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Conservation has embraced advances in big data and related digital technologies as key to preventing biodiversity loss, especially in the identification of areas of conservation priority based on spatial data, which we call the big geospatial data turn. This turn has led to the proliferation of useful methods and tools, including global geospatial maps. But these methods may also undermine moves toward rights-based and inclusive conservation approaches that consider plural values and perspectives. We built on the burgeoning literature to call for greater attention to be paid to the datasets, methodological choices, and the assumptions global mapping for biodiversity conservation is based on. In increasingly prioritizing the use of big geospatial data, conservation professionals risk forgetting that maps show only partial information and limit the diversity of ways of seeing and representing the world. Big geospatial data collected through remotely sensed technologies must still be situated in time and place and provided with appropriate political-economic and sociocultural contexts. Further, global mapping efforts remain primarily the purview of Global North researchers, even given the push to make data open access. Instead of uncritically calling for more data, we urge conservationists to contextualize and situate big geospatial data carefully so as to build a field that achieves socially just and ecologically effective conservation outcomes.
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http://dx.doi.org/10.1111/cobi.70145 | DOI Listing |
Conserv Biol
September 2025
Global Affairs Program, George Mason University, Fairfax, Virginia, USA.
Conservation has embraced advances in big data and related digital technologies as key to preventing biodiversity loss, especially in the identification of areas of conservation priority based on spatial data, which we call the big geospatial data turn. This turn has led to the proliferation of useful methods and tools, including global geospatial maps. But these methods may also undermine moves toward rights-based and inclusive conservation approaches that consider plural values and perspectives.
View Article and Find Full Text PDFJ Adv Res
September 2025
Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China. Electronic address:
Introduction: Numerical optimization plays a key role in improving the efficiency of solar photovoltaic (PV) systems and solving complex engineering problems. Traditional optimization methods often struggle with finding optimal solutions within a reasonable timeframe due to high-dimensional and non-linear problem landscapes.
Objectives: This study aims to introduce a novel swarm intelligence algorithm, the Beaver Behavior Optimizer (BBO), inspired by the cooperative behaviors of beavers during dam construction.
Infect Dis Poverty
July 2025
Background: Artificial intelligence (AI) remains poorly understood and its rapid growth raises concerns reminiscent of dystopian narratives. AI has shown the capability of producing new medical content and improving management through optimization and standardization, which shortens queues, while its complete reliance on technical solutions threatens the traditional doctor-patient bond.
Approach: Based on the World Economic Forum's emphasis on the need for faster AI adoption in the medical field, we highlight current gaps in the understanding of its application and offer a set of priorities for future research.
Nat Commun
July 2025
Geospatial Sciences Center of Excellence (GSCE), Department of Geography, South Dakota State University, Brookings, SD, USA.
Land cover conversions (LCC) have substantially reshaped terrestrial carbon dynamics, yet their net impact on carbon sequestration remains uncertain. Here, we use the remote sensing-driven BEPS model and high-resolution HILDA+ data to quantify LCC-induced changes in net ecosystem productivity (NEP) from 1981 to 2019. Despite global forest loss and cropland/urban expansion, LCC led to a net carbon gain of 229 Tg C.
View Article and Find Full Text PDFWaste Manag
August 2025
School of Economics and Management, Fuzhou University, Fuzhou 350116, China.
Understanding the spatiotemporal dynamics and key drivers of recycling enterprises is essential for optimizing resource recovery systems and advancing sustainable development in China. This study adopts a multisource big data approach, integrating geospatial, economic, and environmental datasets from 300 cities between 1987 and 2024, and applies spatial analysis and Random Forest models to examine 5,171 registered recycling enterprises. Results reveal strong spatial concentration in eastern coastal provinces like Jiangsu (over 500 enterprises), Shandong, and Zhejiang.
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