Computers, Society, and Nature

The article on critical GIS strongly emphasizes the subjectivity and human fallibility in GIS. The authors call for a more communicative, open, and inclusive approach. Their concerns appear to lie in the construction of truly participatory GIS that is open to all for exploration and examination at all steps.

I agree with ClimateNYC in that this reading seems to have circled back to our original debate of GI science vs. GI system. Through all of our class lectures, a new (to me) perspective is viewing the problem as an issue of public perception. The authors emphasize a strong divide between experts and novices to GIS, and I agree based on the learning curve of writing custom scripts/functions and variable recognition of the data’s limitations. Aitken and Michel’s call for a more “communicative rationality” seems to question the necessity of such a debate and pushes for a merging of the two. A quick note: at the beginning of the course, I stood on the GI science side of the fence.

The general trend of development in GIS functionality has been to make processes more accessible and user-intuitive. This has been shown in the cutting-edge human-computer interface examples shown by Peck. Simultaneous and interactive multi-user visualizations work towards this goal at the software level. These design challenges necessitate frequent debate and exchanges with users to optimize the systems for as wide an audience as possible.

The life-path of computers is a helpful comparison. They originally started out as expensive, feared research-oriented machines found only in the cloisters of MIT and government agencies. Curious techies got involved with programming and hardware development and became the first “hackers”. The development of games and minicomputers were truly pivotal moments in popularizing computers.The quick adoption of geospatial tools such as Google Maps and the GeoWeb is the popularization parallel for GIS. Application and theory will become increasingly intertwined with more intuitive tools available and may lead to a redefinition of the importance of place (in a digitalized world where the friction of distance has been lessened).  

At the end of the day, the increasing accessibility of data may make GIS an example of a science submerged in the social realm. Computer science still remains, and it is likely that GI science will as well. Concerns about underlying politicization and deceptive “objectivity” will fade as GIS methods become more and more ubiquitous. This reading has further shifted my views on GI science vs. GI systems towards a middle ground, and whether it is realistic to view the science-system as separate components.

– Madskiier_JWong

Often in class, there has been discussion of how much a user of GIS needs to know about the GIScience behind a particular concept or application of GIS. The HCI article by Haklay and Tobon bring this issue to the forefront once more in trying to understand how people interact with computers and GIS in particular.

My impression of this paper and the topic of HCI in general is that experiments, workshops, discussions etc. focused primarily on understanding the difficulties users encountered in working with a GIS and then adapting the software and interface to better cater to the needs of the people. Although this can certainly be helpful for users if the required improvements can be incorporated into a GIS, it does not teach the users the GIScience behind the GIS leaving them vulnerable to making assumptions or conclusions without considering such issues as the uncertainty that could be present in their final product or query response. Teaching the users of these GIScience issues falls under HCI as the method of teaching during an experiment or workshop would likely be very influential on how well participants navigated and used the GIS. As such teaching could be an important area for additional research in HCI.

Catering to different learning styles was not greatly mentioned in the paper although I think this would be another way to improve HCI. GUIs are very helpful in this regard as they cater to visual learners and to some extend those who learn best by doing and trying things as what steps or uses a tool has if it can be visualized. However, those who require explanation or a demonstration to learn may be disadvantaged by GUIs. Tutorials and video demonstrations could be incorporated into GISs to explain how to accomplish particular tasks. Incorporating various learning styles into the GIS would assist users with self-help and reduce, but likely not eliminate, the need for face to face explanation.

-Outdoor Addict

I particularly enjoyed reading Haklay and Tobón’s article, as public participation in urban planning is a topic that I take great interest in. The article notes that especially in an age of increased personal computer usage, empowering users of GIS is crucial to not only improve individual and social development in communities, but to also gain a greater understanding of the design and capabilities GIS systems.

In urban planning, encouraging public participation is often a tricky endeavor. While reasons for being unable to participate vary greatly, one cause includes the inability of individuals to travel to meetings due to, for instance, mobility issues or scheduling conflicts. As a result, it is possible that those who are able to participate represent a relatively small portion of a community. Perhaps improving GIS to be more accommodating to all types of users, from the novice to the expert, will enable participants to move up Arnstein’s “ladder of participation” to a level of greater citizen involvement and power.

The article provides two examples of citizen workshops, which both provide insight into how a GIS can be better designed to facilitate usage. From the first example, I think that one of the main points is the importance of a system’s learnability and flexibility. In this case, learnability refers to “the time it takes a person to reach a specified level of proficiency.” This process will vary for every user, and as such, a GIS tailored to improve urban planning participation may have to include many different features to involve, for example, those who have limited vision. Moreover, flexibility is defined as “the extent to which [a system] can accommodate tasks or environments it was not originally planned for.” This point very much relates to another issue Haklay and Tobón bring up, which is that creators of a GIS must be constantly involved in the design process, which may arguable be a never-ending activity.

