Archive for February, 2013

« Older Entries
Newer Entries »

Spatial statistics analysis integration with GIS

Friday, February 8th, 2013

Anselin and Getis authored a 1992 paper on spatial statistics and the problems that persisted in the critical analysis portion of GIS. They divide GIS into four stages – input, storage, analysis, and output – and discuss the interface between storage and analysis and output and analysis. This area of GIS is where, according to the authors, many of the pitfalls of the process of transforming reality to visualization occur. One issue of concern is the integration of a database with the tools to analyze the data. Anselin and Getis offer three possibilities, including full integration in the GIS software, tools that analyze from outside the GIS, and a common format to easily switch between the spatial analysis and the GIS.

One of my primary concerns in reading an article on the fallbacks of technology is that technology changes so rapidly as to make the article nearly obsolete not long after its publication. I mention this here because in the 21 years since the publishing of the article, GIS has advanced leaps and bounds in its toolbox and user interface. Regarding the database-spatial analysis concern noted above, for instance, ArcGIS now includes at least two of the three offered possibilities. Arc is able to read several spreadsheet formats that allow for the integration of Excel if desired. Otherwise, Arc comes with a multitude of analysis tools, ranging from simple geometry calculation to more complex map algebra and interpolation methods. While databases are, for the most part, easier to organize within Excel, there is often little need to work outside of the GIS at all. Arc (and other software) comes with complicated and useful spatial algorithms that make many of the issues noted by Anselin and Gertis largely antiquated.

– JMonterey

Tags:, ,
Posted in 506 | Comments Closed

Integrating Spatial Statistical Analysis into GIS

Thursday, February 7th, 2013

Anselin & Getis give a good overview of the issues that pertain to the integration of Spatial analysis into GIS. Although it is quite a dated paper(1992), it does a good job of highlighting exactly why better integration wold be beneficial for the entire field of GIS. As noted by the authors, one of the key functions of a GIS is the analysis portion, which in turn encompasses spatial statistical analysis. They correctly identify this function as vital for more complex and in depth case studies in the future.

Technology has evolved a great deal since the time Anselin & Getis wrote their review. Modern day GIS now include many spatial statistic tools built right into the system. For example, for several courses I have used the “spatial analyst” toolbox in ArcGIS to perform statistical analysis of raster datasets. This toolbox holds a wide array of functions, ranging from calculating the statistics of objects in a raster (zonal statistics), to combining different rasters based on the measure of central tendency of the data (cell statistics).  In addition, there are now even excellent standalone programs made to specifically analyze statistics. Some of these programs, such as R, allow the user to perform complex statistical analysis of datasets. In addition, more complex programs, such as STATA, include a spatial component that allows the user to perform spatial statistical analysis on datasets.

Overall, the authors do a good job of providing an overview of the problems, as well as the benefits of better integration between spatial statistical analysis and GIS. However, many of the issues raised throughout their paper have been solved over the years, with the evolution and growing complexity of GIS. Spatial statistical analysis is an important component in any GIS. In the present day we have the ability the perform complex spatial analysis within GIS programs such as ArcGIS, something Anselin & Getis could only dream about at their time of writing their paper.

-Victor Manuel

Tags:
Posted in 506 | Comments Closed

How to Transfer the Only Answer

Thursday, February 7th, 2013

“The primary purpose…is to define a common vocabulary that will allow inter-operability and minimize any problems with data integration.” Maybe I am misinterpreting the statement, in which case it would be beneficial to have an ontology for papers on ontology. From what I gather, ontology strives to describe data in a standardized, easily translatable manner. Would that not require culling the outlying definitions, or creating an entirely new definition to categorize. In which case, do we not lose the small nuances and differences? Why are those not as valuable as the opportunity to integrate?

This runs head long as a counter argument to the pro-integration sentiment in Academic Autocorrelation. It is the differences that GIS benefits from. Given our current methods of capturing data, and the sheer scale on which projects are now attempted, it is unlikely that one will ever capture the truth. Rather, it is a representation of the truth from the instant we perceive it. Our interface with our environment consists of no more than five senses, which when compared with other species are rudimentary at best. Furthermore, it is is surprisingly easy to replace reality with something that is not, in which case, though, it still is, according to the viewer, their reality. Thus, the broad range of subjectivity in interpretation is a beneficial burden.

If an ontology were to be imposed on our knowledge set it would constrain our perception, as limited as it is, and yet facilitate transfer across parties. If truth is sacrificed in favor of knowledge transfer, it is the responsibility of the individual to balance accordingly. Unless I am lost myself, in which case I look forward to further clarification.

