Geography 450 Schedule






Vancouver, June 2010 (Elvin Wyly)
"..quantitative approaches to economic geography can and should be liberated from their needless association with mainstream economics and its own vision of science, truth, and evidence, and made part of an emancipatory economic geography.  They can be marshalled to effectively critique mainstream economics on its own terms, to incorporate the insights of economic thinking that lie outside the mainstream, to develop understandings of the spatial dynamics of capitalism at the micro- and macro-scales, to conceptualize other possible worlds, and to create space for views of what constitutes a valid argument and the nature of empirical validation that depart substantially from a logical positivist worldview."  Paul Plummer and Eric Sheppard (2001).  "Must Emancipatory Economic Geography be Qualitative?"  Antipode 33(2), 194-199, quote from p. 198
Chicago, December 2010 (Elvin Wyly)
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Neighborhoods:
Suggestions, possibilities, and paths of discovery

The "strategic positivism" essay was a short commentary on several panels of papers presented at the annual meeting of the Association of American Geographers, which were then published in a special issue, "Critical Quantitative Geographies," guest edited by Mei-Po Kwan and Tim Schwanen.  For overviews, see:

Mei-Po Kwan and Tim Schwanen (2009).  "Quantitative Revolution 2:  The Critical (Re) Turn."  Professional Geographer 61(3), 283-291.

Tim Schwanen and Mei-Po Kwan (2009).  "'Doing' Critical Geographies with Numbers."  Professional Geographer 61(4), 459-464.

When "strategic positivism" was published, I half expected a SWAT team to do a nighttime raid to revoke my geographer's license.  But somehow I got away with it.  Emboldened, I continued to scratch my head about the history of this thing, this epistemology-cum-epithet.  I came up with a cool idea for a title:  inspired by Bill Bunge and other radicals who saw no conflict between science and social justice, I came up with the idea of "city" under the square-root sign (which also denotes the 'radical' or 'root').  Then I realized that I needed to go back, all the way back, to the beginning of "positivism" itself.  This means reading Comte.  Nobody reads Comte.  What exactly am I doing here, getting lost in the most obscure, dusty corners of the library?

Sometimes, that obscurity makes all the difference.  Dig through the once-dominant movements that were tossed aside or overthrown -- for good and bad reasons -- and you'll often get a refreshing view of today's events through the lens of your scholarly counterpart elsewhere in a time-space continuum that is poised somewhere between Einstein and the "religion of humanity."  That's the phrase Comte had for his subjective synthesis of science and spirituality.  It's a side of the strange godfather of positivism that almost noone remembers.

Elvin Wyly (2011).  "Positively Radical."  International Journal of Urban and Regional Research 35(5), 889-912.

The point of all this for me was simple:  positivism was unstable, partial, situated, and contingent -- indeed, even subjective -- from the very beginning.   This means that we can remake positivism.  We can put it in service to the grand critical epistemological pluralism of Marxism, critical race theory, feminism and queer theory, post-colonial studies, and all other partners for social justice and human emancipation.

In other words:  I value the scientific method.  I want to work for the good guys.  The good guys are led by the feminists, the Marxists, the antiimperialists, the pacifists, the environmentalists, the postcolonial theorists ... pretty much everyone inclined to question the priorities of the Bad Guys.  For my entire lifetime, we've been divided against one another into what David Harvey (channelling Raymond Williams) has diagnosed as "militant particularism."  With our deep sensitivities on matters of inclusion and diversity, we on the Left are often all too quick to shy away from the tense struggles with our allies to find common ground -- and thus we allow a diffusion of Left interests such that individual factions are bought off, co-opted, or buried in bureaucracy.  For these reasons, I have little interest in trying to identify the finer points of distinction between various progressive and Left causes.  While I'm prepared to genuflect to the vague inclusive mantra of "difference," I don't want it to get in the way of our mobilization against the much larger threat.  Let's come together and fight the Bad Guys to make a better world.  Let's emphasize our unity until that day when we have the luxury to focus on our divisions.

There's a lot of committed activists, organizers, and allies out there working day-to-day, trying to advance the cause of social justice.  I want to have strategy sessions with these organic intellectuals in the streets and in the trenches.  Then I want to go back to the University and work with students to sift through data to support the cause.

*

There's a growing and popular emphasis on visualization in mathematics and statistics.  Consider, for example:

Hans Rosling (2010).  "200 Countries, 200 Years, Four Minutes."  London:  BBC Four.

For a comprehensive history of quantification in geography, see

David Livingston (1992).  "Statistics Don't Bleed."  In The Geographical Tradition.  Malden, MA:  Blackwell.





Week Three:  January 24.  Politics of Data.  Facts, assumptions, and starting points.  Primary and secondary data; institutional considerations; units of observation; summary data vs. microdata; cross-sectional vs. longitudinal data; unique features of spatial data; critical data; introduction to STATA.
Week Four:  January 31.  Simple Descriptive Statistics.  de+scribere (to write).  Frequencies, sums, percentages; measures of central tendency; location quotients, growth quotients, and other standardized indices; contingency tables and the simple chi-square test; creating tables and charts in Excel.
Week Two:  January 17.  Urban QuestionsAn overview of Departmental data collections, thanks to Jose Aparicio, who has helped thousands of students express their geographical imaginations.  An overview of the kinds of urban questions I encourage you to explore in this course.  Conversations with students and colleagues doing the kinds of urban research you may wish to pursue in this course.
Week Five:  February 7.  Sampling.  Purposes of sampling; anecdotes, case studies, representation, and generalization; types of samples; sampling strategies; the central limit theorem.
Week Six:  February 14.  Inferential Statistics. Drawing inferences about a population from a sample.  Probability distributions; hypothesis tests and confidence intervals; simple hypothesis tests in STATA.
February 20-25.  Spring Break.

Note lab hours for the break:

-------- Original Message --------
Subject:     Lab hours during the break
Date:     Mon, 13 Feb 2012 11:35:45 -0800 (PST)
From:     Jose Donato Aparicio <jdaparic@geog.ubc.ca>
To:     Brad Maguire <bmaguire@geog.ubc.ca>, Brian Klinkenberg
<brian.klinkenberg@geog.ubc.ca>, alan mcconchie
<alan.mcconchie@geog.ubc.ca>, Juan-Pablo Mendez-Gonzalez
<pablo.mendez@geog.ubc.ca>, Elvin Wyly <elvin.wyly@geog.ubc.ca>
CC:     michael <michael.more@geog.ubc.ca>, Samuel Walker
<samuel.walker@geog.ubc.ca>, Jacob Wall <jwall@geog.ubc.ca>, Alejandro
Cervantes <larios@geog.ubc.ca>

Good morning everyone

Please notify your students of the lab hours during reading break, if
applicable:

Monday through Friday from 8:30am to 4:30pm

I will be around if they were to need help.

Cheers

Jose

Week Seven:  February 28.  Correlation and Ordinary Least-Squares Regression. Measuring the association between an outcome (dependent variable) and one or more influences (independent variables).  Scatterplots, positive correlation, negative correlation, and non-linearity; correlation coefficients; bivariate ordinary least-squares (OLS) regression; multiple regression.  Correlation and regression in STATA.

