SPATIAL DATA VISUALIZATION ANALYSIS & MAPPING (PhD)
Doctoral School in Economics
The course aims at introducing participants to the collection, management, analysis, mapping, modelling and visualization of spatial data in a GIS environment. Participants will learn both theoretically and practically what the specificities of spatial data and methods are, how to map and analyze geographical patterns through basic digital cartography, spatial analysis, spatial statistics, spatial regressions, how to visualize and communicate the results effectively and reflexively, and will be introduced to some examples of applications in spatial data visualization, geospatial analysis, environmental analysis, urban analysis, statistics and economics.
Instructors | Filippo Celata, Luca Salvati, Valerio Leone Sciabolazza (Sapienza); Sara Caramaschi (Polimi); Alice Corona (Unibo); Federico Martellozzo (Unifi).
1) Introduction to GIS and geodata | F. Celata | 6 June, 11-13 15-18 | Introduction to GIS software. Specificities and sources of spatial data. Spatial coordinates, scales, geometries and partitions. Georeferencing and geocoding. Geoprocessing. Mapping with ArcGIS.
2) Spatial statistics and urban analysis | F. Celata | 8 June, 14-18 | Introduction to spatial statistics. Point processes, spatial clustering, density maps. Measurement, mapping and interpretation of global and local spatial autocorrelation. Exploratory spatial data analysis with ArcGIS.
3) Spatial data visualization | A. Corona | 9 June, 10-13 15-18 | Spatial data visualization best practices: examples, principles and tools to effectively communicate data, with a focus on accessibility, audience-driven design and critical thinking. Introduction to Datawrapper.
4) The visual in social, urban and geographical research | S. Caramaschi | 10 June, 15-18 (online) | Encountering the visual in social science research. The ability to construct and read the visual as a means of research communication. Practical form(s) of knowledge creation and representation.
5) Raster analysis and environmental applications | F. Martellozzo | 13 June, 9-13 | Raster data structure and interoperability. Interpreting and using environmental geodata. Surface interpolation and zonal statistics. Linear and spatial autoregressive models. Applications with ArcGIS, QGis and Geoda.
6) Causal inference with spatial data | V. Leone Sciabolazza | 14 June, 11-13 15-18 | Using spatial datasets to design credible counterfactual data, control for common shocks, and identify sources of exogenous variation for estimating the impact of various ‘treatments’. Applications with R.
7) Spatial interpolation and regressions | L. Salvati | 20 June, 15-18 | Introduction to spatial interpolation. Regression analysis with spatial data: from OLS to Geographical Weighted Regressions. Global and local applications with ArcGIS and other free software.