Spatial-temporal Characteristic Research Articles

Policy, innovation, market or behavior driven effects: energy green transition and environmental management in Chinese heterogeneous cities

Energy green transition (EGT) is currently one of the main measures for countries around the world to address the contradiction between economic growth and increasingly deteriorating environmental and climate issues. Cities are the center of energy consumption. The key to EGT lies in urban energy green transition. Therefore, the focus of this study is on the driving mechanism of urban EGT. Firstly, the spatial-temporal characteristic of EGT in Chinese heterogeneous cities is analyzed by using methods such as gravity model. Secondly, the possible paths includes policy driven, innovation driven, market driven, and behavior driven for urban EGT are discussed through theoretical analysis. Finally, combined with panel data of 236 Chinese cities in 2007-2022, this study empirically analyzes the complex driving mechanism of urban EGT. Results show that: (1) The EGT in Chinese cities is continuing. From the perspective of urban heterogeneous, EGT in 1-tier and 2-tier cities is significantly faster than that in 3-, 4-, and 5-tier cities. The EGT speed in eastern cities is the fastest, while that in northeastern cities is the slowest. The difficulty of EGT in energy resource-based cities is actually the greatest. From the perspective of spatial features, the spatial center of EGT in Chinese cities generally shows a changing trend from northwest to southeast. (2) Policy driven, innovation driven, market driven, and behavior driven constitute the complex driving mechanism of urban EGT, and policy driven is the primary driving force for this round of EGT. (3) Positive effect of economic development level and education level improvement on EGT in Chinese cities is significant while resource endowment and population agglomeration level exhibit significant inhibitory effects. (4) There are significant differences in the core driving force for EGT in heterogeneous cities. Both policy driven effect and market driven effect have the highest impact in 1- and 2-tier cities. Innovation driven effect, market driven effect, and behavior driven effect are only significant in eastern and central cities. In energy resource-based cities, innovation driven effect of green innovation is not significant. This study can assist government departments better in formulating relevant policies to support energy transition, promote technological innovation, design market mechanisms, and guide energy consumption behavior.

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

A two-stage stochastic collaborative planning approach for data centers and distribution network incorporating demand response and multivariate uncertainties

As a critical infrastructure to support the development of digital economy, the scale of data center (DC) power consumption has been growing continuously in recent years. The plug-in of a large number of DCs to the distribution network (DN) presents both opportunities and challenges for the secure, stable, and sustainable operation of the system. In order to address the planning issues arising from the integration of DCs into the DN, this paper proposes a two-stage (planning stage and operation stage) collaborative planning method for DCs and the DN while considering the source-demand uncertainty. Firstly, spatial-temporal demand response characteristic of DCs is explicitly elaborated, and based on the energy consumption model, a two-stage stochastic planning model considering conditional value at risk (CVaR) for DCs and the DN is constructed using probabilistic stochastic simulation, which obeys low-carbon and security, and satisfies the source-network-load constraints of the system. The siting and sizing for DCs and renewable generation, feeder expansion for the DN are determined to minimize the investment cost during the planning stage, while the power output for the devices considering uncertainties from load and renewable energy are optimized to minimize the operation cost during the operation stage. Secondly, the L-shaped decomposition algorithm is proposed to solve the two-stage stochastic optimization problem by reconstructing the problem into a master problem and subproblems, where the dual multiplies are calculated to generate feasibility cuts and optimality cuts to achieve iterative solving. Finally, numerical case studies on a 33-bus distribution system demonstrate that the participation of DCs in demand response can achieve a significant reduction in the investment and operation cost of the DN, and further promote the consumption of distributed renewable energy sources.

Dynamic spatial–temporal graph convolutional recurrent networks for traffic flow forecasting

Traffic flow forecasting is crucial for making appropriate route guidance and vehicle scheduling schemes in intelligent transportation systems. However, recent graph-based methods leverage pre-defined static graphs to describe the spatial–temporal characteristic of road networks. The interactions of two road segments are changeable under the influence of natural environmental and socioeconomic factors, while these methods are not sufficient to capture the complicated dynamic correlations of different nodes. To address these problems, we propose a novel dynamic graph-based deep learning framework with dynamic graph recurrent network for traffic flow forecasting, called Dynamic Spatial–temporal Graph Recurrent Neural Networks. In this framework, a novel dynamic graph generator is designed to obtain the dynamic representation of nodes, which employs multi-head attention network and dynamic node embedding to capture hidden spatial dependency more effectively. To infer the edge status of dynamic graph at different times, the generated dynamic graph is trained as special time series data via dynamic graph recurrent neural network for downstream time-series prediction. In contrast to methods straightforwardly concatenating static graphs and dynamic graphs, a novel fusion framework integrates two-channel convolutional networks with penalty terms and a gate fusion layer to extract dynamic spatial dependency from multiple graphs for improving forecasting accuracy and reducing computational consumption. Experiments on three real-world datasets are carried out to evaluate the superior performance of our model. Compared with previous state-of-the-art baselines, the proposed method performs much better with 10%–26% improvements on three datasets. The results also indicate that our model is robust against emergent traffic situations.

