spending on tourism is expected to top $413 billion this year, according to the World Travel and Tourism Council. It’s a competitive market to say the least. Advanced data analytics has made it possible for cities to be smarter about tourism, tourists and business travelers.
Example: Most governments have a good handle on total tourist and travel revenue. But transaction data has added a new level that can serve them a more detailed look that can predict whether a city is attracting business or leisure travelers. It can predict where both classes of trade will come from and what visitors will spend on when they get there. Osaka, Japan is the fastest growing city in terms of visitors and expenditures. That’s a good claim to make to travelers and useful for overall marketing. Drill down into the transaction data and it is evident that Osaka’s growth comes from leisure travel (88 percent of visitors). It has the highest percentage (43.4 percent) of any city for shopping. The Japanese National Tourist Organization has emphasized those shopping areas in its marketing and web presence.3 Spending on accomodations (23.5 percent) is far less than other cities however, such as Paris at 44.8 percent. That data might represent an opportunity for a different marketing approach.
When applied at a product level, the data-driven approach can also show the opportunities that lie underneath the data. For example, Mastercard Destination Insights use the same data mentioned in the Retail Location Insights suite to generate a different report. Destination Insights define top origination countries for travelers who are spending in the destination market, seasonality of travel spend, and spend behavior of tourists in identified origination countries.
Data For Good
All the aforementioned programs deploy data in the relatively strict interest of generating commerce. Data can also be used to engage underserved, low-income or underprivileged populations. Cities and private partners across the globe have taken up the cause of data philanthropy (Data For Good) to share data and best practices in this area. “Open Data” policies can help cities focus services on underserved populations, identify new business opportunities and compare those results.
Chicago and New York City are two cities working hard to fulfill the promise of open data. New York passed a groundbreaking local law in 2012 that mandates every city agency must publish all of its public digital data by 2018. So far, the city has uploaded more than 1,500 datasets to its Open Data Portal, which has received more than five million hits since it was launched in July 2015. Chicago issued a similar mandate in 2012; through initiatives like the Smart Chicago Collaborative, which develops and tests tools built on open data, it has become a national hub for civic technology.
Mastercard and other companies support open-data initiatives with funding, expertise and actual data. In Mastercard’s case it considers granting access to limited datasets — in a way that fully protects consumer privacy — to assist with research and inclusive growth initiatives. Through its grant of data, Harvard researchers are now exploring new frontiers of inclusive growth — from the impact of tourism on emerging economies to the role of knowledge exchange between countries. It is also partnering with the White House’s Data Driven Justice Initiative, which is an effort to use data to help advance criminal justice reform. The Center was able to perform an analysis to demonstrate the impact crime has on merchant locations and local job opportunities in Baltimore.
Cities are the powerhouse of global economic growth and must function efficiently as they grow. And in order to function, commerce and citizens need to flow freely for everyone. Solving for location- based opportunities, transportation issues, tourism engagement and open data sharing can be a foundation for future strategy.
About the Author
Rohit Chauhan leads the Advanced Analytics organization for Mastercard Advisors globally. Advanced Analytics supports the Consulting Services, Information Services and Managed Services groups by centralizing and optimizing the superior analytical, modeling, data sourcing, product development and associated delivery capabilities of the Advisors organization.
Rohit's team of analysts, modelers, technical engineers and product managers have developed analytical platforms and toolsets which are used to translate raw transaction data into actionable behavioral insights through predictive models and segmentation methodologies for clients worldwide.
