Atmospheric aerosol pollution across China:a spatiotemporal analysis of satellite-based aerosol optical depth during 2000–2016

Increasing attention has been paid to the deterioration of air quality in China during the past decade.This study presents the spatiotemporal variations of aerosol concentration across China during 2000–2016 using aerosol optical depth(AOD)from the atmospheric product of Moderate Resolution Imaging Spectroradiometer.Percentile thresholds are applied to define AOD days with different loadings.Temporally,aerosol concentration has increased since 2000 and reached the highest level in 2011;then it has declined from 2011 to 2016.Seasonally,aerosol concentration is the highest in summer and the lowest in winter.Spatially,North China and Sichuan Basin are featured by high aerosol concentration with increasing trends in North China and decreasing trends in Sichuan Basin.North,Southeast and Southwest China have been through increasing days with low AOD loading;however,Northeast China has experienced increasing days with high AOD loading.It is likely that air quality influenced by aerosols has notably improved over North China in spring and summer,over Southwest and Southeast China in autumn,but has degraded over Northeast China in autumn.

A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies

Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil

This data-driven work aims to analyze and classify the spatiotemporal distribution of all Brazilian states considering data so diverse as the number of Covid-19 cases,deaths,confirmed cases per 100 k inhabitants,mortality per 100 k inhabitants and case fatality rates as health indicators.We also considered population,area and population density as geographic indicators.Finally,GDP and HDI were taken into account as economic and social criteria.For this task data were collected from April 3rd until August 8th,2020,corresponding to epidemiological weeks 14e32,reaching three million cases and a hundred thousand deaths.With this data it was possible to classify Brazilian states using multivariate methods into possible groups by means of non-hierarchical(k-means)cluster as well as factor analysis.It was possible to group all states plus the Federal District into five clusters,taking into account these 10 variables over the first five months of the epidemic.Group changes between states were observed over time and clusters,and between three and four factors were found.However,even with great difference on health indicators during days,the number of clusters remains fixed.Also,S^ao Paulo and Rio de Janeiro states were ranked at top list taking into account all epidemiological weeks.Correlations were observed between variables,such as the number of Covid cases and deaths with GDP for most of epidemiological weeks.Some clusters were more critical due to specific variables,including cities that are main hotspots.These multivariate findings would provide a comprehensive description of the ongoing Covid-19 epidemic and may help to guide subsequent studies to understand and control virus transmission.

Overview of recent land cover changes,forest harvest areas,and soil erosion trends in Nordic countries

Mapping spatiotemporal land cover changes offers opportunities to better understand trends and drivers of envi-ronmental change and helps to identify more sustainable land management strategies.This study investigates the spatiotemporal patterns of changes in land covers,forest harvest areas and soil erosion rates in Nordic countries,namely Norway,Sweden,Finland,and Denmark.This region is highly sensitive to environmental changes,as it is experiencing high levels of human pressure and among the highest rates of global warming.An analysis that uses consistent land cover dataset to quantify and compares the recent spatiotemporal changes in land cover in the Nordic countries is missing.The recent products issued by the European Space Agency and the Copernicus Climate Change Service framework provide the possibility to investigate the historical land cover changes from 1992 to 2018 at 300 m resolution.These maps are then integrated with time series of forest harvest areas be-tween 2004 and 2018 to study if and how forest management is represented in land cover products,and with soil erosion data to explore status and recent trends in agricultural land.Land cover changes typically involved from 4%to 9%of the total area in each country.Wetland showed the strongest reduction(11,003 km^(2),−11%of the wetland area in 1992),followed by forest(8,607 km^(2),−1%)and sparse vegetation(5,695 km^(2),−7%),while agriculture(15,884 km^(2),16%)and settlement(3,582 km^(2),84%)showed net increases.Wetland shrinkage dominated land cover changes in Norway(5,870 km^(2),−18%),followed by forest and grassland with a net gain of 3,441 km^(2)(3%)and 3,435 km^(2)(10%),respectively.In Sweden,forest areas decreased 13,008 km^(2)(−4%),mainly due to agriculture expansion(9,211 km^(2),29%).In Finland,agricultural areas increased by 5,982 km^(2)(24%),and wetland decreased by 6,698 km^(2)(−22%).Settlement had the largest net growth in Denmark(717 km^(2),70%),mainly from conversion of agriculture land.Soil erosion rates in Nordic countries are lower than the global average,but they are exacerbating in several locations(especially western Norway).The integration of the land cover datasets with maps of forest harvest areas shows that the majority of the losses in forest cover due to forestry operations are largely undetected,but a non-negligible share of the forest-to-agriculture(up to 19%)or forest-to-grassland(up to 51%)transitions overlap with the harvested sites.Forestry activity in the study region primarily involves small-scale harvest events that are difficult to be detected at the 300 m resolution of the land cover dataset.An accurate representation of forest management remains a challenge for global datasets of land cover time series,and more interdisciplinary international efforts are needed to address this gap.Overall,this analysis provides a detailed overview of recent changes in land cover and forest management in Nordic countries as represented by state-of-the-art global datasets,and offers insights to future studies aiming to improve these data or apply them in land surface models,climate models,landscape ecology,or other applications.

