Case Study In Florida Research Articles

Identifying Points of Interest (POIs) as sentinels for infectious disease surveillance: a COVID-19 study

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.

Medicaid Expansion Case Study: Differences Between Florida and New York Medicaid Expansion Policy

Health access is an essential aspect of social equity. Expanding Medicaid under the Affordable Care Act (ACA) has increased healthcare access for more than 21 million people in the United States, and 10 states have chosen not to participate in Medicaid expansion. This study investigates the factors influencing states’ decisions on Medicaid expansion under the ACA and its implications for social equity. It uses a comparative case study of New York (expansion) and Florida (non-expansion) to reveal the complex determinants of Medicaid expansion decisions. These determinants are crucial for understanding and promoting social equity in pursuing public purposes. Political affiliations, lobbying, and a state’s economic climate significantly shape Medicaid policies. Unfortunately, grassroots advocacy groups encountered significant challenges in Florida, where health associations tended to align closely with their state political parties. Despite the challenges, organizations must advocate for improved healthcare access and Medicaid expansion to address social equity concerns.

State-of-the-Practice of a State Utilization of Traffic Simulation: Case Study of Florida, U.S.

Traffic simulation tools are widely used in analyzing and enhancing the forecasting, planning, design, operations, and management of transportation systems. Despite the increasing use of traffic simulation through the years, there are still many needs and issues facing transportation agencies and modelers in their use of traffic simulation. These needs and issues have not been sufficiently surveyed and summarized in the literature. The goal of this study is to identify the current applications, needs, and issues of traffic simulation modeling based on a survey of public and private agencies in Florida, U.S. The study conducted an online survey of traffic simulation modelers in Florida, followed by phone interviews with a subset of the respondents to the survey. The survey addressed the types of the utilized traffic analysis and simulation tools, utilized simulation guidance, utilized traffic data and performance measures, calibration processes and resources utilized by agencies, the current practice of future year demand forecasting, and perspective on the simulation of emerging technologies. The results from this study will help understand the present situation of applying traffic simulation and future needs to enhance simulation modeling capabilities and practices.

Feasibility of Using Green Laser for Underwater Infrastructure Monitoring: Case Studies in South Florida

Scour around bridges present a severe threat to the stability of railroad and highway bridges. Scour needs to be monitored to prevent the bridges from becoming damaged. This research studies the feasibility of using green laser for monitoring the scour around candidate railroad and highway bridges. The laboratory experiments that provided the basis for using green laser for underwater mapping are also discussed. The results of the laboratory and field experiments demonstrate the feasibility of using green laser for underwater infrastructure monitoring with limitations on the turbidity of water that affects the penetrability of the laser. This method can be used for scour monitoring around offshore structures in shallow water as well as corrosion monitoring of bridges.

Risk Assessment and Insurance Underwriting Decision Model: A Case Study of Gansu Province and Florida

Extreme weather is driving up economic losses and insurance costs, underscoring the urgency for enhanced disaster risk management and policy reform for sustainability. This study presents a novel Risk Assessment Model for analyzing insurance underwriting risk in specific regions, driven by the increasing economic losses and insurance costs due to extreme weather. The model integrates a comprehensive evaluation system with four primary and twelve secondary indices, applying the Entropy Weighting Method (EWH) and Analytic Hierarchy Process (AHP) to allocate weights and quantify risk (Q_unfavourable) effectively. It also adopts the As Low as Reasonably Practicable (ALARP) principle for setting underwriting decision thresholds, based on risk and profitability. Case studies from Gansu Province, China, and Florida, USA, validate the model's practicality and its potential to enhance insurers' underwriting strategies and policy decisions across varied regions, marking a significant improvement in underwriting risk management and strategic insurance policy formulation.

