Spatial-temporal Patterns Research Articles

Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation

Distinguishing commuters from non-commuters is important for transportation planning and traffic demand management. A framework to identify commuters using public transit is proposed based on a spatial-temporal clustering algorithm. The framework extracts commute trips by mining spatial-temporal travel patterns depending on whether the travelers' trip chains are complete. The commuting features in terms of travel frequency, time regularity, and spatial regularity are utilized to compare the difference in travel behavior between commuters and non-commuters. The framework is applied to trip data collected from an incentive program implemented by the Metropolitan Transport Commission of Korea. The data records spatial and temporal information on travelers' boarding and alighting points, as well as actual origins and destinations. One trip record constitutes a complete trip chain for travelers who joined the incentive program. The results show that commuters exhibit a higher travel frequency and more regular spatial-temporal travel patterns than non-commuters. It is found that not all commuters travel during morning and evening peaks; some commuters leave work after evening peak. The proposed identification framework based on the spatial-temporal clustering approach is capable of exploiting the inherent spatial-temporal travel patterns of travelers to achieve a reliable identification of commuters.

Spatial–Temporal Pattern of Coordination between the Supply and Demand for Ecosystem Services in the Lhasa River Basin

Quantifying the spatiotemporal patterns of the coordination between ecosystem service supply and demand is vital for regional sustainable development. To reveal the dynamic pattern of the coordination of ecosystem service (ES) supply and demand in the Lhasa River Basin, we quantified the supply of the following four ESs using the InVEST model from 2000 to 2018: carbon sequestration (CS), water conservation (WC), habitat quality (HQ), and soil conservation (SC). Using socio-economic data, including land development degree, GDP, and population density, the ES demand was quantified. The ES supply–demand ratio (ESDR) and coupling coordination degree (CCD) model were used to evaluate the coupling relationship and coordination of ES supply and demand. The spatial autocorrelation analysis was used to determine the spatial correlation and changes in the ES supply–demand coupling coordination degree. The results indicate that the distribution of ESDR exhibited significant spatial heterogeneity. The area with ES supply far greater than demand was always in the upstream area of the Lhasa River, while the ES demand of Chengguan District far exceeded supply. Grasslands and forests were the main contributors to ESDRs, providing positive ESDRs for three services, SC, HQ, and WC, with a total proportion above 80%. From 2000 to 2018, the mismatch between ES supply and demand was gradually spreading upstream, while the upstream areas had a relatively high CCD. The spatial correlations of the CCD in the Lhasa River basin all showed statistically significant differences (p < 0.01). The high–high aggregation areas were concentrated in the northeast of the Lhasa River basin, while the low–low aggregation areas were centered around Chengguan District. This study provides reference values for optimizing the land use spatial patterns in ecologically vulnerable areas with the goal of sustainable development.

Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment

As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification.

Planning scale flood risk assessment and prediction in ultra-high density urban environments: The case of Hong Kong

Climate change has significantly increased the risks associated with urban flooding. However, most research on flood risk assessment focuses on large-scale climate changes and impacts, leaving a research gap in the high spatial resolution of flood risk assessment in inter-urban areas. This gap makes it difficult to guide regional planning for the government. Therefore, this study aims to explore the risks of floods in ultra-high-density cities under climate change at the planning scale, using Hong Kong as a case study. We comprehensively assessed the flood risk index (FRI) in the built environment in 211 tertiary planning units (TPUs) from the three dimensions of vulnerability, exposure, and hazard from 2006 to 2021. We also employed a prediction model to forecast the spatial–temporal patterns of FRI in the next 5, 10, and 15 years and evaluated the uneven distribution of flood risks. The results show that the FRI of TPUs increased yearly, which poses higher threats to agglomerative areas of transportation and functional facilities. Additionally, future FRI will further impact coastal TPUs in western Hong Kong, resulting in more negative impacts on high-building areas. Therefore, urban planning should prioritize integrating flood management and risk mitigation measures.

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Spatial-temporal patterns and influencing factors for hemorrhagic fever with renal syndrome: A 16-year national surveillance analysis in China

