College of Liberal Arts
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Browsing College of Liberal Arts by Department "Geography and Environmental Studies"
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Item A Cross-Level Exploratory Analysis of “Neighborhood Effects” on Urban Behavior: An Evolutionary Perspective(Multidisciplinary Digital Publishing Institute, 2015-11) Weaver, RussellIt is now generally accepted that spatially-based neighborhood or contextual attributes influence individual behaviors. However, studies of contextual effects often operationalize “neighborhoods” as static, single-level administrative units that are chosen for data availability rather than theoretical reasons. This practice has led to new calls for sound conceptual models that guide data collection efforts and statistical analyses related to these phenomena. While many such models are in use or being proposed in the social sciences, this article argues that research in the field of evolutionary studies offers alternative and interesting ways of investigating neighborhood effects. Accordingly, the article pursues two objectives. First, it makes connections between neighborhood effects research in the social sciences and relevant literature in evolutionary game theory and evolutionary urban geography. Second, these interdisciplinary interactions guide the development of a cross-level conceptual model of neighborhood effects on urban social behavior. The conceptual model is then translated into an empirical model that tests whether and how property maintenance behavior in a selected U.S. study area changes as a function of neighborhood context. The findings reveal that neighborhood effects operate at multiple, interacting spatial levels in the study area, which suggests that conventional single-level administrative boundaries are not equipped to capture these effects. While they are proffered as exploratory, the results nonetheless imply that insights from evolutionary research can add depth and theoretical grounding to contextual effects studies in the social sciences.Item A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon(Multidisciplinary Digital Publishing Institute, 2018-09) Zhao, Chunhong; Jensen, Jennifer L. R.; Weng, Qihao; Weaver, RussellThis study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.Item A Review of the Applications of Remote Sensing in Fire Ecology(Multidisciplinary Digital Publishing Institute, 2019-11) Szpakowski, David M.; Jensen, Jennifer L. R.Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.Item African American Experiences in the Historic Dunbar Neighborhood in San Marcos, Texas: A Case Study of Counter-Life Stories(Multidisciplinary Digital Publishing Institute, 2020-10-03) Ashford-Hanserd, Shetay; Sarmiento, Eric; Myles, Colleen C.; Rayburn, Steven W.; Roundtree, Aimee K.; Hayton, Mary-Patricia; Ybarra, Edward; Benitez, Sarai; Clifford, Theresa M.; Pierce, Christopher; Williams, Chad D.; Maleki, ShadiThe purpose of this participatory research project is to examine the lived experiences (counter-life stories) of current and former Dunbar residents and congregants of Dunbar churches to demonstrate how local stories counter the dominant perspective about the experiences of American Americans in the Dunbar community. Once a thriving community at the center of civil rights activities in Hays County, Texas, the neighborhood has evolved in many ways in the past several decades, contrary to popular belief. This case study employs counter-life story methodology to uncover the hidden truths about Dunbar residents and congregants’ experiences to generate new knowledge about the experiences of African Americans in San Marcos, Texas, and Hays County. Thematic analysis of unfiltered commentary from Dunbar community members revealed three emergent themes: history of racism and slavery, impact of environmental and social racism, and rebuilding and restoring the community. Individual and shared strengths make the community unique and resilient. In-migration of new community members has been outpaced by outmigration. Finally, issues of taxation, representation, and the ongoing deterioration of neighborhood infrastructure are forefront in community members’ minds. In sum, the bedrock of personal and community values and hard work has not changed, but external forces continue to affect the community and compel it to pivot and make plans for change. Personal and communal strengths make the community unique and resilient. Future work will enlist geographic data and methods to help further investigate changes over time.Item Anomalous blocking over Greenland preceded the 2013 extreme early melt of local sea ice(Cambridge University Press, 2018-07) Ballinger, Thomas J.; Hanna, Edward; Hall, Richard J.; Cropper, Thomas E.; Miller, Jeffrey; Ribergaard, Mads H.; Overland, James E.; Hoyer, JacobThe Arctic marine environment is undergoing a transition from thick multi-year to first-year sea-ice cover with coincident lengthening of the melt season. Such changes are evident in the Baffin Bay-Davis Strait-Labrador Sea (BDL) region where melt onset has occurred ~8 days decade−1 earlier from 1979 to 2015. A series of anomalously early events has occurred since the mid-1990s, overlapping a period of increased upper-air ridging across Greenland and the northwestern North