As technology advances, so as our maps. In this talk, we will explore the ever growing open map data that can help us understand, validate, and explore socio-economic indicators with the aid of network theory and machine learning techniques. Participants will learn what data are available in OpenStreetMap that can be used to profile cities and municipalities for socio-economic conditions based on proxy variables. These variables include existence of businesses through establishments and buildings, road network densities and complexities, topographical features, vegetation and water indices, etc. Machine learning models will be used for categorization aided by graph metrics. Results will be cross validated with existing city/municipality tags.