rickwoodramblings

How to make high-speed rail stack-up

2011-08-07T17:11:00.000-07:00

On the face of it, the latest of a long line of studies into fast rail appears to show, yet again, that it is going to be hard to justify the huge up-front capital costs. An estimated cost of $80 billion (give or take $20-odd billion, but who cares about loose change?), which is not going to be recouped in any significant way from fares. So lets not bother, right?

But hang on, aren't we missing something here? Well, only the main cost-recovery avenue -- value (re)capture. The idea is hardly new, but for some reason I can't fathom it is rarely discussed in detail in these fast rail feasibility studies. For those unfamiliar with the idea, it's quite simple. The government is going to shell out huge buckets of money building a high-speed rail line. Anyone owning property near the rail stations will immediately see the value of that property increase. Since these property owners haven't done anything themselves, and are just benefiting from the government's spending, it's fair for the government to recoup the increase. At this point people often start quibbling about difficulties in estimating the benefits, or object to trying to claw back money from old ladies who just happen to own a house near one of the stations. None of that is relevant except in political terms, and for the moment lets stay away from the politics: we can come back to that later.

So how important is value-capture, relative to fare-box revenue? Very. Lets do our own back-of-the envelope calculations and see. First up, lets simplify things just to make the case for value recapture clear. Let's just take the Sydney-Newcastle-Canberra section of the track. The best-guess cost for this in the most recent study is $29.3 billion; let's just call it $30 billion.

Total cost: $30 billion

A further simplification will help bring out my point, and allow us to avoid complicated calculations that just obscure my main point. Let's say that instead of linking Sydney, Canberra and Newcastle, we just build south-west from Sydney out to empty sheep paddocks near Golbourne, and North to some easily developable land near the Central Coast or Newcastle. So, we have two lines, with 4 stops along each line (as in the most recent study), and a train commute of 40 minutes or under for each station.

Now, suppose we develop a small 2km radius 'town' around each station. Since we've built out to sheep paddocks this should be no problem. A 2km radius town with 8 lots per hectare will give us 10000 lots in each town. How much would each lot be worth? With a sub-40minute commute to Sydney, I'd say at least three or four hundred thousand. But lets say after servicing and other costs, the net increase is $200k for each lot. So, 8 stations, 10000 lots per station, $200k per lot, gives us $16 billion dollars:

Land-capture capital recovery: conservatively, $16 billion

So with some pretty conservative assumptions we have recouped half the capital cost. Getting a little more creative should allow even greater cost recovery. Developing a 3km^2 town at each station, or developing at higher densities, are easy adjustments that would increase revenue and get you to near full cost-recovery without any fare-box revenue.

So what about fare-box revenue? In our 8 towns, with 10000 dwellings, lets say there are 10000 workers, 30% of which commute using the rail line, with the other 70% commuting by car or some other mode. So, 3000 workers in each town commuting each way 200 days a year gives us 9.6 million trips. Call it 10 million. Add another 10 million non-work trips. Say each trip costs $10. So, that's $200 million a year, or a 0.6% p.a. gross return on $30 billion invested, before we take into account operating costs. Fiddle with the above numbers any way you like, it wont change the fact that fare-box revenue is almost small enough to forget about altogether, and certainly much less important than value-capture as a funding mechanism.

Net fare-box revenue: approximately nil.

Bottom-line: if you want to fund high-speed rail, heck, in fact rail of any kind, you need to put value recapture front and centre of your approach to cost-recovery. Why isn't it already? Because property owners would prefer to pocket the benefits from the rail-line themselves, and many will fight tooth-and-nail to do so. Building the line to some empty sheep paddock mostly avoids this, because there are only a handful of large land-owners likely to be involved, and that's why I took that approach in the above example, but in reality there would be a lot of opposition to such a move ("It's crazy to build a rail line out to nowhere! We need to strengthen the centres we already have!"). And if you do build to existing centres, it is politically difficult to claw back the increased land values (not that any Australian government have really tried very hard....). In fact, building the line out to empty paddocks is probably not the best plan, economically, anyway, provided that you have a mechanism to recoup the increased value from existing land-holders.

And the reason land-value capture works so well (if you can do it, politically), is because our dysfunctional planning systems have made supply of new dwellings, at low cost, so difficult. So there is a lot of suppressed demand for accessible locations. A rail line is, essentially, a way of 'manufacturing' new accessible locations. Releasing land on the urban fringe is not the same thing -- a point missed by many who claim that the fix to our housing supply problems is just to release more land. This would help a bit, but the real demand is not for land, but for dwellings with good accessibility (to jobs, and other things), and accessibility can only really be supplied by transport infrastructure.