AMac

 

Tags:
Posted in General | Comments Closed

Academic Autocorrelation

Thursday, February 7th, 2013

Nelson talks of the future challenges the incoming generation of spatial statisticians and analysts will face. One, in particular, is the dilution of geography’s influence over the trajectory of the field of spatial analysis. According to a survey of some 24 respondents, there is a risk of “training issues” if “spatial sciences are adopted by many groups and lack a core rooted in geography.” This is a very isolationist way of thinking. If a field is to be dominated entirely by one group of common thinking individuals, it is bound to hit a dead end.

A nondescript, military-in-mind, ramshackle structure was constructed in Cambridge, Massachusetts during World War II. Its purpose was to develop and perfect radar, an instrument that was instrumental in the war effort. Like all wars, once it was over, the building had served its purpose and was intended to be demolished. Tight for space, Massachusetts Institute of Technology crammed a hodgepodge of disciplines into the structure. Before it’s demolition, 50 years later, it had come to be known as the “Magic Incubator.” Numerous technological advances stemmed from the building, many of which could have been accomplished without work across multiple, previously, unrelated disciplines.

Spatial analysis can gain from the weakening of geography’s grip on the subject, allowing different minds with different problems to use and adapt the tool as needed. Until then, spatial analysis will be on the path to innovation, with little invention branching off.

AMac

Tags:
Posted in General | Comments Closed

Unraveling the mystery of ontology

Thursday, February 7th, 2013

Ontologies are such an interesting and abstract field to me. In the lab I work in, there are many people who develop ontologies (some spatial, some not) and I always struggle to comprehend what they are or how I could ever explain them to someone. They seem to be classification systems or ways of understanding trends in different types of data. For example, one project is based on looking through blog posts on vaccines and classifying the content as “pro-vaccine”, “anti-vaccine” or neutral. The idea of how you would do this was completely abstract to me before I read this paper, now I can see how it fits into some of these concepts. The ‘sentiment’ of the blog is similar to a “secondary theory” relating to the content. As with many analytical models, ontologies to capture spatial trends are more complicated than their aspatial counterparts, but also raises a whole new set of interesting challenges (e.g., issues of scale). Looking forward to the presentation tomorrow and maybe finally  really understanding ontologies!

-Kathryn

[PS My spell-check in firefox seems to thing “ontologies’ isn’t a word and wants to change it to gerontologist…]

Posted in General | Comments Closed

Different people, different ontologies

Thursday, February 7th, 2013

There is no one formal ontology for GIScience purposes. Agarwal notes Uschold and Gruninger (1996)’s four types of ontologies: ‘highly informal’, ‘semi-formal’, ‘formal’, and ‘rigorously formal’. Agarwal continues to outline other academics’ categories of ontologies, which can be loosely fit into the aforementioned four types. Most interesting to me are the ‘highly informal’ ontologies, which can comprise general or common ontologies and linguistic ontologies. How can these ontologies be incorporated into GIScience and into a GISystem? Do they need to be translated into a more formal or meta-ontology in order to be properly analysed, reproduced, and/or applied broadly across different applications? These are questions I don’t have answers for.

Agarwal acknowledges the lack of semantics in the ontological specifications. He notes that “explicit stating and consideration fo semantics allows better merging and sharing of ontologies” (p. 508)– perhaps it is from here, in the recognition of varying semantics across cultures and people, where we can move from informal to formal ontologies. Concepts can therefore be qualified with a criteria stemming from the merging and sharing of ontologies, and consequently increase our understanding and better our analyses.

-sidewalkballet

Tags:
Posted in General | Comments Closed

Spatial stats within geography

Thursday, February 7th, 2013

Nelson’s article gives a thorough overview of spatial statistics through a synthesis of literature and surveying of professionals in the field. The article is well structured as Nelson walks the reader through the different sections. From this article, spatial statistics can be linked the past topics that we’ve studied in GIScience, such as the importance of good user-centred GUIs and wide distribution of applications on the web and data visualisation.

Nelson has a subsection entitled “Geography as the Home for Spatial Analysis” where she situates spatial analysis within geography. She comments on the trend of certain subjects migrating into different disciplines (or forging their own) as geographers give up leadership. If the growing fields within geography leave the discipline, what do we have left? Are geographers equipped to meet the demands of the growing fields? Nelson continues on to acknowledge how geographers are not trained to think mathmetically, statistically, nor computationally — strains of thought which are required for spatial stats. She raises questions on to what extent spatial stats should be involved in geography’s curriculums. I think McGill does a good job with our two required stats courses, but I would like to see more application of statistical methods in other courses.

