To understand how development policies and exogenous factors like fluctuations in oil prices are likely to impact urban areas, planners often develop "scenarios" that can be programmed into simulation models to assess how these types of factors could play out into the future. At NCSG, we're working on a project like this that we call PRESTO. As part of the project, we're doing some extensive modeling of transportation flows, housing choices, and environmental impacts. We've got a strong transportation model in the MSTM, and we're working to build a fully integrated land use/transportation model by incorporating SILO. These kinds of models are beloved in the urban planning world because they can help inform economic development policy and get a handle on the environmental impacts of urban growth. Those are important things, but truth be told, they are not all that interesting to me.
My background in sociology is still what drives my primary interst in urban studies, and I still consider myself an applied/urban sociologist more than a planner. For that reason, I'm much more interested in the ways that land use models like SILO and UrbanSim, which are really agent-based location choice models, can help address issues like income segregation, racial segregation, and social inequality. So as part of our scenarios work--and to really dive headfirst in urban modeling--I thought it would be a fun experiment to see if I can get a very basic implementation of UrbanSim up and running while we continue to work on SILO.
The interaction between spatial structure and social mechanisms is the core of my doctoral research. The Moving to Opportunity program was one of the most important social experiments ever administered and it is a rich data source for studying so-called neighborhood effects (with some caveats). I'm using MTO data to build statistical models that identify which community structures are most likely to facilitate social mobility, particularly for low-income families of color.
The first paper I wrote using using these data was compiled into a report for the Urban Institute and looks at the locational attainment of MTO households with and without access to cars. It is available here
I also modified some of the code from Fletcher Foti's activemaps to create a visualization tool that shows how MTO participants moved into neighborhoods of varying quality, and how access to 'communities of opportunity' changed over time. Unfortunately, the data are protected by a rigorous confidentiality agreement, and even after the precise household locations were obfuscated I was not granted approval to make it available
It is no secret that growing up in Baltimore is categorically different than growing up in Bethesda. Neighborhood mechanisms operate differently in communities and stratified access to quality education systems, safe environments, well-paying jobs, and any other criterion of privilege all have spatial implications that impact human development and shape the prospects of social mobility for families living in different neighborhoods. Opportunity mapping is a technique developed by the Kirwan Institute as an attempt to visualize that phenomenon
NCSG was contracted to conduct an opportunity mapping analysis for the Baltimore region as part of a new regional plan, and a large part of my doctoral research focuses on the ways that techniques like opportunity mapping can be used infuse research on neighborhood effects into urban policy
During the project I built a prototype 'opportunity map builder' application that allowed our team to recombine and visualize different indicators on the fly during meetings with regional stakeholders. I've been developing a new web-based platform with Facet Decision Systems that helps anyone with internet access build an opportunity index using indicators of their choosing. Although my dissertation research focuses on neighborhood effects and identifying the 'most important' spatial mechanisms that promote social mobility, there are a host of reasons why anyone should have the ability to explore the data and build an index of their choosing. The project is still under heavy development, but the tool is accessible here
In January of 2015, I was awarded the Innovative Student Contribution to Planning Tools Award by the Open Planning Tools Group for my work on OppMap
The Purple Line is a proposed LRT that (if built) will connect to the DC metro system, running east to west in suburban Maryland from New Carrollton to Bethesda. The line is anticipated to stimulate economic growth in the state, and provide additional TOD benefits like increased transit ridership, but it will also cut directly through one of the state's most diverse and culturally rich communities, the International Corridor.
Obviously, this has varying implications. Although the line will provide greater regional accessibility and get people from their homes to their jobs quicker and easier, it will also increase land prices throughout the corridor which could lead to gentrification and displacement.
The NCSG launched the PLCC as a research initiative and collaboration among local stakeholders to help promote equitable economic development and mitigate displacement along the purple line corridor
Well-designed hardware/software platforms have the potential to improve and reshape cities dramatically, making them smarter, more equitable, and more efficient in any number of ways. As a millenial who grew up with the internet and the tech boom, this is a natural passion of mine. This project, funded by the National Cooperative Highway Research Program, takes a look at popular sketch planning tools like UrbanFootprint, CommunityViz, and EnvisionTomorrow+, and the different ways they are being applied by MPOs and other planning agencies to address regional sustainability