August 03, 2009
In the summer issue of ESRI's ArcNews,
Dr. Paul Torrens, director of the Geosimulation Research Laboratory and
associate professor at the School of Geographical Sciences and Urban
Planning at Arizona State University, penned an article titled "Process
Models and Next-Generation Geographic Information Technology." The
article provides a truly unique vision on how GIS should work by
incorporating more dynamic data and having users develop a better
understanding of how geospatial phenomena really work, specifically
those "processes" which control complex spatial situations. Editor in
Chief Joe Francica interviewed Dr. Torrens about his ideas, space,
time, cellular automata, Web architecture, "spimes" and the "Internet
of things." Because of the length of the interview, it has been divided
into two parts. This is part one. Part two will appear on Wednesday.
Directions Magazine (DM): In your article, you discuss the need to
"consociate" geospatial data models with more dynamic and adaptive
processes. Can these "process models" be viewed as a more natural way
of thinking and rationalizing geospatial phenomenon to allow users to
achieve everyday business objectives? Please define and give examples.
Paul Torrens (PT): Traditionally, geographic information systems
have emphasized patterns and GIS developers have paid comparatively
little attention to the role of processes in representing or analyzing
spatial phenomena. The premium placed on pattern is pretty obvious in
GIS: static maps dominate as a user interface to underlying spatial
databases and we interact with that interface using patterns such as
buffers, filters and so on, or by literally employing click-patterns
with a mouse. Similarly, much of the spatial analysis that we perform
on spatial data is pattern-based: point-pattern analysis and measures
of spatial structure and spatial composition are common examples. These
functionalities are incredibly useful, but the emphasis that GIS places
on pattern belies the fact that spatial phenomena are composed of both
patterns and processes and that the two really work together in
synergy. GIS are abstract models of the real-world and so the absence
of a rich set of process tools to describe that world is troublesome
when we try to ally our GIS to real-world systems. This is beginning to
change, as GIS become more tightly coupled with dynamic simulation
models, which may serve as a dynamic engine for a GIS. And as these
process models are beginning to be encapsulated as data models in the
core of a GIS.
One can, for example, store snapshots of hurricane patterns in a GIS,
and these may even be updated regularly through what we call
comparative statics (i.e., a series of snapshots tied together in a
temporal sequence, like frames in a movie sequence), but these are
really just a freeze-frame sample of a complex, dynamic and adaptive
system that is really driven by processes that control evaporation and
condensation, wind movement, shifts in atmospheric pressure and so on
through very complex mechanisms of non-linear interaction,
path-dependency, positive and negative feedback, and phase transitions.
A GIS generally has no native underlying model of how those processes
operate over space and time and so, despite its wonderful capabilities
for parsimoniously representing the geography of complicated systems,
without being coupled to a process model, the GIS cannot tell us where
a hurricane is likely to go next or what the dynamics of its
interactions will be once it makes landfall. Yet, these are the details
that are of critical interest for citizens, emergency management and
first responders, policymakers and even insurance companies.
Retail site selection and marketing are other examples where process
models could have a dramatic impact. Many retail businesses currently
use GIS to map their customer base and the integration of point-of-sale
geo-referencing, such as tagging cash register transactions with
customers' ZIP Codes or phone number area codes to determine their home
location, for example, has automated much of this and has allied GIS to
mail-marketing and consumer preference analysis. The use of GIS-enabled
geodemographics in this way yields a relatively rich description of who
your customers are, where they come from, and what their transactional
activity is and this information can easily be cross-linked to a wealth
of demographic and economic detail that adds additional value to the
data points that are harvested at a point of sale. Demographic details
can easily be pulled per ZIP Code or area code to classify consumers,
negating some of the need for revealed and stated preference surveys.
But none of this information really tells you why the customer chose a
particular store or transaction in a particular location at a
particular time. Moreover, looking to the long-term (which is the time
horizon for store location decisions, for example), as customers
transition through the lifecycle, their value platforms for consumer
goods and their preferences for services change, and the relationship
between these attributes and the geography of their homes and
workplaces shifts. Quite often, people change location when key phase
shifts occur in the lifecycle: moving to a university, starting in
their first jobs, getting married or starting a family, retiring and so
on. The urban geography of American cities is in a continuous flux and
the demographics of urban populations are changing constantly; this is
especially true in the current housing and employment market. Static
geodemographic systems lack a sophisticated scheme to preemptively
estimate or forecast those changes; to do this, they need to be coupled
with models of population dynamics, demographics, regional migration,
intra-urban migration, even to models of the urban economy, land
development and transportation dynamics.
