IMAGE OF THE WEEK
Austin Brown won third place in the 2016 EMS Undergraduate Student Poster Exhibition. His team’s entry was, “Recession of the Ampatuni and Ausangate Glaciers.” Denice Wardrop, Mike Nassry, and Joe Bishop were the project advisers. You can see his CAUSE digital story on the department homepage.
Angela Rogers was accepted as a College of Earth and Mineral Sciences Administrative Fellow for 2017.
Lauren Fritzsche was elected as the new graduate representative. She joins Ramzi Tubbeh, E.-K. Kim, and Peter Koby as the department’s graduate representatives. Her term will run from January to December 2017.
Josh Inwood was quoted in a news story on CBS “KKK members insist they’re not ‘white supremacists’”
Guido Cervone received NCAR visitor funding.
Laura Clemente-Harding received an NCAR ASP scholarship.
Supporting Women In Geography exceeded its donation goal and were able to sponsor an additional family through Centre County Women’s Resource Center. A family of three and a family of four received holiday baskets. Left, a picture of one of the baskets.
Coffee Hour spring line-up announced
Spring semester Coffee Hour will begin on January 20 with Derek Alderman speaking about MLK streets as monuments to the Civil Rights Movement and also extensions of the ongoing, unfinished struggle for civil rights. Two Miller Lectures will be held this semester. The first, with Lynn Staehelli, will be on January 27. The second, with Tonny Bebbington, will be on April 14. The list of speakers can be found on the department website. Details about each talk are added as they are confirmed. Coffee Hour is the Department of Geography’s ongoing Friday lecture series. Coffee Hour has been held during the spring and fall semesters since 1968.
Dean of EMS tapped for NSF post
The National Science Foundation has named William E. Easterling III, professor of geography and dean of Penn State’s College of Earth and Mineral Sciences (EMS), to serve as director for the Directorate for Geosciences (GEO) in Washington D.C., which supports fundamental research spanning the atmospheric, earth, ocean and polar sciences.
Easterling will step down as dean on May 31, 2017, and will begin his four-year NSF appointment on June 1, 2017. He will remain a member of the Penn State faculty during the four years with NSF.
Apartheid’s lingering effects on HIV and AIDS
Though it was abolished more than two decades ago, Apartheid continues to affect communities in South Africa. In this political system, which lasted from 1948 to the 1994 democratic elections, people were racially classified and forced to live in segregated geographic areas. Within rural South Africa, these spatial containers were called “homelands,” or Bantustans.
“If we were driving through South Africa today, we would easily identify the former Bantustan border because you can see fairly substantial income inequalities from one village to the next. The former KaNgwane Bantustan is a very high population-density area that is surrounded by privately owned farms producing sugar cane and other products for foreign markets,” said Brian King, associate professor of geography. “My work has shown that South Africa’s legacies of racial classification and spatial regulation have played a role in that, and how these spaces continue to shape health and livelihood possibilities in the contemporary era.”
RECENTLY (OR SOON TO BE) PUBLISHED
A step-by-step approach to improve data quality when using commercial business lists to characterize retail food environments
By Kelly K. Jones, Shannon N. Zenk, Elizabeth Tarlov, Lisa M. Powell, Stephen A. Matthews and Irina Horoi
In BMC Research Notes
Access DOI: 10.1186/s13104-016-2355-1
Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans’ Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this approach.
An Impressionistic Cartographic Solution for Base Map Land Cover with Coarse Pixel Data
By Paulo Raposo, Cynthia A. Brewer, Kevin Sparks
In Cartographic Perspectives
Access DOI: 10.14714/CP83.1351
Several every-day cartography applications do not require sharply precise base maps, and in fact benefit from their generalization or deliberate obscuration, such as tourist or transit maps. Additionally, raster data fine enough for a given map scale are not always available. We present a method of creating an impressionistic land cover base map for topographic mapping in which the above two conditions are true, using the National Land Cover Database (NLCD) of the US Geological Survey (USGS). The method is based on reclassification, upsampling, constrained randomization at class boundary edges, and deliberate use of colors with very similar lightness values. The method spans both scientific geospatial data treatment and artistic cartographic design, and both generalizes and enhances the data. The processing, automated in ArcGIS™, is detailed, and examples of the product are provided.
Agro-environmental Transitions in African Mountains: Shifting Socio-spatial Practices Amid State-Led Commercialization in Rwanda
By Nathan Clay
In Annals of the American Association of Geographers
Agricultural commercialization has been slow to take hold in mountain regions throughout the world. It has been particularly limited by challenges of mechanization, transportation access, and governance. Efforts at green-revolution style development have met with persistent failures in highland sub-Saharan Africa, where agricultural systems are often finely tuned to complex and dynamic social–ecological contexts. In Rwanda, a mountainous country in east central Africa, development efforts have long aimed to transition away from largely subsistence-based production that relies on high labor input toward commercial farming systems that are rooted in capital investment for marketable goods. Since 2005, Rwanda’s land policy has become increasingly ambitious, aiming to reduce the 85 percent of households involved in agriculture to 50 percent by the year 2020. The country’s Crop Intensification Program (CIP) compels farmers to consolidate land and cultivate government-selected crops. Although state assessments have touted the productivity gains created through the CIP, others speculate that households could be losing access to crucial resources. Research from both sides, however, has focused squarely on the CIP’s immediate successes and failures without considering how households are responding to the program within the context of the complex and variable mountain environment. Drawing from political ecology and mountain geography, this article describes recent state-led agricultural commercialization in Rwanda as a partial and contested process. By analyzing complex land-use and livelihood changes, it fills an important conceptual and empirical research gap in understanding the environmental and social dynamics of the agrarian transitions of the highlands of Africa.
Supervised classification of civil air patrol (CAP)
By Elena Sava, Laura Clemente-Harding, Guido Cervone
In Natural Hazards
Access DOI: 10.1007/s11069-016-2704-3
The mitigation and response to floods rely on accurate and timely flood assessment. Remote sensing technologies have become the de facto approach for observing the Earth and its environment. However, satellite remote sensing data are not always available, and it is crucial to develop new techniques to complement them with additional sources. This research proposes a new methodology based on machine learning algorithms to automatically identify water pixels in Civil Air Patrol (CAP) aerial imagery. Specifically, a wavelet transformation is paired with multiple classifiers to build models that discriminate water and non-water pixels. The learned classification models are first tested against a set of control cases and then used to automatically classify each image separately. Lastly, for each pixel in an image, a measure of uncertainty is computed as a proportion of the number of models that classify the pixel as water. The proposed methodology is tested on imagery collected during the 2013 Colorado flood.