Temporal Hotspot Research Articles

Human activities exacerbate river network degrading in the Qinhuai River basin

Rivers are undergoing significant changes under the pressures of natural processes and human activities. However, characterizing and understanding these changes over the long term and from a spatial perspective have proven challenging. This paper presents a novel framework featuring twelve indicators that combine geometric and spatial structures for evaluating changes in river network patterns. Through global principal component analysis, these indicators were integrated into a comprehensive river network pattern index (RNP). Employing Pearson correlation analysis, geographically weighted regression, geographic detector models, and the Shapley Value, the study quantitatively analyzed various stressors' impacts and relative contributions on river network changes from the 1960s to 2015s. The results showed a clear trend of degradation over time, particularly with frequency and density declining by 57 % and 48 %, respectively. The changes across subbasins varied temporally and spatially, with the 1980s emerging as a significant temporal hotspot and six spatial hotspots identified among twenty subbasins. The analysis showed that agriculture was significantly negatively associated with RNP, while the relationship between urbanization and RNP was inverted N-shaped. To address the negative effects of human activities, a shift from uniform management approaches is crucial. In agricultural areas, adopting more intensive farming practices could help mitigate negative impacts on RNP. For highly urbanized regions, city planning should consider the interactions between urbanization and other factors affecting RNP. Overall, incorporating an understanding of RNP's spatial-temporal dynamics and driving factors into spatial planning is critical for creating effective and sustainable management strategies for human-river interactions.

Temporal and spatial distribution characteristics and hotspot area identification of wintering geese in Lake Poyang

Temporal and spatial distribution characteristics and hotspot area identification of wintering geese in Lake Poyang

Knowledge Landscape and Trend Analysis of Chinese Rural Education Research in the Perspective of Rural Revitalization

Abstract Chinese rural education research has gone through three stages of development: the period of slow exploration, the period of steady accumulation, and the period of broad development. This paper aims to clarify the knowledge landscape and trends of Chinese rural education research in the context of rural revitalization. To this end, based on the co-word analysis method, this paper collects terms, co-occurrence frequency statistics, and cluster analysis of academic literature on rural education research to give the current situation of literature based on spatial and temporal distribution and hotspot analysis of high-frequency keywords. The number of articles published in rural education research increased year by year from 2017 to 2021 with an average annual growth rate of 16.45%, and the total number of articles reached 465. Among the authors of the published articles, there are 25 core authors with three or more articles, accounting for 5.46% of the total number of all authors. From the distribution of hotspots of high-frequency keywords, the frequency of rural revitalization and rural education as the upper concept was 146 and 87 times, respectively, and the research on rural education showed a trend of diversification. Under the perspective of rural revitalization, academic literature research based on co-word analysis can clarify the policy orientation and research hotspot dynamics in the field of rural education and rural revitalization, examine the intrinsic correlation between rural revitalization and rural education, and explore the path options for promoting the development of rural education and realizing rural revitalization.

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management.

A Proposed Big Data Analytics Model for Crimes Predication based on Spatial and Temporal Criminal Hotspot

A Proposed Big Data Analytics Model for Crimes Predication based on Spatial and Temporal Criminal Hotspot

Domestic Value Creation Research: Current Status, Hot Topics and Trends

The issue of "value creation" has been discussed for a long time, and this paper takes 1203 papers on value creation research published in CNKI database from 2017 to 2022 as the research sample, and conducts bibliometric and visualization analysis with the help of CiteSpace software. The paper focuses on the hotspots and trends of domestic value creation research through keyword co-occurrence analysis, cluster analysis, temporal trend analysis and hotspot word emergence analysis, and concludes that: the hotspots of domestic value creation research can be divided into two time phases, the first phase is directly related to value creation and the second phase is obviously interdisciplinary in nature;the research frontiers mainly focus on "digital economy", "artificial intelligence", "ecosystem"and "value proposition", etc.

