Author

Nguyen, Nhan

Institution (department)

OU Health Science Center (Department of Biostatistics and Epidemiology)

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Background: Oklahoma had a higher incidence and mortality rate of colorectal cancer (CRC) than the U.S. overall rates between 2007 and 2012. In 2000, Oklahoma also had a higher number of fast food restaurants (FFRs) per square mile (ranked 15th) and per population (ranked 18th) than the other states in the U.S. Objectives: The specific aims of the study were (1) to explore the association between fast food density and CRC risk in Oklahoma between 2003 and 2012 while controlling for other related-risk factors including age, gender, race/ethnicity, and poverty; (2) develop a model that predicts CRC risk at the census tract level; and (3) identify high risk areas for CRC. Methods: Two statistical approaches were applied to find the best model fit for study data, which included a Geographically Weighted Poisson Regression (GWPR) and a non-spatial Log-linear Poisson Regression model. Results: The spatial relationship between the outcome and explanatory variables was not found thus a non-spatial Log-linear Poisson Regression was used. There was no significant relationship between fast food density and CRC risk in Oklahoma between 2003 and 2010 after accounting for other risk factors in the model (p-value = 0.0645). This association was also observed for male and below poverty populations (p-value = 0.7196 and 0.1816, respectively). Besides that, there was a significant inverse association between CRC risk and the age population of persons < 50 and 50-64 years (p-value < 0.0001 and p-value = 0.0016, respectively). Race/ethnicity was significantly related to the risk of CRC as well (p-value < 0.0001). Discussion: Although the relationship between fast food density and CRC risk was not significant found in Oklahoma, the model-based map for the Oklahoma CRC incidence rates indicated that rural areas have a higher incidence rate of CRC than urban regions. This suggested that CRC might correlate with other important risk factors that were not able to be included in this model instead of the fast food density alone such as screening rates, percentage use of healthcare coverage, obesity rates, diabetes (particularly Type II) rates, percentage of alcohol intake, smoking rates, and percentage of educational attainments measuring at the census tract level. Thus, future research in this area needs to replicate the study in fully concern with these foregoing limitations. Moreover, the high incidence rate of CRC among rural populations in Oklahoma calls for research into effective rural interventions.

Author

Zhou, Yuting

Institution (department)

University of Oklahoma (Department of Microbiology and Plant Biology)

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Tallgrass prairie is an important ecosystem type and a major feed for beef cattle in the Southern Great Plains (SGP: Kansas, Oklahoma, and Texas). Frequent drought in the SGP affects the production of tallgrass prairie and ultimately the beef cattle production. It is, therefore, necessary to map drought vulnerable areas to help ranchers adapt cattle industry to drought conditions. In this study, we analyzed Land Surface Water Index (LSWI) calculated from near infrared and shortwave infrared bands of Moderate Resolution Imaging Spectroradiometer (MODIS) and quantified the spatial-temporal dynamics of agricultural drought in the tallgrass prairie region of the SGP during 2000-2013. The number of days with LSWI < 0 during the thermal growing season (start and end dates as well as duration of land surface temperature > 5 °C) was defined as the duration of drought to generate drought duration maps for each year. Following the decreasing rainfall gradient from east to west in the SGP, counties in the west experienced whole growing season drought (WGSZ) more (three or more out of 14 years with WGSD), middle counties had one to two months summer drought, and eastern counties experienced less drought (mainly one year with WGSD and less than one month of summer drought). The LSWI-based drought duration map showed similar patterns with Evaporative Stress Index (ESI) and U.S. Drought Monitor (USDM) in wet, summer drought, and whole growing season drought years. Our drought map has identified the vulnerability of counties to different droughts (summer drought and whole growing season drought) in the SGP. This fine resolution (500 m) drought map has the potential to show the drought condition for individual ranch, which can be used to guide drought mitigation activities and livestock production.

Author

Leal G, Eleazar

Institution (department)

University of Oklahoma (Computer Science)

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We propose TKSimGPU, an algorithm that incorporates parallelization strategies in order to answer top-K trajectory similarity queries. We ran experiments comparing the throughput of top-K trajectory similarity queries performed on multicore CPUs and GPGPUs against a naïve GPU implementation using a large-scale real world trajectory dataset. The experiments show that TKSimGPU achieves a 3.37x speedup in query processing time over exhaustive search on a GPU, and a 4.9x speedup in query processing time on a 12-core CPU architecture.

