How easily can you get from your house to a restaurant or park? In what neighborhoods is crime concentrated? From what cities are businesses fleeing? Where are communities mixed, with people of diverse incomes, races and ages, and with different types of housing and land use?
When properly analyzed, big data can cut through false assumptions and answer such questions with a level of specificity not possible otherwise.
Researchers with the Metropolitan Futures Initiative at UCI’s School of Social Ecology do exactly that, processing huge amounts of data related to crime, demographic and socioeconomic trends in Southern California. Established in 2011, MFI produces quarterly reports and hosts symposiums, forums and webinars on its findings.
“These insights are valuable for urban planners and others seeking to make cities more livable and equitable,” says MFI director John Hipp, professor of criminology, law & society as well as urban planning & public policy and sociology.
Generating those insights demands detailed analysis and close examination. Take urban accessibility: It’s one thing to count the number of restaurants, gas stations, parks and stores in a certain ZIP code and then generalize that a higher concentration indicates greater accessibility for residents.
It’s a little more accurate to measure the linear distance, as a bird flies, between a house and all nearby amenities. But even that doesn’t account for how the street network actually allows a person to get to those places.
However, it’s another thing entirely to map the distance from a particular home to all such destinations in a 1-mile radius on the street network, and then do the same thing for every house in a neighborhood to come up with an average – and then do the same thing for all neighborhoods in the region.
The MFI team, using cutting-edge data analysis techniques, took the third approach in a recent project on urban accessibility in Southern California.
“These calculations are very computationally intensive. We were basically mapping the distance between each of Southern California’s 5 million homes and all amenities within a mile,” Hipp says. “We were calculating those distances hundreds of millions of times and then plotting that geographically on top of a map. It’s a sharper picture than with other methods.”
Because MFI researchers need mountains of numerical information to make these calculations, they utilize public tax assessor data, commercial databases of business locations, and crime databases assembled by UCI’s Irvine Laboratory for the Study of Space & Crime, among other sources.
For another study, the MFI team sought to learn how common it is for businesses to relocate and how far they typically move. Using commercial data for 1997 to 2014, members tracked the site of every business in Southern California and determined, year to year, if they had remained in the same location, shut down or moved somewhere else.
Focusing on those that had relocated, the researchers assessed the distances of moves, the characteristics of the old and new neighborhoods, and whether these patterns differed from industry to industry.
“We were able to cut through the generalizations about business relocations, and we found that businesses almost always relocate within the same city or to adjacent cities,” Hipp says. “This insight is really valuable for economic development agencies and city officials trying to boost growth.”
For a different MFI study, on the relationship between race and income in Southern California, Hipp and his team analyzed demographic and income data in a novel way to pinpoint the average neighborhood composition for individuals in particular racial or ethnic groups and in particular income categories. This allowed them to view neighborhoods from the vantage points of people across a wide spectrum.
For example, the researchers determined the percentage of college graduates in the neighborhood of each non-Hispanic white person in the lowest income bracket. By then averaging all those percentages, they were able to show the educational level of the typical neighborhood of someone fitting that demographic profile.
MFI members applied the same method to crime, income, employment status, population density and housing type. They performed the same calculations for all income categories and racial and ethnic groups in Southern California – millions of calculations. The end result revealed where neighborhoods were racially mixed, where educated people clustered and where violent crime was concentrated.
“These data give policymakers, social justice advocates and city planners a rich tool to really see what neighborhoods look like and how policies can be effectively leveraged to improve lives,” Hipp says.