Temperature sensitive electricity demand and policy implications for energy transition: a case study of Florida, USA

The demand for electricity is soaring, propelled not only by population and GDP growth but also the pressing effects of climate change. This study seeks to address the uncertainties surrounding future electricity demand by projecting monthly consumption in Florida, USA, taking into account diverse climate scenarios and their potential impacts. Our approach involves utilizing the degree-day method and constructing an energy consumption regression model grounded in historical data. Key variables, including population, employment, GDP, electricity prices, temperature, and daylight hours, are systematically analyzed. This model acts as the fundamental basis for forecasting future electricity needs in residential, commercial, and industrial sectors across the state of Florida up to the year 2050, considering different climate scenarios. Under the Representative Concentration Pathway (RCP) 4.5 scenario, the residential sector foresees a substantial 63% increase in electricity demand from 2001–2019 to 2050. Under the more extreme RCP 8.5 scenario, this surge climbs to 65%. Meanwhile, the commercial and industrial sectors are expected to witness a 47% and 54% upswing in demand under RCP 4.5 and RCP 8.5, respectively. Intriguingly, heightened demand for cooling during scorching summers outweighs the reduced need for heating in winter, particularly in the residential sector. The current renewable energy policies fall short of addressing the impending climate-driven surge in electricity demand. To combat this, our recommendation is the implementation of a Renewable Portfolio Standard, aimed at significantly enhancing the proportion of renewables in Florida's electricity mix. This paper concludes with a set of crucial policy recommendations, imperative for steering a sustainable transition to renewable energy and effectively managing the impacts of extreme heat on people's lives. These recommendations serve as a strategic roadmap for navigating the evolving landscape of electricity demand amidst the complex challenges posed by climate change.

Assessing socio‐environmental suitability and social license of proposed offshore aquaculture development: A Florida case study

AbstractOffshore aquaculture is a growing industry, but a lack of social acceptance is limiting development, including within the USA. We used the Gulf Coast of Florida, where there has been industry and government interest in development, as a case study to explore offshore aquaculture potential and methods for integrating stakeholder concerns into offshore aquaculture development. We assessed (1) social acceptance of offshore aquaculture in the Florida Gulf Coast using public comments; (2) site suitability for offshore development using social, biological, and technical data; and (3) potential impacts of offshore aquaculture on communities using socioeconomic vulnerability indices. We found that many stakeholders distrust policymakers and industry and have concerns about potential environmental impacts. We created species‐specific suitability maps for red drum (Sciaenops ocellatus) and almaco jack (Seriola rivoliana), demonstrating that large areas of the Gulf are suitable for offshore aquaculture development. We show that many coastal and fishing‐reliant communities have existing vulnerabilities that aquaculture development could affect, but the public comments did not reflect these. To gain social acceptance, industry and government agencies will need to better incorporate public feedback into planning processes in a meaningful way. Consulting local communities and adapting projects in response to their concerns can help to secure social license for offshore aquaculture.

An integration modeling framework for individual-scale daily mobility estimation

This paper presents a novel microsimulation framework (BayABM) that integrates population synthesis, Bayesian Networks (BNs), and activity-based models (activity-BM) to model individual heterogeneity in mobility. The population synthesis simulates a synthetic population with socioeconomic and household-level attributes. The Bayesian Network is adopted to learn and compute the probability distributions of the mobility attributes and infer the mobility that most likely arises at a given time. A hierarchical location model with a random forest is developed to refine the destination choice based on the probability obtained from BN. A total of six mobility attributes are simulated, i.e., travel frequency, travel purpose, travel destination location, travel mode, activity duration, and travel duration. The strengths of the proposed framework are: (1) it can model the individual mobility dynamics; (2) it can generate individual mobility for a large-scale population; and (3) it has broader coverage with better representativeness and less strict data requirement. The proposed model is tested with a Miami-Dade County, Florida case study. A total of 7,253,672 travels for 2,680,607 persons and 860,380 households across the county are generated from a public travel diary survey. Results show that individuals at 20–50 age and 0–20 age travel most frequently, with a total of 3,413,710 and 2,074,677 activities per day. The travel peak of a day occurs at 11:00 and 18:00. The results demonstrate the efficacy of the enhanced model, especially in large-scale applications.