As a final aside, some economists have pointed out that because the benefits of rail systems get capitalized into a relatively small number of land-holders, this makes rail distributively unfair, as costs are shared and benefits and concentrated. The benefits of a motorway, on the other-hand, are much more widely spread. This is a valid point, and can't be addressed without re-thinking value capture. Just to be clear: I'm not arguing for motorways here, just making it clear that making rail work requires us to have a discussion about the fairness of recouping private gains from public infrastructure investment.

Connecting to mysql from python the odbc way from mac

2011-05-04T20:21:00.000-07:00

This post just describes how to set up python to talk to a mysql database on a mac. Written for reference, and for co-workers who will shortly need to do something like this.

I wanted to be able, in a python script, to talk to a remote MySQL database from my mac. As I went about this I found the documentation a little scattered -- I couldnt find a single page giving the instructions, and so I've just written up the steps here.

I assume that you have python installed already.

Step 1: Install pyodbc

First step is to install pyodbc if you havent got it installed already. I downloaded pyodbc from http://code.google.com/p/pyodbc/downloads/list (I downloaded the source distribution because there were no binaries for mac. The source distribution is the one that ends in .zip)

After unzipping this, going into the directory that is created by the unzip and typing python setup.py install at the command prompt should install everything without you needing to do anything else.

Step 2: Check that you have an odbc manager installed

pyodbc wont do all the work for you -- it relies on an odbc manager to ferry things back and forth to databases. On a Mac you should have an odbc manager installed already by default. My machine has one called iodbc: if I type iodbctest at the command prompt* I get the following:

iODBC Demonstration program
This program shows an interactive SQL processor
Driver Manager: 03.52.0607.1008

Enter ODBC connect string (? shows list):

If I then type '?' as suggested, I get an empty list... which explains that there are no drivers installed. What is a driver? Its a database-specific connector that lets the odbc manager talk to a particular database (MySQL, Postgresql, SQLServer, etc). So the last link required in the communication chain is a mysql driver.

* Apparently you can get to iodbc through Applications->Utilities as well if you want to look for it in the Mac GUI.

Step 3: Install your odbc drivers

OK, so we now need to install specific odbc drivers for each database we want to connect to. In this case, I'm just going to install MySQL. I grab the odbc driver from http://dev.mysql.com/downloads/connector/odbc (picking the Mac .dmg file just to make the installation easy). A few clicks on the .dmg file and the driver is installed.

I can check that it is installed by running iodbctest again from the command prompt. This time when I type '?' to see what drivers are installed I get:

Enter ODBC connect string (? shows list): ?

DSN | Driver
------------------------------------------------------------------------------
myodbc | MySQL ODBC 5.1 Driver

OK, so that completes the pipeline. Now in python I can use pyodbc, which talks to iodbc (or whatever ODBC manager you have installed), which uses the mysql odbc driver to talk to a MySQL database.

So lets do that and check that it works

Step 4: connect to your database from python

OK, so now we should be able to connect to your database in python.

But one last thing you need to do is to work out what connection string you need to connect to your database. A helpful resource with example connection strings can be found at http://www.connectionstrings.com/. Here is what I did in python to connect to my database:

>>> import pyodbc
>>> connstr="Driver={MySQL ODBC 5.1 Driver};Server=your.server.;Port=3306;Database=databasename;User=username; Password=nottelling;Option=3;"
>>> conn=pyodbc.connect(connstr)
>>>

and from here you can just play with the conn object as you like in python! So, for example, to look at a table:

>>> res = conn.execute("select * from sometable")
>>>for r in res:
>>> print r.next()

Rental yields will increase, but not the way they'd like....

2011-04-09T05:07:00.000-07:00

In response a growing chorus of analysts pointing out that Australian property seems to have both limited prospects for capital growth and low rental yields, many property market boosters respond that yields will increase (even the boosters have given up spruiking capital growth as a reason to buy property -- that boat has sailed).

Now, for once I am in agreement with the property markets boosters -- yields must increase. But there are two ways for yields on residential property to increase. Increasing rents is one (and clearly the one the boosters have in mind). But falling prices will do the job just nicely also. Just saying.

newspapers snoozpapers

2011-04-09T04:48:00.000-07:00

On a recent flight back from Perth, I found myself growing increasingly exasperated at the poor quality of the journalism in the two newspapers available -- the Fin Review and the Australian.

For some time I've been hoping, against all evidence to the contrary, that the Fin would not follow the Sydney Morning Herald's slide into mindless he-said she-said reporting. But reading the Fin during the last week has not been encouraging. Why read the Fin if not for some actual analysis? But what we get now on almost every issue are the verbatim opinions of pundits -- financial, political, social. Knowing what Bill Gross' views are on inflation and government indebtedness, while interesting, is complementary to, not a replacement for, detailed reporting.

The Saturday article on 'bond vigilantes' was especially unsatisfying -- with extensive quotes from 'senior bond traders' together with repetition of all the tired old news-bites about looming US inflation and sovereign debt concerns. Did the article contain any references to actual evidence or analysis enabling readers to evaluate such claims? US core inflation figures? Long term bond yields consistent with either rampant inflation or sovereign default? No, just appeals to financial market authority-figures.

