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Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

The ecological environment of the Loess Plateau, China, is extremely fragile under the context of global warming. Over the past two decades, the vegetation of the Loess Plateau has undergone great changes. This paper aims to clarify the response mechanisms of vegetation to climate change, to provide support for the restoration and environmental treatment of vegetation on the Loess Plateau.

The Savitsky–Golay (S-G) filtering algorithm was used to reconstruct time series of moderate resolution imaging spectroradiometer (MODIS) 13A2 data. Combined with trend analysis and partial correlation analysis, the influence of climate change on the phenology and enhanced vegetation index (EVI) during the growing season was described.

The S-G filtering algorithm is suitable for EVI reconstruction of the Loess Plateau. The date of start of growing season was found to gradually later along the Southeast–Northwest direction, whereas the date of the end of the growing season showed the opposite pattern and the length of the growing season gradually shortened. Vegetation EVI values decreased gradually from Southeast to Northwest. Vegetation changed significantly and showed clear differentiation according to different topographic factors. Vegetation correlated positively with precipitation from April to July and with temperature from August to November.

This study provides technical support for ecological environmental assessment, restoration of regional vegetation coverage and environmental governance of the Loess Plateau over the past two decades. It also provides theoretical support for the prediction model of vegetation phenology changes based on remote sensing data.

This paper aims to provide empirical evidence on the El Nino and its effects on maize production in three municipalities: Ejura, Techiman and Wenchi in the transitional zone of Ghana. Using a mixed approach, the study details the effects of the El Nino on rainy season characteristics, particularly, rainfall amounts and distribution, onset and cessation of rains, duration of the rainy season and total seasonal rainfall and how it impacted smallholder maize production.

The study used a mixed method approach in collecting and analyzing data. For stronger evidence building, (Creswell, 2013) the authors combined interviews and focus group discussions (FGD) to collect the qualitative data. Semi-structured questionnaires were administered to extension officers, management information system officers and other relevant personnel of the Ministry of Agriculture in the three municipalities. Six FGD’s were held for maize farmers in six communities in all three municipalities.

The study shows that the 2015 El Nino had dire consequences on farm yields, subsequently affecting farmer’s incomes and livelihoods. The study further finds that complex socio-cultural factors, some unrelated to the El Nino, aggravated the effects on maize farmers. These include the lack of adequate climatic information, predominance of rain-fed farming, a lack of capacity to adapt and existing levels of poverty.

The study recommends inter alia, appropriate use of seasonal rainfall forecasting to enhance better farming decision-making and the development of elaborate climate variability interventions by national and local agencies.

This paper aims to assess the progress in the provision of basic services in urban slums in Bangladesh during the transition period of millennium development goals (MDGs) to sustainable development goals (SDGs).

The study used a mixed method of research. The empirical part of the research was conducted in three Blocks of Rupsha slum in Khulna city. Randomly selected 120 households were interviewed through a structured questionnaire; three focus group discussion sessions (FGDs) were also conducted. Progress in the slum residents’ access to basic services during the transition from MDGs to SDGs is tacked based on primary data. The User Satisfaction Index (USI) and Network Analysis tools in ArcGIS are used to identify the gaps in service provision.

Findings show that a very significant proportion of families (56.67%) encounter an acute level of difficulties to gain smooth access to water services. About 89% of respondents have only access to a common or shared toilet facility where one common toilet is used by 20–25 persons. About 31% of families are unable to send their children to primary school even after four years of the adoption of SDGs. Achievements in most indicators of basic services in the slum are in general lower than the national level. Moreover, there exists spatial variability within the same slum. After four years of the transition from MDGs to SDGs, most of the services are poorly satisfying the residents of the Rupsha slum, and water service provision is in worse condition. The findings of this study have unveiled that while achievement in target areas is appreciable at the macro level, at the micro-level; however, good achievement in the provision of few basic services in the low-income settlement is more rhetoric than reality. Therefore, a lot more work needs to be done during the SDG phase to give the slum residents a decent quality of life as they have missed the MDGs’ train.

Study single-out works need to be done during the SDGs phase to give the slum residents a decent quality of life as they have missed the MDGs’ train.

Changes in climate may have both beneficial and harmful effects on crop yields. However, the effects will be more in countries whose economy depends on agriculture. This study aims to measure the economic impacts of climate change on crop farming in Bangladesh.

A Ricardian model was used to estimate the relationship between net crop income and climate variables. Historical climate data and farm household level data from all climatic zones of Bangladesh were collected for this purpose. A regression model was then developed of net crop income per hectare against long-term climate, household and farm variables. Marginal impacts of climate change and potential future impacts of projected climate scenarios on net crop incomes were also estimated.

The results revealed that net crop income in Bangladesh is sensitive to climate, particularly to seasonal temperature. A positive effect of temperature rise on net crop income was observed for the farms located in the areas having sufficient irrigation facilities. Estimated marginal impact suggests that 1 mm/month increase in rainfall and 10°C increase in temperature will lead to about US$4-15 increase in net crop income per hectare in Bangladesh. However, there will be significant seasonal and spatial variations in the impacts. The assessment of future impacts under climate change scenarios projected by Global Circulation Models indicated an increase in net crop income from US$25-84 per hectare in the country.

The findings of this study indicate the need for development practitioners and policy planners to consider both the beneficial and harmful effects of climate change across different climatic zones while designing and implementing the adaptation policies in the country.

Literature survey of the Web of Science, Science Direct and Google Scholar indicates that this study is the first attempt to measure the economic impacts of climate change on overall crop farming sector in Bangladesh using an econometric model.

