From mega-regions to mega commutes: US commuting working paper

My previous post provided some images from a recent piece of work I did on mapping tract-to-tract commuting patterns in the contiguous United States. This post provides a bit more background and extracts from a working paper, plus some of the original map outputs from the project - which are different in style (kind of a night time view). The focus is also more on mega commutes and mega-regions (think Gottmann's 'megalopolis'). I also provide a bit more detail on the method and data.

A constellation of cities in the Midwest

Being a member of the Regional Studies Association for a good few years now, I've followed various debates about regions, city-regions and mega-regions - including the very interesting work on mega-regions by the America2050 project of the Regional Plan Association. I also have a longstanding interest in commuting flows (and mapping them) so I set myself the challenge of mapping micro-level commuting flows in the contiguous United States in the hope of identifying what I expected would be some interesting mega-region commutes. I also hoped, in the context of this data, that I would discover some of the mega commutes identified by Rapino and Fields of the US Census Bureau. On both counts I wasn't disappointed. The first map below shows the entirety of the lower 48 states and the commuting patterns come out quite clearly.

Journeys to work in the contiguous United States

Obviously, some areas are more interesting than others, so I zoomed in on various areas, including California and the Northeastern United States. The map below shows travel to work patterns in California, and you can clearly see the wider Los Angeles metro area as one large commuter region, the Bay Area as another (but more polycentric), and also the settlement and journey to work patterns in the Central Valley, from Redding in the north down to Bakersfield in the South. This shows the urban settlement patterns in the state of California, but also the spatial configuration of the commuting connections between places.

If you take a closer look at the working paper behind these maps you'll find out more about the data. What I found most interesting were the locations where 'mega commuting' was prevalent, so I looked at the top 20 Census tracts in the Northeastern US with the highest number of people commuting there - i.e. over 50 miles each way. As you can see from the table below, this is dominated by New York City, but Washington DC also features. The total volume may not seem much, but remember that these are quite small Census tracts, with only a few thousand people.

Mega commuting in the Northeastern United States

I then did something slightly different - I wanted to filter the data in a more scientific manner. Since the data provided by US Census Bureau includes a margin of error (MOE) value for each individual tract-to-tract flow, I calculated the coefficient of variation for each individual flow line (there were just over 4 million). These were based on a 90% confidence level, so the formula was simply:

((MOE/1.645)/Commuting Estimate) x 100

I used a rather generous cut-off and then displayed only those flows which had a coefficient of variation of less than 40. The results are shown in the map below. We can see the expected pattern of commuting but - hold on a minute - what are those really long distance lines? Surely people don't 'commute' vast distances like this. Well, it turns out that this might actually be true because many of these lines begin and end in military locations or other places associated with regular, long distance moves for work and since the American Community Survey asks respondents how they usually got to work ‘last week’, it's entirely plausible that a number of people will work away from home and that this will lead to the kinds of patterns we see below. Or, to put it another way, don't think of these long lines as journeys people travel every day! 

If you want to read more about it, you can click below to see the working paper, which also includes links to high resolution versions of the images shown here.

American Commute: working paper

Mapping the American Commute

Update, 20 September 2015: scroll to the bottom of the post if you want to download the data.

One of my summer projects this year has been attempting to map the American commute, following earlier work on a similar subject. Put simply, I've attempted to put together a map which shows commuting connections between locations in the contiguous United States, using the most fine-grained data I could find. Some of the results of this went into a recent piece in WIRED, and also CityMetric, and the larger piece of work it's based on is part of on-going research into the best ways of mapping commuting flows. The main images are below, followed by some more technical information. For now, all you need to know is that these images show commuting connections of 100 miles or less between Census tracts in the lower 48 states. You'll have to forgive me if your city isn't labelled! 

Higher resolution image available here

And now some zoomed in versions...

Zoom in of the west coast

Texas, and beyond!

Interesting patterns of connectivity in the Midwest

Look closely for some interesting inter-connections

The famous BosWash megalopolis

But this just shows where people live, doesn't it? Yes it does. But it also shows how the places where people live connect with other places from a functional economic point of view, at a fairly fine-grained level. It offers a slightly different view than just looking at the urban fabric alone which, I might add, is interesting in itself. Mapping flows like this is not exactly new, as this paper from Arthur Robinson (1955) on Henry Drury Harness (1837) demonstrates. Nonetheless, I haven't seen anyone map travel to work at this resolution for the United States, so I thought I'd have a go myself. 

