Jonathan Gescher
Biographic Data
| ID | 8836299 |
|---|---|
| NAME | Jonathan Gescher |
| GIVEN NAMES | Jonathan |
| FAMILY NAME | Gescher |
| SIGNATURE | GESCHER J |
| AFFILIATIONS | Leibniz Institute for Regional Geography |
| ORCID | 0009-0008-6309-0513 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Downhill from here? Why shrinking regions are not necessarily ‘left-behind’
This paper explores the socio-spatial polarisation between thriving agglomerations and de-populating or stagnating, largely rural, areas in Germany, answering the question to what extent shrinking regions can also be seen as ‘left behind’. We find that demographic shrinkage is a challenge mostly for peripheral and rural districts. Our analysis shows no significant link between socio-economic development and population growth, highlighting the imp…
Breaking the Binary
Between 2001 and 2022, Germany's overall growth rate averaged 0.2% per year. Yet, of the 3285 towns and cities with more than 5000 inhabitants, only 52 grew in all 21 years under consideration (1.6%); 85 (2.6%) shrank in all years, leaving 3148 (95.8%) with a combination of the two. Using a novel dynamic time warping approach, we explore the processes of municipal population change beyond growth and shrinkage. We identify clusters of similar popu…
How Can Clusters of Population Trajectories be Identified? Comparing the Potential of Dynamic Time Warping and Sequence Analysis
Reducing complexity while retaining the maximum amount of information is a key challenge for population analysis. Solving this challenge becomes a necessity when looking at numerous areas over extended periods, which defy manual pattern recognition efforts. This paper introduces Dynamic Time Warping (DTW) as a novel method for population time series clustering, capable of creating distinct, well‐separated groups for process‐centered population an…
No prominent works on this page.
How Can Clusters of Population Trajectories be Identified? Comparing the Potential of Dynamic Time Warping and Sequence Analysis
Reducing complexity while retaining the maximum amount of information is a key challenge for population analysis. Solving this challenge becomes a necessity when looking at numerous areas over extended periods, which defy manual pattern recognition efforts. This paper introduces Dynamic Time Warping (DTW) as a novel method for population time series clustering, capable of creating distinct, well‐separated groups for process‐centered population an…
Downhill from here? Why shrinking regions are not necessarily ‘left-behind’
This paper explores the socio-spatial polarisation between thriving agglomerations and de-populating or stagnating, largely rural, areas in Germany, answering the question to what extent shrinking regions can also be seen as ‘left behind’. We find that demographic shrinkage is a challenge mostly for peripheral and rural districts. Our analysis shows no significant link between socio-economic development and population growth, highlighting the imp…
Breaking the Binary
Between 2001 and 2022, Germany's overall growth rate averaged 0.2% per year. Yet, of the 3285 towns and cities with more than 5000 inhabitants, only 52 grew in all 21 years under consideration (1.6%); 85 (2.6%) shrank in all years, leaving 3148 (95.8%) with a combination of the two. Using a novel dynamic time warping approach, we explore the processes of municipal population change beyond growth and shrinkage. We identify clusters of similar popu…
Dynamic time warping (2 works) · Population (2 works) · Regional resilience and development (2 works) · Urbanization and City Planning (2 works) · Cluster (spacecraft) (1 work) · Cluster analysis (1 work) · Data Management and Algorithms (1 work) · German (1 work) · Identification (biology) (1 work) · Key (lock) (1 work)