This paper aims to present a case study-based approach to identify resource-poor communities with limited abilities to cope with the adverse effects of climate change. The study area is the Nkonkobe Local Municipality, in the Eastern Cape which is one of South Africa’s provinces ranked as being extremely vulnerable to the adverse effects of climate change because of high incidences of poverty and limited access to public services such as water and education. Although adaptive capacity and vulnerability assessments help to guide policy formulation and implementation by identifying communities with low coping capacities, policy implementers often find it difficult to fully exploit the utility of these assessments because of difficulties in identifying vulnerable communities. The paper attempts to bridge this gap by providing a user-friendly, replicable, practically implementable and adaptable methodology that can be used to cost-effectively and timeously identify vulnerable communities with low coping capacities.

A geostatistical approach was used to assess and evaluate adaptive capacities of resource-poor communities in the Nkonkobe Local Municipality. The geospatial component of this approach consisted of a multi-step Geographical Information Systems (GIS) based technique that was improvised to map adaptive capacities of different communities. The statistical component used demographic indicators comprising literacy levels, income levels, population age profiles and access to water to run automated summation and ranking of indicator scores in ArcGIS 10.2 to produce maps that show spatial locations of communities with varying levels of adaptive capacities on a scale ranging from low, medium to high.

The analysis identified 14 villages with low adaptive capacities from a total of 180 villages in the Nkonkobe Local Municipality. This finding is important because it suggests that our methodology can be effectively used to objectively identify communities that are vulnerable to climate change.

The paper presents a tool that could be used for targeting assistance to climate change vulnerable communities. The methodology proposed is of general applicability in guiding public policy interventions aimed at reaching, protecting and uplifting socio-economically disadvantaged populations in both rural and urban settings.

The approach’s ability to identify vulnerable communities is useful because it aids the identification of resource-poor communities that deserve priority consideration when planning adaptation action plans to deliver support and assistance to those least capable of effectively coping with the adverse effects of climate change induced vulnerabilities.

The purpose of this paper is to investigate the patterns and trends of drought incidence in north east highlands of Ethiopia using monthly rainfall record for the period 1984-2014.

Standard precipitation index and Mann – Kendal test were used to analyze drought incident and trends of drought occurrences, respectively. The spatial extent of droughts in the study area has been interpolated by inverse distance weighted method using the spatial analyst tool of ArcGIS.

Most of the studied stations experienced drought episodes in 1984, 1987/1988, 1992/1993, 1999, 2003/2004 and 2007/2008 which were among the worst drought years in the history of Ethiopia. The year 1984 was the most drastic and distinct-wide extreme drought episode in all studied stations. The Mann–Kendal test shows an increasing tendencies of drought at three-month (spring) timescale at all stations though significant (p < 0.05) only at Mekaneselam and decreasing tendencies at three-month (summer) and 12-month timescales at all stations. The frequency of total drought was the highest in central and north parts of the region in all study seasons.

This detail drought characterization can be used as bench mark to take comprehensive drought management measures such as early warning system, preparation and contingency planning, climate change adaptation programs.

Climate change affects the geographic and seasonal range of malaria incidence, especially, in poor tropical countries. This paper aims to attempt to conceptualize the potential economic repercussions of such effects with its focus on Ethiopia.

The paper is conceptual and descriptive in its design. It first reviews existing literature and evidence on the economic burdens of malaria, and the impacts of climate change on malaria disease. It then draws the economic implications of the expected malaria risk under the future climate. This is accompanied by a discussion on a set of methods that can be used to quantify the economic effects of malaria with or without climate change.

A review of available evidence shows that climate change is likely to increase the geographic and seasonal range of malaria incidence in Ethiopia. The economic consequences of even a marginal increase in malaria risk will be substantial as one considers the projected impacts of climate change through other channels, the current population exposed to malaria risk and the country’s health system, economic structure and level of investment. The potential effects have the potency to require more household and public spending for health, to perpetuate poverty and inequality and to strain agricultural and regional development.

This paper sheds light on the economic implications of climate change impacts on malaria, particularly, in Agrarian countries laying in the tropics. It illustrates how such impacts will interact with other impact channels of climate change, and thus evolve to influence the macro-economy. The paper also proposes a set of methods that can be used to quantify the potential economic effects of malaria. The paper seeks to stimulate future research on this important topic which rather has been neglected.

Using a combination of the geographical information system (GIS) and the Canadian water quality index (WQI), the current study sought to provide a long-term general assessment of the water quality of the Shatt Al-Arab River (SAAR), focusing on its suitability for living organisms. Likewise, SPSS statistics was used to develop a nonlinear WQI regression model for the study area.

The study required four decades of data collection on some environmental characteristics of river water. After that, calculate the WQI and conduct the spatial analysis. Eight variables in total, including water temperature, dissolved oxygen, potential hydrogen ions, electrical conductivity (EC), biological oxygen demand, turbidity, nitrate and phosphate, were chosen to calculate the WQI.

Throughout the study periods, the WQI values varied from 55.2 to 79.83, falling into the categories of four (marginal) and three (fair), with the sixth period (2007–2008) showing the most decline. The present research demonstrated that the high concentration of phosphates, the high EC values, and minor changes in the other environmental factors are the major causes of the decline in water quality. The variations in ecological variables' overlap are a senior contributor to changes in water quality in general. Notably, using GIS in conjunction with the WQI has shown to be very effective in reducing the time and effort spent on investigating water quality while obtaining precise findings and information at the lowest possible expense. Calibration and validation of the developed model showed that this model had a perfect estimate of the WQI value. Due to its flexibility and impartiality, this study recommends using the proposed model to estimate and predict the WQI in the study area.

Even though the water quality of the SAAR has been the subject of numerous studies, this is the only long-term investigation that has been done to evaluate and predict its water quality.