12 research outputs found


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    Every year, natural hazards such as hurricanes, floods, wild fires,droughts, earthquakes, volcanic eruptions, and ice storms destroy millions of trees across the World and cause extensive damage to their species composition, structure, and dynamics. Recently within the last decade, ice storms has caused catastrophic damage to trees, infrastructures, power lines in Oklahoma, and has taken over several dozen human lives. However, studies pertaining to the vulnerability and assessment of tree damage from ice storms in Oklahoma are almost non-existent. This study aims to fulfill thatgap by first integrating remote sensing (RS) and geographic information systems (GIS) to assess and estimate tree damage caused by the December 8-11, 2007 ice storm that struck the north-central part of Oklahoma. It also explores the factors that contributed to the tree damage and created multiple regression models based on the factors. Finally, it examines the vulnerability of trees to ice storms by creating an ice storm tree damage vulnerability indexfor the City of Norman, Oklahoma.The integrated RS and GIS method assessed tree height and crowndamage with high degree of accuracy. The thickness of ice accumulation has emerged as the most important predictor, followed by tree branch angle and pre-storm crown, wind, stem, and branch diameters for tree damage from ice storms. Results indicate that the vulnerability index accurately predicted several areas that are highly vulnerable.Results from this study are significant from both theoretical, and methodological and implication perspectives. The present study contributes significantly by identifying the geographic conditions of the City of Norman that make its urban forestry vulnerable to ice storm damage. In doing so, it initiates steps for future tree vulnerability research. Methodologically, the study contributes significantly to geospatial technology paradigm in geography by integrating RS and GIS to assess tree damage not only on achange/no change basis, but also by quantifying the damage. Finally, the methods and techniques developed in this study can not only assess damage from future ice storms, but can also quantify damage from other natural disasters in other parts of the world as well

    Detection of Land Use/Land Cover Changes and Urban Sprawl in Al-Khobar, Saudi Arabia: An Analysis of Multi-Temporal Remote Sensing Data

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    While several studies examined land use and land cover changes in the central and western parts of Saudi Arabia, this study is the first to use remote sensing data to examine the decadal land cover changes in Saudi Arabia’s eastern coastal city of Al-Khobar between 1990 and 2013. Specifically, it utilized ISODATA classification method to classify Landsat TM, ETM+, and OLI data collected from 1990, 2001, and 2013 and then detected changes in the land cover within the study area. It then measured urban sprawl by calculating the relative Shannon’s entropy index values for the three years. With overall classification accuracies greater than 85%, the results show that urban built-up areas increased by 117% between 1990 and 2001 and 43.51% from 2001 to 2013. Vegetation increased by 110% from 1990 to 2001 and by 52% between 2001 and 2013. The entropy index values of 0.700 (1990), 0.779 (2001), and 0.840 (2013) indicates a high rate of urban sprawl and the city dispersing near the outskirts and towards the neighboring cities of Dhahran and Dammam. Future studies should examine the current challenges faced by the city’s residents due to urban expansion and attempt to find ways to resolve them in the near future

    Examining the Walking Accessibility, Willingness, and Travel Conditions of Residents in Saudi Cities

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    Rapid urban expansion and population growth in Saudi cities over the past four decades have increased vehicular accidents and traffic congestion and have impacted the daily walking conditions of the residents. Walking has various health and environmental benefits. In North American and European countries, three factors have been found to motivate a resident to walk within their community: their accessibility to community social and business facilities, their perception and willingness, and the safety conditions of the roads and sidewalks within their community for walking. This study examined these factors and their role in the walking habits of the residents in the neighborhoods of Doha and Dana districts in Saudi Arabia’s eastern city of Dhahran. Data were collected through field observations and by randomly sampling and interviewing 200 residents. Geographic Information Systems (GIS) and SPSS statistical software were used for data analysis. The results show that most of the community facilities are randomly placed in the districts. Mosques are the closest facility to each resident with an average accessibility distance of 242m. Almost 43% of the respondents prefer daily walking while the rest are hesitant due to hot weather during summer and narrow and poorly designed sidewalks. The sidewalks were also found to be blocked by trees, street signals, and illegally parked vehicles. Future studies should explore the accessibility to facilities, willingness, climate, and health conditions of the residents, and the road and sidewalk conditions for walking in other cities of the Kingdom.Applied Science, Faculty ofNon UBCEngineering, School of (Okanagan)ReviewedFacult

    Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia

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    Examining the relationships between vehicle crash patterns and urban land use is fundamental to improving crash predictions, creating guidance, and comprehensive policy recommendations to avoid crash occurrences and mitigate their severities. In the existing literature, statistical models are frequently used to quantify the association between crash outcomes and available explanatory variables. However, they are unable to capture the latent spatial heterogeneity accurately. Further, the vast majority of previous studies have focused on detailed spatial analysis of crashes from an aggregated viewpoint without considering the attributes of the built environment and land use. This study first uses geographic information systems (GIS) to examine crash hotspots based on two severity groups, seven prevailing crash causes, and three predominant crash types in the City of Dammam, Kingdom of Saudi Arabia (KSA). GIS-based geographically weighted regression (GWR) analysis technique was then utilized to uncover the spatial relationships of traffic collisions with population densities and relate it to the land use of each neighborhood. Results showed that Fatal and Injury (FI) crashes were mostly located in residential neighborhoods and near public facilities having low to medium population densities on highways with relatively higher speed limits. Distribution of hotspots and GWR-based analysis for crash causes showed that crashes due to “sudden lane deviation” accounted for the highest proportion of crashes that were concentrated mainly in the Central Business District (CBD) of the study area. Similarly, hotspots and GWR analysis for crash types revealed that “collisions between motor vehicles” constitute a significant proportion of the total crashes, with epicenters mostly stationed in high-density residential neighborhoods. The outcomes of this study could provide analysts and practitioners with crucial insights to understand the complex inter-relationships between traffic safety and land use. It can provide useful guidance to policymakers for better planning and effective management strategies to enhance safety at zonal levels

    Modeling Future Land Cover Changes and Their Effects on the Land Surface Temperatures in the Saudi Arabian Eastern Coastal City of Dammam

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    Over the past several decades, Saudi cities have experienced rapid urban developments and land use and land cover (LULC) changes. These developments will have numerous short- and long-term consequences including increasing the land surface temperature (LST) of these cities. This study investigated the effects of LULC changes on the LST for the eastern coastal city of Dammam. Using Landsat imagery, the study first detected the LULC using the maximum likelihood classification method and derived the LSTs for the years 1990, 2002, and 2014. Using the classified results, it then modeled the future LULC for 2026 using the Cellular Automata Markov (CAM) model. Finally, using three thematic indices and linear regression analysis, it then modeled the LST for 2026 as well. The built-up area in Dammam increased by 28.9% between 1990 and 2014. During this period, the average LSTs for the LULC classes increased as well, with bare soil and built-up area having the highest LST. By 2026, the urban area is expected to encompass 55% of the city and 98% of the land cover is envisioned to have average LSTs over 41 °C. Such high temperatures will make it difficult for the residents to live in the area

    Sources, pattern, and possible health impacts of PM2.5 in the central region of Bangladesh using PMF, SOM, and machine learning techniques

