This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Housing is one of the most significant components of sustainable development; hence, the need to come up with sustainable housing solutions. Nevertheless, the sales of houses are steadily falling due to the unaffordability of houses to many people. Based on the expanded community acceptance model, this research examines the relationships between sustainable housing and quality of life with the moderating factors of knowledge, technology, and innovation in Shenzhen. Additionally, it aims to delineate the principal dimensions influencing quality of life. The study employs purposive sampling and gathers data from residents of Shenzhen via a Tencent-distributed survey. Analysis was conducted using Smart Partial Least Squares (PLS) 4.0. Results indicate a positive correlation between economic sustainability in housing and quality of life. Contrarily, the social and environmental aspects exhibited negligible impacts on quality of life. Knowledge, technology, and innovation were identified as significant moderators in the correlation among all three sustainable housing dimensions and quality of life. The findings are anticipated to enhance understanding of the perceived impacts of sustainable housing on quality of life in Shenzhen and elucidate the role of knowledge, technology, and innovation in fostering this development.

The growth of buildings in big cities necessitates Design Review (DR) to ensure good urban planning. Design Review involves the city community in various forms; however, community participation remains very limited or even non-existent. There are indications that the community has not been involved in the Design Review process. Currently, DR tends to involve only experts and local government, without including the community. Therefore, this research aimed to analyze the extent of opportunities for community participation by exploring DR analysis in developed countries and related policies. In-depth interviews were also carried out with experts and Jakarta was selected as a case study since the city possessed the most intensive development. The results showed that the implementation of DR did not consider community participation. A constructivist paradigm was also applied with qualitative interpretive method by interpreting DR data and community participation. The strategy selected was a case study and library research adopted by examining theories from related literature. Additionally, the data was collected by reconstructing different sources such as books, journals, existing research, and secondary data from related agencies. Content and descriptive analysis methods were also used, where literature obtained from various references was analyzed to support research propositions and ideas.

The tourism sector is exponentially expanding across the globe. Despite different forms of tourism, community-based tourism has evolved with new dimensions of development. Assessing the sustainable development of the sector is a top priority in order to adopt the new forms. Therefore, in this study, the association between community-based tourism and its sustainable development was measured under the lens of collaborative theory and social exchange perspective. Non-probabilistic judgmental sampling techniques were applied, and 201 respondents were assessed. Data analysis was conducted using structural equation modeling (SEM). The study grounded with residents’ perspectives and attested that community-based tourism directly enhanced residents’ economic conditions with a better environment, and the relationship between residents and tourists enhanced the tourism industry’s sustainable development. Stakeholders like government and local administrations play a significant role in exploring community-based tourism. This outcome of the research will be a substantial resource for local administrations, governments, researchers, policymakers and practitioners.