Machine Learning-Based Method for Predicting Compressive Strength of Concrete

Accurate prediction of the compressive strength of concrete is of great significance to construction quality and progress. In order to understand the current research status in the concrete compressive strength prediction field, a bibliometric analysis of the relevant literature published in this field in the last decade was conducted first. The 3135 journal articles published from 2012 to 2021 in the Web of Science core database were used as the database, and the knowledge map was drawn with the help of the visualisation software CiteSpace 6.1R2 to analyse the field at the macro level in terms of spatial and temporal distribution, hotspot distribution and evolutionary trends, respectively. Afterwards, we go into the detail and divide concrete compressive strength prediction methods into two categories: traditional and machine-learning methods, and introduce the typical methods of each. In addition, a boosting-based ensemble machine-learning algorithm, namely the gradient boosting regression tree (GBRT) algorithm, is proposed for predicting the compressive strength of concrete. 1030 sets of concrete compressive strength test data were collected as the dataset, of which 60% were used to train the model, 20% to validate the model and 20% to test the trained model. The coefficient of determination (R2) of the GBRT model was 0.92, the mean square error (MSE) was 22.09 MPa, and the root mean square error (RMSE) was 4.7 MPa, which is an excellent prediction accuracy compared to prediction models constructed by other machine-learning algorithms. In addition, a five-fold cross-validation analysis was carried out, and the eight input variables were analyzed for their characteristic importance.

When and Where Did They Strand? The Spatio-Temporal Hotspot Patterns of Cetacean Stranding Events in Indonesia

Analyses of the spatial and temporal patterns of 26 years of stranding events (1995–2011 and 2012–2021, n = 568) in Indonesia were conducted to improve the country’s stranding response. The Emerging Hot Spot Analysis was used to obtain the spatial and temporal hotspot patterns. A total of 92.4% events were single stranding, while the remaining were of mass stranding events. More stranding events were recorded between 2012 and 2021 in more dispersed locations compared to the previous period. Within the constraints of our sampling limitations, East Kalimantan and Bali were single stranding hotspots and consecutive hotspots. East Java and Sabu-Raijua in East Nusa Tenggara were mass stranding hotspots. Temporally, Raja Ampat (West Papua) experienced a significant increase in case numbers. The presence of active NGOs, individuals or government agencies in some locations might have inflated the numbers of reported cases compared to areas with less active institutions and/or individuals. However, our results still give a good understanding of the progression of Indonesia’s stranding responses and good guidance of resource allocation for the stranding network. Several locations in Indonesia that need more efforts (e.g., more training workshops on rescue and necropsies) have been identified in this paper. Suggestions to improve data collection (including georeferencing tips) have also been included.

Dynamic Coverage Optimization for 5G Ultra-dense Cellular Networks Based on Their User Densities

This paper has proposed a user-density-based coverage optimization technique for ultra-dense cellular networks. Antenna tilting is a promising coverage optimization technique to be used in 5G networks, that significantly improve the signal to interference plus noise ratio (SINR) by choosing the appropriate angle of tilt. In this paper, the cellular coverage has been optimized for scattered user densities/user hotspots using an adaptive antenna tilting mechanism that steers the beams towards the temporal hot spot in the coverage area. The proposed method has the competence to improve the desired SINR level and coverage area for a group of users rather than a single user. In this work, a reinforcement learning (RL) algorithm has been implemented to optimize the tilt angle. The performance of the proposed technique has been evaluated in the simulation platform considering a three-sectored multicellular mobile network where the groups of user clusters are distributed randomly. The result confirms the improvement in RSS and SINR values in the group of users having high density with maximum user satisfaction.

Towards a new urban geography of expenditure: Using bank card transactions data to analyze multi-sector spatiotemporal distributions

The spatial distribution of commercial activities is vital to support healthy lifestyles and to achieve livable public spaces and environmental, social and economic sustainability in our cities. However, commercial activities require a constant flow of expenditure for their own viability. As a result, understanding the spatial and temporal distribution of expenditure is fundamental, although the lack of detailed, complete data sources has impeded this task until now.Bank card data paves the way for a new urban geography of expenditure, thanks to its fine spatial and temporal granularity along with the uniform coverage of all commercial sectors. In this paper, we analyze temporal, spatial, and spatiotemporal distributions of expenditure at the intraurban scale of the city of Madrid (Spain), combining spatial statistical tools (Getis-Ord General for global autocorrelation and Getis-Ord Gi* hot spot analysis for local autocorrelation) with k-means cluster analysis and spatiotemporal tools (Time Series Clustering analysis and Temporal Hot Spot Analysis).Our analysis confirms the strong center-periphery gradient described in previous literature, but with a CBD integrated by distinct specialized areas. The paper demonstrates that bank card data has a great potential to support a new geography of expenditure that could strengthen decision-making in planning and retailing.

