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This dissertation consists of three empirical essays on labor, health, and real estate economics. The first essay theoretically and empirically analyzed the effects of the costs of firing an employee and hiring a replacement in a labor market with imperfect information. The theory suggested that increased expected firing or replacement costs contributed to a ``lemons effect" for the fired worker through the negative signal received in the labor market regarding the worker's ability. To test this theory, data from the Displaced Worker's Supplement to the Current Population Survey from 2004 to 2014 was used. The results were mixed, but suggested that workers in the United States who were displaced from their job experienced decreased probabilities of finding reemployment as firing costs increased. The essay also examined whether this ``lemons effect" contributed to larger wage decreases, but the estimates did not support this conclusion. The second essay estimated the impacts of the 2001 elimination of the Medicare 24-month waiting period for non-elderly Amyotrophic Lateral Sclerosis (ALS) patients. Using data from the National Hospital Discharge Survey, this essay estimated the effects of the elimination on health insurance coverage and utilization of health care services. By applying a difference-in-difference OLS estimation technique, it was estimated that, as a result of the waiting period elimination, non-elderly ALS patients were more likely to be insured, but there was a significant crowd-out of private insurance. These non-elderly patients who were admitted to the hospital with serious symptoms were also more likely to be transferred to long- or short-term care facilities while non-serious patients were more likely to receive a high (four or more) number of medical services while hospitalized. In the third essay, the effects of a new suburban casino on local housing prices were evaluated. Similar to the second essay, a difference-in-difference approach was applied, but it was combined with a spatial hedonic pricing model. Using data from a GIS product from the Maryland Department of Planning and local-area data from the American Community Survey, the effects that the opening of Maryland Live! Casino had on home sales prices of properties located in primary (one-mile radius) and secondary (one to three miles) impact areas were estimated. The results of the estimations indicated that the opening of the casino had a positive impact on housing prices in the primary impact area and this impact likely began during the construction period. No impacts, however, were evident in the secondary impact area. / Economics

In 1895, French sociologist Gustave Le Bon proclaimed the era of crowds upon us, in his influential work, The Crowd. Le Bon's work was translated into English a year later, inspiring a number of similar works by American sociologists, and almost single-handedly creating the discipline of crowd psychology. Interest in the new masses was not limited to sociologists, however. Due to advances in transportation and communication technologies, and the rise of the city, the problem of "in the mass" came to pervade the atmosphere of America, at the turn of the twentieth-century. Thus, American writers also wrestled with the difficulty of representing this catch-all entity "crowd," often speculating about what the psychology of the crowd might mean for the future of democracy. But, whereas early crowd theory was overwhelmingly conservative in its depiction of the crowd mind as a site of primitive impulses, irrational emotions, and affective contagion, authors like Frank Norris and Sherwood Anderson, though largely ceding to this description, saw in the crowd the possibility for an entirely new social consistency. Contrary to sociological prescriptives designed to brace the individual against the imminent threat of crowd contagion, however, Norris and Anderson identify what contemporary theorist Roberto Esposito terms the "immunitary regime" as the true difficulty to overcome. For Esposito, the biopolitically engendered immunitary dispositif protects modern individuals from "a risky contiguity with the other, relieving them of every obligation toward the other and enclosing them once again in the shell of their own subjectivity" (Terms 49). It is this hard shell of subjectivity that Norris and Anderson attempt to break down in their works. In this way, the two authors represent a small segment of a genealogical thread in American fiction--one stretching from Whitman, to Steinbeck, and beyond--that takes a gambit on what Badiou calls the "communist hypothesis." Perhaps most importantly, though, the texts of Norris and Anderson demonstrate, either deliberately or otherwise, that such a gambit must preclude any recourse to substantialist notions of innate gregariousness, primitive sympathy, or herd instinct. Thus, while refusing to endorse the immunitarian paradigm as the final word on being-together, Norris and Anderson demonstrate how we must work and think through immunity to arrive at an adequate concept of collective life in the modern era. While other studies of the crowd or the masses often ask what the multitude stands for, in a metonymical or metaphorical register, this one asks how it is formed, how it functions, and what it could mean for the possibility of collective life in modernity. Similarly, whereas other studies often judge a particular representation of the crowd against a preformed model of what constitutes the properly political, the following study attempts to unearth the crowd's immanent possibilities to potentially change those very models. / English

This master’s thesis investigates the application of anomaly detection techniques to analyze crowdmovements using cell location data, a topic of growing interest in public safety and policymaking. Thisresearch uses machine learning algorithms, specifically Isolation Forest and DBSCAN, to identify unusualmovement patterns within a large, unlabeled dataset. The study addresses the challenges inherent inprocessing and analyzing vast amounts of spatial and temporal data through a comprehensive method-ology that includes data preprocessing, feature engineering, and optimizing algorithm parameters. Thefindings highlight the feasibility of employing anomaly detection in real-world scenarios, demonstratingthe algorithms’ ability to detect anomalies and offering insights into crowd dynamics.