The adaptive nature of consumer trends is even more complex if we
consider the near-term, which can influence product selection in retail
stores and management of the related supply chain. In the near-term,
many fashions, fads and subcultures emerge and diffuse very rapidly and
very widely, sometimes on the order of a day, temporally, and globally
in terms of spatial reach. The pace of adaptation is accelerating as
new technologies are adopted (Twitter tweets, flash mobbing,
crowd-sourcing of online consumer review and reputation systems).
Because of an essential resolution mismatch, static geodemographics
contextualized in an ecology of ZIP Codes and decennial census
information misses much of these trends, which is why there is growing
interest in the use of higher-resolution geographic information
technologies to fill in these gaps. This can be accomplished using
location-based services triangulated by cell phone position and allied
to micro-transactions such as swipes of consumer loyalty cards and
credit cards, and also coupled to individual products using RFID tags,
for example. Again, process models can take GIS out of a static
information ecology and can animate the dynamics between the "dots" of
these data points, potentially to the scale of the choreography of
customers within a store and the individual selection of a product on
the shelf. The implications of, say, shifting product displays, could
be assessed on the order of a day and, from the bottom up, the knock-on
implications for the supply chain on the order of weeks can be managed,
for example.
DM: You talk about the need to fuse space and time into GIS because
today much of it is used only for simple visualization. How would you
expect the next generation of GIS solutions to better integrate the
time dimension for spatial analysis?
PT: This is already happening, although much of the innovation is
taking place outside traditional GIST research and development, but in
ways that are very closely allied with geographic information systems.
The rapid proliferation of mobile Internet and communications
technologies (cell phones, hand-held gaming devices, Wi-Fi enabled
laptops, Bluetooth peripherals, RFID inventory systems, in-car
navigation devices) has necessitated development of entirely new
database systems for handling moving objects and for indentifying and
classifying events as interactions of those objects with each other and
socio-technical systems (humans and the cell phone network, employees
and products in a stock room, for example). We see this in our everyday
lives: when we turn on our cell phones we expect them to work,
regardless of location. Similarly, we take it for granted that a
company like UPS can deliver a package anywhere in the United States
(maybe even in the world, although there are obviously locations that
are beyond its reach) and that we can track the movement of that
package through way-stations in near real-time.
Georeferencing moving objects in this way constitutes a first layer of
a larger possible information architecture; semantic analysis, which
would "make sense" of these vast stores of data in the context of
movement within a complex socio-technical system, constitutes the
second layer. Development of this second layer is already underway:
John Krumm and Eric Horvitz at Microsoft Research have a project to
machine-learn driver trajectories from in-car GPS data, as a
"predestination" system that would forecast likely activity patterns.
Similarly, various efforts have been developed to couple car position
data (from GPS devices, drivers' cell phones or through Wi-Fi
networking of in-car devices on the road) with process models to
generate real-time traffic reports. Kai Nagel and his group at the
Technical University of Berlin have been working for some time on
linking intelligent transport systems to dynamic agent-based traffic
models that will perform near real-time forecasting of likely traffic
outcomes from congestion, for example. It seems to be only a matter of
time before these now disjointed information ecosystems align and allow
for a many-system viewpoint (driver behavior, ramp-to-ramp traffic
conditions, construction projects, electronic billboard notifications,
staffing at toll booths) to unfold. Once again, process models are the
catalyst that allow a system-of-systems architecture to unfold in a way
that accommodates the complexity of interactions between diverse system
elements. GIS is a likely candidate for organizing data for those
systems, because of the widespread ability of location tags to couple
diverse data sources.
In terms of visualization, or geovisualization if we consider
human-computer interaction in a digital context, developments in
space-time GIS are also ushering in new forms of interfacing for GIS,
challenging the dominance of the map as a portal to spatial data. In
academic GIS research and development, many researchers (including my
own group) have turned to artificial intelligence in search of tools
that can better model real-world processes and their evolution
dynamically, whether this is through agent-based modeling,
machine-learning or semantic analysis. The dynamics of these processes
need to be visualized in ways that are not traditionally catered to in
a typical GIS and so researchers have turned to computer-animated
design and to commercial game engines for methodology that can push
beyond the limitations of static cartographic interfaces to spatial
data. In turn, this has led to the emergence of "immersive" virtual
globe cartographic interfaces, such as NASA Worldwind and Keyhole's
World Viewer (now Google Earth), which work with standard GIS data
formats by essentially visualizing maps in 2.5D (2D with extrusion) and
3D. In each case, these products can be visualized on a desktop or any
client device using browser-based visualization schemes such as
Silverlight and Flash.
