Mobility models, which stimulates movement patterns of individuals or groups, play pivotal roles in assisting urban planning, transportation, and public health. As these mobility models are progressively used to create new government policies and allocate resources in cities, it is crucial to consider the impact of amplifying or perpetuating existing biases or unfairness. Currently, the existing research is aimed at generating synthetic traces from real historical data and protecting the privacy of traces, but rarely on the fairness dimension of these mobility traces. This research specifically investigates the fairness dimension of mobility models. The fairness will be determined by analyzing the Common Part of Commuters (CPC) for different sensitive groups. CPC is the metric that is used in measuring how accurate the synthetic traces are compared to the real data. The different sensitive groups will be created by grouping the extremities of CDC’s Social Vulnerability Index (SVI), which considers a range of factors including socioeconomic status, household composition, race/ethnicity/language, and housing/transportation. The research will result in a package extension that will allow all users to analyze for fairness in Mobility Models between the wealthy and less privileged regions. We anticipate that mobility models will have a higher average and higher distribution of CPC in the more privileged regions.

Nostalgic contents are social media posts that refer to past collective memories or events. When crisis events do occur, it affects the way of living life. In these unprecedented times, people turn to social media to express their concerns and feelings. By studying the engagement and interactions of users in social media, we can create new ways of understanding nostalgic longing. This research explores the nostalgic activity of tweets during crisis events. The NLP (Natural Language Processing) classifier is a pre-trained algorithm that enables us to detect whether the tweet is nostalgic or not by using references to human language and then classifying them into categories. The performance of our classifier is 98% accurate. This accuracy ensures the detection of nostalgic tweets is correct when formulating an analysis. Once the nostalgic tweets are obtained from the classifier I can begin performing a deeper analysis of the tweets by using machine learning tools. A descriptive analysis of time is used to gain insight into how people react to events and the progression of the nostalgia feeling humans have. Especially pre-crisis, during a crisis, and post-crisis are time periods that are significant because it gives insight into the progression of human behavior. Sentiment analysis is also performed on the data to understand how people feel about certain events. This is a useful method to gain information about whether there is a positive or negative reminiscent during the time the tweet is posted. The analysis has shown that less than 1% of tweets are nostalgic and the contents tend to be more negative than positive. The content of the tweets ranges from informational to political with a reminiscent of the time during crisis. The results will help us understand human behavior and how it can be leveraged as public assistance during a crisis.

The "Missing Maps" research project targets remote secluded communities across large regions of the globe. In this project, we train a model that leverages satellite images to find settlements, houses, and villages so that humanitarian organizations and community health systems can know about every community in the area. This has a high impact and helps non-governmental organization and local policymakers to meet the needs of people in rural areas and plan relief efforts in cases of crisis or natural disasters. There is extensive research on training neural networks to recognize buildings on satellite images for big cities like New York or Las Vegas, but not on rural satellite images to identify remote communities. In the "Missing Maps" research project, we use ensemble methods that combine multiple machine learning models to solve the problem holistically and improve accuracy as a result while adapting it to a diverse variety of continents and areas. Some of the methods explored in this research are based on community detection using neural networks and advanced image inpainting. The models are trained using the latest datasets, such as OpenEarthMaps and OpenBuildings. This project diversifies satellite image analysis and addresses the biases in algorithms that are only targeting urban areas.