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Street Networks - Alternative models, measures & their merits

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Abstract

Cities concentrate intensive human activities requiring highly complex networked infrastructure for movement, public transport and myriad other spatial interactions. The planning and management of multi-modal street networks for diverse users therefore requires an understanding of urban layout beyond motorised vehicle networks as simple linear conduits of movement. In seeking to address these issues, there has been a profusion of studies of street networks in recent years, with increasing attention from network scientists such as physicists, in addition to studies from transport, geographical and urban fields. These studies take different approaches to representing street networks, each with a different focus, sophistication and level of detail. The models used are based on paradigms grounded in different traditions, often with little reflection upon which is the appropriate representation of the system for a particular application. The different approaches typically generate different results without necessarily comparing means of representation and methods of analysis for different modes and contexts. A few studies have made comparisons but none has yet been done systematically across a wider set of approaches. In this international, inter-disciplinary contribution, we identify and characterise different representations of street networks, and associated measures, and compare analytic results for a sample area to bring out the commonalities, differences and relative merits of the different approaches. This review is a first step to build a foundation for deeper and more consistent understanding of the meaning and significance of the different models, and of their utility for particular applications.
Street Networks
Alternative models, measures & their merits
J. Gil1, K. Kropf2, L. Figueiredo3,
G. Stavroulaki1, M. Tomko4& S. Marshall5
AESOP Annual Congress: Göteborg, 11 July 2018
1Chalmers University of Technology; 2Oxford Brookes University; 3Universidade Federal
da Paraíba (UFPB); 4University of Melbourne; 5University College London (UCL).
Today’s Presentation
1. The Context
2. The Challenge
3. Creating a comparable set of models
4. Results
5. Discussion
6. Next Steps
The Context
“To represent an empirical phenomenon as a
network is a theoretical act… the appropriate
choice of representation is key to getting the
correct result.Butts (2009)
The Context
‘Street network studies’
Street layout &
Urban morphology
Transport planning &
transport geography
Network science
‘20 years of network science’
Nature, 19th June 2018
Distinctive aspects of
street networks
Settings for general human behaviour
not just traffic movement
Multi-modal
Activity in three dimensions not just
linear through movement
Destinations in their own right
Link significantly to fronting buildings
(networks extend inside buildings)
Hierarchical distinctions between main
streets and side streets (not directly
captured if broken into discrete links)
The Challenge
There are multiple ways of representing and
analysing street networks
These tend to (implicitly) use different
assumptions and be applied in different
ways
… and tend to be published in different
journals, without consistently relating to
one another
The Challenge
Divergence between ‘conventional’
approaches from geography, transport
planning and physics; and ‘alternative’
approaches from urban morphological
traditions
There is a lack of knowledge about the
relative merits of these different models and
measures for specific purposes…
Hence our study….
Street environment data sets used to create network models
From networks to models and their representations
Street environment data sets used to create network models
From networks to models and their representations
Network
model
Graph
representation
Street environment data sets used to create network models
From networks to models and their representations
Network
model
Graph
representation
Alternative models and their graph representations
Network
model
Graph
representation
Marshall, Gil, Kropf, Tomko & Figueiredo (forthcoming)
Space syntax
(Hillier et al)
Figueiredo ,
Porta et al,
Jiang et al
Tomko, etc. Marshall,
Kropf, etc.
‘Conventional’
approaches
Marshall, Gil, Kropf, Tomko & Figueiredo (forthcoming)
How to reconcile this diversity of network models and representations?
What are the merits of these different models and the measures?
