Catherine Ross delivering a research seminar in 2019

Fall 2020 Research Seminar Series

Fall 2020 Research Seminar Series

Join us for the School of City & Regional Planning 2020 Fall Research Seminar Series! This year our lecture series will take place virtually via BlueJeans Meetings. We have a great line up this semester and hope to see you online!

Paige Clayton, Georgia Tech

"Don’t go it alone: Firm survival, funding, and the influence of local venture mentoring organizations"

Friday, September 4, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

Venture mentoring (VM) organizations represent a potentially efficient model for supporting start-ups when compared to capital and fixed-cost intensive entrepreneurial support organizations (ESOs) such as accelerators and incubators.

Yet, VM organizations have been left out of the organizational sponsorship literature, leaving a gap. Based on a comparison of multiple ESOs’ dominant learning mechanisms, this paper argues that VM organizations impact firm outcomes through the bridging and education mechanisms of their mentoring services.

This paper finds VM participation has a positive impact on the amount of funding start-ups receive when compared to two matched control groups. Furthermore, there are boundaries to this support in terms of niche effects and survival. Implications are that entrepreneurs should strategically use multiple ESOs to match their needs.

Elora Lee Raymond, Georgia Tech

"Preventing Evictions after Natural Disasters: the Role of Landlord-Tenant Law"

Friday, September 18, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

Extreme flooding events will become more common as climate change accelerates. Researchers have asked for a more detailed understanding of how and why residents relocate following natural disasters, arguing that these decisions and processes are complex. In this research we examine how displacement proceeds through an eviction process following extreme flooding events, and suggest policy remedies.

Forced moves through an evictions process are associated with a series of negative outcomes for households and families: worsening neighborhood quality followed by a series of relocations, long-term losses in household wealth, and increases in stress-related illnesses. If a household must relocate in the year following a natural disaster, an eviction process is arguably one of the worst processes through which relocation can happen. 

We examine the rise in eviction rates after natural disasters and extreme flooding in five states: the Florida Panhandle (April, 2014 flooding), Mobile, Alabama (April, 2014 flooding), South Carolina (Hurricane Joaquin, 2015), St. Paul, Minnesota (2014 derechos) and New Jersey (Hurricane Sandy, 2012). We stratify our case selection by levels of statutory protections for tenants in state law, examining evictions following natural disasters in states with strong tenant protections (New Jersey, Minnesota), states with mixed frameworks (Alabama, South Carolina), and states with pro-business tenant frameworks (Florida). In the three cases with weaker tenant protections in their legal framework, we find strong, significant increases in eviction rates in disaster-affected neighborhoods relative to adjacent areas with no disaster declaration. By contrast, in the two states that are protective of tenants, following flooding events, there is no statistically significant rise in evictions relative to adjacent areas. We close with policy recommendations for states and local governments to provide tenant assistance and eviction prevention measures in the year following a disaster.

Ariane Middel, Arizona State University

"Human Thermal Exposure in Cities - Novel Sensing and Modeling to Build Heat-Resilience"

Friday, October 2, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

Dr. Middel is a recent NSF CAREER recipient and will discuss the approach and methodology for her research as proposed in the CAREER project. She will present her published work based on a novel tool she has designed for measuring senseable heat called "MaRTy". Her future project aims to build a model that simulates what MaRTy can measure. The first half of the talk will be about measurements with MaRTy and the second half will be focused on the research design of the future study (with some preliminary data to help along the way).

Omar Isaac Asensio, Georgia Tech

"Automatically discovering behavioral failures in electric vehicle mobility: Policy and regional analysis using transformer deep learning"

Friday, October 16, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine-learning based on 12,720 electric vehicle (EV) charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the rapidly expanding population of EV drivers in 651 core-based statistical areas in the United States.

We deploy supervised machine-learning algorithms to automatically classify unstructured text reviews generated by EV users. Extracting behavioral insights at a population scale has been challenging given that streaming data can be costly to hand classify. Using computational approaches, we reduce processing times for research evaluation from weeks of human processing to just minutes of computation. Contrary to theoretical predictions, we find that stations at private charging locations do not outperform public charging locations provided by the government. Overall, nearly half of drivers who use mobility applications have faced negative experiences at EV charging stations in the early growth years of public charging infrastructure, a problem that needs to be fixed as the market for electrified and sustainable transportation expands. 

I will discuss a series of papers on the use of transformer deep learning models, bidirectional encoder representations from transformers (BERT) and Transformer-XL (XLNet), to automatically discover topics of attention in a nationally representative sample of EV user reviews. This capability can potentially boost management and infrastructure service issues in the EV charging market, which is expected to grow to $27.6 billion USD by 2027.

Clio Andris, Georgia Tech

"Planning for Interpersonal Relationships: Data, Analytics, and a Romantic Case Study"

Friday, October 30, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

Urban planning philosophies support planning for altruism, social wellbeing and the public good--and more recently, for happy neighborhoods, support of mental health and cultivation of social capital. This work advocates for a shift in planning towards interpersonal relationship and social network-oriented planning. This shift is important in today’s increasingly virtual, lonely world. It is also especially timely amid the COVID-19 pandemic and planning strategies to re-introduce the public to shared spaces in its aftermath.

GIS data, measurement methods, and analytical approaches can help planners create and valuate spaces to support interpersonal relationships and social life. Part one will inventory new data and creative approaches for capturing social life and relationships in different geolocated areal distinctions—from Craigslist’s Missed Connections to Bowling Alone. It will also describe how these data can be integrated into fundamental planning tools and quantitative methods. Part two will briefly present a real-world survey of 124 romantic couples in a college town, and a mixed-method GIS/qualitative analysis. The results show how couples spend quality time together, how the area supports romantic relationships and how can it be improved. Finally, the talk will pose an open discussion with the group about the caretaking role of planners, the role of spaces in introducing us to one another, opportunities for stakeholders to embrace new social-oriented data, and how planners can balance the mixture of virtual and physical space in future quests to support social life and interpersonal relationships.

Julian Agyeman, Tufts University

"Just Sustainabilites in Urban Policy, Planning and Practice"

Friday, November 13, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

In his talk, Julian will outline the concept of just sustainabilities as a response to the ‘equity deficit’ of much sustainability thinking and practice. He will explore his contention that who can belong in our cities will ultimately determine what our cities can become. He will illustrate his ideas with examples from urban planning and design, urban agriculture and food justice, and the concept of sharing cities.

Prentiss Dantzler, Georgia State University

"Housing Assistance and Neighborhood Conditions: Lessons Learned from the U.S. and Canada"

Friday, November 20, 2020 | 12:30 p.m. - 1:45 p.m. EDT | BlueJeans

Housing costs are causing many people to wonder if they will be able to afford to stay in their homes. As a result, many countries have tried to address housing affordability by providing financial assistance to those who cannot pay their monthly housing expenses on their own. Because the government serves as the landlord, many have questioned whether or not the government should supply housing for those who cannot afford it given the neoliberal turn of state intervention.

This neoliberal turn is argued to be seen locally in the case of housing vouchers and social mixing initiatives encouraged by agencies undertaking public housing restructuring across the U.S., since the 1970s, and Canada, in the 1990s. Within North America, the two largest housing authorities are located in New York, NY and Toronto, ON, respectively. And while there is a lot of literature on housing assistance in New York and other large U.S. cities, there is little research on the Greater Toronto Area (GTA). Given the increasingly unaffordable nature of the GTA, this study focuses on the relationships between housing assistance programs and neighborhood conditions. 


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