Decoupling effect and spatial-temporal characteristics of carbon emissions from construction industry in China

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of “double carbon target”. The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is “high in the east and low in the west, high in the south and low in the north,” showing obviously regional differences.

Direct observation of spreading precursor liquids in a corner.

Precursor liquid is a nanoscale liquid creeping ahead of the macroscopic edge of spreading liquids, whose behaviors tightly correlate with the three-phase reaction efficiency and patterning accuracy. However, the important spatial-temporal characteristic of the precursor liquid still remains obscure because its real-time spreading process has not been directly observed. Here, we report that the spreading ionic liquid precursors in a silicon corner can be directly captured on video using in situ scanning electron microscopy. In situ spreading videos show that the precursor liquid spreads linearly over time ([Formula: see text]) rather than obeying the classic Lucas-Washburn law ([Formula: see text]) and possesses a characteristic width of ∼250-310nm. Theoretical analyses and molecular dynamics simulations demonstrate that the unique behaviors of precursor liquids originate from the competing effect of van der Waals force and surface energy. These findings provide avenues for directly observing liquid/solid interfacial phenomena on a microscopic level.

Discussion on the spatial-temporal inhomogeneity characteristic of horizontal-axis wind turbine's wake and improvement of four typical wake models

Previous studies on wake of horizontal-axis wind turbines (HAWTs) mainly focus on the spatial distribution but ignore the time-varying characteristics of the wake profile which highly affect the operation strategy of the downstream HAWTs to ensure the high-efficiency and low-fatigue load. This paper proposed a mathematical method for studying the spatial-temporal inhomogeneity of wake profiles. Considering the anisotropy of wake expansion, the turbulence intensity was modified to avoid repeated experimental calculation of empirical parameters such as wake expansion coefficient. In addition, the super-Gaussian function was used to modify the near wake model, and the modified near wake model was combined with four typical far wake models to obtain four temporal wake models that can express the time-varying characteristics of the wake. The accuracy of the improved four temporal wake models were verified by wind tunnel test, and the relative errors were analyzed. Results show that the prediction errors of the modified two-dimensional temporal wake model are basically within 4%, which is smaller than the other three wake models. The method presented in this paper can effectively describe the spatial distribution and time-varying characteristics of wake expansion, and can provide a reference for optimizing the control strategy of HAWTs.

An enhanced hybrid deep neural network reduced-order model for transonic buffet flow prediction

In the transonic flight environment, transonic buffet has significant influence on the aerodynamic characteristics of aircraft. In the face of the strong nonlinear characteristics of transonic buffet, the reduced-order model (ROM) technology based on deep learning (DL) is introduced to study the flow field. In this article, an enhanced hybrid deep neural network (eHDNN) consisting of Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (ConvLSTM) neural network is constructed as the ROM of unsteady transonic buffet flow field. By mining the spatial-temporal characteristic of buffet flow directly from large-scale flow field data, the eHDNN can predict complex flow characteristics such as shock wave motion in the unsteady buffet flow. Structural similarity information and nonlinear improvement strategies are introduced to improve the nonlinear expression ability of eHDNN. The predicted result is consistent with the numerical simulation and has high degree of prediction accuracy. In addition, the eHDNN has good generalization ability, which can predict the spatial-temporal evolution of buffet flow field at unknown angles of attack. The prediction method has good application prospect of the transonic buffet flow control and prediction, which also provides experience for DL modeling in other complex and strongly nonlinear flows.

