Abstract
We describe the procedure for compiling a list of industrial monuments in the regions of Germany and the resulting data set. This includes detailed insights into the absolute and relative numbers of industrial monuments, categorized into production, traffic, and supply. In addition, we provide an overview of the regional distribution of the number of industrial monuments in Germany. Baden-Wuerttemberg and Bavaria exhibit the largest shares of industrial monuments nationwide, followed by Saxony and North Rhine-Westphalia. Spatial analyses reveal significant regional variations in monument density per population and area, reflecting the historical and cultural heritage shaped by industrialization. The data set, publicly available on the Internet, fills a gap in documenting and analyzing industrial heritage in Germany.
1 Motivation and Background of the Project
Industrial monuments are important testimonies to the economic history and cultural heritage of a region (Bole 2021; Harfst, Wust, and Nadler 2018; Paasi 1986; Storm 2011). They represent a regional culture of remembrance (collective memory) and can, therefore, make a significant contribution to explaining the development of a region.[1] According to the definition of the International Committee for the Conservation of Industrial Heritage (TICCIH), industrial monuments are remnants of economic activity that are of historical, technological, social, architectural, or scientific value.[2] This includes in particular buildings, plants, and machinery, as well as sites for the processing, storage, and transportation of raw materials and products, including the corresponding infrastructure (e.g., bridges).[3] As a rule, the term is restricted to industrial monuments in the sense of manufacturing industry, i.e., remains of agricultural production are excluded.
In Germany, the monument authorities of the respective federal states are responsible for documenting and, if necessary, maintaining the existing industrial monuments. In some federal states, the monument system is organized quite differentially. There are also significant differences between the federal states and their monument authorities with regard to the definition and categories of industrial monuments. As a result of these different responsibilities and procedures, the lists of monuments are often structured quite differently and are only to a limited extent compatible with each other. This results in a lack of cross-regional information on the industrial monuments existing in the regions of Germany, which has so far made it almost impossible to use such information for regional research.
Against this background, the idea was born to create the first ever directory of industrial monuments in German regions, which makes it possible to analyze connections between regional history, collective memory, and a variety of economic and social indicators. This directory, which we present here, was compiled in the years 2021–2024 as a by-product of our research work at the Faculty of Economics at Friedrich Schiller University Jena.[4] One of the main objectives of collecting and processing the data was to develop a standardized definition of industrial monuments to make the sometimes very different definitions used by the monument authorities as comparable as possible.
The data represent a measure of the extent of industrial tradition in regions compared to other regions and, therefore, allow to analyze long-term legacies and effects of historical industrial structures (Bleakley and Lin 2012; Stuetzer et al. 2016). For example, the data can contribute to understanding the mechanisms behind the formation of a place-based collective memory of industrial heritage. Previous research using less granular data has shown that collective memory about past economic structures – as carried by industrial monuments and other remnants of past economic setups – can have a significant effect on current levels of regional entrepreneurship (Fritsch et al. 2019) and voting behavior (Greve, Fritsch, and Wyrwich 2023). More generally, the data enable future contributions to the growing literature on collective memory on current economic behavior and outcomes (Fouka and Voth 2023; Ochsner and Roesel 2024). With these possibilities, the data also contribute to the large literature focusing on how historical developments and patterns (e.g., industrial tradition) shape future economic developments (e.g., Dell 2010; Nunn 2009).
Cultural studies could complement this ongoing research on collective memory by using the data to focus on the cultural and symbolic significance of industrial monuments to the local community and their role in shaping local identity. In addition, a regional database of industrial monuments can also serve as a rich resource for a variety of other research endeavors when combined with other regional data. This includes studies that trace the development of industrial technologies and innovations through the study of industrial monuments. The database can also support research on tourism and economic development by assessing the economic impact of the preservation and promotion of industrial heritage on the local economy, which is another important area of study. This report first describes the methodology of data compilation (Section 2). Following this, the procedure for harmonizing the data and identifying industrial monuments is presented (Section 3). Section 4 provides an overview of the spatial structure of industrial monuments in Germany. Finally, we draw a brief conclusion and provide an outlook for future work (Section 5).[5]
2 Data Sources
In order to record the existing information on monuments and their availability, the websites of the monument authorities of all federal states were examined. While some monument authorities directly provide a file on the Internet with information on the monuments in their area of responsibility without access restrictions, most monument authorities only make the information available by retrieving data for each individual monument. As such a retrieval per monument was not feasible for us due to the very high workload involved, we asked the monument authorities concerned to send us electronic directories in list form.
