Social Dynamics in Urban Context (SoDUCo)
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| License | CC Attribution 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
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Transcript: English(auto-generated)
00:07
Hi everyone, and thanks for coming. Sorry for that, it's not our fault, it's right. Okay, my name is Pierre André Lény, and I'm a French GIS consultant, and Bertrand Dumeneu is a researcher.
00:28
We are going to present you a beginning project called Social Dynamics in Urban Context. The goal is to build some open tools and data sets to explore the spatial and social history of Paris,
00:47
in a period from the revolution to the middle of the 18th century. It is a project funded by the French national agency called INR. And I will let Bertrand for the first part.
01:03
Yeah, okay, thanks. So, hi. This talk is about studying social facts in history that are located in space. And the goal is that the idea is as stressed out by Andrew Abbot, a sociologist of the Chicago School.
01:23
If you study the spatial properties of social facts, this is the only way you can understand it fully and very deeply. And in fact, social facts, they are not only located, they are in a kind of... There is a kind of spatial complex relationships between social facts and social phenomena and the geographical space.
01:47
First of all, social facts, they shape geographical space. Like, you can imagine in a city, if you create a new street, this is resulting from political and economical processes.
02:04
But also, the geographic space has an influence on society. And I think maybe the simple example of that is gentrification, where changes in the shape of a neighborhood will have some effect on who will live there.
02:22
And those two relationships, they are in fact interlocked and they form a kind of loop which is evolving through time. And when historians are working on social phenomena, they are studying this evolving loop in time using both the space, geographical space, and social data.
02:42
And nowadays, we can use GIS, of course, to study this kind of phenomena. And this is a very simple example that we are doing right now at my lab about the re-gentrification of Paris during the 19th century, where the idea is to study the evolution of wealth inequalities in the city over one century
03:04
with an eye on the morphological changes in the geographical structure of the city. And we are using a very common GIS stack, mainly QGIS and PostGIS, and extracting data from multiple data sources covering the whole period.
03:24
Of course, spatial sources like maps, clusters, and atlases at different times in the 19th century, and social sources, mainly census and inheritance registers, which provide us with people's names, with their addresses,
03:41
and some information about their income, how much tax they pay, or the value of their possessions. And of course, we need to extract geographic vector data from that to be able to contactively study and analyze these phenomena.
04:00
So we extracted the different road networks to study the structure of the city, and also the addresses contained in the maps in order to build a gadgetry of historical addresses. And we did the same extracting data from the census registers and inheritance registers,
04:21
and we geocoded this data using the historical gadgetry we built before. And this allows us to study the structural change in the city using the different road networks, dating from the end of the 18th century to the end of the 19th century,
04:43
and study how local changes in the shape of the network has global effects on its structure. I know it's heat maps. We also have ways to... It allows us to map what's happening.
05:04
It's not in the slide. It's not an animation. I don't know. It does nothing.
05:58
Click on the PDF and control.
06:02
It should be okay. Yeah, great. Thanks. Okay, so this is regentrification of Paris, where the richest inhabitants were living in the very center of the city in the beginning of the 19th century, then moved slowly to the west during the century, and finally came back.
06:22
There were new rich people in the city at the end of the century due to the creation of new boulevards and new city squares in the center of Paris. In 2019, this kind of work is pretty common in historical sciences because GIS in historical sciences is finally a thing.
06:41
This is mainly due to the use and the extensive use of QGIS in academics. We have really to thank the contributors in the community of QGIS. We can't do anything without QGIS.
07:00
Also, we rely on massive amounts of open archival documents, such as maps, for example, which are published openly by digital libraries on the web. There is a global movement in the academy to publish and to open the research data, which also helps by public policies, at least in Europe, that encourages
07:24
and even sometimes force, in France, to open research data. But there are still some problems with some things to do. First, the universe of geographic historical data is made of small data sets
07:41
that have a low level of redundancy and a low overlaying because historians are working most of the time on very specific parts of space and a very specific period of time. They rely on a few... I mean, historical sources are scarce. There are not a lot of historical sources in one place.
08:06
Also, building geohistorical data and extracting geohistorical data is a difficult task because you often have to do it partially, manually. It's a matter of building geospatial temporal data with an aggregative process
08:24
where you are going to take some pieces of information and bring them together from historical sources. Most of the time, the data sets come with very few documentation, so there is a lack of lineage metadata. It makes the data hard to reuse and sometimes hard to understand,
08:43
and it's pretty complicated to know if there is bias in the data, which is a problem when you try to model and study historical facts. So the idea of the project is that we could improve those points
09:03
by being specific tools to help historians collaborate and build in an iterative and aggregative way to build and share geohistorical data to analyze all states of the geographical space and locate social facts.
09:23
So that's why we are starting the SODUCO project. As Pierre said, it's funded by the French National Research Agency. It started just a few months ago. It's a four-year project, and we have almost half a million euros to hire mostly postdocs, engineers, and PhDs to hire PhD students.
09:47
It brings together people from different horizons, like people from GI science with the IGN, historians from the School of Higher Studies in Social Sciences,
10:00
computer sciences, people from the National Archives of France, and people like Pierre who have a foot in the industry and the world of phosphorgy. It relies on different previous projects, which involved most of the team, and just one example of this is a first collaborative experiment.
10:21
The goal was to digitize the Cassini map of France, which is a single snapshot covering its historical map that covers the entire country in the late 18th century. It's very unique because it's global, it's very detailed, and it's pretty homogeneous for historical sources.
