刚刚接到他电话，称他找到工作了！真心替他高兴！
这几个月，虽然他每天乐呵呵的，可想想都知道，他背负着多么大的压力。自费来美国上学，毕业几个月了一直在找工作。或许几度萌生退意，终于咬牙坚持下来。如今如愿找到工作，可以继续在美国发展，还有什么比这个更让人高兴的呢！
高兴之余，我竟忍不住哭了。这是我在美国最好的朋友啊！他找到工作了，也意味着他也要走了。几个月前想到他要毕业要走，心里就挺难过的。结果他工作迟迟未定，这事也就放下了，似乎忘记了他迟早要先走这事。每周嘻嘻哈哈吃饭打牌不亦乐乎。如今这一天突然就来了…
祝福你吧，我的好兄弟。假以时日，你肯定会成大事！
10/17/2014
7/21/2014
Yo? Yo!
I read an article recently that top managers of Tencent such as Ma Huateng invested in Yo, a plain mobile app. It is really astonishing that such a boring app could involve so many users and raise so much investment. It is said to be launched on April Fool's day as a joke, and it turned out to be so popular. Basically, it could do nothing but send your friends a signal 'Yo!' So if you miss your boyfriend, you send him a 'Yo!'; if you try to wake up someone, you continuously send him 'Yo!'... You can send a "Yo!" whenever you want to draw attention without saying anything.
The phenomenon raises two questions: 1. why people like it? 2. Why business invests in it?
That people like it seems to indicate a trend that the social media is getting lighter. From long blogs to 140-character-limit Twitter, and to the no-word-at-all Yo, people are getting lazier in typing and outputting. It is like a hand wave or a 'hi' greeting in real life; people do not need to say anything essential to appear friendly. However, I doubt how far people would go with it as they cannot customize their message. Sometimes people do want to chat after greetings, right? Maybe the function should be integrated into an IM app, say, by tapping the portrait of a friend, you send him a "Yo"? Isn't it similar to QQ's function ‘shaking your friend'?
Then why business is optimistic about it? The article I read contributes it to the notification system. Yo has the potential to take over the notification system of the cell phone. Indeed, the notification is the first see when a phone is waked. The notification is able to push key information to the user. For instance, markets could push sales information by sending a 'Yo'. It is like a horn signal or a sales banner in reality. The signal succeeds to convey the necessary information without requiring response. This is important as it does not necessarily interrupt the user's on-going activity. The idea is similar to activity awareness. On the one hand, you need to keep your collaborators aware of your activity; on the other hand, you do not want to be interruptive. The design of notification is part of the design of technological support of activity awareness.
I forgot to mention that we moved into our new home last week. It is a two-floor townhouse, with two bedrooms. This place is really perfect for us, and for our coming baby. We put a lot of efforts in designing and decorating our new home. Actually we should enjoy the process much more than the result itself. To design is to imagine the sweet scene we will be in the future. This is, perhaps, similar to the design of technology.
The phenomenon raises two questions: 1. why people like it? 2. Why business invests in it?
That people like it seems to indicate a trend that the social media is getting lighter. From long blogs to 140-character-limit Twitter, and to the no-word-at-all Yo, people are getting lazier in typing and outputting. It is like a hand wave or a 'hi' greeting in real life; people do not need to say anything essential to appear friendly. However, I doubt how far people would go with it as they cannot customize their message. Sometimes people do want to chat after greetings, right? Maybe the function should be integrated into an IM app, say, by tapping the portrait of a friend, you send him a "Yo"? Isn't it similar to QQ's function ‘shaking your friend'?
Then why business is optimistic about it? The article I read contributes it to the notification system. Yo has the potential to take over the notification system of the cell phone. Indeed, the notification is the first see when a phone is waked. The notification is able to push key information to the user. For instance, markets could push sales information by sending a 'Yo'. It is like a horn signal or a sales banner in reality. The signal succeeds to convey the necessary information without requiring response. This is important as it does not necessarily interrupt the user's on-going activity. The idea is similar to activity awareness. On the one hand, you need to keep your collaborators aware of your activity; on the other hand, you do not want to be interruptive. The design of notification is part of the design of technological support of activity awareness.
I forgot to mention that we moved into our new home last week. It is a two-floor townhouse, with two bedrooms. This place is really perfect for us, and for our coming baby. We put a lot of efforts in designing and decorating our new home. Actually we should enjoy the process much more than the result itself. To design is to imagine the sweet scene we will be in the future. This is, perhaps, similar to the design of technology.
6/30/2013
Doctoral Workshop in Seattle
This is a three-day workshop in Seattle about the NSF funded program on social computational systems (SoCS). It is actually a PI meeting, but has some sessions to give feedback to doctoral students. Amazingly, it is my first workshop in my academic life, and it has been a really good start.
It's my first time to Seattle. Long before coming to USA, I've learnt the beauty and charm of Seattle. There is nothing thrilling in this city, but its quietness is attractive enough for people to live there for a whole life.
I gave a poster presentation on the first and second day. It's about some preliminary design features to meet challenges of online geodeliberation. It was my first time doing a poster presentation, and it was really challenging. Although poster presentation is more informal than presentation, it doesn't mean it is less challenging. The real purpose of poster presentation is not really report your work, but to rise people's interest in your work, and give a vivid talk about the importance of your work and the challenges you are facing. Also people tend to move around, you need to give them a quick overview of your work, rather than go into too much detail. It may always be frustrating to restart over again as new set of people are joining, so sometimes don't hurry to talk from the very begin. Sometimes you can wait till they ask you some questions and jump directly to that point. Of course you have to keep the balance.
The workshop is a great opportunity for me as I met many professors and students (from University of Washington, MIT, harvad, Ohio State, Oregon State, Virginia Tech, etc. )who are working on social computing, which seems a very promising direction. Many are utilizing social media data (e.g. twitter data) to identify interesting patterns, using data mining techniques. Another popular topic is crowdsourcing, which takes advantage of the power of the public.
As a professor summarized, computer scientists always try to think of solutions to a problem, while social scientists devote themselves to better understanding of the problem, and we working on social computing try to bridge the gap, and always start with people, observe their current practices and identify how their behavior could be improved and facilitated, and design technology to support meet those challenges.
Finally I'd like to put a word I learnt from the workshop:
It's my first time to Seattle. Long before coming to USA, I've learnt the beauty and charm of Seattle. There is nothing thrilling in this city, but its quietness is attractive enough for people to live there for a whole life.
I gave a poster presentation on the first and second day. It's about some preliminary design features to meet challenges of online geodeliberation. It was my first time doing a poster presentation, and it was really challenging. Although poster presentation is more informal than presentation, it doesn't mean it is less challenging. The real purpose of poster presentation is not really report your work, but to rise people's interest in your work, and give a vivid talk about the importance of your work and the challenges you are facing. Also people tend to move around, you need to give them a quick overview of your work, rather than go into too much detail. It may always be frustrating to restart over again as new set of people are joining, so sometimes don't hurry to talk from the very begin. Sometimes you can wait till they ask you some questions and jump directly to that point. Of course you have to keep the balance.
The workshop is a great opportunity for me as I met many professors and students (from University of Washington, MIT, harvad, Ohio State, Oregon State, Virginia Tech, etc. )who are working on social computing, which seems a very promising direction. Many are utilizing social media data (e.g. twitter data) to identify interesting patterns, using data mining techniques. Another popular topic is crowdsourcing, which takes advantage of the power of the public.
As a professor summarized, computer scientists always try to think of solutions to a problem, while social scientists devote themselves to better understanding of the problem, and we working on social computing try to bridge the gap, and always start with people, observe their current practices and identify how their behavior could be improved and facilitated, and design technology to support meet those challenges.
Finally I'd like to put a word I learnt from the workshop:
"I alone can't change the world, but I can throw a stone across water to create many ripples".
― Mother Teresa
1/31/2013
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.
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.
12/28/2012
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?
11/15/2012
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。
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。
10/28/2012
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。
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。
10/13/2012
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.
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.
9/03/2012
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.
