The following are the top 20+ student projects as selected by the TechniCity instructors. There were many, many great projects overall! Click on the title to read more about the project. See the 2013 projects here.
My previous blog post discussed the racial and ethnic diversity of graduate students in U.S. urban planning programs (see Are U.S. Planning Programs “Diverse”?). I received some very insightful feedback, mostly supporting the idea that we need to better define what “diversity” means if planning academics and planning professionals consider this a priority. I wasn’t accusing PAB of intentionally focusing on “whiteness” in their description of “student diversity,” rather, I was pointing out how diversity ends up being perceived relative to the racial/ethnic categories that are used to report student and faculty composition. The fact of the matter is that skin color dominates the “diversity” conversation.
The second part of my descriptive analysis of program racial/ethnic diversity focuses on faculty, as well as a comparison of faculty to students. It’s no surprise that faculty members of urban planning programs are predominantly white. Data from PAB’s Annual Report Online Database (AROD) show the breakdown of full-time, part-time, and adjunct faculty for planning programs. Out of 1,806 total faculty, nearly 80% are white. This figure is composed of 71% white full-time faculty, 77% white part-time faculty, and 85% white adjunct faculty. Figure 1 illustrates a stark gender imbalance as well as the large numbers of white adjuncts hired by planning programs. The predominance of white adjuncts may be a reflection of the planning profession (i.e., the available pool) or who we choose to hire. While the proportions of white full-time faculty and graduate planning students are quite close (71% and 70% respectively), the gap widens with respect to part-time and adjunct faculty.
Figure 2 shows a relatively flat distribution with about half of planning programs having 80% or greater white faculty members and less than one-fifth having under 70% white faculty members.
Comparing program proportions of white faculty and students shows a significant positive correlation between the two groups (see Figure 3). It would be interesting to assess the role of recruiting resources and other institutional factors that may be influencing this relationship. It would also be interesting to see how/if this relationship has changed over time.
As shown in the post on student racial and ethnic diversity, I also looked at the numbers of race/ethnicity categories represented by planning program faculty members. This is a crude measure of variation because it doesn’t account for the evenness of numbers between categories, however, it is used here for illustrative purposes. The numbers of categories are slightly different for the faculty data from the AROD and the data from the ACSP Guide. The AROD includes 9 race categories for faculty:
- Black or African American
- American Indian or Alaska Native
- Native Hawaiian and Other Pacific Islander
- Some Other Race Alone
- Two or More Races
The ACSP Guide includes 8 race/ethnicity categories for students:
- Hispanics of Any Race
- African American
- Native American/Pacific Islander
- Asian American
- Other/Don’t Know
- Non-US Citizens
Figure 4 shows the relationship between the numbers of faculty and student categories represented by planning programs. Similar to Figure 3 there is an observable positive correlation between the numbers of groups represented by planning faculty and the students within programs. The upper right-hand corner highlights 13 “diverse” programs that have at least 5 groups represented by both faculty and students. Table 1 shows the list of these schools.
Table 1 – Racial/Ethnic categories represented by faculty and students
Your comments are appreciated.
 I combined these data with data from the ACSP Guide used in the student analysis. This resulted in complete data for 65 U.S. planning programs.
For some time the planning profession and planning educators have shown concern about being too “white”. Our country continues to experience significant demographic changes, especially in terms of race and ethnicity, most notably becoming less “white”. The Planning Accreditation Board (PAB) has emphasized that planning programs should be racially diverse, which translates into being less “white” and/or more “non-white” – for the purposes of better representing the populations we serve. Regarding student diversity PAB language states:
“Student diversity: The Program shall adopt appropriate recruitment and retention strategies, including curricular strategies, to achieve its aspirations for a diverse student body, and shall document actual progress in implementing those strategies. The Program shall foster a climate of inclusivity that appreciates and celebrates cultural difference through its recruitment and retention of students. Students shall possess, in the aggregate, characteristics of diversity (e.g., racial and ethnic background) that reflect the practice settings where graduates work or where professional needs exist in the Program’s region of recruitment and placement. Notwithstanding, the demographic mix is not a static concept, and all planning programs should seek to be in the forefront of a diverse society.”
It can be argued that “racial and ethnic background” is a very narrow indicator of student diversity and should be reconsidered in light of the broader concept of diversity. Why don’t we include sexual orientation, political perspectives, socio-economic background, musical tastes, etc? Fostering inclusion involves self-identification which may be seen by some as potential for discrimination. As planning programs consider their relative student diversity (by PAB criteria), I thought it would be interesting to look at program-level metrics on race and ethnicity. According to PAB, of the nearly 5,000 students in accredited programs, 69.5% (full and part-time) are non-Hispanic white, which compares to 62.6% for the U.S. In addition, these programs are 53% male and 47% female. So planning programs have some work to do. The aggregate numbers tell part of the story, and the question then becomes whether the unit of analysis is the discipline or individual programs.