If you spend some time looking at the big version of the map you can begin to see how places connect and where there are obvious disconnections, even between places that are not that far apart. One thing that you can pick up from the complete dataset (but not this batch of maps) is the growth of mega-commuting, as explained by Melanie Rapino and Alison Fields of the United States Census Bureau. 

Background information: the data I used is the most recent tract-to-tract journey to work dataset from the American Community Survey. This dataset covers journeys to work between the c74,000 census tracts in the United States and the complete dataset has around 4million interactions. I mapped this in QGIS, using methods I've described previously on this blog. The tricky bits were dealing with the messy FIPS codes, dealing with the size of the dataset, and trying to decide what to label. There is quite a bit of error in the dataset (as acknowledged by the ACS people) and each individual flow line has a margin of error value associated with it, from which I also calculated the coefficient of variation. This is explained in a more detailed working paper, which I expect to publish in the coming months.

Update, 20 September 2015: there has been quite a bit of interest in the underlying dataset I put together to create the maps, so I have decided to make the whole shapefile available here in the hope that others will find it useful and be able to produce some interesting analysis or visuals from it. I'm hoping someone will do a cool interactive web map of it, but it might be quite technically challenging. If you do use it, make sure you read the associated working paper, which explains the process and the underlying data. One word of warning: the uncompressed file is pretty big so you'll need a good computer.

Mapping the American Commute: download the data (213MB, zipped shapefile)

Mapping the Polycentric Metropolis: journeys to work in the Bay Area

I’ve recently been writing and thinking about polycentric urban regions, partly because I’m interested in how places connect (or not) for one of my research projects, and partly because I’ve been experimenting with ways to map the connections between places in polycentric urban regions. There was quite a lot of the latter in Peter Hall and Kathy Pain’s ‘The Polycentric Metropolis’ from 2006 but given that the technology has moved on a little since then I thought I’d explore the topic in more detail. Mind you, I’ve also been looking back on Volumes 1 to 3 of the Chicago Area Transportation Study of 1959 as a reminder that technology hasn’t moved on as much as we think – their ‘Cartographatron’ was capable of mapping over 10 million commuting flows even then (though it was the size of a small house and required a team of technicians to operate it – see bottom of post for a photo).

Are you part of the big blue blob?

Anyway, to the point… What’s the best way of mapping polycentricity in an urban region? For this, I decided to look at the San Francisco Bay Area since it has been the subject of a few studies by one of my favourite scholars, Prof Robert Cervero of UC Berkeley. Also, a paper by Melanie Rapino and Alison Fields of the US Census Bureau identified the Bay Area as the region with the highest percentage of ‘mega commuting’ in the United States (traveling 90 or more minutes and 50 or more miles to work). Therefore, I decided to look at commuting flows between census tracts in the 9 counties of the Bay Area, from Sonoma County in the north to Santa Clara County in the south. I’ve used a cut-off of 30 miles here instead of the more generous 50 mile cut-off used by Rapino and Fields. I also mapped the whole of the United States in this way, but that’s for another day.

The series of maps below illustrate both patterns of commuting in the Bay Area and the different approaches I’ve taken in an attempt to capture the essence of polycentrism in the area. I don’t attempt to capture the misery of some of these commutes, since for that I’d need a different kind of technology. But, I do think the animations in particular capture the polycentric nature of commuter flows. If you’re represented by one of the dots in the images below, thanks a lot for taking part!

Let’s start with a simple representation of commutes of over 30 miles from San Francisco County (which is coterminous with the City of San Francisco). The animated gif is shown below and you can click the links to view the sharper video file (mp4) in your browser (so long as you're on a modern browser). The most noticeable thing here is the big blue blob© making its way down from San Francisco to Palo Alto, Mountain View and Cupertino in Santa Clara County. In total, the blue dots represent just over 15,000 commuters going to 803 different destination census tracts. I’m going to take a wild guess and suggest that some of these commutes are by people who work at Stanford, Google and Apple. But it probably also includes people working at NASA Ames Research Center, Santa Clara University and locations in San Jose. 

View video file in browser - or click image to enlarge gif

These patterns aren’t particularly surprising, since there has been a lot of press coverage about San Francisco’s bus wars and commutes of this kind. However, there is a fairly significant dispersal of San Francisco commuters north and east, even if the numbers don’t match those of the big blue blob. By the way, from San Francisco it's about 33 miles to Palo Alto, 39 miles to Mountain View, 42 to Cupertino and 48 to San Jose. 

