Data sources

First of all, data is collected from all these sources:

• Genealodzy.pl (Geneteka 2025): Genealodzy is an open-source database with historical records from Poland and Polish–Lithuanian Commonwealth territories. This digital archive is used to find birth records in Malopolska between the 1500s and the Great War. The most common surnames found in this archive (50,227 surnames, comprising more than 80% of all the records) are used for further analysis.

• FamilySearch.org (FamilySearch 2025): FamilySearch is an open-source database with various worldwide historical records on population. This source is used to find all the available records on each surname found in Malopolska birth records. This information is relevant to finding the geographical distribution of a given surname in the whole of Europe over time.

• Wikipedia (Wikipedia 2025): Wikipedia is an open-source platform containing a well-structured dataset of the most typical surnames for each European country. This data is used as an additional source to assess a surname’s origin by both analyzing suffixes and adopting Fuzzy Logic (Python library: thefuzz) (TheFuzz 2025) to find a probabilistic match with surnames from Malopolska birth records. However, as Wikipedia records are not historical but are based on current surname distribution by country, the origin might be biased. This is why the study assigns higher weights and importance to older historical records.

• Genezanazwisk.pl (Naruszewicz-Duchlińska 2012): Geneza Nazwisk is an open-source database containing detailed surname etymology and origin. It is used as an additional source to validate surname origin. This website has been chosen over similar sources created by other scholars as the database structure is suitable for automated web scraping.

• Automated Google search (Google LLC 2025): I designed a script to collect information when searching for a given surname’s origin on the Google search engine. This source is used as an additional method to assess surnames’ origin, but as the results can come from any website and not necessarily verified sources, less weight and importance are given compared to older historical records.

It is important to mention that both Genealodzy and FamilySearch are run by the Church of Jesus Christ of Latter-Day Saints. Particularly, in the Genealodzy archive, which is our starting point for collecting surnames of people inhabiting Malopolska between the 1500s and 1914, we would expect to see more records from communities other than Christian. For example, only 0.14% of records of the entirety of Poland are labelled as Jewish. This number goes down to 0.07% for Malopolska. It is far below the estimates of 10% (Conrad, Graf, and Wille 2023). In addition to that, we did not find records labelled as Muslim. Therefore, the reader who wants to obtain a percentage distribution for all migrant groups inhabiting Malopolska over the centuries should adjust the results by the estimated size of these communities (Figure 2).

Figure 2

Occurrences of all surnames found in Malopolska across Europe.

Coordinates

After collecting the input data, coordinates are added to all the locations found for a given surname in the whole of Europe over time. This step is essential to standardize locations and solve inevitable inconsistencies in the naming conventions. In fact, borders and regions’ names have mutated drastically over the centuries, and on the other hand, each digital archive follows different guidelines and structures. The Python module used for finding and adding coordinates is geopy.geocoders (GeoPy Contributors 2018). Coordinates are then used to establish the diocese where the record is from and map it. Shapefile maps are open-source maps of fourteenth-century dioceses created by Stanford University (Stanford University Libraries 2025), and they are used in this study to provide more granularity (for example, to divide Italy into Northern Italy, Eastern Italy, and other parts) and customization of the regions, which would be otherwise unavailable in other maps.

Algorithm I

After regions are defined and all available birth locations are assigned to their regions, the study proceeds with probabilistic estimates of surnames’ origin through a multi-step algorithm. For simplicity, it has been divided into three parts. The initial estimates are produced in this first part of the algorithm, and next, they will be sequentially validated by Algorithm II and finally by Algorithm III. First of all, for each surname, the region of the oldest birth record is identified. Secondly, distributions across all the regions are calculated based on all the historical records available on that given surname. We acknowledge that a surname may have originated from one region and then thrived in another, and therefore, the distributions have been adjusted with logarithmic weights to ensure the oldest record receives greater importance in estimating surnames’ origin. Weights are assigned to the distributions following the formula below:

$\text{exp}\left(-\frac{\left(Rw-Ro\right)}{\left(Rn-Ro\right)}\right),$

where w belongs to (0,n)

Ro- Is the year of the first birth record found

Rn- Is the year of the last birth record found

Rw- Is the year of a given birth record

Once weights are assigned, the algorithm proceeds with a so-called Forward Walk to identify the most likely region of origin. The benefit of the Forward Walk is that it can capture long-term population movements replicating, to a certain extent, the steps taken by people while spreading throughout Europe over the centuries. In order to achieve that, a 2D matrix of the regions is designed so that regions that are geographically bordering each other are also bordering in the matrix (Figure 3). For each surname, two Forward Walks are performed. One starts from the region of the first birth record available, and one starts from the region with the highest prevalence of that given surname. In both cases, the algorithm starts analyzing the neighbouring regions in the matrix. Suppose the total percentage distribution of the starting region together with its neighbouring regions sums up to at least 90%. In that case, the algorithm stops as the high distribution around the starting point provides enough confidence in the region of origin.

