Slides cg geneanet_geomed(1)

Spatial Aspects of Collaborative
Demography via Genealogical Data
Arthur Charpentier
(CREM UMR CNRS 6211, Université de Rennes 1 & Actinfo Chair)
Ewen Gallic
(CREM UMR CNRS 6211, Université de Rennes 1 & Actinfo Chair)
GEOMED 2017
i3S, Porto, 7–9 September 2017

Introduction Data preparation Preliminary results Conclusion
Historical demography
What is historical demography?
the description, analysis and understanding of population, in
the past
using quantitative methods
Which use for historical demography? (Dupâquier, 1981)
Biology: understanding the structures of families and house-
holds
sociology: studying fertility, birth and death rates; disentan-
gling between hereditary and environmental characteristics
Demography: rebuilding the population,
Economics: studying population migration
A. Charpentier & E. Gallic Données généalogiques - 2/22

Historical demography: a vast literature
A pioneer analysis of historical demography: Henry (1956)
Followed by a lot of articles exploiting longitudinal data, e.g.,:
Matthijs and Moreels (2010) (COR∗ database)
Antwerp, Belgium, 1846–1920, ≈ 57k obs.
Mandemakers (2000)
The Netherlands, 1812–1922, ≈ 77k obs.
Bouchard et al. (1989) (BALSAC)
Québec, Canada, since 17th
century, ≈ 2M events, ≈ 575k
individuals
Bean et al. (1978)
mainly Utah, USA, since 18th
century, ≈ 1.2M individuals

Historical demography: limits
These studies face some issues:
often limited to a small sample of individuals, in particular
geographic areas (possible bias)
gathering information from available sources is expensive and
time consuming

Historical demography and Big Data:
collaborative data
New perspective are emerging with the big data era
Collaborative genealogy data might overcome some issues:
less costly for researchers
may cover wider geographical areas
no need for sampling
Question: Can we use these data to study population?

Big Data and Genealogy: (short) literature
A promising new strand in the literature uses big data to study:
lifespan: Fire and Elovici (2015) with WikiTree.com online
data (+1M rows)
exceptional longevity: Gavrilova and Gavrilov (2007) with
online genealogy data (+75M deceased individuals)
Cummins (2017) with family trees from FamilySearch.org
(402, 204 rows)
Kaplanis et al. (2017) with family trees from Geni.com (86M
proﬁles)

Big Data and Genealogy: Issues
Some issues remain:
possible representativeness bias
unknown quality of records
heterogeneity in information collected

Outline
1 Introduction
2 Data preparation
3 Preliminary results
4 Concluding remarks

Geneanet
http://www.geneanet.org/
Since 1996
First European genealogy website:
+2 million members
+4 billion individuals
Geographic distribution:
40% in France
30% in the rest of Europe
25% in the US
5% in the rest of the world

Raw data
Here, we focus on:
people born between 1800 and 1804, in "Maine-et-Loire"
(France)
and their oﬀspring
Number of rows: 100, 081

Raw data
ID_user ID_ns ID_num Name Surname Sex date_b date_m
1 daage besnard|jean|1 575 BESNARD Jean 1 18000227 18280121
2 denisgallienne besnard|louis|1 22771 BESNARD Louis 1 18040603 18251110
3 domiassi besnard|jean| 1748 BESNARD Jean 1 18000227 18280121
4 dutheilfr besnard|pierre| 729 BESNARD Pierre 1 18001221 18270703
5 dvivier1 besnard|louis|1 65196 BESNARD Louis 1 18001215 18291027
date_d type place Lat Long ID_num_m ID_num_f
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
4 N Gennes, 49350 47.34083 -0.23278 99 59
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Raw data
Row: event(s) for an individual (Birth, Marriage, Death)
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
4 N Gennes, 49350 47.34083 -0.23278 99 59
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Raw data
date of event
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
4 N Gennes, 49350 47.34083 -0.23278 99 59
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Raw data
date of event
place of event (name, latitude, longitude)
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
4 N Gennes, 49350 47.34083 -0.23278 99 59
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Raw data
Individuals identiﬁed by (ID_user, ID_ns)
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
4 N Gennes, 49350 47.34083 -0.23278 99 59
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Raw data
Possible to link parents
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
4 N Gennes, 49350 47.34083 -0.23278 99 59
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Individuals
Individuals might appear multiple times in the raw data

