Research Projects
Filtered by: Smart Cities and Connected Communities
CHS: Small: Collaborative Research: Making Information Deserts Visible: Computational Models, Disparities in Civic Technology Use, and Urban Decision Making
Principal Investigator(s):
Research Areas: Information Justice, Human Rights, and Technology Ethics > Smart Cities and Connected Communities
Enhancing understanding of how civic technologies are used and how information inequalities manifest in a city by examining Boston's 311 system for reporting non-emergency issues to the city government and then using computational and qualitative approaches to identify, categorize, and understand the kinds of information disparities that are becoming institutionalized.
Principal Investigator(s):
Research Areas: Information Justice, Human Rights, and Technology Ethics > Smart Cities and Connected Communities
Enhancing understanding of how civic technologies are used and how information inequalities manifest in a city by examining Boston's 311 system for reporting non-emergency issues to the city government and then using computational and qualitative approaches to identify, categorize, and understand the kinds of information disparities that are becoming institutionalized.
Long Term Multi-Instruments Land Surface Reflectance Record and Applications
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Smart Cities and Connected Communities
The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products.
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Smart Cities and Connected Communities
The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products.
NSF Convergence Accelerator Track J: MidAtlantic Food Resiliency Network – Securing the Future of Food through a Multi-Mindset Approach
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The Mid-Atlantic Food Resiliency Network (MFRN) aims to improve food security in the Mid-Atlantic, starting in Prince George’s County. Collaborating across multiple disciplines, the MFRN will develop tools and systems to reduce hunger, waste, and food deserts. Initiatives include understanding food behaviors, repurposing food waste, and training future food security leaders.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The Mid-Atlantic Food Resiliency Network (MFRN) aims to improve food security in the Mid-Atlantic, starting in Prince George’s County. Collaborating across multiple disciplines, the MFRN will develop tools and systems to reduce hunger, waste, and food deserts. Initiatives include understanding food behaviors, repurposing food waste, and training future food security leaders.
SCC-IRG Track 1: Inclusive Public Transit Toolkit to Assess Quality of Service Across Socioeconomic Status in Baltimore City
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.