Attribute-based modelling for simulation of urban structure
Camilo Cruz – Universidad de Chile – Faculty of Architecture and Urbanism
camilocruzg@fau.uchile.cl
Abstract
The main interest of our research is to explore the possibilities of
geometrical urban modelling, through the study of the fundamental structure
of existing urban environments. This analysis-based approach looks at the
minimal constituent elements of the urban structure, searching for conditional
relationships between them, in order to build descriptive indicators, such as
entropy, that can be used as the main inputs for the simulation of urban
patterns and/or the spatial analysis of behavioral urban phenomena.
This project focuses on the mathematical and geometrical modeling of
urban patterns, as a first step towards the understanding of the subjacent set of
rules that articulate cities, in order to incorporate them in the practice of urban
design practice.
Introduction
The work presented in this paper constitutes the first approach in the
development of a design framework, aimed to translate into visible form the
ideas and decisions that the stakeholders involved in the urban development
process make, through the implementation of computational tools, capable of
performing analysis and simulation through 3D visualization techniques.
The scope of the project is to establish a relation between the
morphological characteristics of existing cities, the behaviour of populations
and the ideas of different urban stake holders, in order to be able to evaluate
and generate plausible new urban scenarios.
‘A city is a relatively large, dense and permanent settlement of socially
heterogeneous individuals’ (Wirth 1938). Today, cities concentrate more that
50% of the world’s population, and are becoming more and more complex as
time goes by. Therefore, it appears to be a necessity to take a few steps back
from the traditional practice of design, as it has been applied to the
development of built environments, in order to define a different approach that
considers the dynamical character of the processes of urbanization, as well as
the capacity of the tools that we have available.
There are many initiatives pursuing similar goals, basically categorized in
two groups: behavioral simulation, that is the development of operational
models capable of dynamically representing urban phenomena, and
geometrical modelling, oriented towards the visualization of urban spaces.
On one hand, behavioral simulation mostly concentrates on transportation,
mobility, location of jobs and households and land value, among other urban
topics. The output of such models act as guidelines that inform decision makers
about the economic, social or demographic consequences (to mention some of
the possibilities) of taking a specific line of action.
Geometrical modeling, on the other hand, is strongly oriented towards the
generation of 3D graphics and visualization of urban environments, focusing
more on the plausibility of the product, than on the relation of the generative
process with real-life city production.
There are some efforts that sit in between both groups, such as Space
Syntax (Hillier et al. 1976), which through a topological approach to the analysis
of built space, has been very effective at predicting certain kinds of behaviour.
The approach we present in this paper is intended to articulate the analysis
and simulation of urban environments, with a strong emphasis on visualization,
through the implementation of a set of tools capable of retro feeding each
other in an iterative process that should open the possibility of translating
abstract ideas, such as intensification or density, into a scenario that can be
immediately tested and modified, in order to adapt it to the real needs of the
stake holders involved in the process.
Related work
Traditionally, the emergence and development of cities has been a
consequence of the cultural development of a certain society, and as such,
urban environments constitute a reflection of a group of people’s social
structure and relationship with the territory they inhabit (Mumford 1961).
According to the work of Kostof, there are two ways of urbanizing a
territory: spontaneous development, produced in time, according to the needs
of the moment, or the planned interventions, masterminded and founded in a
precise moment in history (Kostof 1993; De Landa 1997).
Particular shapes and patterns can be associated with each of these modes
of territorial occupation, allowing a trained observer to recognize them, even
though in most contemporary cities they don’t exist in isolation from one
another (Alexander et al. 1978; Kostof 1991). Those patterns have their origin in
ideas and ideals, that are implemented through a decision making process that
ends up with a designed plan.
It is possible to say then, that there is a gap between those ideas and its
materialization, produced by addressing the task through methods that are
rooted in a tradition that considers design as a mean to achieve definitive
solutions, therefore producing rigid outcomes, instead of allowing flexibility
through the consideration of problems as dynamic conditions (Beirão & Duarte
2005).
An example of a contemporary approach is the work of Alexander Lehnerer
(Lehnerer 2009), who points towards a transformation of the practice of urban
design, from traditional design, to the construction of rule structures oriented
towards the generation of scenarios.