This low-quality style of reporting has been prominent in real-estate journalism for a long time, where puff pieces quoting real estate agent and industry mouthpieces have forced those interested in any thoughtful analysis to other sources. (Leith van Onselin, for example, has posted manytimes on the issue -- such as here, here, and here).

As for the Australian, the problems there are quite different. Not being a regular reader, I was unprepared for the breadth and depth of the editorial lunacy, from Greg Sheridan's column railing against the economy wrecking carbon tax to Chris Merritt's nanny-state rant. In political and news sections, Peter van Onselin's column offered the only relief (Peter is Leith's brother -- there must be something in the genes). In Business, Glenda Korporaal's piece was also thoughtful and balanced enough to be out-of-place.

Not content with its plentiful home-grown lunacy, the Australian even found space to import some, with William McGurn's WSJ piece praising Paul Ryan's fairytale deficit reduction plan -- entirely neglecting to mention that the plan relies entirely on heroic assumptions (2.8% unemployment in 2021, anyone?).

I'm too young to become a grumpy old man about these things, but they're forcing my hand dammit!

2010-08-02T19:32:00.000-07:00

Just a reminder to myself on converting between coordinate systems in ArcGIS.

I often want to convert data from one coordinate system into another, and I frequently forget how to do this. This is just a post to myself so that I remember how to do this..

Below is just a cached copy of this page, replicated here in case the original page goes away.....

Converting your vector files into different Coordinate Systems Using ArcGIS 8.2

EXAMPLE: converting NAD83 MTM ZONE8 to WGS84 UTM ZONE18

open ArcToolBox
choose Data Management Tools → Projections → Project Wizard (shapefiles, geodatabase)

select the shapefile to be converted, then its original coordinate system will be list in the dialog. Click Next.

define output file name and stored location; click NEXT.

giving desired coordinate system; click Select Coordinates System

click Select to define a new coordinate system

choosing Geographic (lat/long) or Projected (x/y) coordinate system.

For WGS84 ZONE18N, choose Projected coordinate system → UTM → WGS84 → WGS84 UTM ZONE18N. Click NEXT.

Detail information of WGS84 UTM ZONE 18N will be list in the dialog. Click NEXT

A Geographic transformation (optional) will be required since (in this case) NAD27 and WGS84 are different datum. A list of option will be offered in the function; choose the newest transformation (or fit the case). Finally, your output file should be projected in the new coordinate system.

Schumacher's foresight and the folly of complex forecasting models

2010-03-30T03:31:00.000-07:00

I've been reading Schumacher's "Small is Beautiful" recently -- quite an old book now but still worth a read. He writes very clearly, and the subject matter of the book is mostly still relevant, and the key ideas still thought-provoking. He shows uncommon foresight on a few topics. For example, he talks about the near certainly of a future where oil is scarce & expensive, and he was doing so before even the 1970's oil shocks. He also talks about the folly of eroding natural capital long before it was common to do this. But perhaps most interestingly, he identifies the process where a void in values (created by the decline of religion) was being filled by economics, and despite being an economist, he thought this dangerous. He thought it far safer to rely on traditional cultural & religious value systems than to subject everything to economic cost benefit analysis. I think this last point is especially interesting because, while most economists will probably baulk at it, recent events lend weight to Schumacher's view. For example, look at all the economic debate and analysis that has gone into climate change, and look where it has got us. Wouldn't we have done better developing a value system where caring for our planet was more than just an economic consideration, but something sacred?

Schumacher wasnt a hippy or anything either, nor was he a fuzzy-minded economist who hated the reduction of everything to numbers because he himself couldnt do any maths. In fact, he was a competent mathematician. This makes some of what he has to say about model-building in the later chapters of his book particularly interesting. Being a bit of a data-cruncher and model builder myself, I liked the quotes in particular, which are right on the money, and which I think should be compulsory reading for any model builder:

"Crude methods of forecasting ... are not likely to lead into the errors of spurious verisimilitude and spurious detailing -- the two greatest vices of the statistician. Once you have a formula and an electronic computer, there is an awful temptation to squeeze the lemon until it is dry and present a picture of the future which through its very precision and verisimilitude carries conviction. Yet a man who uses an imaginary map, thinking it a true one, is likely to be worse off than someone with no map at all; for he will fail to inquire wherever he can, to observe every detail on his way, and to search continuously with all his senses and all his intelligence for indications of where he should go.

The person who makes the forecasts may still have a precise appreciation of the assumptions on which they are based. But the person who uses the forecasts may have no idea at all that the whole edifice, as is often the case, stands and falls with one single, unverifiable assumption. He is impressed by the thoroughness of the job done, by the fact that everything seems to 'add up;, and so forth. If the forecasts were presented quite artlessly, as it were, on the back of an envelope, he would have a much better chance of appreciating their tenuous character and the fact that, forecasts or no forecasts, someone has to take an entrepreneurial decision about the unknown future."

