Large-scale Human Activity Research Articles

IoT sensors as a tool for assessing spatiotemporal risk to extreme heat

The safety of urban populations sensitive to extreme heat is under increasing threat. Few studies examine the potential benefits of deploying IoT environmental sensors in the urban context and their integration with large-scale human activity data. This paper examines the deployment of IoT sensors in high-resolution extreme heat risk assessment in the case of Seoul, South Korea. This study conducted spatiotemporal analysis on heat exposure with IoT sensors, compared it with an existing land surface temperature map for validation, combined it with human activity data for risk assessment, and finally discussed the benefits of IoTs in detecting abnormal weather events. The results show that extreme heat risks and characteristics vary by age group, and socio-demographic nature overlaps with contextual factors concerning climate risk. This paper discussed possible policy implications to better deal with recurring climate hazards using IoT sensors.

Late Pleistocene sedimentary environment reconstruction and evolution in the Houtao Plain

Reconstructing the Late Pleistocene sedimentary environment history of the Houtao Plain has great significance not only for revealing the evolution of the Yellow River but also for identifying the formation of paleolake and the northern Ulan Buh Desert. This paper presents the results of the sedimentary facies framework based on a grain-size multi-parameter analysis optically stimulated luminescence and 14C dating for a drilling core DKZ06 with a length of 20.04 m from the Houtao Plain. Grain-size multi-parameter analysis was combined with the grain size of modern environmental sediments to the division of the core sedimentary facies in this area. Sedimentary facies of core DKZ06 indicate that the formation of the Houtao Plain area is dominated by fluvial channel deposition and river changed mainly by frequent fluvial channel since ∼35 ka. The two episodes of shallow lake deposition from a drilling core, suggesting that there will not be a mega-paleolake in the Houtao Plain, and the frequent channel migration of the Yellow River may have caused a series of furiotile lakes during the Late Pleistocene. The appearance of aeolian sand at the top of core DKZ06 combined with previous stratigraphic records from the adjacent areas indicates that the origin of the northern Ulan Buh Desert is relatively late and it formed only 2,000 years ago. We propose that a combination of river migration and large-scale human activity may be responsible for desert formation. The evolution of sedimentary environments has a close correlation with climate change during the Late Pleistocene. During the interglacial stage (MIS3), the sedimentary environment has a particular lacustrine and fluvial–lacustrine environment because of relatively warm–humid conditions. During the glacial stage (MIS2), the Houtao Plain was dominated by a fluvial sedimentary environment because of a relatively cold and dry climate. The sedimentary environment is complex and changeable during the Holocene (MIS1), which was likely due to the frequent climate fluctuation.

Divergences Between Resting State Networks and Meta-Analytic Maps Of Task-Evoked Brain Activity

Background: Spontaneous human neural activity is organized into resting state networks, complex patterns of synchronized activity that account for the major part of brain metabolism. The correspondence between these patterns and those elicited by the performance of cognitive tasks would suggest that spontaneous brain activity originates from the stream of ongoing cognitive processing. Objective: To investigate a large number of meta-analytic activation maps obtained from Neurosynth (www.neurosynth.org), establishing the extent of task-rest similarity in large-scale human brain activity. Methods: We applied a hierarchical module detection algorithm to the Neurosynth activation map similarity network, and then compared the average activation maps for each module with a set of resting state networks by means of spatial correlations. Results: We found that the correspondence between resting state networks and task-evoked activity tended to hold only for the largest spatial scales. We also established that this correspondence could be biased by the inclusion of maps related to neuroanatomical terms in the database (e.g. “parietal”, “occipital”, “cingulate”, etc.). Conclusion: Our results establish divergences between brain activity patterns related to spontaneous cognition and the spatial configuration of RSN, suggesting that anatomically-constrained homeostatic processes could play an important role in the inception and shaping of human resting state activity fluctuations.