By approaching the planning and urban design problem through the
understanding that there is a broad space of solutions to it, instead of one fixed
possible outcome dictated by the criteria of the designer in charge, a need for
tools that allow us to search for solutions that meet the requirements of a
certain idea and assess scenarios, strongly resonates.
Some very important efforts have been pushed forward in the field of
analysis. The most notable example is the work of Space Syntax (Hillier et al.
1976), which aims towards the understanding of the built environment as a
socio-spatial dynamic construct, through the analysis of its topological
attributes, by measuring space through depth and convex maps, as the
structure that supports interactions (Bafna 2003). Hillier’s approach has proven
very effective at anticipating how people interact with architectural space, the
fact that it disregards its geometric characteristics leaves room for performing
further and more accurate analysis.
Beirao and Duarte (Beirão & Duarte 2005) have also worked in urban
analysis, through the use of shape grammars (Stiny 1980), in which the methods
proposed by Stiny are used to assess urban morphology and then to generate
sets of rules for design.
In the fields of transportation and regional planning, great body of work has
been developed, dedicated to analyze urban functions and attributes from an
econometric perspective (Waddell 2002), and at a very large scale
(metropolitan areas), mainly focusing on location, and developing urban growth
models based on assumptions, disregarding morphological aspects of urban
environments. Therefore, the results of such studies constitute guidelines to
inform decision makers about possible scenarios, but still leaving room for
traditional designers to interpret those ideas into a fixed outcome.
Computer scientists on the other hand, have been more effective at
developing simulation systems capable of generating three dimensional visuals
of urban environments.
The work of Parish and Muller (Parish & Müller 2001) represents the
foundations for a variety of projects that are being developed in search for
planning tools. Their approach is very comprehensive, since it is capable of
generating from road maps to buildings, using sequences of subdivisions and
extrusions based on data maps provided by the user. Therefore, their proposal
disregards the analysis part, leaving a gap between reality and the simulated
space.
The city simulator developed by Parish and Muller has been used and
extended to produce two platforms: the first one is a commercial suite,
CityEngine, developed as an equivalent to a BIM software for planners. It is
capable of deploying urban elements on a given territory as an action of design,
by using the same approach of subdivisions (Schirmer & Kawagishi 2011) that
originated in the concept of shape grammars. The second is Synthicity and has a
more academic approach. Their aim is to achieve integration between
behavioural and geometric urban simulation, in search of relations between
both paradigms (Vanegas et al. 2009).
This paper represents the starting point of a new project, oriented towards
capitalizing on the space of opportunities that appears as a consequence of the
approach to design as a mean to unveil and evaluate solutions. We aim to look
for the generation and analysis of different scenarios, through the
implementation of integrated tools, capable of assessing, simulating and
reassessing the scenarios produces, in order to understand if they meet the
requirements proposed by the stakeholders that are discussing the future of a
certain city.
Methodology
As it has already been mentioned, the work presented in this paper
represents the first steps of a research project aimed towards the construction
of a framework for the analysis and simulation of three dimensional urban
environments through the use of computational tools.
The approach developed is based on two premises: a) it is possible to use
geometry and statistics to measure and describe the spatial attributes of cities,
and b) it is also possible to use that data to generate new, without a design
intervention (understood in a traditional sense).
Therefore, it becomes necessary to implement a two part system, capable of
performing analysis, in order to measure the attributes of urban environments,
and simulation, to validate the analysis and explore the possibilities of the
attributes recovered from the analysis, through the generation of new scenarios
from the parametric manipulation of input attributes.
Since urban environments are highly complex structures, at this stage we
propose to address the problem in a fragmented manner. That way it becomes
possible to work with a small amount of variables, allowing us to focus in how
they interact, in order to model that reality.
The physical aspect of cities can be broken down as a collection of
architectural objects, sitting on plots that are arranged into blocks organized by
a transportation network (building-plot-block-road network).
