SETOSim - an ABM of Evacuation on the coast of the Seto Inland Sea

A few colleagues and I had a paper published last year! I wanted to talk briefly about some work we did in creating the paper, which was entitled "Simulating emergencies with transport outcomes Sim (SETOSim): Application of an agent-based decision support tool to community evacuation planning". Rather than rehashing the paper, I'd like to talk a little about the context in which the work came about.

The research was originally conceived by Dr Hitomi Nakanishi of the University of Canberra, Australia. With the support and guidance of Professor Yoshihiro Suenaga of Kagawa University, Japan, we developed a simulation of evacuation behaviour situated within the city of Takamatsu, Japan.

Dr Hitomi Nakanishi and Professor Yoshihiro Suenaga on a research boat trip in 2018. Professor Suenaga was showing us how the coastline at different points in the Seto Inland Sea gave rise to weather hazards. He often conducts research scuba dives to gain a better understanding of the sea floor.

Takamatsu is Hitomi's home town; it is also the home of Kagawa University. It is, by Japanese standards, a "relatively small" town - meaning it is home to a mere half a million people and has professional sports teams and its own light rail system. It sits on the coast of the Seto Inland Sea, which exposes it to storm surges and various other alarming manifestations of weather.

The port of Takamatsu as seen from a nearby viewpoint We cleverly visited on a day when there were no typhoons, and were therefore not blown away.

In light of this, we built an agent-based model (ABM) of households making decisions about how, when, and where to evacuate during storm surge events. The ABM was able to make use of Professor Suenaga's storm surge models to explore evacuation events for notional future weather events - an exciting combination of different models!

An example of one of Professor Suenaga's tidal current simulations in the Takamatsu port area. See the paper linked at the beginning of the post for more information.

In 2018 Professor Suenaga came up with funding which allowed us to travel to Takamatsu and hold a symposium. We invited local people to come and hear about the evacuation model we had built. They were asked to fill out a survey about their own experiences evacuating during storm surge events. We later expanded  that survey to make it available to an even larger population, allowing us to gain a better understanding of local experiences and perceptions.

A local newspaper reporting on our symposium!

Based on these surveys, we expanded our work. Hitomi and I returned in the summer of 2019 to work further on the model and to share it with representatives of local government and community groups. Given that my Japanese is...shall we say, limited, my role in these meetings was to run situations and to help visualise different scenarios. I was also able to answer technical questions about how simulation could be used and what kinds of maps or metrics could be drawn from our work.

Professor Suenaga (back left) and me (back center) sharing the simulation results with the Ritsurin Town Community Group.

I found it hugely interesting to see how people responded to the simulation. Local government officials were keen to see the overall performance of the system, which is great and one of the ways that I truly think this kind of simulation can contribute to these kinds of planning efforts. Equally, local community members found it interesting to see their own communities contextualised in emergency events. This latter point was perhaps the most exciting thing for me. A lot of community members mentioned how difficult it was to decide to evacuate, and how abstract the whole thing felt; seeing a simulation of the journey to shelters, and the actions of their neighbours, made the situation more clear to them. They began to discuss reaching out to other neighbourhood groups to better coordinate evacuation efforts in the future, making sure that their plans synched.

This has been one of my favourite projects, and it's one that I think has the real potential to be of use. I hope to return to it - and to Takamatsu - in the not too distant future!

Hitomi leaving なタ書, a bookstore in Takamatsu.

Freight Traffic Control 2050

I wanted to write a bit about a project which has just wrapped up: Freight Traffic Control 2050, an EPSRC-funded grant and originally a collaboration among five universities.

The focus of the work was on trying to understand the last mile of freight delivery. Freight is something I've often excluded from my research into road usage and traffic because the data is usually proprietary. I primarily work with open data, so it has proved difficult to gain access to this kind of information for my own analysis. Unfortunately for me, while freight vehicles represent only a small percentage of vehicles on the road, their characteristic driving patterns mean that they have a disproportionate impact on congestion, emissions, and road safety.

Through the partnerships FTC 2050 had developed, I had access to a wealth of data which had been collected by team members and shared by our project partnerships. Being able to combine this kind of information with agent-based modelling techniques was a great opportunity, and I'm so grateful to the project team members and partners for making it possible!

The rest of the team did a bunch of great analysis of the data, while I developed a simulation framework to track the effectiveness of different delivery strategies together with Kostas Cheliotis. We were able to incorporate their findings into the behavioural framework of the drivers, which is something we'll hopefully be publishing soon.

In the interim, we've finally made our simulation framework public on GitHub. There's a short wiki, which we intend to develop further.

This work ended up garnering quite a bit of interest; we were interviewed for an article in the New Scientist (behind a paywall here) and later for BBC Radio 4 (both live on the radio program You & Yours and in a longer segment on the podcast Smart Consumer - please enjoy my now weirdly trans-Atlantic accent!).

The project was a great experience and I'm sad to see it come to an end. We've got some follow-on funding to bring the simulation to festivals throughout the UK, so if you pass by the Science Tent at a big festival, keep an eye out for FTC 2050 work!

