The increasing usage of the space above and underneath the land surface brings up the need of controlling its occupation. The localization of the infrastructure underground network is a major challenge, and for most of the countries, this information is not yet available or not easy to access. At this juncture, the cadastre of the network location is of fundamental importance for the water management supply systems. ISO 19152 from 2012 -Land Administration Domain Model (LADM) -addresses the standardization and integration of common features of cadastre systems in a simple way, and may be applied to several aspects of land administration. Within this context, this research aims to develop a model of subsurface water utility networks, based on the standard proposed by LADM. The implementation of the model suggested by ISO 19152 was tested using the COMPESA cadastre of the water utility network as a study case. The modernization and the technological advances adopted by the company facilitated the understanding of the geographical database underlying structure and its adaptation to the international standard. The modeling was produced using UML language, and DBDesigner for the physical implementation, executed in connection with PostgreSQL/PostGIS and QGIS, was applied.
Introduction: COVID-19 is a major public health concern in this century. The causative agent SARS-CoV-2, is highly contagious and spreads continuously across territories. Spatial analysis is of enormous importance in the process of understanding the disease and its transmission mechanisms. We aimed to identify the risk areas for COVID-19 and analyze their association with social vulnerability in Maceió, Alagoas. The study was conducted in 2020. Methodology: This is an ecological study to evaluate the incidence, mortality and case fatality rate of COVID-19 and their relationship with 12 indicators of human development and social vulnerability. Multivariate and spatial statistics were applied. A 95% confidence interval and a 5% confidence level were considered. Results: The spatial scan statistic revealed the existence of six high-risk clusters for the incidence of COVID-19. The regression model showed that social indicators, such as literacy of people, residents of private households, households with more than four residents, and resident brown population, were associated with COVID-19 transmission in Maceió-AL. The disease affected localities whose populations are exposed to a context of intense socioeconomic vulnerability. Conclusions: Based on the results, it is necessary to adopt measures that take into account the social determinants of health in order to minimize the damage caused by the pandemic.
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