Finally, for Lower Saxony, the required data were obtained by web scraping from the website of the State Office for the Preservation of Monuments. In this way, it was finally possible to compile raw data on the existing monuments for all 16 federal states.
The differences between the federal states in the recording of monuments are clearly reflected in the structure of the data provided in each case. These data also differ in terms of their content of information, which has an impact on their preparation and harmonization, especially on the identification of industrial monuments. Although in most federal states, the data were available in the form of electronic tables; in some federal states, only text documents (lists) were made available. Table 1 shows the format in which the raw data were available and the classification of the monuments into different categories by the respective monument authority.
Overview of the raw data.
Federal state | Data source | Access | Monument categories |
---|---|---|---|
Baden-Wuerttemberg | Baden-Württemberg state office for the preservation of monuments | Table available on request | None |
Bavaria | Bavarian state office for the preservation of monuments | Table available on request/raw data available for download | Monument, ensemble |
Berlin | Berlin state monuments office | Table available for download | Architectural monument, ground monument, ensemble, garden monument |
Brandenburg | Brandenburg state office for the preservation of monuments | List available for download | Monuments, excavation protection areas, monument areas, architectural and artistic monuments |
Bremen | State office for monument preservation Bremen | List available on request | Movable monument, entire complex, individual monument |
Hamburg | Hamburg monument protection office | Table available for download | Object, ensemble |
Hesse | Hesse state office for monument preservation | Table available on request | Individual monument, ensemble |
Mecklenburg-Western Pomerania | Mecklenburg-Western Pomerania state monument preservation office |
Lists and tables available on request | None |
Lower Saxony | Lower Saxony state office for the preservation of monuments | Self-generated list via web scraping | Archaeology, architectural monuments (individual and groups), excavation protection areas |
North Rhine-Westphalia | Lower monument authority in the Rhineland | Per lower monument authority table received on request | None |
Lower monument authority Westphalia-Lippe | None | ||
Rhineland-Palatinate | State monument preservation Rhineland-Palatinate | List available for download | None |
Saarland | Saarland state monuments office | Table available on request/list available for download | Individual monument, ensemble |
Saxony | State office for monument preservation Saxony | Table available on request | Garden monument, cultural monument, technical monument |
Saxony-Anhalt | State office for monument preservation Saxony-Anhalt | Table available on request | Architectural monument, movable cultural monument, monument area, small monument area, small monument |
Schleswig-Holstein | State office for monument preservation Schleswig-Holstein | Table available on request | Tangible assets, buildings, landmarks |
Thuringia | Thuringian state office for monument preservation | Table available on request | Individual monument, ensemble |
3 Preparation of the Data
3.1 Harmonization of Data
Twelve federal states provided a categorization of existing monuments, which made it possible to narrow down our target category of “industrial monuments” from the outset. Where it was possible to make such a preselection, information was requested on the categories “architectural monument,” “ground monument,” “individual monument,” “object,” “cultural monument,” and “technical monument”; monuments in the categories “garden monument,” “foundation monument,” “excavation protection area,” and “small monument” were not included, as these are not to be regarded as industrial monuments in any case. In order to avoid double counting of individual monuments, “ensembles” and “complete complexes” (e.g., urban districts) were not included. The very rare “movable monuments,” which were only recorded in two federal states with a very small number of cases, were not taken into account as they cannot be clearly assigned to a specific location (region) in case of doubt. As a result, machine houses, for example, are assigned to a specific region as industrial monuments, whereas the machines themselves are not considered.
While some federal states specify a concrete object type per monument (e.g., church, brewery, residential building, etc.), in other federal states (Bavaria, North Rhine-Westphalia, Brandenburg, Mecklenburg-Western Pomerania, and Rhineland-Palatinate) this information is part of a short description of the respective monument. If the object type was part of a short description, this entry for each monument was truncated to the first 50 characters (letters and punctuation marks), while the information on the object type, which is always at the beginning of a short description, was retained. The reason for this procedure was that the deleted text was generally not relevant for our utilization efforts, and there was a risk that this text would lead to a false positive classification as part of the further process of identifying the industrial monuments using keyword matching.
In a number of federal states, no information on the year of construction of a monument was available. In those federal states where this information was generally available, the information proved to be so incomplete that we decided not to include it in the file.
When merging data from the individual monument authorities, the formats were harmonized and variable designations adapted. For each of the monuments included, the harmonized dataset includes information on the corresponding district, the corresponding federal state and the region type, as well as information on the object type (see Section 3.3).[6] For econometric analyses, it is advisable to control for these differences in the data per federal state using dummy variables (regional fixed effects).
