Computers, Society, and Nature

Using eye movements as a human-computer interface HCI, as Poole and Bali point out in their article, has many advantages. Perhaps the most useful advantage is its applications for disabled people who would not otherwise be able to use a keyboard or a mouse. The way eye movements has made great strides; what used to need very invasive methods can now be accomplished with an infrared camera.

Detecting eye movements is one thing, but interpreting these metrics in order to infer some form of thought is something entirely different and more complex. This process is built off the eye-mind hypothesis, which is exactly what it sounds: if your eyes are drawn or fixated on something, this provides some insight into the thought process behind these actions. These conclusions can then be used to analyze and improve the design of interfaces. The applications from this process are endless – ranging from a better cockpit interface to reduce pilot error all the way to an improvement of doctors in performing medical procedures.

The main difficulty in this technology lies in interpreting the various eye movement metrics. If someone blinks a lot is this indicative of a low work load, or do they simply have dry eyes? As the authors point out, we are limited by technology to process the enormous amount of data generated, and it must be done at a reasonable costs. With current technology, algorithms are in need of constant recalibration. Another con is that for every handicapped person this technology may help, it also may exclude people with lazy eyes or those in need of hard contact lenses.

Pointy McPolygon

After reading Elwood and Lanter’s articles I had the image of a complete shift. With his user-centered interface, Lanter was concerned with the interaction between the user and the system. The user-centered interfaces are so developed now. Someone mentioned the perfect example in this blog; two years-old baby are able to use iPads! Can it be more user-friendly than that? Interfaces are so user-centered that people generate an enormous amount of heterogeneous data, and are using geotagging and geoblogging. I think that users are directly interacting with the system now and not only with the interface. Furthermore, users are interacting with each other. On the other end, ‘system designers’ (in Lanter’s words or ‘GIScientists’ in Elwood’s perspective) have to figure out ways to manage this phenomena. The problem has turned back to the ‘system’. It is interesting how the ‘system’ from Lanter’s point of view refers to the software system design. Although what I mean by ‘system’ refers to a broader point of view and is about social, political, technological systems (Oh no not the GIS tool/Science again!!!). Geovizualisation technologies have an impact on all spheres of the society. I’ll give a few examples of that (from Elwood’s paper). Political: renaming places and the negotiation of colonial and postcolonial histories or the promotion of activist activities; Social: posting information on bad neighbors!; technological: interoperability of heterogeneous data, transformation of meaning when different people work with the data….

S_Ram

The article by Sarah Elwood touches on issues relating to how the evolution of spatial data has spurred new (or continue to fuel existing) questions regarding how we can begin to handle these datasets in such a way that we can analyze them and make some meaning from it. As new geovisualization technologies emerge, and as long as people continue to freely post geospatial information that can be collected, it poses a double edge sword. Information about the livelihoods of people on a micro level has never been so accessible, yet challenges we face with new innovative geovisualization technologies is what Elwood calls a conundrum of “unprecedented volumes of data and unprecedented levels of heterogeneity” (Elwood, 2009). By applying GIScience theory and research such as assessing the ontology of the data, mathematical algorithms, visual modeling techniques etc. has contributed to research regarding data integration, and heterogeneous qualitative data – issues that extends beyond new geovisualization technology.

Still, even with bigger, better, newer algorithms that can automate data integration we must recognize that categorizations in a particular datasets is context dependent. Therefore, labeling something can carry a lot of weight. What people define as a “bad neighborhood” can have a multitude of meanings (bad as in high crime, noise, a particular demographic that you do not mix well with?), which can have high social and political implications. If datasets are to be combined, then finding a proper categorization scheme for the dataset must also be thrown into the mix of challenges of data integration.  Perhaps this is where metadata can really shine through if it can provide the context of how the dataset was derived and defining the categories its chosen. I don’t know about you, but my appreciation for “data about data” has definitely grown since GEOG 201.

-tranv

I often find papers like this can be very theoretical and I can struggle to grasp the real crux of the research, but I found that Elwood presented some fairly theoretical topics clearly by providing examples. [something about the other paper]

As spatial components of new tools and technology become increasingly ubiquitous, I find that there is now an expectation for data to be presented spatially, and some disappointment when it’s not available. These papers made me think about how I expect spatial information to be easily available and consumable for me. For example, just discussing where to go skating with some classmates, we were irritated with the idea of having to use a website that just listed the rinks with their general location (http://ville.montreal.qc.ca/portal/page?_pageid=5977,94954214&_dad=portal&_schema=PORTAL) and were thrilled to find the alternative which provided the skating rinks mapped across the island (http://www.patinermontreal.ca) – to be honest, I was fully expecting a mapped version to be available and would have been shocked if there wasn’t.  Apartment-hunting without Padmapper or similar is pretty miserable, since “where” is usually the most important variable in any potential home. Clearly non-GIS users have grown to have a higher level of spatial literacy with products from Google and spatial functions in technology, but they are also coming to expect consumable spatial information to be available.

-Kathryn

This paper raises some interesting points about what interfaces are trying to do – it might seem like they are just trying to create an attractive user experience, but in an ideal world they should allow a user to learn the software through “trial and error” and ultimately come to the correct conclusions about how the software functionality works. Seems like high expectations, but clearly useful to design software for the people who will be using it!

