Target Audience

The tutorial will be of interest to a multi-disciplinary audience that has interest in spatial data collection and analysis with an application focus in cities. Social scientists that would like to engage with geo-referenced data on a practical level, computer scientists that would like to experience a new application domain focus in urban environments and people with background in geography, architecture or planning that would like to improve their data science skills could benefit from attending. While only basic knowledge of Python is required to attend the tutorial and an understanding of the ecosystem of online services, more sophisticated programmatic examples will be described. These will be provided in an off-the-shelf manner so attendees will be able to use them even if they have an abstract understanding of the underlying systems discussed (e.g. demonstrating how APIs are used in the context of data collection won’t require knowledge of server programming).

​Some familiarity with network science concepts is advisable, yet all practical examples will be covered with appropriate references and explanation of the network theory concepts. There will be limited use of machine learning algorithms through Python’s scikit-learn library to simulate a number of application examples, though sophisticated knowledge in the area won’t be required. By the end of the tutorial will have improved their programming, analytic and modeling skills in the area of urban data science. They will have experience in performing spatial data analysis using a wide variety of datasets considering different application scenarios and have example code that can be easily modified for further use.

Location