Could someone point me to publications about this in journals with impact
factor larger than 0.01
Thanks,
Rui
On Mon, 22 Jan 2007 12:25:26 +0000, Professor Bill Hillier
<[log in to unmask]> wrote:
>Dear Chengke LIU - When Alain Chiaradia of Space
>Syntax Limited analysed the traffic engineering
>data for Nantes in France, he also found little
>correlation at low radius. However, with each
>increase in radius the correlation improved and I
>think eventually reach .8 at radius-n. We think
>the reason for this is that even when it includes
>minor roads, traffic engineering data is normally
>very skewed towards towards more integrated
>lines, and movement on these lines can be
>expected in general to reflect longer trips and
>so be expected to correlate better with higher
>rather than lower radius (see Penn et al 1998
>Configurational modelling of urban movement
>networks in Environment and Planning B).
>
>But, as you say, it is of course also the case
>that movement on the main networks is more
>subject to management and control factors than,
>say, the street pattern of a local area, and this
>may well affect things - as of course will your
>not mapping the pattern of connections as they
>really are.
>
>Two points of clarification. First, see Hillier
>et al (2000) Self-generated neighbourhood
>consolidation in informal settlements, Urban
>Design International 5, 2 61-96 for a study of
>vehicular movement in Santiago, Chile,
>
>Second, the aim of space syntax has always been
>to show the independent impact of spatial
>configuration on movement, but of course many
>other factors shape movement, and to give a
>fuller account of real movement rates these
>factors can be built into the regression model,
>as in the Penn reference. However, it is better
>deal with space separately, since only in this
>way can we isolate the effect of spatial design
>on movement, and the consequences of this in
>subsequent development like the influence of
>movement rates on land use patterns - as shown
>for example in the Santiago study cites above.
>These effects are likely to follow in different
>degrees from any level of correlation between
>spatial configuration and movement.
>
>However, referring to other correspondents, I do
>not see how the correlations found in Hillier &
>Iida (2005) Network effects and psychological
>effects: a theory of urban movement in eds Cohn A
>& Mark D Spatial Information Theory Lecture
>Notes in Computer Science 3603, Springer Verlag,
>pp 473-490 can be called anything other than
>remarkable for single variables. They also of
>course show unambiguously that it is geometric
>and topological factors, not metric factors, that
>shape movement at all but the most localised
>levels in the urban grid. - Bill
>
>
>
>
>
>
>>Dear Professor Alan Penn
>>
>>Thanks for your quick reply. I still have some questions to your comments.
>>
>>1.The input traffic flow data – I got 308 counting stations on both major
>>roads and minor roads covering Kowloon, the number of sample axial lines is
>>214. I do think they can cover the range of both traffic flow and spatial
>>configuration variables. I was told the traffic flow data was collected by
>>sensor belts installed on the ground road. But I am not sure the detail of
>>data collecting. Does that really matter? Actually the data I used was got
>>from The Annual Traffic Census 2005, published by Hong Kong Transportation
>>Department. According to your words, both the hand gathered and automated
>>gathered data have some problem, what data should I use for this kind of
>>research.
>>
>>2.The axial mapping – It’s obviously not an easy process to ensure all the
>>underpass and over pass are disconnected, especially in Hong Kong. But even
>>they are not, the correlation would not be so bad, if the spatial
>>configuration really matters.
>>
>>
>>3.Attribution of multiple counts to a single line segment. Some stations
>>locate along one axial line, what do you mean by putting these in as
>>separate data points in the stats to start with.
>>
>>Your advices are really helpful, I wish I could get more. Thanks
>
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