I wish I could say I had never fallen for the temptation to go for a complex model with 'rich' outputs, when it would have been essentially as accurate, and less misleading, to present a few back of the envelope projections.....

More arcgisscripting hacking

2010-03-08T18:36:00.001-08:00

As I mentioned in an earlier post, I've been fumbling around trying to learn how to get a python program that can talk to ARCGis. After getting around some initial irritations, I'm actually making some headway. I found this series of posts particularly helpful. As a reminder to myself, and as help for anyone else grappling with this, I'll post the first non-trivial script I've come up with here. This script is still pretty simple -- it opens up a shapefile and reads all the information associated with the shapefile. Here it is:


import arcgisscripting
import sys

#This is my first attempt to do some python 
#scripting with ArcGIS Geocomputation object

#first we create the gis scripting object
gp = arcgisscripting.create(9.3)

gp.Workspace = "C:/tmp"
#allow old files to be overwritten
gp.OverWriteOutput = True

#lets print out some information about a layer
shapefile= "C:/tmp/test.shp"
desc = gp.Describe(shapefile)

#From Describe object
print desc.DataType
print desc.CatalogPath

#From FeatureClass object. The Describe object 
#that is returned by the above call to Describe 
#is also a 'FeatureClass' object. I havent quite 
#got my head around the data model used by the
#geoprocessing object, so I don't quite understand 
#how we know which fields are available and which 
#arent after a call to describe.... but these work 
#for the shape file I'm using.
print desc.ShapeType
print desc.FeatureType
print desc.ShapeFieldName

#list the fields. This is the same as 
#returned by ListFields()
for f in desc.Fields:
....print "    "+f.Name


#now go and read each line in the spatial 
#data table associated with the shapefile.

#first we get a 'read' cursor
readCursor = gp.SearchCursor(shapefile)

#make sure we are at the start
readCursor.Reset()

#now go through and read (and print out) each row
row = readCursor.Next()
while (row != None):
....#print the contents of the row
....resstr = "ROW: "
....for f in desc.Fields:
........resstr = resstr + str(row.GetValue(f.Name)) + " , "   
....print resstr
   
    row = readCursor.Next()


#done!

pure eye-candy

2010-03-07T19:03:00.001-08:00

As part of the UrbanIT research project I've been co-opted into at UNSW, I've been writing some code to 'visualize' the contents of various databases holding information about the built environment. To be very brief, the aim of the project is to integrate the existing disparate data-sets that are out there to create a single unified view of the urban environment. So, you may have data on (say) the energy efficiency of different buildings, the cost of maintaining those buildings, the price of those buildings, etc etc. All this data is tucked away in different databases and you need (essentially) an integration layer sitting on top to, well, integrate it all. Once you do this, you can do relatively complex spatial queries across multiple data sources. Convert the output to KML and you get some pretty pictures like this:

This is a mashed-up visualization of two seperate queries -- one asking for the buildings within 60m of a particular lat/long, coloured by their main use (commercial:blue, industrial:red,residential:green,other:grey). The second is a query on a particular residential building with the individual apartments coloured by their estimated value (warmer colours == more expensive apartments).

Sorry, I cant provide the KML itself, as the data associated with the buildings shown in the picture is pretty rich, and is confidential.

arcgisscripting breaks python print function

2010-02-28T21:57:00.000-08:00

I've bitten the bullet and finally decided to learn how to write python scripts to work with ArcGIS. The first problem I came across was that standard output appears to be silently redirected without any warning. So when you want to do some basic poking around like:

import arcgisscripting

gp = arcgisscripting.create(9.3)

tools = gp.ListTools("*")

for tool in tools:
....print(gp.Usage(tool))

this turns out not to work, because behind the scenes somewhere the GIS processing object is playing silly-buggers with stdout, so print doesn't work like it should. You can force stdout to be 'normal' by doing something like this:

import arcgisscripting

oldstdout = sys.stdout
gp = arcgisscripting.create(9.3)

#this call to ListTools has the undocumented strange
#side-effect of changing sys.stdout
tools = gp.ListTools("*")
gp_stdout=sys.stdout

for tool in tools:
....usagestr=gp.Usage(tool)
....sys.stdout=oldstdout
....print(usagestr)

I hope this isnt a sign of what I'm in for while learning this stuff -- stange undocumented behind-the-scenes funny-business.

importing raster data into ArcGIS

2010-02-21T21:44:00.000-08:00

This is not so much a blog post as a reminder to myself on how to do something.