Week Eight:  March 6.  Logistic Regression.  Measuring the association between a binary outcome and one or more influences (independent variables).  Natural logs; the logistic function; maximum likelihood estimation; assessing model fit; odds ratios.  Logistic regression in STATA.
Week Nine:  March 13.  Somewhat Less Ordinary Versions of Regression. Partial decomposition analysis; interaction terms; the expansion method; local indicators of spatial association (LISA) and spatial regression.  LISA and spatial regression in GeoDA.
Week Twelve:  April 3.  Group and Individual Presentations
Week Nine:  March 13.  Principal Components Analysis and Factor Analysis. Measuring the association between many characteristics (variables).  Purposes of PCA and factor analysis; geometric illustration of principal components; eigenvalues, eigenvectors, loadings, and scores; rotations.  PCA and factor analysis in STATA.
Week Ten:  March 20.  Classification and Cluster Analysis. Epistemologies, applications, and implications of classification; the distance measure of similarity; linkage rules; assessing accuracy and meaning in cluster solutions.  Cluster analysis in STATA.
Week Eleven:  March 27.  Graphical Rhetoric.  Cartographic communication; default=disaster; map interpretation and thick description; power corrupts, but PowerPoint corrupts absolutely; Tufte's principles for graphical elegance.
Infrastructure:
Data, documentation, and resources







 
Week One:  January 10.  Introduction.  Course purpose; Theoretical foundations; getting to know your colleagues; inventory of interests and expertise.
"The Radical Statistics Group ... was formed in 1975 as part of the radical science movement associated with the establishment of the British Society for Social Responsibility in Science ....  Members are 'radical' in being committed to helping build a more free, democratic and egalitarian society.  Members of Radstats are concerned at the extent to which official statistics reflect governmental rather than social purposes.  Our particular concerns are the mystifying use of technical language to disguise social problems as technical ones; the lack of control by the community over the aims of statistical investigations, the way these are conducted and the use of the information produced; the power structures within which statistical and research workers are employed and which control the work and how it is used; the fragmentation of social problems into specialist fields, obscuring connectedness."  RadStats (2009).  About Radstats.  http://www.radstats.org.uk/about.htm, accessed 5 February.  London:  The Radical Statistics Group.
Neighborhoods
Suggestions, possibilities, and paths of discovery

David Listokin, Elvin Wyly, Ioan Voicu, and Brian Schmitt (2003).  "Known Facts or Reasonable Assumptions?  An Examination of Alternative Sources of Housing Data."  Journal of Housing Research 13(2), 219-251.

Jason deParle (2012).  "Harder for Americans to Rise from Lower Rungs."  New York Times, January 4.

Rick Mercer (2010).  "The Rant:  Stephen Harper and the Census."  Toronto:  CBC.

National Oceanic and Atmospheric Administration (2010).  "Carbon Tracker:  Watching the Earthe Breathe."  Boulder, CO:  Earth System Research Laboratory.

Frank Luntz (2007).  Interview on Hot Politics.  Frontline.  Washington, DC:  Public Broadcasting System.

Hasan M. Elahi (2011).  "You Want to Track Me?  Here You Go, FBI."  New York Times, October 29.

Elvin Wyly (2001).  "Notes on Data."  Piscataway, NJ:  Department of Geography, Rutgers University.

Gary King (1995).  "Replication, Replication."  Political Science 28(3), 444-452.

Gary King (2006).  "Publication, Publication"  PS:  Political Science and Politics 39(2), 119-125.

Gary King (2007).  "An Introduction to the Dataverse Network as an Infrastructure for Data Sharing."  Sociological Methods & Research 36(2), 173-199.

Some interesting discussions of politics and data took center stage at the Geography Department Colloquium on January 24, 2011.  Simon Donner's presentation offered a particularly valuable engagement with the collisions of journalistic attention and scientific practice.  See

Simon Donner (2011).  "Publish/Publicity or Perish?"  Public presentation.  Vancouver:  Department of Geography, University of British Columbia.

The "digital individual" is being automated in a storm of shifting politics and epistemologies.  WTF?, you ask?  Read this, and then this, and this, then (if you wish to pursue the "paradigms and personalities" or "Pucci Solution" options, this could be the basis of a valuable project; it could even be an empirical strategic positivism piece if you were to compile data on how much of this stuf has happened, and if you could find some logical urban dimension to it all).

City Center
Required Reading

Please skim through the project options page here as well as the explanation below.

The central objective of this course is to provide a forum where you can engage in the actual practice of urban research.  "What exactly do you mean by urban research?" you ask.  When it comes to 'the urban,' I'm an imperialist:  I see the urban in almost everything, especially now that our world is, for the first time in human history,majority-urban.  In terms of research methods, I'm a pluralist:  I respect and learn from a wide range of narratives and methodologies.  But having an open mind is not the same thing as having expertise.  I like reading ethnography, for example, but I don't have any expertise in this at all.  What I specialize in is multivariate statistical techniques as applied to public-sector, secondary data sources on urban social and economic processes.  Or to put it more simply:  crunch numbers, draw maps, tell stories.  I don't have an a priori set of methods you're expected to learn in this course.  The methods you'll learn will depend on the topic you choose and the stories you want to tell.  You have a lot of freedom in your choice of topic and your choice of method; but we do have one limitation.  We can't do any research that involves personally identifiable information, or contact with human subjects, that would require the formal approval processes of the University's Behavioural Research Ethics Review Board.  If you want to do interviews, focus groups, or other research involving human subjects, I recommend Geography 371 instead.

The central requirement of this course is a final paper.  You should aim for about twenty-five pages double-spaced of main text, not counting maps, tables, or other graphics you wish to include, and not counting the references (in whatever style you choose).  You have the choice between a group project or an individual project.  Do not be deceived by the least-effort mental calculation that you might be thinking right now ("25 pages / 1 person = hard, 25 pages / 5 people = 75% easier!").  Collaborative work is wonderful, but in many ways it's much harder, particularly in a field like ours where there is so much pluralism in assumptions, inspirations, and approaches to the craft of research itself.

I ask that you consider three options for the paper, which I've decided to call 1) Strategic Positivism, 2) Paradigms and Personalities, and 3) Your Index.  I prefer the first option on strategic positivism -- this is the kind of work that's in my mind for how the course is designed -- but I realize that it's not for everyone.

Let me explain what I mean for each of these options

Strategic Positivism

The first option is simple:  find an urban issue you care about, then marshall some combination of data, maps, charts, or other analytical/graphical tools that help you accomplish your goals.  For some of you, those goals will be inductive and exploratory, and so you'll use methods to help you explore interesting patterns or relationships amongst various phenomena.  But some of you may have a lot of specialized knowledge or interest in a particular issue -- and so this will be an opportunity for you to see how working with data, maps, and statistics might help you make your arguments more effectively.  (Note that I have no interest in telling you what to think on any issue of politics or public debate -- I am much more interested in helping you improve how you think, and how you marshall evidence to strengthen your claims.)  If you've taken a course with Brian Klinkenberg, or Dan Hieberts statistics in geography class, or a class with Sally Hermansen, you'll be able to do a lot of the analytical stuff in your sleep.

You have a lot of freedom in your choice of topic.  But if you need ideas and suggestions, I've compiled a page of project options based on work done by graduate students and other colleagues who have taken this course in the past.  There are also some more suggestions to the right on this page.