Individuals
merged in successive steps
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Individuals
close names (e.g., Jean or Jehan) accounted for using a string
distance metric
1 16810000 NM Longué, 0180 47.37806 -0.10806 4457 574
2 18831027 ND Cunault, 49350 47.30833 -0.15389 994 1620
3 18560000 NM Longué, 49180 47.37806 -0.10806
5 18490717 N Pommeraye, 49244 47.35528 -0.86028 43116 4063

Individuals
close names (e.g., Jean or Jehan) accounted for using a string
distance metric
We started with 100, 081 rows and ﬁnally obtained 58, 541
individuals in Maine-et-Loire
ID ID_m ID_f Name Surname Sex Date_b Long_b Lat_b
16194 NA NA besnard jean 1 18000226 -0.11 47.38
73508 NA NA besnard jean 1 18000228 -0.23 47.34
70834 NA NA besnard jean 1 18000307 0.02 47.47
13915 NA NA besnard pierre 1 18000418 -0.15 47.31
2128 NA NA besnard jeanne 2 18000530 -0.83 47.27
ID Date_m Long_m Lat_m Date_d Long_d Lat_d
16194 18280121 -0.10806 47.37806 18711003 -0.11 47.38
73508 NA NA NA 18130131 -0.23 47.34
70834 18330206 0.01694 47.46667 18560107 0.02 47.47
13915 18270703 -0.15389 47.30833 NA NA NA
2128 18280708 -0.82583 47.26694 18850820 -0.83 47.27

Genealogy
Using the parents’ IDs, it is possible to construct family trees

Family tree
We start with an individual: François Menard, born in 1801-
01-24 in Maine-et-Loire
François
Menard
1801-01-24
(Feneu,
49460)

Family tree
Then we look up for individuals with François Menard born
in 1801-01-24 as "father"
François
Menard
1801-01-24
(Feneu,
49460)
François
Menard1
1843-12-12
(Feneu,
49460)
Renée
Menard
1837-09-29
(Feneu,
49460)
Julie Menard
1835-11-04
(Feneu,
49460)

Family tree
And then retrieve these individuals’ mother(s)
François
Menard
1801-01-24
(Feneu,
49460)
Julie Poulard
1802-05-31
(Feneu,
49460)
François
Menard1
1843-12-12
(Feneu,
49460)
Renée
Menard
1837-09-29
(Feneu,
49460)
Julie Menard
1835-11-04
(Feneu,
49460)

Family tree
We then look up for the grandchildren
François
Menard
1801-01-24
(Feneu,
49460)
Julie Poulard
1802-05-31
(Feneu,
49460)
François
Menard1
1843-12-12
(Feneu,
49460)
Renée
Menard
1837-09-29
(Feneu,
49460)
Marie Hery
1874-07-27
(Feneu,
49460)
Louise Hery
1873-04-28
(Feneu,
49460)
Julie Menard
1835-11-04
(Feneu,
49460)
François Jolie
1874-06-19
(Feneu,
49460)
Jean Jolie
18700915
(Feneu,
49460)
Joseph Jolie
1868-09-18
(Feneu,
49460)
Pierre Jolie
1866-04-07
(Feneu,
49460)
Julie Jolie
1864-07-05
(Feneu,
49460)

Family tree
And complete with the second parent
François
Menard
1801-01-24
(Feneu,
49460)
Julie Poulard
1802-05-31
(Feneu,
49460)
François
Menard1
1843-12-12
(Feneu,
49460)
?
?
Renée
Menard
1837-09-29
(Feneu,
49460)
Marie Hery
1874-07-27
(Feneu,
49460)
Louise Hery
1873-04-28
(Feneu,
49460)
Pierre Jolie
1837-06-19
(Soulaire-
et-Bourg,
49460)
Julie Menard
1835-11-04
(Feneu,
49460)
François Jolie
1874-06-19
(Feneu,
49460)
Jean Jolie
18700915
(Feneu,
49460)
Joseph Jolie
1868-09-18
(Feneu,
49460)
Pierre Jolie
1866-04-07
(Feneu,
49460)
Julie Jolie
1864-07-05
(Feneu,
49460)

Migration
With these family trees, we are able to study migration of
people from generation to generation
Using the information given on the place of the events of their
life