For the purpose of this work, we will be focusing on the analysis and
simulation of the transportation network, since it constitutes the higher
structural level of the urban construct. However, roads, avenues and other
transportation corridors are still very complex elements to model. Therefore we
will be only considering the structural character of networks, which means that
roads are interpreted as arcs connected to one another, intersections are
interpreted as nodes, and they are deployed and distributed over a flat
territory. Accordingly, many attributes, such as width of the roads, number of
lanes, presence of sidewalks, width of sidewalks, presence of green mass
(trees), vertical slope, etc. have not been incorporated to the process, which
will allow us to build up a structure of relations between attributes starting at
the barebones of the system, with the intention of expanding its scope as the
research progresses.
The two part system consists of an analysis tool, implemented as a script,
written in python for grasshopper (inside Rhinoceros) capable of reading a
shape file containing all the streets as separate units that go from one node to
another; and a simulation script, that has yet to be written, using the same
environment.
At this stage we are considering two basic components as our main study
objects: arcs and nodes.
Arcs
Arcs are defined as a line that connects two points. Each one of those two
points can represent either an intersection with other arcs, or a cul de sac.
Each arc is identified with a unique number that allows us to refer to that
particular object within the whole system.
The attributes that describe each arc are the coordinates of the points at
each end, written as a Cartesian ordered pair in the (x,y) form, the Euclidean
distance between those two points and the length of the arc, both expressed in
meters, the average mean of the arc, in relation to the x axis (fig. 1), and finally
the angles (in degrees) measured at both ends of the arc, from the
corresponding point to 1/20th of the length of the arc, in relation to the north of
the system (fig. 2a). The angle measurement method proposed allows us to
evaluate the relation between connected arcs in the system (fig. 2b).
INSERT FIG 1
Caption: Fig. 1 Attributes of an arc. Source: Author’s elaboration.
INSERT FIG 2
Caption: Fig. 2a Angle measurement on a regular urban pattern. Fig. 2b Angle
measurement on an irregular urban pattern. Source: Author’s elaboration.
Nodes
A node is defined as a point shared by two or more arcs (fig. 3a). The only
exception is that when only two arcs share one particular point, and the relative
angle between those arcs, measured at the shared point, is within the range
that goes from 160° to 200°, those arcs are considered ‘continuous’, and the
node is excluded from the system (fig. 3b). This process also defines the
‘continuity’ attribute that will be addressed when the data analysis is discussed.
INSERT FIG 3
Caption: Fig. 3a Regular node. Fig. 3b Exception for a 2 arc node. Source:
Author’s elaboration.
Due to computation limitations, it is highly inefficient to perform node
analysis for a whole city, therefore, the use of analysis areas has been
incorporated.
Each node is assigned a unique ID within a specific analysis set.
The attributes that describe a particular node are location, expressed as a
Cartesian ordered pair (x,y), ‘congestion’, defined as the number of arcs that
converge on it, ‘orientation’, which is the angle of the arc that is closer to the
vector that represents the north direction, the relative angles between the arcs
that configure the node, and finally the number of ‘continuous’ pairs of arcs,
according to the angle range that has already been mentioned (fig. 4).
INSERT FIG 4
Caption: Fig. 4 Attributes of a node. Source: Author’s elaboration.
Data collection and Analysis
In order to collect the attributes described above, a python script has been
developed and implemented into Grasshopper for Rhinoceros 5.0. The program
performs a parse through the whole database, in the form of a shape file, and
translates it into a list of arcs, identifying endpoints, curves and, assigning an ID
number for each one of them.
At this point, it is required from the user to define an analysis area, which
will determine the elements on which further data collection should be
performed.
The area selection is done by placing a single point on the map and defining
the lengths of the sides (fig. 5), generating a rectangle.
INSERT FIG 5
Caption: Fig. 5 Attributes of a node. Source: Author’s elaboration.
The arcs captured inside the analysis area are further analyzed, in order to
obtain all the aforementioned attributes from them.
The output of the data collection process are three ‘comma separated value’
(.csv) files, containing a) general system attributes (table 1), b) node attributes
(table 2), c) and the attributes for all the arcs (table 3).
INSERT TABLE 1
Caption: Table 1 General attributes. Source: Author’s elaboration.
INSERT TABLE 2
Caption: Table 2 Nodes attributes. Source: Author’s elaboration.