Paradigms and Personalities

Your second option is what I call "Paradigms and Personalities."  This is stolen from the title of a book edited by Jim Wheeler and Brian Berry, reflecting on the history of Urban Geography in the United States in the second half of the twentieth century.  Think "Biography Channel meets Urban Geography."  In other words:  if you have math anxiety or you just find it boring to work with numbers and maps, then consider studying the history of how certain techniques and assumptions have been used in our field over the years.  If you've taken a course with Trevor Barnes, Derek Gregory, Jim Glassman, Juanita Sundberg, Gerry Pratt, or Jamie Peck, you'll know that it's crucial to understand the history of certain ideas and ways of producing knowledge.

It's a good idea to read enough about a particular approach to get a fair understanding before you attack its every epistemological and ontological foundation.  I've been amazed at some people's ability to use the most sophisticated theory to rip apart every last assumption of, say, ordinary least-squares regression ... without being able to explain in clear terms exactly how one goes about calibrating an OLS model.  The more you learn about the internal logic of the methods you're describing, the more valuable your comments and criticisms.

If you're interested in this option, the very best work to inspire you is Trevor Barnes's ongoing research project on the history of geography's quantitative revolution.  There are a few other citations to the right on this page to get you started if you choose this path.

Another variation on this option is what I'll call the Pucci Solution, in memory of Frank Pucci.  Frank, a comrade from grad school days, really hated quantitative stuff, and he thought that the spatial analysts' writing was atrocious.  He always suspected that the quantitative types were up to something nefarious.  He began using the phrase "quantmag" as an all-purpose epithet for ... "quantitative maggots."  We lost Frank several years ago, but I never forgot his brash, hard-hitting assessments.  If you're dubious of the growing quantification that seems to pervade (post)modernity, then you may want to consider the Pucci Solution.  This is basically an analysis of the consequences of quantification in a particular realm of urban change or urban policy.  Your analysis would offer a critical perspective on the way certain kinds of data are collected, or the way certain types of measurement, modeling, and analysis are used in particular policies, corporate practices, or social movements.  I see headlines every day that call out for an in-depth analysis of this sort:  recent things I've read deal with the increasing range of information collected by Fair Isaac and Company in their calculation of consumers' credit scores; the increasingly aggressive competition amongst local school districts (and local schools) to respond to benchmark standardized testing outcomes; the longstanding policy debates over the measurement of poverty and unemployment...

... or look at this.

Your Index

Your third option is to build a small collection of strategic statistical anecdotes, and then spin a story around them.  Think of this as a mashup of Harper's Index with a standard-issue college term paper.  In my spare time lately, I've been collecting a tiny sample of statistical anecdotes, and you may be interested in taking a look; but note that if you choose this option you'll need to do a better job than I did in organizing them and constructing a full term-paper narrative on an issue that you care about.  I'd recommend you consider aiming for anywhere between 5 and 15 'items'; think creatively about whether you want to open with a full list of your index, or if you want to use each 'anecdote' separately as a section-heading.  Either option can work quite well.

Neighborhoods
Suggestions, possibilities, and paths of discovery

Strategic Positivism

Here are a few additional thoughts and reflections on the "strategic positivism" option.  Would you like to see an example of what I mean by this phrase?  The best and most current example I have to offer is this:

Elvin Wyly and C.S. Ponder (2011).  "Gender, Age, and Race in Subprime America."  Housing Policy Debate 21(4), 529-564.

I presented a ten-minute version of this paper at "Context and Consequences:  The Hill-Thomas Hearings Twenty Years Later."  Washington, DC:  Georgetown University School of Law, October 6, 2011.  The text is here, and the images are here, the full verbose version of the article behind the short talk is here, and the webcast of the entire event, with all the people far more distinguished and intelligent than I, is here.

Now if you just page through it quickly, you'll see it begins with a lot of quotes and deeply political issues that really get us energized; it ends with a lot of criticism and calls to action.  In between, it's got a lot of evidence, in the form of data and multivariate statistical analysis.  This is the sandwich I'd like you to imagine for your chosen topic:  begin with a manifesto, then use some data, maps, or models to help you build your case, then offer your conclusions and your call for action.  The tools you use don't have to be super-detailed, advanced multivariate inferential techniques; you can get a lot out of simple descriptive tabulations.  But depending on the questions you want to explore, you may indeed find it worth the investment to learn a multivariate technique.  The story we wanted to tell in "Gender, Age, and Race," for example, simply had to be multivariate if we were going to respond to the foaming-at-the-mouth American conservatives who still claim that the risky lending that triggered the global financial crisis was all the fault of irresponsible minority borrowers and bleeding-heart liberals who were trying to force banks to make loans to minorities.  If we were going to build a solid case against these idiots, we had to have a set of techniques that compared African American women to White men and White women with the same kinds of incomes, loan terms, and and so on. 

So for that article, we simply had to use a multivariate approach.  I hope you don't find the details too boring or intimidating.  I should also note that this is a major rewrite and revision -- the paper was completely rewritten from the ground up, after an entirely new round of statistical analyses, after a first draft received a round of peer reviews.  We also had to go through an application and approval process, which involved signing legal contracts, in order to get access to some specialized data.  And this paper is on a topic I've been studying for more than a few years now.  So perhaps you might find it more interesting to see the kinds of papers I wrote when I was a bit closer to your stage.  If you're interested, here are a few samples of what I've been able to dig out of my old stacks o' paper.

Here's the first paper I ever got published in a "refereed" professional journal.  In my second year of doctoral study, my friend Dan Hammel suggested we do something together.  He knew a lot of the literature on gentrification, and I knew some methods that intrigued him. Many of the gentrification studies he had read used criteria to identify study areas that weren't really consistent or comparable; so he asked me, "is there a way we can find out what observable changes distinguish gentrifying areas from the rest of the inner city?  Are the changes dominated by economic factors (income, home ownership), or social/cultural differences (household composition, race/ethnicity, education, etc.)?  We started walking and driving through neighborhoods of Minneapolis and St. Paul in January or February of 1994, and eventually submitted a paper, went through revisions, and got it published about two years later.

Daniel J. Hammel and Elvin K. Wyly (1996).  "A Model for Identifying Gentrifying Areas with Census Data."  Urban Geography 17(3), 248-268.

"Ah," you say, "that's not fair as an example, because you're not giving us two years to submit our final papers."  Right you are.  So here are a few examples of papers I wrote at earlier stages.  The Milwaukee density paper was written for a mixed course with graduate and undergraduate students; I took this shortly after I completed my MA, when I had just been allowed to continue on in the doctoral program.  This submission was one of four short projects required in the course.

Elvin Wyly (1993).  "Residential Density Patterns in Milwaukee, Wisconsin."  Unpublished paper for Metropolitan Analysis, Excercise 1.  Minneapolis:  Department of Geography, University of Minnesota.

Around the same time, I was working with my advisor, and another grad student, on some contract research.  I wouldn't have been able to make it through grad school if John hadn't gotten folks at the Department of Transportation interested in geography and metropolitan growth dynamics.  Here's a short summary of a project we worked on for about a year.

John S. Adams, Melissa J. Loughlin, and Elvin K. Wyly (1994).  Transportation Use in Minnesota:  An Analysis of the 1990 Census of Population and Housing.  [4 Volumes and Project Summary.]  Report 94-28, Minnesota Department of Transportation.  Minneapolis:  Department of Geography, University of Minnesota.