BackgroundChina is confronted with the significant menace posed by hemorrhagic fever with renal syndrome (HFRS). Nevertheless, the long-term spatial-temporal variations, regional prevalence patterns, and fundamental determinants' mechanisms for HFRS remain inadequately elucidated. MethodsNewly diagnosed cases of HFRS from January 2004 to December 2019 were acquired from the China Public Health Science Data repository. We used Age-period-cohort and Bayesian Spacetime Hierarchy models to identify high-risk populations and regions in mainland China. Additionally, the Geographical Detector model was employed to quantify the determinant powers of significant driver factors to the disease. ResultsA total of 199,799 cases of HFRS were reported in mainland China during 2004–2019. The incidence of HFRS declined from 1.93 per 100,000 in 2004 to 0.69 per 100,000 in 2019. The incidence demonstrated an inverted U-shaped trend with advancing age, peaking in the 50–54 age group, with higher incidences observed among individuals aged 20–74 years. Hyperendemic areas were mainly concentrated in the northeastern regions of China, while some western provinces exhibited a potential upward trend. Geographical detector model identified that the spatial variations of HFRS were significantly associated with the relative humidity (Q = 0.36), forest cover (Q = 0.26), rainfall (Q = 0.18), temperature (Q = 0.16), and the surface water resources (Q = 0.14). ConclusionsThis study offered comprehensive examinations of epidemic patterns, identified high-risk areas quantitatively, and analyzed factors influencing HFRS transmission in China. The findings may contribute to the necessary implementations for the effective prevention and control of HFRS.

Thawing process of high-ice-content frozen soil subjected to saturated steam

Engineering measures based on permafrost protection to inhibit high-ice-content permafrost thawing and subsidence in some areas of the Qinghai-Tibetan Plateau (QTP) are challenging owing to global warming. Hence, early permafrost thawing is an effective method for reducing the later settlement of the upper infrastructure. Saturated steam can thaw permafrost rapidly, but the research on the process and mechanisms of thawing permafrost with steam is lacking. In this study, steam-thawing tests were conducted on four types of frozen soil with high ice contents (20 %, 30 %, 40 %, and 50 % by volume). The temporal and spatial migration patterns of temperature and moisture in permafrost subjected to steam and the resulting thawing rates were analyzed by real-time monitoring of temperature and the volumetric water content (VWC) during the thawing process. Based on this, the mechanism of steam thawing permafrost was analyzed. The results showed that the continuous injection of saturated steam into the permafrost for 2 h resulted in complete thawing of permafrost with all four ice-contents, but the thawing rates were significantly different. The injection of steam led to an increase in VWC of thawed soil within a 5 cm radius by 1.26–1.68 times. The thawing of permafrost at different ranges was dominated by two different modes, pressure-driven water steam migration leading to permafrost thawing at a radius of about 10 cm and thawing of permafrost at the rest of the range by hydrothermal migration induced by temperature and humidity gradient. Compared to other conventional thawing methods, steam thawing is more suitable for targeted pre-thawing of deeply permafrost layers with high ice content.

Visitors' consistent stay behavior patterns within free-roaming scenic architectural complexes: Considering impacts of temporal, spatial, and environmental factors

GPS positioning data are increasingly utilized in environmental behavior studies to explore the spatial-temporal behavioral patterns of individuals. However, individuals' stay behavioral pattern and its influencing factors, which are particularly significant for the design and management of scenic architectural complexes, have not been thoroughly examined. Using GPS trajectory data collected from the Palace Museum in Beijing (China), this paper investigated the visitors' stay behavior patterns associated with temporal, spatial, and environmental influencing factors. Types of stay behavior and characteristics of stay in main stay areas were automatically recognized using Python algorithms for further and quantitative analysis. Results showed that visitors' stay time exhibited a consistent pattern regarding psychological time allocation, a relatively unsignificant pattern regarding lunch hour, and no clear pattern regarding fatigue feature. Grouped regression analysis showed positive linear relationships with similar slopes between the average stay length and the number of stay occurrences in each type of stay area. Partial correlation analysis revealed the underlying connection between the impact of seats and greenery on stay behavior. Individually, each of the two environmental elements showed limited effect on stay frequency and stay length, while incorporating greenery into seating areas would notably increase both stay frequency and stay length.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery.

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Housing burden across Chinese cities: Spatio-temporal patterns and influential factors

Over the past several decades, the issue of housing burden has emerged as a critical global challenge, with China serving as a pivotal case study amid its urbanization surge. Previous research has mainly focused on the purchasing aspects of housing burden within isolated urban contexts, often overlooking the significance of rental burden. Employing a panel dataset of 270 Chinese cities from 2014 to 2020, this article analyzes the spatial-temporal patterns of purchasing and rental burden across those cities. Furthermore, based on a proposed analytical framework, this article empirically examines the factors influencing the housing burden. This article finds that the housing burden in China is mainly concentrated in some major cities and city-regions, and the influential factors of the purchasing and rental burden vary. This article contributes to a comprehensive understanding of China's landscape of housing burden, which is critical for designing more efficient and inclusive housing policies globally.

