Monument inventories have existed in Europe since the 19th century, but only a few are still available digitally today. The following article discusses how relevant data on the monument inventory of the German Democratic Republic (GDR) and the Federal Republic of Germany (FRG) can be found in (digital) archives/repositories and why it makes sense to transfer information from analog sources into digital formats. Furthermore, the data quality of current digital data on monument inventories in Germany is examined, followed by the derivation of further steps for the visualization of various monument inventories. Three different approaches will be evaluated: 1. the use of Python, 2. the use of FactGrid and 3. the use of WissKi in combination with a Flask framework. These approaches will be demonstrated using the case study of the Berlin monument inventory at the time of the merging of monument lists from the GDR and the FRG.

Inventories and Maps of Monument Inventories in Germany
Digital maps depicting the current inventory of monuments exist in great numbers; however, thus far, they have always been limited to individual federal states or monument regions and only rarely designed to be interoperable or reusable.
Furthermore, they all lack a temporal component, creating the impression that monument registries are an immutable product. The project outlined here contributes to the creation of an interactive platform aimed at highlighting historical changes in the monument inventory, strengthening awareness of cultural heritage in Germany, and promoting dialogue on monument conservation and inventory processes.
The focus of the project lies in the development of an innovative, map-based web application that allows for the visualization of temporal and spatial relationships and changes. This not only fosters understanding of the history of the monument inventory but also facilitates the identification of trends in societal and political engagement with it.
Due to the multitude of lists, inventories, and other data and source collections, this will be exemplified within the scope of the presentation using the federal state of Berlin.

Data Quality and Methodology

When examining the data and sources, three problems immediately become apparent:
1. Outdated street and object designations that hinder clear assignment,
2. General lack of geodata/georeferencing, and
3. The challenge of capturing analog information as well as transparency regarding the provincial nature of the data.
Furthermore, questions arise regarding how geodata is modeled and processed. This includes the standardization or transformation of coordinates. In the case described here, the decision was made for the decimal degree system. In the absence of coordinate data, spatial reference information was assigned through geocoding using Python. Regarding GIS data quality, efforts were made to adhere to the appropriate ISO 19157. Additional information on objects that could be extracted from the topographies was also stored in an OpenDocument database at first hand.
Three different approaches are presented in this study with the aim of evaluating possible ways for creating an application that is as sustainable and lightweight as possible and that can be connected to other projects and initiatives, such as the National Research Data Infrastructure.

1. Inventories of Monuments in a Python Environment
   In terms of the methodological approach, it may be surprising that established GIS software is not used. However, this decision was consciously made because working in a Python environment does not require software installation, thus being resource-efficient and flexible (also regarding different operating systems). Moreover, the focus of the project study presented here lies on the visualization and analysis of geodata representation rather than the capture and management of extensive datasets. Working with Python also allows for easy export options for visualizations, enabling straightforward embedding into web applications. Additionally, various forms of visualization can be quickly implemented and easily adjusted and re-visualized depending on the question at hand. The folium library builds on the strengths of the Python ecosystem in data processing and the strengths of the Leaflet.js library in mapping, making it easy to manipulate data in a Leaflet map. In addition to representing data in the form of points (markers), polygons can also be created.

2. Inventories of Monuments in FactGrid
   Using FactGrid to store and analyze information about heritage inventories offers several compelling advantages. FactGrid provides a structured and collaborative platform specifically designed for storing and organizing historical data. This structured approach ensures that information related to heritage inventories can be systematically cataloged and easily retrieved, facilitating efficient research and analysis. Furthermore FactGrid supports the creation of interconnected datasets, allowing researchers to explore complex relationships between different elements of heritage inventories. By linking related data points within the FactGrid framework, users can uncover hidden patterns, trends, and connections that may not be immediately apparent through traditional methods of analysis.

3. Inventories of Monuments in WissKI in combination with a Flask-Framwork
   Combining WissKI with a Flask framework presents a compelling approach  for analyzing monument inventories in space and time due to several key  advantages. WissKI provides a structured environment specifically designed for  managing and analyzing complex historical data, including monument  inventories. Its flexible data model allows for the organization of  spatial and temporal information in a coherent manner, enabling  researchers to capture the nuanced relationships between monuments,  locations, and historical contexts. By integrating Flask with WissKI, researchers can create interactive  visualization tools and analytical interfaces that leverage the  structured data stored within WissKI, enabling dynamic exploration of  monument inventories across different spatial and temporal dimensions. Moreover, the combination of WissKI and Flask enables scalability and  interoperability, allowing researchers to seamlessly integrate  additional data sources and analytical tools as needed.