A Review of Common Factors among Successful and Failed Efforts to Eradicate Invasive Vertebrates in Florida

Species invasions can alter ecosystems, decimate populations of native species, facilitate the spread of infectious diseases, and threaten industries like fisheries and agriculture. Significant resources and financial investments are often directed towards invasive species management (ISM) to prevent future introductions, to remove smaller populations, and to contain expanding populations. While prevention is the most important goal of ISM, focus may shift to removal (eradication) when prevention fails. Eradication attempts are costly and have not been inclusive of all invasive populations. These efforts are inherently difficult because they require an abundance of resources, must be executed before the population expands, can be disrupted by public opposition, and often require years of monitoring to confirm success. Regardless, even failed eradication attempts provide important lessons to potentially improve future eradication efforts. This review of 20 case studies in Florida assesses successful and failed eradication attempts to identify common factors that contributed to each outcome. More than half of the detailed failed eradication attempts either lacked communication from or were outright disrupted by stakeholders. Most successful eradications targeted fish or small mammals and removed <200 individuals. Nearly half of the successful eradications occurred on islands or within isolated bodies of water. Despite the abundance of invasive herpetofauna in Florida, there has yet to be a documented successful eradication effort targeting these species. While many barriers exist to publishing eradication outcomes, future eradication efforts should prioritize documentation to provide guidance to similar efforts. Future research should address the role of media coverage and outreach efforts regarding eradication success, especially efforts targeting charismatic species.

Generating Interest in Foundational Computer Science in High Schools: A Northwest Florida Case Study

Computer Science drives innovation and discovery, and Computer Science classes in High Schools, particularly AP Computer Science Principles, have also been linked to better performance in other High School courses, but a Code.org report shows that the state of Florida, particularly the Northwest Florida region, is far behind the rest of the nation in offering Computer Science classes at the High School level. This paper discusses strategies that are can be implemented to improve High School Computer Science offerings in like-regions that have lower Computer Science offerings. The paper also discusses the challenges that come up in addressing this issue and presents potential avenues for addressing these challenges. One major challenge that is addressed is creating the culture of inclusive computing by getting more women and under-represented groups interested in taking Computer Science courses. Though this is a Northwest Florida case study, most of the strategies discussed in this paper would be applicable to any other part of the country with low Computer Science offerings with a similar socio-economic background.

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.

Enhancing the Retail Food Environment Index (RFEI) with Neighborhood Commuting Patterns: A Hybrid Human-Environment Measure.

The Retail Food Environment Index (RFEI) and its variants have been widely used in public health to measure people’s accessibility to healthy food. These indices are purely environmental as they only concern the geographic distribution of food retailers, but fail to include human factors, such as demographics, socio-economy, and mobility, which also shape the food environment. The exclusion of human factors limits the explanatory power of RFEIs in identifying neighborhoods of the greatest concern. In this study, we first proposed a hybrid approach to integrate human and environmental factors into the RFEI. We then demonstrated this approach by incorporating neighborhood commuting patterns into a traditional RFEI: we devised a multi-origin RFEI (MO_RFEI) that allows people to access food from both homes and workplaces, and further an enhanced RFEI (eRFEI) that allows people to access food with different transportation modes. We compared the traditional and proposed RFEIs in a case study of Florida, USA, and found that the eRFEI identified fewer and more clustered underserved populations, allowing policymakers to intervene more effectively. The eRFEI depicts more realistic human shopping behaviors and better represents the food environment. Our study enriches the literature by offering a new and generic approach for assimilating a neighborhood context into food environment measures.

Regional Yield Estimation for Sugarcane Using MODIS and Weather Data: A Case Study in Florida and Louisiana, United States of America

Sugarcane is an important sugar source in America and is mainly planted in the states of Florida and Louisiana. The purpose of this study was to predict the sugarcane yield in these two states from 2008 to 2016. Three statistical sugarcane yield models (i.e., the CNDVI, K–M, and SiPAR models) were applied to predict yield in America, using remote sensing and weather data. To verify the robustness of models, model parameters obtained in places (i.e., Reunion Island and Southwestern China) far away from America were used. The results showed that the SiPAR model outperformed the CDNVI and K–M models for yield prediction. Solar radiation was an important constraint factor to ensure the statistical model’s robustness under different conditions. The CNDVI model had the lowest robustness because of the absence of solar radiation, although it could reflect the yield trend to some extent. The K–M model failed to predict the low sugarcane yield, owing to the lack of consideration of temperature and soil water stresses. Florida had a low sugarcane yield in the west and southwest; however, Louisiana had high sugarcane yield in the same directions. This study demonstrated the robustness of the SiPAR model and investigated the sugarcane yield status in Florida and Louisiana. It can be a reference for similar studies in the future.