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！
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：
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！
8/08/2012
来美第二天
在陈旭师兄家的沙发中胡乱睡了一觉，没倒过时差来，只睡了两个多小时。
早上8点出发去学校了。这里的公交车系统算是美国比较发达的了，可以去小镇的好多地方，但因为是暑假，班次比较少，约20-30分钟一班。
PSU学校很大，可是并不让人惊艳。建筑都是中规中矩的，既没有西方传统的高大宏伟，也没有历史沉淀的古典优雅。大部都是砖红色的，像内地新建的高中。不过校园内高大的树木和广袤的草坪给人很宁静的感觉，还有随处可见的松鼠，让人忍俊不禁。
先去了Boucke Building的Global Program处做了document checking，然后在hub的Id+这个办公室办了学生证和PNC银行卡。两者可以绑定，非常方便。之后是web access account的激活，差不多报到手续就结束了。系里的报到得等弄完SSN（social security number）后填完I-9表格（表示有受佣资格，可以做assistant）后才可以，干脆放在orientation时做。
中午在hub里的panda express吃的中餐，点了西兰花和牛肉，味道不错。
下午去了学校最大的图书馆Pattee Library，有点小失望。虽然藏书应该很丰富，但布置毫无特色，像内地的图书馆，在书架边上放着一列大桌子供人学习，没有典雅的台灯，没有励志的壮语。
晚上到了传说中的parkway plaza，房间旧了一点，但还挺宽敞，室友人也不错，发现可以住，就当晚托学长把东西运过来了。师兄给了我一张单人床(matress + box)，碰巧另一个师兄要搬走，去他那淘了不少东西，晚上两辆车浩浩荡荡地就开过来了。好玩的是，床垫和下面的box是放在车顶运过来的，我坐在车厢里用手扶着，感觉就像视频里开挂了的印度卡车一样。
终于有了自己的住所，以后像家一样好好经营。
早上8点出发去学校了。这里的公交车系统算是美国比较发达的了，可以去小镇的好多地方，但因为是暑假，班次比较少，约20-30分钟一班。
PSU学校很大，可是并不让人惊艳。建筑都是中规中矩的，既没有西方传统的高大宏伟，也没有历史沉淀的古典优雅。大部都是砖红色的，像内地新建的高中。不过校园内高大的树木和广袤的草坪给人很宁静的感觉，还有随处可见的松鼠，让人忍俊不禁。
先去了Boucke Building的Global Program处做了document checking，然后在hub的Id+这个办公室办了学生证和PNC银行卡。两者可以绑定，非常方便。之后是web access account的激活，差不多报到手续就结束了。系里的报到得等弄完SSN（social security number）后填完I-9表格（表示有受佣资格，可以做assistant）后才可以，干脆放在orientation时做。
中午在hub里的panda express吃的中餐，点了西兰花和牛肉，味道不错。
下午去了学校最大的图书馆Pattee Library，有点小失望。虽然藏书应该很丰富，但布置毫无特色，像内地的图书馆，在书架边上放着一列大桌子供人学习，没有典雅的台灯，没有励志的壮语。
晚上到了传说中的parkway plaza，房间旧了一点，但还挺宽敞，室友人也不错，发现可以住，就当晚托学长把东西运过来了。师兄给了我一张单人床(matress + box)，碰巧另一个师兄要搬走，去他那淘了不少东西，晚上两辆车浩浩荡荡地就开过来了。好玩的是，床垫和下面的box是放在车顶运过来的，我坐在车厢里用手扶着，感觉就像视频里开挂了的印度卡车一样。
终于有了自己的住所，以后像家一样好好经营。
8/07/2012
来美第一天
北京时间2012年8月6日6点12分，在贵博的寝室醒来，开始洗漱。7点，坐上第一班经过联合苑开往大学站的小巴。搭MTR到沙田，坐上直达机场的通天巴士（air bus，中文翻译更霸气）A41。8点20分，到达香港机场 terminal 1。
Delta的值机柜台在D区，排了一条长长的队伍。称行李时发现超重了，27Kg，比规定多了4Kg，无奈只好交了580港币，后来一想其实可以把箱子里的小书包和衣物拿出来，因为看很多人其实拿了两三件小包登机。
9点在机场吃了大快活。可能最后一次吃大快活的早餐了，点的滑蛋炸鱼柳，竟然感觉味道不错。
9点20进入安检、出关，9点40到达31号闸口。不一会儿就登机了。
从香港直飞底特律(Detroit)，历经约15个小时。飞机上的电影大部分竟没有字幕，发现自己还真听不懂对白，在美国有的受了。
美国东部时间下午1点20左右，飞机在Detroit降落。本来想把沿途拍下来给亲爱的作参考，后来发现指示牌非常清楚，又有工作人员指导，肯定不会走错，惟一需要注意的是入关有两个通道：US citizens和foreign visitors，我们要属于后者。拿了行李，开始入关，将护照 、I-20和飞机上填的I-94给海关人员，他会在I-20上盖章，并把I-94的出境卡钉到护照上，这两个文件非常重要，要小心保管。
走两步是入关物品检查，会有只警犬闻你的行李，警犬竟不是那种大狼狗，而是那种特可爱的beable（听后面的美国小男孩说的）。海关会问你带了多少现金，要求不多于10000美金。
海关的人其实并不严肃，会和你聊天。当我说要去Penn State时，他长长地叹了一口气：Oh boy。原来是最近发生的橄榄球虐童事件，看来给学校的声誉造成了很大的影响。
整个转机手续用了约40分钟就完成了，因此之前担心的转机时间不够（2小时）根本不是问题。2点多就坐在B6闸口等去State College的飞机。可是却迟迟不来。据说是打印不出乘客的信息？Paperwork之类的，听不太明白。不过那个检票的大叔态度挺好，很详细地向我们解释晚点的原因。
终于在5点40登机了。飞机真的是非常小，从闸口到飞机没有以往的通道，只用简单的一个木板架住，我的登山包放不进飞机的行李箱，只好拿去托运。起飞没多久我就沉沉地睡着了，醒来时竟然已经着陆了，没能鸟瞰一下整个State College。
下飞机时享受了国家领导人的待遇，从机舱沿着台阶走下来的，四周非常空旷，虽然已经下午7点10分，但太阳仍然高照，天空蓝的出奇，我一下子被这种乡村的空旷的宁静的美丽震憾了。
拿了行李没多久，于博师兄就来了。路上得知他的老婆和妈妈都在，马上就有baby了，在美国有个温馨的house，让我马上憧憬起我和瓜瓜的未来。晚上在麦当劳随便吃的快餐，6刀多。然后晚上和他家里人一起去了Walmat，买了点生活用品。那个超市很大，还卖家具，24小时营业。神奇的是收银台没有人，是自助check out的。将物品的条形码一刷就好。有趣的是，你还必须在刷完条码后把这件东西放入旁边的购物袋内，否则是不能刷第二件物品的。
这几天暂住一个叫陈旭的师兄这，在copper beach W. Aaron Dr.。睡沙发，竟然睡不着，12点躺下竟然2点半就醒了。折腾到3点半，索性起来，记下来美国的第一天。
第二天，也就是今天，去学校办各种证件。看看这个我未来要呆五六年的地方长什么样。
