IMAGE OF THE WEEK
SWIG’s workshop at the Eberly Science College’s annual ENVISION: Stem Career Day for Young Women held on January 26. Their workshop was called, “Seeing Like a Satellite,” and taught participants about land use/land cover change through satellite imagery. Image: Michelle Ritchie
Gregory S. Jenkins will give a talk on Wednesday, Feb. 20 on “Natural, Human and Climate Change Drivers in Africa and the Need for Interdisciplinary Research and Communication,” at 12:30 p.m. in 158 Willard Building.
Center for Landscape Dynamics Grad Award Lightning Talks rescheduled for Tuesday, Feb. 26, noon-1:30 p.m. in 319 Walker Building and the new due date for the Grad Award proposals is March 15.
Two online geospatial program students, Danielle Barlow and Samuel Cook, have been selected as Student Assistants for the USGIF Symposium, the premier event of the geospatial intelligence business and profession, being held June 2-5, 2019, in San Antonio, Texas.
Seth Dixon (’09g) curates a Geography Education ScoopIt topic page.
The University of Toronto is hosting the Geohealth Network Conference: Building Capacity for Health Geography on April 30. For more information and to submit an abstract: https://www.geohealthnetwork.com/
Save the date: The Penn State Geography Alumni and Friends Reception during the AAG annual meeting is scheduled for Friday, April 5 at 7:00 p.m. at LiLLies. More details to come soon.
Food insecurity is often addressed from an agricultural perspective, yet forests provide important and unique contributions to nutrition in many regions. The contributions of forests to nutrition are quite varied, flowing through surprisingly complex pathways. Furthermore, the extent to which the availability of nutritious forest foods depend on the type, amount, and configuration of forests is largely under-appreciated. Here, we explore some of the ways remote sensing can better characterize forest-nutrition linkages.
- Friday, Feb. 22
- 3:30 p.m. in 319 Walker Building, Coffee and refreshments
- 4:00 in 112 Walker Building, Lecture
- Coffee Hour To Go webcast
People around the world paint their walls different colors, buy plants to spruce up their interiors and engage in a variety of other beautifying techniques to personalize their homes, which inspired a team of researchers to study about 50,000 living rooms across the globe.
In a study that used artificial intelligence to analyze design elements, such as artwork and wall colors, in pictures of living rooms posted to Airbnb, a popular home rental website, the researchers found that people tended to follow cultural trends when they decorated their interiors.
Robert Brooks is quoted.
The Trump administration’s stated goal for a rule defining which wetlands and waterways get Clean Water Act protection: Write a simple regulation that landowners can understand.
“I believe that any property owner should be able to stand on his or her property and be able to tell whether or not they have a ‘water of the U.S.’ on their property without having to hire an outside consultant or attorney,” acting EPA Administrator Andrew Wheeler told the Senate Environment and Public Works Committee in mid-January.
But scientists who specialize in the study of wetlands and waterways say it’s not that simple.
Inside 50,000 living rooms: an assessment of global residential ornamentation using transfer learning
Xi Liu, Clio Andris, Zixuan Huang, Sohrab Rahimi
EPJ Data Science
The global community decorates their homes based on personal decisions and contextual influences of their larger cultural and economic surroundings. The extent to which spatial patterns emerge in residential decoration practices has been traditionally difficult to ascertain due to the private nature of interior home spaces. Yet, measuring these patterns can reveal the presence of geographic culture hearths and/or globalization trends.
In this work, we collected over one million geolocated images of interior living spaces from a popular home rental website, Airbnb (http://airbnb.com), and used transfer learning techniques to automatically detect the presence of key stylistic objects: plants, books, decor, wall art and predominance of vibrant colors. We investigated patterns of home decor practices for 107 cities on six continents, and performed a deep dive into six major U.S. cities.
We found that world regions show statistically significant variation in decorative element prevalence, indicating differences in geographic cultural trends. At the U.S. neighborhood level, elements were only weakly spatially clustered and found to not correlate with socio-economic neighborhood variables such as income, unemployment rates, education attainment, residential property value, and racial diversity. These results may suggest that American residents in different socio-economic environments put similar effort into personalizing and caring for their homes. More broadly, our results represent a new view of worldwide human behavior and a new application of machine learning techniques to the exploration of cultural phenomena.
