A presentation of the ARCHIVES Project to the ISCRAM-MED Conference

ArchivesGeo-

historical

modeling of 1

Alexis Drogoul UMI 209 UMMISCO, IRD/UPMC [email protected]

Simulating the past to better manage the present: geo-‐historical modeling of past catastrophes

ISCRAM med 2014 invited talk

mailto:[email protected]

Vietnam is a country structured by water: the Red River delta in the North and the Mekong River delta in the South

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Hanoi is a city literally built on water

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Flooding in Hanoi is menPoned since 1000 years (in the imperial chronicles), then in the French colonial archives from 1890 to 1954, and since then in official reports. On average, 1 major flood every 3 years.

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2008 2014

2013

Recent ones are mainly caused by heavy rain episodes.

The policy against flooding has been constant over Pme: building dykes systems (~4000 km)

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1927

2013

1905

« Hanoi ciPzen and city planners regularly forget they live near a river… »

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Flood zone RiverDyke

West Hanoi

The analysis and transmission of past disasters is an integral part of disaster management

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Prevention!• Land use planning !• Learning from events!• Technical measures

The experience of past disasters allows local knowledge to be used to develop community responses that both help to raise awareness of risks and also help prepare for improved future disaster response and reconstruc<on

Inspired by Integral Risk Management Cycle, FOCP 2012

Issue 1: The availability and accessibility of the data concerning this event Issue 2: The construc<on of relevant informa<on from these data Issue 3: The reconstruc<on of a coherent «story» from these informa<on !This is what historians do, but it would be helpful to be able to do it in a more systema8c way as this concerns hundreds of thousands of events.

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However, being able to learn from a past event requires addressing some issues

In the last 10 years, informa8on technology has become ubiquitous in disaster risk management and there are hundreds of solu8ons developed

!!!!!!!!!!!!But they require the availability of large datasets of digital informa8on about each event

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For predic<ng risks For assessing risks For mi<ga<ng risks For launching alerts For educa<ng people For organizing rescue .... !

Digital informa8on is the data stored in computers, which can be automa8cally harvested and analyzed to produce useful knowledge about a disaster

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From real-‐<me monitoring... ... to post-‐assessment

In the last 10 years, as soon as a disaster occurs, rich digital informa8on is produced, disseminated, and immediately analyzed

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Immediately aGer Fukushima, 572.000 new TwiHer accounts have been created in Japan

today1900 20001800170016001000500 1500

However, the quan8ty of digital informa8on about past risk events is strongly dependent on when in history they have happened

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Past Future

Digi8za8on of physical documents

Produc8on of digital documents

cf. F. Kaplan, 2013, hIp://Laplan.wordpress.com/2013/03/14/lancement-‐de-‐la-‐venice-‐8me-‐machine/

http://fkaplan.wordpress.com/2013/03/14/lancement-de-la-venice-time-machine/

today1900 20001800170016001000500 1500

A first step can be to make more informa8on available through the exploita8on and automated analysis of available digi8zed contents

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Past Future

Ins8tu8onal analysis

(Web)mapping

Social network analysis Social network analysis



Analysis of digital informa8on

But how to benefit, for past events, from the abundance of the informa8on on contemporary catastrophic events ?

How can we reproduce the dynamics of the event itself so as to beHer understand its impacts ? !How can we have a closer look at the social dynamics of the management of the event ? !How can we follow the behaviors of the mul<ple actors of an event in order to understand their rela<onships ? !How can we recreate the equivalent of Facebook, Google Maps, YouTube, TwiIer for past events ? (F. Kaplan, 2013) !!

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Geo-‐historical modeling is one way to extrapolate the informa8on available in order to « tell stories » and produce new digital informa8on through simula8ons

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Past Future



Analysis of digital informa8on

Simula8on of digital models

3D reconstruction of Rialto neighborhood

in 1500 ab. based on the documents

of Venetian archives

The diversity, amount and accuracy of the Venetian administrative documents are unique in Western history. By com-

bining this mass of information, it is possible to reconstruct large segments of the city’s past : complete biographies,

political dynamics, or even the appearance of buildings and entire neighborhoods. The documents are intricately

interweaved, telling a much richer story when they are cross-referenced.

Text recognition in ancient hand-written

documents

Venice Time Machine

Geo-‐historical models are not supposed to be faithful reproduc8ons (i.e. « movies »).

