The widespread diffusion of smartphones, grid sensors and Internet technology has created vast new spatial data sets. One example is residential electricity consumption for each house in California for each 15 minutes. Another example is the universe of all UBER rides in San Francisco in a given month. To analyze these data in order to spot patterns and test hypotheses requires three skills. First, the analyst must be able to manipulate these huge data bases in order to create spatial databases plus maps and other geovisualizations. Second, the analyst must have a sophisticated understanding of spatial economics in order to have a framework for understanding potential causal relationships that can be inferred from the data. Third, the analyst must be trained in spatial analytics and statistics to be able to generate interesting new facts that form the basis for testing the spatial and economic theories and producing new actionable knowledge.
This degree trains students in spatial economics and spatial sciences. By combining the insights from these two different fields, there are significant synergies. The geospatial curriculum teaches best practices in spatial data creation, mapping and data manipulation while simultaneously also teaching students how the economist’s perspective informs one’s understanding about “why” such patterns are observed.
Trained students will gain new insights about emerging business opportunities, environmental trends, and urban crime and congestion trends. Such spatial patterns are directly tied to real estate valuation and to identifying emerging opportunities and challenges for companies operating in cities around the world. Given the large (and growing) number of Big Data startups in the Los Angeles area, we will use our network of contacts to place our students as interns in these firms.
The program is 32 units in length.