3.2 Identification of Industrial Monuments
To further identify industrial monuments in the harmonized data, we essentially performed two processing steps. In a first step, keywords were first worked out by means of which the recorded devices, installations, and buildings can be assigned to the areas of production, traffic, and supply. The area of “bridges and aqueducts” is not assigned to the industrial monuments, as it is not possible to distinguish between private and commercial use, and is shown as a separate category. The “cross-catalog of technical monuments” by Föhl and Wolf (1988) was used for the allocation to the areas of production, traffic, and supply. This is a catalog that is divided into 11 lead sectors, 73 classification categories, and 410 key points (see Table A1 in the Online-Appendix).
These 11 lead sectors represent economic areas that are subdivided into associated branches of industry using the classification categories (see Table 2). For example, the lead sector “raw materials industry” includes the classification categories of iron ore mining, hard coal mining, and lignite mining. The lead sectors were initially assigned to the areas of production, traffic, and supply. Finally, the keywords relevant to us are formed from the remaining key points. For example, the keyword “foundry” is used from the keyword “iron and steel foundries” in order to identify both iron and steel foundries as well as other possible designations of foundries when comparing the raw data of the various federal states.
Classification of the lead sectors into the areas of production, traffic, and supply.
Lead sector | Production | Traffic | Supply | |
---|---|---|---|---|
1 | Raw materials industry | x | ||
2 | Bulk goods industry | x | ||
3 | Processing industries | x | ||
4 | Public supply | x | ||
5 | Power plants based on natural energy | x | ||
6 | Steam and explosion engine plants and buildings | x | ||
7 | Promotion | x | ||
8 | Communication equipment | x | ||
9 | Bridges and aqueductsa | |||
10 | Residential and commercial buildings | x | ||
11 | Other objects | x | x | x |
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Note: aBridges and aqueducts are not included in the classification and are recorded as a separate category in the data, as it is not possible to distinguish between private and commercial use.
In a further processing step, the keywords were used to carry out a matching process in which one of the keywords was searched with the contents of the variable “object type” (see Table 3). If there was such a match, it was assumed that a monument is an industrial monument. This procedure leads to a certain harmonization of the sometimes very different information provided by the monument authorities on the industrial monuments. As a result, industrial monuments in the areas of production, traffic, and supply can be differentiated on this basis. For the file to be made available, the number of industrial monuments in each of these categories was determined for each district and included in the file.
Keywords for the areas of production, traffic, and supply.
Production | Mining, scaffolding, shaft building, machine house, pay hall, washing hall, adit, winding tower, malakow, glassworks, coal bunker, coke bunker, cooling tower, pump house, grain mill, grain mill, factory, brewery, malthouse, distillery, winery, ironworks, steelworks, steel mill, rolling mill, wire drawing mill, foundry, forge, smelting works, refinery, hammer mill, spinning mill, weaving mill, dye works, printing works, sawmill, grinding mill, press shop, forge, locksmith’s shop, publishing house, workshop, cold store, grinding mill, pressing mill, stamping mill, warehouse, trade |
Traffic | Railroad station, local traffic, customs, lock keeper’s house, lighthouse, harbor, dock, harbor warehouse, hangar, post office |
Supply | District heating, waterworks, water collection, well, cistern, dam, water tower, waste water, power plant, storage plant, tensioning plant, fire station, fire department, power plant, water mill, wind power plant, windmill, steam plant, engine plant, steam boiler plant, filling station, department store |
3.3 Overview of the Variables
Table 4 summarizes the variables contained in the dataset and their meaning. All information is available at the level of rural districts and independent cities (NUTS3 regions) and can be aggregated for these types of region using the information on the respective labor market (Federal Institute for Research on Building, Urban Affairs and Spatial Development BBSR 2014) and spatial planning region (variables amr and ror). We use the territorial status in 2015 as a basis, which means that we provide information for a total of 402 districts and independent cities.[7]
List of variables.