Author

Thompson, Emily

Institution (department)

University of Oklahoma (Department of Geography and Environmental Sustainability)

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The urban population in the United States increased by 12.1% from 2000 to 2010, but this change is not uniform for all urban areas. While many studies are devoted to changing urban land cover patterns as a result of population growth, this study specifically investigates the changes of a shrinking city. Detroit reduced from a peak population exceeding 1.8 million in 1950 to 714 thousand in 2010, a decline of 61.4%. Between 2000 and 2010, Detroit shrank by 24% from 951 thousand to 714 thousand people. This study uses the ArcGIS software to investigate the relationship between percent population change and land cover changes experienced by the Detroit Metropolitan region between 2001 and 2011. For this study, I use the 2001 and 2011 National Land Cover Dataset’s Land Cover, Percent Impervious Surface, Percent Tree Canopy and Lidar data. I also use the 2000 and 2010 US Census Bureau population data.

Author

Holtzman, Laura

Institution (department)

University of Oklahoma (Geography and Environmental Sustainability)

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Detecting abrupt changes in time series of remotely sensed data is an important approach to monitoring land use and land cover change. Time series change detection can be used to analyze several types of data including temperature, carbon emissions and NDVI. There are several statistical methods to detect breaks in time series. Breaks for Additive Seasonal Trend (BFAST) uses an additive decomposition model to differentiate trend, seasonal and noise components in a time series and determines moments of abrupt change in the overall trend. BFAST was published in 2010 and has been validated with Normalized Difference Vegetation Index (NDVI) time series from Moderate Resolution Imaging Spectroradiometer (MODIS). Applying BFAST to the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI product will allow us to compare and evaluate break detection methods that have been validated with MODIS NDVI. The GIMMS3G product is a global bimonthly NDVI data set, extending from July 1981 to December 2011, and is derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard 7 different NOAA (National Oceanic and Atmospheric Administration) satellites. In comparison to MODIS, the GIMMS product has a longer NDVI time series, and the data are from several satellite sensors. In this study, we will use BFAST on the global GIMMS3G product to test the sensitivity of the BFAST algorithm in determining break changes.

Author

Klemm, Toni

Institution (department)

OU / South Central Climate Science Center (Geography and Environmental Sustainability (DGES))

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Agriculture is one of the most weather- and climate-dependent industries. Unseasonal wet or dry climate, such as the recent droughts and rainfall in the south-central US, can lead to crop damage with severe consequences for regional and national economies. Seasonal climate forecasts, tailored for the agricultural community, could help reduce crop losses by providing skillful forecasts for the coming seasons. My proposed research uses online surveys and spatial statistics to explore ways in which tailored seasonal climate forecasts can help winter wheat producers in Oklahoma make better long-term decisions and assess whether climate model output is skillful enough to create such tailored forecast products. I see my research as a stepping stone in applied climate research towards creating operational seasonal climate forecasts and reducing crop losses for winter wheat farmers and agricultural producers in general.

Author

Buerger, Claude

Institution (department)

University of Oklahoma (Department of Geography and Environmental Sustainibility)

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Using Euclidean raster generation, measurement, and reclassification this project searches for ideal places to stand along Mobile's Parade Route A during Mardi Gras Parades. This projects looks at places determined to be good to stand along Mobile’s (Alabama) Parade Route A. Factors include: distance to bars, distance from trees, and distance to walls. This project was aimed at students so proximity to bars was considered the highest factor.

Author

Duan, Yi

Institution (department)

University of Oklahoma (Department of Economics)

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Previous literature has focused on combining national accounts data with household survey data to build a world income distribution and calculating inequality index based on that. However, it is subjected to inaccuracy and errors. This study uses night lights data (1992-2013) as a proxy for economic activities to build a uniform world income distribution. Since night lights cover almost all the inhabitant areas, for the first time, we can build a world income distribution on the same scale. Once the world income distribution is established, inequality index can be calculated accordingly. Using coefficient of variation (CV) as the measure of inequality, this study shows that overall world inequality tends to drop slights over 1992-2002, but increases dramatically after that and becomes stable after 2009. Between-country inequality accounts for a bigger share in the overall inequality, but the overall inequality pattern is driven by within-country inequality. Preliminary regression results show that foreign aid tends to decrease between-country inequality while increase within-country inequality.

Author

Clark, Race

Institution (department)

OU (CIMMS)

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The rapid recent expansion of remote sensing techniques and datasets has enabled scientists to produce flood forecasts in regions of the world where traditional hydrological information is either sparse or non-existent. Successfully working within this new paradigm requires the use of modern geographic information system (GIS) techniques. Here, we present a strategy for processing digital elevation models (DEMs) into a series of derivative datasets required for hydrological modeling applications. We demonstrate that these techniques work worldwide, at a wide range of resolutions, and with both open-source and proprietary GIS software. Additionally, we demonstrate the development of a new Windows graphical user interface for DEM processing, specifically intended for use with our partner organizations in several developing nations. Finally, we show how these techniques are combined with other geo-referenced global datasets to enable capacity building, training, and hydrological modeling activities around the world.

