Bandung, the capital of West Java, is the province with the largest population in Indonesia.Urban development is then interrelated with the number, structure, dynamics of the population, also the size of the area. The pattern of population distribution is an important point for the government in determining a City's Spatial Plan. Thus, this study aims to build a mapping system for population distribution and prediction in Bandung City, Indonesia. It will be very beneficial for the government, especially in making decisions for planning an area. The system implemented using the Google Maps platform APIs and the method of calculating Geometric models and processed data is data obtained from the Central Bureau of Statistics of the city of Bandung in the form of a sub-district catalogue in the reprocessed numbers. The results of the data processing are visualized into the Geographic Information System Web with information features in the form of population mapping, density, and prediction of the future population for each sub-district in the city of Bandung. Based on the results of testing the implementation of the Geometry model with the Black box method, the system's functionality has shown the appropriate results.

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Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patient's medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidencebased medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Healthe-Child project.

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