Spatial statistical analysis needs geographers — maybe not to perform the analysis, but for spatial interpretation. Geographers need spatial analysis to increase rigour in our studies and validity as a department.

Interestingly, this blog post calls for geographers wanting to become spatial statisticans to round out their education with a math or stats degree. It takes more than just geography.

-sidewalkballet

PS: For future thought– Nelson says, “data are increasingly being viewed as public properties” (p. 86)… hmmm…

Posted in General | Comments Closed

Do Mountains Exist? Do I exist? And What is Love?

Thursday, February 7th, 2013

In their article “Do Mountains Exist: towards an ontology of landforms”, Smith & Marc question the existence of mountains as everyday objects. While this question seems ridiculous at first, they point out that everyday objects can either be organisms or artifacts – of which mountains are neither. They do not have a distinct boundary from their surroundings, nor do they have any characteristics that differentiate them from other similar landforms like hills. Suddenly, the question seems like an interesting one.

 

Philosophical debates aside, mountains do exist as geographic landforms. Answering questions like “being” and “existing” for these landforms seem somewhat irrelevant when we know that, as the authors point out, beetles will congregate on mountain tops nonetheless – and these beetles are surely not debating the meaning of their home, they just know that they want to be there. This in itself gives it meaning as an object; however, it perhaps is not an everyday object. In the same way one might tell someone to sit in a specific chair, it is very difficult to do this with specific landforms unless a comprehensive ontology is developed. A complete ontology would incorporate both geography, and philosophy.

 

These geographic features are not specifically defined in terms of the geographic ontology.  For this to be done, the exact scientific nature and history of this planet would need to be assessed. While a mountain is easily visible when standing before it, it is not so obvious when incorporating the irregular shape of the earth and many slight changes in elevation features along its surface.

 

Landforms are also neglected in philosophical ontology, since they are not distinct entities. For these reasons, ‘mountains’ do not appear in geographic databases or in scientific models. While all of these philosophers and geographers dispute what makes a mountain a mountain, it remains that if you ask a 5 year old to point to the mountain, he will, no questions asked.

 

Pointy McPolygon

 

Posted in 506 | Comments Closed

Ontologies & Information Systems

Thursday, February 7th, 2013

Ontology has often been a topic of heated discussion. Specifically, the ontology of whether certain objects, theories, etc. often raises complex ethical questions. However, ontology with regards to Information Systems differs in that it focuses on the study and clarification of certain concepts, with the objective of formulating them into frameworks that are both logical and well understood.

Ontology plays an important, and often unrecognized part within Information Systems. As different people do research within their field, the way in which the gather, record, and organize their data is shaped by their onologies. More specifically, their beliefs, values, ideas, etc. influence what they perceive as important, resulting in datasets that are often unique and idiosyncratic. These idiosyncrasies make it difficult to standardize data across fields, which in turn hampers cross-field research and analysis. Therefore, an important step going forward will be to develop a sort of “Master Ontology”, a standardized and universally accepted framework. This framework would synthesize the various conceptualizations of different communities of data users in order to make data organized, standardized, and transferable.

In their conclusion, Smith and Mark remark that a complete ontology of the spatial world is needed not only to comprehend both primary theory (common sense), as well as field based ontologies that are used to model natural phenomena such as runoff and erosion. I believe this to be extremely important, as people from diverse fields must collaborate to build comprehensive, and most importantly, standardized databases in the field.

-Victor Manuel

Tags:
Posted in 506 | Comments Closed

Exploratory and Confirmatory Spatial Analysis has come a long way, but…

Thursday, February 7th, 2013

As with many of the papers in this class, the topics presented are still extremely relevant to the field of GIS, however we have made leaps and bounds in terms of technology since it was written (1992 in this case).  Computing power and the development of appropriate algorithms have allowed GIS analysts to drastically improve the so called manipulation, exploration and confirmation processes brought forth by Anselin and Getis.  While I have only been familiar with a program such as ArcGIS for a few years, I would argue that the spatial analysis capabilities have drastically improved since the 90s.  It is obvious that GIS is no longer about the display and visualization of spatial data, as the ability to perform exploratory and confirmatory analysis has become the norm.  These sorts of procedures have become more “automated”, per se, and allow for more “plug it and chug it” methods to spatial analysis.

