Posts Tagged ‘Geovisualization’

Significance of Geovisualization

Monday, October 27th, 2014

A visual representation of data has always been known to be very helpful to exhibit information to the viewers and non-expert users in any field of study, particularly for study requiring data analysis. For instance, a map is an excellent example of a visual representation of the world or part of the world itself. In the Kraak’s article, the emphasis was on the usefulness of geovisualization and it is also argued that other graphic representations aid in simulating visual thought process, using the Minard’s map of champagne de Russie 1812-1813 with some geovisualization techniques.

 

In this posting I’d like to mention a couple of other examples that underline the importance of visualization to explore data, as well as the advantages from it. Graphic visualization can often reveal patterns that are not necessarily seen on tables and charts. For instance, a thematic map is known for its ability of displaying a connection between specific themes and geographic areas. This type of visualization highlights on spatial variations of one or a small number of geographic distributions. An example of thematic map can be found in the following link:

http://1.bp.blogspot.com/_RpkOLpWs7KA/TFOt-UruAvI/AAAAAAAAAG8/4U1xBqz_d38/s1600/cartogram.jpg

Another visualization tool is an application from ESRI, called Story Map Swipe. It is a tool used to create the story map more interactive and one can use this Swipe tool back and forth to compare one map to another very easily and quickly, therefore the impression one receives may differ or accentuated. An example of the Swipe can be found in the following link:

http://tmappsevents.esri.com/website/swipe-sandy-custom/

 

These types of visualization of data will definitely stimulate the visual thought process of any viewer and user compare to represent same data as a simple table or charts. Furthermore it may lead to a hint for new hypothesis or even an innovative solution to a problem.

ESRI

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Marginalized communities and qualitative data

Friday, February 10th, 2012

Throughout reading Elwood’s article, marginalized communities came to mind, mostly because of the certain level of rigidity in her review of emerging geoviz technologies. I found it particularly interesting of the comparison that was made between ‘public’ and ‘expert’ technologies, where the status-quo of GIS comprises of the ‘expert’ (standardization of data) realm is threatened by the ‘public’ (wiki, geo-tagging, Web 2.0, VGI) realm. I agree with Andrew “GIS” Funa’s point on standardization. What is our inherent need to do this with all of our data? And what happens when standardization cannot be applied? More specifically, how relevant is an expert technology to marginalized communities if no one is willing to apply that technology?

There is a mention of ‘excitement’ and high hopes, which authors have for new geoviz technologies to represent urban environments; however the article does not expand any further. The article does, however, note the term ‘naive geography’ and its “qualitative forms of spatial reasoning” (259). Presuming one can safely state that representing marginalized populations is a qualitative problem, ‘expert’ technologies tend to not focus on these issues. According to Elwood, qualitative problems are more difficult than quantitative problems, “where exact measurements or consistent mathematical techniques are more easily handled” (259). So what do we do about unstructured, shifting, context-dependent human thought? So should we not try to digitally represent these data because it may be too difficult to decipher? To draw linkages and discover patterns? Will qualitative data always be at a loss because it will not fit an exact algorithm? I think we should take the spark of hope that MacEachren and Kraak gave us and strive beyond some of the limitations outlined by Elwood.

-henry miller

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So many challenges, so many opportunities

Friday, February 10th, 2012

MacEachren and Kraak address the notion of visualizing the world and what this exactly entails. The article was written over a decade ago and is still as relevant today as it was then, and centuries ago. “…80 percent of all digital data generated today include geospatial referencing” (1). A powerful sentence that altered my perspective on geographic visualization (geoviz), when I first read this article a few years ago. There is so much to explore, to reveal; the sky is the limit.  Geoviz is about transformations and dichotomies; the unknown versus known, public versus private, and high versus low-map interaction (MacEachren, 1994). It aims to determine how data can be translated into information that can further be transformed into knowledge. MacEachren and Kraak provide a critical perspective into the world of geoviz and its vexing problems. They do a good job in convincing us that a map is more than a map. Maps have evolved by means that “maps [are] no longer conceived of a simply graphic representations of geographic space, but as dynamic portals to interconnected, distributed, geospatial data resources” (3). “Maps and graphics…do more than ‘make data visible’, they are active instruments in the users’ thinking process” (3).

