Javascript must be enabled to continue!
Quantifying Historical Migrations Using a Multi-Step Probabilistic Algorithm and Surname Distributions over the Centuries: A Case Study of Malopolska
View through CrossRef
Contemporary studies on historical migrations gradually demystified the idea that the population in the past was stable and homogenous, and unveiled dynamic and complex connections throughout the whole of Europe. However, even if we are in the era of digitalization and big data, there is no standardized methodology to investigate historical migrations, and there are major challenges in quantifying and providing consistent estimates of migration groups. This research aims to tackle these challenges by proposing a replicable approach, consisting of a multi-step algorithm that uses probabilistic analysis to quantify migration and estimate the most likely migrants’ origin based on surname distributions over the centuries. Particularly, the focus is on the Malopolska case study, on both international and Polish migrations to the region from the 1500s to the Great War. The proposed approach to quantification of historical migrations can be a helpful tool to use in combination with existing methodologies to validate and increase the accuracy of the estimates of other case studies in Europe.
Les études contemporaines sur les migrations historiques ont peu à peu démystifié l’idée selon laquelle la population passée était stable et homogène, et ont mis en lumière des connexions dynamiques et complexes à l’échelle de l’Europe entière. Cependant, même à l’ère de la numérisation et des mégadonnées, il n’existe pas de méthodologie standardisée pour étudier les migrations historiques, et il reste de grandes difficultés à quantifier et fournir des estimations cohérentes des groupes de migrants. Cette recherche vise à relever ces défis en proposant une approche reproductible, fondée sur un algorithme en plusieurs étapes qui utilise une analyse probabiliste pour quantifier la migration et estimer l’origine la plus probable des migrants à partir des distributions de noms de famille au fil des siècles. L’étude se concentre en particulier sur le cas de la Malopolska, en analysant les migrations internationales et polonaises vers cette région, du XVIᵉ siècle jusqu’à la Grande Guerre. L’approche proposée pour la quantification des migrations historiques peut constituer un outil utile, à combiner avec les méthodologies existantes, afin de valider et d’affiner les estimations d’autres études de cas en Europe.
Title: Quantifying Historical Migrations Using a Multi-Step Probabilistic Algorithm and Surname Distributions over the Centuries: A Case Study of Malopolska
Description:
Contemporary studies on historical migrations gradually demystified the idea that the population in the past was stable and homogenous, and unveiled dynamic and complex connections throughout the whole of Europe.
However, even if we are in the era of digitalization and big data, there is no standardized methodology to investigate historical migrations, and there are major challenges in quantifying and providing consistent estimates of migration groups.
This research aims to tackle these challenges by proposing a replicable approach, consisting of a multi-step algorithm that uses probabilistic analysis to quantify migration and estimate the most likely migrants’ origin based on surname distributions over the centuries.
Particularly, the focus is on the Malopolska case study, on both international and Polish migrations to the region from the 1500s to the Great War.
The proposed approach to quantification of historical migrations can be a helpful tool to use in combination with existing methodologies to validate and increase the accuracy of the estimates of other case studies in Europe.
Les études contemporaines sur les migrations historiques ont peu à peu démystifié l’idée selon laquelle la population passée était stable et homogène, et ont mis en lumière des connexions dynamiques et complexes à l’échelle de l’Europe entière.
Cependant, même à l’ère de la numérisation et des mégadonnées, il n’existe pas de méthodologie standardisée pour étudier les migrations historiques, et il reste de grandes difficultés à quantifier et fournir des estimations cohérentes des groupes de migrants.
Cette recherche vise à relever ces défis en proposant une approche reproductible, fondée sur un algorithme en plusieurs étapes qui utilise une analyse probabiliste pour quantifier la migration et estimer l’origine la plus probable des migrants à partir des distributions de noms de famille au fil des siècles.
L’étude se concentre en particulier sur le cas de la Malopolska, en analysant les migrations internationales et polonaises vers cette région, du XVIᵉ siècle jusqu’à la Grande Guerre.
L’approche proposée pour la quantification des migrations historiques peut constituer un outil utile, à combiner avec les méthodologies existantes, afin de valider et d’affiner les estimations d’autres études de cas en Europe.
Related Results
Hydatid Disease of The Brain Parenchyma: A Systematic Review
Hydatid Disease of The Brain Parenchyma: A Systematic Review
Abstarct
Introduction
Isolated brain hydatid disease (BHD) is an extremely rare form of echinococcosis. A prompt and timely diagnosis is a crucial step in disease management. This ...
Inventory and pricing management in probabilistic selling
Inventory and pricing management in probabilistic selling
Context: Probabilistic selling is the strategy that the seller creates an additional probabilistic product using existing products. The exact information is unknown to customers u...
Breast Carcinoma within Fibroadenoma: A Systematic Review
Breast Carcinoma within Fibroadenoma: A Systematic Review
Abstract
Introduction
Fibroadenoma is the most common benign breast lesion; however, it carries a potential risk of malignant transformation. This systematic review provides an ove...
Influences on flood frequency distributions in Irish river catchments
Influences on flood frequency distributions in Irish river catchments
Abstract. This study explores influences which result in shifts of flood frequency distributions in Irish rivers. Generalised Extreme Value (GEV) type I distributions are recommend...
Abstract 820: Decolonizing data: Diversifying cancer registries to include SWANA
Abstract 820: Decolonizing data: Diversifying cancer registries to include SWANA
Abstract
Southwest Asian/North African communities (SWANA) make up over 3-4% of immigrants in the U.S. and yet their health status is largely unknown because these e...
A Sparse CoSaMP Channel Estimation Algorithm With Adaptive Variable Step Size for an OFDM System
A Sparse CoSaMP Channel Estimation Algorithm With Adaptive Variable Step Size for an OFDM System
Compressive sampling matching pursuit (CoSaMP), as a conventional algorithm requiring system sparsity and sensitive to step size, was improved in this paper by approximating the sp...
Chest Wall Hydatid Cysts: A Systematic Review
Chest Wall Hydatid Cysts: A Systematic Review
Abstract
Introduction
Given the rarity of chest wall hydatid disease, information on this condition is primarily drawn from case reports. Hence, this study systematically reviews t...
Probabilistic Linguistics
Probabilistic Linguistics
For the past forty years, linguistics has been dominated by the idea that language is categorical and linguistic competence discrete. It has become increasingly clear, however, tha...

