Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO₂), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = $5.97\times {10}^{-7}$) and Spearman rank correlation coefficient = 0.77 (p-value = $1.58\times {10}^{-8}$) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.

The introduction of the Industry 4.0 and Society 5.0 concepts has been undoubtedly challenging, and the readiness towards them could be fundamentally enhanced by strategic management and entrepreneurial governance. Bureaucracy in the majority of developing countries, including Indonesia, is an impediment due to the delays in bureaucratic reform and weak patterns of communication and coordination between their institutions. This study aims to analyze the readiness towards the era of Industry 4.0, and Society 5.0 in Indonesia from the perspectives of strategic management of the bureaucracy and entrepreneurial government. We undertake a case study on the organization of the Deputy for Human Resources of the Indonesian Ministry of Empowerment of the State Apparatus and Bureaucratic Reform and use a mixed method that simultaneously combines quantitative and qualitative methods. The resulted data from observations, in-depth interviews, and focus group discussions were then analyzed using path analysis, descriptive methods, and qualitative approaches. Our results finding shows that there is a strategic value in data-based policies, and the ownership of data from various perspectives is strategically used as a direction for policymakers. One of the impacts of the Industry 4.0 and Society 5.0 concepts is that the world has become increasingly connected. Hence, there are no boundaries between systems. Bureaucratic strategic management and entrepreneurial government have a significant effect on the readiness towards the Industry 4.0, and Society 5.0 concepts, in Indonesia, either partially or simultaneously.