Can Social Media Help Us Understand The Impact of Climate Change on Forests in The US?

While social media data are increasingly being used in the study of pressing environmental problems, their ability to monitor environmental changes has scarcely been assessed. Understanding this viability is highly important as climate change increasingly impacts public health, and behavior. We examine social media photographs associated with wildfires in Yellowstone National Park to assess if images and content can adequately capture environmental change associated with large-scale landscape impacts - wildfires - using computer vision, natural language processing and spatiotemporal analysis. We find that social media posts associated with wildfire events rarely capture the fires themselves, while landscape impacts including burnt trees and early succession are more frequently the topic of photography. Furthermore, we find that computer vision has challenges with capturing these phenomena. While capturing wildfires proved difficult, developing multimodal analysis including natural language processing, spatial, trend and computer vision analysis at scale may open opportunities for more general understanding of social media’s efficacy for monitoring environmental change

Tapping into Social Media Data to Identify the Public\u27s Most Valued Landscapes

Today, millions of people are using social media to share information and images about the places they visit for outdoor recreation and leisure. This fact sheet reviews recent research which analyzed over 7.5 million photos posted to Instagram, Flickr, and Panaramio to examine which European landscapes individuals value most. The research is the first of its kind to use social media data to identify the public’s most valued landscapes across an entire continent

Mapping Landscape Values Using Social Media

Social media data are providing scientists with a variety of new ways to examine how and why individuals value particular natural landscapes. In this fact sheet, we review cutting edge research that used millions of photos posted to Instagram, Flickr and Panaramio to examine which European landscapes individuals value most. The research is the first of its kind to use social media data to identify the public’s most valued landscapes across an entire continent. The research is also the first to compare the spatial agreement between geotagged imagery uploaded to different platforms

FUTURES-AMR: Towards an Adaptive Mesh Refinement Framework for Geosimulations

Adaptive Mesh Refinement (AMR) is a computational technique used to reduce the amount of computation and memory required in scientific simulations. Geosimulations are scientific simulations using geographic data, routinely used to predict outcomes of urbanization in urban studies. However, the lack of support for AMR techniques with geosimulations limits exploring prediction outcomes at multiple resolutions. In this paper, we propose an adaptive mesh refinement framework FUTURES-AMR, based on static user-defined policies to enable multi-resolution geosimulations. We develop a prototype for the cellular automaton based urban growth simulation FUTURES by exploiting static and dynamic mesh refinement techniques in conjunction with the Patch Growing Algorithm (PGA). While, the static refinement technique supports a statically defined fixed resolution mesh simulation at a location, the dynamic refinement technique supports dynamically refining the resolution based on simulation outcomes at runtime. Further, we develop two approaches - asynchronous AMR and synchronous AMR, suitable for parallel execution in a distributed computing environment with varying support for solution integration of the multi-resolution results. Finally, using the FUTURES-AMR framework with different policies in an urban study, we demonstrate reduced execution time, and low memory overhead for a multi-resolution simulation

Aesthetic Characteristics of the Front Range: An Analysis of Viewsheds Provided by Boulder OSMP Lands

The city of Boulder’s Open Space and Mountain Parks (OSMP) lands offer residents and visitors a variety of unique recreational, scenic, and cultural experiences that are often captured and shared publicly via social media. Given the diversity of OSMP lands, visitor experiences likely differ based on the aesthetic and biophysical features that can be viewed from these landscapes. For instance, the peaks of the iconic Flatirons provide visitors with different scenic views than the low-lying grasslands in the southeastern area of the city. Furthermore, visitor use and enjoyment of OSMP lands could be directly related to the landscape features that are visible from these different locations. Understanding how visible landscape features vary across OSMP lands can help managers target their planning efforts to improve the quality of outdoor recreation experiences, and potentially identify new locations for outdoor recreation infrastructure (e.g., trails, pavilions, etc.) that offer the ability to see the regions most desirable landscape features. This study: (1) identifies points in the landscape where users are often inspired to take photographs; (2) maps the landscapes most often viewed by visitors; (3) summarizes the types of landscape features viewed from OSMP lands; and (4) determines how these landscape features vary across LCAs

Landscape Values and Aesthetic Preferences Across the Front Range

Boulder’s Open Space and Mountain Parks (OSMP) lands are managed to provide a diverse set of benefits valued by Boulder’s residents as well as tourists. Not all OSMP lands provide the same set of benefits however. Understanding how the values associated with OSMP lands vary across the region can provide managers with insights into how best to allocate resources so that they yield the maximum public benefit. In addition to an understanding of the values visitors associate with OSMP lands, management can benefit from knowledge of how different features of the landscape impact user experiences, both positively and negatively

Modeling Landowner Interactions and Development Patterns at the Urban Fringe

Population growth and unrestricted development policies are driving low-density urbanization and fragmentation of peri-urban landscapes across North America. While private individuals own most undeveloped land, little is known about how their decision-making processes shape landscape-scale patterns of urbanization over time. We introduce a hybrid agent-based modeling (ABM) – cellular automata (CA) modeling approach, developed for analyzing dynamic feedbacks between landowners’ decisions to sell their land for development, and resulting patterns of landscape fragmentation. Our modeling approach builds on existing conceptual frameworks in land systems modeling by integrating an ABM into an established grid-based land-change model – FUTURES. The decision-making process within the ABM involves landowner agents whose decision to sell their land to developers is a function of heterogeneous preferences and peer-influences (i.e., spatial neighborhood relationships). Simulating landowners’ decision to sell allows an operational link between the ABM and the CA module. To test our hybrid ABM-CA approach, we used empirical data for a rapidly growing region in North Carolina for parameterization. We conducted a sensitivity analysis focusing on the two most relevant parameters—spatial actor distribution and peer-influence intensity—and evaluated the dynamic behavior of the model simulations. The simulation results indicate different peer-influence intensities lead to variable landscape fragmentation patterns, suggesting patterns of spatial interaction among landowners indirectly affect landscape-scale patterns of urbanization and the fragmentation of undeveloped forest and farmland

Understanding European Regional Diversity - Lessons learned from Case Studies

The content of this report is a deliverable to the FP 7 project RUFUS (Rural future Networks) concerning the case studies made within the project. As a deliverable in a EU framework project it reports extensively on the methods and empirical data collected in the project’s case studies. The work has as an overarching motive to translate research findings into implications that are relevant for policy makers in the EU. The conclusions from the case studies are therefore of two types – the findings made and the implications they might give for policy making within the field of rural development