Here’s my response to these questions below~

Think about and describe - what are these projects trying to do?

These projects aim to make an interactive education model of an event or an object or a speech. These projects aim to educate the reader through an interactive perspective, allowing the user to filter through certain data and observe the data set thorough specific perspectives which they user would like to explore. 

Are the projects doing something that a book or article couldn’t do? If so, what?

The projects are specifically created to be non-static and non-linear. In other words, they are (in a way) deviating highly from what a book or article can do. A book or article tends to give you information from a certain perspective (perhaps the author’s or the editorial team’s perspective) no matter how neutral they try to get. In contrast to that, the projects that we are looking at can give us the facts in a more neutral manner, allowing us to explore everything from every perspective possible through tools that allow us to sort through the data. Furthermore, there is a sense of interactivity and non-linearity as they way of exploration is left up to the user, rather than having to follow a set path.

How would you classify each of these projects (e.g., history, literature, philosophy …)?

I would classify the projects as such –

1. Aegaron (Ancient Egyptian Architecture Online) is a historical project. 2. Quantifying Kissinger would be a dual categorized project, both historical and political in nature 3. Slave voyages would be classified as historical and geographic in nature (looking at human geography and the movement of people) 4. Kindred Britain would be historical and humanistic in nature as it explores the linkages between important historical figures 5. King’s “I have a dream” Speech would be political and philosophical in nature to me.

What do you like about these projects?

I like how these projects have a self-exploratory vibe about them and how they encourage thinking and connections to different topics from their main central topic. 

What do you dislike or how would you improve them?

Today I’ll be looking at the key words node and network graph. A node is essentially a point in a network or diagram at which lines or pathways intersect or branch. Network graphs are essentially graphs which describe relationships between entities, which in visualizations, are often represented as nodes.

An example of this would be using Scott Weingart’s example in his text, “Networks Demystified 8: When Networks are Inappropriate”. On the left is an example of the image which appeared –  Flight patterns over U.S. depicted in a network graph, with each individual branch connecting to each other through nodal points.

Personally, I found Networks an interesting topic to look at, especially with what Weingart says. In looking at data visualization and network data, we often look so much into the possibilities and future that we forget that there are limitations to what can be done. Furthermore, we don’t often stop to consider whether what can be done, should be done. In Weingart’s text, he highlights several key problems about network structures.

“Network structures are deceitful. They come with a few basic assumptions that are very helpful in certain areas, but extremely dangerous in others, and they are the reason why you shouldn’t analyze a network without thinking through what you’re implying by fitting your data to the standard network model. In this case, the assumption to watch out for is what’s known as a lack of memory.”

In a way, this problem Weingart mentions can be related to what we explored last week – the problem of Capta has surfaced, where humanistic interpretation has caused us to “misinterpret” or disregard the assumptions network structures inherently display.

Furthermore, the idea of a lack of memory is important in looking at the individualism of a person, of a place and understanding the character of a location. Not all two cities evolve the same way, and network structures tend to miss out this point. Not all questions can be answered with a single network, and not all networks can be mapped over each other, with the same sort of understandings being produced.

Another problem mentioned was the ideas of Multimodality. The problem of Multimodality is summed up as such: while networks can help us understand many problems, not all tools are built to allow and enable us to understand multimodal networks.

In the words of Weingart himself:

The data set which I chose to look at is the City Budget, Adjustments and Expenditures since Budget Fiscal Year 2012.

The data sets are expressed numerically for spending, while each numerical data set is represented by three overarching categories: department, fund name and account name. These three overarching categories cover exactly what the city budget should denote: How much money the fund requires, which department it belongs to, and which account (and inferentially, what purpose) this money is used for.

A single record would include a single instance of fund, department, account and numerical representation of how much the record needs.

Wallack and Srinivasan’s definition of ontology (according to the reading) extends the pre-existing digital divide conceptualizations to further investigate the important issue of mismatches between the ontologies of state-created information systems and local communities’ representation of their contexts. Critically, they “argue that the reconciliation of community and state logics and framings is critical for effective engagement with communities as well as formulation and implementation of development policies. We suggest several paths toward overcoming mismatched ontologies: education and communications strategies to enable communities and states to translate across ontologies and fill in significant gaps; re-assignment of policy responsibilities to minimize information loss; and several mechanisms that would enable communities to be directly and productively engaged in developing shared ontologies.”

In other words, they believe that educating people on how to read the data and proper communication of what the data truly means will bridge the divide between how the state structures it’s data and what the data actually means in a physical tangible sense. Within the example I chose, the divide between understanding the economic benefit of the monetary division from each fund is important – especially because each fund’s money has the ability to affect the community (both in terms of infrastructure and societal change). I believe this data will be useful for people who would like to raise a case for more budget from the city council – for example for people who would require more funding for the disabled, or increase in budget to improve the arts scene. This data will also be useful to track and monitor the state’s actions – certain data may be used to cover up spending in other areas, and from there we can make inferences into how transparent the government and state is.

The data set claims to show how spending within the city has been increased since 2012 to 2014, of which in 2014 total fiscal spending was $28,082,788,853.31. The argument presented by the City budget demonstrates their ability to reduce total spending through the reduction of the city budget to $27,710,867,160.47 in the current fiscal year.

