Hi! I’m Oforiwaa Pee Agyei-Boakye. I’ve been an intern at the Weigle Information Commons since last fall but this is my first blog post. So far, the internship has been exciting as my involvement with educational technology at the Weigle Information Commons connects with my work at the School of Design. The Weigle Information Commons and the Vitale Digital Media Lab support students with their visuals (which is of grave importance to me as a designer) and assist students with up-to-date statistical software programs.
Coming from the School of Design, specifically the City and Regional Planning department, I have explored software including PhotoShop, Illustrator, AutoCad, Indesign, ArcGIS, and R for my projects. Although projects in the School of Design sound like they may be only visual, we engage with statistical data analysis as well.
Over the years, data analysis has evolved through various stages as the volume of data has increased. Technology kept pace with that and developed R; in fact, most data analytics have switched from Excel to R. R is a free open source statistical program with a steep learning curve, and it is getting increasingly popular. It has Mac, Windows and Linux operating system versions. Students and professionals whose work involves lots of data use it extensively. An advantage of R is the fact that it can be used to do increasingly complex models.
In city planning, R is mostly used for data correlation, regression modeling and logit modeling. I used it in my Quantitative Methods classes, Introduction to Transportation Planning last fall, and currently am using it for a Planning by Numbers class this spring. A basic familiarity with descriptive and inferential statistics helps to make better and more effective use of R.
City Planners use it to assess planning and urban policy data in order to address a planning problem or question. Applications of R in City Planning include: (i) analyzing population, economic, and settlement patterns across Metropolitan and Statistical Areas; (ii) understanding the determinants of housing and real estate prices; (iii) understanding mortgage foreclosure patterns; (iv) identifying the characteristics that explain travel behavior and mode choice; (v) identifying the factors contributing to Presidential election wins; and (vi) understanding the determinants of homelessness by metro area.
For example, to analyze Philadelphia housing, rental and real estate demand, R studio will be used to analyze housing and census data. Housing census datasets such as how many Philadelphia residents live more than one person per room, how many structures are dilapidated, or what rent prices run these days, can predict that.
The R language is not easy to learn initially, but once you grasp it data analysis is simple. R also integrates nicely with other visual design programs that WIC provides assistance with – from poster creation to PhotoShop and Illustrator, and general help in the Media Lab. The Commons can help with R software support (see library guide!), and custom workshops – or stop by to see our statistics tutor, Doug Allen, for specific questions on Tuesdays and Wednesdays this semester. Be on the lookout for more about R at the Commons this semester!
PennWIC 