Movie LATCH

Here is my info for this part of the project.  I think that I am not sure that MPAA ratings fall into heirarchy.  I think I may move this to category.  Although people may classify this as a heirarchy.

3 Types of data

1.       Movie Performance (sales volume, weekly sales, box office rating, movie title)

2.       MPAA Ratings (MPAA ratings)

3.       Audience Ratings (Average viewer rating, movie title)

Organize according to LATCH

1.       Movie Performance

a.       Alphabetical: movie title

b.      Time: all-time, yearly, monthly, weekly, release date, movie length

c.       Category: genre, sales

d.      Hierarchy: top ten movies

2.       MPAA Ratings

a.       Alphabetical: movie title

b.      Category: genre

c.      Hierarchy: MPAA Ratings (G, PG, PG-13, R, NC-17)

3.       Audience Ratings

a.       Alphabetical: movie title

b.      Category: genre, number of reviews, average viewer rating

 

Temporal Map / Groups of Info

Release Date: Last week | Last 30 days – current week | Last 365 days – current 30 days | Previous

Genre: comedy | romantic | horror |action | sci-fi | foreign

Movie sales rating: 1 - 10 | 11 - 50 | 50 - 100

Average viewer rating: 1-5 | 5 <>= 7

MPAA Ratings: G | PG | PG-13 | R | NC – 17

Movie Title: A – G | H – M | N – Z ** Will have to look at movie titles an first letter frequency

Follow Up

The concentration of this project is to gather data encompassing gas prices.

So in terms of data organization...

Locations would be divided into states and major metropolitan cities.
The data would be shown in chronological order to study the rise and fall of gas prices.
Not only will I be tracking down gas prices, but also the prices of barrels of oil in order to see whether or not the price of oil is reflected upon the prices of gas.
Also, in order to further examine the trends in prices, the data will be broken down by season and compare holiday/vacation trip weekends to regular weekends.

book latch

Again, the primary goal of this project is to compare when and where books are written to when and where they take place. These are possible organizational methods for each LATCH principle:

Location:

City by city; By region/country; Limit to books in one region/country; English-speaking countries; Throughout the universe, By the distance from where the book was written to where it is set

Alphabetical:

By book title; By author's name

Time:

By year; By decade; By the difference between the year the book is written and the year it's set

Categorical:

By genre, By language, By author's birthplace, By tone, Past vs. present vs. future, Non-fiction vs. fiction, Fictional settings vs. real settings

Hierarchy:

Place written, place set-->time written, time set-->genre

Location and time will be most important because they directly relate to the primary data. Providing different categories like genre or time change would provide secondary functions to explore trends in the data. One challenge will be that the data doesn't not all have the same accuracy. One book could be set in Manhattan in 1987 and another across Europe over three decades.

Visualizing movement through space and time (both forwards and back) will be big a challenge as well. One way to do it is with a map with symbols or colors that indicate time change, or conversely, a timeline that uses symbols to indicate a change in place (this would probably be harder). Another way to visualize this is to make a three dimensional map, where time is the third dimension. A top view would isolate the location variable and a side view would isolate time variable. 

data breakdown

I'm going to focus on the following data sets for each Pittsburgh neighborhood:

population
area (sq. miles)
household income
educational attainment
occupation

I'm also going to include the same information for Pennsylvania, Allegheny County, and Pittsburgh in order to allow for some zooming in and out and a big picture idea.

I'd like to have the main navigation be a map of the neighborhoods, but I don't want that to be the only place where information is located. Perhaps as you zoom in more information appears alongside the map or in a rollover image. Maybe.

Also, it would be nice to include images that give a sense of the personality of the neighborhoods, so it's not just another dry set of numbers. I think that's what I dislike most about census data--there's no "feel" for the area. Ideally I could do a mashup of the census data and geolocated flickr photos, but I don't have the technical skills for that. Perhaps I could fake it in Illustrator.

Those are my thoughts for now. I still need to do the temporal map.

Pittsburgh census info by neighborhood

After class last night I was thinking that I set myself up for a lot more up front research than I'd really like (I'd rather spend more time on working out the visualizations). So I did some searching around and found this interesting resource: 2000 City of Pittsburgh Neighborhood Census Report (pdf). The document is well-organized, but there's no easy to way to get a big picture view of the data at once. Nor is it easy to compare neighborhoods without flipping back and forth through the pages and matching up the tiny numbers. So I think I'll work with this and see what kinds of visualizations I can come up with.

Digital self - Know your self

Everybody is using Internet, but nobody really cares about what are we actually doing on the Internet. The internet covers everything we need in our daily life. We search for food, map, housing, jobs, friends, and even potential significant others. During the time we build a digital self on the Internet that we barely know about - the browser may knows about this better than ourselves because it stores our daily browsing histories. I found it particularly interesting to observe everybody's daily browsing history that changes over time, which adds up a image of a digital being - your digital self on the Internet, mirroring your inner identity and value.

I want to build a dynamic data information visualization piece, through which you could compare your real self and your digital self, in order to know better about yourself.

Current Problem:
No visualizations for individual browsing history

Audience:
Every Internet User

Context:
Everyday - the data may fluctuate with the time.

Informal Project Proposal

Being from an area notorious for its high gas prices, I sweat every time I go to the pump. I just don't understand how prices can fluctuate so erratically and hurt my bank account so deeply. I want to look into the reasons behind the rise and fall of gas prices including comparisons of prices across the nation and how oil is heavily connected to our economy. Many ordinary citizens do not know that 1 barrel of oil is 158.987295 liters nor do they understand how heavily dependent we are on oil and the causes and effects of this dependence. I want to ask questions like...
Why are some areas more expensive than others (national/international)
How much does United States political actions (i.e. War in Iraq) hurt our supplies of oil.
How did gas prices effect people's personal lives (traveling, means of transportation to work...)
Traffic
What products did we buy in response to these prices and how automobile manufacturers responded to these prices.
How are cars different now?
The actual price of oil in the market compared to what people pump into their automobiles.
How much of this resource is left on our planet.
Where are they drilling and how many new places are we drilling in?

The purpose of this investigation is to try to help the public understand why gas prices went up so much and to emphasize our great reliance on this diminishing commodity.