In the past few exercises you used QGIS to visualize and symbolize data, analyze existing data, and create new data.  In this module you will use what you have learned about symbology and displaying data to communicate spatial information in a map composition.  Maps can contain spatial information, figures, images, graphs, and other elements to communicate information.  In this exercise you will create a map showing distribution of ozone concentrations in Mexico City. 

In this lesson, you will work with socio-economic, demographic, and air quality data for Mexico City municipalities.  You will derive new data, based on existing attributes, learn how to join non-spatial attributes to spatial data, work with data selections, and learn how to conduct more sophisticated spatial analysis, using different types of data.

Identifying Demographic and Physical Characteristics of Urban Areas

In this module, you will perform many common GIS analytical functions such as proximity and overlay analysis in order to identify which cities are located at a lower elevation and close to the coast.  You will work with elevation, cities, downscaled climate model output in netCDF format and coastline data.  

This is an introductory module for anyone interested in learning QGIS. 

In this first exercise, we will be working with global climate model output from the Community Earth System Model (CESM) in order to map possible future temperature and precipitation change by the middle of the 21st century.  We will map temperature change thresholds and identify which regions may be affected by these changes.  This first exercise will focus on global change.