Out of the many challenges that we still face (also by Elwood) there are some that have been tackled successfully. The one I will focus on is ‘interfaces’ in relation to digital earths. Arguably, I believe that no one would have imagined the progress made with digital earths, especially Google Earth (GE) back in 2001. GE remains untouchable in its user-friendly display, mash-ups are through the help of Volunteered Geographic Information(VGI), including programmers who are contributing free software, interoperable with GE (GE Graph, Sgrillo). However, the abstract versus realism issue is relevant as ever. The quality and accuracy of the data may be low yet the information visualized will look pristine, and vibrant, thus deceive the user to believe otherwise. How do we then address levels of accuracy? Abstraction? Realism? Thus, we have challenges but we also have progress. MacEachren and Kraak’s article refocuses our attention on the pertinent obstacles that we should be mindful when exploring, discovering, creating or communicating geoviz. To move away from the “one tool fits all mentality” (8). To unleash the creativity from within.

MacEachren’s simple yet powerful geovisualization cube.

 

-henry miller

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Heterogeneity in Geovisualization Research

Friday, February 10th, 2012

In the Paper of Sarah Elwood 2008, one of the most important features of current Geovisualization research is concluded as “heterogeneity”. First, the sources from which geographic information are collected for visualization is heterogeneous. Nowadays, users can publish their geospatial information through GeoWeb applications, mobile technologies, and social network media. Moreover, remote sensing technologies continuously provide earth observation data with fine spatiotemporal and spectral resolution. And different geospatial databases open another portal for geographic information science research.

Secondly, the geospatial information with Geovisualization becomes heterogeneous. Currently, Geovisualization is no longer limited within professional community, but users can customize it with well-designed Geovisualization tools. Due to different user interests, the geospatial information that they choose to visualize are heterogeneous. For example, GoogleMap can display the information about Chinese restaurants in Montreal, but users still need to access restaurant discussion board to determine which one they will go for diner. All those geospatial information is displayed to users via different Geovisualization tools.

Thirdly, the usages of the heterogeneous Geovisualization tools are heterogeneous. Some GeoWeb are developed for government management, so the geospatial information is carefully analyzed for decision-making support. For emergence system, we require the geospatial data are collected and updated in real-time and geographic location information should be provided with high accuracy. Although these two systems might be developed based on GoogleMap, their architecture are quite different due to their heterogeneous usage.

Finally, the users of Geovisualization system are also heterogeneous. They can be travel agency, business analyst, research scientists and so on. The heterogeneity of Geovisualization has greatly increased the complexity of GIS research, which require corresponding heterogeneous research methodologies.

–cyberinfrastructure

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Cognition’s Role in Geovisulation Research Programs

Thursday, February 9th, 2012

In their article outlining the research challenges faced by the field of Geovisualization, Alan MacEachren and Menno-Jan Kraak pose the problem of cognition as a direct relationship between how external, dynamic visual representations can “serve as prompts for creation and use of mental representations” (7). They note that the existing lack of paradigms for how to conduct research into the cognitive processes at work in geovisualization projects or into their usability as a major problem in this field. However, I wonder if this doesn’t put the cart before the horse.

Much of the existing research into geospatial cognition seeks to understand how the human mind works in processing spatial data, particularly how such data is acquired, processed and translated into knowledge. Before we can hope to create user interfaces utilizing geovisualization techniques, shouldn’t we follow this approach and attempt to understand how these digital interfaces might impact cognition of spatial data? The authors set out the goals of establishing a cognitive theory that supports and assess the usability of “methods for geovisualization” and those that take advantage of dynamic, animated displays (7). Yet this feels like we are trying to support the cognition of a new field without trying to understand how it actually impacts cognition.

