Data Collection for Social Scientists
About
One of the main challenges in using digital data in the social sciences is knowing how to collect it: where to find existing data, how to judge whether it can be trusted, and how to gather original data when nothing suitable exists. This course covers both sides, from data repositories, the U.S. Census Bureau and geocoding to satellite imagery and field data collection with a phone.
| Module | What you will learn | Tools |
|---|---|---|
| Introduction to Data Collection | When to use existing data or collect your own; vector and raster data; judging data quality; FAIR data | Concepts |
| Finding Data in Repositories | Where to find free vector data, how to read metadata, and how to load data from the web | Web browser, QGIS |
| Data from the U.S. Census Bureau | Census geography, the Decennial Census and the ACS, margins of error, and the Census API | Web browser, Python notebooks |
| Geocoding and Adding Spatial Context | Turning addresses into points, checking match rates, and downloading features from OpenStreetMap | Python notebooks |
| Acquiring Imagery | Scanned historic maps, declassified CORONA imagery and Sentinel-2 satellite data | Web browser, QGIS |
| Collecting Data in the Field | Methods and apps, designing a schema, GPS accuracy, ethics and quality checks | Concepts |
Several modules use one example: access to grocery stores in Lafayette and West Lafayette, Indiana. The grocery access case study in the Case Studies course completes it, with a QField field mapping demonstration and a network analysis in QGIS.
Prerequisites
- A free CyberFaCES account to run the notebooks
- A free Census API key (see Setup below)
- QGIS installed on your computer (see Setup below)
- For the notebooks: basic Python, such as running cells and working with a
pandasDataFrame - No prior GIS experience is needed
Objectives
- Find, evaluate and cite existing geospatial datasets
- Download census boundaries and statistics with the Census API, and judge their reliability
- Find and download historic maps and satellite imagery
- Geocode addresses and add context from OpenStreetMap
- Design a field data collection workflow with consistent, well-documented data
This course 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.
| Type | Course |
|---|---|
| Level | Expert |
| Length | 4 hours |
| Topics |
python QGIS Concepts Community: Spatial Turn |
Modules
-
Introduction to Data Collection
Module 1 • 35 minutes
When to use existing data or collect your own; vector and raster data; judging data quality; FAIR data
-
Finding Data in Repositories
Module 2 • 35 minutes
Where to find free vector data, how to read metadata, and how to load data from the web
-
Data from the U.S. Census Bureau
Module 3 • 1 hour
Census geography, the Decennial Census and the ACS, margins of error, and the Census API
-
Geocoding and Adding Spatial Context
Module 4 • 1 hour
Turning addresses into points, checking match rates, and downloading features from OpenStreetMap
-
Acquiring Imagery: Scanned Maps and Satellite Data
Module 5 • 50 minutes
Scanned historic maps, declassified CORONA imagery and Sentinel-2 satellite data
-
Collecting Data in the Field
Module 6 • 40 minutes
Methods and apps, designing a schema, GPS accuracy, ethics and quality checks
Instructor
Aniket Dev Roy
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