Delta的值机柜台在D区，排了一条长长的队伍。称行李时发现超重了，27Kg，比规定多了4Kg，无奈只好交了580港币，后来一想其实可以把箱子里的小书包和衣物拿出来，因为看很多人其实拿了两三件小包登机。
9点在机场吃了大快活。可能最后一次吃大快活的早餐了，点的滑蛋炸鱼柳，竟然感觉味道不错。
9点20进入安检、出关，9点40到达31号闸口。不一会儿就登机了。
从香港直飞底特律(Detroit)，历经约15个小时。飞机上的电影大部分竟没有字幕，发现自己还真听不懂对白，在美国有的受了。
美国东部时间下午1点20左右，飞机在Detroit降落。本来想把沿途拍下来给亲爱的作参考，后来发现指示牌非常清楚，又有工作人员指导，肯定不会走错，惟一需要注意的是入关有两个通道：US citizens和foreign visitors，我们要属于后者。拿了行李，开始入关，将护照 、I-20和飞机上填的I-94给海关人员，他会在I-20上盖章，并把I-94的出境卡钉到护照上，这两个文件非常重要，要小心保管。
走两步是入关物品检查，会有只警犬闻你的行李，警犬竟不是那种大狼狗，而是那种特可爱的beable（听后面的美国小男孩说的）。海关会问你带了多少现金，要求不多于10000美金。
海关的人其实并不严肃，会和你聊天。当我说要去Penn State时，他长长地叹了一口气：Oh boy。原来是最近发生的橄榄球虐童事件，看来给学校的声誉造成了很大的影响。
整个转机手续用了约40分钟就完成了，因此之前担心的转机时间不够（2小时）根本不是问题。2点多就坐在B6闸口等去State College的飞机。可是却迟迟不来。据说是打印不出乘客的信息？Paperwork之类的，听不太明白。不过那个检票的大叔态度挺好，很详细地向我们解释晚点的原因。
终于在5点40登机了。飞机真的是非常小，从闸口到飞机没有以往的通道，只用简单的一个木板架住，我的登山包放不进飞机的行李箱，只好拿去托运。起飞没多久我就沉沉地睡着了，醒来时竟然已经着陆了，没能鸟瞰一下整个State College。
下飞机时享受了国家领导人的待遇，从机舱沿着台阶走下来的，四周非常空旷，虽然已经下午7点10分，但太阳仍然高照，天空蓝的出奇，我一下子被这种乡村的空旷的宁静的美丽震憾了。
拿了行李没多久，于博师兄就来了。路上得知他的老婆和妈妈都在，马上就有baby了，在美国有个温馨的house，让我马上憧憬起我和瓜瓜的未来。晚上在麦当劳随便吃的快餐，6刀多。然后晚上和他家里人一起去了Walmat，买了点生活用品。那个超市很大，还卖家具，24小时营业。神奇的是收银台没有人，是自助check out的。将物品的条形码一刷就好。有趣的是，你还必须在刷完条码后把这件东西放入旁边的购物袋内，否则是不能刷第二件物品的。
这几天暂住一个叫陈旭的师兄这，在copper beach W. Aaron Dr.。睡沙发，竟然睡不着，12点躺下竟然2点半就醒了。折腾到3点半，索性起来，记下来美国的第一天。
第二天，也就是今天，去学校办各种证件。看看这个我未来要呆五六年的地方长什么样。
6/29/2012
GeoInformatics 2012
2012年6月15日至17日，GeoInformatics 2012在香港中文大学举办，由我们实验室承办。GeoInformatics是CPGIS这个华人地理信息协会每年都要举办的国际性会议。1992年，在美国纽约Buffalo大学，几个年轻的留美博士生自发组织成立了这样一个华人协会，领头的就是现在的林珲教授。20年后，当时的协会成员或成为工业界的领头人，或成为国内外顶尖大学的学者、教授，共同支撑着GIS学科的发展。我能想像这种从无到有的成就感，也很感叹这帮人敢想敢干的精神。
这届的会议非常成功，也邀请到了许多国际GIS大佬，如Goodchild, Michael Batty等。甚至还请来了GIS之父Tomlinson. 当2米多高的Tomlinson拄着拐杖颤颤巍巍地走上讲台，以一个父辈的身份，用颤抖的、接近哭腔的口吻对我们说“Now, I talk to you directly, the future of GIS”时，我仿佛接受了一次洗礼，似乎那个学科发展的巨担已传递到了我们肩头。
会上我还意外地碰到了John Radke，让我一下子想起了三年前的我。那会儿我利用寒假的时间去美国UC Davis交流，期间去了UC Berkeley. 因时间仓促，事先没联系好那边的老师，只在网上查了地理系的地址，就凭着一股年轻人的蛮不讲理去了。在系楼的过道上看到了John Radke的名字，只知道是和GIS相关的，就和秘书说我要找他。没想到他并没有怪我来得莽撞。很奇怪的，我们并没有讨论GIS的话题，而是就中国的问题争论起来。他说他小时候在中国长大，对中国很失望，中国的政府和学术很腐败；我便和他说现在已经不一样了，你大可以再去看一下。争论的最后我们都按捺不住脾气了，一小时的谈话就终止了。现在想来，当时的我真是好笑，而John的风度也的确让我印象深刻：一个大牌教授突然被一个不知哪来的毛头小孩打扰，还莫名其妙地和他讨论了一小时中国问题！记得讨论完的我异常兴奋，回去后还给他发了邮件，贴在下面，以供玩味。
这次会议是我认识GIS圈里人的大好机会，在这里把CPGIS的一些会员贴出来，供膜拜学习（不保证信息完整准确）。
1. 宫鹏。19岁即于南京大学地理系本科毕业，21岁于南京大学地理系硕士毕业，25岁于加拿大滑铁卢大学地理系获得博士学位，现任美国UC Berkeley大学教授，清华大学地球系统科学研究中心主任。
2. 丁跃民。美国Verizon Communication公司信息技术部资深经理。Verizon主要运营无限服务，美国最新的4G网络就是他们运营的。
3. 龚健雅。江西人。毕业于华东地质大学（今东华理工大学）。中国科学院院士，武汉大学教授，武汉大学测绘遥感信息工程国家重点实验室主任。吉奥公司总工。
4. 周成虎。福建人。南京大学陆地水文专业学士，中科院地理所博士。陈述彭学生。中科院地理所副所长，资源与环境信息系统国家重点实验室主任。
5. 李荣兴。上海同济大学surveying and mapping专业学士、硕士，Technical University of Berlin 大学 Photogrammetry and Remote Sensing博士。曾是NASA火星探索计划科学家。现任Director, Mapping & GIS Laboratory，Department of Civil and Environmental Engineering and Geodetic Science， The Ohio State University
6. 李斌。广东人。Ph.D., Syracuse University。现为Central Michigan University地理系教授。
7. 柳林。中山大学地理科学与规划学院院长。美国辛辛那提大学教授。
8. 林戈。美国内布拉斯加大学医学中心副教授。
9. 夏福祥。浙江衢州龙游人。浙江大学毕业。现为ESRI 资深构架师。
10. 周启鸣。香港浸会大学地理系教授，地学计算与分析研究中心主任。
11. 王野乔。美国罗德岛大学自然资源科学系教授。
12. 关蔚禾。哈佛大学地理分析中心研究服务部主任。
13. 涂汉明。Octagon Research公司临床信息技术部总监。
14. 陶阅。湖北人。武汉大学毕业，后赴加拿大卡尔加里大学用两年半时间获得博士学位。32岁获终身教授职称。开发了GlobalView等软件，现为PPLIVE的CEO.