County Town Comprehensive Service Functions in China: Measurement, Spatio-Temporal Divergence Evolution, and Heterogeneity of Influencing Factors

Strengthening the service function of small towns, using its fundamental role in the urban system to drive rural development, is the main issue that needs to be addressed urgently in numerous developing countries. County towns are unique types of small towns in China. Analyzing the spatial-temporal patterns and differentiation mechanisms of comprehensive service functions of county towns in China from a geographic point of view can not only provide a basis for the macro-control of county towns but also provide typical regional research results for the study of urban systems and urban–rural coordination in developing countries. Based on Point of Interest (POI) data of 1788 county towns in China, this study analyzes the evolution of spatial and temporal differentiation of comprehensive service functions and influencing factors by using modeling methods such as Getis-Ord Gi* analysis, the random forest model, and Multiscale Geographically Weighted Regression (MGWR). The obtained results show that (1) from 2012 to 2021, the average value of the comprehensive service function index (CSFI) of county towns in China shows a significant increase, and the proportion of county towns with medium–high service levels and above increases from 3.41% to 54.50%; (2) spatially, the comprehensive service function of county towns is characterized by the basic pattern of “high east, low west; high south, low north”, which keeps getting stronger. During the study period, eastern China has always been a high-level region, northwestern and southwestern China have always been low-level regions, and northeastern China has been a stagnant region, while central, northern, and southern China have been fast-growing regions; (3) county general public budget revenues, value added of secondary industry, GDP per capita, county town resident population, altitude, and GDP per capita of affiliated prefecture-level cities to which it belongs are the key factors influencing the comprehensive service function of county towns in China. The county general public budget revenue indicator, which represents the governmental capacity, has the strongest influence; and (4) the results of the MGWR analysis indicate that there is spatial and temporal heterogeneity in the intensity of the above-mentioned key influencing factors on the development of comprehensive service functions of county towns in China. Based on this finding, differentiated strategies should be proposed to policy makers and urban planners in different regions in order to effectively enhance the level of comprehensive service functions of county towns in China.

Temporal-spatial characteristics and sources of heavy metals in bulk deposition across China

With the rapid development of industries, agriculture, and urbanization (including transportation and population growth), there has been a significant alteration in the emission and atmospheric deposition of heavy metal pollutants. This has consequently given rise to a range of ecological and environmental health issues. In this study, we conducted a comprehensive two-year investigation on the temporal and spatial distribution characteristics of heavy metals in atmospheric deposition across China based on the Nationwide Nitrogen Deposition Monitoring Network (NNDMN). The atmospheric bulk deposition of Lead (Pb), Arsenic (As), Nickel (Ni), Selenium (Se), Chromium (Cr) and Cadmium (Cd) were 6.32 ± 1.59, 4.49 ± 0.57, 1.31 ± 0.21, 1.05 ± 0.16, 0.60 ± 0.06 and 0.21 ± 0.03 mg m−2 yr−1, respectively, with a large variation among the different regions of China. The order for atmospheric deposition flux was Southwest China > Southeast China > North China > Northeast China > Qinghai-Tibet Plateau and rural area > urban area > background area. The concentrations of heavy metals in bulk deposition exhibit seasonal variation with higher levels observed during winter compared to summer and spring, which are closely associated with anthropogenic activities. The Positive Matrix Factorization (PMF) results indicated that combustion, industrial emissions and traffic are the primary contributors to atmospheric deposition of heavy metals. The single factor pollution index (Pi) of heavy metals is consistently below 1, and the composite pollution index (Ni) is 0.16 across China, indicating that atmospheric heavy metal deposition is at a pollution-free level. The comprehensive potential ecological risk index of heavy metals is 11.8, with Cd exhibiting the highest single factor potential ecological risk index at 7.09, suggesting that more attention should be paid to Cd deposition in China. The present study reveals the spatial-temporal distribution pattern of atmospheric heavy metals deposition in China, identifying regional source characteristics and providing a theoretical foundation and strategies for reducing emissions of atmospheric pollutants.

Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020

Typhoons are increasing in inland areas and affecting agriculture under climate change. Assessing the large-scale impact of typhoons on crops is essential for improving agricultural resilience. However, typhoon impacts are typically intricate and uncertain, depending on the different stages of crop growth and the indicators used for assessment. Here, we leveraged satellite data to examine the spatial-temporal patterns of typhoon-induced impacts on maize during the milk stage in Northeast China in 2020. We found that land surface water index (LSWI) showed a significantly higher negative relative change of 161.5% in regions severely affected by typhoons, surpassing that of normalized difference vegetation index (NDVI, 19.7%), near-infrared reflectance of vegetation (NIRV, 31.7%), and enhanced vegetation index (EVI, 25.2%), suggesting the outperformance of LSWI in monitoring the impact of typhoons on maize. Based on LSWI, we revealed the spatial-temporal distribution of typhoon impacts. Specifically, significant impacts on maize growth were observed in central Northeast China, particularly in Heilongjiang (49% of maize fields) and Jilin provinces (53% of maize fields). Furthermore, week-long lagging typhoon impacts on maize growth were generally found but lasted until the end of the maize growing season. We also found the typhoon impacts were partially mitigated by spatial and temporal compensations. Regions with above-average maize growth offset the losses experienced in areas severely affected by typhoons. Additionally, better maize growth in the early stages enhanced its resilience against subsequent typhoon impacts. Despite the mitigating effects on maize production, typhoons may increase the risk of maize mold, which warrants further investigation. Given the projected increase in future climate extremes, this study provides valuable insights into the rapid assessment of typhoon impacts using satellite data and cloud computing and supports decision-making in crop management for sustainable agricultural production.

