Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing need for accurate weather predictions, we need large samples of data from different data sources for an accurate estimate. There are a number of data sources that keep publishing data periodically. These data sources have their own server protocols that a user needs to follow while writing client for retrieving data. This project aims at creating a generic semi-automatic client mechanism for retrieving scientific data from such sources. Also, with the increasing number of data sources there is also a need for a data model to accommodate data that is published in different formats. We have come up with a data model that can be used across various applications in the domain of scientific data retrieval.

Indiana University-Purdue University Indianapolis (IUPUI) / Geospatial analysis is a generic term describing several technologies or methods of computational analysis using the Earth as a living laboratory. These methods can be implemented to assess risk and study preventative mitigation practices for Public Health. Through the incorporation Geographic Information Science and Remote Sensing tools, data collection can be conducted at a larger scale, more frequent, and less expensive that traditional in situ methods. These techniques can be extrapolated to be used to study a variety of topics. Application of these tools and techniques were demonstrated through Public Health research. Although it is understand resolution, or scale, of a research project can impact a study’s results; further research is needed to understand the extent of the result’s bias. Extreme heat vulnerability analysis was studied to validate previously identified socioeconomic and environmental variables influential for mitigation studies, and how the variability of resolution impacts the results of the methodology. Heat was also investigated for the implication of spatial and temporal resolution, or aggregation, influence on results. Methods studying the physical and socioeconomic environments of Dengue Fever outbreaks were also studied to identify patters of vector emergence.

This dissertation consists of three chapters which as a whole address development issues in Nigeria. These chapters relate to issues on conflicts, rural labor, education, weather shocks, health, and aid. Chapter 1 studied the effects of conflicts on the labor adjustment patterns of rural households in Northern part of Nigeria. I relied on variation in the conflict incidence and employed the use of instrumental variable technique in order to identify the effect of conflict on several household labor variables. The result reveals that conflict within 20km radius of household has led to adjustment of labor away from agriculture to nonagricultural sectors. Also, it has led to substitution of household agricultural labor with hired labor. The demand pressure on hired labor was also found to have increased the wage rates of agricultural hired labor. Chapter 2 investigates the effects of conflicts, depending on the nature, on educational attainment of individuals in Nigeria. Specifically, I looked at how two forms of conflicts with different nature and perpetrators - Boko Haram insurgence and Farmers and Herders conflict – affects educational attainments in Nigeria. I employed the Difference-in-Difference (DiD) technique and found that both forms of conflict hurts completed years of education of individuals that are exposed to them. However, the magnitude of the effects of farmers and herders conflict tends to be larger than that of boko haram insurgence. Chapter 3 studies the effectiveness of World Bank aid projects in reducing the adverse effects of weather shocks on children’s health in Nigeria. The study revealed that children’s’ exposure to weather shocks in their month of birth and in utero have adverse effects on their Weight-to-age- z-score (WAZ) and height-to-age z-score (HAZ). However, the availability of aid projects within 20km radius of these children helps to reduce such negative effects of such weather shocks.

Severe weather outbreaks are fairly common events that occur multiple times a year. Many studies have attempted to define and quantify these outbreaks, however, no work has been done to directly relate synoptic-scale processes to outbreak intensity using the N15 ranking index. It is believed that a statistically significantly strong relationship between outbreak severity and quantified synoptic-scale parameters exists and can be utilized to predict the severity of an upcoming outbreak using the N15 ranking index. Utilizing the NCEP-NCAR Reanalysis dataset, synoptic-scale variables were chosen and standardized into domains created from areal coverages. A series of tests were completed, including stepwise regression, principal component analysis, and a bootstrap cross-validation method to find the most significant variables and best domain size. The findings from this study suggest that synoptic-scale processes do not have a strong relationship to severe weather outbreak intensity and that future work would be necessary.

Tornadoes are always dangerous, but inside a motor vehicle they are especially problematic due to an automobile’s smaller size and structure. Tornado fatalities in motor vehicles have not decreased in recent years despite downward trends in total tornado fatalities. Receiving tornado warnings is difficult inside a motor vehicle, particularly when alone in the vehicle. Controlled access highways are also difficult places to find shelter as access is limited to interchanges, while buildings at interchanges may not be able to withstand an intense tornado. Electronic signs and audible messages on mobile communications devices are the most suitable technologies available for reaching motorists. At each interchange, shelters capable of withstanding a violent tornado should be constructed or retrofitted, allowing motorists – and nearby residents – a safe place to shelter nearby. Constructing such shelters will take considerable time and significant cost but will go a long way towards the goal of a Weather-Ready Nation.

We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to detrmine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter-clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter-clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.

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Weather conditions may impact traffic flow in different ways. Both the human decisions regarding the trip (route, mode, time) and the trip itself can significantly vary. Driver’s behavior may be affected by weather resulting in, among others, deterioration of the network ’s travel times and speeds. Therefore to study and analyse travel times under different weather conditions, represents an important instrument to support Intelligent Transport Systems (ITS). With the correct knowledge and information, travellers would be able to plan their trips in a cost-efficient way, while traffic managers could take advantages of these predictions to deploy control strategies (e.g. weather-responsive signal timing plans). In this project Automatic Number Plate Recognition (ANPR) data from summer 2012 to summer 2013 from three different arterial routes in Stockholm city is used in order to analyze travel times, at a link level, under different weather conditions. To determine to what extent weather variables such as rain, snowfall, temperature and visibility impact the speeds in the network, weather data is integrated with traffic data (ANPR) and analyzed through linear regression models. Results show that there is in fact a negative effect on speed but also on speed’s variability. This knowledge can be useful for trip planning and for traffic management under different weather conditions.

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