Author

Zheng, Yaoyao

Institution (department)

Hydrometeorology and Remote Sensing Lab (Schoo of Meteorology)

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Our understanding of the coupling between the terrestrial and the atmospheric parts of hydrological cycle is circumscribed by limited observations of soil moisture. The recently launched NASA's Soil Moisture Active Passive (SMAP) mission provides a high-resolution, global, space-borne quantitative estimation of shallow layer soil moisture. Comparison of soil moisture between SMAP and the International Soil Moisture Network (ISMN) provides insight into the use of satellite and in-situ-measurement information for quantification and prediction of soil moisture. The relationship between soil moisture and antecedent precipitation is empirically derived from rainfall rate and 5-cm soil moisture from Soil Climate Analysis Network (SCAN). Similar empirical relationship is obtained by incorporating the SMAP soil moisture and the Multi-Radar/Multi-Sensor (MRMS). Evaluation of the discrepancies between the relationships derived from in-situ measurement and remote sensing can be used to assess their ability to provide sufficient skills needed in hydrological and climate application. Effect of soil constituent on the quantification of soil moisture using precipitation is also investigated. This research recognizes the potential of multiplatform remote sensing for improving the quantitative estimation of soil moisture with large temporal and spatial coverage.

Author

Zhang, Yao

Institution (department)

University of Oklahoma Norman Campus (Department of Microbiology and Plant Biology)

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Carbon dioxide, one of the most important greenhouse gases (GHG), has continuously rise in atmosphere concentration ever since the beginning industrial revolution. Recent studies suggest the increasing CO2 concentration has caused the global warming, increasing frequency of extreme climate events, and increasing plant growth. Gross primary production (GPP)--the carbon fixed by plant through photosynthesis--is one of the most important process and the major driver of the global carbon cycle. During the past decades, numerous approaches has been made to improve the predictability of the GPP through ground, atmospheric and space observations, but there still remains a large range of GPP estimates among different method. In this study, we present the most recent GPP estimates from the VPM model for Oklahoma, this GPP product has 500m spatial resolution and 8-day temporal resolution. It will be beneficial to understand the climate change impact on terrestrial carbon cycling and provide valuable information for decision makers.

Author

wang, Jie

Institution (department)

University of Oklahoma Norman Campus (Department of microbiology and plant biology)

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Woody plant encroachment happened globally on many ecosystems over the past century. The southern great plains of USA was reported five- to sevenfold greater woody plants expansion than other regions of USA. It severely altered the grassland ecosystem structure and affected the ecosystem processes such as water cycle and biogeochemical cycle. However, a time series of maps based on historically observed woody plant encroachment are few or do not exist at the regional scale .The objectives of this study is to (1) develop an algorithm to extract the distribution of red cedar in Oklahoma; and (2) produce a time series maps of red cedar from 1980s; and (3) produce a stand age map to explain the encroachment process. A phenology-based algorithm was developed based on 25-m Phased Array Type L-band Synthetic Aperture Radar image in 2010 and 30-m Landsat Thematic Mapper (TM) images from 1984-2010.

Author

ZOU, Zhenhua

Institution (department)

University of Oklahoma (Department of Microbiology and Plant Biology)

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Open surface water bodies are important water resource for irrigation, livestock, wildlife, and human livelihood in Oklahoma. We used tens of thousands of Landsat TM/ETM+ images from 1984 to 2014 to track the dynamics of open surface water bodies. Both water-related spectral indices and vegetation indices were used to identify water bodies for individual images. We generated annual maps of open surface water bodies at 30-m resolution, including both year-long and seasonal surface water body types. Our result shows that on the average year-long open surface water body area over the 31 years is ~2307 km2, accounting for 1.27 % of the entire Oklahoma state (181,195 km2). Out of all year-long open surface water body pixels identified from 1984 to 2014 (4.3 million pixels, ~3132 km2, see map below), only 45% (~1415 km2) has water throughout the 31 years. In drought and pluvial years (e.g., 2006, 2007), both small water bodies and large water bodies had large changes. Severe drought in 2011/2012 resulted in the smallest amount of water bodies in the state. These water body maps could be used to support water resource management, crop and livestock production, and biodiversity conservation in the state.

