Posts Tagged ‘GIScience’

We are where we live

Monday, October 20th, 2014

Up to now several topics such as public participation GIS, Cyberinfrastructure, Agents, Social Network Analysis methods, etc. were discussed. These topics are pretty much about manipulating geospatial data, since that is what we do in GIScience, I think. And when it comes down to the data, humans are all about providing data. There are many use of such data and because of its enormous quantity, one can collect and retrace back or even create the target’s profile based on tweets, purchases made, photos, friends, used search keywords, etc. Therefore one can use software to estimate the living setting of the user quite accurately using such information available in the web. As for geospatial data, specifically, one can collect, manipulate and sometime visualize the data as a map and one may believe such digitalized representation of a space is in fact identical to the actual space, and it was possible because it is mere a space.

However, in EPBG, a space is not just a space. In my understanding, EPBG argues that there are such a strong relationship with one’s behavior and the way one perceive the surrounding, and not the surrounding itself. It was quite intriguing, because in that sense, if two individuals who are located in completely different location may behave quite similarly if their way of processing the external information for the different settings are equivalent and vice versa.

So far in GIScience, humans are all about providing data. Whereas in EPBG, it is human, specifically the brain, that collects the information from the past experiences, memories, etc. from a specific space and re-create a place, therefore each space representing a unique place for each individual and that leads to the specific behavior.

This reminds me of a quote “You are what you buy”

And make me think that in fact, we are not only what we buy/eat, but we are also where we live. Since there is a clear distinction with people living in North America settings, like us, versus Europe, Africa, Middle East, and Asia, in terms of level of education, daily life style, diet, language, etc. Furthermore, even within the North America, depending on which regions distinguishes us and one can observe such distinctions even within a city like Montreal. And there comes the issue of MAUP.

Long story short, it is not whether we, as humans, our behaviors are shaped by the environment per se nor these shape the environment, but it is rather bidirectional: we perceive the environment the way we have been affected by, therefore both are intrinsically correlated.

This is such a headache because psychology is not my strongest field. Nevertheless, I find this subject quite absorbing.

ESRI

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Social Network Analysis and GIScience

Monday, October 6th, 2014

Social Network Analysis(SNA), in my understanding, is to analyze social relationship that can represent any type of link that one individual can have with another individual. There are 2 distinct methods, a quantitative approach and a qualitative approach to conduct the analysis. Each method obviously has its own advantages. Interestingly, in Gmma Edwards’ article, it is argued that a third option, which is a mixed-method approach to network analysis combining both quantitative and qualitative approaches are appropriate for SNA. This was very refreshing article. Especially when it came to my mind that the social network study and GIScience both have common features. Among others, the use of relational database was one of them.

In the SNA, the relationships between actors, such as flow and exchange of resources, the flow of information and ideas, the spatial embedding of network ties, etc. are generated and analysed.

Whereas in GIScience, the relational data are collected, stored and managed as well, but perhaps a different format/method than how it is being done in SNA, and  such software is called as Relational Database Management System(RDMS).

Of course, the objective or the way they use the relational data may slightly differ, but I think that it would be quite interesting to practice SNA by adding the geographic aspect on top of it and visualize it on an actual geographic map to display actors and lines rather than an empty space, for certain subject. That way, it could be easier to figure out a new relationship or a meaningful observation that one couldn’t find it previously.

ESRI

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Agents Agents Agents

Monday, September 22nd, 2014

This article review other articles and provide a brief definition on terms that are quite difficult to find, even in Google, such as ‘Artificial Life Geospatial Agents’ (ALGA) representing a computer model that may be independent programming code interacting with other code or a single piece of software itself that use computational models to imitate  an individual’s behavioral responses to an external stimuli. It is a crucial tool to model interactions and behaviors between humans, animals and the natural environment.

 

Unlike ALGA, ‘Software Geospatial Agents’(SGA) is used to manage information and making decisions in hardware and software environment, and it is designed to manage geographically explicit information, such as a geographic coordinate, on behalf of an entity, which can be a person, a software or even hardware.

 

These agents share couple of common points. For instance, they are both a predominant type of agents in GIScience and they both perceive and respond rationally to new situations to new situations and their environment In addition, they are enable to handle the unique qualities of geospatial data as well.

This article demonstrates further explanations and examples to demonstrate the minimum requirements for a piece of software code to be considered as an “agent” in the AI literature and then, the authors question the existence a Geospatial Agent and underline its importance to both ALGA and SGA. They argue that as much as AI requires spatial information, without it, AI is likely to fail. It sounded quite convincing and all until they mentioned how geographic coordinates as a part of IP specifications could benefit the SGA and Internet community…my skeptical ego just woke up and oh well…Nonetheless of my regard in that specific example, this article in overall did a good job in reviewing other agents-related articles and explaining the roles and definitions of the intelligent agents and of course underlined the uniqueness and importance of geospatial agents that are playing and will be playing in the future by handling geospatial data, which makes it so unique and valuable.

