This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing $70\%$ of the urban space was used to train the models, and the remaining $30\%$ was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of $90.0\%$ for Dhaka, $91.5\%$ for Nairobi, $94.75\%$ for Jakarta, $82.0\%$ for Guangzhou city, $94.25\%$ for Mumbai, $91.75\%$ for Cairo, and $96.75\%$ for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.

The Indonesian spectrum usage fees—the so-called Biaya Hak Pengguna Frekuensi Izin Pita Frekuensi Radio (BHP IPFR)—are currently calculated using a formula determined by the three following main parameters: the frequency band, the country’s economic parameter, and the nationwide population. As spectrum usage fees are proportional to the width of the bandwidth, the current formula would result in an extremely high price when applied to 5G-mmWave private networks, with the cost burden being a direct consequence for the service operator. In this paper, we propose the formulation of a new spectrum usage fee for 5G-mmWave private network implementation in Indonesian industrial areas. To do so, we evaluate the current formula, adopt the framework offered by the ITU-R SM.2012-5 (06/2016), and use an industrial reference index—the Indonesia Industry Readiness Index 4.0 (INDI 4.0) score. We test the proposal by applying the new formula to calculate the 5G-mmWave private network spectrum usage fee for the Jakarta industrial area. The result shows that the new formula gives a lower spectrum usage fee than the current formula, which benefits 5G-mmWave private network service operators. Such savings can be regarded as a government subsidy for the service operators to use in various ways in the industry, providing further economic benefits. Using the input–output model, we prove that despite the proposed new formula brings a lower spectrum usage fee, resulting in a loss in state income, it will lead to a much greater positive impact on the national economic output. Applying the new formula will eventually have a multiplier effect on various sectors and encourage digital economic growth and national digital transformation, especially for vertical industries in Indonesia. This study may serve as a guideline or initial reference for Indonesian policymakers and service operators for applying the CAPEX and OPEX cost of using the new spectrum for 5G-mmWave private network service implementation and estimating the economic multiplier for 5G-mmWave private network service deployment in industrial areas. It can also be used as a benchmark case for other countries to apply spectrum usage fees for private networks in industrial areas.

Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively.

This study explores how using mobile applications for everyday business processes can help small and medium enterprises (SMEs) in Indonesia become more competitive. We combined two well-known theories—one on human behavior and one on consumer values—to understand better what influences SMEs to adopt mobile apps. We collected survey data from 69 SMEs and analyzed it using a structured modeling approach. The findings show that SMEs are more likely to use mobile apps when they have a positive attitude toward them and feel in control of their use. However, we also found something unexpected: Consumer choice behavior, such as valuing uniqueness or emotion in products, actually weakens effect on app use. This suggests that while mobile apps can help SMEs grow, simply offering more features or variety may not always lead to greater use. The study offers useful lessons for policymakers, app designers, and SME support programs aiming to promote digital tools for business growth.

In this study, we wanted to see how as a representative of a society newspaper portrayed COVID-19. For this purpose, this study considered two countries United Kingdom (UK) and Bangladesh (BD), and analyzed how COVID-19 as an external event was focused in the newspapers. To conduct the analysis, we handpicked a set of covid related feature terms, and using Latent Dirichlet Allocation (LDA) we verified the coherency of the chosen terms. The main finding of this study is that, initially as a new event COVID-19 found huge importance in the newspapers, but with gradual time progression, this became a new normal event and lost its initial insurgence of focus and took a stable condition. We also observed that despite being quite different demographically, geographically, economically along with being affected differently due to covid, UK and BD exhibit quite similar characteristics in portraying covid in newspapers. The decisions were arrived at by applying different types of Natural Language Processing (NLP) tools and statistical analysis. For experimentation, we collected all the published news articles from two newspapers, the Guardian and the Daily Star from January 1, 2020, to March 31, 2021.

The rapid outbreak of COVID-19 has initiated the development of mobile applications aiming at helping public health authorities to slow down viral diffusion. The proliferation of these applications engenders challenges to forge a balance between ‘public health utility’ and ‘personal privacy’. This paper scrutinizes various applications that collect personal data according to their functions and data protection compliance. These applications are mostly of three broader categories- contact tracing, self-assessment, and quarantine enforcement. We conduct systematic categorization based on five parameters-type of owner or provider, host platform, functionalities, the existence of privacy policy, and state of the source code. A total of 122 apps encompassing 83 countries were assessed during a research period of 20 days (June 1 to 20, 2020). Findings suggest that although the majority of the applications publish a privacy policy, many applications do not give information in detail, making the issue of privacy obscure. The majority of the applications collect various sensitive personal data irrespective of their functionalities, provider, and platform. Most applications are not open source raising concerns over trust and transparency. The findings are valuable to policymakers who are formulating short, mid, and long-term technology policies to strike a balance between functionality and personal privacy.