Spatial-temporal Data Analysis Research Articles

Top-k sentiment analysis over spatio-temporal data.

In recent years, social media has become much more popular to use to express people's feelings in different forms. Social media such as X (i.e., Twitter) provides a huge amount of data to be analyzed by using sentiment analysis tools to examine the sentiment of people in an understandable way. Many works study sentiment analysis by taking in consideration the spatial and temporal dimensions to provide the most precise analysis of these data and to better understand people's opinions. But there is a need to facilitate and speed up the searching process to allow the user to find the sentiment analysis of recent top-k tweets in a specified location including the temporal aspect. This work comes with the aim of providing a general framework of data indexing and search query to simplify the search process and to get the results in an efficient way. The proposed query extends the fundamental spatial distance query, commonly used in spatial-temporal data analysis. This query, coupled with sentiment analysis, operates on an indexed dataset, classifying temporal data as positive, negative, or neutral. The proposed query demonstrates over a tenfold improvement in query time compared to the baseline index with various parameters such as top-k, query distance, and the number of query keywords.

Utilizing IOT and Geospatial Analytics for Sustainable Fisheries Management

This study developed a software application that integrates Internet of Things (IoT) devices and weather data to visualize prime fishing locations using advanced spatial data techniques. The application features a dashboard that processes and displays real-time data, providing insights into fishing trends, fisherman activities, boat locations, and environmental conditions. The application uses the Graham scan method to generate a GIS grid heatmap for visualizing fish populations and trends, enhancing fisheries management capabilities. Comprehensive testing and refinement ensured the application's usability and adaptability. The results demonstrated high user satisfaction, with a 91% rating in usability and accuracy. The Graham Scan method successfully mapped fishing zones, achieving a 97.96% overlap in spatial-temporal data analysis, proving essential for data-driven decision-making in sustainable fisheries management.

Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches

Graph neural networks (GNNs) haven proven to be an indispensable approach in modeling complex data, in particular spatial temporal data, e.g., relating to sensor data given as time series with according spatial information. Although GNNs provide powerful modeling capabilities on such kind of data, they require adequate input data in terms of both signal and the underlying graph structures. However, typically the according graphs are not automatically available or even predefined, such that typically an ad hoc graph representation needs to be constructed. However, often the construction of the underlying graph structure is given insufficient attention. Therefore, this paper performs an in-depth analysis of several methods for constructing graphs from a set of sensors attributed with spatial information, i.e., geographical coordinates, or using their respective attached signal data. We apply a diverse set of standard methods for estimating groups and similarities between graph nodes as location-based as well as signal-driven approaches on multiple benchmark datasets for evaluation and assessment. Here, for both areas, we specifically include distance-based, clustering-based, as well as correlation-based approaches for estimating the relationships between nodes for subsequent graph construction. In addition, we consider two different GNN approaches, i.e., regression and forecasting in order to enable a broader experimental assessment. Typically, no predefined graph is given, such that (ad hoc) graph creation is necessary. Here, our results indicate the criticality of factoring in the crucial step of graph construction into GNN-based research on spatial temporal data. Overall, in our experimentation no single approach for graph construction emerged as a clear winner. However, in our analysis we are able to provide specific indications based on the obtained results, for a specific class of methods. Collectively, the findings highlight the need for researchers to carefully consider graph construction when employing GNNs in the analysis of spatial temporal data.

Analysis of the spatial-temporal evolution of Green and low carbon utilization efficiency of agricultural land in China and its influencing factors under the goal of carbon neutralization