Effects of human activity intensity on habitat quality based on nighttime light remote sensing: A case study of Northern Shaanxi, China

Habitat quality is a crucial expression of the value of ecosystem services. Habitat quality issues caused by human activities are troubling dilemmas worldwide, and there is an urgent need to assess the impact of the large-scale human activity intensity on habitat quality. In this study, Northern Shaanxi, China, is used as an example to demonstrate how the impact on habitat quality can be explored by simulating the intensity of human activities using nighttime light remote sensing data from 2000 to 2020. Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data, Visible Infrared Imaging Radiometer Suite-Day-Night Band (VIIRS-DNB) data, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used in this study. The results showed that from 2000 to 2020, human activity intensity in Northern Shaanxi increased by 361.4 %, and the habitat quality decreased by 2.3 %. Habitat quality was more spatially clustered and spatially dependent than habitat degradation. Human activity intensity and habitat quality were significantly correlated. Habitat quality in Yulin city decreased significantly compared with that in Yan'an city. The differences in human activity types and economic structures of Yulin and Yan'an were an important factor for this phenomenon. The assessment of the impact of human activity intensity on habitat quality using nighttime light data is feasible and can be applied in other larger regions worldwide.

Concept2Robot: Learning manipulation concepts from instructions and human demonstrations

We aim to endow a robot with the ability to learn manipulation concepts that link natural language instructions to motor skills. Our goal is to learn a single multi-task policy that takes as input a natural language instruction and an image of the initial scene and outputs a robot motion trajectory to achieve the specified task. This policy has to generalize over different instructions and environments. Our insight is that we can approach this problem through learning from demonstration by leveraging large-scale video datasets of humans performing manipulation actions. Thereby, we avoid more time-consuming processes such as teleoperation or kinesthetic teaching. We also avoid having to manually design task-specific rewards. We propose a two-stage learning process where we first learn single-task policies through reinforcement learning. The reward is provided by scoring how well the robot visually appears to perform the task. This score is given by a video-based action classifier trained on a large-scale human activity dataset. In the second stage, we train a multi-task policy through imitation learning to imitate all the single-task policies. In extensive simulation experiments, we show that the multi-task policy learns to perform a large percentage of the 78 different manipulation tasks on which it was trained. The tasks are of greater variety and complexity than previously considered robot manipulation tasks. We show that the policy generalizes over variations of the environment. We also show examples of successful generalization over novel but similar instructions.

Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data

In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

LANDSLIDE INVESTIGATION ASPECTS

Landslide processes are a distinct phenomenon with a huge impact on human way of life. Those gravitational movements in the upper layers of the crust can have a dramatic effect in river valleys, coastal regions and mountainous terrains. The reasons for their existence and development lie in naturally occurring processes in the lithosphere and the atmosphere and large-scale human activity. Investigating various natural and anthropogenic phenomena is vital, with attention to climate change processes. In the report of the European Commission (2012) an alarming level of erosion and abrasion is observed, with more than 630 000 areas with landslide processes. According to MRRB in Bulgaria there are near 2000 zones with active movements. The current investigation focuses on a fragment of the Bulgarian coast prone to landslide that was researched in 2017. The first registered movements date back to 2010. Characterizing for this case is the relatively quick developments of the movements, which affect urban areas. This publication revolves around a complex engineering investigation, which provides an understanding how and why activation processes in the researched zone form, while using well-known proven methods. The used techniques for investigation focus on usability issues, as well as on the opportunity to corelate different sets of data, gather important information and reinforce it with monitoring. The focus here is on these three combined steps: information, prediction, monitoring.

The avalanche-like behaviour of large-scale haemodynamic activity from wakefulness to deep sleep.

