Featured Grid: Climatology and Home Design

In a book pub­lished in the 1950s enti­tled Cli­ma­tol­ogy and Archi­tec­ture, the author presents a matrix of rooms in a house vs. com­pass direc­tion. The author’s rec­om­mended place­ments are indi­cated with a sym­bol. Here’s the orig­i­nal graphic:

Climatology and House Design (Original Graphic)

It’s an inter­est­ing graphic but the reader needs to “work” a lot to under­stand and find mean­ing and insight. The pat­terns in the graph are almost ran­dom and there is no eas­ily dis­cern­ble con­clu­sion; the reader asks “OK, so what do I do wit this? How do I act on it?” In most busi­ness pre­sen­ta­tions, one of two things would hap­pen (1) Eyes would glaze over and no one would draw a con­clu­sion from the chart (2) every­one would have a dif­fer­ent opin­ion on what the graph means and there would be a long dis­cus­sion on what it means and how to inter­pret it. I decided to try to improve on the graphic by try­ing to group the data to make it eas­ier to dis­cover mean­ing in it. Sort­ing in alpha­bet­i­cal order makes it eas­ier to find a room, and also some con­tigu­ous blocks:

Climatology (alphabetical sort)

Still, alpha­bet­i­cal order is a bit ran­dom, and doesn’t seem to dis­close any­thing at all. I decided to sort by flex­i­bil­ity (ie which rooms could be placed in all 8 direc­tions, fol­lowed by which rooms could be in 7 and so forth). Here is what it looks like:

Climatology and Home Design

Here the sort helps in find­ing mean­ing in the data. It doesn’t explain every nuance, but gives a basis for gen­er­al­iza­tion into three blocks: (a) rooms like the garage and bath­rooms which can be located any­where in the house. Bed­rooms are sim­i­lar, except for avoid­ing a west­ern expo­sure (warmer or hot in sum­mer — this book was writ­ten before wide­spread use of air-​conditioning) (b) liv­ing rooms — ter­races, sun porch, play rooms which have the most nat­ural light and warmth and © util­ity rooms which are placed in the cooler parts of the house (north­ern exposure).


First post for 2014. After almost 2 years of blog­ging, I want to reshape the blog a bit. There are three steps to mak­ing data dri­ven deci­sions: 1) Col­lect, aggre­gate, and process the right data. 2) Ana­lyze, orga­nize, and visu­al­ize to begin to find mean­ing. 3) Get insights, draw con­clu­sions, and take action Begin­ning in […]

Product Grid Example: Nooka Watchfinder

Nooka cre­ates non-​traditional watches that dis­play time with com­bi­na­tions of dots, bars, and charts rather than num­bers and hands. Their Watchfinder app is a prod­uct grid that lets you click on color, ideograms of watch faces, and sort by color, name, price or face. Inter­est­ing graph­i­cal vari­ant on prod­uct grid. for some exam­ples of Nooka […]

Featured Data Graphic: Visualizing City Data

Inter­est­ing mul­ti­vari­ate graphic shows mul­ti­ple city ast­trib­utes: sur­face area (cen­tral square), height of tallest build­ing, pop­u­la­tion, aver­age price of prop­erty, num­ber of vis­i­tors (4 right tri­an­gles) and mean tem­per­a­ture and rain­fall (charts). Each city is arranged by lat­i­tude (east to west) and lon­gi­tude (north– south) in a rough grid. Data is from pub­li­cally avail­able data […]

Featured Chart: Car Ferry Congestion by Day

Wash­ing­ton State Fer­ries have an effec­tive visual grid show­ing hour, day, and con­ges­tion for vehi­cle fer­ries on dif­fer­ent routes in the Puget Sound. The exam­ple below shows Ana­cortes to the San Juan Islands in early sum­mer. Fri/​Sat are most con­gested between 9am and 4pm where pas­sen­gers would have to wait in line for mul­ti­ple ferry […]

Big Data Bookshelf: Data Flow 2

Data Flow 2 presents many use­ful exam­ples of data visu­al­iza­tion which are orga­nized by the form of the chart or info­graphic: Data Maps — maps and car­tograms Data Process — process or flow charts Data Blocks — bar carts, colum­nar charts, 3d blocks Data Cir­cles — cir­cles, pie charts It’s a use­ful book with lots […]

Featured Chart: Linguistic Geography of Bubbler vs. Water Fountain

This week’s chart is a visu­al­iza­tion of usage for “Water Foun­tain” vs. Drink­ing Foun­tainvs. “Bub­bler”. Water foun­tain pre­dom­i­nates in the south and north­east, while drink­ing foun­tain in the west. “Bub­ble” is regional in the upper Mid­west. The inter­ac­tive appli­ca­tion is avail­able from Joshua Katz a PhD can­di­date in sta­tis­tics at NC State. There are a […]

Big Data Bookshelf : DAX Formulas for PowerPivot

Power­pivot is an addin for Microsoft Excel which enables analy­sis of larger data sets (>1M rows) within Excel and also more sophis­ti­cated analy­sis that typ­i­cally would involve mul­ti­ple SQL queries with com­plex data joins. I blogged about learn­ing Pow­er­pivot last year. One of the dif­fi­cult things is the lack of usable doc­u­men­ta­tion or cook­books on […]

Big Data Bookshelf: Data Points: Visualization that Means Something

In the newly released book Data Points: Vsu­al­iza­toin that Means Some­thing, Nathan Yau (Flow­ing Data blog) shares his ideas on under­stand­ing, visu­al­iz­ing, and ana­lyz­ing data. The exam­ples are use­ful and imple­mentable. Many focus on sets of graphs with mul­ti­ple dimen­sions of data, rather than a sin­gle chart type. The author also presents ideas and examples […]

Navigating Taxonomic Content

Effec­tive con­tent tool from Wikipedia for nav­i­gat­ing con­tent relat­ing tax­o­nomic nav­i­ga­tion for the ani­mal king­dom. This exam­ple shows the Euro­pean Hedge­hog. It com­bines a photo, con­ser­va­tion sta­tus, bino­mial name (Latin name) tax­o­nomic clas­si­fi­ca­tion , and maps of dis­tri­b­u­tion. It’s pos­si­ble to nav­i­gate from species to fam­ily to order and then back to spe­cific gen­era and […]