Atlantic. We investigate an extreme early melt event observed in spring 2013. (~6σ below the 1981–2010 melt climatology), with respect to preceding sub-seasonal mid-tropospheric circulation conditions as described by a daily Greenland Blocking Index (GBI). The 40-days prior to the 2013 BDL melt onset are characterized by a persistent, strong 500 hPa anticyclone over the region (GBI >+1 on >75% of days). This circulation pattern advected warm air from northeastern Canada and the northwestern Atlantic poleward onto the thin, first-year sea ice and caused melt ~50 days earlier than normal. The episodic increase in the ridging atmospheric pattern near western Greenland as in 2013, exemplified by large positive GBI values, is an important recent process impacting the atmospheric circulation over a North Atlantic cryosphere undergoing accelerated regional climate change.Item Applying Place-Based Social-Ecological Research to Address Water Scarcity: Insights for Future Research(Multidisciplinary Digital Publishing Institute, 2018-05) Castro, Antonio J.; Quintas-Soriano, Cristina; Brandt, Jodi; Atkinson, Carla L.; Baxter, Colden V.; Burnham, Morey; Egoh, Benis N.; Garcia-Llorente, Marina; Julian, Jason P.; Martin-Lopez, Berta; Liao, Felix Haifeng; Running, Katrina; Vaughn, Caryn C.; Norstrom, Albert V.Globally, environmental and social change in water-scarce regions challenge the sustainability of social-ecological systems. WaterSES, a sponsored working group within the Program for Ecosystem Change and Society, explores and compares the social-ecological dynamics related to water scarcity across placed-based international research sites with contrasting local and regional water needs and governance, including research sites in Spain and Sweden in Europe, South Africa, China, and Alabama, Idaho, Oklahoma, and Texas in the USA. This paper aims to provide a commentary on insights into conducting future solutions-oriented research on water scarcity based on the understanding of the social-ecological dynamics of water scarce regions.Item Arctic Change and Possible Influence on Mid-Latitude Climate and Weather: A US CLIVAR White Paper(2018-03) Cohen, Judah; Zhang, Xiangdong; Francis, Jennifer; Jung, Thomas; Kwok, Ronald; Overland, James E.; Taylor, Patrick; Lee, Sukyoung; Coumou, Dim; Handorf, Doerthe; Semmler, Tido; Vihma, Timo; Smith, Doug; Ballinger, Thomas J.The Arctic has warmed more than twice as fast as the global average since the mid 20th century, a phenomenon known as Arctic amplification (AA). These profound changes to the Arctic system have coincided with a period of ostensibly more frequent events of extreme weather across the Northern Hemisphere (NH) mid-latitudes, including extreme heat and rainfall events and recent severe winters. Though winter temperatures have generally warmed since 1960 over mid-to-high latitudes, the acceleration in the rate of warming at high-latitudes, relative to the rest of the NH, started approximately in 1990. Trends since 1990 show cooling over the NH continents, especially in Northern Eurasia. The possible link between Arctic change and mid-latitude climate and weather has spurred a rush of new observational and modeling studies. A number of workshops held during 2013-2014 have helped frame the problem and have called for continuing and enhancing efforts for improving our understanding of Arctic-mid-latitude linkages and its attribution to the occurrence of extreme climate and weather events. Although these workshops have outlined some of the major challenges and provided broad recommendations, further efforts are needed to synthesize the diversified research results to identify where community consensus and gaps exist. Building upon findings and recommendations of the previous workshops, the US CLIVAR Working Group on Arctic Change and Possible Influence on Mid-latitude Climate and Weather convened an international workshop at Georgetown University in Washington, DC, on February 1-3, 2017. Experts in the fields of atmosphere, ocean, and cryosphere sciences assembled to assess the rapidly evolving state of understanding, identify consensus on knowledge and gaps in research, and develop specific actions to accelerate progress within the research community. With more than 100 participants, the workshop was the largest and most comprehensive gathering of climate scientists to address the topic to date. In this white paper, we synthesize and discuss outcomes from this workshop and activities involving many of the working group members.Item Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed(Multidisciplinary Digital Publishing Institute, 2021-09-16) Farzin, Mohsen; Avand, Mohammadtaghi; Ahmadzadeh, Hassan; Zelenakova, Martina; Tiefenbacher, JohnDue to numerous droughts in recent years, the amount of surface water in arid and semi-arid regions has decreased significantly, so reliance on groundwater to meet local and regional demands has increased. The Kabgian watershed is a karst watershed in southwestern Iran that provides a significant proportion of drinking and agriculture water supplies in the area. This study identified areas with karst groundwater potential using a combination of machine learning and statistical models, including entropy-SVM-LN, entropy-SVM-SG, and entropy-SVM-RBF. To do this, 384 karst springs were identified and