These interfaces are beginning to be standardized in GIS: ESRI's
products now maintain interoperability with COLLADA (Collaborative
Design Activity), for example, which is a format for exchanging 3D
assets in CAD using standard XML schemes. Because of their foundation
in XML, COLLADA assets can co-exist with similar mark-up schemes for
GIS data. This allows for geographic objects to be exchanged between
CAD and GIS (as well as virtual globes) rather seamlessly. Other
examples, such as Microsoft's Virtual Earth (now "Bing Maps for
Enterprise") are coupled to dynamic visual simulation software (ESP,
for example), which will allow for the display of dynamic processes. In
my group we have developed immersive, 3D animated "peoplescapes" that
are interoperable with agent-based models, network graphs, GIS and
spatial databases.
A further advantage of the growing synergy between GIS, visualization
and animation is the ability to move beyond the mouse as an interface
device and toward recent-generation controllers (Nintendo's Wii remote
or data gloves, for example). These permit greater flexibility
and degrees of freedom in manipulating geographic objects, whether they
are on a screen or in a CAVE (Cave Automatic Virtual Environment), and
allow for additional senses to be used, such as tactile feedback, which
can assist in developing GIS for populations that may benefit from
non-visual interactivity with geographic data. The proliferation of
touch-screen technologies, whether on cell phones, tablet PCs,
touch-screens or on dedicated "surface computers"
(some of which are capable of visualizing many layers of information
simultaneously: see Microsoft's Second Light project), is ushering in
entirely new forms of gesture-based interaction with spatial data
through GIS. Recent advances in using hands, feet and bodies - via 3D
camera and imaging - as the actual controller for gaming (see 3DV Systems and Microsoft's Project Natal)
will likely accelerate these developments even further. These require
processes to model body movement and gesturing and to classify those
movements as space-time processes and events that can be interpreted in
what is, essentially, a specialized GIS. They also need models that can
then translate those data in a meaningful geographic context on-screen.
DM: Many of our readers may not be familiar with the use of cellular
and agent-based automata in simulations. Can you explain the concept
and provide some examples of how it would be used to simulate
geospatial processes?
PT: Cellular and agent-based automata are tools that can be used to
build very detailed and complex simulations of spatial entities and
processes. They are, fundamentally, media for processing and
computation. Each automaton works as a computer, just like a PC's CPU,
and automata actually work in the same ways that our brains function to
process information. An automaton is capable of storing a finite amount
of data and these data can take on any digital form: they could be
spatial data, numerical data or text, for example. In addition to its
own store of data, the automaton is able to accept data from other
entities, from other automata, from software or from a computer user's
mouse activities, for example. The automaton can share these data, or
its own data, with anything that it interacts with and in this way we
can think of an automaton as being capable of communication. So
essentially, we have an automaton that works as a database. In
addition, the automaton is endowed with a set of processes, which can
be considered to work as rules, heuristics, algorithms, methods and so
on. The role of these processes is to perform operations on data, to
conflate data, search through data, to prune data and so on. Usually,
the processes are designed to contextualize the data and to thereby
convert the data into information. The results of this processing can
also be shared with other automata or digital entities. Processes can
also be introduced to describe how an automaton moves through an
environment, thereby controlling how its information is exchanged and
what its interactions might be. By adding processing to the automaton's
database functionality, the automaton gains a level of intelligence, in
so much as it is capable of (often proactively) extracting knowledge
and meaning from data.
Agent-based automata use agency to shape their processing abilities and
there are a variety of schemes in which agency can be interpreted:
human agency, collective agency, emotional behavior, utility
maximization and so on. When agents apply their agency introspectively,
we usually refer to them as "individual-based models." When agents work
with other agents (or with an environment, whether that is a technical
environment, a social environment or a physical environment) to
accomplish some goal, we usually refer to the automata as a
"multi-agent system." When agents are used to represent nodes in a
network (neurons in the brain, for example) and the exchange of
information between nodes, we commonly build them as artificial neural
networks. Artificial neural networks can be used to model real neural
networks (decision making, for example) or they can be used to
distribute processing of large tasks, when we use them in image
processing for analyzing satellite imagery, for example. When the
automaton is considered as residing in a discrete spatial unit (perhaps
within a lattice of related spatial units), say within an individual
parcel boundary within a city, for example, we regard the automata as
"cellular automata" and information exchange takes place between cells
based on diffusion.