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    Particulate Matter 2.5 (PM2.5) is a major environmental and public health threat in Bangladesh. It is important to explore the relationship between PM2.5 and other variables to mitigate its adverse health impacts. This study aims to understand the sources, patterns, and health impacts of PM2.5 in five central districts of Bangladesh using fourteen variables. These variables have been analyzed by PMF, SOM, Machine Learning, and Multi-regression analysis. This paper has found that PM2.5 is correlated positively with NO2 (0.55), BC (0.45), CH4 (0.38), and NOx (0.22), while correlated negatively with Rainfall (−0.10), CO (−0.33), and SO2 (−0.24). In PMF modeling, the R2 values of settlement density (1.00), SO2 (0.99), DEM (0.94), Rainfall (0.77), NO2 (0.74), and Brickfield (0.66) have found as the most correlated variables. In this study, the dominant variables NO2, CO, Rainfall, O3, AOT, CH4, and BC are found in Factor 1; SO2, settlement density, and DEM are found in Factor 2; and population density and brickfield are found in Factor 3. In SOM mapping, most of the variables are concentrated in the north-eastern, central, and south-eastern parts of the study area. The prediction of PM2.5 using machine learning is significant, showing reasonable R2 for Random Forest (0.85), Extreme gradient boosting (0.81), and Stepwise Linear (0.76). The impact of PM2.5 on child Acute Respiratory Infection (ARI) is significant (p = 0.002, R2 = 0.75); while child mortality is not significant (p = 0.268; R2 = 0.55). These results will be useful for creating and implementing local and regional PM2.5 mitigation plans. Concerned institutions and academia may also use these outputs for reducing health impacts, particularly child mortality and acute respiratory infections

    Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

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    Motorization has been escalating rapidly in developing countries, posing a severe challenge to sustainable urban mobility. In the past two decades, car-sharing has emerged as one of the most prominent alternatives to facilitate smart mobility solutions, thereby helping to reduce air pollution and traffic congestion. However, before its full-scale deployment, it is essential to understand the consumers’ acceptance of car-sharing systems. This study aimed to assess the public perception and acceptance of the car-sharing system through a stated preference (SP) questionnaire in the city of Lahore, Pakistan. The collected data contained detailed information on various service attributes of three alternative modes (car-sharing, private car, and taxi) in addition to the sociodemographic attributes of respondents. Data analysis and interpretation were performed using econometric models such as the Multinomial Logit Model (MNL), the Nested Logit Model (NL), and the Random Parameter Logit Model (RPL). Study findings revealed that several generic attributes such as travel time, travel cost, waiting time, and privacy were predicated as significant influential factors towards the adoption of car-sharing. Sociodemographic attributes, including age, education, monthly income, the individuals who had driver’s licenses, and frequency of travel in a week, were also found to be significant. The findings of the current study can provide valuable insights to stakeholders and transportation planners in formulating effective policies for car-sharing

    Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

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    Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods

    The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies

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    Road traffic crashes (RTCs) are one of the most critical public health problems worldwide. The WHO Global Status Report on Road Safety suggests that the annual fatality rate (per 100,000 people) due to RTCs in the Kingdom of Saudi Arabia (KSA) has increased from 17.4 to 27.4 over the last decade, which is an alarming situation. This paper presents an overview of RTCs in the Eastern Province, KSA, from 2009 to 2016. Key descriptive statistics for spatial and temporal distribution of crashes are presented. Statistics from the present study suggest that the year 2012 witnessed the highest number of crashes, and that the region Al-Ahsa had a significantly higher proportion of total crashes. It was concluded that the fatality rate for the province was 25.6, and the mean accident to injury ratio was 8:4. These numbers are substantially higher compared to developed countries and the neighboring Gulf states. Spatial distribution of crashes indicated that a large proportion of severe crashes occurred outside the city centers along urban highways. Logistic regression models were developed to predict crash severity. Model estimation analysis revealed that crash severity can be attributed to several significant factors including driver attributes (such as sleep, distraction, overspeeding), crash characteristics (such as sudden deviation from the lane, or collisions with other moving vehicles, road fences, pedestrians, or motorcyclists), and rainy weather conditions. After critical analysis of existing safety and infrastructure situations, various suitable crash prevention and mitigation strategies, for example, traffic enforcement, traffic calming measures, safety education programs, and coordination of key stakeholders, have been proposed