Or to go even deeper into the dusty archives.  When I was an undergraduate, Peter Gould allowed me to take a small human geography seminar that was mostly grad students.  We read Hagerstrand, Glacken, Lovelock's Gaia ... and then Harvey got me really interested in Baltimore.  I tried to make sense of Baltimore's restructuring in the context of regional changes underway across different parts of Maryland:

Elvin Wyly (1987).  "Baltimore and Maryland:  Spatial Economics."  Unpublished paper for Geography 497B.  University Park, PA:  Department of Geography, The Pennsylvania State University.

Seen through today's eyes, the paper I wrote is really quite embarrassing, and so I do not include it here as an example for you to emulate step-by-step (especially the maps hand-crafted with Koh-i-Noor pens on mylar, a craft that disappeared with the dinosaurs).  I include it here for two very different reasons:  honesty and the collective.  Honesty matters:  I really want you to do work on the level of "Gender, Age, and Race in Subprime  America."  But I have to be honest that it took twenty years of false starts and lame excuses before I was really capable of doing work like this.  But the collective also matters:  in the best of all possible worlds, you'll be able to take the best of what I'm able to do with all those gray years of experience in the field, and you'll be able to do it better than I.  I have a vivid memory of a colloquium discussion at Penn State in 1987 or 1988:  the department had invited a hot rising star of the field to deliver a lecture on the history and theory of the discipline, and afterwards there was an interesting discussion of generational changes in the discipline, and how things seemed to be getting more competitive.  Peter Gould reminded us of our obligation to the generations who came before:  it is our job to help our students do better than our best achievements.  "If the net curve doesn't go upward, we squander the promise of the Enlightenment and the possibilities of human progress."  This isn't a direct quote, of course, but it's what my aging memory tells me that I took away from seeing that discussion so many years ago.  So:  do your best work, and I'm convinced you'll be able to make a contribution that is far better and more useful than what I wrote in 1987.

By the way:  that hot rising star?  Derek Gregory.

Paradigms and Personalities

If you choose this option, you'll want to start reading right away to get a 'feel' for the ongoing conversation of us geographers.  We're a curious bunch, aren't we?  We seem to have a hard time defining what is that we do -- and, given society's widespread ignorance of geography, this means we're often the punchline to jokes about the useless and obscure nature of university life.  The great curse of our discipline is that we will not give you The One Right Way to define what it is you've studied.  The great joy of our discipline is that you get to assert your own manifesto for why geography matters.  Here are a few of my thoughts on this topic.  Or if you want to see how geographers can have a sense of humor about stereotypes of the field, see this.

After the laughter, then you can get serious.  Use this course as an opportunity to explore serious questions.  What is the relationship between "the urban" and the different influences on geographical inquiry?  How have the relations between the humanities and the sciences shaped our field?  What are the new possibilities that have been created with geography's non-positivist turn?  What are the opportunity costs of this shift? 

Here are some recent articles you should use as starting-points for inquiry.

Trevor J. Barnes (2010).  "Taking the Pulse of the Dead:  History and Philosophy of Geography, 2008-2009."  Progress in Human Geography 34(5), 668-677.

Mark Ellis (2009).  "Vital Statistics."  Professional Geographer 61(3), 301-309.

Susan Hanson (1993).  "Never Question the Assumptions, and Other Scenes from the Revolution."  Urban Geography 14(6), 552-556.

Robert J. Stimson (2008).  "A Personal Perspective from Being a Student of the Quantitative Revolution."  Geographical Analysis 40(3), 222-225.

Brian J.L. Berry, Daniel A. Griffith, and Michael R. Tiefelsdorf (2008).  "From Spatial Analysis to Geoscience."  Geographical Analysis 40(3), 229-238.

Arthur Getis (2008).  "A History of the Concept of Spatial Autocorrelation:  A Geographer's Perspective."  Geographical Analysis 40(3), 297-309.

Richard Morrill (2008).  "Is Geography (Still) a Science?"  Geographical Analysis 40(3), 326-331.

Ron Johnston (2008).  "Quantitative Human Geography:  Are We Turning Full Circle?"  Geographical Analysis 40(3), 332-335.

Peter Haggett (2008).  "The Spirit of Quantitative Geography."  Geographical Analysis 40(3), 226-228.

Peter Haggett (2008).  "The Local Shape of Revolution:  Reflections on Quantitative Geography at Cambridge in the 1950s and 1960s." Geographical Analysis 40(3), 336-352.

Emilio Casetti (1972).  "Generating Models by the Expansion Method:  Applications to Geographical Research."  Geographical Analysis 4(1), 81-91.

Trina Hamilton (2009).  "Power in Numbers:  A Call for Analytical Generosity in New Political Strategies."  Environment and Planning A 41, 284-301.

Luc Anselin, Ibnu Syabri, Youngihn Kho (2006).  "GeoDa:  An Introduction to Spatial Data Analysis."  Geographical Analysis 38(1), 5-22.

Luke Bergmann, Eric Sheppard, and Paul S. Plummer (2009).  "Capitalism Beyond Harmonious Equilibrium:  Mathematics as if Human Agency Mattered."  Environment and Planning A 41(2), 265-283.
 
"... if positivism is a social and political movement, that means we can play a role in reshaping what the project is all about.  I suggest that critical and radical urbanists have a responsibility to reclaim positivism from its conservative kidnappers. We must move quickly:  while philosophers and historians of science invariably locate the high point of positivism in the safe, distant past (Comte, the Vienna Circle, the U.S. Cold War science infrastructure), recent trends indicate that we are just now approaching the worldwide networks of human knowledge and learning first imagined by Comte in the 1820s.  The positivist era is right now.  The question is what kinds of politics it will have."

Elvin Wyly (2012).  "Automated (Post)Positivism."  Unpublished draft.  Vancouver:  Department of Geography, University of British Columbia.
Quick Links, Week by Week
Infrastructure
Data, documentation, and resources







Books on Reserve
at the Geographic Information Centre, Geography Room 112

Some of the required readings throughout the term will be drawn from these sources, and I highly recommend you skim through these sources from time to time for ideas and inspiration.

Ronald Abler, John S. Adams, and Peter Gould (1971).  Spatial Organization:  The Geographer's View of the World. Englewood Cliffs, NJ:  Prentice-Hall.

David Harvey (1969).  Explanation in Geography. London:  Edward Arnold.

Martin Cadwallader (1996).  Urban Geography:  An Analytical Approach.  Upper Saddle River, NJP:  Prentice-Hall.

James E. Burt, Gerald M. Barber, and David L. Rigby (2009).  Elementary Statistics for Geographers.  New York:  Guilford Press.

Richard P. Greene and James B. Pick (2011).  Exploring the Urban Community:  A GIS Approach.  Upper Saddle River, NJ:  Prentice-Hall.

Peter R. Gould (1999).  Becoming A Geographer. Syracuse, NY:  Syracuse University Press.
 
Scratch pad:  random tidbits

The primitive HTML editor I use for this page isn't the best, so it's a bit labor intensive to code links, and there's no convenient "scratch pad" to store things that you don't know where you're going to put.  (Sorry, I'm an old dog, and I'm reluctant to learn new tricks, "trick" defined in this digital age as software, social networking stuff, or up-to-date digital equipment; not long ago, students who glimpsed my jurassic cellphone began calling it a Fisher-Price phone...!). 

