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1. Analyzing the Impact of Decarbonizing Residential Heating on the Electric Distribution Grid

   https://doi.org/10.1145/3607114.3607119  

   Wamburu, John; Bashir, Noman; Irwin, David; Shenoy, Prashant (June 2023, ACM SIGEnergy Energy Informatics Review)

   Heating buildings using fossil fuels such as natural gas, propane and oil makes up a significant proportion of the aggregate carbon emissions every year. Because of this, there is a strong interest in decarbonizing residential heating systems using new technologies such as electric heat pumps. In this paper, we conduct a data-driven optimization study to analyze the potential of replacing gas heating with electric heat pumps to reduce CO 2 emission in a city-wide distribution grid. We conduct an in-depth analysis of gas consumption in the city and the resulting carbon emissions. We then present a flexible multi-objective optimization (MOO) framework that optimizes carbon emission reduction while also maximizing other aspects of the energy transition such as carbon-efficiency, and minimizing energy inefficiency in buildings. Our results show that replacing gas with electric heat pumps has the potential to cut carbon emissions by up to 81%. We also show that optimizing for other aspects such as carbon-efficiency and energy inefficiency introduces tradeoffs with carbon emission reduction that must be considered during transition. Finally, we present a detailed analysis of the implication of proposed transition strategies on the household energy consumption and utility bills, electric grid upgrades, and decarbonization policies. We compute the additional energy demand from electric heat pumps at the household as well as the transformer level and discuss how our results can inform decarbonization policies at city scale. 

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2. Predicting building types using OpenStreetMap

   https://doi.org/10.1038/s41598-022-24263-w  

   Atwal, Kuldip Singh; Anderson, Taylor; Pfoser, Dieter; Züfle, Andreas (December 2022, Scientific Reports)

   Abstract Having accurate building information is paramount for a plethora of applications, including humanitarian efforts, city planning, scientific studies, and navigation systems. While volunteered geographic information from sources such as OpenStreetMap (OSM) has good building geometry coverage, descriptive attributes such as the type of a building are sparse. To fill this gap, this study proposes a supervised learning-based approach to provide meaningful, semantic information for OSM data without manual intervention. We present a basic demonstration of our approach that classifies buildings into either residential or non-residential types for three study areas: Fairfax County in Virginia (VA), Mecklenburg County in North Carolina (NC), and the City of Boulder in Colorado (CO). The model leverages (i) available OSM tags capturing non-spatial attributes, (ii) geometric and topological properties of the building footprints including adjacent types of roads, proximity to parking lots, and building size. The model is trained and tested using ground truth data available for the three study areas. The results show that our approach achieves high accuracy in predicting building types for the selected areas. Additionally, a trained model is transferable with high accuracy to other regions where ground truth data is unavailable. The OSM and data science community are invited to build upon our approach to further enrich the volunteered geographic information in an automated manner. 

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3. Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data

   Falcon, William; Schulzrinne, Henning (April 2018, Sixth International Conference on Learning Representations)

   null (Ed.)

   In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy. 

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4. Replication Data for: Mapping super-gentrification in large US cities, 1990 to 2020

   https://doi.org/10.7910/dvn/bsaf99  

   Lauermann, John; Wang, Ziqi; Feldman, Anna; Wu, Yuanhao; Smash, Nathan; Lauermann, John (January 2025, Harvard Dataverse)

   This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already gentrified. Building a national longitudinal tract database of gentrification intensity indicators, we analyze where this process has occurred across the 45 most populous metropolitan regions. We develop a method for quantifying metro-specific gentrification indices, then compare the class and racial demographics of super-gentrified tracts against other kinds of affluent places. We also interpret these national patterns with a case study of gentrification’s broader geographies in greater New York City. While super-gentrification is most commonly researched in global mega-cities, we found a wider geography including substantial suburban and smaller city patterns. We also found that super-gentrified neighborhoods are less racially diverse than other gentrified neighborhoods, and are more demographically similar to historically affluent (but not recently gentrified) neighborhoods. The study contributes a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already gentrified. 

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5. Mapping super-gentrification in large US cities, 1990–2020

   https://doi.org/10.1080/02723638.2025.2528418  

   Lauermann, John; Alexander, Zoe; Wang, Ziqi (July 2025, Urban Geography)

   This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already been gentrified. Building a national longitudinal tract database of gentrification intensity indicators, we analyze where this process has occurred across the 45 most populous metropolitan regions. We develop a method for quantifying metro-specific gentrification indices, then compare the class and racial demographics of super-gentrified tracts against other kinds of affluent places. We also interpret these national patterns with a case study of gentrification’s broader geographies in the New York City metropolitan region. While super-gentrification is most commonly researched in global mega-cities, we found a wider geography, including substantial suburban and smaller city patterns. We also found that supergentrified neighborhoods are less racially diverse than other gentrified neighborhoods, and are more demographically similar to historically affluent (but not recently gentrified) neighborhoods. The study contributes to a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already been gentrified. 

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