Analysis and modeling of changes in online shopping behavior due to Covid-19 pandemic: A Florida case study

The emergence of eCommerce and online shopping commenced a new episode in human life and changed trading patterns. Online shopping provided access to a broader range of products and facilitated their delivery, which increased demand. To respond to the increased demand, more heavy commercial vehicles need to be on the roads to deliver orders. This is while the road infrastructure is not ready for such a swift shift, and most roads and bridges were planned and constructed during the 19s when online shopping was not coined yet. The continued increase of heavy vehicles on roads can intensify the deterioration of roads and structures such as bridges. Therefore, there is a significant need for an update on new shopping trends, especially changes in people's behavior due to the ongoing Covid-19 pandemic, and to assess if the pandemic permanently changed the trends of in-store and online shopping. This study first examines the NHTS 2017 data to find the attributes that are significant to online shoppers' behavior. Then a survey is developed to scrutinize Covid-19 effects on the online shopping behavior of users before, during, and after the Covid-19 pandemic. 206 records of data are interpreted through descriptive analysis and discrete choice modeling of users' responses to find the most significant attributes affecting their online shopping behavior. The findings of discrete choice modeling and descriptive analysis support that people tend to go back to stores after the pandemic. The findings of this study show that online and in-store shopping would be balanced after the pandemic and would pursue their normal trends as they were before the pandemic. Based on the findings of this study, it is hard to state that online shopping can vanish in-store shopping due to Covid-19. People still need to go to stores to fulfill their needs for the joy of shopping, interactions with other people, and touching the products they would like to buy. Therefore, transportation stakeholders need to pay special attention to both in-store and online shopping for their planning and operation management of ground transportation infrastructure.

Drone logistics for uncertain demand of disaster-impacted populations

The paper introduces a stochastic optimization-based approach to address the logistics for the timely delivery of aid packages to disaster-affected areas utilizing a fleet of drones when the set of demand points is unknown. The major problem addressed is to locate a set of drone take-off platforms so that with a specified probability α, the maximum total disutility (or cost) under all realizations of the demand locations is minimized. A set of discrete scenarios defines the uncertainty set of the demand points. A Chance Constrained Programming (CCP) formulation is developed to select a set of platform locations whose disutility distribution produces minimum α percentile. For each platform location set, and each demand scenario, the total disutility is defined as the total delivery time for serving the demand points plus a penalty for unvisited demand points. For every set of drone platform locations, referred to as a candidate combination of platforms, the resultant disutility distribution is obtained by solving a space-time drone scheduling subproblem for all possible demand scenarios. The drone scheduling subproblem optimally schedules and sequences a set of trips for each drone so that the total disutility is minimized. Owing to the computational complexity of the proposed approach, an approximation method is developed that decomposes the problem into three tractable stages. The first stage identifies a set of most preferable platform combinations. The second stage develops an approximation algorithm based on a greedy approach to mitigate the extensive computational requirements for solving the large number of drone scheduling subproblems. The last stage builds upon the properties of a Sample Average Approximation (SAA) method and of the CCP formulation to select the optimum set of platforms. Finally, the performance of the proposed stochastic approach is evaluated through a series of computational experiments and a case study of Central Florida. The results reveal interesting insights and demonstrate the effectiveness of the proposed logistics system for drone delivery of humanitarian aid packages.