Agricultural land is the most basic input factor for agricultural production and an essential component of terrestrial ecosystems, which plays a vital role in achieving carbon neutrality. Giving full play to the carbon-neutral contribution of agricultural land is a crucial part of China's economic transformation and green development. It incorporates carbon and pollution emissions from agricultural land use into the unexpected outputs of the Green and Low-carbon Utilization Efficiency of Agricultural Land (GLUEAL) evaluation system. The study utilized several advanced analytical tools, including the super-efficient Slacks-Based Measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method, Geodetector, and Geographically and Temporally Weighted Regression (GTWR) model. The objective was to examine the spatial-temporal evolution of GLUEAL and identify the factors that influenced it in all 31 provinces of China from 2005 to 2020. The results show that: (1) The overall spatial-temporal evolution of GLUEAL showed an increasing trend, but the disparity between provinces and regions became wider. (2) Most provinces have not yet made significant spatial and temporal jumps. They have high spatial cohesion with specific "path-dependent" characteristics. (3) The Geodetector results reveal that the Number of Rural Labor Force with Higher Education (NRLFHE) and Technology Support for Agriculture (TSA) have insufficient explanatory power on average for GLUEAL. Agricultural Economic Development Level (AEDL), Urbanization Level (UL), Multiple Crop Index (MCI), Planting Structure (PS), Degree of Crop Damage (DCD), Financial support for agriculture (FSA), and Agricultural mechanization level (AML) had stronger explanatory power on average for GLUEAL and were important factors influencing GLUEAL levels. (4) The average influence of AEDL, UL, FSA, and AML on GLUEAL changed from negative to positive. The average influence of MCI and DCD on GLUEAL was negative, and the average influence of PS on GLUEAL changed from positive to negative. This study provides a comprehensive description of the spatial and temporal evolution of GLUEAL in China. It reveals the key factors influencing GLUEAL and analyzes their spatial variations and impact patterns. These findings offer robust evidence for government policymakers to formulate policy measures for sustainable agricultural development and optimized resource allocation, promoting the transformation of agricultural land towards green and low-carbon practices and advancing the achievement of sustainable development goals.

Spatio-Temporal Coupling Evolution of Urbanisation and Carbon Emission in the Yangtze River Economic Belt.

The distribution characteristics of urbanisation level and per capita carbon emissions from 2006 to 2019 were investigated by the ranking scale rule, using 108 cities in the Yangtze River Economic Belt of China. A coupling coordination model was established to analyse the relative development relationship between the two, and exploratory spatial-temporal data analysis (ESTDA) was applied to reveal the spatial interaction characteristics and temporal evolution pattern of the coupling coordination degree. The results demonstrate that: (1) The urbanisation level and per capita carbon emissions of the Yangtze River Economic Belt show a stable spatial structure of 'high in the east and low in the west'. (2) The coupling and coordination degree of urbanisation level and carbon emissions show a trend of 'decreasing and then increasing', with a spatial distribution of 'high in the east and low in the west'. (3) The spatial structure exhibits strong stability, dependence, and integration. The stability is enhanced from west to east, the coupling coordination degree has strong transfer inertia, and the spatial pattern's path dependence and locking characteristics show a trend of weak fluctuation. Therefore, the coupling and coordination analysis is required for the coordinated development of urbanisation and carbon emission reduction.

Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional "double carbon" goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15'57″~112°25'43″ E and 27°43'13″~27°49'21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the "northwest-southeast" pattern to the "north-south" pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.

The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization

Urban-land development and utilization is one of the main sources of carbon emissions. Improving the green and low-carbon utilization efficiency of urban land (GLUEUL) under the goal of carbon neutrality is crucial to the low-carbon transition and green development of China's economy. Combining the concept of green and low-carbon development in urban land use, carbon emissions and industrial-pollution emissions are incorporated into the unexpected outputs of the GLUEUL evaluation system. The super-efficient slacks-based measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method and Geographically and Temporally Weighted Regression (GTWR) model were used to analyze the spatial-temporal transition and the influencing factors of GLUEUL in 282 cities in China from 2005 to 2020. The result shows that: (1) From 2005 to 2020, the green and low-carbon land-utilization efficiency of Chinese cities shows an increasing temporal-evolution trend, but the gap between cities is gradually widening. (2) From the spatial-temporal dynamic characteristics of Local Indicators of Spatial Association (LISA), regions with the highest GLUEUL have strong dynamics and instability, while cities at the lowest level have a relatively stable spatial structure. On the whole, the local-spatial-transfer direction of GLUEUL of each city is stable, with certain path-dependent characteristics. (3) There are differences in the degree of influence and direction of action of different factors on GLUEUL. The economic development level, industrial-structure upgrading, financial support, wealth level, and green-technology-innovation ability have positive effects on overall GLUEUL, with industrial-structure upgrading promoting GLUEUL the most, while urban population size, foreign-investment scale, and financial-development level play a negative role. This study can provide some empirical and theoretical references for the improvement of GLUEUL.

