Savannah Blight, Civic Data Science for Equitable Development

Friendly Cities has a new collaboration with the City of Savannah to strategize decisions to address vacant and blighted properties in the community. Neighborhoods experiencing blight suffer from safety concerns and lower housing values, leading to suboptimal living conditions for already marginalized residents. Savannah’s Housing & Neighborhood Services Department also estimated that every blighted property bleeds $1300 of public funds annually. These funds are used to cover the unrecoverable costs of overgrown grass, litter, illegal dumping, securing open structures, and demolishing properties. In 2019, local voters approved the use of 10 million dollars to acquire and redevelop of at least 1,000 abandoned, blighted residential properties over the next 10 years to effect meaningful change in neighborhoods long neglected or exploited by profit-driven investors. However, strategizing which properties to intervene first and what kinds of interventions can be best applied for equitable development remains a challenge. Our research revolves around understanding the spatio-temporal impacts of variables that contribute to neighborhood growth and decline at the parcel levels and derive if-then rules to classify and match properties with appropriate interventions. We also attempt to mitigate the “algorithmic bias” (as defined as applying rules differently to minority populations) in code enforcement. Additionally, we will also collect social capital data to benchmark measures of neighborhood vitality. This project is funded by the Georgia Smart Communities Challenge 2020 jointly with Brian Brainerd of the City of Savannah. Graduate Research Assistant Xiaofan Liang is leading the analysis. The links attach media reports from Savannah government and Georgia Tech.

Threads Across the Urban Fabric: Personal Relationships in the City

Urban neighborhoods can exhibit a lack of inter-neighborhood connectivity, leaving the city with disparate social networks. Quantitative research on these layouts, and on segregation, measures the configuration of areal (Census) units—evidencing stark boundaries of socio-economic differences. Adding personal relationships to the GIS data tells a different story: that some ties cut across segregated boundaries, evidencing crucial relationships.

Through a partnership with Big Brothers Big Sisters (BBBS), we model geolocated mentorships as personal day-to-day interaction across urban neighborhoods. We use a novel dataset of over 12,000 BBBS “Bigs” and “Littles”, in seven U.S. American cities (Philadelphia, Tampa, Kansas City, Dallas, Houston, Denver and Los Angeles). Using statistical tests and GIS, we find that “matches” live closer than simulated pairs. Despite being geographically close, matches create socio-economic bridges. Little neighborhoods have a higher percentage of African Americans and significantly fewer bachelor’s degrees, more unemployment, and lower median household income than Big neighborhoods. Neither matches nor simulated pairs align with the network of urban commuting patterns. We also find that Bigs hail from inner-city areas, suggesting civic engagement and ‘giving back’ of (often young) adults who choose to live in the city.

Results suggest that social-capital-facilitating organizations create a spillover effect by facilitating bridges between neighborhoods and disconnected social networks, benefiting the larger city. This work can help inform research in geography, city science and sociology and advance the quantitative study of economic social systems within the urban environment.

Poster Winner, Urban Geography Specialty Group Poster Competition: Understanding the 21st Century City,Association of American Geographers Annual Meeting, 2019.

See related paper at: Andris, C., Liu, X., Mitchell, J., O’Dwyer, J., & Van Cleve, J. (2019). Threads across the urban fabric: Youth mentorship relationships as neighborhood bridges. Journal of Urban Affairs, 1-16. Special Issue Edited by Z Neal and B Derudder.

Challenges for Social Flows

The social flow is a linear geographic feature that evidences an individual’s decision to connect places through travel, telecommunications and/or declaring personal relationships. These flows differ from traditional spatial networks (roads, etc.) because they are often non-planar, and provide evidence of personal intentionality to interact with the built environment and/or to perpetuate relationships with others.

We use tons of social flow data in today’s research, and so, we create new typologies, address new problems, and redefine social distance as the manifestation of social flows. We describe challenges for leveraging these data with commercial GISystems in terms of representing, visualizing, manipulating, statistically analyzing and ascribing meaning to social flows.

Measuring Geographic Pull Power: A Case Study of College Athletics

Student athletes increase the diversity of schools. Which universities draw students from distant and diverse locales? We put together “pull power statistics” for 160,000 student-athletes from more than 1,600 university team rosters at 128 schools over various years. Stats include mean distance traveled, count of unique hometowns, percentage of international student-athletes, and a new distance decay “apex” method to rank schools by their pull power. Western U.S. (like U. Idaho and U. Arizona) and private schools (like Harvard) lead the ranks. Schools like Rutgers, Catholic University, UMBC and U. Illinois-Chicago have a lot of local student athletes.

Using Yelp to Find Romance in the City: A Case of Restaurants in Four Cities

Findings: There’s a certifiable difference in urban hot spots and demand depending on couples’ relationship stage. Key factors for selecting a romantic date spot include ambiance (cozy & classy atmosphere), high prices, and downtown locations. Couples with families (children) tend to prefer less expensive restaurants, with great service and (probably) accessible parking. Special occasion locations are concentrated in downtown areas, especially for Las Vegas and Pittsburgh, but are more distributed for sprawling cities like Phoenix and Charlotte.

Method: Natural Language Processing of Yelp reviews to scan for correlations of certain keywords, cross-referenced with locations of restaurants.

Good For: If you are opening a bar or restaurant and would like to influence the potential clientele, or if you would like to glean insight into the romantic life cycle.