So this will be my spot for disorganized stuff...

Ken Zimmerman, Elvin Wyly, and Hilary Botein (2002).  Predatory Lending in New Jersey:  The Rising Threat to Low-Income Homeowners.  Newark, NJ:  New Jersey Institute for Social Justice.

Andrew Barr, Jonathan Rivait, and Richard Johnson (2012).  "Syria's Martyrs."  The National Post, January 14, p. A14, reproduced on The Huffington Post.

Shaila Dewan and Robert Gebeloff (2012).  "Among the 1 Percent, Many Variations."  New York Times, January 14.

John A. Eterno et al. (2012).  The Crime Numbers Game:  Management by Manipulation.  London:  CRC Press/Taylor & Francis.

Cool visualizations and interpretations: Mark Byrnes (2011).  "Buffalo, Then and Now (1902-2011)."  The Atlantic Cities, December 15.

Reflect on this provocation, then read Torrens' article; focus on the words, and don't stress out about the equations (they give me headaches too!).  Then discuss.  "Agent-based modeling is, perhaps, the ultimate postmodern (Dear 1988) form of geocomputation, because any agent perspective can potentially be deployed in simulacra (Baudrillard 1994)." 

Think about the possible uses and performative meanings of what Torrens has built.  Should progressives and radicals work with agent-based modelers like Torrens to refine their plans for nonviolent but insistent, creative, and effective mobilizations?  Can flash mobs be mobilized for social justice? 

Paul M. Torrens (2012).  "Moving Agent Pedestrians Through Space and Time."  Annals of the Association of American Geographers 102(1), 35-66, quote from p. 61.

If you don't learn quantification, it will be learned upon you.  It's scary.  Read the Peters article, then look at the Goss article, then ... think ... how have the real material spaces of targeting consumers (and consumer-voters, to borrow from Tiebout) changed with the acceleration of all forms of digitally-mediated communication?

Jon Goss (1995).  "We Know Who You Are and We Know Where You Live:  The Instrumental Rationality of Geodemographic Systems."  Economic Geography 71(2), 171-198.

Jeremy Peters (2012).  "For GOP Ads, 'CSI' but Not Letterman."  New York Times, January 29.

Are the Tories Reading My Syllabus? 

See:

Climate Action Network Canada (2012).  "Feds List First Nations, Green Groups as Oilsands 'Adversaries.'"  Ottawa:  Climate Action Network Canada.

The Government compiles a database -- this is the governmentality that makes all of us data-keepers suspect! -- and then its manekin demurs. 

"Environment Minister Peter Kent denied the labelling of 'allies' and 'adversaries' reflected Ottawa's approach.  "I think that's a gross mischaracterization of reality," Kent said Thursday.  "I think that any of our messaging, whether in Canada, elsewhere on the continent, in Europe or in Asia is based on facts and science.  We do recognize there are some groups characterized by my colleague as 'radical' and they are very narrowly focused on certain areas they perceive to be unacceptable in a variety of ways.  We intend to fully push back and to counter that but again respectfully and with facts and with science."

See Mike de Souza (2012).  "Federal Push for Oilsands Lists Allies, 'Adversaries.'"  Vancouver Sun, January 27, p. B3.

So public-relations spin and Tricky-Dick dirty tricks are now officially labeled "facts and science"?  What about the facts and science on climate change, and the distinctive carbon ecosystem of petroleum extraction in the Athabasca basin?

Your mission, should you choose to accept it.  You have three tasks.  One, follow the CANC link above, and get the Freedom of Information Act documents referred to in the press reports. Two, compile a list of the names of allies and adversaries.  Three, do a search similar to Josh On's "They Rule," and build networks out from the organizations named.  Use whatever sources out there you can to find connections, and draw them, either on paper, or in a piece of software if you prefer that way.  Your purpose is to find the main points of overlap and interconnection among organizations that are listed on opposite sides of the fence.  In other words, how many degrees of separation might there be in finding common ground?  Do a map and a short essay advising your chosen audience how to try to build connections.

Yes!  Yes, they are reading our syllabus!

"Many Canadian police agencies 'actively suppress' racial data when delivering their annual crime reports to Ottawa -- a trend that is both disturbing and growing, according to a study released Wednesday."  The study, led by Paul Millar at Nipissing University in North Bay, "says the continued 'whitewashing' of criminal data makes it virtually impossible for researchers to gauge whether police are dealing with racial and ethnic minority groups in an equitable manner."  An RCMP spokesman, true to form, demonstrated the Right's savvy co-optation of the langauge of privacy to achieve regressive goals:  "asking a victim or accused person to identify their race 'may give rise to human rights and privacy concerns.'"  Quoted in Douglas Quan (2012).  "Police Suppressing Racial Data, Study Finds."  National Post, February 2, p. A2.

A Gentle Reminder on Lab Procedures from Jose
January 20, 2012

"On lab related business, please remind students not to save files to any of those Windows' friendly folders (Desktop, My documents, my images, my downloads,etc...). These locations are part of the profile associated to each username in the lab. The profile travels up to/down from the server every time a student logs in/logs out. As students work through the semester they download datasets and then quickly forget about them sitting in those out of the way folders.

When students are conducting data searches, I would encourage them to:

1) Download/unzip/examine datasets in C:\data.

2) Copy the relevant datasets to the H:\ drive.

3) if they want to keep the original downloads I would suggest they bring a USB key.

4) Clean the 'My Downloads' folder periodically if there are recurrent data transfers. I believe that is the default location for any browser when downloading data.

Have a good weekend.

Cheers

Jose"
Thanks to all who completed the survey.  Here's one way of summarizing just one of the questions on the survey -- your preferences for various kinds of research and study.  Here's a worksheet with the methods ranked from 1 (you love it) to 14 (you can't stand it). 

Here are the data in comma-delimited format.  In class, you should download this file to C:\DATA on your local computers, and then we'll explore the data a bit.

Here is the short introduction to STATA that we'll discuss in class. 

For another illustration of Multidimensional Scaling, see the Congressional Ideology Mappings of Govtrack.us!

For additional resources on learning STATA, browse through the list of choices available here.

Here's a copy of my musings on various conceptual and "ologies" issues.

Here are a few other thoughts on data that may be useful or interesting.
Neighborhoods
Suggestions, possibilities, and paths of discovery

Thanks to those who shared written ideas for what they'd like to do.  For those who didn't turn in anything last week, I will submit a proposal to you.  Next week, I'm going to pitch you on a few projects that *I* would like to do, if only I had the time and help.  Since I don't have as much time as I need, maybe you can supply the help and we can work on this stuff together.  I'll pitch three or four ideas for projects; one is something I've had in the back of my mind for a while; another is something I recently planned to complete in the summer.  Maybe you can help me on it. 

I won't force you to be part of my project, but the burden of proof shifts for those who did not submit written proposals last week:  if you want to opt out of working on one of my projects, then you should have a pitch ready for me.  If you give me evidence that you're committed to a particular idea and you've invested a bit of time, then you can do your own project.  But otherwise I hope you'll join me -- we've got lots of interesting work to do.

Here's a short background paper on a few simple descriptive statistics.