Variable | Meaning |
---|---|
Region name | Name of the region |
kreis | County code |
nuts3 | NUTS3 code |
amr | Number of the labor market region as defined in 2014 |
amr_type3 | Number of the structural type of the labor market region as defined in 2014 |
amr_type3_bez | Designation of the structural type of the labor market region as defined in 2014 |
amr_name | Name of the labor market region as defined in 2014 |
ror | Number of the spatial planning region as defined in 2011 |
ror_name | Name of the spatial planning region as defined in 2011 |
fl15 | Area according to 2015 in km2 |
bev15 | Number of resident population 2015 |
bundesland | Name of the federal state |
produktion | Number of industrial monuments in the area of “production” |
versorgung | Number of industrial monuments in the area of “supply” |
verkehr | Number of industrial monuments in the area of “traffic” |
alle | Number of industrial monuments of all areas |
bridge | Number of bridges and aqueducts |
produktion_km | Number of industrial monuments in the area of “production” per 10 km2 |
versorgung_km | Number of industrial monuments in the area of “supply” per 10 km2 |
verkehr_km | Number of industrial monuments in the area of “traffic” per 10 km2 |
alle_km | Number of industrial monuments of all areas per 10 km2 |
produktion_bev | Number of industrial monuments in the area of “production” per 1,000 inhabitants |
versorgung_bev | Number of industrial monuments in the area of “supply” per 1,000 inhabitants |
verkehr_bev | Number of industrial monuments in the area of “traffic” per 1,000 inhabitants |
alle_bev | Number of industrial monuments of all areas per 1,000 inhabitants |
With information on area (in km2) and population in 2015, two measures are available to relate industrial monuments to the size and density of the region. Information on the number of industrial monuments in the categories of production, supply, transport, and bridges and aqueducts is included for each district. All information on monuments is related to the period 2021–2024.
3.4 Data Access
The dataset (Fritsch et al. 2024) is freely available from the Leibniz Institute for the Social Sciences (GESIS) at https://doi.org/10.7802/2821 and on the Homepage of Michael Fritsch at http://m-fritsch.de/material/in Excel format (.xlsx). The data are available in three regional delimitations: for districts or NUTS3 regions (variable kreis or nuts3), for labor market regions (variable amr), and for spatial planning regions (variable ror).
4 Spatial Structure of the Industrial Monuments
Table 5 shows the absolute number of industrial monuments identified by us for each federal state and their percentage share of the total number for Germany. This information is also included separately for the areas of production, supply, and transport, as well as for bridges and aqueducts, which we do not include in the industrial monuments in the narrower sense, but represent an independent category.
Number and proportion of industrial monuments by federal state.
Federal state | Number of industrial monuments | Share (%) nationwide | Production | Share (%) nationwide | Supply | Share (%) nationwide | Traffic | Share (%) nationwide | Bridges and aqueducts | Share (%) nationwide |
---|---|---|---|---|---|---|---|---|---|---|
Baden-Wuerttemberg | 5,697 | 19.86 | 1,542 | 14.83 | 3,399 | 29.53 | 756 | 11.16 | 1,214 | 17.14 |
Bavaria | 5,797 | 20.21 | 2,280 | 21.93 | 1,944 | 16.89 | 1,573 | 23.21 | 926 | 13.08 |
Berlin | 505 | 1.76 | 223 | 2.14 | 101 | 0.88 | 181 | 2.67 | 65 | 0.92 |
Brandenburg | 926 | 3.23 | 326 | 3.14 | 266 | 2.31 | 334 | 4.93 | 74 | 1.04 |
Bremen | 45 | 0.16 | 12 | 0.12 | 13 | 0.11 | 20 | 0.30 | 3 | 0.04 |
Hamburg | 462 | 1.61 | 284 | 2.73 | 82 | 0.71 | 96 | 1.42 | 197 | 2.78 |
Hesse | 2,247 | 7.83 | 506 | 4.87 | 1,042 | 9.05 | 699 | 10.32 | 955 | 13.48 |
Mecklenburg-Western Pomerania | 1,049 | 3.66 | 317 | 3.05 | 312 | 2.71 | 420 | 6.20 | 52 | 0.73 |
Lower Saxony | 1,678 | 5.85 | 568 | 5.46 | 869 | 7.55 | 241 | 3.56 | 770 | 10.87 |
North Rhine-Westphalia | 2,691 | 9.38 | 1,176 | 11.31 | 961 | 8.35 | 554 | 8.18 | 315 | 4.45 |
Rhineland-Palatinate | 1,544 | 5.38 | 361 | 3.47 | 699 | 6.07 | 484 | 7.14 | 322 | 4.55 |
Saarland | 479 | 1.67 | 193 | 1.86 | 79 | 0.69 | 207 | 3.05 | 135 | 1.91 |
Saxony | 2,978 | 10.38 | 1,799 | 17.30 | 649 | 5.64 | 530 | 7.82 | 1,403 | 19.81 |
Saxony-Anhalt | 1,040 | 3.63 | 421 | 4.05 | 305 | 2.65 | 314 | 4.63 | 221 | 3.12 |
Schleswig-Holstein | 585 | 2.04 | 166 | 1.60 | 199 | 1.73 | 220 | 3.25 | 113 | 1.60 |
Thuringia | 960 | 3.35 | 224 | 2.15 | 589 | 5.12 | 147 | 2.17 | 317 | 4.48 |
Germany (total) | 28,683 | 100.00 | 10,398 | 100.00 | 11,509 | 100.00 | 6,776 | 100.00 | 7,082 | 100.00 |
We were able to identify a total of 28,683 industrial monuments, most of which are located in Baden-Wuerttemberg (19.86 %) and Bavaria (20.21 %), followed by Saxony (10.38 %) and North Rhine-Westphalia (9.38 %). The largest number of industrial monuments (just under 11,500) are in the supply area; just over 10,000 industrial monuments are in the production area, and around 6,700 industrial monuments are in the traffic area. A total of around 7,000 monuments are bridges and aqueducts.