From the second workshop example, I think that one of the key issues is the accessibility of a GIS. The development of a web-based urban planning or e-government platform meant that individuals could remotely access information or be included in decision-making. I think that this process has profound implications for those with mobility issues, as already mentioned, in that one no longer has to travel a great distance to participate.

Lastly, while increasing citizen engagement may appear at first to be entirely positive, one has to wonder to what extent should this be encouraged. On the one hand, it may not be possible to completely cater a system to the needs of every individual. In other words, at some point generalizations have to be made, which may hinder how those with varying levels of expertise or capability interact with a system. On the other hand, citizen engagement is often unquestionably considered a positive aspect that should always be fostered. Perhaps this notion itself should be questioned, because, for example, it is possible that too much public engagement can lead to the reduced capacity of organizations to operate efficiently.

– jeremy

Based solely upon the reading, I am not quite sure yet how exactly we need to be able to include temporal analysis into maps. As the article says, the digital map itself is strongly related to its analogue roots. Do we really need to be able to analyse temporally through maps? Aren’t the current discrete time step methods enough? What about the geovisualisation we saw where Hans Rosling visualises 3 socioeconomic indicators over time, on a 2D graph. Non-map based methods could be simpler to interpret than the solutions proposed. The basemap with overlays solution proposed in the article is not a good one. It is, first of all, seemingly restricted to vector data. Secondly, I think trying this approach can make things difficult to comprehend (visually), especially with many time slices. The third solution – the space-time composite could also be tricky. Accessing the data at a certain point in time may be simple enough, but if you’re comparing two or three time slices which all have a very large number of polygons, does it still make sense to visualise it. Don’t people tend to view discrete polygons as different objects? Here, the different polygons can actually represent the same thing, but just iterations of it over time. Won’t it be confusing to therefore view two time slices on the same basemap?
Somehow, I feel that looking at a time series in a graph is more intuitive than the map. We’ve already had the map metaphor ingrained in ourselves, I doubt it would be easy to teach a ‘temporal map metaphor’ to people.

I was also thinking about how this sort of thing would work technically. When data isn’t accessed a lot, it tends to get compressed and archived to save storage space. If we’re doing temporal analysis and grabbing slices from here and there, would we have to just keep all our data uncompressed? While technology has advanced very rapidly since the article has risen, we’re still present with the problem of large datasets, and performing temporal analysis with discrete time slices is still probably a chore these days.

-Peck

Time has been and continues to be a mysterious entity for philosophers and physics. It doesn’t make any sense to talk about things in space with time, since no entity can exist without also having duration as one of its attributes. We also know from special relativity that space is intrinsically tied to time through the Lorentz Transformation, but this really only comes into play when traveling at extremely high velocities. However, there are many ways in which time is considered different from space. One can move in all directions in space but only in forward in time. I really liked Table 2 where Langran and Chrisman (1988) presented the analogies of GIS concepts between space and time. It helped me to clarify my thinking about temporality and how these two concepts can be combined.

While reading the article, I found myself asking the similar questions as Outdoor Addict. What constitutes as a change? And at what temporal scale should these changes be observed? For me, the first question is closely related to cartographic scale and must be considered in tandem with the specific research question at hand. If the research is concerned with the location of maple trees then perhaps a new map tree will constitute a change worth recording. Conversely, if the research question is concerned with the expansion or reduction in the size of a maple tree forest then the growth of one additional maple tree may not be enough to count as a mutation. An “event” in the latter example would be related to a percentage change in the forest boundaries. The cartographic scale one chooses will have a direct influence on the frequency of mutations and thus, on the appropriate temporal scale. This also leads to questions about precision for temporal data. When an event occurs, how precise should the temporal records be? To the minute? to the second? To the hundredth of a millisecond? It is likely that different kinds of events will have different temporal requirements.

Although I liked the map/overlay model the authors proposed, I imagine it is not the best way to visualize the data, especially when many polygons are undergoing mutations and data is collected at very fine scales (i.e. every 5 minutes).  For me, spatial-temporal visualization must involve some sort of animation/video that enables the user to select the speed it is played. This reminds me of the Agent-Based Models we saw in class.

Ally_Nash

Prior to reading Langran and Chrisman’s article, my understanding of temporal geographic information was limited to time lapse videos of static snapshots, which visually display change. After reading the article, however, I was able to better comprehend the importance of temporal models that enable direct comparisons of how objects are changing. Models such as these allow for a greater understanding of how change is occurring at specific times, whereas snapshots seem to merely illustrate the general notion of change.

Outdoor Addict questions how decisions are made as to what constitutes an event and I think this is a valid concern. In abiding by data storage limitations, for example, we may deem a change to be irrelevant and discard it. However, what if this change is considered to be important at a future date? Perhaps the idea of examining snapshots is still holding me back—certain technologies that allow changes to be constantly tracked might need to be considered to a greater degree. In thinking about this, a parallel may lie in a Geography 407 class discussion, where dialogue revolved around sensors designed to continually track animals in forests. In this case, every motion that is detected is recorded, while durations of no motion are not. Can anyone else think of similar examples?