The use of agent-automata in geographic modeling (what is termed as
geosimulation) is motivated by a number of factors. Agent-automata are
completely malleable to parameterization, such that an automaton can
really represent anything that can be expressed digitally. They
constitute what is known as universal computers. This means that an
agent-based modeling architecture can be used to build simulations of
lots of different things, which has advantages over more traditional
forms of modeling that might fit one or two purposes only. Multi-agent
systems can also be very efficient in solving problems that lend
themselves to distributed computing, i.e., a problem is broken-down
into lots of little pieces and each piece is handed off to a dedicated
agent, with some system-level rules for reconstituting the results. So,
we can use agent-automata to model traffic on highways, for example, at
the scale of every single car on the road in an entire city, and we can
examine how system-level effects emerge from the one-to-one
interactions of drivers. This relates to the use of agent-automata to
model complex adaptive systems, which are systems in which things like
emergence, feedback, bifurcations, and fractal scaling are important.
Most traditional models are not suitable for modeling complex systems
because they are constrained by limits of independence, linearity and
rationality, for example. Automata also have a close affinity to GIS
data models, such as networks, rasters and vectors. In a way, a lattice
of automata with many states is the same concept as cross-indexing many
raster-layers in a GIS, but with the added advantage of embedded
process models. Unlike in GIS, automata can easily be extended to three
dimensions (even four dimensions if you consider space and time as
separate).
I have worked extensively with automata as the engine for processes in
urban simulations. I have built, over the years, models of suburban
sprawl in which the automata are patches of land in an urbanizing
landscape and agency is used to model how developers and settlers might
build and populate that landscape. In this case, I am essentially using
the model to build a synthetic city in a computer, so that I can
experiment with possible trajectories of urbanization (smart growth,
sprawl, green belts, edge cities, exurbanization and so on) in ways
that are impossible to do in the real world. I have also built
agent-based models in which automata are used to represent individual
householders, property developers, their decisions to move within a
city as they transition through the lifecycle, decisions to develop or
redevelop land at particular densities and for particular activities,
and the community- and city-level outcomes of their interactions as
social neighborhoods, gentrification phenomena and so on. In the last
few years, I have been building very detailed introspective automata to
model real people. Here, my focus has been on infusing realistic
behavioral geography and spatial cognition into their agency so that
they can behave realistically, doing the right things in the right
places and at the right times, so that they can move through their
environments using spatial thinking, and so that they can structure
their activities and interactions with each other and their
surroundings using spatial cognition.
One of the benefits of agent-based modeling is its almost limitless
extensibility, but this poses problems, particularly when agents are
modeled at high resolutions, because the models approximate the level
of detail that is present in the real-world analog of the thing you are
trying to simulate. There is a potentially insatiable level of
uniqueness that can be built into the agents and in this sense they are
the perfect tool for postmodern science. Unlike in other geographic
modeling fields, such as climate modeling, there has not really been a
concerted effort to build fundamental laws for agent-based models,
partly because for the systems that they are being built to explore -
human systems, for example - no universal laws exist; geography is
always a unique qualifier in these cases. So, models are often built
anew per-application, which slows the pace of development in this area
as an academic field. I have been working to circumnavigate these
issues, to some extent, by building standardized and reusable schemes
for agent-based modeling. Some years ago, when I was studying with
Michael Batty's group at the Centre for Advanced Spatial Analysis, I
partnered with Itzhak Benenson at Tel Aviv University to build what we
called a "Geographic Automata System" that would standardize the
automata components needed in urban simulation, and we actually
borrowed on GIS design principles to do that. Now, I am working on a
similar scheme for agent-based models that involve human movement
(path- and way-finding, locomotion, steering, collective motion and so
on).
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| This is a great interview and it is outstanding to see the integration of a vast number of technologies around GIS. A lot of what Professor Torrens is talking about is in play now in the Intelligence Community. He has done a good job of explaining how these technologies can be used elsewhere. |
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| Very interesting information in this article, makes you think about the possibilities. I was lucky enough to take a class taught by Prof Torrens and he has some great ideas for the future of GIS. Keep up the great work & go Devils! |
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| I would love to see the principles discussed by Torrens applied to morphogenesis. Imagine how cool an immersive model of embryogenesis would be!! |
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