That being said, the authors bring up a vital point by saying that in some cases, “better theoretical notions may be needed.”  To me, this is essentially a warning to GIS analysts, telling us not to rely solely on whatever new algorithms or spatial analyst tools may be needed.  When one is working with the massive complexity of spatial data that is at our fingertips today, it is imperative that we are familiar with the data itself.  We must still predict what sorts of patterns we may see.  If the exploratory process unveils some sort of new model of our environment, we need to know why that is so.  Otherwise, we reach a point where the user is no longer relevant, which will be detrimental.  So, yes, we have made great progress in the use of spatial geostatistics.  However, we must be careful how far we take this and always be conscious of the types of decisions we make when analyzing spatial data.

 

-Geogman15

Posted in General | Comments Closed

Trends in Spatial Statistics

Thursday, February 7th, 2013

This article outlines the progression of the geography and spatial statistics since the quantitative revolution. Moving from net importers of techniques to net exporters in spatial analysis methods indicates how geographers have made their mark in academia. The book list offered by the author also demonstrates how vast the field has grown in multiple directions where few books were cited by multiple interviewees. Does this indicate that the breadth of knowledge in the field is so massive that a commonality such as a seminal book/article does not exist? It was noted that interviewees expressed their concern about the blurred line between GIS and spatial analysis. I’m not convinced that a clear distinction can be made – while seeing a “cluster” may be obvious, those who seek to understand the reasoning, and causes of the spatial distribution of a phenomenon will require specialized knowledge in theory and methodology to make those distinctions.

How society understands spatial data has also changed. As the proliferation of user friendly geospatial tools continues to pique the interest of the public to the discipline, it appears that the field has positively responded by providing freeware GIS tools which allow the distribution of more advanced spatial analysis techniques to further inform their understanding. As the discipline of geography continues to be enriched by new perspectives from several disciplines, spatial data will continue to evolve and become larger spatially, temporally and in volume. Handling these datasets require a reassessment of the tools we have to use and determine whether an adjustment can be made to address the new challenges in spatial statistics or if a new formulation needs to be made. But if we can address these issues and capitalize on the integrating spatial statistical with GIS to confirm hypothesis and move away from GIS as exploratory tool then the S moves from “system” to “science”.

-tranv

Posted in 506 | Comments Closed

Do Mountains Exist?

Thursday, February 7th, 2013

The deep question with which the paper starts delves into the definitions of existence and comprehension of geographic features around us. The coming of predicate logic was the first attempt to consolidate questions about existence in a scientific framework, thus binding existence to a variable. However, to answer questions about categories and objects, predicate logic faces a challenge as these definitions are by nature recursive. As rightly pointed out by Barry Smith and David M. Mark the question then becomes two folds: “do token entities of a given kind or category K exist?”  and “does the kind or category K itself exist? ”. Predicate logic in itself is good at explaining logical entailment but fails to take into account the how humans perceive things. Thus, it may be right to say that mountains exist as they are part of the perceived environment.

Information Systems on the other hand adopted a different definition of ontologies. It considers ontologies as a set of syntax and semantics to unambiguously describe concepts in a domain. The objects are hence classified by information Systems in to categories and the categories are in turn arranged into a hierarchical structure. However, such an arrangement was futile in describing things like mountains, soils or phenomenon such as gravity. One central goal of ontological regimentation is the resolution of the incompatibilities which result in such circumstances. Hence the concept of fields was developed to efficiently categorize these “things”.

However, there are still doubts with naming of such “things” like mountains. Obviously, Mt. Everest exists because all the particles making Mt. Everest exist but exactly what particles are called Mt. Everest. This is the inherent problem in dealing with fields which are by nature continuous, lacking discrete boundaries.

Ideally the entire field of Ontology should be able to explain the entire set of things which are conceptualized and perceived with no ambiguity. This requires tremendous insight and reflection about why do the things exist in the first place.

– Dipto Sarkar

 

Posted in 506, General, geographic information systems | Comments Closed

Ontologies and GIScience

Thursday, February 7th, 2013

“Ontological considerations in GIScience” by Agarwal states the issues of ontologies and the communication of the data contained within the various ontology types is problematic to understanding. One way I have worked with in the past to resolve the issue of relating different ontologies is setting a accessible reference grid over the different ontology of the same area and creating a cross-referencing database that links both the human and scientific data. For example, if I selected a grid a side bar would open that would display human based data (impressions, oral histories, etc.) and any other data for that same location. In addition, neighboring grid cells can be linked to see if they represent similar data, and a referencing system based on type of ontology characteristic created. This last part is similar to what I believe Agarwal maybe alluding to in the last part of his article.