mapped. Sixteen factors that are related to karst potential were identified from a review of the literature, and these were compiled for the study area. The 384 locations were randomly separated into two categories for training (269 location) and validation (115 location) datasets to be used in the modeling process. The ROC curve was used to evaluate the modeling results. The models used, in general, were good at determining the location of karst groundwater potential. The evaluation showed that the E-SVM-RBF model had an area under the curve of 0.92, indicating that it was most accurate estimator of groundwater potential among the ensemble models. Evaluation of the relative importance of each of the 16 factors revealed that land use, a vector ruggedness measure, curvature, and topography roughness index were the most important explainers of the presence of karst groundwater in the study area. It was also found that the factors affecting the presence of karst springs are significantly different from non-karst springs.Item Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem(Multidisciplinary Digital Publishing Institute, 2016-01) Jensen, Jennifer L. R.; Mathews, Adam J.We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R² values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R² values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area. View Full-TextItem Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models(Public Library of Science, 2020-07-28) Pourghasemi, Hamid Reza; Pouyan, Soheila; Farajzadeh, Zakariya; Sadhasivam, Nitheshnirmal; Heidari, Bahram; Babaei, Sedigheh; Tiefenbacher, JohnInfectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the—polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.Item Augmenting Geovisual Analytics of Social Media Data with Heterogeneous Information Network Mining-Cognitive Plausibility Assessment(Public Library of Science, 2018-12) Savelyev, Alexander; MacEachren, Alan M.This paper investigates the feasibility, from a user perspective, of integrating a heterogeneous information network mining (HINM) technique into SensePlace3 (SP3), a webbased geovisual analytics environment. The core contribution of this paper is a user study that determines whether an analyst with minimal background can comprehend the network data modeling metaphors employed by the resulting system, whether they can employ said metaphors to explore spatial data, and whether they can interpret the results of such spatial analysis correctly. This study confirms that all of the above is, indeed, possible, and provides empirical evidence about the importance of a hands-on tutorial and a graphical approach to explaining data modeling metaphors in the successful adoption of advanced data mining techniques. Analysis of outcomes of data exploration by the study participants also demonstrates the kinds of insights that a visual interface to HINM can enable. A second contribution is a realistic case study that demonstrates that our HINM approach (made accessible through a visual interface that provides immediate visual feedback for user queries), produces a clear and a positive difference in the outcome of spatial analysis. Although this study does not aim to validate HINM as a data modeling approach (there is considerable evidence for this in existing literature), the results of the case study suggest that HINM holds promise in the (geo)visual analytics domain as well, particularly when integrated into geovisual analytics applications. A third contribution is a user study protocol that is based on and improves upon the current methodological state of the art. This protocol includes a hands-on tutorial and a set of realistic data analysis tasks. Detailed evaluation protocols are rare in geovisual analytics (and in visual analytics more broadly), with most studies reviewed in this paper failing to provide sufficient details for study replication or comparison workItem Can Shrinking Cities Demolish Vacancy? An Empirical Evaluation of a Demolition-First Approach to Vacancy Management in Buffalo, NY, USA(Multidisciplinary Digital Publishing Institute, 2018-08) Weaver, Russell; Knight, JasonPublicly-funded demolition of vacant structures is an essential tool used in shrinking cities to eliminate nuisances and, often, reduce vacancy rates. Concerning the latter, however, when shrinking cities implement large-scale demolition programs independent of complementary planning efforts, it is reasonable to expect impacts on vacancy to be negligible. Among other reasons, demolition operates only on the outflow of existing vacant structures and largely fails to grapple with inflows that add to vacancy over time. This article evaluates an ambitious demolition program in Buffalo, NY, USA, that sought, explicitly, to lower the municipality’s overall vacancy rate. Evidence from statistical changepoint models and Granger tests suggest that, while Buffalo’s overall vacancy rate, measured as undeliverable postal addresses, appeared to decrease around the time of the program, the drop was not linked to elevated demolition activity. The same finding holds for the subarea in which demolitions were spatiotemporally clustered. Although this lack of efficacy is potentially because the city failed to demolish its targeted number of structures, we argue that the likelier explanation is