Temporal Hotspot Identification using Dynamic Life Cycle Inventory: Which are the Critical Time-spans within the Product Life Cycle?

Dynamic Life Cycle Assessment (LCA) accounts for the temporal variation of the LCA model. Due to the relative lack of temporally-differentiated characterization factors for the Life Cycle Impact Assessment stage, the most common way to perform dynamic LCA is to limit the dynamic component to the Life Cycle Inventory (LCI) stage. In the manufacturing context, this would translate into a shop-floor connected through Internet of Things (IoT) technologies, to continuously track inventory variables such as technological and environmental flows as well as throughput rate. LCA impacts could then be assessed for each time-step, following their temporal distribution, which would have been overseen with a traditional static LCA. Hotspot analysis can be used to analyze the most relevant drivers of LCA impacts. It can be repeated for each impact category, is embedded in commercial LCA software and, can be done at life cycle stage, process, technological and elementary flow levels. We define temporal hotspots as critical time-spans, responsible for a significant share of LCA impacts. Here we show how to identify temporal hotspots in the context of a dynamic LCI. This would allow for the assessment and management of a new level of differentiation for the hotspot analysis, the temporal one. Moreover, traditional levels of hotspot identification (e.g. process or flow) can be used in an assessment focused on the critical time-spans. To guide readers through the implementation of the methodology, we test it on a hypothetical case study. The methodology could inform decision makers looking for detailed data-driven solutions to reduce LCA impacts of those life cycle stages for which dynamic LCI data is available. Since dynamic LCI data rarely covers the full life cycle, a hotspot analysis with static data is still needed, as a preliminary assessment.

Analysis of Temporal Characteristics of Burglary Crime

Social security order had generally stabilized, and various major criminal crimes had been effectively controlled, but the problem of burglary crime was very serious. Burglary crime had gradually shown the different characteristics from the past, making it increasingly difficult to control. Data of Burglary crime from 2015 to 2019 in Danyang City, Jiangsu Province, was collected. The main contents of the study included temporal trend analysis based on time series, temporal hotspot analysis based on Biharmonic Spline Surface Interpolation, and temporal correction analysis based on Time Period Probability. Characteristics of burglary cases in yearly scale, monthly scale, and daily scale were extracted in this paper. This study found that the burglary cases in Danyang showed different temporal distributions on different time scales. This study further enriched the spatio-temporal analysis methods of burglary crime, and put forward targeted and reliable suggestions for police departments.

Exploring Research Trends and Building a Multidisciplinary Framework Related to Brownfield: A Visual Analysis Using CiteSpace

Brownfield has become one of the critical issues in modern cities. Over the past few decades, a considerable number of papers on brownfield research have been published. This study reviewed 773 documents themed with “brownfield” in the Web of Science core database between 1980 and 2020 and used the CiteSpace software to sort out the spatial and temporal distribution, knowledge groups, subject structures and hotspot fields, and evolutionary trends of global brownfield research. The analysis focuses on distribution of lead authors and their institutions, high-frequency categories and keywords, high influential journals, author contribution, and evolutionary trends based on coword analysis, coauthor analysis, cocitation analysis, and cluster analysis of documents. On the basis of the aforementioned keywords, clusters, and citation bursts analysis, this paper establishes a multidisciplinary framework for brownfield research, suggesting the main research directions for the future development, which provides theoretical support and practical guidance for the research direction of future brownfield research.

Temporal Patterns and Hotspot Identification of Vehicle Collisions with the Roe Deer (Capreolus pygargus) on Jeju Island, South Korea

Wildlife–vehicle collisions (WVCs) are increasing worldwide, posing a threat to both humans and wildlife in terms of human injury and loss of wildlife. Understanding the temporal and spatial non-randomness of WVCs is an essential process for effective WVC management. Roe deer (Capreolus pygargus) is the most prevalent wildlife species in WVCs on Jeju Island, South Korea. This study analyzed individual temporal patterns and spatial hotspots (clusters) based on roe deer–vehicle collisions (RDVC) on Jeju Island from 2014 to 2017. RDVCs occur frequently from July to October, and RDVCs are higher in male deer than with females in all seasons, except in winter. The clusters in the high-risk periods were more regionally distributed compared to the clusters in the low-risk periods. Regardless of the period, some road sections have been identified as crucial clusters that should be given priority for road fencing to mitigate human-deer conflicts. Our findings could also provide road administrators with practical options for management such as temporarily imposing vehicle speed limits and installing signboards.