Figure 3

Matrix representing defined regions in Europe. Each number represents one region.

On the contrary, if the sum of the distributions does not reach at least 90%, the algorithm keeps walking forward to the neighbouring region with the highest prevalence of that given surname and keeps summing up all the distributions of the neighbouring regions. This forward walk keeps moving forward as described until the sum of the distributions reaches at least 90%, or until there are no further regions to investigate. The total distribution reached is the new confidence score assigned to the starting point region (region of the oldest record or region of highest prevalence). This output will then be used by the second algorithm together with other sources to validate surname origin estimates.

Algorithm II

The second part of the multi-step algorithm is validating the estimates of surnames’ origin generated by Algorithm I. In order to do that, for each surname, the algorithm performs three validations called Three Passes, which, based on comparisons with other sources, reinforce more or less the estimates on surnames’ origin. To minimize biases, each Pass begins the validation across the same sources from a different starting point.

The Highest Prevalence Pass uses as a starting point the region of the highest prevalence of a given surname and its respective confidence score assigned by Algorithm I. As a first step, the algorithm checks whether this region matches the one where the surnames’ suffix is most likely to belong. If there is a match, a multiplier is used to recalculate the score and adjust the confidence in the region. If there is a match with another high-prevalence region where the surname has been recorded, the percentage distributions of other regions are adjusted accordingly, and all the estimates are then normalized. Based on these results, the starting point region can be either confirmed or reassigned. Then, the algorithm compares the region with the one of the first birth record, and if there is a match, the score is adjusted as in the previous step. Afterward, the algorithm compares the region with the one identified by the automated Google search and again adjusts the score if there is a match. Next, the algorithm compares the region with the one identified using Fuzzy Logic to find a probabilistic match with the most common surnames by country stored in Wikipedia, and the score is adjusted accordingly. Finally, the last score adjustment is given by comparing it with the region assigned by Geneza Nazwisk.

The First Record Pass uses the region of the first birth record and its respective confidence score assigned by Algorithm I as a starting point. As an initial step, the algorithm checks whether this region matches the one where the surnames’ suffix is most likely to belong. If there is a match, a multiplier is used to recalculate the score and adjust the confidence in the region. Secondly, the algorithm compares the region with the one from the highest prevalence pass, and if there is a match, the score is adjusted as in the previous step. Finally, the algorithm follows the same steps as above. It compares the region with the one from the automated Google search, the one from the Wikipedia dataset, and the one assigned by Geneza Nazwisk, again adjusting the final score accordingly.

The Suffix Pass uses as starting point the region where the suffix of a given surname is the most common. Firstly, the algorithm compares the region with the one with the highest prevalence, and if there is a match, the score is adjusted. Secondly, the algorithm compares the region with the one of the first birth record, and if there is a match, the score is adjusted. Finally, the algorithm follows the same steps as above. It compares the region with the one from the automated Google search, the one from the Wikipedia dataset, and the one assigned by Geneza Nazwisk, adjusting the final score in the same way as above. When all Three Passes are completed, the regions and their adjusted scores are used to feed Algorithm III, which will make a final decision on surnames’ origin.

Algorithm III

The last part of the multi-step algorithm consists of a set of rules that make decisions on the most likely region of origin of a given surname based on the regions and their respective scores produced by the Three Passes in Algorithm II. The prediction accuracy can be derived from the rule numbers: the first rules hold greater accuracy than the last ones. The following rules are applied, depending on the results previously obtained for each surname:

• Rule 1: If the region from the First Record Pass matches the region from the Highest Prevalence Pass, the final region of origin is the same.

• Rule 2: If the region from the First Record Pass does not match the region from the Highest Prevalence Pass, and both have the same confidence score, the algorithm takes the region of the first birth record as the final region of origin.

• Rule 3: If the region from the First Record Pass does not match the region from the Highest Prevalence Pass, and they do not have the same confidence score, the algorithm picks the region with the highest score.

• Rule 4: If the region from the First Record Pass and the region from the Highest Prevalence Pass are not available due to a lack of historical records, the algorithm takes the region from the Suffix Pass as the final region. However, this rule is applied only when a given suffix has high and medium confidence and therefore has more than 100 records available in Wikipedia database.

