Spatial Analytics of the Built Environment

The course will investigate a number of qualitative and quantitative methods to measure and analyze urban spatial problems relevant to contemporary urban planning practice. The course is based in part on literature on spatial analysis and in part on newly emerging topics in urban analytics. Aiming to offer students tools for integrating spatial information and decision making into planning and design solutions, the course is structured around four experiments:

· Pedestrian route choice analysis
· Understanding business location and patronage
· Mapping spatial inequality
· Making sense of big, aggregate data

Each experiment will run for four weeks, during which groups of participants are asked to tackle a real-world urban analytics exercise from beginning to end, starting with a introduction of theory and methods, followed by data collection and analysis and ending with a presentation of findings in class.

Each experiment is conducted in teams. By exposing participants to different experimental set-ups that move from conceptualization and experimental design, to data collection, analysis, to the presentation and interpretation of findings, the course aims to prepare students for applied urban analysis projects. There is no mid-term exam or big final review; each of the experiments counts equally, distributing the workload evenly throughout the semester. Lab sessions introduce students to relevant software applications.

The course is very hands-on. We use multiple software platforms including ArcGIS, Rhino and Excel, along with some functionality that is new and experimental. If you do not enjoy experimentation and have no interest in quantitative analysis, this may not be the right course for you. But if you are willing to explore and embrace some uncertainty, you should experience enough to become a self-sufficient learner in urban analytics and visualization, and might discover a whole new lens through which to study, plan and design built environments.

None, but prior experience with ArcGIS and Rhinoceros3D will be very helpful.

Learning Objectives
By the end of the course, students will be able to:
· Use four powerful urban analysis techniques in practice: pedestrian route modeling, location accessibility analysis, comparative mapping, and data visualization.
· Explain how each of the analysis technique works in detail to stakeholders.
· Apply these methods in architecture, urban planning, design, public policy and real-estate development.
· Identify the appropriate spatial analysis techniques for different urban analytic problems.
· Formulate analysis projects from beginning to end, presenting the findings with clarity and precision.

Measurable Outcomes
Each of the four experiments uses different analysis methods and produces a team report. The reports are key evidence of understanding for grading.

· Demonstrate their understanding of data collection, computational spatial analysis and results interpretation as demonstrated via experiment reports and presentations.
· Utilize appropriate spatial analysis methods to solve given urban analysis problems.
· Formulate the given problems in reports.
· Explain their analytics choices and solutions with clarity.
· Analyze the strengths and weaknesses of proposed spatial analysis solutions.