A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data

Abstract

In this research, we introduce a system that utilizes open government data and machine learning algorithms to extract meaningful insights about cities and zones in the United States. It is estimated that 4% of the world’s population occupies the United States of America. Remarkably, the US is considered the largest country to host prominent websites on the internet [16]. It is estimated that 43% of the top one million websites in the world are hosted in the United States (see Figure 1); promoting it as the largest influential country in producing data on the web (followed by Germany hosting only 8%) [16]. Although most data content on the web is unstructured, the US government adopted the initiative to release structured data related to different fields such as health, education, safety, development and finance. Such datasets are referred to as Open Government Data (OGD) and are aimed at increasing the transparency and accountability of the US government. Our aim is to provide a well-defined procedure to process raw OGD information and produce expressive insights regarding different zones within a city, differences between cities, or differences among zones located in different cities.