The first example above doesn’t reveal anything like the whole story, though. There are actually quite a lot of commuters who travel in the opposite direction from Santa Clara County to San Francisco but more widely the commuting patterns in the Bay Area – a metro area of around 7.5 million people – resembles a nexus of mega-commuting. This is what I’ve attempted to show below, for all tract-to-tract connections of 10 people or more, and no distance cut-off. The point is not to attempt to display all individual lines, though you can see some. I’m attempting to convey the general nature of connectivity (with the lines) and the intensity of commuting in some areas (the orange and yellow glowing areas). Even when you look at tract-to-tract connections of 50 or more, the nexus looks similar.

Click image to view larger version

Stronger connections - click image to view larger version

If we zoom in on a particular location, using a kind of ‘spider diagram’ of commuting interactions, we can see the relationships between one commuter destination and its range of origins. In the example below I’ve taken the census tract where the Googleplex is located and looked at all Bay Area Commutes which terminate there, regardless of distance. In the language of the seminal Chicago Area Transportation Study I mentioned above, these are ‘desire lines’ since this represents ‘the shortest line between origin and destination, and expresses the way a person would like to go, if such a way were available’ (CATS, 1959, p. 39) instead of, for example, sitting in traffic on US Route 101 for 90 minutes. According to the data, this example includes just over 23,000 commuters from 585 different locations across the Bay Area. I've also done an animated line version and a point version, just for comparison.

Commuting connections for the Googleplex census tract

Animated spider diagram of flows to Mountain View

Just some Googlers going to work (probably) mp4

Looking further afield now, to different parts of the Bay Area, I also produced animated dot maps of commutes of 30 miles or more for the other three most populous counties – Alameda, Contra Costa and Santa Clara. I think these examples do a good job of demonstrating the polycentric nature of commuting in this area since the points disperse far and wide to multiple centres. Note that I decided to make the dots return to their point of origin – after a slight delay – in order to highlight the fact that commuting is a two way process. The Alameda County animation represents over 12,000 commuters, going to 751 destinations, Contra Costa 25,000 and 1,351, and Santa Clara nearly 28,000 commuters and 1,561 destinations. The totals for within the Bay Area are about 3.3 million and 110,000 origin-destination links.

Alameda County commutes of 30+ miles mp4

Contra Costa County commutes of 30+ miles mp4

Santa Clara County commutes of 30+ miles mp4

Finally, I’ve attempted something which is a bit much for one map, but here it is anyway; an animated dot map of all tract-to-tract flows of 30 or more miles in the Bay Area, with dots coloured by the county of origin. Although this gets pretty crazy half way through I think the mixing of the colours does actually tell its own story of polycentric urbanism. For this final animation I’ve added a little audio into the video file as well, just for fun.

A still from the final animation - view here

What am I trying to convey with the final animation? Like I said, it's too much for a single map animation but it's kind of a metaphor for the messy chaos of Bay Area commuting (yes, let's go with that). You can make more sense of it if you watch it over a few times and use the controls to pause it. It starts well and ends well, but the bits in the middle are pretty ugly - just like the Bay Area commute, like I said.

My attempts to understand the functional nature of polycentric urbanism continue, and I attempt to borrow from pioneers like Waldo Tobler and the authors of the Chicago Area Transportation Study. This is just a little map-based experimentation in an attempt to bring the polycentric metropolis to life, for a region plagued by gruesome commutes. It’s little wonder, therefore, that a recent poll suggested Bay Area commuters were in favour of improving public transit. If you're interested in understanding more about the Bay Area's housing and transit problems, I suggest watching this Google Talk from Egon Terplan (54:44).

Notes: the data I used for this are the 2006-2010 5-year ACS tract-to-tract commuting file, published in 2013. Patterns may have changed a little since then, but I suspect they are very similar today, possibly with more congestion. There are severe data warnings associated with individual tract-to-tract flows from the ACS data but at the aggregate level they provide a good overview of local connectivity. I used QGIS to map the flows. I actually mapped the entire United States this way, but that’s going into an academic journal (I hope). I used Michael Minn’s MMQGIS extension in QGIS to produce the animation frames and then I patched them together in GIMP (gifs) and Camtasia (for the mp4s), with IrfanView doing a little bit as well (batch renaming for reversing file order). Not quite a 100% open source workflow but that’s because I just had Camtasia handy. The images are low res and only really good for screen. If you’re looking for higher resolution images, get in touch. It was Ebru Sener who gave me the idea to make the dots go back to their original location. I think this makes more sense for commuting data.