The danger of such an approach is that we are simply writing theory to support pre-articulated goals. Shouldn’t we instead start from a blank slate and then ask what types of cognitive impacts geovisualization might have for how the public processes geospatial data? For example, one researcher into geospatial cognition found that people who learn geographic data from maps as opposed to experiential data (as in navigating an environment) often had better recall of the data and more accurate perceptions of spatial relationships. Shouldn’t we try to first figure out how cognition of geovisualized data fits into this paradigm before just drafting a research agenda for it?

–ClimateNYC

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Geovisualization: room for collaboration and virtual environments

Thursday, February 9th, 2012

The article by MacEachren and Kraak’s is a great article to read because not only did they highlight important challenges in geovisualization but also attend to overarching issues and what kind of actions are needed to address them (which I particularly enjoyed). I strongly agree with the authors when they point out that if we are to meet these challenges, there needs to be an increased emphasis on collaboration between disciplines and countries. Further, researchers themselves must appreciate other perspectives and make a real effort to understand how other disciplines understand the issue by keeping up with “complementary research” and getting involved with collaborative work.

The article by MacEachren and Kraak’s is a great article to read because not only did they highlight important challenges in geovisualization but also attend to overarching issues and what kind of actions are needed to address them (which I particularly enjoyed). I strongly agree with the authors when they point out that if we are to meet these challenges, there needs to be an increased emphasis on collaboration between disciplines and countries. Further, researchers themselves must appreciate other perspectives and make a real effort to understand how other disciplines understand the issue by keeping up with “complementary research” and getting involved with collaborative work.

One area of research related to geovisualization that sparked my interest is the potentials of virtual environments. The tension between the need for abstraction or realism in visualization is intriguing to me and would be something I am interested in to explore in more depth. Although abstraction is appropriate/useful for certain problems, the experiential qualities VE offers could be very beneficial for geographic decision-making and alternative thinking, especially since the scales of some geographic problems are very large (climate change) and thus more difficult to envision. Further, a realistic geovisualization of our environment with dynamic access to the information on the Internet could prove to be extremely valuable for educating students.

-Ally_Nash

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Sptatial cognition and geovisualization

Thursday, February 9th, 2012

The topic of spatial cognition (and closely related, naïve geography) was relevant to the issues discussed by both Elwood as well as MacEachren & Kraak. The ways humans learn geographic concepts and reason about space is required for geovisualization to “handle qualitative forms of spatial knowledge” (Elwood, 259) and for building “human-centered approach to geovisualization” (MacEachren & Kraak). I believe developments in this field are urgently needed and have far-reaching implications not only for geovisualization but also for building ontologies. In fact, Smith and Mark also touch on the lack of research by stating “We know of no data on the ages at which young children acquire or master the basic concepts of naïve geography and the associated kinds of objects…” (10).

With a growing amount of geo-located SMS, pictures and videos, how can we process these qualitative information without grasping how it is that the contributors comprehend their surroundings? Since users are also contributors in the Web 2.0 environment, it is evitable that we must dedicate resources to understand these users. For instance, how do people learn and remember directions? How do people from different cultures use landmarks, whether natural or man-made? Only by understanding how people build their relationship with geographic space can we take more initiative in the geovisualizing process and derive meaning out of spatial descriptions (near, far..). As a side note, I imagine it would also be important to first identify what the source data was initially intended for because the context could influence how spatial forms are perceived and described. For example an emergency text message and a text message trying to rent out an apartment could be very different — the first message is influenced by panic and thus, the users might have a distorted conception of distances whereas the second message is motivated by the intention to sale and thus everything might be described as “near” the apartment.
-Ally_Nash

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35mm Photos are to Digital Photos as Paper Maps are to GIS

Thursday, February 9th, 2012

I agree with sah. I’m excited about geovisualization! It is truly amazing how maps have become a dynamic user interface! Even when I first started studying Geography several years ago, maps on paper were almost obsolete. On some levels I want to feel nostalgic, as I do for the era of film cameras, but ultimately GIS is far more practical. In his 1965 article titled New Tools for Planning, Britton Harris writes that “so long as the generation and spelling out of plans remain[s] an arduous and slow process, opportunities to compare alternative plans [are] extremely limited” (Harris 1965). Geovisualization and electronic, dynamic databases allow us to be more creative with existing information.