Increasing evidence suggests that responsiveness is associated with critical or near-critical cortical dynamics, which exhibit scale-free cascades of spatio-temporal activity. These cascades, or 'avalanches', have been detected at multiple scales, from in vitro and in vivo microcircuits to voltage imaging and brain-wide functional magnetic resonance imaging (fMRI) recordings. Criticality endows the cortex with certain information-processing capacities postulated as necessary for conscious wakefulness, yet it remains unknown how unresponsiveness impacts on the avalanche-like behaviour of large-scale human haemodynamic activity. We observed a scale-free hierarchy of co-activated connected clusters by applying a point-process transformation to fMRI data recorded during wakefulness and non-rapid eye movement (NREM) sleep. Maximum-likelihood estimates revealed a significant effect of sleep stage on the scaling parameters of the cluster size power-law distributions. Post hoc statistical tests showed that differences were maximal between wakefulness and N2 sleep. These results were robust against spatial coarse graining, fitting alternative statistical models and different point-process thresholds, and disappeared upon phase shuffling the fMRI time series. Evoked neural bistabilities preventing arousals during N2 sleep do not suffice to explain these differences, which point towards changes in the intrinsic dynamics of the brain that could be necessary to consolidate a state of deep unresponsiveness.

Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.

HIB-tree: An efficient index method for the big data analytics of large-scale human activity trajectories

Research on traditional trajectory data has been widely carried out, and the popularization of mobile social network and location based service have enabled traditional trajectory data correlate with more human activity information, named as activity trajectory. How to generate large-scale, high-quality activity trajectories and provide efficient indexing structure to support big data management and analysis, has become a new challenge. This paper proposes a new method to generate activity trajectory, which produces relevant keywords for activity trajectory based on word embedding, then an efficient index HIB-tree and an enhanced similarity algorithm based on point popularity and keyword influence factor are proposed to recommend activity trajectory. Experiments show that the performance of HIB-tree is better than the existing index methods of activity trajectory.

A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016

In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and Social Network Analysis (SNA), we drew several conclusions: (1) NTL related studies have become a research hotspot, especially after 2011 when the second generation of NTL satellites, the Suomi National Polar-orbiting Partnership (S-NPP) Satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was on board. In the same period, the open-access policy of the long historical dataset of the first generation satellite Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) started. (2) Most related studies are conducted by authors from USA and China, and the USA takes the lead in the field. We identified the biggest research communities constructed by co-authorships and the related important authors and topics by SNA. (3) By the visualization and analysis of the topic evolution using the co-word and co-cited reference networks, we can clearly see that: the research topics change from hardware oriented studies to more real-world applications; and from the first generation of the satellite DMSP/OLS to the second generation of satellite S-NPP. Although the Day Night Band (DNB) of the S-NPP exhibits higher spatial and radiometric resolution and better calibration conditions than the first generation DMSP/OLS, the longer historical datasets in DMSP/OLS are still important in long-term and large-scale human activity analysis. (4) In line with the intuitive knowledge, the NTL remote sensing related studies display stronger connections (such as interpretive frame, context, and academic purpose) to the social sciences than the general remote sensing discipline. The citation trajectories are visualized based on the dual-maps, thus the research preferences for combining the environmental, ecological, economic, and political science disciplines are clearly exhibited. Overall, the picture of the NTL remote sensing research is presented from the scientist-level, topic-level, and discipline-level interactions. Based on these analyses, we also discuss the possible trends in the future work, such as combining NTL studies with social science research and social media data.

Geomorphic change in Dingzi Bay, East China since the 1950s: impacts of human activity and fluvial input

This study examines the geomorphic evolution of Dingzi Bay, East China in response to human activity and variations in fluvial input since the 1950s. The analysis is based on data from multiple mathematical methods, along with information obtained from Remote Sensing, Geographic Information System and Global Position System technology. The results show that the annual runoff and sediment load discharged into Dingzi Bay display significant decreasing trends overall, and marked downward steps were observed in 1966 and 1980. Around 60%–80% of the decline is attributed to decreasing precipitation in the Wulong River Basin. The landform types in Dingzi Bay have changed significantly since the 1950s, especially over the period between 1981 and 1995. Large areas of tidal flats, swamp, salt fields, and paddy fields have been reclaimed, and aquaculture ponds have been constructed. Consequently, the patterns of erosion and deposition in the bay have changed substantially. Despite a reduction in sediment input of 65.68% after 1966, low rates of sediment deposition continued in the bay. However, deposition rates changed significantly after 1981 owing to large-scale development in the bay, with a net depositional area approximately 10 times larger than that during 1961–1981. This geomorphic evolution stabilized following the termination of large-scale human activity in the bay after 1995. Overall, Dingzi Bay has shown a tendency towards silting-up during 1952–2010, with the bay head migrating seaward, the number of channels in the tidal creek system decreasing, and the tidal inlet becoming narrower and shorter. In conclusion, largescale development and human activity in Dingzi Bay have controlled the geomorphic evolution of the bay since the 1950s.