The Cartographatron: Information and images on the 'Cartographatron' used in the Chicago Area Transportation Study (1959) are shown below.

From p.39 of CATS, 1959, Vol 1

From p.98 of CATS, 1959, Vol 1

The beating heart of the City of London

I've had a rush of blood to the head so here I am with a second blog in two days. I'm getting some slides ready for tomorrow's Modelling World 2015 talk in London, which is all about visualising mobility (see below) so I wanted to add in a couple of new visuals on commuting in and out of London. Visualisation can often be just a lot of fancy graphics. This can be useful in itself for a number of reasons (e.g. capturing attention on an important issue, drawing attention to unusual patterns in a dataset) but since I've been working with commuting data in England and Wales I wanted to focus on flows into and out of the City of London. 

This interests me for a number of reasons, including i) commuting can play a significant role in wealth creation and it also needs to be understood in relation to how we measure GVA; ii) commuting is often very stressful and damaging to the individual - particularly long commutes - so I'm interested in the kinds of distances involved and this can be seen easily on a map; iii) commuting can often be environmentally damaging - though this isn't what I'm mapping here; iv) commuting in and around London is often about green belt hopping so I was curious to see how much commuting comes from beyond the metropolitan green belt; and v) commuting is a two-way process and affects places at both ends and in between due to travel. 

So, here's what I did. I took the MSOA-level commuting data for England and Wales (table WUEW01 here), used a bit of QGIS, extracted frames from QGIS using the MMQGIS plugin, then patched it all together in GIMP to create an animated gif. One for inflows, one for outflows and one for in and outflows (thanks to Ebru Sener for the idea). It might run a little slowly in the blog post in a browser but see below for the images. Just to clarify, I've only shown flows of 25 or more into the City of London. Those not familiar with the data should be aware the the 'City of London' refers to the small area in the centre of London and not the entirity of Greater London! An obvious point but one worth repeating in case anyone is confused. A Greater London map would have many more data points, covering most of England.

Commuting flows (>=25) into the City of London

Same as above, but going back the way

The 'pulse' of the City of London

You should be able to get a better view of the images by clicking on them individually and if you want them to work more quickly try saving them to your own machine.

The Polycentric South East

Tweets yesterday from Michael Edwards and Joseph Kilroy reminded me of a map I produced last year in which I showed commuting patterns in South East England, minus London. I did this to get a sense of the polycentric nature of travel to work in the South East as this has been a topic of many previous studies - including the famous Hall and Pain book - but none (to my knowledge) using the 2011 Census data I mapped. The other reason for me blogging about this today is that I'm speaking on a similar topic at Modelling World 2015 this Thursday in London. Enough words, time for some maps, which I've refreshed for this week.

The first map below shows all commuting in the South East of England in 2011, without place names. As you can see, I've removed London from the equation, both in relation to travel to work flows and from the underlying map canvas. This gives a slightly different perspective than the one we're used to. The second map is the same as the first but I've added the names of local authorities in order to help identify places. Click any of the images to enlarge.

Commuting in South East England, 2011

Same as above, but with labels

Now, here's what it looks like when you add London back in... Kind of brings to mind astronomical metaphors, as hinted at in a previous study by the RTPI. I should add that the definition of a supernova is 'a star that suddenly increases greatly in brightness because of a catastrophic explosion that ejects most of its mass' so this might be stretching things slightly... Then again, if what people are saying about the displacement of the poor from London this might actually be spot on.

The 'London Supernova'

Finally, I've produced a zoomed-in version closer to London where you can see some of the flows which go through/over the capital. I don't fancy that commute!

Flow mapping with QGIS

[Now updated with sample data file - see Step 1.]
I've written quite a bit about flow mapping with GIS in the past, including on this blog, and in a couple of academic papers. Previously, I'd used ArcView 3.2, ArcGIS 9 or 10 and MapInfo. MapInfo in particular has been my 'go to' GIS for mapping large flow matrices, thanks to a very short line of MapBasic code explained to me by Ed Ferrari. Others, such as James Cheshire, have used R to great effect, but this post is instead about flow mapping with QGIS, which I am extremely impressed with for its flow map capabilities. I've posted many of my QGIS flow maps on my twitter but in this post I want to explain a little bit about the method so others can experiment with their own data. Here's an example of a flow map created in QGIS - though in this case it's not a very satisfying result because of population distribution, county shape and so on*.

US county to county commuting

So, to the method. If you want to create these kinds of maps in QGIS, it's mostly about data preparation. I should also add that I currently use version QGIS 2.4 but I believe the method is the same in any version. Here's the ingredients you need.

