Depression is a common chronic disorder. It often goes undetected due to limited diagnosis methods and
brings serious results to public and personal health. Former research detected geographic pattern for
depression using questionnaires or self-reported measures of mental health, this may induce same source
bias. Recent studies use social media for depression detection but none of them examines the
geographic patterns. In this paper, we apply GIS methods to social media data to provide new perspectives
for public health research. We design a procedure to automatically detect depressed users in
Twitter and analyze their spatial patterns using GIS technology. This method can improve diagnosis
techniques for depression. It is faster at collecting data and more promptly at analyzing and providing
results. Also, this method can be expanded to detect other major events in real-time, such as disease
outbreaks and earthquakes.