GIS as Communication Tool

Previously I viewed GIS as more of a professional tool -- although I tries to turn it into commercial use by common people. GIS was used in the context of one person, one computer. The user take advantage of GIS to make spatial analysis. I believe that is largely why GIS has long been targeted to experts only.

In fact, another big facility of GIS is to serve as a communication tool, among a group of people, or even a whole community, in addressing a spatial issue. The difference between the two uses of GIS is data. In the first case, data has been collected from various sources (often in large amount), and ready to be cleaned and analyzed. In contrast, in the communication context, data is originally stored in user's mind. As he describes his spatial proposition or spatial experience, he either sketches a map or makes reference to a spatial feature. In other word, the map is dynamically created, and is dynamically changing.

The communication problem is actually a problem of information flow. In a situation of two-people dialogue, information flows from one participant's mind to a physical or digital information carrier (often a map in spatial issue), and from the information carrier to the other participant's mind. The process involves the translation from spatial mental model to system conceptual model, and again from conceptual model to mental model. There are two difficulties in the translation: 1) how to minimize the loss of information; 2) how to keep information on the receiver end as consistent as possible as that on the sender end.

The problem becomes even more complex when the social scale becomes larger, to a community, for example. People of various backgrounds are involved in discussion. As they make spatial reasoning and deliberation, they communicate their spatial proposals, assertions, comments, and arguments, which form a large reservoir of information. Furthermore, different pieces of information is highly related to each other. This relation might be linguistic structure, topic embedding, or spatial related. How to keep management of the complex structure of information is worth considering.

According to Walton and Krabbe's typology, human dialogues can be categorized into six: information-seeking, inquiry, persuasion, negotiation, deliberation, and eristic dialogues. Each type of dialogue bears different characteristics, and requires different GIS model.

GIS as a communication tool can be a very promising direction, which involves multiple disciplines like linguistics, social science, cognition science, HCI, etc.

Some reflections on reading a paper

The first semester in PSU has ended. One of the best lessons I've learnt is how to read and review a paper. There are two papers that serve as a guidance:

Fong, P. W. L. (2009). Reading a computer science research paper. ACM SIGCSE Bulletin, 41(2), 138. doi:10.1145/1595453.1595493;

Smith, a. J. (1990). The task of the referee. Computer, 23(4), 65–71. doi:10.1109/2.55470

The reading process can be divided into three tasks: comprehension, evaluation, and synthesis. The latter is dependent on the former and requires deeper understanding of the paper.

To fully comprehend paper in the shortest time, read the paper asking yourself four questions:

1. What is the research problem? Around this problem, motivation of the research may be addressed. Possible situations may include: there is some weakness in existing research approaches; there is some crisis in the current research field; or the paper challenges the existing research paradigm.

2. What are the claimed contribution of the paper? Try to find something new in the paper. The new thing may include: 

• a new question, 
• a new understanding of an existing research problem, 
• a new methodology, 
• a new algorithm, 
• a new proof technique, 
• a new formalism or notation, 
• a new evidence to substantiate or disprove a previously published claim, 
• a new evaluation method, or  
• a new research area.

3. How the paper substantiates the claim? A paper becomes scientific only if it is strongly supported, or it becomes a mere opinion. To support the authors' claim, some methodology must be used, which may include: 

• theorems
• experiments
• data analyses
• simulations
• user studies
• case studies
• examples

4. What are the conclusions? What are the lessons learnt from the paper? 

Most often, all the four components can be found in the abstract and introduction sections. When writing  an article ourselves, we should also make them explicit in the two sections.

Evaluation goes along with each component. Ask yourself the following questions:

1. Is the research problem significant? Does the work enable practical applications, deepen understanding, or explore new design space?

2. Are the contributions significant? Are the author simply repeating the state of the art? Are the authors aware of the relation of their work to existing literature? Are there any real surprises?

3. Are the claims valid? Has the right theorem been used? Any errors in proofs? What assumptions are made? Comparing apples and oranges? Experimental setup problems?