• Rule 5: Similarly to Rule 4, if the region from the First Record Pass and the region from the Highest Prevalence Pass are not available due to lack of historical records, the algorithm takes the region from the Suffix Pass as the final region. However, this time the rule applies only to suffixes which have between 30 and 100 records available in Wikipedia database.

• Rule 6: If surnames are not clearly identified, suffixes are primarily used to identify the region of origin of a given surname. However, the set of suffixes used in this rule are based on researches conducted by scholars. The most important suffixes are the following:

  • - Rus suffixes: -WICZ (Gliński 2016; Baranivska 2021)

  • - Ukrainian suffixes: -KO and -UK (Baranivska 2021)

  • - German suffixes: -ACH, -HN, -HL,- EIT, -ERT, -DEL, -INK, -KEL, -MAN, -MANN, -SCH, -ICH, -HEL, -ATZ (Baker 2005) or containing characters such as “Ä, Ö, Ü, ä, ö, ü, ß” (only if a surname has been previously identified as belonging to Eastern or Central European regions)

  • - Polish suffixes: -SKI, -SKA, -CKI, -CKA, -ZKI, -ZKA (Gliński 2016)

  • - Neofici group: Neofici consist of people who converted to the Catholic faith and after baptism took a new surname. Frequently, the new surnames were created from weekday names or months (Gawryszczak 2014).

Once the most likely region of origin is found for all the surnames available in Malopolska birth records from the 1500s to the Great War, we can quantify the historical migrations over the centuries calculating the percentage of migrants coming from different regions in Europe.

Migrations until 1650

Between the 1500s and 1600s, Malopolska was the capital region of the Kingdom of Poland and was an attractive destination for Western Europeans for multiple reasons. On one side, Metropolitan Krakow was a wealthy trading centre that offered great opportunities for traders, artisans, artists, and individuals seeking a better life. On the other side, the Commonwealth was free from religious wars and counter-reformations (Wasiak 2022), making it a refuge for migrants escaping violence in the West (Davies 1996, 504). Political power was also strong, proven by the Prussian Homage to the Kingdom of Poland in 1525 in Krakow and King Sigismund’s success in secularizing the Teutonic Order, which stunned even the Pope (Nowakowska 2018).

According to Conrad, Graf, and Wille (Conrad, Graf, and Wille 2023), the entire Kingdom of Poland before the union with Lithuania in 1569 was estimated to have 70% of Polls, 15% of Ruthenians, and approximately 10% of Germans, the rest of the population being Armenians, Jews, Tatars, Vlachs (Romanians, Moldavians), and other (Conrad, Graf, and Wille 2023). Prior to the Golden Age in the 1400s, a group finding refuge from religious intolerance consisted of Czech Hussite migrants (Hanczakowski 2019). Later historical sources point to other migrant groups, such as the Dutch, predominantly protestants escaping religious prosecution and wars (Kowalski 2019). Kowalski describes Germans, Italians, French, and various ethnicities being welcomed by the protestant community of Krakow (Kowalski 2019). Finally, the mass migration of Scots is another key event in Polish history (Devine and Hesse 2012), with an estimated presence of 30,000 Scottish families in Poland (Kowalski 2016). In the 1500s, goods from Scotland constituted around 10% of total Gdansk imports, and in the first half of the 1600s, Scottish and English ships accounted for 12% of those entering Gdansk harbour (Kowalski 2016).

We can review the overall migration patterns described in previous studies and loosely compare our results (Figure 5) with the study conducted by Conrad, Graf, and Wille (Conrad, Graf, and Wille 2023) on the entirety of Poland before the union with Lithuania. The estimates provided by our algorithm show that up to 1650 Polish surnames constituted 77.7% of all available records (comparable with the estimate of 70% provided by the aforementioned authors). Northwestern Europeans (particularly ethnic Germans) were found to be most prevalent in this period, with a presence of 7.81% (relatively close to the 10% of Germans estimated by Conrad, Graf, and Wille). Eastern European surnames were 5.82%, and 2.68% were from the neighbouring Kingdom of Hungary (labels: Hungary, Slovakia, Romania, Croatia). The results also show a presence of 3.25% of Ukrainian and Belarus surnames: Conrad, Graf, and Wille estimated 15% of Ruthenians in the whole of Poland, and as expected, the number turned out to be far lower in Malopolska as the group was native to Red Rus and mostly present in the East. Surnames from the British Isles were nearly 1.75%, overlapping with the documented Scott migration to Poland (Devine and Hesse 2012). To put numbers into context, this is similar to the number of Poles in the United Kingdom in 2017 at the peak of migration (1.51%) (Statista 2020; Statista 2021). Other relevant migration groups found were Czech (1.85%), French (1.06%), and Italian (0.89%).

