Spatio-Temporal Analysis of Drought in the North-Eastern Coastal Region of Vietnam Using the Standardized Precipitation Index (SPI)

Spatio-temporal analysis of drought provides valuable information for drought management and damage mitigation. In this study, the Standardized Precipitation Index at the time scale of 6 months (SPI-6) is selected to reflect drought conditions in the North-Eastern coastal region of Vietnam. The drought events and their characteristics from 1981 to 2019 are detected at 9 meteorological stations and 10 Chirps rainfall stations. The spatio-temporal variation of drought in the study region is analyzed on the basis of the number, duration, severity, intensity, and peak of the detected drought events at the 19 stations. The results show that from 1981 to 2019 the drought events mainly occurred with 1-season duration and moderate intensity and peak. The number, duration, severity, and peak of the drought events were the greatest in the period 2001-2010 and were the smallest in the period 2011-2019. Among the 19 stations, the drought duration tends to decrease at 11 stations, increase at 7 stations, and has a slight variant at 1 station;the drought severity tends to decrease at 14 stations, increase at 4 stations, and has not a significant trend at 1 station;the drought intensity tends to decrease at 17 stations, increase at 1 station, and has a slight variant at 1 station;and the drought peak tends to decrease at 18 stations and increase at 1 station.

Spatio-temporal Characteristics and Geographical Determinants of Air Quality in Cities at the Prefecture Level and Above in China

In recent years, the large scale and frequency of severe air pollution in China has become an important consideration in the construction of livable cities and the physical and mental health of urban residents. Based on the 2016-year urban air quality index(AQI) data published by the Ministry of Environmental Protection of China, this study analyzed the spatial and temporal characteristics of air quality and its influencing factors in 338 urban units nationwide. The analysis provides an effective scientific basis for formulating national air pollution control measures. Four key results are shown. 1) Generally, air quality in the 338 cities is poor, and the average annual values for urban AQI and air pollution in 2016 were 79.58% and 21.22%, respectively. 2) The air quality index presents seasonal changes, with winter > spring > autumn > summer and a u-shaped trend. 3) The spatial distribution of the urban air quality index shows clear north-south characteristic differences and a spatial agglomeration effect; the high value area of air pollution is mainly concentrated in the North China Plain and Xinjiang Uygur Autonomous Region. 4) An evaluation of the spatial econometric model shows that differences in urban air quality are due to social, economic, and natural factors.

Spatio-Temporal Air Pollutant Characterization for Urban Areas

Urban pollution has now become increasingly recognized as an important determinant of air pollution in developed countries. The effect of urban air pollution in developing countries, on the other hand, has not been adequately addressed in the data Spatio-temporal time series. Thus, this study was intended to characterize the effect of urbanization on air pollution for an urbanized Klang Valley, Malaysia using Spatio-temporal data from 2008 to 2017. The Air Pollution Index (API) data and local pollutant concentration were employed to establish the links between urban air pollution. The analysis will be supported by determining the source of pollutants during the study period using Principal Component Analysis (PCA). The study identified that Carbon monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3) are the major air pollution that has contributed to degrading air quality in the Klang Valley due to the vehicles, combustion process, and industries.

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

STGI:a spatio-temporal grid index model for marine big data

Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.

Spatio-Temporal Characteristics of Standardized Precipitation Index in the Taihu Basin during 1951-2000

Spatial and temporal characteristics of standardized precipitation index （SPI）, which is widely used for drought/flood monitoring, are investigated in this study. The purpose is to obtain a reasonable primary scheme of zoning on the basis of drought/ wetness conditions in the study area. Spatio-temporal distributions of SPI with the time scales of 3 months and 12 months are investigated with the datasets of precipitation in the Taihu basin during past decades （1951-2000）. Results indicate that SPI series of 3 months show random fluctuation while that of 12 months behaves like I/f noise. SPI series of 3 months show little trend while that of 12 months show significant trend at several stations. Drought magnitude （DM） is also estimated on the basis of SPI values to assess drought condition. No trend is detected in DMs with time scales of both 3 months and 12 months. Spatial variability of DM is analyzed by mapping the DM with 12 months for extreme drought and wetness, and regional characteristics are analyzed for DM.