INSERT TABLE 3
Caption: Table 3 Arcs attributes. Source: Author’s elaboration.
After the data collection is finished, a second script, developed in
‘Processing’ (http://www.processing.org) allows us to process and visualize the
results, generating a series of calculations and the classification of data into
indicators and graphs that describe the analyzed area (fig 6.).
INSERT FIG 6
Caption: Fig. 6 Analysis panel. Source: Author’s elaboration.
The analysis is performed at three levels, according to the data collection.
The first level (fig 6a) shows ‘node density’ (in nodes per hectare), ‘arc
density’ (in linear meters of arc per hectare), which allow us to understand the
‘intensity’ of the area. Another important piece of information built within this
first level is the average ratio between Euclidean distance and length of each
arc, which gives us a rough idea about the general character of the area in
terms of morphology. A ratio equal to 1 means that all the arcs are perfectly
straight, and as it gets smaller, it is possible to assume that the roads on the
map draw curvy lines.
The second level corresponds to information about the arcs (fig 6b). This
area of the panel shows two numeric elements, the average arc length and the
standard deviation for that average, and a histogram that shows the
distribution of arc lengths, in increments of 30 meters, within the area of
analysis. These pieces of information offer a first approach to the understanding
of the regularity of the analyzed pattern, since they allow us to see the whole
range of arcs involved in that particular layout.
Finally, the third level shows us all the information about the nodes (fig. 6c),
separated in three sub areas: the first one addresses the congestion of the
nodes, showing the average number of arcs per each node and the standard
deviation of that average, alongside with a histogram that illustrates the
distribution of node congestion, making evident the existence of cul de sacs and
providing a more accurate idea regarding the geometry of the pattern. In this
section, a continuity indicator is included, by calculating the number of
‘continuous’ connections occurring at each node, through an average. The
second sub-set of information depicts the rotation of the nodes in the system,
showing average, standard deviation and a histogram. The third sub-section
shows the relative angles between the arcs that configure the nodes, also
calculating average, standard deviation and a histogram.
The whole idea behind this process is to depict a general image of the
network being analyzed, which could allow the user to define, for instance, if it
was planned or a spontaneous growth, and to describe its main attributes, in
order to replicate them.
Simulation
After the analysis, and in order to validate it, we have designed a basic
simulation algorithm, based on the fundamental components of the
transportation network structure and the information obtained through the
analysis process.
The simulation is proposed as an iterative process that minimizes the
number of discretional operations for the user, which are reduced to 2: a)
defining a work area, which means to draw a rectangle that sets the limits of
the simulation, avoiding an infinite loop; and b) place the initial node, in order
to define the starting point for the simulation.
After those decisions are made, the simulator draws the first iteration of
urban structure by selecting attributes from existing data, obtained in the
analysis process, through the use of the Monte Carlo method in a very basic
form:
The probability of selecting a given attribute is weighted by the probability
distribution of that attribute in the collected data (fig. 7).
INSERT FIG 7
Caption: Fig. 7 Monte Carlo simulation diagram. Source: Author’s
elaboration.
The sequence of actions executed by the simulator is as follows: (fig. 8)
INSERT FIG 8
Caption: Fig. 8 Simulation Algorithm. Source: Author’s elaboration.
Example
The whole development of the tools described above has been done using
the city of Santiago, Chile, as the main data base, due to the ease of access to
.shp files and other pieces of data.
The main input for the data collection is a GIS road map of the entire city.
The analysis has been performed for a series of areas of the city (fig. 9),
selected according to their apparent characteristics, in order to prove if there is
a relation between the form observed and the data collected.
INSERT FIG 9
Caption: Fig. 9 City map and analysis areas. Source: Author’s elaboration.
The results of the analysis are displayed in figure 10.
INSERT FIG 10
Caption: Fig. 10 Analysis results. Source: Author’s elaboration.
The simulation process has not been automated yet. Therefore, a manual
process of simulation has been performed, using the data for one of the areas
analyzed, in search for a first validation of the process.
The results are illustrated in figure 11.
INSERT FIG 11
Caption: Fig. 11 Simulation results. Source: Author’s elaboration.