What gets left out of the budget is a humanistic effect which demonstrates the effects of the reduction of the budget. With cuts in spending, areas which need signifiant help may drop as well – from shelters to pension funds to funding for the jobless.

The digital archive I chose for this exercise is the BENNETT (WALTER E.) PHOTOGRAPHIC COLLECTION. Visually, I found it to be a captivating series, of which is extra sensory due to the added visual element, which reduces the need for an imaginative description. The photographs also act as a historical repository to depict (in the photographer’s perspective),  what significant events occurred during the time in which the photos were taken. However, what’s missing is a description of how the information in the photos can be related to significant events in the world and also within the society it was captured in. While inferences can be made, we must also note that photography is not often realistic, as the photographer’s cropping and editing may cause the photo to have a shift away from reality. I would remedy these problems by first including a description of the photo alongside it’s relevance in history. Objective facts about the photo and its events would help create a better picture of the relevance of these photos during that time period and how it relates to today’s world as well.

Week 5

The key terms I’ll be looking at are: The Data Explosion & Data for the Common Good Alongside these two key terms, we’re supposed to be visiting the exhibition in ArtScience Museum titled Big Bang Data that I’ve actually been to before.

In this case, I’d like to explore one of the exhibits that we will be visiting as I found it extremely interesting and pertinent to the idea of The Data Explosion. As per the video you see, the exhibit comprises of several machines that actually convert the data from stock exchanges at the end of the day to form a stunning visual of something akin to the night sky. The exhibition was created in conjunction with IBM and shows how data en masse can actually be used to create something stunningly beautiful. The combination of both economic data (which tends to be a source of stress for many) with solar systems may not be a common topic, but the metaphor behind the exhibit is both stunning and beautiful – data sources with the connotations of stress and industrialization can and are turned into sources of relaxation – something that benefits everyone.

In the same way, the metaphor can be applied to The Data Explosion in which we now have so much data we have no idea what to do with it. In using the data to produce applications which can be made for the common good of people – such as identifying dangerous areas in a city simply through using the mass of data to track which locations people avoid, we are given a chance to do good for the city around us. Perhaps through reinventing ways to keep that specific part of the city safe or even using more collected data to pinpoint and reduce specific problems in which safety is at risk – the data we collect from different sources can all be put together to form something fully functional.

Week 4: An introduction into Data Visualization Keywords: database, relational database, data visualization

I looked at two resources: Data + Design: A Simple Introduction to Preparing and Visualizing Information & "Everything on Paper Will Be Used Against Me:" Quantifying Kissinger in an attempt to understand the idea of databases and data visualization.

I believe that the combination of both looking at theory and an actual data visualization video has allowed me to understand how data visualization works and how useful it can become in distilling large volumes of data objectively. In essence, data visualization (as seen in the Kissinger video) allows us to map significant world events over to specific events occurring within Kissinger’s office. In other words, data visualization distills data in an understandable manner as well, allowing us to map data onto real world situations, giving both the data and the situation occurring context.

In a way, the applications of data visualization are limitless, and while looking around this topic, I was interested in seeing if data visualization could be applied to art. I found that Data Art is actually a big thing, where things like data from Kung Fu Masters can be visualized into flowing fabric, creating a certain sort of movement and fluidity. (You can catch the video here: https://flowingdata.com/2016/05/18/kung-fu-particles/)

So the video lecture for week 3 was highly centered around classification practices, which are characterized by 5 distinct traits. The five are as such: 

(a) Classification distinguishes some objects from others (b) Classification must be relevant to the purpose of the classification scheme (c) Classification must be definite and ascertainable (d) Classification should be permanent (e) Classification should have an enumerable list of possible values which exhaust all possibilities

Following the short lecture on characterization, we were posed some questions at the end of the lecture to answer, and my answers are displayed below. How do we decide which information is relevant to a topic and which we can ignore? How do we decide how to divide up this information? What are the implications of these decisions?

I believe the first question is a key question which is difficult to answer. Theoretically, one would isolate the required information through a search of multiple key words which are related to the topic. However, if we were to do it that way, the multiple key words which we use may not be the only words that could be related to the topic in a grammatical sense. In other words – in defining a list of words to source for information, we may miss out on words that we have not thought of. In my opinion, it’s possible to use a software to help us associate information that contains key words which is relevant to the topic. However, this poses two other questions -  the first being that our search results might be to general and the second which is more important – computers can associate things via literal connections, but conceptual connections (i.e. key words that may not relate literally to the topic but is conceptually related) may be missed out. Thus, to decide which information is relevant and which we can ignore, I believe a combination of digital and human classification is required. Humans are required to define the parameters of association for the technology required to sift through the bulk of the data. Through observation of search patterns and common associations, we can decide the relevance of information through it’s appearances together. Refinement occurs once a process is set up in order to filter through the information more efficiently – in other words, when there’s a dedicated team to help decide which information is useful, the information gathered will be increasingly accurate.

We can decide how to divide up this information according to it’s required usage. In other words, according to the necessity of the information. For example, if we have bulk data on Christmas (the holiday) we could narrow our search according to specifics, such as “what flights will be going to Tokyo on christmas day?” Which helps us define the exact parameters in which we need the specific information. However, in General categorisation, this words very differently. There are no set parameters that we can use to define what we want. Thus, I believe that we can use the same conceptual association to define (in general) the different divisions to the information we have.