Scientists have traditionally studied recreation in nature by conducting surveys at entrances to major attractions such as national parks. This method is expensive and provides limited spatial and temporal coverage. A new source of information is available from online social media websites such as flickr. Here, we test whether this source of “big data” can be used to approximate visitation rates. We use the locations of photographs in flickr to estimate visitation rates at 836 recreational sites around the world, and use information from the profiles of the photographers to derive travelers' origins. We compare these estimates to empirical data at each site and conclude that the crowd-sourced information can indeed serve as a reliable proxy for empirical visitation rates. This new approach offers opportunities to understand which elements of nature attract people to locations around the globe, and whether changes in ecosystems will alter visitation rates.

Visual analytics is often based on the intuition that highly interactive and dynamic depictions of complex and multivariate databases amplify human capabilities for inference and decision-making, as they facilitate cognitive tasks such as pattern recognition, association, and analytical reasoning (Thomas and Cook 2005 Thomas, J.J. and Cook, K.A. 2005. Illuminating the path: the research and development agenda for visual analytics, Los Alamitos, CA: IEE Computer Society Press. National Visualization and Analytics Ctr [Google Scholar] ). But how do we know whether visual analytics really works? This article offers a generic evaluation approach combining theory- and data-driven methods based on sequence similarity analysis. The approach systematically studies users' visual interaction strategies when using highly interactive interfaces. We specifically ask whether the efficiency (i.e., speed) of users can be characterized by specific display interaction event sequences, and whether studying user strategies could be employed to improve the (interaction) design of the dynamic displays. We showcase our approach using a very large, fine-grained spatiotemporal dataset of eye movement recordings collected during a controlled human subject experiment with dynamic visual analytics displays. With this methodological approach based on empirical evidence, we hope to contribute to a deeper understanding of how people make inferences and decisions with highly interactive visualization tools and complex displays.

Knowing the impact of land-use and land-cover (LULC) changes on the distribution of water yield (WY) is essential for water resource management. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we investigated the spatial-temporal variations of WY from 1990 to 2015 in China’s northern semi-arid region of Beijing–Tianjin–Hebei (Jing-Jin-Ji). We quantified the combined effects of LULC dynamics and climatic variation on WY. Furthermore, we identified the relative contribution of main LULC types to WY. For our study region, the built-up area increased by 35.66% (5380 km²) during the study period. In the meantime, cropland, grassland, and wetland decreased continuously. The expansion of built-up area and decline of vegetated land led to an increase of 1047 million m³ (5.1%) in total WY. The impacts of LULC changes on WY were mainly determined by the biophysical characteristics of LULC composition. Vegetated land has relatively lower WY coefficients due to higher rates of evapotranspiration and water infiltration. Built-up areas and bare land have higher WY coefficients as a result of their impermeable surface. The spatial-temporal analysis of WY with specification of WY coefficients by LULC types can facilitate integrated land-use planning and water resource management.

Sustainability challenges demand solutions, and the pace of technological and scientific advances in physical geography and Earth observation have great potential to provide the information needed to address these challenges. This paper highlights five online tools and initiatives that are lifting barriers to address these challenges. The enviroGRIDS project in the Black Sea catchment demonstrates how the use of spatial data infrastructures can facilitate data sharing. Google Earth Engine is providing solutions to challenges of processing big data into usable information. Additionally, application programming interfaces allow outsiders to elaborate and iterate on programs to explore novel uses of data and models, as seen in the Berkeley Ecoinformatics Engine. Finally, collaborative mapping tools, such as Seasketch/MarineMap and the InVEST software suite, allow engagement within and between groups of experts and stakeholders for the development, deployment, and long-term impact of a project. Merging these different experiences can set a new standard for online information tools supporting sustainable development from evidence brought by physical geography combined with socioeconomic conditions.