这届的会议非常成功，也邀请到了许多国际GIS大佬，如Goodchild, Michael Batty等。甚至还请来了GIS之父Tomlinson. 当2米多高的Tomlinson拄着拐杖颤颤巍巍地走上讲台，以一个父辈的身份，用颤抖的、接近哭腔的口吻对我们说“Now, I talk to you directly, the future of GIS”时，我仿佛接受了一次洗礼，似乎那个学科发展的巨担已传递到了我们肩头。
会上我还意外地碰到了John Radke，让我一下子想起了三年前的我。那会儿我利用寒假的时间去美国UC Davis交流，期间去了UC Berkeley. 因时间仓促，事先没联系好那边的老师，只在网上查了地理系的地址，就凭着一股年轻人的蛮不讲理去了。在系楼的过道上看到了John Radke的名字，只知道是和GIS相关的，就和秘书说我要找他。没想到他并没有怪我来得莽撞。很奇怪的，我们并没有讨论GIS的话题，而是就中国的问题争论起来。他说他小时候在中国长大，对中国很失望，中国的政府和学术很腐败；我便和他说现在已经不一样了，你大可以再去看一下。争论的最后我们都按捺不住脾气了，一小时的谈话就终止了。现在想来，当时的我真是好笑，而John的风度也的确让我印象深刻：一个大牌教授突然被一个不知哪来的毛头小孩打扰，还莫名其妙地和他讨论了一小时中国问题！记得讨论完的我异常兴奋，回去后还给他发了邮件，贴在下面，以供玩味。
Prof. Radke,John竟也马上给我回了一封长信：
Hello, I'm the Chinese student who met you this afternoon. I'm from Zhejiang University with GIS as my major.I really admire you, not for your great achievements in GIS, but that you are truly thinking about what we are doing and what we are doing that for. You made me think deep into why I learn GIS. I have too much to say, so it might be a little bit long letter.Money is necessity for life, but I never worry too much about it. I have my dream which means more to me and I keep it all the way I grow up: I want to do something to change the world( big dream, err?) I keep on asking myself: but how? until I met GIS. GIS tools are powerful but they are not interface friendly and are complicated to use. Why not make it as popular as photoshop, thus pushing GIS research one step forward?But after the talk with you today, something new came into my mind. GIS is a tool, and only a tool which can be used in any way or not in any way at all if you are not determined to solve a problem! There exist many problems--both in developing and developed countries--environmental degradation, resource shortage, transportation problem, etc. Actually the government could solve them, but it costs. What we might do is, to make the cost smallest so that the governor is willing to take the risk, with the tool of GIS. And I might turn to politician if necessary.I don't know if it is another naive idea. It just hits my mind on my way back home.As for the "China" you mentioned today, I just feel sorry. But it's not apology. I just feel sad that you didn't see the other side of the country. That's not real China. There are many problems in China like government corruption and environmental degradation. But we have identified the problems and we have taken action. We just need time. Time is indeed impressing but it's never too late to take action. China is no longer the poor country before. It is a big responsible country, just look at 2008 Olympics and the important role China plays in the Copenhagen conference. I truly hope you pay a visit to China once more, and perhaps to Zhejiang University, Hangzhou.By the way, Beijing is the capital of P.R.China, while Capital Nanjing is history. They are two different periods of China of 5000 years old.
Dong Chen,这次再见，很可惜他已经不记得我了。希望有一天，在我学有所成之时，能再和他"talk around GIS problems"。
It was good to meet you. If I could only transfer my experience to you
directly you would see that in my mind I see the beauty of your home
country and in fact all the counties of the world. I also see the future
and the custodians of the future. This is my dilemma. It starts with
humans and their inability to see the bigger more important problems.
They are for the most part goal directed and this is their weakness and
downfall. No doubt China will fix its current problems but will they
learn? The US is young, has experienced many growing pains and I believe
rarely learns form those experiences. If we only had time to go into this
further.
To save a life is to save your own. Save mother earth and look what you
have done.
Governors (politicians) are usually caught up in themselves and by the
time they realize they could have done something good ... their time is up
and a new one enters the scene. Faith in their ability to see the
problems and articulate a solution is likely naive but this is not a
failure of yours but theirs.
Spatial reasoning and understanding brings us closer to recognition of
what is. This is why I have embraced GIS. I see you feel the same. I
salute you for it.
kind regards,
这次会议是我认识GIS圈里人的大好机会，在这里把CPGIS的一些会员贴出来，供膜拜学习（不保证信息完整准确）。
1. 宫鹏。19岁即于南京大学地理系本科毕业，21岁于南京大学地理系硕士毕业，25岁于加拿大滑铁卢大学地理系获得博士学位，现任美国UC Berkeley大学教授，清华大学地球系统科学研究中心主任。
2. 丁跃民。美国Verizon Communication公司信息技术部资深经理。Verizon主要运营无限服务，美国最新的4G网络就是他们运营的。
3. 龚健雅。江西人。毕业于华东地质大学（今东华理工大学）。中国科学院院士，武汉大学教授，武汉大学测绘遥感信息工程国家重点实验室主任。吉奥公司总工。
4. 周成虎。福建人。南京大学陆地水文专业学士，中科院地理所博士。陈述彭学生。中科院地理所副所长，资源与环境信息系统国家重点实验室主任。
5. 李荣兴。上海同济大学surveying and mapping专业学士、硕士，Technical University of Berlin 大学 Photogrammetry and Remote Sensing博士。曾是NASA火星探索计划科学家。现任Director, Mapping & GIS Laboratory，Department of Civil and Environmental Engineering and Geodetic Science， The Ohio State University
6. 李斌。广东人。Ph.D., Syracuse University。现为Central Michigan University地理系教授。
7. 柳林。中山大学地理科学与规划学院院长。美国辛辛那提大学教授。
8. 林戈。美国内布拉斯加大学医学中心副教授。
9. 夏福祥。浙江衢州龙游人。浙江大学毕业。现为ESRI 资深构架师。
10. 周启鸣。香港浸会大学地理系教授，地学计算与分析研究中心主任。
11. 王野乔。美国罗德岛大学自然资源科学系教授。
12. 关蔚禾。哈佛大学地理分析中心研究服务部主任。
13. 涂汉明。Octagon Research公司临床信息技术部总监。
14. 陶阅。湖北人。武汉大学毕业，后赴加拿大卡尔加里大学用两年半时间获得博士学位。32岁获终身教授职称。开发了GlobalView等软件，现为PPLIVE的CEO.
6/28/2012
A dip into NCL
NCL is an interpreted language designed for scientific data analysis and visualization. I use it mostly for visualization, but not seriously, because there is another developed platform for visualization. For NCL, I just use it to check the model results.
So I just got a dip of NCL for the last three days, in an attempt to plot a wind vertical profile with eta level. The wind vector goes along the terrain and the plot is blank where there is a hill, so that the impact of topography could be seen. Shame on myself, I still haven't gone through it. And in terms of time, I have to give up. Here I'd lie to summarize up what I've learned with NCL.
Coordinate variables are the key information to plot right. Variables from model results (in my case, WRF, CMAQ, and SMOKE) usually bear no coordinate, but columns and rows. It is fine if you just want to see the patterns. However, if you want a base map overlaid, the right coordinates have to be assigned. Coordinates are usually stored in another output variable, e.g. XLAT for latitude and XLONG for longitude in WRF. What you have to do is to assign lat/lon to the right dimension of, say, wind. Here is the code:
It comes out that there are two grids in WRF, the staggered grid and the mass grid. Value in mass grid is refers to value in the center of the grid while value in staggered grid means value in the boundary of the grid. So the dimension size of staggered grids is always greater than mass grids by 1. To solve the inconsistency, we average the value to mass grid points.
Finally gsn_csm_pres_hgt_vector (example is here and here) is used to plot the vector. However, the result (shown in figure below, at 24.5N along 113-114E) is quite different from the example, mainly for the three points:
1. I am not sure whether the eta level is terrain-following. Though there are "ups and downs", no specific "hills" are to be found. Of course, it may be a problem of scales.
2. The temperature contour goes along eta level, which is weird.
3. Wind always flows to the east. There seems no "up" wind.
Some other resources:
To plot vectors:
gsn_vector(), gsn_csm_vector(). And the latter is more advanced.
gsn_csm_vector_map() overlays the vector plot on a base map.
To draw vector and scalar simultaneously.
gsn_csm_vector_scalar_map()
So I just got a dip of NCL for the last three days, in an attempt to plot a wind vertical profile with eta level. The wind vector goes along the terrain and the plot is blank where there is a hill, so that the impact of topography could be seen. Shame on myself, I still haven't gone through it. And in terms of time, I have to give up. Here I'd lie to summarize up what I've learned with NCL.
Coordinate variables are the key information to plot right. Variables from model results (in my case, WRF, CMAQ, and SMOKE) usually bear no coordinate, but columns and rows. It is fine if you just want to see the patterns. However, if you want a base map overlaid, the right coordinates have to be assigned. Coordinates are usually stored in another output variable, e.g. XLAT for latitude and XLONG for longitude in WRF. What you have to do is to assign lat/lon to the right dimension of, say, wind. Here is the code:
f = addfile(filename, "r")But wait, there is a mistake here. NCL would prompt dimension inconsistent between U and W. Let's take a close look at it. By the funtion printVarSummary we find the dimension of both U and W is time*level*latitude*longitude. So, why inconsistent?
