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This paper aims to use geographical information systems kriging interpolation technique to examine and map the spatiotemporal variation in rainfall in Guinea Savanna of Nigeria.

Rainfall data, for the periods between 1970 and 2000, are collected from the archives of the Nigerian Meteorological Services, Oshodi Lagos. In this paper, rainfall is considered as the primary and input for crop yield. It is observed that the most important climatic element is rainfall; particularly inter‐annual variation and the spatiotemporal distribution of rainfall. Three spatial interpolation methods are chosen for this research work: inverse distance weighting method and the spline (completely regularized) as the determinist methods; and ordinary kriging as the stochastic methods. In order to analyze the interpolation quality, an evaluation by cross validation has been carried out. Ordinary kriging method was discovered suitable for this paper because it allows the sharpest interpolation rainfall data and is the most representative.

The results of the analysis show that rainfall varies both in time and space. Rainfall variability is very high in most of Northern Guinea Savanna (e.g. Yola, Minna, and Ilorin) with values of coefficient of variation (CV) between 26 and 49 percent while in Southern Guinea Savanna, the CV is very low especially, in Enugu (9 percent), and Shaki (8 percent). These anomalies (such as decline in annual rainfall, change in the peak and retreat of rainfall and false start of rainfall) are detrimental to crop germination and yield, resulting in little or no harvest at the end of the season.

The paper concludes that geospatial techniques are powerful tools that should be explored further for realistic analysis of the effects of seasonal variability in rainfall.

Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose of this paper is to provide some insights into response patterns during the 2013 ice storm in the city of Vaughan, Ontario, Canada, using temporal and spatial analyses.

The City of Vaughan Fire and Rescue Service data set containing all responses to fire and other emergency incidents from January 1, 2009 to December 31, 2016 was used. The 2013 Southern Ontario ice storm occurred from December 20, 2013 to January 1, 2014, and, for this study, December 20–31 is considered the “study period.” Temporal, spatial and spatiotemporal analyses of responses during the study period are carried out and are compared with the same period in other years (2009–2012 and 2014–2016).

The findings show that temporal patterns of response attributes changed significantly during the 2013 ice storm. Similarly, the spatial pattern of responses during the 2013 ice storm showed some major differences with other years. The spatiotemporal analyses also demonstrate significant variations in responses in the city during different hours of the day in the ice storm days.

This study is the first study to examine the spatiotemporal patterns of responses made by a fire department during the 2013 ice storm in Canada. It provides some insights into the differences between response volumes, temporal and spatial distributions during large emergency events (e.g. ice storm) and normal situations. The results will help in mitigating the number of responses in the future through public education and technological changes. Moreover, the results will provide fire departments with information that could help them prepare for such events by possible reallocation of resources.

The purpose of this study is to assess the spatiotemporal patterns of future meteorological drought in the Yellow River Basin under different representative concentration pathway (RCP) scenarios.

Delta method is used to process the future climate data of the global climate models, then analyzed the spatiotemporal variation trend of drought in the Yellow River Basin based on standardized precipitation evaporation index (SPEI) under four RCP scenarios.

This research was funded by the National Natural Science Foundation of China (41901239), Soft Science Research Project of Henan Province (212400410077, 192400410085), the National Key Research and Development Program of China (2016YFA0602703), China Postdoctoral Science Foundation (2018M640670) and the special fund of top talents in Henan Agricultural University (30501031).

This study can provide support for future meteorological drought management and prevention in the Yellow River Basin and provide a theoretical basis for water resources management.

The purpose of this study is to explore spatiotemporal factors that affect the empirical analysis of whether crime rates in buffer areas surrounding abandoned properties transferred to a Land Bank that differed among three regimes: before transfer, during Land Bank stewardship and after disposition and whether those differences were associated with differences in relative crime activity in the neighborhoods in which they were located.