Regression-Based Feature Selection on Large Scale Human Activity Recognition

In this paper, we present an approach for regression-based feature selection in human activity recognition. Due to high dimensional features in human activity recognition, the model may have over-fitting and can’t learn parameters well. Moreover, the features are redundant or irrelevant. The goal is to select important discriminating features to recognize the human activities in videos. R-Squared regression criterion can identify the best features based on the ability of a feature to explain the variations in the target class. The features are significantly reduced, nearly by 99.33%, resulting in better classification accuracy. Support Vector Machine with a linear kernel is used to classify the activities. The experiments are tested on UCF50 dataset. The results show that the proposed model significantly outperforms state-of-the-art methods.

Telling Time: Chemistry Education in the Anthropocene Epoch

An International Union of Geological Sciences working group is expected to soon formalize a determination that we have moved from the Holocene to the Anthropocene Epoch on the geologic time scale. In addition to reaching consensus on the scientific evidence for this change, this initiative is meant to raise awareness in other scientific communities of the effects of large-scale human activity on fundamental earth system parameters. In parallel, work is being done to understand the resiliency of our planet to the large human footprint, and to define and quantify the planetary boundaries that define a safe operating space for humanity. Many of these planetary boundaries are quantified by chemical measurements. We explore the implications of these parallel developments for chemistry educators.

Sandbank occurrence on the Dutch continental shelf in the North Sea

Sandbanks, the largest of bed patterns in shallow sandy seas, pose a potential risk to shipping. They are also valuable elements of natural coastal protection, dissipating the energy of waves. In the Southern Bight of the North Sea, several sandbank areas have been reported in the literature. However, based on an objective crest–trough analysis of the bathymetry of the Dutch continental shelf, the present study shows that sandbanks are more widespread than commonly considered. These banks are relatively low, presumably explaining why they have not been documented before. This widespread occurrence of sandbanks in the North Sea is in agreement with theoretical predictions based on stability analysis techniques. The possible interference between large-scale human activity and low-amplitude open-shelf ridges implies that one should be careful not to overlook these patterns if none should appear in a preliminary (visual) assessment. The only part of the Southern Bight in which no ridges can be seen is a circular area with a diameter of about 50 km near the mouth of the river Rhine. Here, freshwater outflow affects the direction of tidal ellipses and residual flow, and suppresses the formation of open ridges.

Soil erosion response to climatic change and human activity during the Quaternary on the Loess Plateau, China

We review published stratigraphic, archaeological and pedosedimentary evidence in order to reconstruct the history of soil erosion in China. Documentary evidence of climatic and flood events of the Yellow River and modern hydrological and meteorological data are synthesised to analyse the history of past human activity and its effects on soil erosion intensity during four nested periods of time during the Quaternary. The most intensive period of erosion during the Quaternary was in the Holocene. During the Holocene, intervals of intensive soil erosion occurred at 7500–7000 BP, 200 BCE–0 CE, 1000–1600 CE (Christian era) and during the 1930s, 1950s and the later part of the 1960s of the last century. Large-scale human activity including warfare during early Chinese history, population migration, the inner wars in 1930s, the Cultural Revolution and the recent national campaign to aid soil and water conservation are all closely related to the rate of soil erosion on the Loess Plateau and to sediment loads in the Yellow River. Overall, soil erosion during the transition from dry-cool to wet-warm climates was more intense than during wet-warm and cool-dry climatic episodes, but serious accelerated soil erosion has occurred during the last 2,500 years because of man-induced devastation of vegetation and other anthropogenic disturbance of the environment. Modern rates of soil erosion on the Loess Plateau are a combination of both intensive natural and human-induced erosions and are some four times greater than occurred in the geological past. The recent implementation of soil and water conservation measures has decreased sediment load in the Yellow River by 25%.