Place niche and its regional variability: Measuring spatial context patterns for points of interest with representation learning
XiLiu, Clio Andris, Sohrab Rahimi
Computers, Environment and Urban Systems
In the built environment, places such as retail outlets and public sites are embedded in the spatial context formed by neighboring places. We define the sets of these symbiotic places in the proximity of a focal place as the place’s “place niche”, which conceptually represents the features of the local environment. While current literature has focused on pairwise spatial colocation patterns, we represent the niche as an integrated feature for each type of place, and quantify the niches’ variation across cities. Here, with point of interest (POI) data as an approximation of places in cities, we propose representation learning models to explore place niche patterns. The models generate two main outputs: first, distributed representations for place niche by POI category (e.g. Restaurant, Museum, Park) in a latent vector space, where close vectors represent similar niches; and second, conditional probabilities of POI appearance of each place type in the proximity of a focal POI. With a case study using Yelp data in four U.S. cities, we reveal spatial context patterns and find that some POI categories have more unique surroundings than others. We also demonstrate that niche patterns are strong indicators of the function of POI categories in Phoenix and Las Vegas, but not in Pittsburgh and Cleveland. Moreover, we find that niche patterns of more commercialized categories tend to have less regional variation than others, and the city-level niche-pattern changes for POI categories are generally similar only between certain city pairs. By exploring patterns for place niche, we not only produce geographical knowledge for business location choice and urban policymaking, but also demonstrate the potential and limitations of using spatial context patterns for GIScience tasks such as information retrieval and place recommendation.
Examining the Impact of Risk Perception on the Accuracy of Anisotropic, Least-Cost Path Distance Approaches for Estimating the Evacuation Potential for Near-Field Tsunamis
Shannon M Grumbly, Tim G. Frazier, Alexander G. Peterson
Journal of Geovisualization and Spatial Analysis
Coastal hazards that can strike with little or no warning, such as tsunamis, are problematic in terms of population exposure and the threat of loss of life. With projected increases in coastal populations, exposure is likely to increase among these communities. For near-field tsunamis, the evacuation window can be as little as 15 to 20 min, and evacuation can be problematic for numerous reasons, such as population demographics, limited road networks, local topographic constraints, lack of proper education, and
misaligned risk perception of the general populace. It is therefore critical for tsunami evacuation planning and education to be highly effective. To address this need, we employed a participatory mapping approach to explore potential evacuation enhancement by evaluating existing least-cost path pedestrian evacuation models, perception of landscape constraints, and additional risks to nearfield tsunamis in Aberdeen, Washington. Stakeholders were tasked with drawing their understood evacuation routes on maps which were analyzed for approximate time to reach safety and compared to least-cost path pedestrian evacuation models. A quantitative analysis of selected evacuation pathways revealed participants consistently overestimated evacuation
time and did not follow modeled least-cost pathways. The results suggest traditional modeling (e.g., least-cost path and agent-based models) underestimate travel time to safety. Thus, there is a need for additional outreach, notably in communities where traditional evacuation models might create a false sense of security.