1. A file with some kind of flow data, such as commuting, migration, flight paths, trade flows or similar. There should be columns with an origin x coordinate, origin y coordinate, destination x coordinate, destination y coordinate, some other number (such as total commuters) and any other attributes your dataset has (such as area codes and names). Here's an example csv file of global airline flows, if you want to experiment - it's the one from the screenshots below. I put it together using data from OpenFlights - by combining the airports.dat and routes.dat files. 

2. Once you have a file with the above ingredients, you then need to create a new column which has the word 'LINESTRING' in it, followed by a space, an open bracket, then the origin coordinates separated by a space, followed by a comma and a space, then the destination coordinates separated by a space and then a close bracket - as you can see below. You don't actually need to call the column 'Geom' as I have below, but when you import the file into QGIS it will ask you which column is the 'geom' one. You can create the new column in Excel by using the 'concatenate' function. If you're not familiar with it, there are loads of explainers online.

This bit probably takes the most time

3. Once you have your data in this format, you need to save it as a CSV so it's ready to import into QGIS. From within QGIS, you simply click on the 'Add Delimited Text Layer' button (the one that looks like a comma) and then make sure your settings look like the example below.

Make sure you click the right import button

Import CSV dialogue in QGIS - should be on WKT

4. Once you've done this, you simply click OK and wait a few seconds for QGIS to ask which CRS (coordinate reference system) you want to use. Select your preferred option here and then wait a few more seconds and QGIS will display the results of the import. You can then right click on the new layer and Save it as a shapefile, or your other preferred format. In the screenshot example above, the file with c60,000 airline flows took only about 10 seconds to appear on my fairly average PC running 64 bit Windows 7. I also tried it with 2.4 million lines and it only took about a minute. If you try this in ArcGIS - in my experience - it normally doesn't work with that many flows but MapInfo will handle it okay, but take longer. However, QGIS will render it more nicely as it handles transparency in a more sophisticated way and with hundreds of thousands of flows you usually have to set the layer transparency to 90% or higher.

The results, once you've done a bit of symbolisation and layer ordering, will look like some of the examples below.

Rail flows

All commuter flows

Bus flows - no labels, obviously

* I'm still trying to make sense of the US county to county flow map. The spatial structure of the counties and the distribution of the population make it more difficult to filter, so the above example is just a very rough (and not very satisfying) example.

Addendum: since a few people have asked, I've done a new post on how to make the lines appear to glow

A national map of cycling to work

I've recently being doing some visualisation work with the newly released Census commuting data from 2011. I've produced maps of all travel to work, and travel by car, train and bus. I've now done a map of cycling to work (below). This map is particularly interesting in relation to the patterns it reveals but also in relation to the strange long-distance flows we can see. I'm certainly not saying that anyone actually commutes by bike between Manchester and Bristol, as the map may suggest. Click on the big version and have a look around to see if you can spot anything interesting or particularly unexpected. A version with some place name labels can be found here.

Go to the full size version

This data comes from Question 41 of the 2011 Census form, which asked people to say how they 'usually' travelled to work in relation to the mode of transport which accounted for the largest part, by distance, of their journey. The results can look quite beautiful on a map, but they can also be confusing. Look closely at the map above and you'll ask yourself why there are so many long distance cyclists in England and Wales. More seriously, you might begin to question the validity of the data, the honesty of respondents or some other aspect of the results. 

The ability to interrogate datasets in this way is one of the strengths of visualising large datasets in that we can often immediately identify anomalous patterns or results that confound expectations or are just plain wrong. I'm not entirely sure what's going on with the long-distance flows. Perhaps some people take their bike on a train so ticked the 'bike' option, despite the train journey being longer. Perhaps some people live in one part of the country during the week and cycle to work there but then live at their usual address during the weekend and this is registered as their residence on the Census forms. I'm only speculating but this could be one possible explanation. 

In the image below, I've filtered the data so that only flows of 2 or more are shown. This significantly reduces the visual clutter, but also draws out stronger long distance connections between places such as Bristol and Manchester, and indeed Manchester and lots of other places. Take a closer look by clicking the link below this map. I've added some place names to this map to help with orientation.

Go to the full size version

I'd be keen to hear different interpretations on the data. You get similar results when you map the 'walk to work' data so there's definitely something interesting going on with how people have answered the Census question and the data we have to work with. I'm certainly not saying it's 'wrong', more that we need to understand what exactly it tells us. For now, I'll leave it at that.

