Derek B. Van Berkel

Continental-scale quantification of landscape values using social media data

Significance In many landscapes across the globe, we are witnessing an ongoing functional shift away from landscapes managed for extractive activities (e.g., agriculture, mining, forestry) and toward landscapes managed for recreation and leisure activities. Understanding the spatial configuration of this functional shift at regional and continental scales will be crucial for the development of effective landscape and rural development policies in coming decades. We present a rigorous comparison between three social media platforms’ suitability for mapping and quantifying landscape values. We also introduce a predictive model capable of quantifying landscape values at a continental scale. The utility of the model is illustrated through the identification of specific landscape features that best explain high densities of ascribed value (i.e., landscape value locations). Individuals, communities, and societies ascribe a diverse array of values to landscapes. These values are shaped by the aesthetic, cultural, and recreational benefits and services provided by those landscapes. However, across the globe, processes such as urbanization, agricultural intensification, and abandonment are threatening landscape integrity, altering the personally meaningful connections people have toward specific places. Existing methods used to study landscape values, such as social surveys, are poorly suited to capture dynamic landscape-scale processes across large geographic extents. Social media data, by comparison, can be used to indirectly measure and identify valuable features of landscapes at a regional, continental, and perhaps even worldwide scale. We evaluate the usefulness of different social media platforms—Panoramio, Flickr, and Instagram—and quantify landscape values at a continental scale. We find Panoramio, Flickr, and Instagram data can be used to quantify landscape values, with features of Instagram being especially suitable due to its relatively large population of users and its functional ability of allowing users to attach personally meaningful comments and hashtags to their uploaded images. Although Panoramio, Flickr, and Instagram have different user profiles, our analysis revealed similar patterns of landscape values across Europe across the three platforms. We also found variables describing accessibility, population density, income, mountainous terrain, or proximity to water explained a significant portion of observed variation across data from the different platforms. Social media data can be used to extend our understanding of how and where individuals ascribe value to landscapes across diverse social, political, and ecological boundaries.

Assessment and valuation of recreational ecosystem services of landscapes

Recreational ecosystem services (RES), understood as the numerous benefits people obtain from landscapes and the natural environment, are a topical area of policy, research and society. This Editorial introduces the current state of RES research, provides an overview of the 21 contributions comprising this Special Issue of Ecosystem Services, and outlines opportunities for further research. This issues publications employ diverse methods for assessing and valuing RES at different scales in Europe and beyond. The papers present advancements in mapping and valuation, provide evidence for the contributions of biodiversity and landscapes to the generation of RES and human well-being, and shed light on distributional effects across different beneficiaries. Taken together, contributions emphasize that RES may be a prime vehicle for reconnecting people with nature with positive effects on societal well-being. The diversity of approaches currently applied in RES research reflects much creativity and new insights, for example by harnessing georeferenced social media data. Future research should aim towards harmonizing datasets and methods to enhance comparability without compromising the need for context-specific adaptations. Finally, more research is needed on options for integrating RES information in decision making, planning and management in order to enhance actual uptake in public and private decisions.

pFUTURES: A Parallel Framework for Cellular Automaton Based Urban Growth Models

Simulating structural changes in landscape is a routine task in computational geography. Owing to advances in sensing and data collection technologies, geospatial data is becoming available at finer spatial and temporal resolutions. However, in practice, these large datasets impede land simulation based studies over large geographic regions due to computational and I/O challenges. The memory overhead of sequential implementations and long execution times further limit the possibilities of simulating future urban scenarios. In this paper, we present a generic framework for co-ordinating I/O and computation for geospatial simulations in a distributed computing environment. We present three parallel approaches and demonstrate the performance and scalability benefits of our parallel implementation pFUTURES, an extension of the FUTURES open-source multi-level urban growth model. Our analysis shows that although a time synchronous parallel approach obtains the same results as a sequential model, an asynchronous parallel approach provides better scaling due to reduced disk I/O and communication overheads.

Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR

Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numerous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and temporal determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these landscape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual-sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially variable visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.

Geospatial Analytics for Park & Protected Land Visitor Reservation Data

Reservation databases utilized by parks and protected lands (PPLs) are a source of empirical data that holds a wealth of spatiotemporal information about both destination usage (from the supply side) and visitor characteristics (the demand population). Unfortunately, PPL reservation databases are rarely explored with these goals in mind. Geovisualizations of reservation data can be used to identify longitudinal patterns, trends and relationships that can help PPL managers generate knowledge useful in decision support. To demonstrate the knowledge that can be gained through geospatial analytics of PPL reservation data, 12.5 million reservation records from the recreation.gov database between January 1, 2007 and December 30, 2015 are examined. The database includes 3272 distinct destinations that provided camping, permitting or ticketing on U.S. Federal PPLs. This chapter discusses both the value of, and the methodology for, inductively exploring spatiotemporal PPL reservation data through geovisualization. Efforts such as those described in this chapter can provide decision support to managers of Federal, State and County agencies tasked with tourism and resource management.

Agent based urban growth modeling framework on Apache Spark

The simulation of urban growth is an important part of urban planning and development. Due to large data and computational challenges, urban growth simulation models demand efficient data analytic frameworks for scaling them to large geographic regions. Agent-based models are widely used to observe and analyze the urban growth simulation at various scales. The incorporation of the agent-based model makes the scaling task even harder due to communication and coordination among agents. Many existing agent-based model frameworks were implemented using traditional shared and distributed memory programming models. On the other hand, Apache Spark is becoming a popular platform for distributed big data in-memory analytics. This paper presents an implementation of agent-based sub-model in Apache Spark framework. With the in-memory computation, Spark implementation outperforms the traditional distributed memory implementation using MPI. This paper provides (i) an overview of our framework capable of running urban growth simulations at a fine resolution of 30 meter grid cells, (ii) a scalable approach using Apache Spark to implement an agent-based model for simulating human decisions, and (iii) the comparative analysis of performance of Apache Spark and MPI based implementations.