It required me to re-re-re-read this article over and over because the terminology and concept was very unfamiliar and uneasy for me, but it was still quite interesting and always good to learn new terminologies…sometimes… 😛

ESRI

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A Review of GIScience—Achievement and Challenges

Monday, January 23rd, 2012

What are the most important accomplishments in GIScience over the past twenty years? Which technologies play pivot roles in the development of GIScience? What social effect has GIScience brought by its twenty years’ progress? And what are the challenges we facing in GIScience research nowadays? Goodchild shows the answers in his paper, with insightful opinions from a large number of scientists in this field. Starting from the coining of GIScience, Goodchild introduces the rapid growth of GIScience and its position in the large family of science. Research agenda of GIScience is delineated and the accomplishments are presented from research and institution perspective respectively. Challenges are classified as five groups and discussed with future research directions.

Technologies, especially computer and information technologies have stimulated the development of GIScience, such as Web 2.0, database systems, mobile technologies and so on. The advances of geosensing systems bring new approaches for data capture, which enable detailed earth observation data with improved spatiotemporal resolution. Moreover, geospatial ontology (Web 3.0) changes GeoWeb from a visualization tool to a platform for geospatial information exchange. Cloud computing builds large computing resource pool with virtualized hardware and software, to facilitate the share of geospatial information. Currently, geospatial information is collected, analyzed, visualized, and exchanged with unprecedented amount and speed. As Microsoft has indicates in the fourth paradigm research report (http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_complete_lr.pdf), Goodchild also points out the era of information-intensive research has arrived. The social impact of the fourth paradigm should also be studied as well as its educational challenges. All the research can extend the definition of GIScience and reformat its conceptual framework.

–cyberinfrastructure

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GIScience and uncertainty

Monday, January 23rd, 2012

The article was thought provoking, addressing numerous accomplishments, research agendas and challenges. I appreciated the author’s self awareness and frank statements when addressing his own limitations. At times, it was overwhelming as there were a lot of points covered with 20 years of theoretical and empirical accounts of GIScience.

From the challenges mentioned, I found the notion of uncertainty intriguing; a concept that is highly influential yet largely ignored. Goodchild’s conceptual framework for GIScience elucidates how the human, society and the computer are interlinked by many variables (e.g. spatial cognition, public participation GIS, data modelling). “Uncertainty” dominates the middle of the triangle, however 3 out of the 19 papers — that were most cited, and considered classics over the last 20 years — analyzed by Fisher “report work on uncertainty” (9).

The article notes Tobler’s Law and its implication that “relative errors over short distances will almost always be less than absolute errors” (12). According to Goodchild, this has significant implications for the modeling of uncertainty. From this, it can be inferred that we have confidence in addressing an issue due to its proximity, where a relative error is less intimidating than an absolute error. Goodchild further notes the transition made in our thinking about GIScience from “the accurate processing of maps to the entire process of representing and characterizing the geographic world” (11). The emphasis on the GIScience thought process has been shifted away from accuracy on a micro geographic scale in relation to maps, towards a characteristic and representation on a macro, global geographic scale. Moving from a micro to a macro scale will entail more uncertainty, while the aim is to increase accuracy these are contrary in nature.

Despite uncertainty seen as an obstacle to GIScience progress, Goodchild takes note of it as also being a salient factor in “potential undiscoveries” (6). The process of government’s adoption and application of GIScience, and further work on third, fourth and fifth dimensions, and the role of the citizen through neo-geography and VGI are all very exciting and revolutionary.

Goodchild. (2010). Twenty years of progress: GIScience in 2010.

Henry_Miller

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Affirming GIScience’s Place in the Academy

Sunday, January 22nd, 2012

When we read Michael F. Goodchild’s review of the last 20 years of GIScience, we should be careful to note that he “does not pretend to be entirely objective” (3) in outlining his views. In particular, he goes to great length to argue that GIScience functions as a distinct scientific discipline. Although he does devote some space to the debate over whether GIScience represents a tool or science (4), Goodchild leaves little room for the dissenting view that GIScience could be viewed simply as a tool for other disciplines. In fact, he unequivocally states that the field presents “substantial research issues” that can only be solved by using the methods of GIScience (15-16). Although he calls for other practitioners in the field to reflect on the past 20 years, his aim – both in his manner of treating the subject and in what he writes – appears  to both define and establish GIScience as a sub-discipline or science in the already jam-packed academy.

In noting GIScience’s establishment, past accomplishments and possible future directions, Goodchild writes there’s “no danger” this area of study will “be absorbed into one of its intersecting disciplines” due to the “well-defined, persistent” nature of the problems that this science addresses (16). Goodchild most clearly lays out his agenda of GIScience as a discipline in Figure 1 (“A Conceptual Framework for GIScience”) by organizing various topics in GIScience according to their relationship with human beings, society or computers. This organization resembles a similar one taking place in many departments whereby researchers attempt to locate their own research questions in terms of where they might sit on a spectrum that includes both human or natural science approaches. It implies the universal, organizing principle of GIScience as a lens through which these questions should be viewed. In fact, Goodchild references the definition of geography as a science (4) before providing several definitions of how GIS also represents a lens or science (6).

As we noted in class, defining GIScience in this manner holds important implications for the discipline and for the universities where it’s taught. Just as the creation of distinct statistics departments or environmental science programs can both shape the educational program for students and the funding opportunities for researchers, Goodchild’s view of GIScience could influence future developments in the field. Having just come from a graduate marine science program that treated GIS only as an important tool worthy of a certificate showing proficiency, I can see how these questions could be central in defining how universities or other fields treat GIScience as it grows and evolves.

– climateNYC

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