N.B. Why didn't I include Scotland and Northern Ireland? The data are not out yet. It's not some ploy to exclude anyone and I know the blog title says 'national' so forgive me if that threw you. I intend to expand the analysis in due course.

Comparing Populations: Night Time vs. Day Time

Esteemed Canadian and fellow researcher Brian Webb, from the University of Manchester, recently sent me an interesting image which compares the population of Washington D.C. in the day to the population at night. This got me thinking. I did a bit of digging and found some of my old data. Put simply, I had two datasets for wards in the North West of England. One file contained the resident population of wards and the other had the population of wards during the day time (i.e. residents, minus out-commuters, plus in commuters). Out of this came two visualisations, as shown below (red peaks = more people) and a short video.

I also decided to turn this into a very simple animation, which is embedded below. I have also produced a larger version of this on its own page. Note: the video embedded below will keep playing once you click play. The larger version allows you to pause the video and watch at your own pace.

Although these are really just some pretty pictures there are some important points to be made here. We think about the population of places - and the associated local costs and constraints - in relation to resident population but in some areas the day time population is so high that the impact on the local area is far out of proportion to the size of the resident population. Another matter is the well known issue of spatial mismatch or, more generally, understanding the differences between where people live and where people work and the implications of this. 

In short, understanding the spatiality of populations is important for planning and policy purposes - these visuals are just a simple way of telling the story of data. This is important because the data on display here comes from an analysis of a commuting data matrix of 1000 x 1000, or one million cells of data. So, another point here is that data on its own is not information, as we all know.

Experiments in Colour

I've been experimenting with colour and animations a lot recently. This involves producing a lot of material that ends up on the floor of the editing room, so to speak. So, I made an entirely pointless animation of said cuttings... The one below is an animated gif (nerd speak) of the North West of England and its commuting 'intensity' (i.e. spikes for areas where people commute to). The visual effects are just from my experiments - some just for the sake of art. An assault on the eyeballs, to be sure!

Flow Map Layout

I've been experimenting with mapping flow data (again) and this time have been looking at Flow Map Layout, by Phan et al. at Stanford. There is a short paper on it, and a slideshare presentation, but basically it offers a slightly different approach to flow mapping.

I experimented using UK commuting data for 2001. I looked at the top 50 flows (by district) into Greater London. This equates to more than 550,000 commuters going in to London but it excludes intra-London moves obviously. It's a bit tricky at first when you are trying to get used to it but when you do you can produce some nice images... Click on the image below to see it full size.

You can move things around in the (basic) mapping interface and it is actually quite flexible. There are some display options for colours and edge routing, etc.

The largest inflow was from Epping Forest, with around 26,000 commuters.

Manchester Commuter Inflows

A short post today on visualizing commuting flows. I've used some 3D GIS techniques to create a commuting surface for the North West of England (based on wards). Areas with peaks represent high in-commuting. Manchester dominates the North West pattern, as seen below.

I added in flow lines to this map in order to see which 'peaks' were being by-passed on the way to Manchester, and from where. The lines on the map below represent flows of 25 or more from individual wards in the North West. Not a huge number per ward, but it all adds up. When you think of how many people are doing this and how far they come it is significant however...

This is only really a rough draft, but it does communicate quite a bit of information and it tells a familiar story of urban commuting. Click on the map to see it in full size.

Geovisualization

Following on from the previous post, I dug out some of my previous work, did a bit of experimenting, editing and touching-up to produce some new geovisualizations. I believe that geovizualisation is about more than just making pretty maps - it is about communicating a lot of spatial data in an effective way, which could not otherwise be easily digested.

In short, geovisualization is part spatial data analysis, part graphic design, part art. In this post, I've tried to use a combination of techniques in order to produce the set of images below. The data displayed is commuting for wards in North West England in 2001. I used a GIS to create the raster images and convert them to 3D, I used some image editing tools to add labels, and I used some more advanced techniques for the colour/grey background focus ones.

Click on the individual images to view in full size at best resolution - the smaller versions below are not super high quality. Areas with high red spikes = areas of high in-commuting and blue = areas of high out-commuting. In effect, the red areas are where people work and the blue areas are where people live, though in reality there is of course some overlap.

Image 1 - Commuting in NW England, 2001

Image 2 - Same image as above, but with labels

Image 3 - Same image, with different colours

Image 4 - More colour experimentation

Image 5 - Colour focus area for Manchester

Image 6 - Colour focus area for Liverpool