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VERSION:2.0
PRODID:-//Vrije Universiteit Amsterdam//NONSGML v1.0//EN
NAME:PhD defence M.J. Ton
METHOD:PUBLISH
BEGIN:VEVENT
DTSTART:20260313T134500
DTEND:20260313T151500
DTSTAMP:20260313T134500
UID:phd-defence-m-j-ton@8F96275E-9F55-4B3F-A143-836282E12573
CREATED:20260924T143353
LOCATION:Main building VU, 1105, Aula, De Boelelaan, 1081 HV, Amsterdam
SUMMARY:PhD defence M.J. Ton
X-ALT-DESC;FMTTYPE=text/html: Modeling Migration a
nd Global Population Patterns
Natural disasters d
rive migration less than thought: new data offers a more realistic pi
cture
Natural disasters do influence internal migrati
on in the United States, but the effect appears to be smaller than is
often assumed. According to climate scientist Marijn Ton, this is be
cause neighboring regions tend to resemble one another closely, and n
atural disasters do not stop at administrative borders. By taking thi
s interconnection between regions into account, it becomes possible t
o make a more realistic estimate of migration—one that will often b
e lower than previously expected.
In addition, Ton’s research
shows that it is important to understand where people live and what
opportunities they have. He therefore developed a global dataset. For
approximately two billion households and more than seven billion ind
ividuals, he compiled a wide range of information. This includes demo
graphic data such as age, household size, and gender, as well as soci
oeconomic data such as education and income. This provides insight in
to where households are located and what options are available to the
m.
By combining this information with migration models, we can
better assess who is able to relocate after a disaster, who remains,
and where measures or support are most urgently needed.
More in
formation on the thesis
DESCRIPTION: Modeling Migration and Global Population Patterns Natural disasters drive migration less than thought: new data of
fers a more realistic picture
Natural disasters do influ
ence internal migration in the United States, but the effect appears
to be smaller than is often assumed. According to climate scientist M
arijn Ton, this is because neighboring regions tend to resemble one a
nother closely, and natural disasters do not stop at administrative b
orders. By taking this interconnection between regions into account,
it becomes possible to make a more realistic estimate of migration—
one that will often be lower than previously expected.In addition, To
n’s research shows that it is important to understand where people
live and what opportunities they have. He therefore developed a globa
l dataset. For approximately two billion households and more than sev
en billion individuals, he compiled a wide range of information. This
includes demographic data such as age, household size, and gender, a
s well as socioeconomic data such as education and income. This provi
des insight into where households are located and what options are av
ailable to them.By combining this information with migration models,
we can better assess who is able to relocate after a disaster, who re
mains, and where measures or support are most urgently needed.More in
formation on the thesis
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