Invoking the Precautionary Principle

The article on the precautionary principle, risk perception, and assessment by Wiedemann and Schutz (2005) deserves praise and careful consideration because of the growing awareness that certain human activities could potentially seriously harm human and environmental health. The precautionary principle (United Nations Conference on Environment and Development 1992) holds forth that a point can presumably be reached when human well-being and environmental health are put at risk by a large-scale human activity or man-made system over which humans have control. At such a point the problem could be identified, a course charted, and precautionary actions taken to ameliorate or prevent a potential threat to human and environmental health on behalf of current and future generations. Despite the incontrovertible element of uncertainty and other limitations of scientific methods, it is implicitly assumed that science plays the vital role of providing humanity with the best knowledge of how the world works and of the placement of humankind in the natural order of living things. To the extent scientific methods are incorporated and used to inform both the proclamation of a problem and the implementation of its remedy, the precautionary principle affords humanity a mechanism to focus attention and to examine data on potential impacts of human activities and systems upon the natural world. With such attentiveness and knowledge, humans become able to make choices and to engage in the regulation of behaviors that are advantageous rather than detrimental to human and environmental health. As a mechanism of science, the precautionary principle becomes a useful tool in raising awareness and determining aspects of human culture that are and are not sustainable. Absolute global human population numbers (Hopfenberg 2003; Hopfenberg and Pimentel 2001), increasing human consumption worldwide (Imhoff et al. 2004), and the seemingly limitless expansion of the world’s predominant human economy (Czech and Daly 2004; Meritt 2001) point to the existence of a rapidly spreading culture that could be characterized by its proclivity for unlimited growth—growth that increasingly outruns humanity’s capabilities to anticipate and address the potential for devastating consequences of growth. Given the current scale and rate of this growth relative to the small, finite, noticeably fragile planet we inhabit, it could be that this cultural predisposition for increasing growth is patently unsustainable and, moreover, could give rise to the potential for recognizable, worst-case scenarios. Global warming; diminishing nonrenewable energy resources; destruction of the ozone layer; biodiversity loss; acid rain; deforestation; solid waste disposal; pollution of the air, water, and land; and desertification are regularly referenced in this context. Not unexpectedly, the evolution of science gives rise to new approaches for examining large-scale human activities such as human propagation and human consumption and man-made constructions such as the prevailing economic system. Although relatively new, “top-down” research focuses on data acquisition and analysis regarding certain human behaviors and global human systems. This development complements the “bottom-up” research with which scientists are so familiar (Cairns 2003). As the adage goes, scientists have had difficulty “seeing the forest for the trees” because traditional scientific methods focus primarily on parts of a large system, not on the large system itself. Another dimension of this change in focus is the development of “joining edge” research, in which leading ideas and best practices from multiple disciplines are brought together in a collaborative effort to examine large, complex systems. This approach complements the more familiar pursuit of progressively narrowing “cutting-edge” research of components of a whole system (Cairns 2003; Kriebel et al. 2001). Perhaps, scientific data to advance human understanding about why global-scale human activities and systems are sustainable or unsustainable could be vital to protecting humanity from endangerment, biodiversity from extinction, and Earth from irreversible degradation, even in these early years of the 21st century.

Environmental impact management of ship based tourism to Antarctica

Tourist visits and landings on the ice free areas of Antarctica is the most recent large scale human activity on this continent. More than 98 per cent of tourists visit by cruiseship, which means less than two per cent of tourism is land based. Environmental impacts are generally transient with pressure placed on repeatedly visited shore based attractions. This article investigates the effects of tourism on the environment in Antarctica, legislation as well as liability issues and how they are being managed.