Author

Holland, Alex

Institution (department)

University of Oklahoma (Department of Geography and Enviromental Sustainability )

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Two of the most visible aspects of urbanization are light pollution and heat pollution. Light pollution occurs when an area is lit by artificial lighting at night. The artificial light can make the sky much brighter than the normal nighttime lighting levels and disrupt circadian rhythms of many nocturnal species. Light pollution can also impact human society by disrupting our circadian rhythms as well. It can affect an area for more than 50 sqkm around a major urban area. Heat pollution is otherwise known as the “heat island effect”. This effect is caused when the rural areas surrounding a city cool off quicker than the city itself. This can lead to a whole host of ecological problems such as delaying or even halting migration of avian species. The “heat island” effect can impact humans by not allowing area to cool at night during the summer leading to an increased risk of heat exhaustion and heat stroke Using Denver (CO), New Orleans (LA), and Oklahoma City (OK) as the study areas, this project analyzes these effects in three areas that have varying climates, topography and urban growth patterns. Denver’s urban area is elongated from north to south with a few suburbs to the east and the mountains to the west. Oklahoma City, OK is located in the Cross Timbers region of the Southern Plains. The urban area is slightly elongated from north to south. The eastern part of the region has forested area while the western areas are more grassland. New Orleans, Louisiana is located in a crescent between the Mississippi River and Lake Pontchartain. There are suburbs south of the Mississippi as well as in St. Tammany Parish on the north shore of the lake; otherwise, the city is surrounded by marshland. We evaluated day and night land surface temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Image Suite (VIIRS) day/night band (DNB), to determine the light and heat impacts of these urban areas on their surroundings. To calculate the impact of land surface temperature, the difference between nighttime LST for urban areas and the LST for a nearby rural area was taken. A month long composite of DNB data was taken for the light pollution. We then compared the geographic extent of these impacts for the different cities. We also used census variables such as population, household income, and land use data to account for socioeconomic differences between the cities. In order to make this data compatible with the remote sensing data we had to convert it from vector to raster form.

Author

Conner, Tim

Institution (department)

University of Oklahoma (Geography)

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The May 24, 2011 tornado outbreak contained an EF5 tornado that tore through Piedmont and El Reno, Oklahoma, leaving a 1609-meter wide damage path stretching 101 kilometers. The purpose of this study was to evaluate three vegetation indices, EVI, NDVI, SR, as well as Principal Component Analysis calculated from Landsat 5 to determine which was most effective to visualize the damage within the study area and to analyze the recovery of natural vegetation and agriculture within the damage path. We discovered that the red and NIR reflectance bands did not reveal the damage path very clearly but the damage was visible in some principal components. We used NDVI to analyze the severity of the agricultural damage (Winter Wheat), and recovery which also appeared to have been stunted by drought.

Author

Magee, Chloe

Institution (department)

University of Oklahoma (Geography and Environmental Sustainability)

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Coastal cities are especially vulnerable to storm surge and flooding from a tropical cyclone. In particular, New Orleans, Louisiana presents a unique example due to the city’s geographically susceptible location between Lake Pontchartrain to the North and the drainage of the Mississippi River into the Gulf of Mexico. By conducting a comparative analysis of New Orleans’ pre-hurricane geography to post-storm flooding, problematic areas in the land cover can be identified for future mitigation and protection from levee failure. LiDAR data provides a digital perspective of the coastline and city for an interdisciplinary analysis of building density variation, elevation and geographic features. The proximity of each of these features to critical emergency response infrastructure demonstrates the resiliency of the town to future disasters. We utilize GIS to create digital elevation models overlayed with points of critical infrastructure. This location also creates a case study for other coastal cities susceptible to flooding impacts by providing data availability investigations and constructive map outputs. Moving forward, the output of such risk and vacation maps will be suggested for distribution from emergency managers as they make critical decisions during crisis situations, with the purpose of future collaboration with emergency officials in order to provide them with accurate and tangible spatial information. Mitigation techniques will be suggested based on findings with an emphasis on the understanding of susceptibility to risks based on geographic location.

Author

Ignac, Benjamin

Institution (department)

University of Oklahoma (DGES)

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While a majority of Americans think climate change is happening, those numbers have decreased from 71% in 2008 to 63% in 2015. Meanwhile, the number of people who deny climate change grew from 10% to 18%. However, recent studies have not detected a clear dichotomy between believers and deniers of climate change. A nationwide survey in 2014 by Yale and George Mason Universities highlights six distinct segments of the US adult population based on their perception of climate change: the Alarmed, Concerned, Cautious, Disengaged, Doubtful and Dismissive. The aim of this study is to survey and analyze climate change perceptions among college students at OU. I used the same survey instrument as the “Six Americas” survey by Yale and George Mason Universities, so I can directly compare my results with theirs and compare my sample with the nationwide survey. I surveyed over 490 college students enrolled in sections of the general education courses in the Fall 2015 semester at the OU College of Atmospheric and Geographic Sciences. College education could have a positive impact on an individuals’ perception of climate change. However, OU is located in a very Christian and conservative state. Could that affect how students perceive climate change? This cross-sectional study answers to the following questions: (1) How do the sampled students score compared to the American average? (2) Are there correlations between the individuals’ attitude towards climate change and their demographic characteristics? (3) Are there any differences in results among the courses of the sampled students?

Author

Zhou, Sha

Institution (department)

Tsinghua University (Department of Hydraulic Engineering)

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N/A