that demolition was not part of a holistic planning strategy. These results have important implications for using large-scale demolition programs as standalone vacancy management policies in shrinking cities.Item Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr(Public Library of Science, 2016-05-09) Yuan, Yihong; Medel, MonicaRecent advances in multimedia and mobile technologies have facilitated large volumes of travel photos to be created and shared online. Although previous studies have utilized geotagged photos to model travel patterns at individual locations, there is limited research on how these datasets can model international travel behavior and inter-country travel flows—a crucial indicator to quantify the interactions between countries in tourism economics. Realizing the necessity to investigate the potential of geotagged photos in tourism geography, this research investigates international travel patterns from two perspectives: 1) We apply a series of indicators (radius of gyration (ROG), number of countries visited, and entropy) to measure the descriptive characteristics of international travel in different countries; 2) By constructing a gravity model of trade, we investigate how distance decay influences the magnitude of international travel flow between geographic entities, and whether (or how much) the popularity of a given destination (defined as the percentage of tourist income in national gross domestic product (GDP)) affects travel choices in different countries. The results provide valuable input to various commercial applications such as individual travel planning and destination suggestions.Item Climate Change, Land Use/Land Cover Change, and Population Growth as Drivers of Groundwater Depletion in the Central Valleys, Oaxaca, Mexico(Multidisciplinary Digital Publishing Institute, 2019-05) Ojeda Olivares, Edwin Antonio; Sandoval Torres, Sadoth; Belmonte-Jimenez, Salvador Isidro; Campos Enriquez, Jose Oscar; Zignol, Francesco; Reygadas Langarica, Yunuen; Tiefenbacher, JohnGroundwater depletion is an important problem driven by population growth, land use and land cover (LULC) change, climate change, and other factors. Groundwater depletion generates water stress and encourages unstainable resource use. The aim of this study is to determine how population growth, LULC change, and climate change relate to groundwater depletion in the Alto Atoyac sub-basin, Oaxaca, Mexico. Twenty-five years of dry season water table data from 1984 to 2009 are analyzed to examine annual groundwater depletion. Kriging is used to interpolate the region’s groundwater levels in a geographic information system (GIS) from mapped point measurements. An analysis of remotely sensed data revealed patterns of LULC change during a 34-year (1986–2018) period, using a supervised, machine-learning classification algorithm to calculate the changes in LULC. This analysis is shown to have an 85% accuracy. A global circulation model (GFDL-CM3) and the RCP4.5 and RCP8.5 scenarios were used to estimate the effects of climate change on the region’s groundwater. Estimates of evapotranspiration (using HELP3.5 code) and runoff (USDA-SCS-CN), were calculated. Since 1984, the region’s mean annual temperature has increased 1.79 °C and urban areas have increased at a rate of 2.3 km2/year. Population growth has increased water consumption by 97.93 × 10(6) m3/year. The volume of groundwater is shrinking at a rate of 284.34 × 106 m3/year, reflecting the extreme pressure on groundwater supply in the region. This research reveals the nature of the direct impacts that climate change, changing LULCs, and population growth have in the process of groundwater depletion.Item Correction: Gharehchahi, S.; James, W.H.M.; Bhardwaj, A.; Jensen, J.L.R.; Sam, L.; Ballinger, T.J.; Butler, D.R. Glacier Ice Thickness Estimation and Future Lake Formation in Swiss Southwestern Alps—The Upper Rhône Catchment: A VOLTA Application. Remote Sens. 2020, 12, 3443(Multidisciplinary Digital Publishing Institute, 2020-11-12) Gharehchahi, Saeideh; James, William H. M.; Bhardwaj, Anshuman; Jensen, Jennifer L. R.; Sam, Lydia; Ballinger, Thomas J.; Butler, DavidNo abstract prepared.Item COVID-19: Evidenced Health Disparity(Multidisciplinary Digital Publishing Institute, 2021-08-05) Iyanda, Ayodeji E.; Boakye, Kwadwo; Lu, YongmeiHealth disparity is an unacceptable, unjust, or inequitable difference in health outcomes among different groups of people that affects access to optimal health care, as well as deterring it. Health disparity adversely affects disadvantaged subpopulations due to a higher incidence and prevalence of a particular disease or ill health. Existing health disparity determines whether a disease outbreak such as coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), will significantly impact a group or a region. Hence, health disparity assessment has become one of the focuses of many agencies, public health practitioners, and other social scientists. Successful elimination of health disparity at all levels requires pragmatic approaches through an intersectionality framework and robust data science.Item Demand for Stream Mitigation in Colorado, USA(Multidisciplinary Digital Publishing Institute, 2019-01) Julian, Jason P.; Weaver, RussellColorado, the headwaters for much of the United States, is one of the fastest