More on Danny Dorling:  Dorling is one of geography's most powerful and eloquent voices for social justice; A few years ago, Sir Simon Jenkins described him as 'Geographer Royal by Appointment to the Left.'  See liner notes for Danny Dorling (2010).  Injustice:  Why Social Inequality Persists.  Bristol:  Policy Press.  Dorling's work combines the measurement rigor of statistical methodology with the political integrity of philosophical commitments to social justice and equity.  He also follows good practice by making his data available for other researchers; all of the data used to create the charts and tables published in Injustice are available here and some other resources can also be found here.

Dorling's most recent book has gotten a bit of press coverage, and he has collaborated to produce a series of short film clips illustrating the five key themes of the book.  Search for "Danny Dorling" on Youtube, or sample this one:

Danny Dorling and Carl Lee (2010).  "A Personal Geography of Injustice."  Part IV, "Greed is Good."  Sheffield:  Department of Geography, University of Sheffield.

For an excellent illustration of location quotients applied to urban-geographic questions, see Dan Hiebert's 2006 Metropolis Census Atlas.

Lewis Lapham, former editor of Harper's, came up with the idea for a concise, distilled way of conveying interesting magnitudes, comparisons, estimates, and contrasts; he described the idea as sort of a "statistical poem."  In 2009, Harper's Index celebrated its twenty-fifth anniversary.  They now have a search engine for the Index.

There's a fascinating intellectual history of the 'spatialization' of simple descriptive statistics.  Simple measures of central tendency and dispersion -- the mean, median, and standard deviation, for instance -- ignore location, and present the world deceptively as a one-dimensional place.  What if we think of applying these descriptive statistics to a simple map, with its two dimensions, one for latitude, one for longitude?  The saga involves the Russian chemist Mendeleev and the establishment of a prominent laboratory for geographical analysis, of a sort, in Leningrad after the 1917 Revolution.  See pages 25-27 of Taylor's excerpt, or the Sviatlovsky and Eells piece.

E.E. Sviatlovsky and Walter Crosby Eells (1937).  "The Centrographical Method and Regional Analysis."  The Geographical Review 27(2), 240-254.

Peter J. Taylor (1977).  Quantitative Methods in Geography:  An Introduction to Spatial Analysis.  Prospect Heights, IL:  Waveland Press, Inc., pp. 18-32.

A central theme of Taylor's chapter is that nearly every simple descriptive statistic -- mean, standard deviation, and so on -- has an explicitly geographical counterpart.  One of these involves a simple technique to adjust for the problem of small numbers in the common cartographic technique of choropleth mapping.  Without adjustment, choropleth maps can be quite deceptive.  Consider two neighborhoods appearing on a map of poverty rates:  in one neighborhood with 100 households, 25 have incomes below the poverty line; in another neighborhood with 4,000 households, 1,000 are in poverty.  The familiar choropleth map portrays these places as identical.  But if we are working on policies that will affect low-income communities -- or if we are working with activists to mobilize political organizing in various parts of the city -- then these two places would merit very different consideration.  If we have limited time and money -- and these valuable resources are always scarce -- then we would have to prioritize our efforts.

Many years ago, Andrew Kirby and Anthony Gatrell came up with the idea of using the chi-square statistic to deal with this problem of small numbers.  The technique involves a comparison between observed and expected distributions across space.  For a description of the technique, see pages 194-198 of Gatrell's chapter.  For an illustration of how I've used the approach, see page 2153 of Wyly et al. (2007), and compare the maps on pages 2155 and 2156.

Anthony C. Gatrell (1985).  "Any Space for Spatial Analysis?"  Chapter 10 in R.J. Johnston, ed., The Future of Geography.  London:  Methuen, 190-208.

Elvin Wyly, Mona Atia, Elizabeth Lee, and Pablo Mendez (2007).  "Race, Gender, and Statistical Representation:  Predatory Mortgage Lending and the U.S. Community Reinvestment Movement." Environment and Planning A 39, 2139-2166.

 
Infrastructure
Data, documentation, and resources

This might seem a bit too simplistic, a bit beneath you.  But we can all use a refresher from time to time.  And every year in one of my other course, approximately twenty percent of the submissions for a metropolitan analysis project have incorrect results because of erroneous calculations of simple concepts like ... "percent."  Not everyone in our little town can be above average unless everyone can calculate the average...!

So you might be interested in these Khan Academy tidbits:

"Describing the Meaning of Percent."

Mean, Median, and Mode

Chi-Square Distribution

A Few Resources for the projects I'll discuss in class:

Trudi Bunting, Alan Walks, and Pierre Filion (2004).  "The Uneven Geography of Housing Stress in Canadian Metropolitan Areas."  Housing Studies 19(3), 361-393.

Elvin Wyly (1999).  "Continuity and Change in the Restless Urban Landscape."  Economic Geography 75(4), 309-338.

Elvin Wyly (2010).  "Feminized Unpaid Labor in the Vancouver Metropolitan Area."  Vancouver, BC:  Department of Geography, University of British Columbia.

Mark Davidson and Elvin Wyly, "Class-ifying London: questions of social division and space claims in the post-industrial city."

Elvin Wyly, Sage Ponder, Pierson Nettling, and Dan Hammel, "The New Meaning of Housing in America."

 
City Center
Required Reading

If you're able to get to the GIC before Tuesday morning, read James E. Burt, Gerald M. Barber, and David L. Rigby (2009).  "Sampling."  Chapter 6 in  Elementary Statistics for Geographers.  New York:  Guilford Press.

If you can't get to the GIC to read Burt et al.'s chapter, then take a look at these instead:

David C. Wheeler, Jason E. VanHorn, and Electra Paskett (2008).  "A Comparison of Design-Based and Model-Based Analysis of Sample Surveys in Geography."  The Professional Geographer 60(4), 466-477.

Matthew G. Hannah (2001).  "Sampling and the Politics of Representation in U.S. Census 2000."  Environment and Planning D:  Society and Space 19, 515-534.



In Class Today:

As you'll recall, the discussion of research projects last week went long, and I never got around to presenting the background refresher on simple descriptive statistics.  So that's what I'll start off with today, followed by a bit of sampling.

Simple Descriptive Statistics
Sampling
Neighborhoods
Suggestions, possibilities, and paths of discovery

"We have never previously conducted a survey on the scale of the voluntary National Household Survey, nor are we aware of any other country that has. The new methodology has been introduced relatively rapidly with limited testing. The effectiveness of our mitigation strategies to offset non-response bias and other quality limiting effects is largely unknown. For these reasons, it is difficult to anticipate the quality level of the final outcome."

Statistics Canada (2011).  "National Household Survey:  Data Quality."  Ottawa:  Statistics Canada.

see also

Statistics Canada (2011).  "2011 National Household Survey:  Response Rates."  Ottawa:  Statistics Canada.

Statistics Canada (2011).  "2011 Census of Population:  Response Rates."  Ottawa:  Statistics Canada.

Predators in Pinstripes

Office of the Attorney General (2012).  "A.G. Schneiderman Announces Major Lawsuit Against Nation's Largest Banks for Deceptive and Fraudulent Use of Electronic Mortgage Registry."  New York:  State of New York Department of Law.

You've just got to see this:

Rachel Maddow (2012).  "Voices for the downtrodden:  Rachel Maddow describes the protest movement to help people resist foreclosure and stay in their homes, and shares video of their unique tactic of singing to interrupt foreclosure auctions.  New York:  MSNBC, February 3.