The MacEachren and Kraak article seems to stress the importance of having a universal map that serves many different fields at the same time (like cyberinfrastructure inferred, this hints at the future and the web 3.0, where the machines are doing a lot of the work on their own, catering to the needs of the user without being prompted). This is where I will raise an issue. I agree that it would be nice to have one map to serve multi-disciplinary studies, but at the end of the day, a tool optimized for a specific field will always do a better, more thorough job than a universal tool. For example, the cross-training running shoe is a good shoe for many different exercises. It allows you to have support in many different directions and is a great shoe for the gym, but you don’t see many basketball players wearing cross-trainers. Furthermore you would never consider wearing a soccer cleat on a gym floor. Don’t get me wrong, a cross-trainer is great, but if you want to get the most out of a shoe, you may want to try a shoe that is sport-specific.

Gone are the days of the 35mm film, quality photos and photo albums;  we’re left with millions of self portraited digital Facebook photos… Quality is rare but the options are now limitless, just like the world of GIS and geovisualization.

Andrew GIS

 

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What About Privacy in Data?

Thursday, February 9th, 2012

Sarah Elwood posits that rapid change took hold of geospatial technologies over the last five years, with the “emergence of a wide array of new technologies that enable an ever-expanding range of individuals and social groups to create and disseminate maps and spatial data” (256). Elwood does an admirable job of fielding some of the pros and cons that stem from this revolution in technology. In particular, she covers changing power relationships as new groups are empowered by creating data, the possible limitations of existing spatial data models and analytical operations, and how problems with the heterogeneity of the data might make it difficult to support across users or platforms (interoperability).

However, her most important alarm bell, I believe, comes when she writes “that the growing ubiquity of geo-enabled devices and the ‘crowd sourcing’ of spatial information supported by Google Maps fuels exponential growth in digital data, and growing availability of data about everyday phenomena that have never been available digitally, nor from so many peoples and places” (257). What happens when governments use this data to spy on citizens or when individuals use this data for the wrong purposes? The United States government clearly has no compunction about monitoring its own citizens (if you follow recent politics there). Elwood, herself, pays short shrift to what this might mean for the privacy of users and, even, just the public caught up in “everyday phenomena.” She notes that some scholars have raised the question of whether or not the rise of these technologies constitute new forms of “surveillance, exclusion and erosion of privacy” (257) but quickly moves on to the exciting promise of these technologies.

In particular, Elwood appears enamored of the potential of these technologies to reveal new social and political truths (261). Yet, as we noted in our IPhone conversation in class, these technologies might be used inappropriately to track us without our knowledge. Individuals in a democratic society have an undeniable right to privacy, but how can they use these new technologies and software and still be sure that their privacy is respected and their data remains anonymous (if needed)? Should some type of system or regulations be put in place to ensure this right? Something like this has been tried in Europe, but what are the lessons? I’m not sure.

–ClimateNYC

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The Challenge of Large-Scale Data and Geovisualization

Thursday, February 9th, 2012

Nowadays, geospatial data are collected in unprecedented speed, and data volume also increases exponentially. We get image data with fine spectral and spatial resolution from remote sensing technologies, volunteer geospatial information from GeoWeb and mobile technologies, and historical records from different geospatial databases. Due to those factors, geospatial research is now facing of large-scale data, and how to extract information from the large-scale data for knowledge discovery becomes an important challenge for Geovisualization, as MacEarchren et al. point out in 2000.

