Browsing by Research Output - Author "Abdul-Rahman, Mohammed"

Critical success factors (CSFs) are important for the success of any project including assessing the resilience of communities to natural and human-made shocks and stresses. Due to limited studies on CSFs for community resilience assessment (CRA), this study was conducted to identify and classify CSFs using resilience experts' opinions from both developed and developing countries and investigate if the same factors apply to the success of CRA in developed and developing countries. Thirty-one factors were identified from the community resilience literature and analyzed using feedbacks from 392 survey questionnaires from twenty-three countries. Analysis carried out to measure the agreements between experts' opinions from developed and developing countries showed no significant disagreement on most of the CSFs. Twenty-eight of the factors were found to be critical to CRA success in both developed and developing countries. The results from factor analysis further classified the 28 CSFs into seven components. Findings from this study provide a guide on the criteria to look out for when adopting a CRA methodology. The results also provide guidelines for community resilience experts to develop better CRA methodologies and help CRA project managers and policymakers to improve CRA success.

Over the last decade, 90% of Big Data has been generated by people living in urban areas. With the advent of Internet of Things (IoT) and the increased use of the internet, Social Media has become an integral part of people's daily lives. Millions of unstructured data are being sent to the cloud every second, providing opinions practically on any discourse. This makes microblogs such as Twitter, Instagram, WeChat, and Facebook smart instruments for urban planners to harvest ‘big data’ on socioeconomics, urban dynamics, transportation, land uses, resilience, etc. This study proposed a framework for social media big data mining and data analytics using Twitter. It demonstrated the functionalities of the framework on a case study using Natural Language Processing and Machine Learning techniques like Latent Dirichlet Allocation and VADER Sentiment Analysis to mine, clean, process, and validate the data. The validated results from the case study showed high accuracy that Social Media Big Data can be used to study the spatiotemporal dynamism of community challenges.