Capturing long-memory properties in road fatality rate series by an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity: A case study of Florida, the United States, 1975–2018

Introduction: Time series models play an important role in monitoring and understanding the serial dynamics of road crash exposures, risks, outcomes, and safety performance indicators. The time-series methods applied in previous studies on crash time series analysis assume that the serial dependency decays rapidly or even exponentially. However, this assumption is violated in most cases because of the existence of long-memory properties in the crash time series data. Ignoring the long-memory dependency could result in biased understanding of the dynamics of road traffic crashes. Method: To fill this research gap, this study proposes an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity (ARFIMA-GARCH) to capture and accommodate the long-memory decencies in the road fatality rate time series. To further investigate how the factors influencing the fatality risks play a role in the long-memory dependence, the effects of exogenous variables are examined in this study. The analysis is conducted based on the road crash fatality data in Florida, USA over 44 years. Results’ Conclusions: The case analysis confirmed the existence of long-memory property in the crash fatality time series data by both the joint tests of Augmented Dickey-Fuller and the Phillips–Perron, and the integer order of differencing (≤0.5) in the proposed models. The model results reveal that gasoline price and alcohol consumption per capita is positively associated with road fatality risks, whereas unemployment rate and rural/urban road mileage are negatively related to the road fatality risks. Practical Applications: The significant influential factors are also found to account for the long-memory serial correlations between road traffic fatalities to some extent.

Assessing international students’ vulnerability to hurricanes: University of Florida case study

Disaster research concerning the behavior of international students at U.S. institutions of higher education is very limited. The main objective of our study is to develop new knowledge about international student's behavior at U.S. institutions of higher education in relation to hurricanes in order to enhance the overall campus crisis management. A mix of quantitative and qualitative methods was used to collect data from University of Florida (UF) main campus in Gainesville, Florida; UF administrative leaders committed to international students' safety in emergencies were interviewed. In addition, 120 international students at UF were surveyed. The data analysis sought to provide insights into one main research question: In a disaster-context, what challenges do international students face that contribute to their vulnerability at UF main campus? Three main groups of challenges were found; Institutional challenges, Students' well-being and daily challenges, and Disaster preparedness challenges. Moreover, considerable variations in students' behavior were found among some demographics, including gender, ethnicity, housing, degree level, and previous hurricane experience. The research findings shed a light on a research area that has been under-investigated, and contribute to an increased understanding of international students' vulnerabilities at U.S. institutions of higher education.

Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study

This paper focuses on the problem of model transferability for machine learning models used to estimate hourly traffic volumes. The presented findings enable not only an increase in the accuracy of existing models but also, simultaneously, reduce the cost of data needed for training the models—making statewide traffic volume estimation more economically feasible. Previous research indicates that machine learning volume estimation models that leverage GPS probe data can provide transportation agencies with accurate estimates of hourly traffic volumes—which are fundamental for both operational and planning purposes—and do so with a higher level of accuracy than the prevailing profiling method. However, this approach requires a large dataset for model calibration (i.e., input and continuous count station data), which involves significant monetary investment and data-processing effort. This paper proposes solutions, which allow the model to be prepared using a much smaller dataset, given that a previously collected dataset, which may be gathered in a different place and time period, exists. Based on a broad selection of experiments, the results indicate that the proposed approach is capable of achieving similar model performance while collecting data for a 5 times shorter time period and utilizing 1/4 of the number of continuous count stations. These findings will help reduce the cost of preparing and maintaining the traffic volume models and render the traffic volume estimations more financially appealing.

Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

The analysis of land use and land cover data provides invaluable support to a variety of land management and conservation activities. However, historically its application has been limited by high costs associated with data acquisition, analysis, and classification. In recent years, freely available satellite imagery and geospatial data sets and rapid advancement in data analysis capabilities provide immense opportunities to understand and solve the real-world environmental problems. Open-source platforms such as Google Earth Engine (GEE) provide a planetary-scale environmental science data and analyses capability at much greater efficiency and accuracy than the traditional workflows. We evaluated the GEE Python API utility for classifying the freely available NAIP aerial imagery of 2017 to derive the land use land cover (LULC) information of a Panhandle area of Florida, USA. We identified eight major LULC classes with an overall accuracy of 86% and Kappa value of 79%. We completed all remote sensing data analyses procedures including data retrieval, classification, and report preparation in the Jupyter notebook, an open-source web application. Computation time for the procedure was less than 15 min. Our results demonstrate the usefulness of this approach for conducting land use and land cover analysis using much less time, money, and human resources. The open-source nature of GEE Python API and its library of remote sensing data could benefit remote sensing projects throughout the world, especially where access to commercial image processing software packages and remote sensing data are limited.

Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields

Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day.