Synthesis requires to think beyond the paper. Some questions can be asked after reading the paper:

1. What are the alternative way to substantiate the claim?

2. Is there any good argument against the case made by the author (contention)?

3. Can the research results be strengthened?

4. Can the research results be extended to other contexts?

5. Is there any relationship between this paper with other literature?

• 
  

师兄毕业了

今天是师兄的毕业答辩。积累了6年，要在短短1小时内说清楚，中间还不停地被老师打断，真的很不容易。

师兄做的问题很大，很复杂，但也很实用。大致讲的是promote collaborative awareness。回顾6年，按时空划分，他做过same time & same place 的 collaboration，different time & different place 的collaboration，而dissertation做的则是 same time & different place的collaboration。应用的场景例子是nuclear release的求援，涉及到一线救护员，人员指挥中心，资源调配中心，交通部门等等。如何让系统支持这些部门协同工作，有效地分配和共享信息，是迫切需要解决的问题。用的方法是event-driven based framework。

再看看自己的工作。三个功课，三个literature review。我计划分别做adaptive GIS, intention recognition, 和GIR的review。第一个基本已经做完，学到不少经验，一定要做好文献的整理，过段时间写一篇关于怎么读paper的心得。

地理与信息

今天读到两则有趣的报导。一则强调地理在当今信息时代重新显示出其重要性 (A sense of place, http://www.economist.com/news/special-report/21565007-geography-matters-much-ever-despite-digital-revolution-says-patrick-lane)，另一则则表示地理优势在信息经济新时代已经越来越不明显 (亚马逊VS沃尔玛：信息经济时代零售霸主逆战，http://www.21cbr.com/html/topic/201210/28-10535.html）。

第一篇文章回顾了历史上对地理的三种看法。第一种是出现在90年代的end of geography。由于互联网的共享性，全世界任何一个角落的人理论上都能享受到共同的资源。在网络上不再有距离的概念（the death of distance)。第二种概念更加极端，认为网上的虚拟世界可以完全替代真实世界。我想这是在21世纪初随着SecondLife这样的cyberspace兴起时的思潮。在这样的虚拟世界里用户可以模拟现实生活中的几乎一切事情，甚至有自己的货币。然而这种想法被证明是不现实的，虚拟世界不可能脱离现实存在，比如虚拟的货币仍需要通过真实货币来兑换。
第三种认为现实世界会影响网上的行为。正如地学第一定律所述（Everything is related and things closer in space are more related），用户更关注身边的事 (local)。随着移动设备的普及和地理定位技术的成熟，企业提供这种local service成为可能。从这个角度讲，geography成为local business越来越重要的因素。

第二篇文章比较了亚马逊和沃尔玛两个零售业巨人。后者是传统的线下销售，前者是这个信息时代特有的产物。近年来亚马逊的营业额逐年上涨，剑指行业老大沃尔玛。沃尔玛通过数十年努力建成的由土地、建筑构成的实体网络正在遭遇前所未有的危机。网络商店的出现，让传统零售业的法宝 location, location, location显得不再那么重要。而目前，两家公司都采取着相似的战略：线上线下的有机结合。

曾经有人疑惑，网络到底需不需要有距离的概念？这涉及到一个问题，即地理到底应该在多大程度上与网络、与信息结合？这里面涉及一系列问题，包括人对（地理）信息的需求，人感知（地理）信息的偏好和习惯，（地理）信息系统如何满足人的认知习惯等等。作为地理信息科学的研究人员，我觉得我们的时代马上到来了。

Academia is not the only option for PhD

It has been puzzling me all the time, even after I've come here for PhD, that how is PhD accepted in industry. There are many negative sayings about PhD: "Permanent Head Damaged", because PhDs are usually stubborn and confined in a narrow field; old aged, and often immersed in old fashioned papers published decades ago; idealist, whose theories and ideas are built in the lack of technological support; etc. So it's exciting that today there is a doctoral career exploration workshop, with most of the speakers graduating from doctoral programs of Penn State. And I should say, I really learn a lot. Or to be more exact, I actually knew those stuff. I simply got lost after I dived into the mess. And the speech today helped me out, and reminded me of my strength as a PhD.

So why does a company hire a PhD. Apparently the company has a different expectation for PhD. The company won't pay a PhD to program. Some of the things that a PhD is expected might be:

1. Direct a team. Think of ideas in what directions should your team go. Be creative and innovative.
2. Theorize your product. Apply the theories in your domain to practice. It's important that your products are well theoretically founded, so that it is convincing for investors and customers. Be rational and persuasive.

We should be aware that the graduation program is not to get us damaged, but to get us fully prepared.
Keep on asking ourselves internally what is my goal, what is my value, what is the need of the world. Find a joint point of the three aspects, and we can make a difference.

For me, I actually already have a very clear path. I'm to study the cognitive aspect of computer systems. The problem for me is what I can do to improve the usability of a system. And I would start from how people perceive information, and apply that understanding to the design of systems. As I have a GIS background, I may focus on GIS as the study case, but I should also not limit myself in GIS. Therefore my strength should definitely not be interface design or system programing, but to provide guidelines to those guys, in a theoretical level, and suggest evaluation methods before the final product is issued to the public.

So what I need to do in the first step is to enrich myself with the theories of human cognition. Also to leverage my strength, I should apply those theories to the GIS field. So a deeper understanding of GIS users and their thinking, reasoning, and behavior is required.