Watch closely as the camera pans left in one of the crowd clips, at the 3:23 mark.  See the guy in the white suit?  That's Reverend Billy. In a strange version of asynchronous distance learning, the Rev and I did some co-teaching with Holly Foxcroft a few years ago.

Rachel Maddow (2012).  "US Economic Picture Brightening:  Jared Bernstein, a senior fellow at the Center on Budget and Policy Priorities, talks with Rachel Maddow about new positive economic statistics and the effort by New York Attorney General Eric Schneiderman with the White House to hold big banks accountable for the damage they did to the economy with bad mortgage deals."  New York:  MSNBC, February 3.

Yikes!  The disclosures are coming fast and furious now!  See

Gretchen Morgenson (2012).  "A Mortgage Tornado Warning, Unheeded."  New York Times, February 4.

And then the full report is here.





Infrastructure
Data, documentation, and resources

 
City Center
Required Reading

Please try to get to the GIC before Tuesday morning, and read:  David Harvey (1969).  "Problems of Explanation in the Social Sciences and History."  Chapter 5 in  Explanation in Geography.  London:  Edward Arnold.

Please also read this before class:

Inferences and Hypothesis Testing
Today we'll spend most of our time in studio and workshop mode.  I'll give a brief lecture on inferences and hypothesis testing, and then we'll have open lab time to work on our projects.  Call me over for advice and recommendations as you continue work on your individual or group project.  For those in one of my working groups, below are some resources we'll be using to guide our work in the next few weeks.

London Group

Today we'll talk about some interpretations of the neighborhood classification of the Greater London region.  You should be familiar with the general argument in our draft paper:

Mark Davidson and Elvin Wyly, "Class-ifying London: questions of social division and space claims in the post-industrial city."

Here's the neighborhood classification map we're working on interpreting.

And then here are some of the articles in the ongoing debate:

Chris Hamnett (2003).  "Gentrification and the Middle-Class Remaking of Inner London, 1961-2001."  Urban Studies 40(12), 2401-2426.

Tom Slater (2009).  "Missing Marcuse:  On Gentrification and Displacement."  City 13(2,3), 292-311.

Chris Hamnett (2009).  "The New Mikado?  Tom Slater, Gentrification, and Displacement."  City 13(4), 476-482.

Tom Slater (2010).  "Still Missing Marcuse:  Hamnett's Foggy Analysis in London Town."  City 14(1-2), 170-179.

Chris Hamnett (2010).  "I am Critical.  You are Mainstream:  A Response to Slater."  City 14(1,2), 180-186.

Tim Butler, Chris Hamnett and Mark Ramsden (2008).  "Inward and Upward:  Marking Out Social Class Change in London, 1981-2001."  Urban Studies 45, 45-67.

Pinstripe Predators Group

Today we need to begin narrowing our focus on institutions that we'll analyze, and then setting up the details of the databases we'll use.  We may find it helpful to consider the general approach used by analysts at the California Reinvestment Coalition. spent a bit of time with Kevin Stein, Associate Director of CRC, when delivering testimony to the Federal Reserve Board in August, 2010:  see

Good Data, Good Deeds.    Presented at the Federal Reserve Bank of San Francisco, August 5, 2010.  The disembodied voice of ekw is here, with a backup copy here, and a full transcript of the entire hearing is here.

Please skim through a few of these resources:

California Reinvestment Coalition.

California Reinvestment Coalition (2011).  The Wall Street Wrecking Ball:  What Foreclosures are Costing Oakland.  San Francisco:  California Reinvestment Coalition.

California Reinvestment Coalition (2011).  Paying More for the American Dream V:  The Persistence and Evolution of the Dual Mortgage Market.  San Francisco:  California Reinvestment Coalition.

Vancouver Suburbanism Group

In addition to the housing-stock and commuting models I discussed with you last week, I'm exploring ways of measuring inequality in different housing submarkets in Vancouver and other metropolitan areas.  Skim through the Walks article below, and then try to make sense of the equations on page 304.  Don't be scared.  Just read the sentences out loud while looking at the equations.  I'm tracking down some of Alan's sources to make sure I really understand what's going on here.  Follow along if you're so inclined...but it's saturday night, and you probably have better things to do than stare at inequality and polarization equations...!

R. Alan Walks and Richard Maaranen (2008).  "Gentrification, Social Mix, and Social Polarization:  Testing the Linkages in Large Canadian Cities."  Urban Geography 29(4), 293-326.

Michael C. Wofson (1997).  "Divergent Inequalities:  Theory and Empirical Results."  Review of Income and Wealth 42(4), 401-421.

Here are the preliminary tables from the analysis of the 2006 Census of Population file.
 
 
City Center
Required Reading

I know, I know ... so sorry for not updating this page until the very last minute.  By this point in the semester, you should be focused on your individual and group projects.  You should consult the books on reserve at the GIC to explore various methods and techniques to use in your inquiry.  For correlation and regression, I recommend any of these:

Chapter 3, "Patterns of Land Use and Land Value," of Cadwallader, Analytical Urban Geography, especially pages 60-63.

Chapter 4, "Statistical Relationships," of Burt, Barber, and Rigby, Elementary Statistics for Geographers.

Chapter 20, "Cause-and-Effect Models," of Harvey, Explanation in Geography.

Chapter 5, "Structuring Geographic Relationships," of Abler, Adams, and Gould, Spatial Organization.

I just returned from New York.  As Calvin and Hobbes once reminded us, the days were just packed ... but there was just a tiny bit of time for a little bit of wandering in a few neighborhoods across the city -- a chance to use that analytical technique described by remote sensing specialists as "ground truthing" data from satellite sensors.  I didn't have as much time as I would have liked for the extended wandering, to explore all the neighborhood dynamics that can so often be overlooked if we never go any deeper than that requisite night-time-view of the galactic metropolis (a temptation to which I always succumb -- see below!).  So many spatial, social, and political transformations.  If you're interested, skim through our "Mapping Public Housing," look at the maps, and then drink a bit from the flood of scholarly and journalist reports on demographic changes documented with the release of more recent census data.  Goldschein's piece is just one place to get you started if this catches your geographical imagination...

Elvin Wyly and James DeFilippis (2010).  "Mapping Public Housing:  The Case of New York City."  City & Community 9(1), 61-86.

Eric Goldschein (2012).  "Gentrification has Made this Old Brooklyn Neighborhood Unrecognizable."  Business Insider, 2 March.
UPDATES

Here's an update on what I've been doing lately on these three projects.  You should be working -- with as much independence or collaboration as you find most productive -- on some component of these (unless you're doing a separate independent or group project).  If you need a copy of the division of labor we discussed a few weeks ago, it's here, and then there are some additional reference links to explore, here.

Pinstripe Predators Group

I have been sifting through the national data, working to measure how racial inequalities have and have not changed in overall mortgage market trends.

Here's a preliminary table extracted from some 60 million loan application register files between 2004 and 2010.

Here's a rough draft of the first few pages of a manuscript...

London Group

I've been reading through the 2005 Office of National Statistics (ONS) edited book describing changes in Socieconomic Classifications in the U.K. Census.  It took a while before I fully understood the logic of the new "National Statistics Socio-Economic Classification" (NS-SEC), and this is of course a major prerequisite before we begin wading through the micro-level data.  I dove into that a bit, however, trying to estimate a series of models predicting home ownership as a function of demographic and social-class variables.