Figure 5

Distribution of surnames by region for the period until 1650 (values greater than 0.3%).

Migrations between 1650 and 1750

The following century marked the slow decline of the Polish–Lithuanian Commonwealth. According to a study conducted by Malinowski and Luiten van Zanden, the economic growth between 1662 and 1776 was half of that between 1500 and 1578 (Malinowski and Luiten van Zanden 2017). This can be attributed to climate change (Izdebski and Guzowski 2025) and the growing instability. Moreover, the rise of colonial empires in the Northwest and alternative migration destinations (Kowalski 2016) affected the migration landscape in Malopolska. In addition, Socinians were expelled from Poland during the wars with Sweden (Conrad, Graf, and Wille 2023), together with some other protestants (including some Scots) who supported the Swedish king (Devine and Hesse 2012). Dutch settlers (Olender) continued to migrate mainly to Northern and Central Poland (Marszał 2022). In this period, an important geopolitical change occurred behind the southern border, where Habsburgs steadily increased their possessions, and people faced the counter-reformation laws of the new Catholic rulers (Michels 2024).

In the results of this study (Figure 6), we can observe indeed an initial decline in surnames from regions such as France, Belgium, and the British Isles. However, German surnames rebounded between 1700 and 1750, which might be associated with a constant influx of Dutch settlers (as previously mentioned, this label was used for multiple ethnic groups) (Wójcik-Żołądek 2014). Similarly, French surnames rebounded, especially those originating from the area around the Rhine River Valley neighbouring Germany. Slovak (0.68%) and Silesia (3.55%) surnames slowly increased as both regions became Habsburg domains. Another group that steadily rose was the Rus (labelled “Belarus”), which reached 4.12% by the end of 1750. This increase is likely associated with the loss of territories by the Polish–Lithuanian Commonwealth in the East. During this period, we also observe a growth in the number of “Neofici” surnames (Jewish converts), who could benefit from Chapter XII and Article 7 of the III Lithuanian Statute from 1588, stating that Jews who converted could join the ranks of nobility (Lipińska and Lipiński 2021).

Figure 6

Distribution of surnames by region for the period 1650–1750 (values greater than 0.27%).

Migrations between 1750 and 1914

Between 1772 and 1795, during the partitions of the Polish–Lithuanian Commonwealth, Southern Poland fell under the Habsburgs, and the new ruler, Maria Theresa, started a colonization campaign in the eastern provinces of the Empire, including Southern Poland. It is estimated that 38,000 migrants moved to the East of the Empire (Wójcik-Żołądek 2014). Her successor, Joseph the Second, continued these policies with new campaigns in the 1780s, bringing around 14,400 people to Southern Poland and 170 new settlements, out of which 120 were purely German speaking (Lepucki 1938). The following century, up to the Great War, presented a strong emigration trend from Poland. It is estimated that between 1870 and 1914, approximately 3.5 million people left Poland (Suleja 2022). In the Galicja region especially, the emigration started in 1880, and this wave notably affected the birth rate (Jura 2002). In the same period, especially between 1850 and 1914, an intense Jewish migration from Southern Poland and from the entire Galicja region to Hungary can be observed, as reported by the study of Csíki (Csíki 2022). The author also states that between 1900–1910, both Poles and Jews (approximately 135,000 and 120,000 people, respectively) left Galicja for Hungary, Silesia, and the Americas.

In general, this emigration trend can also be observed in this study’s results (Figure 7, Figure 8). Ukrainian and Belarusian (Rus) surnames declined compared to previous periods, with Ukrainians levelling around 1.75–1.77%, while Belarusian presence kept decreasing until 1914, reaching 3.08%. French, Belgian, Dutch, and British groups declined further, as did other Western surnames. Malopolska was no longer an attractive migration destination. The only steady increase was among Slovakian and Silesian surnames, as those territories and Malopolska were both part of the Habsburg Empire. In addition to that, the number of German surnames grew compared to the previous period, together with Swiss surnames, which reached 0.28% at its peak in 1800–1850. Interestingly, in the records, we notice a rise in the occurrence of local Southern Polish surnames at the expense of generic Polish surnames ending with suffixes such as -SKI and -CKI.

Figure 7

Distribution of surnames by region for the period 1750–1850 (values greater than 0.27%).

Figure 8

Distribution of surnames by region for the period 1850–1914 (values greater than 0.3%).

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