Spatial and temporal patterns of land surface temperature in Greenland from 2000-2019

Temperature dynamics on the island of Greenland are an important factor in shaping ecological events. Investigating the land surface temperature (LST) patterns is critical for understanding ecological dynamics across different regions. Further melting of the Greenland ice sheet could deva state marine and terrestrial ecosystems. This study used data from Moderate Resolution Imaging Spectroradiometer satellites to understand the seasonal patterns and patterns of LST over the entire island. Focusing on the period between 2000 and 2019, this study used a natural cubic spline model to identify seasonal patterns for all sub-regions. The data were seasonally adjusted and filtered with a second-order autocorrelation component. The spline was fitted again to identify the LST pattern, and a multivariate regression model was then used to adjust for spatial correlation. We illustrate that most of the land surface of Greenland hasstable temperature trends. These observed patterns in LST in Greenland during the study period suggest that the observed ice-sheet melting in Greenland within the last two decades could be due to other factors, not necessarily LST patterns.

FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework

Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers in managing airspace more safely and efficiently. Existing approaches generally perform multi-horizon FTP tasks in an autoregressive manner, thereby suffering from error accumulation and low-efficiency problems. In this paper, a novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) representation in the FlightBERT. Specifically, the FlightBERT++ is implemented by a generalized encoder-decoder architecture, in which the encoder learns the temporal-spatial patterns from historical observations and the decoder predicts the flight status for the future horizons. Compared with conventional architecture, an innovative horizon-aware contexts generator is dedicatedly designed to consider the prior horizon information, which further enables non-autoregressive multi-horizon prediction. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. The experimental results on a real-world dataset demonstrated that the FlightBERT++ outperformed the competitive baselines in both FTP performance and computational efficiency.

Spatial pattern of sea surface temperature and chlorophyll-a trends in relation to hydrodynamic processes in the Alborán Sea

Environmental conditions such as temperature, planktonic biomass, and ocean currents play an important role in the development and distribution of marine species. This work aims to estimate, in a high spatial resolution, the actual trends of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) and to assess the relationship with the local hydrodynamic conditions in the Alborán Sea. To investigate these objectives, time series of SST and Chl-a of satellite sensor data were analyzed during 20 years from January 2001 to December 2020, using the Seasonal-Trend-Loess (STL) decomposition method and the Mann-Kendall seasonality test. The results, obtained with a 95% of confidence, showed that the Alborán Sea basin is subject to sea surface warming evaluated at 0.027 ± 0.008 ° C per year, related to the warming of the Atlantic water mass, which contributes to a decrease of productivity evaluated at -0.0024 ± 0.0003 μg /l per year of Chl-a concentration. These trends are not homogeneous over the entire basin area but show a large regional variation between different parts of the Alborán Sea due to the hydrodynamic process of the Atlantic Jet - Western Alboran Gyre system (AJ-WAG), which is more active especially in summer/autumn seasons and contribute largely to these changes by mixing the waters of the Atlantic Ocean and the Mediterranean Sea.

Sensing the pulse of the pandemic: unveiling the geographical and demographic disparities of public sentiment toward COVID-19 through social media

ABSTRACT Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users’ reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities and inaccurate estimations. This study explores solutions to pinpoint and alleviate the demographic biases in social media analysis through a case study estimating the public sentiment about COVID-19 using Twitter data. We analyzed the pandemic-related Twitter data in the U.S. during 2020–2021 to (1) elucidate the uneven social media usage among demographic groups and the disparities of their sentiments toward COVID-19, (2) construct an adjusted public sentiment measurement based on social media, the Sentiment Adjusted by Demographics (SAD) index, to evaluate the spatiotemporal varying public sentiment toward COVID-19. The results show higher proportions of female and adolescent Twitter users expressing negative emotions to COVID-19. The SAD index unveils that the public sentiment toward COVID-19 was most negative in January and February 2020 and most positive in April 2020. Vermont and Wyoming were the most positive and negative states toward COVID-19.

Spatial and temporal analysis of haemorrhagic septicaemia outbreaks in India over three decades (1987–2016)

Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987–2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.