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Rather, they propose to reconstruct fic8onal reali8es, suppor<ng the explora<on of what-‐if scenarios (e.g., « what if such interven0on op0on had been chosen… ? », «what effect this decision could have had on … ?») and a quasi-‐experimental approach to « historical truth »

Research works on geo-‐historical models belong to rather recent trends in digital humani8es

Geo-‐historical methodologies Flooding risks in Lyon city, C. Combe, J-‐P. Bravard (Univ. Lyon 2)

Simulation of Historical Tsunamis (Japan, Taiwan, US)

Virtual archaeology (Univ. Of Sussex), « Anasazi Culture » (SFI), etc.

!Digital History «Venice Time Machine» (EPFL)

!!Very few references, however, to the modeling of past catastrophes in their social/management dimensions.

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Fig. 27. L’inondation du Rhône en 1840.

The ARCHIVES project, a mul8disciplinary approach to the construc8on of geo-‐historical models of catastrophic events from archived data

International Center for Advanced Research on Global Change, VNU (Geomorphology, Hydrology) IDEES, Univ. Rouen (GIS, hydrological model, Patrick Taillandier) Vietnam National Satellite Center (Red River basin, Nguyen Thi Hoang Anh) !National Archives Center n°1 (Documents and data) Ecole Française d’Extrême-‐Orient (History, Olivier Tessier) IOIT, VAST (Digitizing, Luong Chi Mai) L3I, Univ. la Rochelle (Document recognition, Muriel Visani) !IRIT, Université de Toulouse (Social model, Benoît Gaudou) IT Dept, University of Science and Technology of Hanoi (GIS building, Nasser Gasmi) UMMISCO, IRD (Models coupling, Alexis Drogoul)

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ARCHIVES is organized in three main ac8vi8es, with two outcomes iden8fied

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Chronology and scenarios

Stakeholders

GIS, « physical » models

Digitizing & analysis of documents

Reconstruction of geographical/geophysical/hydrological information

Geo-‐referenced index

Geo-‐historical simulations

Geo-‐historical model

The first proof of concept focused on the floods of July 1926 in Hanoi and its management by French and Vietnamese authori8es

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Delimita8on of the case study: from the 25th to the 31st of July, 1926, in Gia Lâm

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Breach at Gia Quất 28th, evening (old dyke) 29th, at 9 AM (new dyke)

Dykes Breaches

Breach at Ái Mộ 29th, at 4 PM

Hà Nội - downtown

Breach at Lâm Du 29th, between 4 PM and 5PM

Study area: !Gia Lâm (eastern district of Hanoi). !Chronology: !- 25th to 30th of July: increase of water height (~12m) and main dyke breaches !- 31st of July to November: plugging of dykes

First task was to gather, digi8ze, analyze (and some8mes complement) the data available

French colonial civil archives (NAC1 & EFEO, Hanoi) French military archives (Aix-‐en-‐Provence) Vietnamese newspapers (NAC1, Hanoi) Archives of technical services (water management, agriculture, …) (NAC1, Hanoi) City Maps (IGN, France & NAC1, Hanoi) Vietnamese imperial archives (NAC1 & EFEO, Hanoi) Morphology of the Red River bed (VNSC, Hanoi) !!!

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6"

Contour lines (brown)!

Buildings(red)! Red River (blue)!

Lakes (blue)!

The second task consisted in linking these heterogenous data pieces in a geo-‐referenced, 8me-‐indexed database

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This allowed to produce a reasonably realis8c GIS of the hydrographic/urban/geomorphologic condi8ons in which the flooding event took place

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6"

Contour lines (brown)!

Buildings(red)! Red River (blue)!

Lakes (blue)!

The addi<on of temporal informa<on allowed to query and navigate the database and get an idea, locally, about the « <meline » of the event.

The third task was to build a hydrological model, able to replicate the dynamics of the Red River during this period

! GIS Data available

" Digital Eleva<on Model (DEM)

" Shapefile of the dykes

" Shapefile of the buildings

" Shapefile of the Red river

" Shapefile of the lakes

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The GAMA plaiorm was used to implement the models because of its facili8es for handling spa8al data, coupling heterogeneous models and ease of use for non-‐computer scien8sts