Discussion
So far, the development of the project needs to be addressed from two
perspectives: a) the construction of the methodology, and b) the validation of
the process through the observation of the results.
a) As it has been presented in this paper, it is evident that the research
project is the starting point of a work in progress, therefore, there are still many
issues that need to be solved, clarified or revised.
In terms of the methodological approach, the decision of starting to build a
system from scratch responds to the need of having complete control over the
operations performed by the software, as well as a full understanding of the
construction of the tool itself.
This is why the Rhinoceros 5.0 has been selected, since it allows the
development of python code to perform key operations, by using some of the
native software assets to visualize the outcomes.
The implementation of the tools so far, has many limitations that should be
remedied as the project develops. The most notable and constraining is the use
of .shp files as the main data input. The idea is to enable the system to read
.xml files, in order to be able to read and use publicly available databases, such
as the one provided by Open Street Map (openstreetmap.org).
b) From the observation of the results obtained from the case study,
some of the flaws of the system, pointed out above, become more evident. It is
clear that it is necessary to perform a deeper analysis in order to achieve more
accurate results. This can only be performed if the analysis tool gets further
developed and an automated simulator gets implemented, so the iterative
process can be completed and assessed.
However, the possibility of collecting and processing large amounts of
geometrical data from simple urban patterns seems to be an auspicious starting
point for a project that has still to be developed to its full potential in the
upcoming years.
Conclusion
The main scope of this research project, and specifically of this first stage, is
was to lay down the methodological foundations for the development of a large
scale system, capable of performing a variety of operations based on data,
instead of discretional design decisions. Up to this point, it seems that those
foundations are starting to settle down, even though there is a long road ahead.
The experimental and theoretical base for the development of this
methodological framework have demonstrated that there are many practical
applications for such tools, since the focus of the field of urban design is
gradually shifting towards an approach that relates strongly to the reality of
contemporary cities.
References
Alexander, C., Ishikawa, S. & Silverstein, M., 1978. A Pattern Language:
Towns, Buildings, Construction (Center for Environmental Structure Series),
Oxford University Press.
Bafna, S., 2003. Space Syntax: A Brief Introduction to Its Logic and Analytical
Techniques. Environment & Behavior, 35(1), pp.17–29.
Beirão, J. & Duarte, J., 2005. Urban grammars: towards flexible urban
design. In Proc. 23rd Int. eCAADe Conf. pp. 491–500.
Hillier, B. et al., 1976. Space syntax. Environment and Planning B: Planning
and Design, 3(2), pp.147–185.
Kostof, S., 1991. The City Shaped, Urban Patterns and Meaning Throughout
History,
Kostof, S., 1993. The design of cities. Places, (5), pp.85–88.
De Landa, M., 1997. A thousand years of nonlinear history, New York: Zone
Books.
Lehnerer, A., 2009. Grand urban rules, Rotterdam: 010 Publishers.
Mumford, L., 1961. The City in History: Its Origins, Its Transformations, and
Its Prospects, Harcourt, Brace & World. Available at:
http://books.google.com/books?id=q0NNgjY03DkC&pgis=1 [Accessed April 14,
2014].
Parish, Y. & Müller, P., 2001. Procedural modeling of cities. Proceedings of
the 28th annual conference on …, (August), pp.12–17.
Schirmer, P. & Kawagishi, N., 2011. Using Shape grammars as a rule based
approach in urban planning - a report on practice. In S. V. T. Zupancic M.
Juvancic & A. Jutraz, eds. Respecting Fragile Places - Proceedings of the 29th
Conference on Education in Computer Aided Architectural Design in Europe. pp.
116–124.
Stiny, G., 1980. Introduction to shape and shape grammars. Environment
and planning B, 7(November), pp.343–351.
Vanegas, C. a. et al., 2009. Interactive design of urban spaces using
geometrical and behavioral modeling. ACM Transactions on Graphics, 28(5),
p.1.
Waddell, P., 2002. UrbanSim: Modeling urban development for land use,
transportation, and environmental planning. Journal of the American Planning
Association.
Wirth, L., 1938. Urbanism as a Way of Life. American journal of sociology,
44(1), pp.1–24.