Data-driven bus timetabling with spatial-temporal travel time

PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

DaaS: Towards Energy-Efficient Data Collection Optimization for Data as a Service in IoT networks

The Internet of Things (IoT) networks have been adopted ubiquitously to support domain applications. Specially, social robot devices have become an important part of IoT networks as smart devices, where gathering sensory Data functionality can be encapsulated as a IoT Services (denote DaaS), as a foundation for supporting widely domain applications. In this setting, gathering sensory data from service-aware social robots in an energy-efficient manner is of importance for prolonging the network lifetime and promoting proper decision-making. Considering the large-scale and spatial-temporal evolutional characteristic of IoT networks, social robots roam over time to deal with tasks, especially considering the fact that it may hardly be predicted for the regions and time durations that certain anomalies may occur. Therefore, this paper proposes to adopt mobile edge computing to support sensory data gathering. Edge nodes in edge networks gather sensory data from their subordinating social robots in a periodic manner. We design an edge network division method by constructing an improved Sort-Tile-Recursive (STR) tree, which can cluster the edge nodes and decrease unnecessary energy consumption. Experimental results show that our technique is more efficient than traditional ones in decreasing energy consumption.

Energy-efficient sensory data gathering in IoT networks with mobile edge computing

The Internet of Things (IoT) networks have been adopted ubiquitously to support domain applications. Specially, social robots have become an important part of IoT networks as smart devices and are widely used to support versatile domain applications. In this setting, gathering sensory data from social-aware mobile robots in an energy-efficient manner is of importance for prolonging the network lifetime and promoting proper decision-making. Considering the large-scale and spatial-temporal evolutional characteristic of IoT networks, social robots roam over time to deal with tasks, especially when considering fact that it may hardly be predicted for the regions and time durations that certain anomalies may occur. Therefore, this paper proposes to adopt mobile edge computing to support sensory data gathering. Edge nodes in edge networks gather sensory data from their subordinating social robots in a periodic manner. We design an edge network division method by constructing an improved Sort-Tile-Recursive (STR) tree, which can cluster the edge nodes and decrease unnecessary energy consumption. Experimental results show that our technique is more efficient than traditional ones in decreasing energy consumption.

Spatiotemporal variation of ice thickness of Lake Qinghai derived from field measurements and model simulation

湖冰厚度是湖泊在封冻期的重要物理参数，明晰其时空变化特征对于认识气候变暖背景下的湖冰响应规律具有重要的理论价值和现实意义.基于ERA5 Climate Reanalysis气温数据集、MODIS MOD09GQ数据产品和2019年湖冰钻孔测厚数据及雷达测厚数据，重建2000-2019年青海湖冰厚时间序列并分析其时空变化特征.结果表明：①2019年3月实测青海湖湖冰厚度平均增长速率为0.30 cm/d，高于2月份（0.12 cm/d）.基于度日法湖冰生长模型模拟的2018年11月-2019年3月青海湖冰厚平均增长速率为0.34 cm/d，与实际观测数据相比，模拟冰厚误差为±2 cm，但在河流入湖口处和湖区南侧误差较大，且冰厚模拟数值在3月中旬前高估而之后有所低估.②青海湖多年平均冰厚介于32~37 cm，其中2008-2016年湖冰厚度年际变化剧烈，呈现先增大再稳定后减小的趋势.冻结初期湖冰厚度增长迅速，12月和1月湖冰增长速率分别为0.45和0.41 cm/d，2月后冰厚增长速率放缓，2月和3月分别为0.29和0.14 cm/d.③2000-2019年冰厚整体呈现北厚南薄、东厚西薄的空间格局，多年冰厚变化幅度湖区西部较东部稳定，湖冰平均厚度与完全封冻时长及封冻期呈正相关.;Ice thickness is an important physical parameter of a lake in the freezing period. It is of great theoretical and practical significance to understand the spatiotemporal characteristics of lake ice, which helps study how lake ice responds to climate under the background of global warming. Based on the ERA5 Climate Reanalysis temperature dataset, MODIS MOD09GQ data product, and ice borehole and radar thickness measurements in 2019, the thickness of lake ice in Lake Qinghai between 2000 and 2019 was reconstructed and the spatial-temporal variation characteristic was analyzed. The results showed that the average growth rate of ice thickness in March (0.30 cm/d) was faster than that in February (0.12 cm/d) based on the field survey data. The average growth rate of ice thickness in Lake Qinghai from November 2018 to March 2019 was 0.34 cm/d simulated by the degree-day model, with the ice thickness error of ±2 cm compared with the actual observation data. However, the error was large at the entrance of the river and the south side of Lake Qinghai. Meanwhile, the simulation of ice thickness was overestimated before the mid-March but underestimated later. The annual average ice thickness of Lake Qinghai was 32-37 cm, in which, it fluctuated dramatically in 2008-2016, showing the tendency of thickening first, then stabilizing and finally thinning. At the beginning of lake freezing, the ice thickness increased rapidly with the growth rate of 0.45 cm/d in December and 0.41 cm/d in January. After February, the rate slowed down, and it was 0.29 cm/d in February and 0.14 cm/d in March. On the whole, the ice thickness of Lake Qinghai showed a spatial pattern of being thicker in the north and east, whereas being thinner in the south and west, years of spatial change were more stable in the west than in the east. The average thickness of lake ice was positively correlated with complete freezing duration and the freeze-up period.