A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

Estimation of net primary productivity and its driving factors in the Ili River Valley,China

Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.

Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies

In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.

A remote sensing-based agricultural drought indicator and its implementation over a semi-arid region, Jordan

The objective of the study was to develop a remote sensing （i.e., Landsat-8 and MODIS）-based agricultural drought indicator （ADI） at 30-m spatial resolution and 8-day temporal resolution and also to evaluate its performance over a heterogeneous agriculture dominant semi-arid region in Jordan. Firstly, we used principal component analysis （PCA） to evaluate the correlations among six commonly used remote sensing-derived agricultural drought related variables. The variables included normalized difference water index （NDWI）, normalized difference vegetation index （NDVI）, visible and shortwave drought index （VSDI）, normalized multiband drought index （NMDI）, moisture stress index （MSI）, and land surface temperature （LST）. Secondly, we integrated the relatively less correlated variables （that were found to be NDWI, VSDI, and LST） to generate four agricultural drought categories/conditions （i.e., wet, mild drought, moderate drought, and severe drought）. Finally, we evaluated the ADI maps against a set of 8-day ground-based standardized precipitation index values （i.e., SPI-I, SPI-2, ..., SPLS） by use of confusion matrices and observed the best results for SPI-4 （i.e., overall accuracy and Kappa-values were 83% and 76%, respectively） and SPI-5 （i.e., overall accuracy and Kappa-values were 85% and 78%, respectively）. The results demonstrated that the method would be valuable for monitoring agricultural drought conditions in semi-arid regions at both a reasonably high spatial resolution （i.e., 30-m） and a short time period （i.e., 8-day）.

Measuring spatio-temporal autocorrelation in time series data of collective human mobility

Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

Water quality characteristics and assessment of Yongding New River by improved comprehensive water quality identification index based on game theory

The Yongding New River is essential for the water supplies of Tianjin.To date,there is no comprehensive report that assesses the year-round water quality of the Yongding New River Main stream.Moreover,little attention has been given to determining a combined weight for improving the traditional comprehensive water quality identification index(ICWQII)by the game theory.Seven water quality parameters were investigated monthly along the main stream of the Yongding New River from May 2018 to April 2019.Organic contaminants and nitrogen pollution were mainly caused by point sources pollution,and the total phosphorus mainly by non-point source pollution.Dramatic spatio-temporal variations of water quality parameters were jointly caused by different pollutant sources and hydrometeorological factors.In terms of this study,an improved comprehensive water quality identification index(ICWQII)based on entropy weight or variation coefficient and traditional CWQII underestimated the water qualities,and an ICWQII based on the superstandard multiple method overvalued the assessments.By contrast,water qualities assessments done with an ICWQII based on the game theory matched perfectly with the practical situation.The ICWQII based on game theory proposed in this study takes into account not only the degree of disorder and variation of water quality data,but also the influence of standard-exceeded pollution indicators,whose results are relatively reasonable.All findings and the ICWQII based on game theory can provide scientific support for decisions related to the water environment management of the Yongding New River and other waters.

Dynamic Development Characteristics and Driving Factors of High Quality Development Level in China’s Five Major Urban Agglomerations

