Is the location of Public Health Units in Curitiba meeting the citizens’ needs? Filipe Lautert, Luiz Gomes-Jr., Nadia P. Kozievitch, Tatiane A. M. Lautert Universidade Tecnológica Federal do Paraná (UTFPR) {filipe,tatiane}lautert@gmail.com, gomesjr@dainf.ct.utfpr.edu.br, nadiap@utfpr.edu.br Abstract. Guaranteeing adequate health services to the population is a chal- lenge, especially in developing countries where limited resources must be opti- mized in order to reach a larger portion of the population. To properly assess the adequacy of health services and prioritize new investments, it is important to gather a large amount of relevant data, integrated from various sources. This paper presents an ongoing research focusing on Curitiba, one of the largest cities in Brazil. We have aggregated socio-political, geographical, trans- portation and health data from open repositories in order to understand the dy- namics of how citizens choose their health units when required, as well as veri- fy the availability of bus stops close to these units. The paper reports findings from our exploratory analysis, highlighting the cases where the city's admin- istration is on the right track, but also the areas which require more investment. More specifically, using GIS and Data Analysis tools we analyze the occur- rence of medical appointments made outside of the citizens’ residential neigh- borhood and the most frequent diseases they had. We also detail which health units do not have a bus stop in a determined radius. Keywords: Open Data, Urban Planning, Health Units, Georeferenced Data. 1 Introduction As the city grows and health units, bus lines, and its bus stops are implemented, it is important to assess whether the infrastructure can cope with the increasing demand of its population; what was a good distribution of services in terms of urban planning in the recent past may no longer satisfy the citizens’ needs. Geographic Information Systems (SIGs) can assist in this type of analysis, as long as there is sufficient and reliable data being generated by the responsible institutions. According to [ZIVIANI et al 2015], there are many mobility models that are used to describe or predict urban mobility, but most of them use a single source of data to do such analysis. Therefore in this article, we bring a new analysis based on different data sources, being them: health units’ location, patients’ residence location and availability of bus stops near the health units.