Risk management in sustainable smart cities governance: A TOE framework

Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

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Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Blockchains-based smart contracts are disrupting the smart real estate sector of the smart cities. The current study explores the literature focused on blockchain smart contracts in smart real estate and proposes a conceptual framework for its adoption in smart cities. Based on a systematic review method, the literature published between 2000 and 2020 is explored and analyzed. From the literature, ten key aspects of the blockchain smart contracts are highlighted that are grouped into six layers for adopting the smart contracts in smart real estate. The decentralized application and its interactions with Ethereum Virtual Machine (EVM) are presented to show the development of a smart contract that can be used for blockchain smart contracts in real estate. Further, a detailed design and interaction mechanism are highlighted for the real estate owners and users as parties to a smart contract. A list of functions for initiating, creating, modifying, or terminating a smart contract is presented along with a stepwise procedure for establishing and terminating smart contracts. The current study can help the users enjoy a more immersive, user-friendly, and visualized contracting process, whereas the owners, property technologies (Proptech) companies, and real estate agents can enjoy more business and sales. This can help disrupt traditional real estate and transform it into smart real estate in line with industry 4.0 requirements.

A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities

Digital tools and marketing have been widely adopted in various industries throughout the world. These tools have enabled companies to obtain real-time customer insights and create and communicate value to customers more effectively. This study aims at understanding the principles and practices of sustainable digital marketing in the Malaysian property development industry by investigating the extent to which digital marketing has been adopted, the impediments to its adoption, and the strategies to improve digital capabilities for the local context. Digital marketing theories, practices, and models from other industries are adopted and applied to the local property development industry to lay the foundation for making it smart and sustainable. This paper proposes a marketing technology acceptance model (MTAM) for digital marketing strategy and capability development. The key factors used in the model are ease of use, perceived usefulness, perceived cost, higher return, efficiency, digital service quality, digital information quality, digital system quality, attitude towards use, and actual use. The model and hypothetical relationships of critical factors are tested using structural modeling, reliability, and validity techniques using a sample of 279 Malaysian property development sector representatives. A quantitative approach is adopted, using an online questionnaire tool to investigate the behavior of respondents on the current digital marketing practices and capabilities of Malaysian property development companies. The results show that the sample property development companies are driven by the benefit of easily obtaining real-time customer information for creating and communicating value to customers more effectively through the company brand. Further strategies, such as creating real-time interactions, creating key performance indicators to measure digital marketing, personalization, and encouraging innovation in digital marketing are most preferred by local professionals. An adoption framework is provided based on the reviewed models and results of the current study to help transform the Malaysian property development sector into a smart and sustainable property development sector by facilitating the adoption of digital technologies. The results, based on real-time data and pertinent strategies for improvement of the local property sector, are expected to pave the way for inducing sustainable digital marketing trends, enhancing capabilities, and uplifting the state of the property development sector in developing countries.

https://www.mdpi.com/2071-1050/12/13/5402/pdf

Smart digital marketing capabilities for sustainable property development: A case of Malaysia

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

Siddra Qayyum

Papers

Risk management in sustainable smart cities governance: A TOE framework

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

UAVs in disaster management: application of integrated aerial Imagery and convolutional neural network for flood detection

Managing smart cities through six sigma DMADICV method: A review-based conceptual framework