This paper was written a fair time ago. I wonder how many GIScientists and users of GIS have continued to find the developments/improvements in GUI for GIS software fairly unsatisfactory, and turned or returned to spatial analysis through programming? I almost never touch an ESRI product anymore – I’d usually rather work through a problem in a combination of postGIS and R. Yes, partly it’s for automation, but to be honest I do find the software can be unintuitive sometimes. However, teaching postGIS to a computer programmer with no background in GIS (with any “S”) showed me that his grasp of databases and analysis were clearly strong but the lack of visualization really did hinder understanding of the analysis tools and the concept of projections – even in someone with a highly technical background. While programming is incredibly useful in GIS and spatial analysis, the visualization aspect is still crucially important too, even for learning. And (maybe because I was trained visually) I never fully trust my results until I “sanity check” them in a visual way.

Random thought – the “mapping” problem is very interesting and of course relevant to GIS, and it seems like the touchscreen has changed this a lot since it’s so intuitive. I’ve noticed several times anecdotally that very young children can work iPads so early as age 2, while they lack the ability to work a mouse on a regular computer for years. I’ve always assumed it’s an issue of dexterity but maybe it’s more to do with this “mapping.”

-Kathryn

Rinner, from the beginning, claims that user-generated information is not considered a “serious” pursuit. To test his theory, he scrapes user-generated text for geographic references, only to retroactively apply a geo-reference to the post. The researchers, who are responsible for geographically referencing the post, strip away the users involvement in the geographic context. This can introduce a large source of error into the data. Consider, on page 14, a marker’s placement corresponds to the “label in Google’s “map” view.” In the event that a user refers to a general area, choosing to place a label greatly reduces the dimension of the users perspective. Much like a home is not a coordinate, but a structure, and the surround property, land, or neighborhood. Had the researchers allowed the users to actively tag locations to their posts, they would have gained far greater insight into the users intentions.

The creation of a Master Plan cannot be accomplished using the scientific method. Studies can find correlations, in certain areas, but many fall apart when the study area is changed. This may be due to social and cultural differences. In that light, and working on the basis that people are subjective creatures, a crowd-sourced master plan can gain from further empowering the constituents. In the case of the Ryerson Master, providing the user with the ability to choose the location of their post’s geo-tag may have developed another dimension for the study.

As a side note for crowd sourced urban planning, I do not think that even high-scale maps are sufficient. The city is rarely experienced from the air. With that in mind, I am in favor of developing forum in which people can experience the city from the ground. To my knowledge, no such technology yet exists.

AMac

In their paper on the uses of Web 2.0 to support spatial decision making, Rinner et al. address one of the problems that M. C. Er identified in DSS 20 years earlier: group decision making. By using PGIS as a source of data, making decisions for a group of people is made easier. In order to test this, the authors designed Arguumaps, where users could make geographically referenced comments about campus/city life or the Ryerson identity. Though some limitations were present, the power of PGIS is clear in its application of online mapping.

Whether this data is useful in DSS is a whole other questions. The authors argue that this case study shows that it could be a useful tool by having users vote on preferred geographically reference locations. For example, instead of posting comments about their favorite restaurants, users could cast votes on restaurants over a whole array of criteria, thus helping other users pick which restaurants they would want to go to.

Presently, 5 years after this article was written, google implemenets these services on their maps. Great strides have been made in mapping as a support to spatial decision making however much ambiguity still exists over the exact definition of DSS.

Pointy McPolygon

The Rinner et al article explores the intersection of spatial decision support systems (SDSS) and volunteered geographic information (VGI) with their development and piloting of an argumentation mapping web 2.0 application intended to solicit and map spatially-grounded citizen input and discussion. 

Rinner et al single out planning processes as areas where such applications have potential.  By tying user discussions and feedback to explicit locations, the responses can serve as a qualitative gauge by decision-makers as to the relative importance of criteria within a SDSS model.  There is even the opportunity to add a quantitative element to such conversations by integrating a positive/negative rating system to the threaded messages (like a spatial Reddit!). Overall, Rinner et al’s project aims to find effective ways to crowdsource planning decisions and amplify the ideas of citizens using emerging web 2.0 technology.

Argumentation mapping and other geospatial mashups’ applicability to decision support are not without their concerns. As we learned the hard way in GEOG 407, a #neogeoweb 2.0 application is only as good as its programmer, and can only ever be as good as the underpinning API.  Four years after this paper’s publication, many of Rinner et al’s suggestions for improvements to the API are still unaddressed by Google.  Formal integration of the data collected in ArgooMap with SDSS models is still a long way off: for now, it is limited to qualitative uses.  In addition, these digital consultative avenues, even if they are improving in terms of end-user functionality, may still be exclusionary: from the digital divide, to gender dynamics on the internet, to the less affluent simply having less free time to invest in online civic discussion, there is a role for critical geographers and GIScientists to suggest ways for SDSS and its related tools to be more inclusive and thus more likely to be truly democratizing the decision-making process.

-FischbobGeo