When I work with spatial (2D or 3D) data, I generally like to do my own data processing (i.e. write my own software) rather than try and write VB code to work with ArgGIS. If I just want to do something vanilla that ArcGIS handles via its point-and-click interface, I'll use that, but more than that and I prefer to process the data outside of ArcGIS.

However, it is often the case that, after doing some data processing, I'll want to import the data back into ArcGIS to view it and/or do minor tweaking of the sort that ArcGIS does well (contour lines, etc). Sometimes just getting the data back in turns out to be a pain in the arse, so I've decided to put this post up to remind myself how to do it for importing grid (raster) data.

OK, so lets suppose you have some grid/raster data (temperature, rainfall, whatever). Lets say it is in a plain text file in this format:

POINT_X,POINT,Y,ELEVATION
LONG1,LAT1,VALUE1
LONG2,LAT2,VALUE2
LONG3,LAT3,VALUE3
...

(I'm assuming a geographic coordinate system but this should work also for a projected coordinate system. Also, you can have an excel file if you want too.)

Now, we want to use this data to create a raster dataset. Here is how to do it

In ArcMap, click "Add XY Data" and select the text (or excel) file with the x,y,z triples.
Make sure you select the spatial reference system for the dataset (it will be 'undefined' by default)
This should give you a 'point' dataset. Which may not be what you are after. Its not usually what I am after, for example. I usually want a proper raster data layer. So, we need 1 more step....
In the ArcToolBox, choose "Conversion Tools->Point to Raster".

Done. This should now give you raster data that has a specified coordinate system. Other ways of importing text data to raster (ASCIIToRaster, for example) don't seem to let you specify the coordinate system, so I've ended up going through this two step process. Anyone knowing a simpler way, please let me know.

I'm shocked! Good property journalism!

2009-10-06T18:11:00.000-07:00

Its so refreshing to see a well written, thoughtful article on housing, given the general kind of guff that usually passes for property-journalism. Three cheers for crikey.

Penny Wrong on Climate Change

2009-02-22T15:50:00.000-08:00

Heard Penny W(r)ong on Radio National this morning. What a disappointment this government has been on climate change. All that money the government is putting into insulating houses with pink batts... what difference will it make to Australia's emissions? The answer, it is becoming clear, is likely to be ... (drum roll).... none. Zilch. Zippo. Why? Because unless the government increases its emissions reduction target, the energy savings from those batts will simply lower the price of emissions permits in the emissions trading scheme. Think about it... the cap remains the same, so if you save energy in your home (by whatever means), this simply means that you will be making it easier/cheaper for companies to buy the necessary permit. Penny did say that a reduction in emissions from households would make it 'possible' for the government to go for a more ambitious target, but does anyone believe the government will do this? No, I didn't think so. What a sorry mess.

Why cant we get to the point where households can get the financial benefit from emissions reductions? If I cut my power use in half, I should get the cost saving from not having to pay for that energy and the value of those saved emissions in the emissions trading scheme. The power company should not take my savings as a windfall gain. Is the government moving towards this? I don't know (perhaps someone can enlighten me), but based on Penny Wong's comments this morning, it seems that they are not thinking this way....

You know its bad in banking, finance and business when....

2008-11-28T16:21:00.000-08:00

... the Sydney Morning Herald jobs classifieds lists only a single job under that section of the classifieds. And that job is? A 'Debt Recovery Officer'. At least the federal government has finally said that they will be willing to go into deficit to soften the impact of the slowdown...

So where do people want to live?

2008-10-28T20:44:00.000-07:00

Shown below is a picture from some work in progress, where I am looking at the location choice preferences of different household types in Sydney. The picture shows where households with young children (i.e. all less than 15 years) would like to live. The graph shows the spatial variation in preference for detached dwellings only. I do look at the effect of dwelling type, but lets do one thing at a time shall we....

You can pretty safely ignore the scale -- just think warmer colours == 'more desirable'. (If you really want to know, its the log-probability of a couple-with-kids household choosing a dwelling in that region in a discrete choice experiment.... but lets not go there).

And what does it show? Well, couples with kids like the northen harbourside suburbs (i.e. Mosman, and around middle-harbour), and big chunks of the Eastern suburbs (Vaucluse, Centennial Park, etc). No real surprises there.

Other results show that couples with young kids do not like attached dwellings -- they really do want the suburban detached home. Well, most of them anyway.. there is always a distribution of preferences.

And what about couples without kids? The figure (below) shows an estimate for couple households without children where the household reference person is less than 55 years old. They really like the inner-west, dont they?

Back to my knitting

2008-10-21T18:47:00.000-07:00

After a couple of forays into economic matters, I'll get back to the sort of stuff I normally do.

Below is a map (click the image for bigger version) showing the proportion of journey to work trips made by either walking or bicycle, at the time of the 2001 census. Not surprising to see the huge walk/bike mode share near the city.

