Presentación y Discusión de los Resultados

analyzing human behavior and perception. For instance, an important char- acteristic seems to be the contextual richness of the provided information. A rich dataset that encompasses additional information such as who took the photo, what was photographed, or when the picture was taken would be better suited than a simple dataset containing just the location of pho- tographs. Therefore, it can be said that the suitability for visualizing and interpreting information depends (to some degree) on the availability of additional, contextual information. Another criterion for evaluating suitabil- ity could be the comparability of data. If data is minimally processed and characterized in a unique but coherent manner (by the targeted audience, thematic focus or the interface of the web application), then data should be easier to compare using algorithms and methods for processing and visualization. The geographic coverage of the provided data can be seen as a further criterion for evaluating suitability. Obviously, a data source is not suited if the area we are trying to assess is not covered by the available data. Vice versa, a data set with a small coverage area, which exactly fits our study area and other needs, may prove a particularly suitable source. Another factor could be user-reach. Usually, in landscape and urban plan- ning (and in the majority of survey-like approaches), planners are interested in a preferably large base of participants, or people on which a study is based. For assessing landscape perception, a suitable data source would ideally encompass not only a rather large portion of the local population, but also tourists and visiting people because the ELC’s definition of land- scape explicitly embraces these groups (see introduction).

While the above-mentioned criteria are important for evaluating suita- bility, they are difficult to assess without actually retrieving and analyzing the data. Often, this may not be possible due to limitations of time or other restrictions. Furthermore, it is not guaranteed that the interface still dis- torts data in such a way that renders it useless for perceptual analysis. For instance, the location of a Twitter tweet (if provided) usually contains the location from where the message was sent. This means it can be assumed

that, often, the original location of photos may not match the (provided) location from where the tweet was sent (see also Hahmann, Purves, & Burghardt, 2014). As an example, consider someone taking a photo during his or her walk to public transit, on a daily commute to work. He or she is more likely to tweet the photo when sitting in the bus (or the metro, etc.), when there is time to add a comment, rather than directly sharing the photo (except if there is a specific reason for). The resulting data may still prove as a suitable base for evaluating general user behavior (e.g. where people think about what). However, the data is perhaps only limitedly suited for evaluating landscape perception because the provided infor- mation is not tied strong enough with the perceived space.

Another perspective, which must be considered when evaluating suit- ability, is the way in which the interface itself influences user behavior and perception. For example, in many services, the users are generally guided or directed towards a common goal (collecting humanity’s knowledge in Wikipedia, or mapping the world in OpenStreetMap). Here, a user’s be- havior is influenced by regulatory mechanisms that limit the value for per- ceptual analysis. This is similarly applicable for photo-sharing communities. For example, the photo-sharing application Geograph requests users “to collect geographically representative photographs and information for every square kilometre of Great Britain and Ireland” (Geograph, 2015). Therefore, it can be expected that the users’ spatial behavior is, to some degree, influenced by these (albeit loosely formulated) overall objectives of the service.

Both previous paragraphs relate to what Antoniou et al. (2010) call ‘in- centives to space’ (p.108). The researchers compared the spatial distribu- tion of photos across four different services (Flickr, Panoramio, Picasa, and Geograph), and found that the spatial patterns differ substantially. They further argue that the rules or incentives that the service itself places upon its users influence how spatial content is distributed. For example, Pano-

ramio and Geograph focus on collecting photographs for the greatest pos-

sible geographic coverage. This specifically encompasses sparsely popu- lated rural areas. Flickr and Picasa, on the other hand, do not explicitly try

to influence how users georeference their content. The functionality for georeferencing photos is additional and not the primary motivation of us-

ers. As a result, the photo locations from Flickr are less evenly distributed

and focus on famous, highly frequented public places. In other words, the resulting data is more likely portraying typical human behavior.

Antoniou et al. (2010) conclude that undirected geographic photo con- tent, such as the data from Flickr, is better suited for analyzing human behavior. They further classify this type of data as implicit and, in contrast, directed spatial content as explicit. Other researchers similarly classify crowdsourced data in actively and passively created spatial content (Doan et al., 2011). From a broader point of view, Richter & Winter (2011) de- scribe that “people will contribute such [spatially implicit] content […] only if the collection is facilitated unobtrusively, casually, or […] calmly,” (p.447) and further emphasize that “communication between users and devices must become as natural and unobtrusive as possible.” This directly relates to the earlier considerations for utilizing technology as a silent background agent for collecting and distributing data (see section 1.3.2). Only if tech- nology (i.e. the interface) is designed in a way that it does not influence the behavior of its users, it is suited for collecting behavioral information.

Based on these considerations, two conclusions may be summarized for selecting suitable sources of crowdsourced spatial photo data for stud- ying human perceptual behavior.

• The submitted spatial information must tie the data strong

enough with the actual location of the perceived space. This is true for photo data only if the original location where the photo was taken is submitted.

• Only spatial photo data that is implicitly collected, without the

user’s activeness towards a common goal or (in-situ perceived) consciousness for collective problem solving, is suitable for an- alyzing general user behavior and perception.

Both points reinforce the choice for large, undirected and open photo- sharing communities such as Flickr, with availability of additional function- alities for uploading and commenting on automatically georeferenced photo content. Among the available sources, Flickr seems a particularly suitable candidate because of its relatively open designed API for access- ing content.

However, it is still unclear to what extent exactly photo taking and photo sharing in communities relates to the perception of the landscape. A more definite statement about the data’s suitability for evaluating human behavior and landscape perception is only possible after closely looking at the specific relations of data and the circumstances under which it was collected. This also means that narrowing down the range of suitable data sources is a non-deterministic process. While some services may be left out early in the process, based on data availability, accessibility or other apparent limiting factors, other services may need a closer review. For the data that is collected through general photo sharing communities such as Flickr, this examination is followed in the next section.