Vancouver Suburbanism Group

IWe had good project meetings on the Global Suburbanisms Group convened by Roger Keil at the annual meetings of the Association of American Geographers, in New York.  I've been reading and thinking, working on the different categories of urban housing submarket to focus on.  Once I devise the classification system, then I'll use the simple inequality measure -- the coefficient of variation -- to determine "where' the widest inequalities are.  This fits into a broader conceptual story by helping us test out two contradictory attacks on suburbia.  On the one hand, there's an esteemed tradition of urban scholars who attacked suburbia as conformist -- especially in the massive wave of suburbanization in the mid-1950s.  Even today, however, many urban elites look with disdain at suburbs and suburbanites, because of perception of sameness and conformity.

Perhaps part of that 'sameness' might be economic, and should show up in our analysis of income inequalities.

But in many cases, suburbanization is also seen as undermining the old inner city -- and there is a sense that many people got access to a lot of subsidies, and opportunities to build wealth, through suburbanization.  Over time, it was recognized that suburbs were quite varied.  Suburbia might thus be more unequal that the older sections of the built environment.

I haven't added any new tables other than what's above...but I'm working on this...

 
 
 
London Group

Here are a few updates for the London Group.  After quite some time on the phone with SAS technical support, I managed to figure out the strange riddle of the Small Area Microdata file that seemed to get corrupted.  I'm rebuilding the SAS code that I showed you to look at the relations between occupational class categories and home ownership across the Greater London region.  I'll have finalized tables by next week's class.

I also figured out a work-around from the other glitch we had in class, on the GeoDa software's refusal to save the significance level, and then the annoying tendency of ArcGIS to barf when we tried to open the shapefile.

I fixed it, but I have no idea exactly what sequence of steps actually did the trick -- note that while I use software in this class, this isn't a class in software, it's a class in urban research.  Sometimes urban research can be messy -- that means that you often have to use a mixture of methods and tools.  When one tool fails, take out your rubber bands and duct tape ... or pick up another tool.

So:  below is a locator map with boroughs identified, then the revised LISA map relating Factor 1 to Factor 2 -- after 999 iterations of the randomization algorithm, and showing at a 5 percent significance level -- and then the revised neighborhood ecology map with the borough overlays.

The files for these are huge, so it may take quite a while if you try to download...

Your mission is to help us all learn about some of the local character of these places as we revise the draft manuscript...

best,
Elvin
More London Resources:  SAS Codes and Output Files

Here's the SAS code used to analyze the Small Area Microdata (SAM) files from the UK Office of National Statistics.


Most of the SAS file is format codes, and then there's a few steps to prepare the data and select certain parts of the population for further analysis.  The heart of the actual analysis is here, near the end of the file:

***Ownership Models for Household Reference Persons;
proc logistic data=mysam.mysam2001 simple;
    class frsex hourspwg hnearnra famtypa miginda frnssec8;
    model rev_own=frsex hourspwg hnearnra famtypa miginda frnssec8 / expb rsquare;
    title "London Home Ownership Models for Greater London Region";
    run;
proc logistic data=mysam.mysam2001 outest=mysam.ownmods noprint;
    class frsex hourspwg hnearnra famtypa miginda frnssec8;
    model rev_own=frsex hourspwg hnearnra famtypa miginda frnssec8 / expb rsquare;
    title "London Home Ownership Models by LA";
    by lacode;
    run;
***Ownership Models for those who just moved in within the last year;
proc logistic data=mysam.mysam2001 simple;
    where miginda ne 0;
    class frsex hourspwg hnearnra famtypa frnssec8;
    model rev_own=frsex hourspwg hnearnra famtypa frnssec8 / expb rsquare;
    title "Ownership Model for Recent Movers for Greater London Region";
    run;
proc logistic data=mysam.mysam2001 outest=mysam.ownmov noprint;
    where miginda ne 0;
    class frsex hourspwg hnearnra famtypa frnssec8;
    model rev_own=frsex hourspwg hnearnra famtypa frnssec8 / expb rsquare;
    title "Ownership Model for Recent Movers by LA";
    by lacode;
    run;

***Models predicting who moved into their homes in the last year;
proc logistic data=mysam.mysam2001 simple;
    class frsex hourspwg hnearnra famtypa frnssec8;
    model rev_mov=frsex hourspwg hnearnra famtypa rev_own frnssec8 / expb rsquare;
    title "Mobility Model for Greater London Region";
    run;
proc logistic data=mysam.mysam2001 outest=mysam.movmods noprint;
    class frsex hourspwg hnearnra famtypa frnssec8;
    model rev_mov=frsex hourspwg hnearnra famtypa rev_own frnssec8 / expb rsquare;
    title "Mobility Model by LA";
    by lacode;
    run;

When we run this batch of code, the software gives us a "log" keeping track of everything SAS did, including any warnings or error messages:


And then SAS also creates a printed output file with the results of the procedures we've asked for:


As you can see, this is a lot of output -- and this is just for the three models for the overall London region as a while.  The output would be much, much longer if we printed out everything for all the models by Local Authority (LA) (notice the "noprint" option in the second portion of each pair of "proc logistic" sections). 

Our mission at this stage is to distill this monstrous output file to a few presentation-quality tables in Excel, and begin taking notes for a few paragraphs of interpretation.  Recall that one part of the paper is focusing on "middle-class" occupations across the London region.  We now have some models that estimate how occupational divisions affect the likelihood of home ownership, across different parts of the London region, while "controlling for" age, household structure, hours worked per week, and so on.  This means that we're able to isolate the independent effect of occupational class on property ownership.

Notice one more thing.  I've highlighted three sections of the code in red.  These are the files created to save the results of the statistical analysis.  I've converted these files to Excel format, and they are here:


So, your mission:  help me re-format the "raw" output of these statistical procedures into a small number of Excel tables that help us make sense of things as we work on our draft.

If you want to see an example, then skim through this manuscript:

C.S. Ponder and Elvin Wyly (2011).  "Gender, Age, and Race in Subprime America," submitted to Housing Policy Debate.

We distilled Stacks o' SAS output into a few key tables to try to tell our story.  The Excel file with all the tables for the manuscript is here.

 
City Center
Required Reading

Edward Tufte (2010).  "Beautiful Evidence." [Excerpts.]  London:  Intelligence Squared.

Go to the GIC, and read pages 13-17, and Chapter 2, of this:

Edward Tufte (2001).  The Visual Display of Quantitative Information.  Cheshire, CT:  Graphics Press, LLC.


Possible Strike

April, 2012:  For information on UBC's labour situation, read this.


Lab Hours
On 22/03/2012 11:16 AM, Jose Donato Aparicio wrote:
> Good day everybody
>
> I will be opening the geography computer labs (114, 115 & 239) on the
> following weekend/holidays between 10am and 5:30pm:
>
> Saturday, March 31st
> Friday, April 6th
> Monday, April 9th
>
> I will be around but TAs are not required to be present.
>
> Other relevant days to keep in mind:
>
> Thursday, April 5th - Last day of class
> Wednesday, April 11th - First day of final exams
>
> Cheers
>
> Jose
>