Three-dimensional aerial base station location for sudden traffic with deep reinforcement learning in 5G mmWave networks

Data volume demand has increased dramatically due to huge user device increasement along with the development of cellular networks. And macrocell in 5G networks may encounter sudden traffic due to dense users caused by sports or celebration activities. To resolve such temporal hotspot, additional network access point has become a new solution for it, and unmanned aerial vehicle equipped with base stations is taken as an effective solution for coverage and capacity improvement. How to plan the best three-dimensional location of the aerial base station according to the users’ business needs and service scenarios is a key issue to be solved. In this article, first, aiming at maximizing the spectral efficiency and considering the effects of line-of-sight and non-line-of-sight path loss for 5G mmWave networks, a mathematical optimization model for the location planning of the aerial base station is proposed. For this model, the model definition and training process of deep Q-learning are constructed, and through the large-scale pre-learning experience of different user layouts in the training process to gain experience, finally improve the timeliness of the training process. Through the simulation results, it points out that the optimization model can achieve more than 90% of the theoretical maximum spectral efficiency with acceptable service quality.

Visceral leishmaniasis: Spatiotemporal heterogeneity and drivers underlying the hotspots in Muzaffarpur, Bihar, India.

BackgroundDespite the overall decrease in visceral leishmaniasis (VL) incidence on the Indian subcontinent, there remain spatiotemporal clusters or ‘hotspots’ of new cases. The characteristics of these hotspots, underlying transmission dynamics, and their importance for shaping control strategies are not yet fully understood and are investigated in this study for a VL endemic area of ~100,000 inhabitants in Bihar, India between 2007–2015.Methodology/Principal findingsVL incidence (cases/10,000/year) dropped from 12.3 in 2007 to 0.9 in 2015, which is just below the World Health Organizations’ threshold for elimination as a public health problem. Clustering of VL was assessed between subvillages (hamlets), using multiple geospatial and (spatio)temporal autocorrelation and hotspot analyses. One to three hotspots were identified each year, often persisting for 1–5 successive years with a modal radius of ~500m. The relative risk of having VL was 5–86 times higher for inhabitants of hotspots, compared to those living outside hotspots. Hotspots harbour significantly more households from the two lowest asset quintiles (as proxy for socio-economic status). Overall, children and young adelescents (5–14 years) have the highest risk for VL, but within hotspots and at the start of outbreaks, older age groups (35+ years) show a comparable high risk.Conclusions/SignificanceThis study demonstrates significant spatiotemporal heterogeneity in VL incidence at subdistrict level. The association between poverty and hotspots confirms that VL is a disease of ‘the poorest of the poor’ and age patterns suggest a potential role of waning immunity as underlying driver of hotspots. The recommended insecticide spraying radius of 500m around detected VL cases corresponds to the modal hotspot radius found in this study. Additional data on immunity and asymptomatic infection, and the development of spatiotemporally explicit transmission models that simulate hotspot dynamics and predict the impact of interventions at the smaller geographical scale will be crucial tools in sustaining elimination.

Research on Innovation Posture of Automated Driving Technology Based on Patentometrics

As an emerging technology that integrates multidisciplinary and multi-technology, Automated Driving is triggering a new round of scientific and technological revolution and industrial transformation, thus attracting the attention of all countries in the world. Based on the Derwent patent database, this paper collects relevant patent information related to Automated Driving from 1998 to 2016 by using software tools such as patent measurement and CiteSpace, and empirically analyzes the temporal distribution, structural distribution, hotspot and frontier technologies, and geographical distribution of automated driving. The study found that automated vehicles have rapidly developing in the past 5 years, which indicates that the technical innovation of automated driving is in an emerging stage. There are geographical differences in innovative activities and capabilities in the field of automated driving. The United States has the absolute competitive advantages, while each country has different technological innovation capabilities in different technical fields. Finally, this paper puts forward some suggestions of our country to develop the technology and industry of automated driving in the future.