This study analyzed crime incidents occurring between 2010 and 2018 in 0.1-mile buffer areas surrounding 31 abandoned properties sold by the Land Bank and their neighborhoods in which those properties were located. Using Copulas, researchers compared concordance/discordance in the buffer areas across the three regime states for each property and approximately matched time periods for associated neighborhoods.

In a substantial number of cases, the relative crime activity levels for buffer areas surrounding individual sold properties as measured by the Copulas shifted from concordant to discordant states and vice versa. Similarly, relative crime activity levels for neighborhoods shifted from concordant to discordant states across three matched regimes. In some cases, the property and neighborhood states matched, while in other cases they diverged. These cross-level interactions indicate that criminal behavioral patterns and target selection change over time and relative criminal activity. The introduction of Copulas can improve the reliability of such models over time and when and where they should be customized to add more granular insights needed by law enforcement agencies.

The introduction of Copulas can improve the spatiotemporal reliability of the analysis of criminal activity over space and time.

Spatiotemporal considerations should be incorporated in setting interventions to manage criminal activity.

This study provides support for policies supporting renovation of abandoned properties.

To the best of authors’ knowledge, this research is the first application of Copulas to crime impact studies. As noted, Copulas can help reduce the risk of applying intervention or enforcement programs that are no longer reliable or lack the precision provided by insights into convergent/divergent patterns of criminal activity.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Rapid development of China's rural industry since 1980s has a massive ripple effect on rural China. However, recent rural industrial development in China has received little scholarly attention. The study aims to investigate the development process and spatial differences of China's rural industry as well as its driving forces.

The study is based on the panel data constituting 30 provinces of China during 1984–2011. The factor analysis is used to identify the most significant factors affecting the growth of China's rural industry. The multiple regression analysis is applied to distinguish the driving forces affecting spatiotemporal variations in China's rural industry.

China's rural industry developed rapidly at an average rate of 26% per year during the period 1984–2011. The period 1990–2011 was characterized by remarkable increased spatial disparities in China's rural industrial development. Specifically, such development was markedly better in the east coast areas than in the inland areas in the west. Five driving forces of rural industrial development were recognized representing regional economic fundamentals of market, industry foundation, transportation, communication and degree of opening-up.

This paper explores the trajectory of the development of China's rural industry and the related regional economic factors. It suggests that the development of rural enterprises across the regions should be valued by Chinese government as a driving force for reducing regional disparities and future rural revitalization.

This paper aims to analyze the spatiotemporal heterogeneity of regional total factor productivity (TFP) growth and explores how haze pollution and different levels of new-type urbanization affect China’s economic growth.

This paper constructs an index for evaluating the TFP growth of China’s 31 provinces by integrating slack-based measures and the Global Malmquist (GM) productivity index. Meanwhile, the panel threshold estimation method is used to examine the complex relationships among haze pollution, new-type urbanization and TFP growth.

The results reflect conspicuous spatiotemporal heterogeneity in TFP growth in China. Interestingly, the influence of haze pollution on TFP growth is limited by the “critical mass” of new-type urbanization in China. When new-type urbanization does not cross the first threshold, haze pollution has a negative but non-significant effect on TFP growth. When new-type urbanization crosses the first threshold but not the second, haze pollution has a significant positive impact on TFP growth. When new-type urbanization crosses the second threshold, haze pollution significantly and positively affects TFP growth with the strongest positive effect.

This study innovates by combining haze pollution and TFP growth and proposing an integrated framework from the perspective of new-type urbanization, providing insight into how different degrees of new-type urbanization impact the mechanism between haze pollution and TFP growth. Using panel data in China and emphasizing green development, a sustainable economy and new-type urbanization, this study contributes to the current studies on haze pollution and economic development based on developed countries.

The purpose of this paper is to introduce an interval finite element method (IFEM) to simulate the temperature field of mass concrete under multiple influence uncertainties e.g. environmental temperature, material properties, pouring construction and pipe cooling.