Spatial Humanities and Geo-computation for Social Sciences:Advances and Applications

Humanities and Social Sciences(HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics(including RS: Remote Sensing;GIS: Geographical Information System;GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences(SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover(LUCC), Specially Protected Natural Areas(SPNA) analysis, editing behavior analysis of Volunteered Geographic Information(VGI), electricity consumption anomaly detection, First and Last Mile Problem(FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

High-risk spatiotemporal patterns of cutaneous leishmaniasis:a nationwide study in Iran from 2011 to 2020

Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.

青藏高原典型牧区生态系统服务与人类福祉的时空协调发展研究

In this study,the interplay between ecosystem services and human well-being in Seni district,which is a pastoral region of Nagqu city on the Qinghai-Tibet Plateau,is investigated.Employing the improved InVEST model,CASA model,coupling coordination model,and hierarchical clustering method,we analyze the spatiotemporal patterns of ecosystem services,the levels of resident well-being levels,and the interrelationships between these factors over the period from 2000 to 2018.Our findings reveal significant changes in six ecosystem services,with water production decreasing by 7.1%and carbon sequestration and soil conservation services increasing by approximately 6.3%and 14.6%,respectively.Both the habitat quality and landscape recreation services remained stable.Spatially,the towns in the eastern and southern areas exhibited higher water production and soil conservation services,while those in the central area exhibited greater carbon sequestration services.The coupling and coordination relationship between ecosystem services and human well-being improved significantly over the study period,evolving from low-level coupling to coordinated coupling.Hierarchical clustering was used to classify the 12 town-level units into five categories.Low subjective well-being townships had lower livestock breeding services,while high subjective well-being townships had higher supply,regulation,and support ecosystem services.Good transportation conditions were associated with higher subjective well-being in townships with low supply services.We recommend addressing the identified transportation disparities and enhancing key regulatory and livestock breeding services to promote regional sustainability and improve the quality of life for Seni district residents,thus catering to the diverse needs of both herdsmen and citizens.

A survey of urban visual analytics:Advances and future directions

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models.Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities.To promote further academic research and assist the development of industrial urban analytics systems,we comprehensively review urban visual analytics studies from four perspectives.In particular,we identify 8 urban domains and 22 types of popular visualization,analyze 7 types of computational method,and categorize existing systems into 4 types based on their integration of visualization techniques and computational models.We conclude with potential research directions and opportunities.

