If you don't have the money or means to purchase your required GIS data, or if the data doesn't exist, then you may need to collect the data yourself. If your goal is to openly share the resulting data with the public, then you may consider enlisting the public in your data collection efforts. VGI and crowdsourcing are two concepts that come into play when enlisting the public or non-domain experts in the collection of GIS data.
VGI
In 2007 Michael Goodchild published a paper in which he elaborated on the idea of volunteered geographic information (VGI). This kind of data is collected by citizens acting as sensors to gather information about the world around them. The citizens then feed this information into a centralized GIS database, often employing a user interface that has been simplified to the degree that specialized training is not required.
VGI has since become a hot term in geographic information science as thousands of people contribute to the OpenStreetMap digital map of the world (discussed later). Governments evaluate the possibilities of creating "citizen reporting" apps that allow anyone to upload information about potholes, graffiti, etc., with the objective of bringing them to the attention of local authorities.
Crowdsourcing
Crowdsourcing is the idea of using the power of a crowd to collect data that is too vast, heterogeneous, or expensive to be collected by other types of sensors. Consider how many people you would have to hire in order to write an encyclopedia with 30 million articles in 250 languages. The crowdsourced website Wikipedia has been able to create a project of this scope solely through crowdsourcing. Other applications of crowdsourcing include combing remotely sensed imagery to find lost people or vehicles, recording old weather measurements from ship logs in order to create climate databases, and transcribing census records to create searchable genealogical indexes.
Crowdsourcing is a particularly good fit for tasks that require an element of human cognition not easily performed by machines. Amazon has even made a business out of crowdsourcing through its Mechanical Turk service. This allows you to hire a crowd of unknown individuals to perform tasks for a particular fee, often pennies for each task. Using an architecture that is conceptually similar to cloud computing, you can scale the task up to as many volunteers as you need.
The concept of crowdsourcing is a good fit for VGI, particularly when a vast amount of data must be collected under time pressure; however, not all VGI projects use crowdsourcing. Some of them are focused on gathering information from a small sample of people or a focused group of domain experts. Cinnamon and Schuurman (2012), for example, enlisted a set of emergency medical professionals at a single hospital to submit information about the locations of local auto accidents. Using tablet computers, the paramedics tapped the screen or typed an address to record the locations of the accidents. The researchers called this type of guided process facilitated VGI (f-VGI), after Seeger (2008). These readings are available in the Lesson 9 module on the course Canvas site if you're interested in learning more about them.
The human factor
The introduction of humans into the sensory element of data collection presents some interesting advantages and challenges. One advantage is that there are a lot of humans potentially available. Some of them even appear to have a lot of time on their hands! This means that tasks can be scaled up quickly and the data can be collected (or corrected) in a hurry. Humans also have the ability to care about projects and become passionate about them, increasing the amount and quality of data collected and creating an endless source of free organization and labor. It's not always necessary to hire the Mechanical Turk when you're enlisting people in a project they really believe in.
However, humans, by nature, make mistakes in some ways that computers may not. They get tired, they commit typos, they make subjective judgments, and so forth. Furthermore, the technical skills and physical infrastructure (e.g., Internet access) required for VGI participation may not be uniformly distributed throughout your study area. Finally, humans carry particular biases and interests that may skew the types of data collected.
Anyone employing VGI in scientific research or mission-critical applications should be aware of these limitations. The next section of this lesson provides some examples of how these advantages and limitations of VGI have affected OpenStreetMap.
References
- Cinnamon, J., & Schuurman, N. (2013). Confronting the data-divide in a time of spatial turns and volunteered geographic information. GeoJournal, 78(4), 657–674.
- Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211–221.
- Seeger, C. J. (2008). The role of facilitated volunteered geographic information in the landscape planning and site design process. GeoJournal, 72(3-4), 199–213.