Abstract:Key Messages
The implications of the big data revolution for the environmental sciences are potentially significant and require critical interrogation.
Thematic examination of big data definitions can encourage scientists to consider how big environmental data may alter environmental scientific priorities.
In the environmental sciences, big data are most valuable when complementary to (and conversing with) traditional data and approaches.
“…New technologies have produced a vast amount of data often referred to as “big data.” Salmond et al () have noted that the abundance of data affects how data are handled and how science is done, which has ramifications throughout the seven activity spheres. Salmond et al () discuss the impact of big data on our understanding of environmental sciences, pointing out that the phenomenon of big data is seen by some as moving the way that science is done from theory‐driven to an iterative process through the analysis of big datasets. Whilst new technologies may seemingly push scientific investigation toward detailed analysis of the reality, the production of big datasets could as likely encourage the development of questions to specifically fit the available data.…”
Section: Technologies and Activity Spheresmentioning
confidence: 99%
“…Datascapes emerge as the focus of analysis and the source of explanation, rather than the physical entities from which the datascapes are derived. As Salmon et al () note: If landscapes can be converted into datascapes, then what is needed is not analysis of landscapes but analysis of datascapes. Further, these changes in observational and methodological priorities could lead towards the prioritization of reanalysis of open source/public datasets over field science, triggering the “rise of the data scientist” (Levy 2015) and the death of the environmental expert (Death 2015).…”
Section: Technologies and Activity Spheresmentioning
confidence: 99%
“…Further, these changes in observational and methodological priorities could lead towards the prioritization of reanalysis of open source/public datasets over field science, triggering the “rise of the data scientist” (Levy 2015) and the death of the environmental expert (Death 2015). Salmond et al (, 59)…”
Section: Technologies and Activity Spheresmentioning
confidence: 99%
“…These operational constraints required in the field highlight that decisions about control and calibration have been transferred from codified laboratory protocols to decisions by a researcher in the field. This introduces the potential for additional and new sources of variability, an observation made by Salmond et al (2017). Researchers may attempt to overcome these issues by relating field measurements to their equivalent in the laboratory to ensure objective and standardized measurements between the field and the laboratory.…”
Section: New Technologies and Production And Labourmentioning
New technologies enable high‐resolution monitoring techniques and the generation of big data and have been heralded as increasing the depth of our understanding of geomorphic phenomena. These technologies, however, also provide us with a convenient entry point into the increasingly constraining political economy of geomorphology. Building on the work of Stuart Lane and of critical physical geographers, this paper traces and examines the multiple roles that new technologies have played in constraining research questions and directing resources. Using the activity sphere framework outlined by David Harvey, the influence of new technologies can be traced around the spheres and their constraining of existing relations within academia and explanation identified.
“…New technologies have produced a vast amount of data often referred to as “big data.” Salmond et al () have noted that the abundance of data affects how data are handled and how science is done, which has ramifications throughout the seven activity spheres. Salmond et al () discuss the impact of big data on our understanding of environmental sciences, pointing out that the phenomenon of big data is seen by some as moving the way that science is done from theory‐driven to an iterative process through the analysis of big datasets. Whilst new technologies may seemingly push scientific investigation toward detailed analysis of the reality, the production of big datasets could as likely encourage the development of questions to specifically fit the available data.…”
Section: Technologies and Activity Spheresmentioning
confidence: 99%
“…Datascapes emerge as the focus of analysis and the source of explanation, rather than the physical entities from which the datascapes are derived. As Salmon et al () note: If landscapes can be converted into datascapes, then what is needed is not analysis of landscapes but analysis of datascapes. Further, these changes in observational and methodological priorities could lead towards the prioritization of reanalysis of open source/public datasets over field science, triggering the “rise of the data scientist” (Levy 2015) and the death of the environmental expert (Death 2015).…”
Section: Technologies and Activity Spheresmentioning
confidence: 99%
“…Further, these changes in observational and methodological priorities could lead towards the prioritization of reanalysis of open source/public datasets over field science, triggering the “rise of the data scientist” (Levy 2015) and the death of the environmental expert (Death 2015). Salmond et al (, 59)…”
Section: Technologies and Activity Spheresmentioning
confidence: 99%
“…These operational constraints required in the field highlight that decisions about control and calibration have been transferred from codified laboratory protocols to decisions by a researcher in the field. This introduces the potential for additional and new sources of variability, an observation made by Salmond et al (2017). Researchers may attempt to overcome these issues by relating field measurements to their equivalent in the laboratory to ensure objective and standardized measurements between the field and the laboratory.…”
Section: New Technologies and Production And Labourmentioning
New technologies enable high‐resolution monitoring techniques and the generation of big data and have been heralded as increasing the depth of our understanding of geomorphic phenomena. These technologies, however, also provide us with a convenient entry point into the increasingly constraining political economy of geomorphology. Building on the work of Stuart Lane and of critical physical geographers, this paper traces and examines the multiple roles that new technologies have played in constraining research questions and directing resources. Using the activity sphere framework outlined by David Harvey, the influence of new technologies can be traced around the spheres and their constraining of existing relations within academia and explanation identified.
“…However, a better description of urban environments alone has yet to deliver an improved understanding of processes that lead to better urban planning and development. Rather, we need innovative ways to explore and analyse this explosion of data ("Big Data") if we are to see any real changes in the ways we plan, manage, and mitigate air pollution, climate change, and their impacts on cities [21]. Mahmood et al [22] show how large data sets from routinely collected sources, in this case ambulance call outs, can be combined with climate data to provide an improved understanding of the impact of climate extremes on ambulance response times in London, UK.…”
Big Data Approaches (BDAs) refers to the combined use of historic datasets, incoming data streams, and the array of related technologies designed to shed new light on societal and environmental complexities through novel organizational, storage, and analytical capabilities. Despite widespread recognition of the commercial benefits of BDAs, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterize BDAs (volume, variety, velocity, and veracity) appear apt in describing the intractable challenges posed by global climate change. This paper employs coastal flood risk management as an illustrative case study to explore the potential applications in the environmental domain. Trends in global change including accelerating sea level rise, concentration of people and assets in low‐lying areas and deterioration of protective coastal ecosystems are expected to manifest locally as increased future flood risk. Two branches of coastal flood risk management are considered. First, coastal flood risk assessment, focusing on better characterization of hazard sources, facilitative pathways, and vulnerable receptors. Second, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings, and response monitoring. Critical commentary regarding technical, contextual, institutional, and behavioral barriers to the implementation of BDAs is offered throughout including a discussion of two fundamental difficulties associated with applying BDAs to coastal flood risk management: the role of BDAs in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches.
This article is categorized under:
Social Status of Climate Change Knowledge > Knowledge and Practice
Climate, History, Society, Culture > Technological Aspects and Ideas
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