Spatial-Temporal Characteristic Analysis of Ethnic Toponyms Based on Spatial Information Entropy at the Rural Level in Northeast China.

As a symbol language, toponyms have inherited the unique local historical culture in the long process of historical development. As the birthplace of Manchu, there are many toponyms originated from multi-ethnic groups (e.g., Manchu, Mongol, Korean, Hui, and Xibe) in Northeast China which possess unique cultural connotations. This study aimed to (1) establish a spatial-temporal database of toponyms in Northeast China using a multi-source data set, and identify their ethnic types and origin times; and (2) explore the geographical distribution characteristics of ethnic toponyms and the evolution of rural settlements by comparing the spatial analysis and spatial information entropy methods. The results found that toponyms reflect not only the spatial distribution characteristics of the density and direction of ethnic groups, but also the migration law of rural settlements. Results also confirm that toponyms contain unique cultural connotations and provide a theoretical basis for the protection and promotion of the cultural connotations of toponyms. This research provides an entropic perspective and method for exploring the spatial-temporal evolutionary characteristics of ethnic groups and toponym mapping.

Spatial non-stationarity and heterogeneity of metropolitan housing prices: the case of Guangzhou, China

Taking a case study of the city of Guangzhou, this paper applies sophisticated approaches, including spatial autocorrelation analysis models, traditional regression models and Geographically Weighted Regressing (GWR) model to analyse and discuss the spatial non-stationarity and heterogeneity characteristics of housing prices, in term of the data of the average annual housing prices of each sub-district/town from 2009 to 2015. In this study, four types of influence factors, i.e., location condition (distance to central business district (CBD)), traffic access level, commercial service facility and public service facility are selected. As results, the spatial-temporal evolution characteristic of the housing prices in Guangzhou is closely related to the layout and development of the urban areas. Furthermore, among the selected factors, location condition is the most important factors affecting the housing prices, followed by commercial service facility, traffic access level and public service facility. Specifically, based on the GWR model analysis, the spatial patterns of the regression coefficients of location condition and the public service facility illustrate “east strong and west weak” of the housing prices in Guangzhou. By contraries, “west strong and east weak” of housing prices according to the traffic access level and commercial service facility. The overall pattern of spatial non-stationarity and heterogeneity of housing prices presents the circle structure characteristics with the centre of the CBD in Guangzhou.

Critical analysis of spatial-temporal morphological characteristic of urban landscape

Remote sensing and Geographical Information System (GIS) data have been used widely to analyze and study the patterns of urban expansions. Urban expansions are intricate dynamic process associated with landscape transformation and its driving factors. Previous studies mainly focused only on identifying urban change; therefore, in this study, we have developed a spatial-temporal morphological model to identify the pattern of urbanization and driving factors contributing the growth pattern. The primary objective of this study is to identify and analyze the urban sprawl of Lucknow city, India, as a pattern and process. Quantification of urban landscape is performed using remotely sensed temporal satellite images of 1990, 1999, 2009, and 2016 over a period of 26 years. An interlink between spatial metrics, gradient analysis, and density index has been developed to analyze the directional growth of the city. Gradient modeling is performed using moving window analysis on a single grid for quantification of the landscape structure. Multi Ring Buffer (MRB) approach has been deployed to measure the extent and trends of urbanization. The quantification of MRB is performed using Shannon’s entropy estimations. The analysis of spatial data is then carried out by splitting the study area into five circular zones of 2 km each in increasing order of radius. The higher value of Shannon’s entropy index shows a highly coalesced urban center with dispersed growth towards the outskirts. Urban gradient analysis is performed to model the landscape parameters and urban growth morphology in 16 different directions. Total 257 sample points from the city center at the interval of 500 m are overlaid on temporal dataset up to 8 km in 16 different directions. To compute the compactness of urban sprawl for the present scenario, density index is evaluated. The outcome from the study indicates an integrated approach for modeling the urban morphology which illustrates the extent of influencing drivers of urbanization in various directions.

Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA)

The spatial pattern of landscape has great influence on the biodiversity provided by ecosystem. Understanding the impact of landscape pattern dynamics on habitat quality is significant in regional biodiversity conservation, ensuring ecological security guarantee, and maintaining the ecological environmental sustainability. Here, combining CA-Markov and InVEST model, we investigated the evolution of landscape pattern and habitat quality, and presented an explanation for variability of biodiversity linked to landscape pattern in Hubei section of Three Gorges Reservoir Area (TGRA). The spatial-temporal evolution characteristic of landscape pattern from 1990 to 2010 were analyzed by Markov chain. Then, the spatial pattern of habitat quality and its variation in three phases were computed by InVEST model. The driving force for landscape variation was explored by using Logistic regression analysis. Next, the CA-Markov model was used to simulate the future landscape pattern in 2020. Finally, future habitat quality maps were obtained by InVEST model predicted landscape maps. The results concluded that, the overall landscape pattern has changed slightly from 1990 to 2010. Woodland, waters and construction land had the greatest variations in proportion among the landscape types. The area of woodland has been decreasing gradually below the average elevation of 140 m, and the area of waters and construction land increased sharply. Logistics regression results indicated that terrain and climate were the most influencing natural factors compared with human factors. The Kappa coefficient reached 0.92, indicating that CA-Markov model had a good performance in future landscape prediction by adding nighttime light data as restriction factor. The biodiversity has been declining over the past 20 years due to the habitat degradation and landscape pattern variation. Overall, the maximum values of habitat degradation index were 0.1188, 0.1194 and 0.1195 respectively, showing a continuously increasing trend from 1990 to 2010. Main urban areas of Yichang city and its surrounding areas has higher habitat degradation index. The average values of habitat quality index of the whole region were 0.8563, 0.8529 and 0.8515 respectively, showing a continuously decreasing trend. The lower habitat quality index mainly located in the urban land as well as the main and tributary banks of the Yangtze River. Under the business as usual scenario, habitat quality continued to maintain the variation trend of the previous decade, showing a reducing habitat quality index and an increasing area of artificial surface. Under the ecological protection scenario, the variation of habitat quality in this scenario represented reverse trend to the previous decade, exhibiting an increase of habitat quality index and an increasing area of woodland and grassland. Construction of Three Gorges Dam, impoundment of Three Gorges Reservoir (TGR), resettlement of Three Gorges Project and urbanization were the most explanatory driving forces for landscape variation and degradation of habitat quality. The research may be useful for understanding the impact of landscape pattern dynamics on biodiversity, and provide scientific basis for optimizing regional natural environment, as well as effective decision-making support to local government for landscape planning and biodiversity conservation.

Spatial–temporal characteristic of BDS phase delays and PPP ambiguity resolution with GEO/IGSO/MEO satellites

GPS precise point positioning (PPP) ambiguity resolution (AR) can improve the positioning accuracy and shorten the convergence time. However, for the BeiDou Satellite Navigation System (BDS), the problems of satellite-induced code bias, imperfections in the error models and the inadequate accuracy of orbit products limit the applications of the BDS PPP AR system, which requires more than 6 h to achieve the first ambiguity-fixed solution. In this study, the accuracy of a wide-lane (WL) uncalibrated phase delay (UPD) is improved after careful consideration of the code bias and multipath. Meanwhile, the accuracy of the BDS float ambiguity is also improved by multi-GNSS fusion and improved precise orbit and clock products, which are critical for high-quality narrow-lane (NL) UPD estimations. With three tracking networks of different scales, including Hong Kong, the Crustal Movement Observation Network of China (CMONOC) and the multi-GNSS experiment (MGEX) networks, the spatial–temporal characteristics of WL and NL UPDs for BDS GEO/IGSO/MEO satellites are analyzed, and the PPP AR is performed. Numerous results show that WL and NL UPDs with a standard deviation (STD) of less than 0.15 cycles can be achieved for BDS GEO satellites, while a STD of less than 0.1 cycles can be obtained for IGSO and MEO satellites. With the precise UPD estimation, for the first time, the BDS PPP rapid ambiguity resolution for GEO/IGSO/MEO satellites is achieved. We found that the average time to first fix (TTFF) of the BDS PPP AR is shortened significantly, to approximately 40 min for Hong Kong and the CMONOC, while the TTFF was 57.4 min for the MGEX networks. With ambiguity resolution, the accuracy of the daily BDS PPP in the east, north and vertical directions improves from 1.74 cm, 1.08 cm, and 5.52 cm to 0.72 cm, 0.54 cm, and 3.21 cm for the Hong Kong network, 2.24 cm, 2.31 cm, and 5.64 cm to 1.18 cm, 0.79 cm, and 3.30 cm for the CMONOC, and 2.71 cm, 1.80 cm, and 6.00 cm to 1.58 cm, 1.15 cm, and 4.33 cm for the MGEX networks. Significant improvement is also achieved for kinematic PPP, with improvements of 40.41%, 34.33% and 37.17% in the east, north and vertical directions for the MGEX networks, respectively.