From the motion detection example, yet another concern arises—there will inevitably be information that sensors cannot detect. In addition, relating to the lecture on scale, the issue is not merely about deciding what objects to include, as previously mentioned, but it is also about determining what level of detail is appropriate. In other words, there is such a thing as too much information. The easy way out would be to yet again rely on the “future technological advances will render this concern irrelevant,” argument, but due to the inescapability of uncertainty, I posit that context and judgement are two of the most important considerations.

Lastly, in answering CimateNYC’s question about the distinction between real time and database time with regards to streaming data, Madskiier_JWong states that information may be incorrect or incomplete and in need of updating at a future date. I would like to add to this that technical issues often arise when dealing with streaming of data. For example, glitches in communication systems or backlogs of data can result in differences between real time and database time. This type of information is valuable, however, as it enables insight into how systems can be better designed.

– jeremy

The user-centered design proposed by Haklay and Tobon (2003) is very close to my own topic of critical GIS in that both topics recognize humans are an important factor in how GIS will be used and valued. Thus, historical, cultural and social aspects play a crucial part in the adoption and success of GIS. I especially appreciate how the article highlighted the fact that the improvement of GIS requires an “iterative process” between the tool and users (society). To maximize the practical usefulness of GIS, researchers must keep in mind that a “good” computer program cannot be judged solely on the number or the complexity of the application but rather on how “usable, safe, and functional” (579) the application is to users.

It makes logical sense that HCI research picked up momentum in the 1980s when personal computers became more affordable. However, I wonder what are differences between humans-computer interaction and human-computer interaction. Or in other words, between how groups interact with technology and how individuals do. Perhaps, decisions that involve a group of people (often in PPGIS), users may tend to listen to the one person with the most “expertise” and disregard their own knowledge of the application. The workshops described in the paper involved a user, a facilitator, and a “chauffeur”. I wonder if people would have interacted differently with the application if they were allowed “free-play” on their own after a short demo of the basic tasks.

Furthermore, I think we should carefully consider what tradeoffs are involved between usability and functionality. By making an application more intuitive and easier to use, are we losing important functions that should be included despite its complexity? Ultimately, this judgment depends on the set of tasks intended to be carried out by the application. However, they are not always easy to predict. For the purpose of planning, shortest path analysis may be extremely insightful, although the results may be difficult to intercept given all the assumptions that goes into the analysis. Moreover, uncertainty will definitely be another tricky area to convey. Therefore, one challenge is to figure out which types of tools should be included in a GIS for naïve users so that the system is both not limiting and not overwhelming.

Finally, the article made be think about the potential backlash of some HCI research. For example, I can imagine that disadvantaged communities may not want the results from the workshops to be published due to, perhaps, the misguided belief that making the system easier to use is equivalent to “dumbing it down” and the negative social stigma that follows. Therefore, the decision for including an opting-in or an opting-out option in HCI research is a sensitive one since this option will have dramatic effects on the number of participants. Personally, I favor having the initial settings to automatically include users in the study because although most people probably want to help and improve on a system that they use, the hassle of opting-in is enough to deter most people to becoming participants. However, due to privacy issues mentioned by the author, the application should explicitly warn users of this option before they can start using the application.

Ally_Nash

Haklay and Tobon stress the need to design both software and hardware that is most convenient for an identified user group and their goals. Cases such as Braille displays for computers are clear examples of a positive, improved human-computer interface. However, when applied to a complex analytical field such as GIS, HCI studies run into the issue of defining the user group. There is no immediate common attribute shared amongst all GIS users unlike the condition of being blind for Braille-users.

The authors are instructive when they indicate that identified difficulties with GIS are more human-based rather than technology-based. The challenge with users of GIS is that experience with the software varies wildly, and some may be unaware that they are a part of a GIS analysis (as a source of information or doing it themselves). It is tempting to simplify displays to extend the potential audience of GIS, and we have seen this in many GeoWeb 2.0 apps and platforms such as Geocommons. Geocommons’ site boasts that users can “Easily create rich interactive visualizations to solve problems without any experience using traditional mapping tools”. Web 3.0 continues in this direction by offering semantic searches and increasingly mobile applications and devices. In the drive to eliminate the distance between computers and humans however, it becomes easier for others to manipulate and use naïve users as data sources. The increased digitization of our world has sparked debates about privacy and perceived privacy issues.

A question HCI studies could ask then is how to best segment GIS users into groups. Can the semantic intelligence of Web 3.0 be used to map common thought processes/links to better identify common goals? This understanding can be used to direct intermediate GIS users to resources that explain the basics of analytic functions, while underweighting papers that describe advanced processes (I frequently ran into advanced “Petri Net” algorithms when searching for the general history of temporal GIS). Are there alternatives to segmenting users by “skill”, which is a vague measure and largely prescriptive?  

-Madskiier_JWong