Although grouping of data may be useful, the variety of ontologies and the evolution of how humans see the world and use of GIS makes this difficult and a problem for GIScience to resolve. I believe the problem is not how we group data but how databases are mostly static in form and unable to expand to new information. That is one reason why creating grids for an area to set a standard may be the best solution, where they are related to dynamic ontology data sets. To simplify, ontologies are dynamic with many ontology layer types, set to a geo-referenced grid.

C_N_Cycles

 

 

Tags:,
Posted in 506 | Comments Closed

Spatial Statistic Skills Being Lost?

Thursday, February 7th, 2013

Nelson’s article, “Trends in Spatial Statistics”, although providing a good summary of past trends seems to be disconnected with what is really happening, or in other words the reasons behind the needs and education within GIS. Geographers are mentioned as  users and not producers of GIS, but this is only because industry, looks at the short term needs and therefore education teach for those needs rather then get more in depth. As a result of geographers looking at industry needs the more advanced courses that are required for spatial analysis are often considered as “too much training” and students do not ask or take these “advanced courses” in a large enough number for them to be offered as geography specific.

I believe that to solve the problem of spatial analysis, more open source courses online that are not time limited are needed to solve the current issues with spatial analysis skills. These courses would allow professional and public assess to the tools needed in the globalization of GIS and the application of spatial analysis techniques. In addition to courses, much of the technical background of GIS program statistical functions are hidden. It may be beneficial to create a functional window that can display the statistical function’s equations and code.

Finally, geography may have once been the only home of spatial analysis, but in today’s global environment other fields may be better suited with their specialization for analysis then geography. I believe that no one can be a master of all disciplines, therefore the skills that geographers thought were important, may not be today for them. In the global context of knowledge and specialization of skills, people are now becoming a cooperative of learning where goals can be achieved to greater success together through an interdisciplinary approach, rather than a single discipline approach.

C_N_Cycles

 

Tags:,
Posted in 506 | Comments Closed

“O”ntology, “o”ntology, ontologies, ontoloGIeS

Wednesday, February 6th, 2013

As the interpretation of ontology varies within different disciples, this reminds me of how the interpretation of spatial phenomenon varies depending on context and what the researching is looking to uncover. A reoccurring theme in this course has been to recognize that the way in which we think, discover, and analyze phenomenon is context specific. How we decide to apply SDSS, visualize space, or determine whether something falls in the category of tool, toolmaking or science depends on the researchers intention and what they wish to convey/uncover. And as expected, the type of ontological approaches and methods is also context specific. As the overarching goal of ontological research is to create “a shared understanding of a domain that is agreed between a number of agents” (Agarwal, 2004) then is it possible to have several ontologies within the boundary the researcher demarcates since shared understandings exist on several levels between humans (ethnically, culturally, gender, age)? As individual positionality is unique to every person, whose shared understanding is being agreed upon?

From what I understand reading this article, GIScience is still in the stages of defining a common set of notions or concepts such that geo-ontologies can only be high level abstractions. Agarwal suggest that the issue is the interdisciplinary nature of GIScience such that the same terminology is conceptualized differently brings excess disciplinary “baggage” to the table. I don’t necessarily see this as a negative – if a shared common understanding can be made within several disciplines (or at least a shared understanding of what it is not) then doesn’t it allow for a more robust definition that can be accepted by more people? And if such a consensus can be achieved, then GIScience can move to the next step in creating this common vocabulary to allow interoperability.

-tranv

Posted in 506 | Comments Closed

Spatial Ontologies

Wednesday, February 6th, 2013

Agarwal’s “ontological considerations is GIS” left me with a lot of questions. The article attempts to outline different conceptions of ontology (both strongly theoretical and technical). Ontology is most simply defined in the final paragraph of the paper as “a systematic study of what a conceptual or formalized model should encapsulate to represent reality”. However, how do we translate personal ontologies into more global technologies? The paper briefly questions what it means to produce an ontology including concepts with variable semantics, that may be vague or differently understood by geographers and those outside the domain. The fractures between disciplines point to the inefficacy of a top-down approach to producing ontologies. Agarwal is correct to question this paradigm, noting the benefits and disadvantages of its counterpart.
Agarwal’s discourse, however, seems to be still firmly couched in the academic context. What would it mean to create a bottom-up ontology of more partipatory platforms? How might we make semantics less fuzzy in the case of non-professional conceptual knowledge? Is it possible, and more importantly, is it even desirable? At the risk of sounding like a broken record, I want again to interrogate the power dynamicsthat inform what becomes a part of what we want to represent reality. There are inherent cultural biases in what we will want to represent, and by maintaining a basis of reality defined by academics, we ignore ontologies that fit outside of dominant strains of thought.

