Introducing sequence analysis to economic geography
(2024) In Progress in Economic Geography 2(1).- Abstract
In this short paper, we introduce sequence analysis methods to economic geography. Sequence analysis is a rich set of research methods that is widely used to analyze temporal variance in several disciplines in the social sciences, including sociology, demography and employment research. However, the toolbox of sequence methods has yet to gain significant attention among economic geographers. Sequence analysis methods can be used to analyze and understand patterns and structures of various phenomena over time. It employs mathematical and statistical techniques to study the sequential order, duration, and transitions between temporal conditions. We argue that sequence analysis holds great potential for advancing research in (evolutionary)... (More)
In this short paper, we introduce sequence analysis methods to economic geography. Sequence analysis is a rich set of research methods that is widely used to analyze temporal variance in several disciplines in the social sciences, including sociology, demography and employment research. However, the toolbox of sequence methods has yet to gain significant attention among economic geographers. Sequence analysis methods can be used to analyze and understand patterns and structures of various phenomena over time. It employs mathematical and statistical techniques to study the sequential order, duration, and transitions between temporal conditions. We argue that sequence analysis holds great potential for advancing research in (evolutionary) economic geography. In the paper, we explain how to use sequence analysis, we ponder on empirical applications for research in economic geography, and we demonstrate its applicability in a use case. We also provide a reproducible R script and manual for the use case in the online appendix.
(Less)
- author
- Losacker, Sebastian LU and Kuebart, Andreas
- organization
- publishing date
- 2024-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Evolutionary economic geography, Sequence analysis, Spatio-temporal data, Time geography
- in
- Progress in Economic Geography
- volume
- 2
- issue
- 1
- article number
- 100012
- publisher
- Elsevier
- external identifiers
-
- scopus:105020753485
- ISSN
- 2949-6942
- DOI
- 10.1016/j.peg.2024.100012
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2024 Elsevier Ltd
- id
- 757e098b-9c6f-40da-924d-7b55d2d45732
- date added to LUP
- 2026-01-27 16:07:06
- date last changed
- 2026-01-27 16:07:26
@article{757e098b-9c6f-40da-924d-7b55d2d45732,
abstract = {{<p>In this short paper, we introduce sequence analysis methods to economic geography. Sequence analysis is a rich set of research methods that is widely used to analyze temporal variance in several disciplines in the social sciences, including sociology, demography and employment research. However, the toolbox of sequence methods has yet to gain significant attention among economic geographers. Sequence analysis methods can be used to analyze and understand patterns and structures of various phenomena over time. It employs mathematical and statistical techniques to study the sequential order, duration, and transitions between temporal conditions. We argue that sequence analysis holds great potential for advancing research in (evolutionary) economic geography. In the paper, we explain how to use sequence analysis, we ponder on empirical applications for research in economic geography, and we demonstrate its applicability in a use case. We also provide a reproducible R script and manual for the use case in the online appendix.</p>}},
author = {{Losacker, Sebastian and Kuebart, Andreas}},
issn = {{2949-6942}},
keywords = {{Evolutionary economic geography; Sequence analysis; Spatio-temporal data; Time geography}},
language = {{eng}},
number = {{1}},
publisher = {{Elsevier}},
series = {{Progress in Economic Geography}},
title = {{Introducing sequence analysis to economic geography}},
url = {{http://dx.doi.org/10.1016/j.peg.2024.100012}},
doi = {{10.1016/j.peg.2024.100012}},
volume = {{2}},
year = {{2024}},
}