Summary and Schedule
Every geospatial project depends on data that is organized, documented, stored safely and checked for errors. This module covers the principles of managing geospatial data during a research project: how spatial data is structured, which formats and coordinate systems to use, how to design a dataset before collecting it, how to organize, version and back up files, and how to judge data quality.
The module is conceptual: it has no programming or software exercises, though it refers to QGIS where that helps. Each episode includes examples and challenges with solutions.
| Episode | What you will learn |
|---|---|
| Introduction to Data Management | The research data lifecycle, why geospatial data needs extra care, and data management plans |
| Geospatial Data Models | Vector and raster data, topology, resolution, and choosing a representation |
| Geospatial File Formats | Shapefile, GeoPackage, GeoJSON, GeoTIFF and others, and which to use when |
| Coordinate Reference Systems | Datums, geographic and projected systems, EPSG codes, and defining versus reprojecting |
| Designing Your Data | Data dictionaries, field types, domains, unique IDs and relationships |
| Organizing, Versioning and Backing Up | Folder structures, file names, portable projects, version control and the 3-2-1 rule |
| Data Quality | Elements of spatial data quality, geometry errors, and quality checks |
Prerequisites
- No prior GIS or programming experience is needed
- Familiarity with spreadsheets and with files and folders on a computer
The companion Data Curation module covers what happens so data can be reused after a project ends: metadata, documentation, preservation, sharing and ethics.
This module is part of Enabling the Geospatial Turn in the Social Sciences, a set of training modules developed at Purdue University with support from the National Science Foundation.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction to Data Management |
What is research data management, and why does it matter for geospatial
work? What are the stages of the research data lifecycle? What is a data management plan, and when is one required? |
| Duration: 00h 40m | 2. Geospatial Data Models |
How do computers represent places and features? What are vector and raster data, and when should I use each? How does map scale affect how a feature should be represented? |
| Duration: 01h 25m | 3. Geospatial File Formats |
What are the common file formats for vector and raster data? What are the limitations of the shapefile? Which format should I use to work with, to share, and to archive my data? |
| Duration: 02h 10m | 4. Coordinate Reference Systems |
What is a coordinate reference system, and why does every layer need
one? What is the difference between geographic and projected coordinate systems? What are EPSG codes, and which ones will I meet most often? Why do layers sometimes appear in the wrong place, and how do I fix it? |
| Duration: 02h 55m | 5. Designing Your Data |
How do I decide what layers and attributes a project needs? What is a data dictionary, and what goes in it? How do field types, domains and unique IDs keep data consistent? How do tables relate to each other and to map layers? |
| Duration: 03h 45m | 6. Organizing, Versioning and Backing Up |
How should I organize the folders of a geospatial project? What makes a good file name? How do I keep track of versions without losing work? How should I store and back up my data? |
| Duration: 04h 30m | 7. Data Quality |
What does “quality” mean for geospatial data? What kinds of errors are common in vector data? How can I check and improve the quality of a dataset? |
| Duration: 05h 15m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
This module is theory only: there is nothing to install and no data to download. You need only a web browser to read the episodes.
Some episodes refer to QGIS, a free and open-source GIS, to show where a setting or tool can be found. If you would like to follow along, you can install it from https://qgis.org (choose the Long Term Release). The other modules in this curriculum (Data Collection, Data Analysis and Data Visualization) include hands-on QGIS and Python exercises.
To practice the challenges, it helps to have:
- a spreadsheet program, for drafting data dictionaries
- a project of your own (or an imagined one) to plan as you go