Simulation as the Catalyst for Geographical Data Science and Urban Policy Making

Abstract

Modern cities in industrialised countries are often characterised by a 'smart' approach to policy. In the 'smart city', urban characteristics are increasingly being understood and planned as a result of the analysis of data that emerge from a multitude of sensors. A key characteristic of these data is that they are produced in real-time by citizens and organisations. The volume and variety of the data, as well problems with poorly understood basses and the inherent complexity of urban processes, make it difficult to derive useful insight for the present or to forecast future urban change.

In this paper we present agent-based modelling (ABM) as a lubricant for geographic data science and policy making in the context of the 'smart city'. We will show how techniques from well-established disciplines such as meteorology can be used to allow ABMs to combine diverse data sets and create holistic in silico representations of the city. This will allow a better understanding of the accuracy and applicability of underlying geographical theories. By utilising the streaming characteristics of many modern data sets to produce short-term, local analyses and forecasts, this new approach can foster a more agile and responsive form of policy-making.

Thanks

Alice Tapper, Jon Ward, Andy Evans

Ongoing work:

Ward, Jonathan A., Andrew J. Evans, and Nicolas S. Malleson (2016) Dynamic Calibration of Agent-Based Models Using Data Assimilation. Open Science 3 (4). doi:10.1098/rsos.150703.

Smart cities and the data deluge

Abundance of data about individuals and their environment

"Big data revolution" (Mayer-Schonberger and Cukier, 2013)

"Data deluge" (Kitchin, 2013a)

Smart cities

cities that "are increasingly composed of and monitored by pervasive and ubiquitous computing" (Kitchin, 2013a)

Large and growing literature

What about forecasting?

Abundance of real-time analysis, but limited forecasting. E.g.:

MassDOT Real Time Traffic Management system (Bond and Kanaan, 2015)

Detect vehicles with Bluetooth to analyse current traffic flows

Centro De Operacoes Prefeitura Do Rio (in Rio de Janeiro)

Advertise some predictive ability, but sparse detail

City dashboards

Forecasting ability is surprisingly absent in the literature

(correct me if I'm wrong!)

Why so little evidence of forecasting?

Proprietary systems? Company secrets?

A methodological gap?

Machine learning will probably help, but black box is a drawback

Maybe agent-based modelling could have the answer

Agent-Based Modelling (ABM)

Autonomous, interacting 'agents'

Model phenomena from the 'bottom-up'

Advantages:

Modelling complexity, non-linearity, emergence

Natural description of a system

Bridge between verbal theories and mathematical models

History of the evolution of the system

ABM Example - Burglary

ABM Problems

1. Computationally Expensive

Not amenable to machine-led calibration

2. Data hungry

Need fine-grained information about individual actions and behaviours

3. Divergent

Usually models represent complex systems

Projections / forecasts quickly diverge from reality

3. Divergence

Drawback with the 'typical' model development process

Waterfall-style approach is common

Calibrate until fitness is reasonable, then make predictions

But we can do better:

Better computers

More (streaming) data

Methodological gap

Dynamic Data Assimilation

Used in meteorology and hydrology to constrain models closer to reality.

Try to improve estimates of the true system state by combining:

Noisy, real-world observations

Model estimates of the system state

Should be more accurate than data / observations in isolation.

Dynamic Data Assimilation

How?

Particle filters

Indoor footfall (Rai and Hu, 2013.; Wang and Hu, 2015)

Kalman Filter

Air traffic (Chen et al., 2012)

Ensemble Kalman Filter

Pedestrian footfall (Ward et al., 2016)

Sequential Monte Carlo (SMC)

Wildfire (Hu, 2011; Mandel et al., 2012)

Ensemble Kalman Filter - Basic Process

1. Forecast.

Run an ensemble of models (ABMs) forward in time.

Calculate ensemble mean and variance

2. Analysis.

New 'real' data are available

Integrate these data with the model forecasts to create estimate of model parameter(s)

Impact of new observations depends on their accuracy

3. Repeat

Ensemble Kalman Filter - Basic Process

Experiment with an EnFK

Very simple ABM

People walking along a street

Every hour, x people begin at point A

CCTV Cameras at either end count footfall

Some people can leave before they reach the end (bleedout rate)

Aim: Estimate the number of people who will pass camera B

Hypothetical 'Truth' Data

Use the model to first generate a hypothetical reality

(Preliminary) Experimental Results

(Preliminary) Experimental Results

Forecast and analysis are barely distinguishable

Virtual observations are closer to 'truth' than the analysis :-(

This is probably due to the degree of randomness in the model

EnKF estimates the model parameter (bleedout rate) accurately :-)

References

Bond, R., and Kanaan, A. (2015) MassDOT Real Time Traffic Management System. In Planning Support Systems and Smart Cities, S. Geertman, J. Ferreira, R. Goodspeed, and J. Stillwell, Eds. Springer International Publishing, pp. 471–488.

Kitchin, R. (2013a). Big data and human geography Opportunities, challenges and risks. Dialogues in Human Geography, 3(3):262–267.

Kitchin, R. (2013b). The Real-Time City? Big Data and Smart Urbanism. SSRN Electronic Journal.

Mayer-Schonberger, V. and Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work and Think. John Murray, London, UK

Ward, Jonathan A., Andrew J. Evans, and Nicolas S. Malleson. 2016. Dynamic Calibration of Agent-Based Models Using Data Assimilation. Open Science 3 (4). doi:10.1098/rsos.150703.