Finally, building a friend network is as important as academic achievement. I should not lock myself in the lab all day long any more. I should get out, talk to people in other areas deeply, and get inspiration.

Information Science

During the overwhelming first week, I have been enriched with many new conceptions or familiar conceptions that didn't make so much sense to me. So many that I don't know which to begin with. Maybe, the name of my department, IST, is a good start.

Founded in 1999, IST (Information Sciences and Technology) is a comparatively new college. It is about Information, Technology, and People. There used to be a debate about the third element: should it be People or Users, users of information and technology? Finally People wins, because proponents argue that we not only care about users who use our technology, but also those who don't. To analyze why they don't use our technology helps us keep moving too. I think that's a good idea, and the idea lays the foundation that IST is a discipline centering on humans.

So what is information science? And what is information?

These seem to be very broad problems, problems we generally never come down to think about. We keep on talking about "information", "information age", "information science" everyday, but we actually take them for granted and never give a second thought about they are "intentionally".

Personally I have been immersed in GIS for four years. Shamefully I now find my understanding of it far from enough. I have always been thinking of it as a system, or a science of geographic information. And more accurately, it's just about geographic data. Open an arbitrary GIS textbook, and you'll find all it deals with is data: data acquisition, data storage, data analysis, and data presentation, although some may touch a bit on the difference between data and information in the beginning of the book. Now I come to realize that GIS can also be viewed as a subset of "information science", in the geographic domain. This gives us broader views of GIS, and can be guided by some more mature theories in information science.

Of course, just like GIS, as a newly emerging discipline (emerged in the 1950s), there are still many disputions over concepts, definitions and theories in information science. The difference is, information science has been drawing wide attention both from government and society from the very beginning, and its wide application appeals to scholars in various disciplines, which guarantee its fast booming over the last few decades, both theoretically and pragmatically.

Information science is driven by problems. To solve a problem, we need information. We seek relative data akin to problem, and organize the data into information trying to answer what, when, where, and who. Finally we come to conclusion "why" this problem happens and "how" to solve the problem, which ends up in the so-called "knowledge". This is the basic data-information-knowledge-wisdom hierarchy proposed by Ackoff 1989.

Technology advances rapidly, so we are faced with ever increasingly large amounts of data and ever increasingly complex systems to automatically deal with these data. But no matter whether we are generating data or consuming data, one thing we have to keep in mind is that our single task is to solve problem. We are not developing awesome software, but designing systems that better assis people to solve a problem. In this sense, "people" should always be the center of research. We try to understand how people learn and use information.

There are three major branches in information science: information retrieval, information relevance, and information interaction. Information retrieval is the basic need in this information overload age. And information relevance is closely related to the effectiveness of retrieval. They are more concerned with algorithms, and user participance is limited, with only a query input. In contrast, information interaction is, in my opinion, more of upper level. It asks the system and the user to work together in an interactive way, to co-solve a problem. It is what I will be engaged in, I believe, the so-called human-computer interaction, or more specifically, human-GIS interaction.

The overwhelming first week

开学第一周，相当慌乱。

共选了五门课，其中三门大课，一门IST的关于怎么阅读文献的，一门IST的information management，还有一门Geography的Geovisual analytics Seminar。每门课都要做大量的文献阅读。

现在才知道原来课可以这么上：不需要你有太多的基础（prior knowledge），而是通过阅读来快速学习，然后通过课堂的分享和讨论来培养critical thinking。想起蔡老师跟我讲的，作为一个phd，你可以一开始不懂某个领域; 但一个星期以后，你不仅要了解，还要能说出个1234来。

这里的学生可能就是这种教育的成果。They keep talking。而这正是我缺乏的：怎么把输入整合之后输出并传播出去？

第一周遇到的很大的问题是阅读文献的速度。四篇文献约100页，我花了两整天才读完（当然也包括经常读不下去倒头睡觉的时间）。以后要注意阅读的方法。The Thinker's Guide to Analytic Thinking中提到的关于reasoning的几个要素，可以借鉴下：

另一个遇到的问题是，我完全听不懂学生的发言。学生的发言与老师的讲话不同，他们极其随意、含糊，而且语速很快、没有停顿。而如果听不懂他们的发言我就很难参与课堂讨论。

在Geovisual Analytics的课上终于见到了传说中的Alan MacEachren，看上去年纪很大了，可是却非常健硕，眼神非常犀利。据说他的课任务非常重，从他的syllabus可见一斑：课程的project希望能至少放会议上发表。Sigh...

不过第一周还是相当充实的，接触了很多新的概念，有一种迅速膨胀的感觉。接下来，加油！