High-quality development is the primary task of comprehensively building a socialist,modern country,as well as the primary task of building urban agglomerations in China.Based on the five development concepts,this paper used the entropy method to measure the High Quality Development Index(HQDI)of the five major urban agglomerations.The results showed that the HQDI of the five major urban agglomerations shows a fluctuating upward trend.First,using the Dagum Gini coefficient to explore the sources of HQDI development differences in urban agglomerations,we found that the main source of HQDI differences in urban agglomerations was inter-regional differences,while intra-regional differences were not important.Second,kernel density estimation was used to test the dynamic evolution trend of HQDI within urban agglomerations.There was a polarisation phenomenon in the HQDI of urban agglomerations,such as the Pearl River Delta urban agglomeration and the Chengdu-Chongqing urban agglomeration.But overall,the degree of imbalance had decreased.Third,using geographic detectors to examine the driving factors of HQDI in urban agglomerations,we found that the main driving forces for improving HQDI in urban agglomerations were economic growth,artificial intelligence technology and fiscal decentralisation.All the interaction factors had greater explanatory power for the spatial differentiation of HQDI,which can be divided into two types:two-factor improvement and non-linear improvement.This study is conducive to improving and enriching the theoretical system for evaluating the high quality development of urban agglomerations,and provides policy references for promoting the high quality development of urban agglomerations.

黄淮海平原耕地功能演变的时空特征及其驱动机制（英文）

Using counties as the basic analysis unit, this study established an evaluation index system for farmland function （FF） from economic, social, and ecological perspectives. The method combining entropy weighting and multiple correlation coefficient weighting was adopted to determine the weights, and the FF indices were calculated for each county. Sub- sequently, the spatio-temporal characteristics of farmland function evolution （FFE） were an- alyzed and the coupled relationships between the sub-functions were explored based on a coupling coordination model. At the same time, the dynamic mechanism of FFE was quanti- tatively analyzed using a spatial econometric regression analysis method. The following ma- jor conclusions were drawn： （1） The farmland economic function generally exhibited a de- clining trend during 1990-2010, and it is essential to point out that it was stronger in under- developed and agriculture-dominated counties, while it continuously weakened in developed areas. Farmland social function decreased in 60.29% of the counties, whereas some counties which were mostly located in north of Zhengzhou and west of Dezhou and Cangzhou, Yantai, and Weihai, clearly increased. A dramatic decline in farmland ecological function occurred around Beijing, Tianjin, and Jinan. Areas located in the northern part of Henan Province and the central part of Shandong Province saw an increase in ecological function. （2） There was a significant spatial difference in the coupling degree and coordination degree of the sub-functions, and the decoupling phenomenon highlighted this. The changes in social func- tion and ecological function lagged behind economic function in developed areas, but these were highly coupled in some underdeveloped areas. （3） FFE in the Huang-Huai-Hai Plain （HHHP） is resulted from the comprehensive effects of regional basic conditions and external driving factors. Furthermore, the transitions of population and industry under urbanization and industrialization played a decisive role in the evolution intensity and direction of farmland sub-systems, including the economy, society, and the ecology. According to the results men- tioned above, promoting the transformation from traditional agriculture to modern agricultureshould be regarded as an important engine driving sustainable development in the HHHP. Taking different regional characteristics of FFE into account, differentiated and diversified farmland use and management plans should be implemented from more developed urban areas to underdeveloped traditional agricultural areas.

Searching Activity Tra jectories with Semantics

With the widespread use of smart phones and mobile Internet,social network users have generated massive geo-tagged tweets,photos and videos to form lots of informative trajectories which reveal not only their spatio-temporal dynamics,but also their activities in the physical world.Existing spatial trajectory query studies mainly focus on analyzing the spatio-temporal properties of the users'trajectories,while leaving the understanding of their activities largely untouched.In this paper,we incorporate the semantics of the activity information embedded in trajectories into query modelling and processing,with the aim of providing end users more informative and meaningful results.To this end,we propose a novel trajectory query that not only considers the spatio-temporal closeness but also,more importantly,leverages a proven technique in text mining field,probabilistic topic modelling,to capture the semantic relatedness of the activities between the data and query.To support efficient query processing,we design a hierarchical grid-based index by integrating the probabilistic topic distribution on the substructures of trajectories and their spatio-temporal extent at the corresponding level of the index hierarchy.This specialized structure enables a top-down search algorithm to traverse the index while pruning unqualified trajectories in spatial and topical dimensions simultaneously.The experimental results on real-world datasets demonstrate the good efficiency and scalability performance of the proposed indices and trajectory search methods.