Report: GEEMaP's third annual research workshop

November 3, 2014 - 12:00am

The third annual GEEMaP research workshop was held on October 18. It was a lively session that included a keynote address by Dr. Tonya Peeples, talks by GEEMaP trainees and faculty, and a poster session by GEEMaP trainees. The workshop was held in the west colloquium suite at the Pappajohn Business Building at the University of Iowa.

Welcome by Kate Cowles, PI

Welcome was by Dr. Kate Cowles, GEEMaP PI and Professor of Statistics and Actuarial Science, The University of Iowa.

Keynote by Dr. Tonya Peeples

The keynote address was by Dr. Tonya Peeples, Professor and Associate Dean for Diversity and Outreach, College of Engineering, The University of Iowa. She spoke about Diversity and Inspiration.

Following are abstracts of the talks and posters.

Geofence Research, by Emily White

A Review of Geofence Research, by Emily White, GEEMaP Trainee
Abstract: A geofence is a virtual perimeter surrounding a point of interest, which is used to trigger certain actions. Events involving a geofence that might trigger an action include entering, exiting and remaining within. For example, a geofence might surround a coffee shop, and a discount may be sent to a user’s smart phone when the user enters the geofence. Alternatively a “smart” thermostat may turn down the heat when a user stays out of a house for a certain amount of time. This work reviews the existing literature on geofence technology and analyzes the major themes. Additionally the capabilities of various geofence APIs (application programming interfaces) are compared.

Green Urban Environments, by Cody Hodson

Green Urban Environments and Academic Performance, by Cody Hodson, GEEMaP Trainee
Abstract: Green environments provide a number of benefits to health and well-being, including cognitive and emotional benefits. Research has linked exposure to natural settings and features with improved focus and decision-making, as well as increased vitality and lower levels of stress; however, the literature does not provide adequate evidence to support a connection between green urban environments and improved academic performance. This positive association likely exists given the results of studies exploring the relationship between natural environments and cognitive as well as emotional function, which have serious implications for academic success. This investigation aims to establish a significant positive relationship between the “greenness” of one’s environment and academic performance using the Twin Cities Metropolitan Area as a case study. The analysis will incorporate socioeconomic and demographic data, environmental data, and standardized test score data. The results of this work will provide insight into the effects of green environments on academic performance that can inform decision makers and stakeholders of the implications that green urban design has for effective education, allowing for the implementation of policy and actions that can lead to a better-educated society and improved health and well-being by extension.

Modeling and Predicting Frac Sand Mining Sites, by GEEMaP Trainees

Modeling and Predicting Mine Sites for Hydraulic Fracking Silica Sand in North Eastern Iowa, by Zachary Bales, Jesslyn Landgren, Cristina Munoz, Austen Smith, Sean Young, GEEMaP Trainees
Abstract: Winneshiek, Allamakee, and Clayton counties in northeastern Iowa feature St. Peter sandstone geology near the surface that could allow potential low cost extraction of fine silica sand. Due to recent expansion of hydraulic fracturing operations used to extract oil and natural gas, demand for a primary ingredient, silica sand, colloquially dubbed “frac sand,” has soared. This rise in demand for silica sand resources has led sand mining companies to approach landowners and county boards to gain land rights and permits to establish new sand mines. Wisconsin has over 100 sand mine sites for this purpose, and now northeastern Iowa is being considered for future mine locations. In order to consider both the challenges and opportunities of sand mining in northeastern Iowa communities, knowledge on where these sites might be located must first be known. Our model uses several Wisconsin geospatial datasets to derive features typical of existing sand mine locations. Geoinformation science (GIS) tools are used to extract features of each grid location in Wisconsin, and each cell is classified as one that either contains a sand mine or does not. The derived statistical model is then able to be applied to a parallel geospatial feature set for northeast Iowa. Identifying potential mine locations will enable further inquiry and knowledge on this current pressing issue.

Social Vulnerability Indices, by Dr. Eric Tate

Social Vulnerability Indices: A Comparative Assessment Using Uncertainty and Sensitivity Analysis, by Eric Tate, Assistant Professor, Department of Geographical and Sustainability Sciences, The University of Iowa
Abstract: Social vulnerability indices have emerged over the past decade as quantitative measures of the social dimensions of natural hazards vulnerability. But how reliable are the index rankings? This presentation details the use of global sensitivity analyses to internally validate the methods used in the most common social vulnerability index designs: deductive, hierarchical, and inductive. Uncertainty analysis is performed to assess the robustness of index ranks when reasonable alternative index configurations are modeled. Across three study areas, the hierarchical design was found to be the most accurate, while the inductive model was the most precise. Sensitivity analysis is employed to understand which decisions in the vulnerability index construction process have the greatest influence on the stability of output rankings. The deductive index ranks are found to be the most sensitive to the choice of transformation method, hierarchical models to the selection of weighting scheme, and inductive indices to the indicator set and scale of analysis.

 Hudson Francis

Poster presentation: Prediction of Engine Sales with a Data-Driven Approach, Hudson Francis, GEEMaP Trainee
Abstract: Models predicting volume of engine sales from historical data are developed. To accommodate seasonal effects, neural networks and autoregressive integrated moving average (ARIMA) approaches are considered. Previous research on the effectiveness of neural networks to model phenomena with seasonality and trend using raw data has been inconclusive. In this study, four predictive models for a linear time series with seasonality are developed and their accuracy is studied. Performance of a dummy variable linear regression model, a seasonal ARIMA model, a neural network model using raw historical data, and a hybrid linear model are compared. The seasonal ARIMA and linear regression models are found to perform better than the neural network model. The hybrid linear model is found to outperform the three individual models.

 Kenneth Wacha

Poster presentation: Potential Carbon Transport: Linking Soil aggregate Stability and Sediment Enrichment for Updating the Soil Active Layer of Intensely Managed Landscapes (IMLs), Ken Wacha, GEEMaP Trainee
Abstract: Currently, many biogeochemical models lack the mechanistic capacity to accurately simulate soil organic carbon (SOC) dynamics, especially within intensely managed landscapes (IMLs) such as those found in the U.S. Midwest. These modeling limitations originate by not accounting for downslope connectivity of flowpathways initiated and governed by landscape processes, and by neglecting the role of soil aggregates in determining sediment enrichment ratios, which provide dynamic updates to the soil active layer (generally top 20-30cm of soil). These hydro-geomorphic processes, often amplified in IMLs by tillage events and seasonal canopy, can greatly impact biogeochemical cycling (e.g., enhanced mineralization) and in turn, have huge implications/uncertainty in determining SOC budgets.