Individuals from Maine-et-Loire
Table 1: Number of observations in each generation
Generation
No
Obs.
Available
Coord. Birth
(%)
Available
Coord.
Marriage (%)
Available
Coord. Death
(%)
0 25,421 99.74 27.78 29.17
1 17,237 97.40 48.20 39.06
2 15,071 98.08 49.19 37.11

Migration
Are children born next to the birthplace of their parents?
We compute the distance between the places of birth of a
child and his or her parents, and keep the closest (if available)
Table 2: Distances between children and their parents (in km)
Mother Father
Generation
Mean
Dist.
Min
Dist.
Max
Dist.
Mean
Dist.
Min
Dist.
Max
Dist.
1 5.86 0 8,091.12 5.16 0 767.85
2 13.67 0 3,334.21 19.83 0 11,850.38

Migration
We then construct three dummy variables:
<10km : TRUE if the closest distance is lower than 10km
Table 3: Distances between children and their parents
Gen.
Nb not
missing
dis-
tance
<10km
<10km
(%)
<50km
<50km
(%)
<100km
<100km
(%)
Nb NAs Total
1 16,751 15,301 91.34 16,514 98.59 16,612 99.17 486 17,237
2 14,565 11,927 81.89 14,014 96.22 14,236 97.74 506 15,071

Migration: from birth to death
We can also look at the spatial movements between birth and
death for an individual
Table 4: Distances between places of birth and death
Gen
Nb not
missing
dis-
tance
<10km
<10km
(%)
<50km
<50km
(%)
<100km
<100km
(%)
Nb NAs Total
0 7,414 7,384 99.60 7,404 99.87 7,410 99.95 18,007 25,421
1 6,547 5,396 82.42 6,213 94.90 6,310 96.38 10,690 17,237
2 5,480 4,063 74.14 4,961 90.53 5,124 93.50 9,591 15,071

What comes next?
Identify a path in space during an individual’s lifespan
birth
marriage(s)
births of children
death
Enlarge the dataset (only people born between 1800 and 1875
here)
Consider more regions

References
Bibliography I
Bean, L. L., May, D. L., and Skolnick, M. (1978). The mormon historical demography
project. Historical Methods: A Journal of Quantitative and Interdisciplinary History,
11(1):45–53. doi:10.1080/01615440.1978.9955216.
Bouchard, G., Roy, R., Casgrain, B., and Hubert, M. (1989). Fichier de population et
structures de gestion de base de données : le ﬁchier-réseau BALSAC et le système
INGRES/INGRID. Histoire & Mesure, 4(1):39–57. doi:10.3406/hism.1989.874.
Cummins, N. (2017). Lifespans of the european elite, 800–1800. The Journal of
Economic History, 77(02):406–439. doi:10.1017/s0022050717000468.
Dupâquier, J. (1981). Une grande enquête sur la mobilité géographique et sociale aux
xixe et xxe siècles. Population, 36(6):1164–1167. doi:10.2307/1532329.
Fire, M. and Elovici, Y. (2015). Data mining of online genealogy datasets for revealing
lifespan patterns in human population. ACM Transactions on Intelligent Systems
and Technology, 6(2):1–22. doi:10.1145/2700464.
Gavrilova, N. S. and Gavrilov, L. A. (2007). Search for predictors of ex-
ceptional human longevity. North American Actuarial Journal, 11(1):49–67.
doi:10.1080/10920277.2007.10597437.
Henry, L. (1956). Anciennes familles genevoises. etude démographique: XVIme - XXme
siècle. Population, 11(2):334. doi:10.2307/1524668.

References
Bibliography II
Kaplanis, J., Gordon, A., Wahl, M., Gershovits, M., Markus, B., Sheikh, M., Gymrek,
M., Bhatia, G., MacArthur, D. G., Price, A., and Erlich, Y. (2017). Quantitative anal-
ysis of population-scale family trees using millions of relatives. doi:10.1101/106427.
Mandemakers, K. (2000). Historical sample of the netherlands. Handbook of interna-
tional historical microdata for population research, pages 149–177.
Matthijs, K. and Moreels, S. (2010). The antwerp cor*-database: A unique ﬂemish
source for historical-demographic research. The History of the Family, 15(1):109–
115. doi:10.1016/j.hisfam.2010.01.002.

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