There is a lack of research on urban sprawl in developing countries, particularly in Sub-Saharan Africa, undergoing significant demographic change. There is an urgent need to conduct more studies on African cities and investigate spatial variations in urban sprawl to fill a knowledge gap in Sub-Saharan Countries (SSC). There have been no studies of urban sprawl in the Somali capital of Mogadishu, a fragile metropolis struggling with the legacy of decades of civil war. This study has two main objectives: (i) to examine sprawl patterns in Mogadishu, Somalia; and (ii) to identify the drivers and impacts of urban sprawl in Mogadishu, Somalia. The study used spatiotemporal imagery from 2006, 2013, and 2021 to identify sprawl patterns. A quantitative method in the form of a cross-sectional survey with 265 participants was then used to identify the drivers and impacts of sprawl, which was then analysed using the structural equation model (SEM). The spatiotemporal analysis results showed sprawl patterns in nine districts and three settlements, mainly scattered and leapfrog patterns. The SEM discovered five significant drivers: low price of land and dwelling (LP), development of transportation infrastructure (DTI), rising income, security reasons, and low commute cost (LCC), in addition to eight significant impacts: less social interaction (LSI), agriculture land and natural habitat loss (AGL NHL), unsafe environment (USE), insufficient health and educational services (IHF IEF), high public services cost (HPSC), insufficient public transport (IPT), less physical activity (LPA), pollution (POL) and mental health issues (MH). Undoubtedly, the impacts found in the study proved that urban sprawl negatively impacted the residents and environment of Mogadishu, which will continue as the security situation in the city improves and more residents are attracted.

Urban sprawl related to rapid urbanisation in developed and developing nations affects sustainable land use. In Sri Lanka, urban areas have mostly expanded in a rather spontaneous, unplanned manner (based on the current settlers’ subjective movement) rather than conforming to the local government’s development plan. This growth inevitably leads to uncontrolled urban sprawl in many Sri Lankan cities, including Batticaloa. So far, Sri Lanka’s planners or researchers have not yet tackled the sprawling developments in this city. Understanding the different forms and patterns of urban sprawl is the key to address sprawling growth. This study aims to identify the characteristics of urban sprawl in the Batticaloa municipal council using Geographic Information System (GIS) and remote sensing technology. Landsat satellite images for the years 2000, 2010, and 2020 as well as 2002, 2011, and 2019 population data were used and analysed using ArcGIS’ maximum likelihood classification tool and the density function, respectively, to delineate the characteristics of urban sprawl. The results revealed that low-density development, leapfrog development, commercial ribbon development, and scattered growth are the influencing characteristics of urban sprawl in the Batticaloa municipality. These characteristics were found mainly in the urban edge of the city and have led to urban sprawl. The finding provides knowledge into recognising the characteristics of urban sprawl with empirical evidence. It affords a clear direction for future studies of urban sprawl in rapidly growing cities that are numerous in Sri Lanka, and the identified characteristics of urban sprawl can be useful in minimising future sprawl. This result can be a tool for future urban planning and management in the Batticaloa municipality.