Just FYI, the dark blue patch out in the north-west (near Richmond) is an army barracks, so lots of army dudes live near thhe barrackds and stroll in I guess. Same for in the south (near Holdsworthy). Other than that, its basically the CBD (incl. North Sydney) and the University of New South Wales, with Sydney's second CBD (Parramatta) also having a reasonable amount of walking and cycling.

The insanity of it all

2008-10-13T20:41:00.000-07:00

How insane is the latest federal government stimulus package measures which increase the first home buyers grants?

The increased grant will boost demand for housing, so the actual beneficiaries are existing investors/owners and new home buyers. Given that both these groups are better off than the average, this seems like a pretty awful redistribution of income. Much better would be tax relief or payments targetted at lower income households. There is some of this in the package, but there could have been more if they werent squandering money propping up the property market.

There is no reason for the government to step in and support house prices. If the government is worried about the economic damage caused by falling home equity, a straight cash handout to all households achieves the same purpose, as well as being much fairer.

To recap: The winners from this policy will be:
a) existing property owners, who will be able to get a better price for their property if they sell it; and
b) Those in a position to purchase a first home or investment property.

Do either of these groups need a handout? First home buyers would be better served through income tax relief or cash payments. They are not well served by a government throwing money at a property market that is already expensive by world standards -- that's just going to make things worse.....

I'm starting to think: Recession in Australia.......

2008-10-02T16:48:00.000-07:00

OK, despite the fact that you almost always come out looking silly when you make economic predictions, I'm going to have a word on this anyway, 'cause I got a feeling that things aint gonna be pretty, and want to put it on the record .....

Despire protestations from our pollies, Australia is headed for a recession. And a bad one at that... . Here is why:

We arent strong enough to surive a synchronized world recession, despite the relative health of our banks.
That means higher unemployment
Given the fact that asset prices (esp. housing) are pretty high, and household gearing is pretty high, this means asset prices are going to ......... drop ......
This just feeds back on itself for a while, with falling asset prices suddenly turning what were 'reasonable' debt to asset ratios into scary ones.... which causes more defaults or fire-sales, which makes asset prices fall further ... which... you get the picture.

I'm not denying the good things that are often pointed out. Our banks are in relatively good shape, and we will have some buffering from China*. But this is not enough. We have a large current account deficit, and actually financing this is going to get very, very difficult. If foreigners stop lending to us, this means domestic savings have to go up (a lot), or general borrowing and investment must plummet. We cannot boost domestic savings that quickly, so we can either flog off everything we can at fire-sale prices, or else business investment will plummet, leading to a recession.

Note that it is also true that Australia's housing supply/demand situation is pretty tight, so one would not normally expect to see much of a fall in prices. To some extent this is true, but the big boom in house prices up to around 2003 was fueled by easy credit sloshing around the world economy, and now that this is going to be cut off, I think prices can fall regardless of the tight supply situation. How far can they fall? My guess is gross rental yields must reach at least 7% for investors to consider buying (from a purely cash-flow point of view, property starts to stack up at that yield), which means prices could fall by a third.......

Hold on to your hats, and for God's sake, don't borrow any money.

To stick my neck out even further, here is how I think things will pan out afterwards:

Rising unemployment, falling asset prices (damaging bank and household balance sheets).
Central banks will lower rates and start pumping out the money. Inflation be damned they'll say !
In the short term, we could see deflation, inflation, who knows... it'll be a blood-bath, but the end result of the money printing will be high inflation.

So, my investment tip is: For now, be in cash, have no debt. In a year or two's time, when everything is a mess and noone can see a way out, get all out of cash, even borrow a modest amount if you can, 'cause the coming inflation wave is going to wash all that cash (and debt) away. Where to put it? Any income generating asset is OK, but resources or commodity-linked assets will be best. Property probably OK too, as long as the yield stacks up.

*Incidentially, while I am at this prediction thing, I'll say that I think India is much more vulnerable than people think, and is in a totally different position to China. I predict India will go into recession also out of this crisis.

More on Travel Behavior

2008-06-12T22:14:00.000-07:00

Here are a few more graphs showing travel behaviour in Sydney.

The first shows average household VKT. This is estimated in the following way:
1) I estimate a simple household travel model that predicts household VKT based on household type, income, and location.
2) I take data from the 2006 census (which tells us where households are living) and estimate how far they will drive per annum

The model is pretty simple, but the trend is clear.

The above map is interesting, but there is a lot going on -- vehicle use changes with income and household type and location, and these all vary spatially.

Another way of looking at it is to think about the independent effect of location, so, what happens if you take the same household and move that household around. How does their vehicle use vary?

I cant do this exactly, for technical reasons I wont bore you with, but the map below is an attempt to get close. The actual method used to obtain this estimate is a little involved, and involves some guesswork, so dont take it as gospel, but it is at attempt to estimate the independent effect of location. In this instance, I take households from a wealthy area (but not stinking rich, i.e. not Vaucluse/Mosman) that has lots of 'couples with kids' households, and 'move' those households around (en-masse). The following map shows average household VKT when you hold income and household type constant, and change location only. I hope that explanation is comprehensible.