Bavaria (21.93 % of all industrial monuments in this category nationwide) and Saxony (17.3 %) dominate the industrial monuments in production. Baden-Wuerttemberg accounts for the third largest share of industrial monuments in this area (14.83 %). A similar picture emerges for the distribution of industrial monuments in the supply area. Here again, Baden-Wuerttemberg (29.53 %) and Bavaria (16.89 %) have the highest shares; the third-highest share is in Hesse (9.05 %). Bavaria accounts for almost a quarter (23.21 %) of all industrial monuments in the traffic area, followed by Baden-Wuerttemberg (11.16 %) and Hesse (10.32 %). The largest number of monuments in the area of bridges and aqueducts can be found in Saxony (19.81 %), Baden-Wuerttemberg (17.14 %), and Hesse (13.48 %).
Table 6 shows descriptive statistics for the total number of industrial monuments, as well as separately for the areas of production, supply, traffic, and bridges and aqueducts per district (402 districts or NUTS3 region). On average, a district has just under 71 industrial monuments, with 26 monuments in production, 29 in supply, and 17 in traffic. The average number of monuments in the area of bridges and aqueducts is just under 18. All districts have at least three industrial monuments. The number of districts without a monument in the area of production/supply/traffic/bridges and aqueducts is 3/4/7/16.
Distribution of industrial monuments at district level.
Mean value | Minimum | Maximum | Standard deviation | |
---|---|---|---|---|
Industrial monuments | 71.35 | 3 | 1,736 | 106.86 |
Production | 25.87 | 0 (3) | 626 | 45.51 |
Supply | 28.63 | 0 (4) | 614 | 42.17 |
Traffic | 16.86 | 0 (7) | 496 | 29.94 |
Bridges and aqueducts | 17.62 | 0 (16) | 330 | 32.52 |
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Note: In parentheses: number of districts without a monument in the relevant category.
A comparison of the absoluvte number of monuments between regions makes little sense if the size of a region is not taken into account. For this purpose, it makes sense to relate the number of monuments to the population or to the area of the region. When calculating the number of industrial monuments in relation to the population, it should be borne in mind that this is not the same as the density of people who have made memorable contributions to the industrial development of the region. The number of industrial monuments in relation to the current population is very strongly influenced by the population development that has taken place over the last few centuries, in particular by population migration.
Figure 1 shows the geographical distribution of industrial monuments per inhabitant. It is noticeable that many eastern German regions have comparatively high values. The high density of industrial monuments, particularly in the south of the former GDR, is apparently due to the fact that these regions were among the most industrialized regions of the former German Reich before the Second World War and the division of Germany, some of which have a long tradition. A high number of industrial monuments per 1,000 inhabitants can also be found in many regions in the center and southwest of Germany. In comparison, the geographical distribution of industrial monuments per 10 square kilometers, especially cities and agglomerations, stand out in Figure 2. Further maps on the distribution of industrial monuments at labor market region level as well as separate analyses of the three areas of production, traffic, and supply can be found in the Online-Appendix.

Number of industrial monuments per 1,000 inhabitants in Germany (districts).

Number of industrial monuments per 10 km2 in Germany (districts).
5 Conclusion and Outlook
Industrial monuments represent important elements of the historical heritage of regions. This report describes the process of creating the first database of industrial monuments in Germany’s regions. With this database, we are closing an important data gap, which helps better analyzing regional development. The data allow for rich analyses on how historical economic structures affect current economic and social behavior and development. Particularly, the data help understanding whether there is a collective memory of industrial tradition that affects regions long into the future.
Initial evaluations of spatial structure of the industrial monuments (Section 4) reveal major regional differences. More in-depth analyses will certainly provide new insights into the influences of regional history and culture on development.
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Supplementary Material
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