U = f->U ; wind in east-west direction, time*lev*lat*lon
W = f->W ; wind in bottom-up direction
lat = f->XLAT(0,:,0) ; time*lat*lon
lon = f->XLON(0,0,:)
znu = f->ZNU(0,:) ; time*lev
lat@units = "degrees_north"
lon@units = "degrees_east"
lat!0 = "lat"
lon!0 = "lon"
lat&lat = lat
lon&lon = lon
lev = znu*1000 ; [-105.1526..-82.84741]
lev@long_name = "eta*1000"
lev@units = "hPa"
lev!0 = "lev"
lev&lev = lev
U!0 = "lev"
U!1 = "lat"
U!2 = "lon"
U&lev = lev
U&lat = lat
U&lon = lon
; And the same for w, left out here
It comes out that there are two grids in WRF, the staggered grid and the mass grid. Value in mass grid is refers to value in the center of the grid while value in staggered grid means value in the boundary of the grid. So the dimension size of staggered grids is always greater than mass grids by 1. To solve the inconsistency, we average the value to mass grid points.
dimU = dimsizes(U)In this case, u is on the common grid. And the same is for w.
nlonU = dimU(3)
u = 0.5 * (U(:,:,:,0:nlonU-2) + U(:,:,:,1:nlonU-1))
Finally gsn_csm_pres_hgt_vector (example is here and here) is used to plot the vector. However, the result (shown in figure below, at 24.5N along 113-114E) is quite different from the example, mainly for the three points:
1. I am not sure whether the eta level is terrain-following. Though there are "ups and downs", no specific "hills" are to be found. Of course, it may be a problem of scales.
2. The temperature contour goes along eta level, which is weird.
3. Wind always flows to the east. There seems no "up" wind.
To plot vectors:
gsn_vector(), gsn_csm_vector(). And the latter is more advanced.
gsn_csm_vector_map() overlays the vector plot on a base map.
To draw vector and scalar simultaneously.
gsn_csm_vector_scalar_map()
6/25/2012
完成论文
查看了下上一篇日志，还是在上月初写的。一晃就是50天，时间过得真快。
没写日志的一个原因是这期间确实没取得实质性的进展。除了为WRF和CMAQ写了两个顶层的控制脚本，就为在其他机器上安装CMAQ进行尝试。这不是技术问题，而是沟通与制度的问题。结果实验室新买的电脑因为权限问题到现在还没配置好，而中大的集群也因升级而暂时无法使用。结果白白浪费了一些时间。不过在此期间，发现CMAQ竟然升级了。最新的CMAQ 为5.0版本，加入了 CMAQ-WRF two-way couple 的新特性。这个升级非常让人兴奋。众所周知，气象场对污染物的分布产生很大的影响，是污染物扩散的驱动力；反过来，污染物的浓度也会对局部地区的温度、相对湿度等产生影响。因此，将这种双向的反馈反应到模式中，会使模拟结果更加准确。
在此期间，还写了MSc的毕业论文。本打算非常认真、严谨地对待这篇文章，但是时间紧迫，平时积累不够（想起Robert说的，每天读半小时文献，写半小时文摘，这些积累使他最后写论文时简单的copy-paste就完成了。真是汗颜）。因此论文不是很让我满意，模拟的结果也没有时间再做改善。
在做毕业论文内容的过程中，我也发现了一些问题：
1. 我拿到的污染源数据中，香港的排放源不完整，甚至是空白的。具体的数据还没有分析，但从CMAQ模拟的结果来看，香港地区除了塔门这个监测点，其他监测点的污染浓度值都与模拟值相差较大。这可以用排放源解释：塔门附近没有大型的排放源，它监测的是背景浓度，而且它离深圳较近。相对而言，深圳的排放源更准确，模拟的污染物浓度也更符合实际。所以塔门模拟得较好。而其他站点，因为排放源的缺失，模拟的浓度值大大低于实际值。
2. 跑模式分析污染物是一件很荒诞的事。在我看来，很多这方面的论文就是为了发表而发表。很多人跑模式，是为了分析过去的某个空气污染事件。当模拟值与实际值不符时，就提高或减少排放源的排放量，直到结果较为满意。试问，这样有什么意义？当然，有些人依此得到较为可信的排放源数据，并希望将它用于未来的模拟。可问题是，怎么证明适用于那个事件的污染源同样适用于其他事件、其他时期？
很高兴我今后不再研究这个领域。
没写日志的一个原因是这期间确实没取得实质性的进展。除了为WRF和CMAQ写了两个顶层的控制脚本，就为在其他机器上安装CMAQ进行尝试。这不是技术问题，而是沟通与制度的问题。结果实验室新买的电脑因为权限问题到现在还没配置好，而中大的集群也因升级而暂时无法使用。结果白白浪费了一些时间。不过在此期间，发现CMAQ竟然升级了。最新的CMAQ 为5.0版本，加入了 CMAQ-WRF two-way couple 的新特性。这个升级非常让人兴奋。众所周知，气象场对污染物的分布产生很大的影响，是污染物扩散的驱动力；反过来，污染物的浓度也会对局部地区的温度、相对湿度等产生影响。因此，将这种双向的反馈反应到模式中，会使模拟结果更加准确。
在此期间，还写了MSc的毕业论文。本打算非常认真、严谨地对待这篇文章，但是时间紧迫，平时积累不够（想起Robert说的，每天读半小时文献，写半小时文摘，这些积累使他最后写论文时简单的copy-paste就完成了。真是汗颜）。因此论文不是很让我满意，模拟的结果也没有时间再做改善。
在做毕业论文内容的过程中，我也发现了一些问题：
1. 我拿到的污染源数据中，香港的排放源不完整，甚至是空白的。具体的数据还没有分析，但从CMAQ模拟的结果来看，香港地区除了塔门这个监测点，其他监测点的污染浓度值都与模拟值相差较大。这可以用排放源解释：塔门附近没有大型的排放源，它监测的是背景浓度，而且它离深圳较近。相对而言，深圳的排放源更准确，模拟的污染物浓度也更符合实际。所以塔门模拟得较好。而其他站点，因为排放源的缺失，模拟的浓度值大大低于实际值。
2. 跑模式分析污染物是一件很荒诞的事。在我看来，很多这方面的论文就是为了发表而发表。很多人跑模式，是为了分析过去的某个空气污染事件。当模拟值与实际值不符时，就提高或减少排放源的排放量，直到结果较为满意。试问，这样有什么意义？当然，有些人依此得到较为可信的排放源数据，并希望将它用于未来的模拟。可问题是，怎么证明适用于那个事件的污染源同样适用于其他事件、其他时期？
很高兴我今后不再研究这个领域。
5/06/2012
所谓认知
一直以为“认知”是心理学领域的东西，而且因为其抽象、玄乎，而本能的排斥它。后来才知道，“认知”早与“地理”扯上关系，在国外，地理是一门“社会科学”，研究地理是为了更好地研究人，将人与地理结合，或者说将人与环境结合，是地理学的崭新的研究方向，而地理、行为、认知则构成了这新方向的关键词。
可是，认知究竟是什么？在我看来，科学的意义就是用逻辑来解释某种现象，而这种现象必须是可重复发生、可用数据定量描述的。那么认知，符合这样的条件吗？
最近看了一篇心理学的论文，虽不足以解开我所有的疑惑，但让我有所启发；更让我印象深刻的是，之前一些模糊的、关于主客观的“思想”，原来可以用严谨的科学语言来表达，让我重新认识了心理学。
这是广州大学心理与脑科学研究中心的叶浩生老师写的《有关具身认知思潮的理论心理学思考》，对具身认知、离身认知等一些“人如何看待世界”的方式进行了综述，并认为具身认知才是正确的理解心理学的基础。