The last map (below) shows average commute time (by origin) in Sydney. This one is compiled as follows:

1) Take ABS journey to work data (They have origin -> destination trip counts by travelzone, which are the spatial regions shown here)

2) Take NSW Transport Data Centre zone->zone travel times (at peak hour).

3) From (1) and (2), calculate average commute per zone.

Again, nothing so surprising here.

Note that the commute time shown here is the average commute for all modes (i.e. car and walk and publilc transport and.. well... everything).

Mode Split, Energy Use, and Job Location

2008-05-19T19:44:00.000-07:00

Ever wondered if the presence of rail has much of an effect on mode choice? Yes, I'm sure it keeps you up at night too. Well, wonder no more, I think the following graph is pretty convincing that rail does have an effect:

(If you want to view this in Google Earth yourself, go here for instructions.)

The scale is hard to read, but basically the colour scale is linear, between 0% and 100% (blue to red), with deep blue being 0% of work trips by car, and deep red in areas where 100% of work trips are by car. The base data is from the 2001 Census (ABS).

Of course there is a lot more going on here. Household type varies with proximity to rail stations (in particular, there are fewer households with kids), so the above graph actually overstates the effect of the rail network, but even if you crunch the stats, you get a strong independent effect.

Another important consideration is where jobs are located. Sydney has a strong CBD, in that there are a large number of jobs in and around the CBD, and so a radial rail network provides reasonably competitive access to a large number of jobs. Job decentralisation, which some argue for, on the basis that it will reduce commute length (and energy use), is unlikely to be effective, for the following reasons

Any decentralized job is much more likely to be driven to, because parking will be available, and public transport will be less competitive
While trip length would decrease, it is easy to overestimate the size of this effect. There is plenty of evidence to suggest that substantial 'excess commuting' would continue. For more specialized jobs, decentralizing may well increase commute length, as workers are not substitutable in such cases.
It ignores economics. Firms locate in, or close to, the CBD for good economic reasons (access to large labour pool. agglomeration economies, etc). Trying to force jobs elsewhere (through planning) will likely impose a cost on those firms who are forced to relocate to a less desirable location.

FYI, the following graph shows the commute energy required for each job in the Sydney CBD. That is, it shows, for each zone, the mean number of MJ of transport energy required for workers to get to jobs in that zone. From 2001 ABS Journey to Work data. Energy is in MJ/worker of primary energy (i.e. energy in the ground).

How to view this in Google Earth at home

You need to save the following KML into a plain text file (Notepad will work), save it as whateveryoulike.kml, and then drag and drop this kml file into Google Earth:

<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://earth.google.com/kml/2.0">
<GroundOverlay>
<description>NONE</description>
<name>JTW_CARONLY_PCTbr.png</name>
<visibility>1</visibility>
<Icon>
<href>http://peter.rickwood.googlepages.com/JTW_CARONLY_PCTbr.png</href>
</Icon>
<LatLonBox id="JTW_CARONLY_PCTbr.png">
<north>-33.54141728667299</north>
<south>-34.10141604662701</south>
<east>151.3614161776215</east>
<west>150.65141715537854</west>
<rotation>0</rotation>
</LatLonBox>
</GroundOverlay>
</kml>

Petrol poverty in Sydney

2008-05-12T16:55:00.000-07:00

Some of my work got into the popular press recently, which is nice, because, afterall, urban planning/economics is meant to be an applied field. Here is a link to the article, (sans graphics I am afraid). The article got picked up by the peak oil crowd, and you can see a couple of different versions of the graphics here and here, for example, but, for convenience, I've put a scanned version of what appeared in the Sydney Morning Herald below.

Update: Paul Krugman picked this up from the Oil Drum, and stuck it on his blog. This is higher profile than I could've expected. I guess the lesson from this is that, if you want to get exposure, it really helps to gave some pretty/garish graphics....

Here is a low-res scan of the pictures that appeared in the paper(they have been tarted up by the paper, who did a nice job on them, but they are based on maps I produced).

The graphs shows average proportion of gross household income spent on petrol (orange is > 6%, yellow 4-6%, green 2-4%, light blue 1-2%, blue < 1%). Left is with petrol at $1.5 per litre, right is with petrol at $2 per litre (assuming zero price elasticity, just to keep things simple).

A few things to note:

Firstly, because these graphs show averages, they mask a lot of variation at the household level. Some individual households in the orange zone, for example, will be spending a lot more than 6% on fuel, which others will be spending a lot less.