Effectiveness of Man-Made Wetland Systems in Filtering Contaminants from Urban Runoff in Milledgeville, Georgia.

A wetland system made up of linked basins was investigated to determine its role in local flood control and contaminant filtration. The study focused on a wetland basin that is dominated by the Celtis laevigata plant and is underlain by clay, with a small sandy layer approximately 1 m below surface. Field and laboratory data were collected to understand the transport and filtration of phosphate, iron, and nitrate. Field results showed the wetland to effectively reduce iron and phosphates from runoff via groundwater flow. These results also showed an increase in the phosphate concentration of surface water while within the basin, resulting from agitation of wetland sediments. Laboratory column experiments showed more than 90% reduction in phosphorus and iron while nitrate concentrations increased above the input concentration. Phosphate and iron were high in wetland water immediately after a rain event. Nitrate concentrations increased as the water filtered through the sediments due to desorption of previously adsorbed nitrates. This wetland could potentially act as a temporal hot spot and rain events as hot moments for these substances. Therefore, the best flood control measure for this site would be to increase residence time in the wetland. This would help to better manage/control the concentration of phosphate, iron and nitrate pollution in surface waters.

Spatial and temporal variation and hotspot detection of kala-azar disease in Vaishali district (Bihar), India

BackgroundAn improved understanding in transmission variation of kala-azar is fundamental to conduct surveillance and implementing disease prevention strategies. This study investigated the spatio-temporal patterns and hotspot detection for reporting kala-azar cases in Vaishali district based on spatial statistical analysis.MethodsEpidemiological data from the study area during 2007–2011 was used to examine the dynamic space-time pattern of kala-azar outbreaks, and all cases were geocoded at a village level. Spatial smoothing was applied to reduce random noise in the data. Inverse distance weighting (IDW) is used to interpolate and predict the pattern of VL cases distribution across the district. Moran’s I Index (Moran’s I) statistics was used to evaluate autocorrelation in kala-azar spatial distribution and test how villages were clustered or dispersed in space. Getis-Ord Gi*(d) was used to identify the hotspot and cold spot areas within the study site.ResultsMapping kala-azar cases or incidences reflects the spatial heterogeneity in the incidence rate of kala-azar affected villages in Vaishali district. Kala-azar incidence rate map showed most of the highest endemic villages were located in southern, eastern and northwestern part of the district; in the middle part of the district generally show the medium occurrence of VL. There was a significant positive spatial autocorrelation of kala-azar incidences for five consecutive years, with Moran’s I statistic ranging from 0.04-0.17 (P <0.01). The results revealed spatially clustered patterns with significant differences by village. The hotspots showed the spatial trend of kala-azar diffusion (P < 0.01).ConclusionsThe results pointed to the usefulness of spatial statistical approach to improve our understanding the spatio-temporal dynamics and control of kala-azar. The study also showed the north-western and southern part of Vaishali district is most likely endemic cluster region. To employ exact and geographically suitable risk-reduction programmes, apply of such spatial analysis tools should suit a vital constituent in epidemiology research and risk evaluation of kala-azar.

Using Volunteered Geographic Information to Assess the Spatial Distribution of West Nile Virus in Detroit, Michigan

West Nile Virus is a vector-borne flavivirus that affects mainly birds, horses, and humans. The disease emerged in the United States in 1999 and by 2001 had reached Michigan. In clinical human cases, the most common symptoms are fever, weakness, nausea, headache, and changes in mental state. The crow is the most common wildlife host in the life cycle of the virus. The state of Michigan, through the Michigan Department of Community Health, collected the spatial locations of over 8,000 dead birds (Corvidae), statewide, during 2002. The large number of samples made spatial and temporal hotspot detection possible. However, the volunteer reporting method produced a dataset with a direct correlation between the numbers and locations of the dead birds and human population density and accurately identifying hotspots remains a challenge. Geographic variation in dead bird intensity was modeled using both global and local spatial clustering algorithms. Statistical models identified overall spatial structure and local clustering. Identification of hotspots was confounded by limited information about the collection procedures, data availability and quality, and the limitations of each method.