Other Economics Commons^™

Hybrid U-Net: Semantic Segmentation Of High-Resolution Satellite Images To Detect War Destruction, Shima Nabiee, Matthew Harding, Jonathan Hersh, Nader Bagherzadeh

Economics Faculty Articles and Research

Destruction caused by violent conflicts play a big role in understanding the dynamics and consequences of conflicts, which is now the focus of a large body of ongoing literature in economics and political science. However, existing data on conflict largely come from news or eyewitness reports, which makes it incomplete, potentially unreliable, and biased for ongoing conflicts. Using satellite images and deep learning techniques, we can automatically extract objective information on violent events. To automate this process, we created a dataset of high-resolution satellite images of Syria and manually annotated the destroyed areas pixel-wise. Then, we used this dataset to …

Futurological Fodder: On Communicating The Relationship Between Artificial Intelligence, Robotics, And Employment, Michael E. Samers Dr

Geography Faculty Publications

This article examines the debate concerning the employment implications of the so-called ‘Fourth Industrial Revolution’ (FIR) or the increasing presence of artificial intelligence and robotics in workplaces. I analyze three ‘genres’ associated with this debate (academic studies including neo-classical and heterodox/post-human approaches, the ‘gray literature’, and popular media) and I argue that together they represent ‘futurological fodder’ or discourses and knowledges that ‘perform’ the FIR and its purported consequences. I contend further that these genres involve a complex mix of ethics and politics, and I conclude with a reflection on the political implications of the FIR debate.

Poverty Mapping Using Convolutional Neural Networks Trained On High And Medium Resolution Satellite Images, With An Application In Mexico, Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz

Economics Faculty Articles and Research

Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These “poverty maps” are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 m2 and 50 cm2 respectively, covering all 2 million km2 of Mexico. Benchmark poverty estimates come from …

Building A Better Model: Variable Selection To Predict Poverty In Pakistan And Sri Lanka, Marium Afzal, Jonathan Hersh, David Newhouse

Economics Faculty Articles and Research

Numerous studies have developed models to predict poverty, but surprisingly few have rigorously examined different approaches to developing prediction models. This paper applies out of sample validation techniques to household data from Pakistan and Sri Lanka, to compare the accuracy of regional poverty predictions from models derived using manual selection, stepwise regression, and Lasso-based procedures. It also examines how much incorporating publically available satellite data into the model improves its accuracy. The five main findings are that: 1) Lasso tends to outperform both discretionary and stepwise models in Pakistan, where the set of potential predictors is large. 2) Lasso and …

Exploring The Neighborhood Preferences Of A Segment Of Millennials In Omaha, Nebraska, Aaron Kloke

Community and Regional Planning Program: Professional Projects

In 2010, Millennials, or those between 18 and 34, surpassed the Baby Boomers in population size. Today, Millennials, also known as Generation Y, make up over 25 percent of the United States’ population. In Omaha, they make up 26.9 percent of the population. The next largest generation in Omaha, the Baby Boomers, make for 19.2 percent of the population. Clearly, this emerging demographic has the ability to change the way we create and design our built environment if it so chooses.

To review how this generation may choose to change the way we design our future neighborhoods, national trends were …

Historical Health Conditions In Major Us Cities: The Hue Data Set, Carlos Villareal, Brian Bettenhausen, Eric Hanss, Jonathan Hersh

Economics Faculty Articles and Research

The Historical Urban Ecological data set is a new resource detailing health and environmental conditions within seven major U.S. cities during the study period from 1830 to 1930. Researchers collected and digitized ward-level data from annual reports of municipal departments that detail the epidemiological, economic, and demographic conditions within each city. They then drafted new geographic information system data to link the tabular records to ward geographies. These data provide a new foundation to revisit questions surrounding the urban mortality transition and the growth of U.S. cities.

Are Residents Who Are Displaced By Gentrification Better Or Worse Off After Relocating?, Alice M. Anigacz

Economics Honors Projects

This honors thesis examines how individuals displaced by gentrification fare after relocation, with changes in wage and income as the primary measures of well-being. Geo-coded Panel Study of Income Dynamics data is used in conjunction with decennial census tract-level neighborhood data to evaluate nationwide occurrences of gentrification and their effects on the displaced between 1990 and 1995, with a focus on whether changing neighborhood effects can account for the change in well-being. Standard OLS regressions not accounting for neighborhood effects find that compared to a nationwide sample, a sample of movers, and a sample of displaced residents, residents displaced specifically …

Bearing The Costs Of Human-Wildlife Conflict: The Challenges Of Compensation Schemes, Philip J. Nyhus, Steve A. Osofsky, Paul Ferraro, H Fischer, Francine Madden

Faculty Scholarship

No abstract provided.

Socioeconomic Index Scores For Metropolitan Connecticut, 1970, William H. Groff, John N. Wright

Storrs Agricultural Experiment Station

No abstract provided.