The temporal and spatial pattern evolution of land-use carbon emissions in China coastal regions and its response to green economic development

Carbon emissions based on land use change have attracted extensive attention from scholars, but the current land use carbon emission accounting model is still relatively rough. Despite the continuous promotion of China’s ecological civilization strategy, whether green economic development promotes carbon emission reduction remains to be studied. This study uses the Exploratory Spatial-temporal Data Analysis (ESTDA) framework system to revise the land-use carbon emission accounting model; it integrates the NDVI adjustment index and systematically analyzes the spatial and temporal patterns and evolutionary path characteristics of carbon emissions from 2000 to 2020 for 130 prefecture-level cities in the eastern coastal region of China, a high carbon emission region. The spatial econometric model is further used to explore the impact of green economy development on carbon emissions. The results show that the spatial distribution of carbon sources and sinks in the eastern coastal cities demonstrates a year-on-year increase during the study period. The spatial distribution of carbon sources is higher in the north than in the south, and the economically developed regions are more elevated than less developed economic areas. Net carbon emissions show prominent spatial clustering characteristics. The south has a more stable internal spatial structure than the north, and the inland has a more stable internal spatial structure than the coast. Green economic development can significantly reduce carbon emission intensity and has a significant spatial spillover effect. The findings imply that policy-makers need to consider the spatial and temporal distribution and spatial correlation of carbon emissions among cities; they can achieve carbon emission reduction by formulating a more reasonable green economy development approach and implementing regional linkages.

Spatial-temporal data analysis of digital twin

Background: Digital Twin (DT) has proven to be one of the most promising technologies for routine monitoring and management of complex systems with uncertainties. Methods: Our work, which is mainly concerned with heterogeneous spatial-temporal data, focuses on exploring data utilization methodology in DT. The goal of this research is to summarize the best practices that make the spatial-temporal data analytically tractable in a systematic and quantifiable manner. Some methods are found to handle those data via jointly spatial-temporal analysis in a high-dimensional space effectively. We provide a concise yet comprehensive tutorial on spatial-temporal analysis considering data, theories, algorithms, indicators, and applications. The advantages of our spatial-temporal analysis are discussed, including model-free mode, solid theoretical foundation, and robustness against ubiquitous uncertainty and partial data error. Finally, we take the condition-based maintenance of a real digital substation in China as an example to verify our proposed spatial-temporal analysis mode. Results: Our proposed spatial-temporal data analysis mode successfully turned raw chromatographic data, which are valueless in low-dimensional space, into an informative high-dimensional indicator. The designed high-dimensional indicator could capture the ’insulation’ correlation among the sampling data over a long time span. Hence it is robust against external noise, and may support decision-making. This analysis is also adaptive to other daily spatial-temporal data in the same form. Conclusions: This exploration and summary of spatial-temporal data analysis may benefit the fields of both engineering and data science.

Divorce as an aspect of demographic behavior of the Volyn region population


 
 
 
 The article gives a general description and a detailed analysis of the divorce process of Volyn region population. The theoretical basis, demographic approaches to the study of divorce and divorce indicators in the region are analyzed. The importance of the divorces consequences and measures of demographic policy, which will be aimed at reducing them as a result of modernization of social and economic life in the current regime of reproduction: low birth rate and increasing life expectancy. Divorces peculiarities of Volyn region population are determined, the main of which are: sociological, psychological and demographic groups of risk factors for divorce. The main stages of family crises and their symptoms are described. The main negative consequences of divorce, which have an impact on the general demographic situation in the region are low birth rate (unrealized childbearing of divorced women); increase in the number of single-parent families in which the child is brought up by one parent; increasing the number of people who do not want to marry again after divorce; decrease of labor activity; deteriorating health, increased morbidity and death rate; increase suicides rate; increased risk of mental illness. It was found out the perspective psychological, legal forms and other assistance in marriage and family. The reasons for the low efficiency of the implementation of specific steps to overcome the processes of divorce in Volyn region are highlighted. Indicators for estimation of divorcing in the region have been calculated. Spatial-temporal data analysis of divorces for the period of 1995–2020 is carried out, regional features of their manifestation are singled out. The maps that reflect the spatial-temporal data differences of divorces in the regions were created. There were identified the main factors and preconditions for divorce in the region.
 
