BEGIN:VCALENDAR 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:2026/phd-defence-m-j-ton@8F96275E-9F55-4B3F-A143-836282E12573 CREATED:20260409T003753 LOCATION:Hoofdgebouw, Aula De Boelelaan 1105 1081 HV Amsterdam SUMMARY:PhD defence M.J. Ton X-ALT-DESC;FMTTYPE=text/html:

Modeling Migration and Global Population Patterns

Natural disasters drive mi gration less than thought: new data offers a more realistic picture

Natural disasters do influence internal migration in t he United States, but the effect appears to be smaller than is often assumed. According to climate scientist Marijn Ton, this is because n eighboring regions tend to resemble one another closely, and natural disasters do not stop at administrative borders. By taking this inter connection 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, Ton’s research shows that it is important to understand where people live and what opportu nities they have. He therefore developed a global dataset. For approx imately two billion households and more than seven billion individual s, he compiled a wide range of information. This includes demographic data such as age, household size, and gender, as well as socioeconom ic data such as education and income. This provides insight into wher e households are located and what options are available to them.

< p>By combining this information with migration models, we can better assess who is able to relocate after a disaster, who remains, and whe re measures or support are most urgently needed.
More informati on on the thesis
DESCRIPTION:
Natural disasters drive migration less than t hought: new data offers a more realistic picture
Natura l disasters do influence internal migration in the United States, but the effect appears to be smaller than is often assumed. According to climate scientist Marijn Ton, this is because neighboring regions te nd to resemble one another closely, and natural disasters do not stop at administrative borders. By taking this interconnection between re gions into account, it becomes possible to make a more realistic esti mate of migration—one that will often be lower than previously expe cted. In addition, Ton’s research shows that it is important to und erstand where people live and what opportunities they have. He theref ore developed a global dataset. For approximately two billion househo lds and more than seven billion individuals, he compiled a wide range of information. This includes demographic data such as age, househol d size, and gender, as well as socioeconomic data such as education a nd income. This provides insight into where households are located an d what options are available to them. By combining this information w ith migration models, we can better assess who is able to relocate af ter a disaster, who remains, and where measures or support are most u rgently needed. More information on the thesis Modeling Migra tion and Global Population Patterns END:VEVENT END:VCALENDAR