Wyatt

Tags:
Posted in General | Comments Closed

Statistics and GIS- a lot has changed

Tuesday, February 5th, 2013

A lot has changed in the last 2 decades since the paper on “Spatial Statistical Analysis and Geographic Information Systems” was published by Anselin and Getis. Today, the central focus of GIS is on spatial analysis and the rich set of statistical tools to perform the analysis. Today the GIS database and analysis tools are not looked upon as different software. Spatial analysis is fully integrated in GIS softwares like ArcGIS and QGIS. Furthermore, for very specialized applications, the modular or the loosely coupled approach is often employed. Software like CrimeStat uses data in established GIS Sofware format, perform analysis on them and produce results for use in GIS softwares.

When it comes to the nature of spatial data, two data models have been widely accepted namely Object based model and field/raster based model. Extensive set of analysis tools have been developed for each of them. Data heterogeneity and relation between the objects are also taken into account by slight improvements over these two models.

Exploratory Data Analysis and model driven analysis have progressed hand in hand and complement each other. While new and innovative visualization and exploration tools help in understanding the data and the problem better. Software has evolved over time to perform complex non-linear estimations required for model based analysis.

However, Statistics and GIS is an ever evolving field and newer methodologies and techniques are developed everyday which pushes the boundary of cutting edge research further and further. Newer challenges in statistical analysis include handling Big Data and community generated spatial information. How these new challenges evolve will be very interesting to observe.

-Dipto Sarkar

Posted in 506, General, geographic information systems | Comments Closed

Spatial Statistics- Producing a canon

Tuesday, February 5th, 2013

Nelson’s summary paper on spatial stats provided a solid framework for dominant strains of thought both in the past and looking forward. One portion of the paper provided a list of important works on the subject with brief descriptions. While I found this to be something of a bizarre format for this sort of paper, I appreciate the question it raises of what might be considered canonical in technical literature. Unsurprisingly, there is discrepancy between what works different spatial statisticians deem most important as guides for newcomers. Nelson himself adds books that he feels were overlooked (or not published at time of survey) revealing he and his reviewers’ own biases.
What I am circling around can be brought to a critical question of: how do we decide what is important, and who gets to decide? This is really what we were asking when trying to peg GIS as a tool or a science. Which aspect of GIS is most important (and critically, why?). While in spatial stats, a basis of formulae and conceptual tools is necessary, where do we go from there? Once we are past the most essential technical aspects of a discipline, defining what is important becomes more subjective. In looking at this particular literature list (which is doubtless helpful to newcomers), I think it is important to question what it means to define what is to be remembered and what is to be forgotten.

Wyatt

Tags:
Posted in General | Comments Closed

accurate maps?

Saturday, February 2nd, 2013

Here’s a link to an index of maps of novels: http://io9.com/5980739/an-index-of-dozens-of-maps-from-epic-fantasy-novels?tag=this-is-awesome

I think maybe some would say ‘art’… But I say maps. It’s about representing the geography of the story told in a book. What do you guys think?

S_Ram

Posted in 506 | Comments Closed

The progress of geovisualization; still lots of work to be done

Friday, February 1st, 2013

In MacEachren and Kraak’s article titled Research challenged in Geovisualization, they argue that the main issues in geovisualization lies in representation, integra- tion with knowledge construction and geocomputation, interface
design, and cognitive-usability issues. The role of geovisualization has changed drastically within the last 10 years, along with developments in other software’s and technologies such as web 2.0.

 

Represent this dynamic data is a far cry from paper maps, where the database and map were static, and connected. One of the other challenges that has surfaced in representation is that of the geovisualization of 3-d and 4-d (time) space. This challenge in representation is still being addressed as the technology grows more powerful. Great strides have been made with interfaces, where 10 years ago many things were only useable by experts, now everyone and their grandma are using them.

 

 

Many of these problems have not yet been solved, however some of them have. Storing of the data is no longer as much of a problem due to cloud storage, and the web2.0 has become much more user-friendly, making it useable by users with limited expertise.

 

Pointy McPolygon

 

 

Posted in 506 | Comments Closed

« Older Entries
Newer Entries »