10:41
So the idea was to extract and digitize manually the road network to perform analysis comparing the network from the 18th century with nowadays. We started with almost 50 people, 50 researchers and historians, and in less than a year we were able to extract the full network
11:05
from the historical georeferenced map of Cassini. We finally digitized it in almost a year. The full dataset is open and published on the Harvard Dataverse,
11:20
and it comes with a data paper to provide documentation, a full documentation about how we created those data. This is just a slide about the top 27 main contributors. Each color is one contributor of this experiment. Thank you, Bertrand.
11:42
So digitizing is very easy, it's a simple process, but what we want to do finally now is to have a reproducible approach to study the evolution of Paris over 150 years.
12:00
We want to analyze social and spatial co-evolutions, producing a critical analysis of primary sources, it's really important, building geographical data in a semi-automatic collaborative way. That's one of the goals. We want to implement and make the approach available to others
12:21
by developing reusable, free and open source tools tailored for geosteutical data. And in fact we want to finally provide a free and open web platform to construct and share data. We have to be punchy because I think we haven't got a lot of time,
12:42
it's not really a success, but sources, like Bertrand says, we have many, many old sources, some Paris trade directories, Paris city maps and atlases, and some of the cadastres. So the goal we set is to build a platform. The main goal is to develop a platform composed by several independent components.
13:06
We are going to build this platform for free and open source tools, not only geospatial but always force, we don't want to reinvent the wheel. We have so many things to do, so even if we have a half a million,
13:21
we have a lot of work, and we want to develop generic tools to permit reuse. So we plan to develop six main components, called georefensor, social data extractor, semi-automatic collaborative digitizer, and collaborative social data editor, a geostorical matcher, and a geocoder.
13:46
So as you can see, we have four years of work. In terms of technical aspects, we plan to have a very central geostorical database using GIS, and we are using first 4G and OHGO stack to develop these tools.
14:10
The platform will be OGC compliant with a lot of WMS, WFS, and probably from WPS. As we said, the project begins, so all things are not really yet ready.
14:27
We are using Docker orchestration for that, and one important thing is that all the projects hosted by a project called Humanoom is a very large research public structure for digital humanities.
14:43
We need this platform and this help to technical infrastructure, servers, and many other things that are really important. I'm sorry, I can't really watch you because I have to watch the screen. First, we have already developed a proof of concept of an historical geocoder.
15:04
I think that we know what it is, geocoding, but we are going to add the date dimension, the temporal dimension. So we have got an in-base geocoder based on the PostgreSQL, on a PLPG SQL, so store procedures, and with an API.
15:23
So it's a proof of concept now of an historical geocoder. It works, you can follow the link if you want. Classic geocoders, you take an address and we will have an answer with our coordinates, and in our case we want to have an address plus a date,
15:41
and sometimes it is a fuzzy date. It's not really absolute date. So like you say, I'm going to use the laser. You have some fuzzy dates, it's more a period than a date, which we are easy to use. So historical geocoder will be address plus date, and the response with big coordinates, and the data source.
16:03
It's really important for us. Concerning the open source model and licenses, it's important. So our goal is really to increase open science, okay, in historical science using 300-person software, and reproducible science need force today.
16:24
The lessons will not be unique. It will be a platform composed by many other and several components, like I said. There are so many, it's a big mess, you know, like open source license with contamination or other things. So we want to always, we are going to always aim for maximum openness.
16:44
Today's licenses used is AfroGPL. It's not the most openness, but it's well adapted to API context, and the component of, I think, the geocoder or the other parts on the MIT license.
17:01
The benefits for Force4j community, it's all the development will be totally open source. We are going to develop open tools. We are going to share our data sets in a collaborative approach. Like the first project, we are very on the crowdsourcing approach,
17:22
and we are open to contribution. That's one of the reasons we are here today, is to continue to have some informal collaboration, or formal. We have some with New York Public Library, and from a genealogist in France, or the world historical gazette.
17:43
It's a goal for contribution today. We have many ideas, but we are finally a small team. The project is a totally free and open source code, like we said. So we are open for contribution, mainly for development at this time,
18:00
at this time of the project. You can have a look at the repository and our website, okay. And we hope that in the future, probably last year or other years later, a possible application for cities with similar sources, maybe like Bucharest or Vienna. And I think it's up now, but we have got normally a conclusion.
18:24
So like we said, it is a French national agency research funded project, in a multidisciplinary way. So we are working with historians, geographers, computer science people.
18:40
You know it's sometimes difficult to be able to work together in different disciplinary. And we have to fight and to success with some challenges finally, like scientific and technical challenges. I think it's one another bullet point, but I forget it.
19:00
So thank you, and it's time for questions. I don't know really if we have. Thank you. For this sort of technical issue, let's hope they will solve it. So one meaningful question. Who has it? Yes.
19:26
Well I'm not sure about meaningful, but just out of curiosity, when you're talking about the geocoder, the historical geocoder, do you also plan a reverse one? Yeah, I will probably let Bertrand respond to that.
19:41
Yeah, so the idea that for now we have the proof of concept made of direct geocoder, but we plan to rely on existing geocoder like Pelias, for example. So there is reverse geocoding in that case. But just for our use case, we're mostly interested in direct geocoding
20:02
because the main use case for historians is to take social data with addresses and place them in the geographical space. So we won't do it ourselves, but if we collaborate and contribute to Pelias to add, for example, the historical side of it
20:23
and integrate the temporal dimension in this case, sure, we will have the reverse geocoding. Yeah, I'm just thinking it would be fun. Okay, so thank you, and sorry for the condition. I think the laptop is done. Thank you very much. We will try to solve it.