离身认知（disembodied cognition）认为思维和身体是分离的，身体只是意识的载体。所谓身心二元论，即心和身分属两个世界，主体和客体是一个“表征与被表征”的关系，主体若能镜像般地表征世界，则能真正认识世界。以计算机为比喻，身体就像硬件，负责数据的输入和信息的输出，而思维像程序一样负责信息的加工处理。从这个意义上讲，认知就是信息的表征和操控，“以抽象的符号表征着外在于我们的带我，然后通过操纵这些符号完成思维”。
反过来说，如今的计算机其实正在模拟人的认知过程，只不过如今的程序还非常稚嫩，如今的硬件也无法如人的身体那样感知到足够丰富的信息。另一方面，既然思维和身体是分离的，那就代表它们是可以替换的；那么如果有一天，物理系统足够复杂，就可以承担人类的智能。这与当时陈老师们在吃饭时谈笑的内容多么相似：随着计算机系统越来越复杂，计算机能自动完成的事件越来越多，可以预见，当计算机足够复杂时，它们能够自己debug，自己reflection，那么它们与人类的距离又有多大呢？这或许是所有计算机专家的梦想，也是许多像《黑客帝国》之类的科幻片的灵感来源。
我本人也非常信仰这种观点。可如今，一种新的看法，即具身认知（embodied cognition）被越来越多人认可，也似乎更“高级”，如同当时只研究环境，后来加入的“人”的因素一样，后者显得更合理。这种观点认为，认知在很大程度上与身体的物理属性相关，脑神经水平上的细节、身体的结构、运动系统等都对认知的形成有重要的影响。“人认识世界的方式是用我们的身体以合适的方式与世界中的其他物体互动，在互动的过程中获得对世界的认识”。它认为认知在本质上并非使用抽象符号的表征和加工，而是一种模拟（simulation），而所谓模拟，是“身体、世界和心智互动过程中产生的知觉、运动和内海状态的复演”。我对此并不十分理解，紧接着文章举了个例子，认为同情心的产生即是因为我们“通过大脑与身体的特殊通道模拟了他人的感受”，同样让我不太理解。
文章还举了一个更有说服力的例子。Yale大学的研究者在2010年做了一个实验，把41个大学生随机分成两组，A组学生双手捧着一杯热咖啡，B组学生捧着一杯冰咖啡。然后两组学生分别对同一个想象中的中性人物的人格特征进行评分。结果显示，A组学生比B组学生更有可能把这个人评估为热情、友好。从而认为，身体上感知到的温暖影响了学生认知上的判断。这个结果的确让人惊讶，但仔细一想却也不是那么奇怪。正如所有人都知道的那样，同样的风景给不同的人可能有不同的感受，有人喜有人悲，最简单的，如果人身体健康，那很可能就是乐观的心态，反之如果他正遭受疾病，他更趋向于悲观的心情，这似乎是理所当然的。
最后说说具身认知的方法论。具身认知告诉我们要把有机体放在它的环境中，视有机体、行为和环境紧密相连。这就回到文首的问题，有关“有机体”的变量是可重复出现的吗？我们知道环境是可以量化的，可人在那一刻是喜是悲却无法估计，也不可能重复发生，那如何用科学的方法来研究？另外，具身认知认为，神经科学和心理学是对同一问题的两种不同的解释，应该将这两种方法结合起来，可是具体如何结合呢？
这些都是具身认知让我不可接受的地方。尽管现代科学都越来越重视“人”的因素，都试图把“人”加入研究的范畴（是否和兴起的社交网络有关？），但我还是很难理解怎样用科学、用数字和规则来研究人的行为，甚至人的认知。
可是，认知究竟是什么？在我看来，科学的意义就是用逻辑来解释某种现象，而这种现象必须是可重复发生、可用数据定量描述的。那么认知，符合这样的条件吗？
最近看了一篇心理学的论文，虽不足以解开我所有的疑惑，但让我有所启发；更让我印象深刻的是，之前一些模糊的、关于主客观的“思想”，原来可以用严谨的科学语言来表达，让我重新认识了心理学。
这是广州大学心理与脑科学研究中心的叶浩生老师写的《有关具身认知思潮的理论心理学思考》，对具身认知、离身认知等一些“人如何看待世界”的方式进行了综述，并认为具身认知才是正确的理解心理学的基础。
离身认知（disembodied cognition）认为思维和身体是分离的，身体只是意识的载体。所谓身心二元论，即心和身分属两个世界，主体和客体是一个“表征与被表征”的关系，主体若能镜像般地表征世界，则能真正认识世界。以计算机为比喻，身体就像硬件，负责数据的输入和信息的输出，而思维像程序一样负责信息的加工处理。从这个意义上讲，认知就是信息的表征和操控，“以抽象的符号表征着外在于我们的带我，然后通过操纵这些符号完成思维”。
反过来说，如今的计算机其实正在模拟人的认知过程，只不过如今的程序还非常稚嫩，如今的硬件也无法如人的身体那样感知到足够丰富的信息。另一方面，既然思维和身体是分离的，那就代表它们是可以替换的；那么如果有一天，物理系统足够复杂，就可以承担人类的智能。这与当时陈老师们在吃饭时谈笑的内容多么相似：随着计算机系统越来越复杂，计算机能自动完成的事件越来越多，可以预见，当计算机足够复杂时，它们能够自己debug，自己reflection，那么它们与人类的距离又有多大呢？这或许是所有计算机专家的梦想，也是许多像《黑客帝国》之类的科幻片的灵感来源。
我本人也非常信仰这种观点。可如今，一种新的看法，即具身认知（embodied cognition）被越来越多人认可，也似乎更“高级”，如同当时只研究环境，后来加入的“人”的因素一样，后者显得更合理。这种观点认为，认知在很大程度上与身体的物理属性相关，脑神经水平上的细节、身体的结构、运动系统等都对认知的形成有重要的影响。“人认识世界的方式是用我们的身体以合适的方式与世界中的其他物体互动，在互动的过程中获得对世界的认识”。它认为认知在本质上并非使用抽象符号的表征和加工，而是一种模拟（simulation），而所谓模拟，是“身体、世界和心智互动过程中产生的知觉、运动和内海状态的复演”。我对此并不十分理解，紧接着文章举了个例子，认为同情心的产生即是因为我们“通过大脑与身体的特殊通道模拟了他人的感受”，同样让我不太理解。
文章还举了一个更有说服力的例子。Yale大学的研究者在2010年做了一个实验，把41个大学生随机分成两组，A组学生双手捧着一杯热咖啡，B组学生捧着一杯冰咖啡。然后两组学生分别对同一个想象中的中性人物的人格特征进行评分。结果显示，A组学生比B组学生更有可能把这个人评估为热情、友好。从而认为，身体上感知到的温暖影响了学生认知上的判断。这个结果的确让人惊讶，但仔细一想却也不是那么奇怪。正如所有人都知道的那样，同样的风景给不同的人可能有不同的感受，有人喜有人悲，最简单的，如果人身体健康，那很可能就是乐观的心态，反之如果他正遭受疾病，他更趋向于悲观的心情，这似乎是理所当然的。
最后说说具身认知的方法论。具身认知告诉我们要把有机体放在它的环境中，视有机体、行为和环境紧密相连。这就回到文首的问题，有关“有机体”的变量是可重复出现的吗？我们知道环境是可以量化的，可人在那一刻是喜是悲却无法估计，也不可能重复发生，那如何用科学的方法来研究？另外，具身认知认为，神经科学和心理学是对同一问题的两种不同的解释，应该将这两种方法结合起来，可是具体如何结合呢？
这些都是具身认知让我不可接受的地方。尽管现代科学都越来越重视“人”的因素，都试图把“人”加入研究的范畴（是否和兴起的社交网络有关？），但我还是很难理解怎样用科学、用数字和规则来研究人的行为，甚至人的认知。
3/31/2012
A first glimpse of CMAQ
In the last three weeks, I did three things:
1. Modify the python script written before to interpolate preciser DEM into WRF.
2. Learn NCL
3. Successfully run CMAQ model.
DEM Interpolation
As mentioned in the last post, I wrote a small python script to interpolate our 30-meter-resolution DEM data into WRF, and tried to make the result more accurate. After the script has been written, however, the validation is another challenging work. First we just used geogrid.exe to take in the output data, and compared the result with that using coarser resolution data, say 30 s(econd) and 2 m(inus), to see the rough trend. The result was OK, but not persuasive enough to claim the correctness, because the terrain is too complex and you can not reach every detail with only eyes!