growing states in terms of both population and land development. These land use changes are impacting jurisdictional streams, and thus require compensatory stream mitigation via environmental restoration. In this article, we first characterize current demand and supply for stream mitigation for the entire state of Colorado. Second, we assess future demand by forecasting and mapping the lengths of streams that will likely be impacted by specific development and land use changes. Third, based on our interviews with experts, stakeholders, resource managers, and regulators, we provide insight on how regulatory climate, challenges, and water resource developments may influence demand for stream mitigation. From geospatial analyses of permit data, we found that there is currently demand for compensatory stream mitigation in 13 of the 89 HUC-8 watersheds across Colorado. Permanent riverine impacts from 2012–2017 requiring compensatory mitigation totaled 38,292 linear feet (LF). The supply of stream mitigation credits falls well short of this demand. There has only been one approved stream mitigation bank in Colorado, supplying only 2539 LF credits. Based on our analyses of future growth and development in Colorado, there will be relatively high demand for stream mitigation credits in the next 5–10 years. While most of these impacts will be around the Denver metropolitan area, we identified some new areas of the state that will experience high demand for stream mitigation. Given regulatory agencies’ stated preference for mitigation banks, the high demand for stream mitigation credits, and the short supply of stream credits, there should be an active market for stream mitigation banks in Colorado. However, there are some key obstacles preventing this market from moving forward, with permanent water rights’ acquisitions at the top of the list. Ensuring stream mitigation compliance is essential for restoring and maintaining the chemical, physical, and biological integrity of stream systems in Colorado and beyond.Item Evaluating Consumer Nutrition Environment in Food Deserts and Food Swamps(Multidisciplinary Digital Publishing Institute, 2021-03-07) Jin, He; Lu, YongmeiThis research examines the consumer nutrition environment in the selected neighborhoods identified as food deserts, food swamps, and food oases in Austin, Texas, by considering food availability, food price, food quality, and food labeling. A food auditing instrument M-TxNEAS (He Jin, San Marcos, TX, USA) was developed to capture the unique dietary culture and food preferences in Texas. A total of 93 food items in 14 grocery stores and supermarkets (GS) and 32 convenience stores (CS) were surveyed. The GS in food swamps and food oases were found to offer significantly more healthy foods than the CS. The availability of healthy food in the GS in the food swamps and food oases is significantly higher than that of the GS from the food deserts; CS in the three neighborhoods did not exhibit a significant difference in healthy food availability. There was no significant difference between the price for the healthy items (lower fat, lower calorie, and whole grain) and that for the regular food options. No significant difference was found for food quality or food labeling between the stores from the different types of neighborhoods. The GS in food deserts are small grocery stores carrying limited ranges of foods. The establishment of larger food stores in the food deserts might not be very rewarding, but opening more small grocery stores with healthier options may alleviate food issues.Item Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study(Multidisciplinary Digital Publishing Institute, 2020-01) Arabameri, Alireza; Blaschke, Thomas; Pradhan, Biswajeet; Pourghasemi, Hamid Reza; Tiefenbacher, John; Bui, Dieu TienGully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.Item Examining personal air pollution exposure, intake, and health danger zone using time geography and 3D geovisualization(Multidisciplinary Digital Publishing Institute, 2014-12) Lu, Yongmei; Fang, Tianfang BernieExpanding traditional time geography, this study examines personal exposure to air pollution and personal pollutant intake, and defines personal health danger zones by accounting for individual level space-time behavior. A 3D personal air pollution and health risk map is constructed to visualize individual space-time path, personal Air Quality Indexes (AQIs), and personal health danger zones. Personal air pollution exposure level and its variation through space and time is measured by a portable air pollutant sensor coupled with a portable GPS unit. Personal pollutant intake is estimated by accounting for air pollutant concentration in immediate surroundings, individual’s biophysical characteristics, and individual’s space-time activities. Personal air pollution danger zones are defined by comparing personal pollutant intake with air quality standard; these zones are particular space-time-activity segments along an individual’s space-time path. Being able to identify personal air pollution danger zones can help plan for proper actions aiming at controlling health impacts from air pollution. As a case study, this paper reports on an examination and visualization of an individual’s two-day ozone exposure, intake and danger zones in Houston, Texas