 
 

Spatial–Temporal Data Analysis-Based Event Detection in Weakly Damped Power Systems

Focusing on tackling the challenging issue of detecting events in weakly damped power systems, this letter proposes a novel event detection approach via spatial-temporal data analysis. Based on the intrinsic spatial-temporal correlations in regional PMU measurements, it efficiently profiles spatial-temporal nearest neighbors (STNNs) of multiple buses’ featured time series. With such discriminative STNN profiles, it is able to detect the occurrence of various events even in the presence of significant frequency oscillations. Numerical test results on the Nordic test system as well as several IEEE test systems demonstrate the efficacy of the proposed approach.

Land Use - Land Cover Change Analysis of Karur Town - A GIS Approach

Land use / Land cover change is one of the most sensitive factors that show the interactions between human activities and the ecological environment. This research study demonstrated the importance of geographical information system and remote sensing technologies in spatial temporal data analysis and also this paper shows a GIS and remote sensing approach for modeling of spatial - temporal pattern of land use and land cover change (LULC) in a fastest growing towns / industrial region of Karur town. QGIS 3.10 version and Arc GIS 10.2 software platforms were utilized in the study for Image processing, LULC mapping and change detection analysis. USGS Earth explorer Landsat series satellite imageries were acquired and LULC maps were prepared for the years 1991, 2000, 2010 and 2020. Supervised classification with maximum likelihood algorithm is adopted for LULC classification. The LULC classes are Built upland, Agricultural land, Barren land and Water body based on NRSA Level – I supervised classification. The Built-up area has drastically increased from 1991 to 2020. It has increased more than double. It was 17 percent in 1991 and increased to 40 percent in 2020. This clearly shows Karur town is the becoming more and more urbanized.

Spatial-temporal evolution patterns of hotels in China: 1978–2018

This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.,A database comprising over 140,000 hotels with more than 30 rooms was created. The exploratory spatial–temporal data analysis (ESTDA) method, based on space–time cube model, was used to explore and visualize the spatial–temporal pattern of hotels.,The Chinese hotel industry can be divided into two development stages, namely, a large hotel-dominant stage before 2000 and a small–medium-sized hotel-dominant stage after 2000. China’s prefecture-level cities were clustered into four tiers. The higher the tier, the earlier the city will initiate hotel development. The Chinese hotel industry has four continuous hotspots (the Yangtze River Delta, Pearl River Delta, Bohai Rim and Sichuan and Chongqing) and some temporary hotspots.,This study lacks quantitative investigation, which could show the underlying mechanism of the evolution of the Chinese hotel industry.,This study is the first to investigate China’s hotel evolution over 40 years by applying big data and the ESTDA method. The systematic and evolutionary exploration will enable hotel researchers to understand the spatial–temporal nature of hotel distribution better. Introducing the ESTDA method into tourism and hotel research also provides an additional tool to researchers. Hotel investors and operators, city and tourism planners and market regulators can learn from the evolution of location patterns to make better where and when decisions.

Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

Generic Constraint-based Block Modeling using Constraint Programming

Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings, for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally, we show an application of the CP framework for model selection using the Minimum Description Length principle.

Межпоколенческие различия в удовлетворенности жизнью и чувстве счастья в России (на материалах Европейского социального исследования)

Проводится дифференциация опыта социализации в молодости четырех условных поколений, а также разделение эффектов социализации, связанных с принадлежностью к определенной совокупности возрастных групп и факторов исторического периода. Используются наиболее полные по сравнению с другими исследованиями эмпирические данные о показателях счастья и удовлетворенности жизнью в России из Европейского социального исследования — сравнительного межстранового мониторинга. К ним применяются количественные методы регрессионного анализа пространственно-временных выборок. Полученные результаты сопоставляются с итогами исследования субъективного благополучия и счастья, проведенного Лабораторией сравнительных социальных исследований НИУ ВШЭ на основе данных Всемирного мониторинга ценностей; а также с итогами Российского мониторинга экономического положения и здоровья населения НИУ ВШЭ; данными ВЦИОМ и других исследований, что повышает надежность полученных результатов. Поколения Х (кризисная социализация) и Y (вторичная социализация в 2000—2014 гг.), а также предположительно формирующееся поколение Z (молодежь, вторичная социализация которой приходится на политически знаменательный 2014 г. и последующие) взрослеют в разных социальных, экономических и политических условиях, что влияет на различия между ними в показателях счастья и удовлетворенности жизнью. Раскрыта и проиллюстрирована связь состояния счастья как с социально-статусными признаками респондентов (уровень образования, доход, семья), так и с принадлежностью к условному поколению. На фоне более высоких показателей счастья молодежи, чем у старших возрастных групп, ее неудовлетворенность от не вполне реализованных социальных ожиданий возрастает. Обоснован вывод о том, что чувство недовольства в российском обществе будет нарастать.
 Благодарность. Статья подготовлена в рамках проекта Российского научного фонда № 19-18-00246 «Вызовы трансформации социального государства в России: институциональные изменения, социальное инвестирование, цифровизация социальных услуг».