Using the assumption that many programs draw from within and supply planners to their own states, I compared each program with the corresponding percentage of white residents in their state. Analyzing the race and ethnicity data for graduate students of ACSP member schools using the Guide to Undergraduate and Graduate Education in Planning (20th Edition). I selected the 96 member schools in the U.S. of which 84 provided complete data. This is admittedly a simplistic approach, however, I think it provides context for further discussion about program-level diversity. There is also the question whether the benchmark should be race-ethnicity at the national, state, regional, or workforce scale.
Figure 1 shows the weak correlation between state % white and program % white and Figure 2 shows the distribution of variance between state % white and program % white for the 84 programs included.
As mentioned, program demographics (% white only) is weakly correlated with state demographics (see Figure 1). This is because planning programs differ in the number of out of state students (including international students) enrolled, even for programs within the same state. Also evident are outliers like Historically Black Colleges and Universities (HBCUs) such as Alabama A&M, Texas Southern, and Jackson State; states with high proportions of white residents like Utah, Iowa, and Maine; and elite schools like Harvard, MIT, and Penn. The “orange zone” shown in Figure 2 includes planning programs with proportions of white students that vary from their state proportions by 30% or more. Based on “whiteness” these programs qualify as hyper-diverse. The results for this group are driven by having much lower percentages of white students compared to their state averages. Whether these 19 schools represent diversity is open to interpretation (see Table 1).
Table 1 – Orange zone programs
On the other hand, schools that fall into the “green zone” shown in Figure 2 are those within 10% absolute difference from their state proportion of white residents. These include the 23 schools shown in Table 2. It should be mentioned that the 30% and 10% thresholds were selected arbitrarily for illustrative purposes and aren’t related to any particular standards. In addition, based on the available data, only 5 of the 84 schools included had margins greater than 10% above their state averages (see Table 3).
Table 2 Green zone programs
Table 3 Below state level
The metrics are an obvious problem here. An appropriate measure would take into account representation across identified groups. Two schools, UCLA and UC Berkeley have students that represent all 8 categories used for reporting student race/ethnicity. Eight other schools represent 7 of the 8 categories. Overall, 56 of the 84 schools represent 4 of the 8 categories (see Figure 3). I’m not recommending this as a diversity measure, but instead using it for descriptive purposes.
In addition, a pressing issue that will be discussed in a follow-up blog post is planning faculty diversity. Data from the ACSP Guide show significant disparities with two-thirds of faculty being male and over 80% being white. This is the product of different dynamics that are being faced by institutions that will require concerted efforts to remedy.
Your comments are welcome.
 Source: U.S. Bureau of the Census, County Population Estimates by Demographic Characteristics – Age, Sex, Race, and Hispanic Origin; updated annually for states and counties. http://www.census.gov/popest/counties/asrh/. 2010 Census of Population and Housing for places; updated every 10 years. http://factfinder2.census.gov.
I recently dusted off my master’s thesis (typewritten) and scanned it into a PDF file. The topic, technology adoption by urban planners, is still of interest to me but the types of technology have changed dramatically since 1985. For my thesis I surveyed all California city and county planning agencies to ask about their adoption of computer technologies. With a quite respectable response rate of 81% (403 out of 497) I was able to show the state of computer use by planners, which existed in only about 60% of planning offices at the time. It was also interesting to note that nearly half of the planning offices felt computers were only having a moderate impact on the planning profession. I have to assume that much has changed in the past 30 years. (Click to view thesis).
As we know, the field of urban planning is far reaching in breadth and depth. This is due to the complex nature of cities, regions, and associated development patterns. Referring to the ambitious field of urban planning, Aaron Wildavsky famously remarked, “If planning is everything, maybe it’s nothing” (Wildavsky, 1973). Is planning everything? And what does that mean for someone trying to understand planning? Using the recent Guide to Undergraduate and Graduate Education in Urban and Regional Planning (20th Edition, dated 2013) published by the Association of Collegiate Schools of Planning, we examined the question of “what is planning?” by analyzing the areas of expertise and interests for over 900 regular faculty listed in the Guide. These are self-reported areas of teaching and research interests that can be used to characterize contemporary aspects of planning. Rather than just reporting the frequency of topics mentioned across planning faculty, network analysis was used to illustrate the range and interconnections between topics. The results are used to report the knowledge domain of U.S. planning faculty (see the full paper here).
Last summer I wrote a brief post about access to scholarship in a MOOC co-taught by Tom Sanchez, Professor in Urban Affairs and Planning, shortly after the course’s initial offering. After TechniCity was offered again this past spring, I thought I would ask Tom more questions about the course. Read more…
Several participants of the TechniCity MOOC have asked for the raw data of comments and other activity from the MindMixer discussion forum. An Excel file of all activity is now available for download here.
Please be sure to share the results of your analyses with the rest of us and post on Twitter using #technicity or on the TechniCity LinkedIn page.