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http://gama-platform.org

draw shape color: color depth:depth; } } species red_river{ rgb color; aspect geometry{ draw shape color:color; } } species lakes { rgb color; int depth; aspect geometry { draw shape color: color; } } species dyke parent: obstacle{ bool was_broken; string break_date_str; int month_break; int day_break; bool has_to_die; bool is_flooded -> {cells_concerned first_with(each.water_height > 0) != nil}; bool is_about_to_be_flooded -> {water_pressure > threshold_to_be_flooded}; string commune_name; float small_dyke_height <- 0.0; int nb_step_flooded <- 0; reflex breaking when: destruction_of_dykes and day = day_break and month = month_break { do break; } action break{ ask cells_concerned { do update_after_destruction(myself); } ask(commune where (each.name = commune_name)){ remove myself from: self.commune_dykes; } do die; } action compute_height { height <- dyke_height - min(cells_concerned collect (each.altitude)); } user_command "Destroy dyke" action: break; action split_dykes (float threshold) { list<geometry> lines1 <- shape.geometries; if (length(lines1) > 1) { loop i from: 0 to: (length(lines1) - 2) { geometry li <- lines1[i]; create dyke { shape <- li ; commune_name <- myself.commune_name; do split_dykes(threshold); } } shape <- last(lines1) ; do split_dykes(threshold); } else { if (shape.perimeter < (threshold * 2) ) { shape <- shape + 10.0; do update_cells; } else { list<point> points <- list(shape points_on threshold); list<geometry> lines <- []; remove last(points) from: points; geometry geom <- copy(shape); loop pt over: points { list<geometry> gs <- list(geom split_at pt); add gs[0] to:lines; geom <- gs[1]; }

http://gama-platform.org

The model designed is a simple diffusion model on a regular grid, which could be easily calibrated using historical data, and could easily adapt to changes in its « environment »

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altitude

water height

heightheight of the highest dykes/

buildings located on the cell

This model proved, once correctly calibrated, to be quite accurate (with respect to the occurrence of some events, like the breaking of dykes)

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The fourth task in ARCHIVES consisted in building a model of the « management » and social response to the event

The data available consisted in: !-‐ the descrip<on of the official administra<ve and military hierarchies (Vietnamese and French ones) -‐ the iden<fica<on of the key actors and their role in the event (through reports and inves<ga<ons led aGer the event), -‐ the flow of their communica<ons (leHers, telegrams) -‐ and various other pieces of informa<on from newspapers, tes<monies and memories.

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The analysis and linking of the documents allowed to reconstruct the structure of the command chain and communica8on flows

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From this descrip8on, a « social model » of the actors was built, focusing on understanding how the flows of orders/informa8on resulted in concrete ac8ons (building of small, temporary dykes)

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A number of simplifica8ons were necessary, so that the model could be calibrated and easily coupled with the hydrological model (through the « dyke » agents)

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!We considered for instance only a top-‐down order and a bottom-‐up information/request chain, using FIPA-‐ACL to manage the communication protocols between agents

ARCHIVES was then tested during a 7-‐days workshop held in Da Lat (Vietnam) in July 2013 with geographers and social scien8sts

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Par<cipants, once trained on the basic model, were encouraged to adopt an approach based on hypothe<cal reasonings, which resulted in a number of addi<ons to the basic model and experiments.

A number of « historical experiments » were conducted by the par8cipants, among them:

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-‐ understanding and modeling the dynamics of the refugees and tes8ng evacua<on policies

-‐ understanding the dynamics of the resources (material ones, like bamboo s<cks, or human ones, like coolies)

-‐ understanding the difference between the official descrip<on of the command chain and the actual communica<on flows observed

-‐ …

ARCHIVES, despite it being quite complete now, is s8ll a preliminary proof of concept.

• The whole project has proved invaluable in

• building a huge dataset (maps, reports, ... ) about this par<cular event in a comprehensive and focused way

• providing archivists and historians with new ways of « represen<ng » and « using » their documents and knowledge

• providing a support for understanding the role of simula<ons in historical research (esp. regarding the differences between theore<cal and actual organiza<ons)

• However, the main challenge for generalizing this approach remains the transforma<on of raw informa<on into digital informa<on

• the automatic generation of actors and their behavior from textual documents (e.g. using process-‐mining tools, SNA…) is a necessary condition to address different events

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The general perspec8ve of such geo-‐historical models is to provide stakeholders with a live historical fic8on, which can be used as an experimental framework

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• For tes8ng prepara<on or management op<ons (including « modern » ones)

• For comparing these op<ons in terms of consequences on society

• For suppor8ng the work of historians in transmibng the memory of events

• For building interac<ve and easily accessible living memories of these events All of this adding to the « digital

informa8on » available with the goal of enhancing the

awareness and prepara8on of contemporary stakeholders

regarding similar risks

A presentation of the ARCHIVES Project to the ISCRAM-MED Conference

Science

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