A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

In spatial-temporal data analysis, location data and its evolution through time are investigated with the goal of uncovering important information to provide novel insights. These insights, for example, may involve congestion identification in transportation, mobility patterns in urban computing, and storm prediction in weather forecasting. Clustering, one data analysis technique, groups spatial-temporal data based on location. Current spatial-temporal data analysis techniques fail to investigate relationships between spatial-temporal clusters, such as splitting from a cluster and merging with another one because of a change of properties over time. These relationships could hold valuable information about the existence of a cluster and its interactions with other clusters and trajectories. In this paper, we introduce a framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split). We describe its architecture and components, as well as a proposed clustering technique, the different approaches for distance calculation, and how we calculate cluster similarity of temporally separated clusters. The result of these operations are used in the identification of cluster relationships over space and time. The analysis of these relationships helps uncover hidden values that could support novel approaches to more effective decision-making. We evaluate our framework with two case studies, based on truck and human trajectories.

The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique

With the rapid development of the Internet of things (IoTs) and modern industrial society, forecasting air pollution concentration, e.g., the concentration of PM2.5, is of great significance to protect human health and the environment. Accurate prediction of PM2.5 concentrations is limited by the number and the data quality of air quality monitoring stations. In Taiwan, the spatial and temporal data of PM2.5 concentrations are measured by 77 national air quality monitoring stations (built by Taiwan EPA). However, the national stations are costly and scarce because of the highly precise instrument and their size. Therefore, many places are still out of coverage of the monitoring network. Recently, under the framework of IoTs, there are hundreds of portable air quality sensors called “AirBox” developed jointly by the Taiwan local government and a private company. By virtue of its low price and portability, the AirBox can provide a higher resolution of space-time PM2.5 measurement. However, the spatiotemporal distribution is different between AirBox and EPA stations, and data quality and accuracy of AirBox is poorer than national air quality monitoring stations. Thus, to integrate the heterogeneous PM2.5 data, the data fusion technique should be used before further analysis.In this study, we propose a new data fusion method called multi-sensor space-time data fusion framework. It is based on the Optimum Linear Data Fusion theory and integrating with a multi-time step Kriging method for spatial-temporal estimation. The method is used to do heterogeneous data fusion from different sources and data qualities. It is able to improve the estimation of PM2.5 concentration in space and time. Results have shown that by combining PM2.5 concentration data from 1176 low-cost AirBoxes as additional information in our model, the estimation of spatial-temporal PM2.5 concentration becomes better and more reasonable. The r2 of the validation regression model is 0.89. Under the approach proposed in this study, we made the information of the micro-sensors more reliable and improved the higher spatial-temporal resolution of air quality monitoring. It could provide very useful information for better spatial-temporal data analysis and further environmental management, such as air pollution source localization, health risk assessment, and micro-scale air pollution analysis.

Half-spectral analysis of spatial-temporal data: The case study of Iranian daily wind speed data

In this paper, we first study the theory of the spatial-temporal half spectral modeling and describe some properties of recently proposed half spectral models. Next, we propose an estimation method for the estimation of spatial-temporal covariance functions in the half-spectral setting. To assess the performance of the proposed half-spectral models, we conduct two simulations, in which we compare the proposed fitting approach with respect to the other classical estimation methods. The proposed methods have great success in fitting parametric space-time covariance functions specifically for massive data sets. Finally, we apply the proposed methods for a real daily wind speed data in Iran.