How virtual reality can help visualise and assess geohazards

Geohazard research requires extensive spatiotemporal understanding based on an adequate multi-scale representation of modelling results.The most commonly applied representation basis for collected data is still the one of a 2D plane,typically a map.Digital maps of spatial data can be visualised and processed by using Geographic Information Systems.It is far less common to use 3D geomodels for the analysis and visualisation of spatial data.For the visualisation of both spatial and temporal hazard components,there are no standardised tools.We claim that a full geohazard assessment is only possible inside a new type of geoscientific and technological environment that is at the same time multi-dimensional,spatiotemporal,integrated,fully interactive(tele-)immersive,and collaborative.Surface and subsurface processes are interacting at various scales that are difficult to be overviewed at once.Virtual Reality(VR)technology could provide an attractive solution to overcome the multi-dimensional and spatiotemporal obstacles.The review of geoscientific applications using VR technology developed by multiple teams around the world shows that some solutions have already been developed many years ago,but widespread use was not possible.This is clearly changing now and soon we will see if VR can contribute to a better understanding of geo-processes.

基于人口流动的广东省COVID-19疫情风险时空分析

Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study.It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk.The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration.It further incorporates a time-lag process based on the time distribution of the onset of the imported cases.In theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures.The research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in Guangdong.The first-tier cities-Shenzhen and Guangzhou-are high-risk regions.The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou,including Dongguan,Foshan,Huizhou,Zhuhai,Zhongshan,are medium-risk cities.The eastern,northern,and western parts of Guangdong,which are outside of the metropolitan areas of the Pearl River Delta,are considered to have low risks.Therefore,the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.

The emergence and evolution of OpenStreetMap: a cellular automata approach

Collaborative mapping projects,such as OpenStreetMap(OSM),have received tremendous amounts of contributed data from voluntary participants over time.So far,most research efforts deal with data quality issues,but the OSM evolution across space and over time has not been noted.Therefore,this study is dedicated to the evolution of the contributed information in order to understand an emergent phenomenon of so-called collaborative contributing.The main objective of this paper is to monitor the evolutional pattern of OSM and predict potential future states through a cellular automata(CA)model.This is exceedingly relevant for numerous OSM-based applications.Descriptive spatiotemporal analysis of the contributions for the time period 2007–2012,using the city of Heidelberg(Germany)as a case study,reveals that early contributions are given three years after the launching of OSM,while after nearly six years,most of the areas are discovered.The simulation results for the validated CA model,predicting OSM states for 2014,provide clear evidence that most of the areas have been explored three years after people began mapping until 2010,and thereafter,the densification process has begun and will cover most parts of the city although the amount of contribution depends on the land use types.

Reshaping the urban hierarchy:patterns of information diffusion on social media

The spatial diffusion of information is a process governed by the flow of interpersonal communication.The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information.Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements.Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked.In this research,we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information.We examined the top 30 populated cities and metropolitan areas in the U.S.and retrieved Twitter data related to two selected topics from these regions,the 2015 Nepal Earthquake and the#JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices.We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications.Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy.

Identifying factors that affect environmental air quality using geographical detectors in the NKEFAs of China

The establishment of the National Key Ecological Function Areas(NKEFAs)is an important measure for national ecological security,but the current ecological and environmental evaluation of NKEFAs lacks research on the air quality in the NKEFAs.This study presented the current status of the air quality in the NKEFAs and its driving factors using the geographic detector q-statistic method.The air quality in the NKEFAs was overall better than individual cities and urban agglomeration in eastern coast provinces of China,accounting for 9.21%of the days with air quality at Level III or above.The primary air pollutant was PM_(10),followed by PM_(2.5),with lower concentrations of the remaining pollutants.Pollution was more severe in the sand fixation areas,where air pollution was worst in spring and best in autumn,contrasting with other NKEFAs and individual cities and urban agglomerations.The main influencing factors of air quality index(AQI)in the NKEFAs were land use type,wind speed,and relative humidity also weighted more heavily than factors such as industrial pollution and anthropogenic emissions,and most of these influence factors have two types of interactive effects:binary and nonlinear enhancements.These results indicated that air pollution in the NKEFAs was not related with the emission by intensive economic development.Thus,the policies taking the NKEFAs as restricted development zones were effective,but the air pollution caused by PM_(10) also showed the ecological status in the NKEFAs,especially at sand fixation areas was not quite optimistic,and more strict environmental protection measures should be taken to improve the ecological status in these NKEFAs.