Consulting Services to Determine the Effectiveness of Vegetation Classification Using WorldView 2 Satellite Data for the Greater Everglades

The purpose of this project was to evaluate the use of remote sensing 1) to detect and map Everglades wetland plant communities at different scales; and 2) to compare map products delineated and resampled at various scales with the intent to quantify and describe the quantitative and qualitative differences between such products. We evaluated data provided by Digital Globe’s WorldView 2 (WV2) sensor with a spatial resolution of 2m and data from Landsat’s Thematic and Enhanced Thematic Mapper (TM and ETM+) sensors with a spatial resolution of 30m. We were also interested in the comparability and scalability of products derived from these data sources. The adequacy of each data set to map wetland plant communities was evaluated utilizing two metrics: 1) model-based accuracy estimates of the classification procedures; and 2) design-based post-classification accuracy estimates of derived maps.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

The analysis of the resort timesharing industry in order to determine the industry's familiarity with timesharing and the industry's conception of the present and future effects of timesharing

The purpose of the study is to examine the impactof the timesharing concept on the resort industry in order to determine the industry's familiarity with timesharing and the industry's conception of the present and future effects of timesharing. The study utilizes two methods of research, primarydata and secondary data, to examine the concept of timesharing. This section includes information on the different forms of timesharing, the legal aspects, the marketing, management, finance and future of timesharing in order to educate the public about the concept. The primary data takes the form of a survey thatquestions hotel/motel operators in the Fort Lauderdale Beach area to determine their attitudes towards the impact of timesharing on the resort industy.

The potential of solid phase microextraction (spme) for the recovery of explosives followed by gas and liquid chromatographic analysis

The potential of solid phase microextraction (SPME) in the analysis of explosives is demonstrated. A sensitive, rapid, solventless and inexpensive method for the analysis of explosives and explosive odors from solid and liquid samples has been optimized using SPME followed by HPLC and GC/ECD. SPME involves the extraction of the organic components in debris samples into sorbent-coated silica fibers, which can be transferred directly to the injector of a gas chromatograph. SPME/HPLC requires a special desorption apparatus to elute the extracted analyte onto the column at high pressure. Re suits for use of GC[ECD is presented and compared to the results gathered by using HPLC analysis. The relative effects of controllable variables including fiber chemistry, adsorption and desorption temperature, extraction time, and desorption time have been optimized for various high explosives.

Utilization of needs assessments by decision makers : a comparative analysis of assessment methods

The major purpose of this study was to ascertain how needs assessment findings and methodologies are accepted by public decision makers in the U. S. Virgin Islands. To accomplish this, the following five different needs assessments were executed: (1) population survey; (2) key informants survey; (3) community forum; (4) rates-under-treatment (RUT); and (5) social indicators analysis. The assessments measured unmet needs of older persons regarding transportation, in-home care, and sociorecreation services, and determined which of the five methodologies is most costly, time consuming, and valid. The results of a five-way comparative analysis was presented to public sector decision makers who were surveyed to determine whether they are influenced more by needs assessment findings, or by the methodology used, and to ascertain the factors that lead to their acceptance of needs assessment findings and methodologies. The survey results revealed that acceptance of findings and methodology is influenced by the congruency of the findings with decision makers' goals and objectives, feasibility of the findings, and credibility of the researcher. The study also found that decision makers are influenced equally by needs assessment findings and methodology; that they prefer population surveys, although they are the most expensive and time consuming of the methodologies; that different types of needs assessments produce different results; and, that needs assessment is an essential program planning tool. Executive decision makers are found to be influenced more by management factors than by legal and political factors, while legislative decision makers are influenced more by legal factors. Decision makers overwhelmingly view their leadership style as democratic. A typology of the five needs assessments, highlighting their strengths and weaknesses is offered as a planning guide for public decision makers.