Spatial-temporal characteristic of drought in northern Nigeria

This paper examines the spatial and temporal characteristics of droughts during the rainy season (May-October) for 1965-2010 over northern Nigeria using monthly rainfall data from station rain gauges. The spatial distribution of rainfall is identified by Standardized Precipitation Index (SPI), which also characterizes specific temporal drought events. Mann-Kendall trend test is applied to analyze the changes in the drought trend and also to confirm the significance of the observed trends. The results of the analysis show major changes in the drought characteristics in northern Nigeria during the major dry episodes (1973-1983) and major wet episodes (1996-2010). For the period 1965-1985, all the stations showed a decreasing trend both in 3 and 6 months time scales. In contrast, 87.5% of the stations showed an increasing trend for the period 1985-2010, with Maiduguri, Nguru, Kano, Katsina and Yelwa showing significant increases, the prolonged dry spell in the 1970’s and 1980’s was shown clearly in some of the analyses. A large number of further observations would be required to put some of these findings on a finer basis for the Sahelian drought characterization. It is recommended that agricultural organizations and disaster management parastatals should be pro-active in drought adaptation and mitigation planning. Keywords : Standardized Precipitation Index, Spatial, Temporal, drought, Mann-Kendall, trend.

Traffic State Spatial-Temporal Characteristic Analysis and Short-Term Forecasting Based on Manifold Similarity

The study on the spatial-temporal characteristics of highway traffic flow is helpful to deeply understand the inherent evolution of highway traffic system and provide a theoretical basis for prediction and control of highway traffic flow. This paper makes an empirical analysis on the spatial-temporal characteristics of highway traffic flow using manifold similarity index and manifold learning technology. The time series of highway traffic flow is converted into the distance series containing manifold features to calculate the manifold distance between multi-section traffic flow data points, which are highly similar to spatial-temporal distribution of traffic flow speed parameters, and then, the levels calibration of traffic state is carried out according to the manifold distance, so as to reveal the distribution rule of spatial-temporal characteristics of highway traffic flow. Its prediction error is obviously lower than the traditional distance measurement method, which has higher accuracy. The research of this paper can provide new ideas and methods to reveal the highway traffic flow evolution and traffic state prediction.

Analyzing temporal–spatial characteristics of drought events in the northern part of Cyprus

Drought in the northern part of Cyprus has become a recurrent phenomenon. In the last few decades, Cyprus has experienced significantly severe drought events occurring periodically, and this trend is now continuing. With rainfall distribution varying considerably across the region and frequent drought conditions, the water resources, agriculture, economy and the environment have been adversely affected. This study aims to investigate spatial–temporal characteristic of drought using Standardized Precipitation Index (SPI) at multiple timescales (3, 6 and 12 months). Monthly time series of 36 years (1977–2013) rainfall data from nine weather stations are used to derive SPI values. Based on different drought categories, this study focuses on propagation of drought from one timescale to another and estimating critical rainfall values during moderate, severe and extreme drought conditions. The analysis revealed that there is a strong correlation among different timescales in detecting drought events. On average, 79 and 78% of 3-month timescale drought propagated into 6- and 12-month drought events, respectively, while 90% of 6-month timescale drought events propagated into 12-month drought events. The derived critical rainfall value for extreme droughts over a 12-month timescale was less than 255 mm/year in the town of Alsancak, while for Guzelyurt, a major citrus growing city, this figure was less than 135 mm/year. The results are validated through drought events detected at various regions of the Mediterranean basin and local flood occurrences during the wet periods and decline in water tables at drought seasons.