Previously, Geovisualization has a tight relationship with Cartography, since it is often utilized to visualize geospatial data in 2D format and provide similar functionalities as maps. But the advancement of technologies, especially Web2.0, has re-formatted Geovisualization as a portal for geospatial information sharing and exchange. With the increasing large-scale data (here large scale means both large volume and high dimension), data mining and pattern recognition are necessary techniques to extract useful information for users. As Web 2.0 brings user-centric computation, how to update knowledge and visualize it with new data turns out to be an interesting topic.

The challenges are concluded as representation, visualization-computation integration, interfaces, and cognitive issues in the paper of MacEarchren et al.. Large-scale data is a common factor in the four types of challenges. Meanwhile, Web 3.0 is approaching, which transforms Internet into a large data source. As computing platform becomes diverse (cloud computing, mobile equipment, and so on), knowledge discovery process is also extended to distributed computing environment. Thus, Geovisualization should also keep pace with this change.

–cyberinfrastructure

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Elwood and Social approaches to data management/visualization

Monday, February 6th, 2012

Elwood’s piece offers an overview of the issues in sorting geographic data. Following an explosion of available geographic data due to geo-tags, GPS units, and volunteered geographic information (VGI), she focuses on the challenge of sorting the data. The Web 2.0 has significantly contributed to this proliferation of data by making user-produced products much easier and more accessible. Elwood raises 3 stumbling blocks in massive data heterogeneity, how to represent qualitative spatial data, and keeping up to date with dynamic data over time.

            This article is useful in demonstrating that “visualization” is not only what is displayed, but also the conscious design behind the collection and organization of the data. The most captivating idea to me involved the context-dependent integration of data, where semantics are accorded nearly a field themselves. Here we find the intersection of the utility of a natural language ontology with data exploration as a subset of geovisualization. Contributors of geographic data are encouraged to work out how their data relates to a broader context/dataset, rather than being forced to think like computers and apply tags or join by attributes to attract the most set of eyes. This seems to be an example of an ideal structural philosophy that affects the public’s attitude and cognition of geospatial data. At the very least, users will be inclined to partially realize the spatial component of their data and its interconnectedness with larger processes. This represents a social approach (and not a technical one) towards data management. Perhaps we can call it the invisible hand of geography?

-Madskiier_JWong

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MacEachren and Kraak and Simple Visualizations

Monday, February 6th, 2012

 

            MacEachren and Kraak explain the importance of geovisualization as a way to merge human vision with domain expertise. Broad applicability in fields such as medical imaging awaits pending the solving of major issues in representation, integration, interface, and cognitive/usability issues. The authors round up their paper by pushing for practical solutions to increase research done on geovisualization.

            I would like to point out that improvements in geovisualization need not necessitate more realistic models. I undertook extensive fieldwork and research to present:

This is a screenshot from the simulation game Dwarf Fortress, whose graphics are entirely based on ASCII. The green triangles represent slope (upwards-pointing triangles represent an uphill slope, downward ones indicate a valley), while different elevation levels are conceived in stacked layer format which can be viewed at the press of a key. Depending on my purposes, this simple representation may be enough to inform my decision of uneven terrain ideal for defending my dwarves (don’t need exact elevation values). The graphics are certainly sufficient for representing how individuals interact and gather resources from the environment (e.g. shortest distance calculations by finding the nearest firewood). A bit contrived I know, but the argument holds for situations such as the Battle of the Boids Agent Based Model shown in class where ‘boids’ were simple triangles, yet were able to show movement patterns. I was also challenged in my raster GIS class where given a DEM of say, Mont Royal, what value would animating it in more realistic 3D have from a purely analytical perspective. I’d like to open this question to other readers (I only came up with being able to debug poor stitch jobs and mismatched elevations with other DEMs at the seams). I concede however that when exploring massive datasets with an abductive approach (no hypothesis in mind), realistic visualizations may offer more creative stimulation to the user.     

MacEachren and Kraak briefly touch on this point by noting a tension between realistic and abstract representations, saying some believe “abstraction is essential for achieving insight”. I feel that the reasons for abstract models tend to be more for practical reasons of limited time and resources than a belief that abstract models are more objective and thus insightful.

-Madskiier_JWong

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