Uncertainties of the significant factors such as the ambient temperature, the adiabatic temperature rise, the placing temperature and the pipe cooling are comprehensively studied and represented as the interval numbers. Then, an IFEM equation is derived and a method for obtaining interval results based on monotonicity is also presented. To verify the proposed method, a non-adiabatic temperature rise test was carried out and subsequently simulated with the method. An excellent agreement is achieved between the simulation results and the monitoring data.

An IFEM method is proposed and a non-adiabatic temperature rise test is simulated to verify the method. The interval results are discussed and compared with monitoring data. The proposed method is found to be feasible and effective.

Compared with the traditional finite element methods, the proposed method taking the uncertainty of various factors into account and it will be helpful for engineers to gain a better understanding of the real condition.

Spatio-temporal variations in precipitation pattern of district Sargodha is one of the most significant researchable questions because of the massive reliance on rainfall for agricultural practice in the study area. The pattern of current rainfall in the study area is unexpectedly changed. The purpose of the present study is to examine the changing precipitation pattern and to link it with climate change.

The study was conducted by using rainfall data of the past 30 years collected from 8 meteorological stations around the study area. The averages of rainfall on monthly basis were temporally arranged, and the fluctuation trends were studied using GIS and statistics. The temporal data of rainfall were compared and contrasted with the precipitation normals of the study area from 1981to 2010. The rainfall deviation in the present study was calculated. The spatial pattern of rainfall was plotted by interpolating the eight points of Punjab around the study area for the first two decades, whereas the past decade was analysed by incorporating five more points of Tehsils in the existing eight. The spatial and statistical representation of data were examined by compare and contrast with the previous findings.

The rainfall in the study area showed remarkable changes in magnitude and spatiality. The rainfall in the district is on the rise, whereas the spatial pattern of rainfall is becoming more complex and anomalous in character. This paper provides convincing evidence about the impact of climate change on the magnitude and spatial patterns of precipitation in the study area.

It will be helpful for understanding the shifts in the rainfall pattern in future as well as for the preparation of response to the issue of climate change and its impacts.

The current manuscript, for the very first time, provided detailed insights about the precipitation pattern shifting during the last 30 years in district Sargodha, Punjab, Pakistan. Furthermore, agricultural sector would likely get severally affected because of seasonal changes in climatic factors like rainfall and have strong food security implications. The current findings will be useful to manage the climate change-related issues in Pakistan and helpful for the policy makers to design a coping strategy for climate change impacts.

This paper aims to study the temporal and spatial variation of vegetation and the influence of climate change on vegetation coverage in the Yellow River basin, China. The current study aimed to evaluate the role of a series of government-led environmental control projects in restoring the ecological environment of the Yellow River basin.

This paper uses unary linear regression, Mann–Kendall and wavelet analyses to study the spatial–temporal variations of vegetation and the response to climate changes in the Yellow River, China.

The results showed that for the past 17 years, not only the mean annual increase rate of the Normalized Difference Vegetation Index (NDVI) was 0.0059/a, but the spatial heterogeneity also yields significant results. The vegetation growth in the southeastern region was significantly better than that in the northwestern region. The variation period of the NDVI in the study area significantly shortened, and the most obvious oscillation period was half a year, with two peaks in one year. In addition, there are positive and negative effects of human activities on the change of vegetation cover of the Loess Plateau. The project of transforming cultivated land to forest and grassland promotes the increase of vegetation cover of the Loess plateau. Unfortunately, the regional urbanization and industrialization proliferated, and the overloading of grazing, deforestation, over-reclamation, and the exploitation and development of the energy area in the grassland region led to the reduction of the NDVI. Fortunately, the positive effects outweigh the negative ones.

This paper provides a comprehensive insight to analysis of the vegetation change and the responses of vegetation to climate change, with special reference to make the planning policy of ecological restoration. This paper argues that ecological restoration should be strengthened in areas with annual precipitation less than 450 mm.