Category: Finished

The American Mafia is known as a violent network of criminals engaged in drug trafficking, violence, and illegal activities. We analyzed a spatial social network (SSN) of 680 Mafia members where connections represent `known associates’ found through a 1960s federal crime investigation by the U.S. Federal Bureau of Narcotics. Members are geolocated to a known home address, and span across 15 major U.S. cities, concentrated in New York City. We ask four different research questions related to family, power, and coordination strategies.

Q1: Do families tend to live in clusters or distributed locations?
Q2: Do criminal associates tend to live near one another and are nearby members likelier to be associates?
Q3: Are high-degree members found near their family centers, high-income areas, or near strategic physical features?
Q4: Does the network exhibit efficiency in geographic space?

The methods we used include:

See: Andris C, DellaPosta D, Freelin B N, Zhu X, Hinger B and Chen H (2021) To Racketeer Among Neighbors: Spatial Features of Criminal Collaboration in the American Mafia. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2021.1884869. [PDF]

In this research, we explored which (types of) places have an independent food culture and which resemble McCities: foodscapes where the food offerings in the landscape can be found just as easily in one place as in many other (often distant) places. We used a dataset of nearly 800,000 independent and chain restaurants for the contiguous United States and defined a chain restaurant using multiple methods. We performed a descriptive analysis of chainness prevalence at the urban area and metropolitan area level. We also identified socioeconomic and infrastructural factors that correlate with high or low “chainness” (a value indicating the likelihood of finding the same venue elsewhere) by combining a random forest model and linear regression. We then examined chainness variation across census tracts with similar sociodemographic values. Using common GIS operations, we evaluated the likelihood of finding chain restaurants of nearby water bodies, coastlines, and highway ramps.

We found that car-dependent small towns, low walkability, a high percentage of African American residents or Trump voters, college towns, and close distance to highways were associated with high chainness. These places tended to be prevalent in the Midwestern and the Southeastern U.S. Independent restaurants were associated with high pedestrian walkability, high population density, Asian, Hispanic and majority-White neighborhoods, tourist areas, areas with urban professionals, and retirement communities. They were also found in coastal cities and waterfronts. These findings, paired with the contribution of a method that quantifies ‘chainness’, opens new pathways for studying landscapes through the lens of commercialism and the service economy. See Environment and Planning B article. See Draft Manuscript. Play with the map!

We investigated what romantic relationships need from the built environment and how these relate to urbanism principles. Our research questions were threefold: (a) Where do couples spend their quality time and what are the characteristics of these places? (b) Does distance between these places and the couples’ residences affect usage? (c) How does State College, PA support romantic ties and how can it be improved? We conducted an online survey of 124 couples in the State College, PA area, and analyzed these data using frequency statistics, OLS regression, and qualitative coding. We also created maps of the relationships’ approximate locations and measured how their locations affected their activities.

We found that restaurants and the outdoors/recreation were most important for sustaining relationships. Couples frequently emphasized activities involving nature and sports/leisure time and rarely mentioned dinner parties, nightclubs, country clubs, or religious facilities. Couples reported that the town satisfied their relationship needs, especially in terms of safety (i.e. well-lit areas and pedestrian-friendly environments). We think this research can be useful during the COVID-19 pandemic, as cities try to support social ties amidst the closure of third places and social activities. Post COVID-19, it may also help inform which facilities to prioritize in terms of opening decisions post-pandemic.

Andris C and Lee S (2021) Romantic Relationships and the Built Environment: A Case Study of a US College Town. Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 1-22. (Online first). [PDF] and [Supplementary Information]

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 developing a human-in-the-loop machine learning model to help the city identify vacant, abandoned, and disinvested (VAD) properties. The human-in-the-loop approach entails closely collaborating with the housing planners to identify areas for interventions, feature selections, active learning (diversity and uncertainty sampling techniques), and interpretable machine learning outcomes. We also compare our model outcomes with human-found VAD properties, check robustness with 2021 data, and conduct an empirical analysis to determine which neighborhoods may be subject to data bias. 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. See Xiaofan’s presentation at ACSP conference here Savannah_ACSP_presentation.