Creating a comparable set of models
Typical operations:
(Filter features)
(Generalise geometry)
(Simplify representation)
Split geometry
Aggregate features
Calculate weights
Label features
Clean topology
Snap junctions
Data sources: Ordnance Survey (OS) Open Data (OS Open Roads, OS Open Map Local, OS Open Greenspace)
https://www.ordnancesurvey.co.uk/business-and-government/products/opendata.html
5 km buffer
Junction model
Steps:
1. Calculate length
2. Convert links to
edges list
(source/target)
attributes
Software: QGIS, PostGIS
Street-segment model
Steps:
1. Calculate length
2. Generate edges
list from
intersecting
street segments
3. Calculate length
edge weight
Software: QGIS, PostGIS
Route structure model
Steps:
1. Analyse street
network and
urban context
2. Identify and sort
strategic routes
3. Label strategic
routes
4. Identify and sort
local route sub-
systems
5. Label local sub-
systems
6. Aggregate
intersecting
routes of same
type as one
feature
7. Create vertex as
centroid of
grouped features
8. Create edges list
from intersecting
features
Software: QGIS, PostGIS
Natural Roads/Continuity model
Steps:
1. Split segments
into straight sub-
segments
2. Calculate azimuth
3. Aggregate sub-
segments into
natural roads:
connection angle
(35 degrees) and
cumulative angle
(70 degrees)
4. Clean topology
5. Create vertices as
centroid of
natural roads
6. Create edges list
from intersecting
features
Software: Mindwalk, QGIS, PostGIS
Intersection Continuity Negotiation (ICN) model
Steps:
1. Calculate azimuth
2. Aggregate sub-
segments into
features:
connection angle
(35 degrees)
3. Clean topology
4. Create vertices as
centroid of
features
5. Create edges list
from intersecting
features
Software: Mindwalk, QGIS, PostGIS
RCL segment model
Steps:
1. Split segments
into straight sub-
segments
2. Snap connections
3. Clean topology
4. Calculate length
5. Calculate azimuth
6. Create vertices as
centroid of
natural roads
7. Create edges list
from intersecting
features
8. Calculate length
edge weight
9. Calculate angle
edge weight
Software: FME, PST, QGIS, PostGIS
Axial model
Steps:
1. Draw axial lines
2. Create vertices as
centroid of
features
3. Create edges list
from intersecting
features
For context:
1. Split segments
into straight sub-
segments
2. Aggregate
segments:
connection angle
(5 degrees) and
cumulative angle
(15 degrees)
3. Generalise (10 m)
4. Extend endpoints
(10%)
Software: CAD, QGIS, PST, PostGIS
Axial segment model
Steps:
1. Split axial lines
into line
segments at
intersection
2. Remove dangling
line ends
3. Clean topology
4. Calculate length
5. Calculate azimuth
6. Create vertices as
centroid of
natural roads
7. Create edges list
from intersecting
features
8. Calculate length
edge weight
9. Calculate angle
edge weight
Software: PST, QGIS, PostGIS
Axial continuity model
Steps:
1. Calculate azimuth
2. Aggregate axial
lines into
continuity lines:
connection angle
(35 degrees) and
cumulative angle
(70 degrees)
3. Trim ends at
joined
intersections
4. Clean topology
5. Create vertices as
centroid of
features
6. Create edges list
from intersecting
features
Software: Mindwalk, QGIS, PostGIS
Named street model
Steps:
1. Not possible due
to incomplete and
inconsistent
naming of the
street segments
Software: QGIS, PostGIS
Results
Comparing graph properties
Software: Python, networkx
Results – Degree distribution
Results – Closeness centrality
Street-segment model
(metric)
RCL-segment model
(angular)
Axial segment model
(angular)
Axial model
(topological)
Natural roads/Continuity model
(topological)
Axial Continuity model
(topological)
Junction model
(metric)
Route structure
(manual classification)
Summary of Results
All graphs are very different (except natural roads and RCL
continuity), hence they are modelling different aspects of the
urban environment
The degree of disaggregate graphs gives a typology of
intersections
The degree of aggregate graphs gives a typology of streets
The urban hierarchies obtained from aggregate models are
similar visually
Route structure gives a clear classification, difficult to obtain
from disaggregate models
Discussion
All models are interpretations of reality, but just use different
selective criteria
RCL data needs pre-processing, and the model is influenced by
assumptions built into the data
Axial model as a starting point requires time to draw, but
provides an appropriate coverage of the pedestrian realm
(pedestrian space not linear!).
Disaggregate models have many steps and analysis parameters,
most important to specify explicitly, most flexible for different
applications
Next Steps
Assess analysis with a purpose: fitness of model/analysis pairs
Apply to more locations
Apply comparison of metrics
Explore different approaches to route structure
Explore relationships between all models
Thank You
jorge.gil@chalmers.se
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