Human behavior is a key factor influencing the spread of infectious diseases. Individuals adapt their daily routine and typical behavior during the course of an epidemic -- the adaptation is based on their perception of risk of contracting the disease and its impact. As a result, it is desirable to collect behavioral data before and during a disease outbreak. Such data can help in creating better computer models that can, in turn, be used by epidemiologists and policy makers to better plan and respond to infectious disease outbreaks. However, traditional data collection methods are not well suited to support the task of acquiring human behavior related information; especially as it pertains to epidemic planning and response. Internet-based methods are an attractive complementary mechanism for collecting behavioral information. Systems such as Amazon Mechanical Turk (MTurk) and online survey tools provide simple ways to collect such information. This thesis explores new methods for information acquisition, especially behavioral information that leverage this recent technology. Here, we present the design and implementation of a crowd-sourced surveillance data acquisition system -- WiSDM. WiSDM is a web-based application and can be used by anyone with access to the Internet and a browser. Furthermore, it is designed to leverage online survey tools and MTurk; WiSDM can be embedded within MTurk in an iFrame. WiSDM has a number of novel features, including, (i) ability to support a model-based abductive reasoning loop: a flexible and adaptive information acquisition scheme driven by causal models of epidemic processes, (ii) question routing: an important feature to increase data acquisition efficacy and reduce survey fatigue and (iii) integrated surveys: interactive surveys to provide additional information on survey topic and improve user motivation. We evaluate the framework's performance using Apache JMeter and present our results. We also discuss three other extensions of WiSDM: Adapter, Synthetic Data Generator, and WiSDM Analytics. The API Adapter is an ETL extension of WiSDM which enables extracting data from disparate data sources and loading to WiSDM database. The Synthetic Data Generator allows epidemiologists to build synthetic survey data using NDSSL's Synthetic Population as agents. WiSDM Analytics empowers users to perform analysis on the data by writing simple python code using Versa APIs. We also propose a data model that is conducive to survey data analysis. / Master of Science

"Crowd counting" is a term used to describe the process of calculating the number of people in a given context; however, crowd counting has multiple challenges especially when images representing a given crowd span multiple cameras or images. In this thesis, we propose a crowd counting camera array and correction (CCCAC) method using a camera array of scaled, adjusted, geometrically corrected, combined, processed, and then corrected images to determine the number of people within the newly created combined crowd field. The purpose of CCCAC is to transform and combine valid regions from multiple images from different sources and order as a uniform proportioned set of images for a collage or discrete summation through a new precision counting architecture. Determining counts in this manner within normalized view (collage), results in superior counting accuracy than processing individual images and summing totals with prior models. Finally, the output from the counting model is adjusted with learned results over time to perfect the counting ability of the entire counting system itself. Results show that CCCAC crowd counting corrected and uncorrected methods perform superior to raw image processing methods.

<p>The rise of technologies and the Internet have provided opportunities to connect logistics demand and supply using the crowd. In this system, named crowd-shipping (CS), a requester doing the shipping selects a courier via a platform. In reality, the idea of CS has been explored by many firms over the last several years. However, there is a lack of fundamental understanding of the issues related to: (1) the markets that are likely to be influenced by CS; (2) the considerations that govern the success of this system; and the (3) the impacts of CS and its design.</p><p><br></p> <p>To address these issues, there is a need of understanding CS system's stakeholders, such as requesters' (i.e. senders') and potential couriers' (i.e. driver-partners') behaviors as well as operations and management of CS firms. This research will address these gaps by conducting a survey to understand driver-partners' behaviors and requesters' behaviors given the CS services availability in the logistics market. Then, pricing and compensation strategies are designed and modeled based on behavior rules of supply and demand generations as well as various CS market penetrations. As such, this research addresses the CS industry in a triad of supply, demand, and operations and management.</p><p><br></p> <p>This research uses advanced econometrics, statistics analysis, mixed integer optimization, and data science techniques to analyze data and generate insights. The contributions of this research are to identify the contributing factors that impact the emerging logistics service. This research also reveals factors that influence the current and future shipping behaviors of requesters, as well as influencing factors of the individuals' willingness to work as driver-partners. The integrated matching and routing models have been developed to examine different pricing and compensation strategies under several market penetration scenarios. `Individual' price and compensation have found to provide the highest profit for CS platform providers.</p><p><br></p> <p>This research provides meaningful knowledge for stakeholders, especially for the CS firms to develop business strategies. Several remarkable benefits that CS firms can obtain include: focusing on some specific population groups to recruit driver-partners (e.g. people with children, middle-aged people having lower incomes, or no car ownership); addressing certain market segments to promote CS services (e.g. tight-window delivery packages, peripheral products, or personal health and medicine items); implementing `individual' or `flatted' pricing and compensation strategies depending on the time of the day, the day of the week, or the market penetration; and improving platform features to incorporate requesters' and driver-partners' expectations.</p>

Within the design of any building, there is a requirement for designers to understand the intended purposes of the building and the elements that influence performance. These elements can be as tangible as providing a lecture hall within a university or relatively intangible such as the environmental temperatures of the rooms. The elements involved are generally recognised within the design industry and a combined force of engineers, architects, and specialist advisors work together to ensure all of the elements are in place for each new design. However, one element affecting performance that has not yet been comprehensively covered (at least for many building types) is that relating to occupant movement and the influence this has on experience and hence performance. For example, the number of times people have to negotiate cross-flow environments in a train station before becoming agitated is unknown. Also, the average distance people will walk through a shopping centre before becoming tired and ending the activity is unknown. Even so, they will both be impacted upon by the design and they will both reflect back on the performance of the design. Before starting this research, it was realised by the research engineer that there was only a limited understanding and application of people flow analyses within industry and, where it existed, it was solely related to transport terminals, pedestrian walkways/crossings, sports stadia arrivals/egress, and evacuation analyses.