Assessing the relative vulnerabilities of Mid-Atlantic freshwater wetlands to projected hydrologic changes
Denice H. Wardrop, Anna T. Hamilton, Michael Q. Nassry, Jordan M. West, Aliana J. Britson
Wetlands are known to provide a myriad of vital ecosystem functions and services, which may be under threat from a changing climate. However, these effects may not be homogenous across ecosystem functions, wetland types, ecoregions, or meso‐scale watersheds, making broad application of the same management techniques inappropriate. Here, we present a relative wetland vulnerabilities framework, applicable across a range of spatial and temporal scales, to assist in identifying effective and robust management strategies in light of climate change. We deconstruct vulnerability into dimensions of exposure and sensitivity/adaptive capacity, and identify relevant measures of these as they pertain to the attributes of wetland extent and plant community composition. As a test of the framework, we populate it with data for three primary hydrogeomorphic wetland types (riverine, slope, and depression) in seven small watersheds across four ecoregions (Ridge and Valley, Piedmont, Unglaciated Plateau, and Glaciated Plateau) in the Susquehanna River watershed in Pennsylvania. We use data generated from the SRES A2 emissions experiment and MRI‐CGCM2.3.2 climate model as input to the Penn State Integrated Hydrologic Model to simulate future exposure to altered hydrologic conditions in our seven watersheds, as expressed in two hydrologic metrics: % time groundwater levels occur in the upper 30 cm (rooting zone) during the growing season, and median difference between spring and summer mean water levels. We then examine the spatial and temporal scales at which each of the components of vulnerability (exposure and sensitivity/adaptive capacity) shows significant relative differences. Overall, we find that relative differences in exposure persist at a very fine spatial grain, exhibiting high variability even among individual watersheds in a given ecoregion. For temporal scale, we find strong seasonal but weak annual relative differences in exposure resulting from a magnification of summer dry‐down combined with winter and spring wet periods becoming wetter. Sensitivities/adaptive capacities show significant differences among wetland types. A comparison between our anticipated hydrologic alterations under climate change and historical changes in hydrology due to anthropogenic disturbance indicates potential shifts in hydrologic patterns that are far beyond anything that wetland managers have experienced in the past.
Immersive Virtual Reality as an Effective Tool for Second Language Vocabulary Learning
Jennifer Legault, Jiayan Zhao, Ying-An Chi, Weitao Chen, Alexander Klippel and Ping Li
Learning a second language (L2) presents a significant challenge to many people in adulthood. Platforms for effective L2 instruction have been developed in both academia and the industry. While real-life (RL) immersion is often lauded as a particularly effective L2 learning platform, little is known about the features of immersive contexts that contribute to the L2 learning process. Immersive virtual reality (iVR) offers a flexible platform to simulate an RL immersive learning situation, while allowing the researcher to have tight experimental control for stimulus delivery and learner interaction with the environment. Using a mixed counterbalanced design, the current study examines individual differences in L2 performance during learning of 60 Mandarin Chinese words across two learning sessions, with each participant learning 30 words in iVR and 30 words via word–word (WW) paired association. Behavioral performance was collected immediately after L2 learning via an alternative forced-choice recognition task. Our results indicate a main effect of L2 learning context, such that accuracy on trials learned via iVR was significantly higher as compared to trials learned in the WW condition. These effects are reflected especially in the differential effects of learning contexts, in that less successful learners show a significant benefit of iVR instruction as compared to WW, whereas successful learners do not show a significant benefit of either learning condition. Our findings have broad implications for L2 education, particularly for those who struggle in learning an L2.
Representing the Presence of Absence in Cartography
Anthony C. Robinson
Annals of the American Association of Geographers
A key cartographic challenge associated with the rise of big data is to show when spatial data observations are missing or to communicate variables that indicate absence. For example, showing where people are tweeting during a disaster might be interesting, but visually identifying where normal signals are missing could in fact highlight the most affected places. Parcel data might be fully present, but attributes of their observations could convey qualities of absence (e.g., abandoned structures). Current geovisualization approaches normally do not show anything at all when data are missing or contain qualities of absence and only in rare cases might use a specific hue to highlight the presence of absence on maps. This work argues that people perceive missingness and absence in a way that is distinct from other spatial data qualities, and we propose a typology of static and dynamic means by which we can draw user attention to the presence of absence. To explore the application of these techniques, I use urban parcel data to visualize patterns of property blight in a Detroit neighborhood. Based on conceptual development and case study application, I propose research challenges to evaluate visual representations of missing and absent information on maps.
Border Thinking, Borderland Diversity, and Trump’s Wall
Melissa W. Wright
Annals of the American Association of Geographers
Donald Trump’s agenda to build a “big” and “beautiful” border wall continues to raise alarms for anyone concerned with social justice and environmental well-being throughout the Mexico–U.S. borderlands. In this article, I examine how the border wall and its surrounding debates raise multiple issues central to political ecological and human geographic scholarship into governance across the organic spectrum. I focus particularly on a comparison of the different kinds of “border thinking” that frame these debates and that provide synergy for those coalitions dedicated to the preservation of diversity throughout the ecological and social landscapes of the Mexico–U.S. borderlands. Key Words: biodiversity, decolonial, feminist, Mexico–U.S. borderlands, neoliberal.