During the period in which the Omicron coronavirus variant was rapidly spreading, the impact of the institutional-social-ecological dimensions on the case-fatality rate was rarely afforded attention. By adopting the diagnostic social-ecological system (SES) framework, the present paper aims to identify the impact of institutional-social-ecological factors on the case-fatality rate of COVID-19 in 134 countries and regions and test their spatial heterogeneity. Using statistical data from the Our World In Data website, the present study collected the cumulative case-fatality rate from 9 November 2021 to 23 June 2022, along with 11 country-level institutional-social-ecological factors. By comparing the goodness of fit of the multiple linear regression model and the multiscale geographically weighted regression (MGWR) model, the study demonstrated that the effects of SES factors exhibit significant spatial heterogeneity in relation to the case-fatality rate of COVID-19. After substituting the data into the MGWR model, six SES factors were identified with an R square of 0.470 based on the ascending effect size: COVID-19 vaccination policy, age dependency ratio, press freedom, gross domestic product (GDP), COVID-19 testing policy, and population density. The GWR model was used to test and confirm the robustness of the research results. Based on the analysis results, it is suggested that the world needs to meet four conditions to restore normal economic activity in the wake of the COVID-19 pandemic: (i) Countries should increase their COVID-19 vaccination coverage and maximize COVID-19 testing expansion. (ii) Countries should increase public health facilities available to provide COVID-19 treatment and subsidize the medical costs of COVID-19 patients. (iii) Countries should strictly review COVID-19 news reports and actively publicize COVID-19 pandemic prevention knowledge to the public through a range of media. (iv) Countries should adopt an internationalist spirit of cooperation and help each other to navigate the COVID-19 pandemic. The study further tests the applicability of the SES framework to the field of COVID-19 prevention and control based on the existing research, offering novel policy insights to cope with the COVID-19 pandemic that coexists with long-term human production and life for a long time.

Due to extensive population growth, urbanization increases urban development and sprawl in the world’s cities. Urban sprawl is a socioeconomic phenomenon that has not extensively incorporated socioeconomic factors in the prediction of most of the urban sprawl models. This study aimed to predict the urban sprawl pattern in 2030 by integrating socioeconomic and biophysical factors. NDBI, Cramer’s V, logistic regression, and CA-Markov analyses were used to classify and predict built-up patterns. The built-up area is the dominant land use, which had a gradual growth from 1990 to 2020. A total of 20 socioeconomic and biophysical factors were identified as potentials in the municipality, affecting the urban sprawl. Policy regulation was the most attractive driver with a positive association, and land value had a high inverse association. Three prediction scenarios for urban sprawl were achieved for 2030. Higher sprawling growth is expected in scenario 3, compared with scenarios 1 and 2. Scenario 3 was simulated with biophysical and socioeconomic factors. This study aids in addressing urban sprawl at different spatial and temporal scales and helps urban planners and decision makers enhance the development strategies in the municipality. Predicted maps with different scenarios can support evaluating future sprawling growth and be used to develop sustainable planning for the city.

The government enacted the Movement Control Order (MCO) to curb the spread of the COVID-19 pandemic in Malaysia, restricting movement and shutting down several commercial enterprises around the nation. The crisis, which lasted over two years and featured a few MCOs, had an impact on Malaysians’ mental health. This study aimed to understand the context of using the word “suicide” on Twitter among Malaysians during the pandemic. “Suicide” is a keyword searched for on Twitter when mining data with the NCapture plugin. Using NVivo 12 software, we used the content analysis approach to detect the theme of tweets discussed by tweeps. The tweet content was then analyzed using VADER sentiment analysis to determine if it was positive, negative, or neutral. We conducted a spatial pattern distribution of tweets, revealing high numbers from Kuala Lumpur, Klang, Subang Jaya, Kangar, Alor Setar, Chukai, Kuantan, Johor Bharu, and Kota Kinabalu. Our analysis of tweet content related to the word “suicide” revealed three (3) main themes: (i) criticism of the government of that day (CGD) (N = 218, 55.68%), (ii) awareness related to suicide (AS) (N = 162, 41.44%), and (iii) suicidal feeling or experience (SFE) (N = 12, 2.88%). The word “suicide” conveyed both negative and positive sentiments. Negative tweets expressed frustration and disappointment with the government’s response to the pandemic and its economic impact. In contrast, positive tweets spread hope, encouragement, and support for mental health and relationship building. This study highlights the potential of social-media big data to understand the users’ virtual behavior in an unprecedented pandemic situation and the importance of considering cultural differences and nuances in sentiment analysis. The spatial pattern information was useful in identifying areas that may require additional resources or interventions to address suicide risk. This study underscores the importance of timely and cost-effective social media data analysis for valuable insights into public opinion and attitudes toward specific topics.