Secondly, these costs are petrol costs only, not vehicle purchase/depreciation, rego, tolls, or other running costs. I took a quick squiz at the latest ABS data (2004 Household Expenditure Survey) which suggests fuel costs to be roughly a third to a quarter of total running costs for cars, on average. With fuel prices up, this proportion is probably a bit out of date, and is also likely to be an underestimate for those in the outer suburbs. Nevertheless, we might expect at least an average of perhaps 15% of gross household income on car travel costs for those in the outer areas, with some households much higher. These sorts of numbers are starting to get pretty significant, and make me think that the housing affordability people should start factoring in transport costs to their measures of affordability. Even if houses on the fringe in Sydney were cheap (which they're not), the extra costs of vehicle purchase and running would make them less affordable, if they were factored in.

The graphs were compiled using a travel model calibrated on NSW Department of Transport Household Travel Survey (3000 households annually across Sydney), so thanks to them. This travel model was applied to the 2006 Census data to work out the graphs you see here. I assume 9 litres per 100km fuel efficiency, because I dont have detailed information about the spatial distribution of the vehicle fleet in Sydney.

Here are the base images I provided to the paper. They're not as pretty as the ones in the paper, unfortunately.

Viewing urban spatial data with Google Earth

2008-04-28T20:24:00.000-07:00

For a while now, I have been using Google Earth as my main GIS-viewer. My area of interest is Urban Economics and spatial data processing in general. Although I generally prefer to write my own data processing software (i.e. I don't tend to use the scripting/programming capabilities of programs such as ArcView or MapInfo, I write my own programs for this instead), I still want to be able to view the results, and, for this, Google Earth is great.

Here are some examples of what can be done, and some datasets that I've collected for work. If you are already using ArcView, or have data in an ESRI file format, and want to be able to view that data in Google Earth, there are some commercial packages around that look quite good, such as Arc2Earth.

I live in, and study, Sydney, mostly, so the following are all specific to Sydney, Australia.

Here is a measure of proximity to commercially zoned land in Sydney. Without going into the exact formula, or the numbers, the warmer colours (on a standard Blue->Red gradient) indicate that there is a lot of commercially zoned land nearby, and the colder colours indicate there isn't much. Generated from publicly available data, from the NSW department of lands (undated, but I assume its current as of around 2001), but with a fair bit of intermediate processing by yours truly.

And here is a measure of the amount of land covered by roads in Sydney (warmer colours on a Blue->Red scale indicate higher proportions of road coverage). In general, roads comprise around 30% of the urban area of most cities, but you can see from this that there is significant spatial variation within this for Sydney (and, I am sure, other cities). The exact measure used is the area (in square kilometres) taken up by road within a 1km radius, so the theoretical range is 0 to Pi, but in this picture, deep red is 2.06 square km, or almost exactly 2/3 of the total available land area. As you may or may not be able to see (depending on how well you know Sydney's geography), the areas with the highest road coverage are actually just adjoining the CBD.

And finally, a map (from ABS 2001 Census data) showing the mode split of journey to work trips in Sydney. Surprisingly, the data here covers almost the entire range, with deep blue being nearly 0% of trips by car, and deep red 100% of trips). The effect of the rail line is pretty evident (note that some of the rail shown by Google is freight lines or otherwise not for passenger use), but there is a lot else going on here -- households without children, for example, are much more likely to live close to rail lines (mostly because they do not have the same strong preference for a detached house and yard) and this household type is less inclined to car use and ownership (independent of whether they live close to a rail line). In short, proximity to rail does have a clear independent effect on car use, car ownership, and mode split, but the graph shown here overstates this effect because household location preferences also relate to transport infrastructure.

How were these created?

Here is the base image file used in the road coverage example above. I generate this with a Java program that:
a) works out from satellite imagery what is road and what isn't
b) works out, for each point on a 5000x5000 grid, what proportion of the area within a 1 km radius is road
c) dumps out a png image showing the proportions.
d) dumps out a KML file that positions the image correctly (lat/long)

All this (barring the 1st step, which can be quite a tricky problem in general, but is made easy in this case for various reasons I wont go into) is pretty easy to do if you know a little programming.

Here is the base image for proximity to commercially zoned land, created with the same software.

Finally, the kml that you need to place the image properly and view it in Google Earth looks something like this: (you will need to change the image URL, and the lat/long for your own purposes).

<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://earth.google.com/kml/2.0">
<GroundOverlay>
<description>NONE</description>
<name>ROAD_COVERAGE_1kmbr.png</name>
<visibility>1</visibility>
<Icon>
<href> http://peter.rickwood.googlepages.com/ROAD_COVERAGE_1kmbr.jpg </href>
</Icon>
<LatLonBox id="ROAD_COVERAGE_1kmbr.png">
<north>-33.55123072596137</north>
<south>-34.08892485815783</south>
<east>151.34688793053158</east>
<west>150.67693273570447</west>
<rotation>0</rotation>
</LatLonBox>
</GroundOverlay>
</kml>