Then we did more accurate validation. First, we made up some experimental data, say, a block of DEM where four quarters were different constant values. The simplified data was easy to check if the output was right or not. Then real data were transferred in WPS, and we did an overlay on the coarser data, and made a "minus" computation in the topography height. In this way, the correctness of the script can be fully validated.
The following work should be to check how much the more precise topography influences WRF. From the geogrid result, you can hardly tell the difference between 1s and 30s terrain. However, from reports by other researchers, the topography does make a difference. The same method is applied: overlay and minus.
NCL
NCL is an interpreted language (like python) designed specifically for scientific data analysis and visualization. As far as I see, the tool has following advantages:
1. Interpreted language. It's easy to use, with compiling and linking problems. Any change can be immediately applied. It is much like python, and can also be run in both interactive mode and batch mode.
2. Powerful I/O ability. It can read almost all scientific data, from ASCII to binary, and netcdf, grib1 & grib2, and shapefile, etc. That's why we abandon GrADS, which can only deal with specific format, and may not be used for WPS, CMAQ and SMOKE.
3. Programming freedom. The language is rather flexible, and give users enough freedom to do what they want. And it is easy to be extended to wrap C and Fortran.
4. Numerous built-in functions. Though I haven't used these mathematical functions, but it is good to know there are many available tools at hands, something like Matlab.
5. Easy map overlay. As it is especially designed for atmospheric and oceanic models, map overlay is very important to study a specific issue.
More features:
There are several different kinds of data in NCL. A variable can have attribute data, coordinate data and missing values. Attribute data explains additional info apart from the "real data", like variable descriptions. Coordinate data facilitates the mapping procedure. Missing value support is another feature that highlights. Many models have a special value that is assigned to those with no sufficient info (e.g. initial condition) to simulate. NCL can recognize these data and do some special tricks.
Also, unlike C, NCL can deal with the whole array. On the other hand, loop though every element in an array is inefficient and not recommended.
CMAQ
It finally comes to CMAQ. Thanks to Prof. Zheng Junyu, now we have SMOKE output, and can run CMAQ. CMAQ consists of many sub-programs. It is annoying to set up configurations one by one. So I write a top script to control these sub-scripts.
It's interesting to learn the shell programming. Linux shell is much more powerful than Windows Dos. It not only has basic commands, but also variables, statements, functions and procedures, and logical expressions. Some confusions I now have is Linux has various shells, like sh, bash, csh, and different shells has some different syntax. Besides, the concept "process" is important, and affects the pass of variables between functions and scripts.
It is really time-consuming to run CMAQ. For basic configurations, there are six output files, including pollution concentration and average concentration, and wet and dry deposition. One problem I recently solved is variables in CCTM output has no coordinates, but only rows and columns, making it difficult to overlay with the map. Actually there is such info, and it resides in MCIP output, in GRDCRO2D file. There are variables LON AND LAT. They are 4-dimension array, with time steps, layers, rows and columns. In fact for coordinates the array is duplicated. Only one dimension is enough, which is also required by NCL (in NCL, coordinate variables must be one dimensional. For longitude, pick columns and for latitude, pick rows).
1. Modify the python script written before to interpolate preciser DEM into WRF.
2. Learn NCL
3. Successfully run CMAQ model.
DEM Interpolation
As mentioned in the last post, I wrote a small python script to interpolate our 30-meter-resolution DEM data into WRF, and tried to make the result more accurate. After the script has been written, however, the validation is another challenging work. First we just used geogrid.exe to take in the output data, and compared the result with that using coarser resolution data, say 30 s(econd) and 2 m(inus), to see the rough trend. The result was OK, but not persuasive enough to claim the correctness, because the terrain is too complex and you can not reach every detail with only eyes!
Then we did more accurate validation. First, we made up some experimental data, say, a block of DEM where four quarters were different constant values. The simplified data was easy to check if the output was right or not. Then real data were transferred in WPS, and we did an overlay on the coarser data, and made a "minus" computation in the topography height. In this way, the correctness of the script can be fully validated.
The following work should be to check how much the more precise topography influences WRF. From the geogrid result, you can hardly tell the difference between 1s and 30s terrain. However, from reports by other researchers, the topography does make a difference. The same method is applied: overlay and minus.
NCL
NCL is an interpreted language (like python) designed specifically for scientific data analysis and visualization. As far as I see, the tool has following advantages:
1. Interpreted language. It's easy to use, with compiling and linking problems. Any change can be immediately applied. It is much like python, and can also be run in both interactive mode and batch mode.
2. Powerful I/O ability. It can read almost all scientific data, from ASCII to binary, and netcdf, grib1 & grib2, and shapefile, etc. That's why we abandon GrADS, which can only deal with specific format, and may not be used for WPS, CMAQ and SMOKE.
3. Programming freedom. The language is rather flexible, and give users enough freedom to do what they want. And it is easy to be extended to wrap C and Fortran.
4. Numerous built-in functions. Though I haven't used these mathematical functions, but it is good to know there are many available tools at hands, something like Matlab.
5. Easy map overlay. As it is especially designed for atmospheric and oceanic models, map overlay is very important to study a specific issue.
More features:
There are several different kinds of data in NCL. A variable can have attribute data, coordinate data and missing values. Attribute data explains additional info apart from the "real data", like variable descriptions. Coordinate data facilitates the mapping procedure. Missing value support is another feature that highlights. Many models have a special value that is assigned to those with no sufficient info (e.g. initial condition) to simulate. NCL can recognize these data and do some special tricks.
Also, unlike C, NCL can deal with the whole array. On the other hand, loop though every element in an array is inefficient and not recommended.
CMAQ
It finally comes to CMAQ. Thanks to Prof. Zheng Junyu, now we have SMOKE output, and can run CMAQ. CMAQ consists of many sub-programs. It is annoying to set up configurations one by one. So I write a top script to control these sub-scripts.
It's interesting to learn the shell programming. Linux shell is much more powerful than Windows Dos. It not only has basic commands, but also variables, statements, functions and procedures, and logical expressions. Some confusions I now have is Linux has various shells, like sh, bash, csh, and different shells has some different syntax. Besides, the concept "process" is important, and affects the pass of variables between functions and scripts.
It is really time-consuming to run CMAQ. For basic configurations, there are six output files, including pollution concentration and average concentration, and wet and dry deposition. One problem I recently solved is variables in CCTM output has no coordinates, but only rows and columns, making it difficult to overlay with the map. Actually there is such info, and it resides in MCIP output, in GRDCRO2D file. There are variables LON AND LAT. They are 4-dimension array, with time steps, layers, rows and columns. In fact for coordinates the array is duplicated. Only one dimension is enough, which is also required by NCL (in NCL, coordinate variables must be one dimensional. For longitude, pick columns and for latitude, pick rows).