In the meantime, my thanks to the project members: Tom Cherrett and Fraser McLeod of the University of Southampton; Tolga Bektas, now of the University of Liverpool; Maja Piecyk, Julian Allen, and Marzena Piotrowska of the University of Westminster; Adrian Friday, Oliver Bates, and Carolynne Lord of lovely Lancaster University; and of course Kostas Cheliotis, formerly of CASA but now happily repatriated!

Walk This Way - Pedestrian Modelling informed by Video Data

In exciting news, a project for which I developed the software has been published in the ISPRS International Journal of Geo-Information. Our paper is entitled "Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis", and explores how data can help inform pedestrian agent-based models. Pedestrian modelling traditionally been challenged by the need to collect data to calibrate and validate models of pedestrian movement. In our model, we use real video data to explore how different navigation metrics incorporate features of the built environment into movement patterns.

The following video shows an example of a model run, and the code to support the work is available here.

 

More information is available here!

Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015), Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis, ISPRS International Journal of Geo-Information, 4(3): 1627-1656.

Thanks to the great team I worked with on this!

AAG 2015

Hi friends! If anyone will be attending AAG 2015, please consider checking out Andrew Crooks and my work on crowdsourced data and agent-based modelling! Andrew will be talking as part of a panel called "Geosimulation and Big Data: A Marriage made in Heaven or Hell?" about some of the work we've done together on ABM and big data.

The talk will include videos highlighting my thesis work, which focused on how social media and mobile phones can influence the spread of crisis-related information through a community, prompting higher-level trends. Check out the video below to see a simulation of the Colorado Springs community reacting to the wildfire of 2012!


Have fun in Chicago!

International Map Year

According to both the International Cartographic Association (ICA) and the United Nations, it's apparently International Map Year! A very happy International Map Year to everyone.

In the past year, I've been enjoying attending mapping parties thrown by the Humanitarian OSM group and using their Tasking Manager to map areas where geographic data is useful but lacking. The team has done a really impressive job of developing both a friendly, useable tool and a community to use it.

In the coming year, I'm especially excited to see what comes of the Missing Maps Project, an effort to map places where humanitarians anticipate crises but lack the spatial data to respond to them. This is similar to the kind of work that amazing groups like MapAction and CrisisMappers end up doing in the heat of the moment. It'll be interesting to see how the availability of good data during the first few hours and days of a crisis impacts the situation.

Happy new year!

New Publication - Triangulating Social Multimedia Content!

In further social media news, my group at GMU has published a paper on how social media can be used to detect the location of events! Our paper, titled "Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter", has just been published in Transactions in GIS.

A three-dimensional perspective of an example of wildfire location assessment.

For more information, please check out my advisor Andrew Crooks' blog post about the paper here or the paper itself below! Thanks, everyone!

Panteras, G., Wise, S., Lu, X., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2014), Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter, Transactions in GIS. (pdf)

MSF Scientific Day: Understanding the Spread of Disease

I wrote earlier about having had the pleasure of working with Ivan Gayton of Medecins Sans Frontieres (MSF) on a project which was shown at MSF's Scientific Day. The goal was to explore how agent-based modelling can help researchers understand how disease spreads in space and time.

ABM is a powerful tool in this context because of its ability to fuse a number of important features which influence the spread of disease. In particular, it can bring together aspects of disease which elude other methodologies:

• ABM can incorporate spatiality in a way that, for example, SIR models can't, capturing the patterns that John Snow highlighted in his early work in GIS
• because ABM can simulate individuals with heterogeneous ages, healths, access to resources, and so forth, it allows for richer representation of disease transmission (i.e. the unusual tendency of Swine Flu to infect the young more than the elderly). System dynamics models of any kind have difficulty doing the same
• human behaviours such as seeking treatment or moving around the environment rather than staying home obviously influence the spread of disease; these dynamic processes can't be addressed by standard GIS techniques
• all of these apply not only to the susceptible individuals, but to the disease and its vectors as well: simulating how tsetse flies move relative to a river or how long a reservoir of bacteria will live on a water pump allows us to project the interactions among these things

Putting my money where my mouth is, Ivan and I have been developing a simulation - or rather, a library to support simulations - which can simply and quickly generate patterns of the spread of disease. The model Ivan presented at MSF Scientific Day highlighted how human-to-human infections such as influenza can spread through an environment, in contrast with water-borne or insect-borne diseases.

A sample visualisation of human-to-human infections is shown in the following video, which reflects a very simple model of individuals moving around the environment throughout the incubation period of the disease, only choosing to seek treatment or stay home during the second phase of the disease. Individuals have homes, and may choose to visit random "friends" based on how distant they are: they therefore frequently make short trips and occasionally travel much longer distances.



This is a very simple case, with very simple behaviours and transmission mechanisms; regardless, it tells the story of how individuals bring diseases home from the larger city to their small towns. It suggests how an epidemic might develop in space and time, and it represents a first step toward understanding how we can respond to and limit the outbreak of disease.

I'm very interested in this project, and while I don't have much time to devote to it, I hope to be writing about it here in the future. In the meantime, I have some (shamefully underdocumented) code available on GitHub here, so do watch both these spaces for updates.