Data Exploration: Neighborhood Crime and Local Business
Neighborhood-level aggregated and anonymized data can offer new insights for understanding factors that impact business success.
While felonies such as homicide make the news, misdemeanor and petty crimes, like larceny, can also be devastating in the long run for cities and their neighborhoods. When customers’ cars are broken into while they shop or when they are harassed or threatened as they leave a restaurant, they will likely opt to dine and shop elsewhere. As a result, stores begin to close, jobs disappear and tax revenue declines.
The question for every city, then, should be where are lower-level crimes happening and how do they affect local businesses and job opportunities?
Recently, the Mastercard Center for Inclusive Growth leveraged Mastercard’s proprietary aggregated and anonymized transaction insights in tandem with public data on commercial robberies to better understand if there are any relationships between such criminal activity and business closings. The effort – which looked at Baltimore, Maryland and Oakland, California – is an initial foray into understanding ways Mastercard’s insights and analytical expertise can provide community organizations, residents and urban planners with insights to better promote inclusive growth in American cities.
In Baltimore, preliminary analysis based on Mastercard insights and public data indicated that in the Fells Point and Locus Point neighborhoods, a spike in commercial robberies in 2013 was followed by an increase in bar and nightclub closings (a proxy for low-wage retail). The results were similar in Oakland in 2014.
The graphs below visualize the analysis and describe the relative magnitude between location and crime incident trends. The values on the vertical axis are scaled and indexed, but preserve the overall relationship between the trend lines.
The Data’s Potential
The analysis is preliminary and the associations are simply an exercise to explore the potential of this kind of neighborhood-level insight. Urban Institute senior research associate Yasemin Irvin-Erickson sees the potential in insights like these, especially when used to identify and understand trends over time. Doing so could allow local leaders, researchers and businesses to see where their communities stand in relation to others.
“Currently, we have a limited number of ways to explore business segment or area trends for any geography smaller than the county level. Anything we have on business sales revenue is largely based on industry specific estimates and not on a community business segment basis,” says Irvin-Erickson.
Aggregated and anonymized transaction insights, in tandem with public data, could be leveraged to explore topics ranging from the impact of policies on retail business to factors contributing to business success, etc.
For researchers studying crime and its impact on local business, aggregated and anonymized transaction insights fill important information gaps. Similar inputs that provide an understanding of trends over time in local communities are scarce, Irvin-Erickson says. “This data set can be very useful in exploring how crime impacts the profitability and growth of local business areas and vice versa.”
The Center will continue to explore these and other possibilities with our data-driven insights and responsibly share results with the research, business and policy communities. We are working with researchers from leading think tanks and universities to help inform their research on equitable economic development related to city revitalization efforts, transit and tourism.
Responsibly sharing insights like these is one part of our commitment to using data for social good. As we wrote in The Call to Action on Data Philanthropy, if governments, nonprofits, academia and the private sector can find a way to work together to fully unlock the power of data, we’ll be better positioned to create sustainable, lasting solutions to society’s greatest challenges.
More on the methodology behind this analysis
We performed location clustering and estimated community segments based on Mastercard’s aggregated and anonymized transaction information to compute a retail density by industry. We’ve derived insights regarding consumer segment spending with both small businesses and large retailers alike. This has helped us to better understand commercial activity in a range of economic environments. We then overlaid the clusters with publicly available data on commercial robbery locations to get a block-by-block understanding of the relationship between lower-level crime and communities.
Featured Photo Credit: Getty