3/16/2012
读《激荡三十年》
很早就听说这本书了，由赫赫有名的吴晓波老师写的，当初因为他与我们强化班的导师同名而让我印象深刻。自然，和其他经济学类书一样，这本书受到了强化班同学的追捧。很多人表示“赞”，或想读，或者读过了的出来发表一堆宏论。我自然不喜这些，因此也就耽搁了。
不过，对经济学类的书的好奇让我又翻看了这本书。似乎第一次看这么“务实”的书。书的内容让我很意外，写的很浅显，也很真实，让不谙世事的我第一次对这些“身边的最实实在在的事”有了一个认识。尤其是读到台州的李书福甚至玉环的那些事时，我深切地体会到书中讲的事情是多么的现实。
所以说经济学是很有意思的一门学科，它不像我平时做的学科如计算机等那么抽象、那么脱离现实，它是研究这个社会如何动作的学科，与古时读书人的清风傲骨隔隔不入。
或许以后也该多读点这类务实的书。有时想想，何必执着于那些所谓的技术呢？
不过，对经济学类的书的好奇让我又翻看了这本书。似乎第一次看这么“务实”的书。书的内容让我很意外，写的很浅显，也很真实，让不谙世事的我第一次对这些“身边的最实实在在的事”有了一个认识。尤其是读到台州的李书福甚至玉环的那些事时，我深切地体会到书中讲的事情是多么的现实。
所以说经济学是很有意思的一门学科，它不像我平时做的学科如计算机等那么抽象、那么脱离现实，它是研究这个社会如何动作的学科，与古时读书人的清风傲骨隔隔不入。
或许以后也该多读点这类务实的书。有时想想，何必执着于那些所谓的技术呢？
3/12/2012
Topography in WPS
Two weeks ago, I tried to run CMAQ. However, it was really beyond my expectation that the pollution emission source data were so difficult to deal with, and finally we had to give up.
SMOKE is a set of programs responsible for the emission data. It takes in pollution inventory and turns it into CMAQ permitted format. In addition, it also divides the inventory spatially into grids and temporally into hours and days as most inventory data we get are annual. The problem is, SMOKE is developed by Americans and for USA use. The standards and administrative divisions are totally different. To take advantage of SMOKE, a specific methodology to adapt to the local is needed. Spanish scholars (R. Borge, et al) have done that. In China, Prof. Zheng Junyu from South China University of Technology spent two years finishing that.
We are lucky that on 2nd March, Prof. Zheng came here and gave us a speech, exactly about pollution inventory preparation. He promised to give us his fruit so that we can go further. This Wednesday we are going there to learn about the skills.
Last week, I tried to replace topography data for WPS with my own data. WPS is the pre-processing software of WRF, preparing geographical and meteorological data for real-case weather simulation. It consists of three sub-programs, among which geogrid is responsible for interpolating static geographical data into grids. WPS is equipped with global geographical data issued by USGS, with 10m, 5m, 2m, 30s resolutions. For better precision, we have to add customized data to geogrid. We've got 1s (30 meters) PRD DEM data, and we can replace the topography data with it. Later, we are going to collect landuse data and do the same replacement.
The work is not so difficult as it seems. Both DEM data and geogrid formatted data are in grids. All I have to do is to read DEM into memory and then output it to static data. GDAL facilitates us greatly with dealing with DEM and WPS provides the output routine. We have to pay attention to obeying the WPS program interface. Also, data sequence in these two data formats are different. GDAL tends to read data from the first row (from north to south), but geogrid routine writes data from the south to the north.
Ater the static data are ready, some configurations have to be modified to instruct geogrid to use the data we specified. After geo_em.d* have been output, we can use NCL to view the result. A sample ncl script can be downloaded here.
In this way, terrain data with better resolution can be applied to WRF, which should affect the simulation result, like wind direction. Of course, the simulation grid is important in determining the significance of this work. Simulation with large domains and low resolution care less about course geography.
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| Fig1. Terrain with USGS 10m data |
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| Fig2. Terrain with 1s data |
3/05/2012
人生的转折点？
此刻，我正坐在303，听着小萝莉的 phd qualify，看着一页一页很华丽的图文，百感交集：这就是我以后要做的？
昨天看了一篇日志，一个满怀壮志的青年不甘于过着每天上班哄妻子带小孩的平淡日子，决意出国，只有外面的大千世界才能容下他博大的雄心。时隔四年，当他每天忙于看paper、做project、写本子，他最大的愿望就是早点毕业，过上高级知识分子应该有的高薪生活，然后有一个幸福的家庭。他不禁感叹到，到最后，我竟然回到了原点。
这或许是困扰所有人的一个问题：我们为什么活着？几天前，我收到了PSU的offer，我当然很兴奋，但我知道我并没有做好准备。我的兴奋只是因为我赢得了【申请】这一仗，我的【被认可】让我有很大的成就感。但是，这其实只是最初的一小步，后面还有5年6年甚至更长的时间。每天，我要做的就是看paper，写paper，申项目，做presentation，很明显我并不愿意过这样的生活，这只是为我今后有更好的生活付出的代价。不知将来我是否会为我今天这样“牺牲”的决定后悔？
美国，多么令人向往的国家。从小，我妈就希望我出人头地，而一个标竿就是出国，留洋。而我也从小迫切地希望能出去走走，去看我妈妈、我外婆、我曾外婆都从没看过的世界。可是，就在两年前，我真的到了美国，三个星期的时间里给我的只有无助、寂寞，也正是这三周灰色的印象，让我在本科毕业时放弃了出国的想法。如今，为何我又萌生了出国的想法呢？说实话，连我自己也说不清楚。或许小池子说的对吧，我心里还是有一颗出去走走的火苗，如果我出去了，或许有一天我会后悔；可如果我没有出去，我将来肯定会后悔。Follow your heart.
Young and to be young, follow your heart.
题目是“人生的转折点？”，这个出国的决定或许会影响我的一生，甚至改变我今后的职业和发展环境。之所以加个问号，是我又怀疑这个决定真的重大到会“改变”我的人生轨迹吗？会不会如其他决定一样，当我面对它的时候，它总是无限倍地放大，而当几年后我回头再看时，它只是我人生中轻描淡写的一笔。或许根本没有什么能真正改变人生，事实上也根本没有改变之说，谁也不知道“人生”原来怎么样。这就是人生，没什么东西是大不了的。对的错的，好的坏的，都筑成了人生的一分子。
最后想起了你的一句话：
我会勇敢的走下去，只要牵着你的手。
这里的路，已不再只是你我的爱情之路，而是你我的人生长路。
昨天看了一篇日志，一个满怀壮志的青年不甘于过着每天上班哄妻子带小孩的平淡日子，决意出国，只有外面的大千世界才能容下他博大的雄心。时隔四年，当他每天忙于看paper、做project、写本子，他最大的愿望就是早点毕业，过上高级知识分子应该有的高薪生活，然后有一个幸福的家庭。他不禁感叹到，到最后，我竟然回到了原点。
这或许是困扰所有人的一个问题：我们为什么活着？几天前，我收到了PSU的offer，我当然很兴奋，但我知道我并没有做好准备。我的兴奋只是因为我赢得了【申请】这一仗，我的【被认可】让我有很大的成就感。但是，这其实只是最初的一小步，后面还有5年6年甚至更长的时间。每天，我要做的就是看paper，写paper，申项目，做presentation，很明显我并不愿意过这样的生活，这只是为我今后有更好的生活付出的代价。不知将来我是否会为我今天这样“牺牲”的决定后悔？
美国，多么令人向往的国家。从小，我妈就希望我出人头地，而一个标竿就是出国，留洋。而我也从小迫切地希望能出去走走，去看我妈妈、我外婆、我曾外婆都从没看过的世界。可是，就在两年前，我真的到了美国，三个星期的时间里给我的只有无助、寂寞，也正是这三周灰色的印象，让我在本科毕业时放弃了出国的想法。如今，为何我又萌生了出国的想法呢？说实话，连我自己也说不清楚。或许小池子说的对吧，我心里还是有一颗出去走走的火苗，如果我出去了，或许有一天我会后悔；可如果我没有出去，我将来肯定会后悔。Follow your heart.
Young and to be young, follow your heart.
题目是“人生的转折点？”，这个出国的决定或许会影响我的一生，甚至改变我今后的职业和发展环境。之所以加个问号，是我又怀疑这个决定真的重大到会“改变”我的人生轨迹吗？会不会如其他决定一样，当我面对它的时候，它总是无限倍地放大，而当几年后我回头再看时，它只是我人生中轻描淡写的一笔。或许根本没有什么能真正改变人生，事实上也根本没有改变之说，谁也不知道“人生”原来怎么样。这就是人生，没什么东西是大不了的。对的错的，好的坏的，都筑成了人生的一分子。
最后想起了你的一句话：
我会勇敢的走下去，只要牵着你的手。
这里的路，已不再只是你我的爱情之路，而是你我的人生长路。
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