In this study, we aimed to further the international discussion on the methodology of applying GIS technology to the editing of large-scale cadastral maps, taking the experience of editing the cadastral map of Toruń from 1910–1915 as an example. We present the concept of building a georeferential spatial database of topographic–historical objects (GSDoT-HO), which includes the stages involved in creating the database, its exemplary structure, and a proposal of good practices in this process, which were developed in the course of previous projects using a geographic information system for Historical Atlases of Polish Towns. Our works included the scanning, calibration, and rectification of a total of 178 sheets of cadastral maps (including 154 sheets of the map of Toruń and 24 sheets of the cadastral map of the then-village of Mokre) at differentiated scales of 1:250, 1:500, 1:1000, and 1:2000. Finally, in the process of vectorization, vector and attribute data were acquired, which made up the final result in the form of GSDoT-HOs. This database was created out of seven information layers with linear or polygon geometries, including the two most important layers, i.e., plots and buildings, which for the then-area of the city of Toruń, contained approximately 5800 and 10,800 vectorised polygon objects, respectively. This article shifts the focus of the discussion of standards in the use of GIS technology to edit Historic Towns Atlases from the development of interactive maps to the construction of a database that should enable comparative studies of urban spaces.

The purpose of the work was to identify the hidden relationship between water consumption and meteorological factors, using principal component analysis. In addition, clusters of similar days were identified based on relationships identified by k-means. The study was based on data from the city of Toruń (Poland). The analysis was based on daily data from 2014–2017 divided into three groups. Group I included data from the entire period, Group II- from warm half-years (April–September), and Group III-from cold half-years (January–March and October–December). For Groups I and II the extent of water consumption was explained by two principal components. PC1 includes variables that increase water consumption, and PC2 includes variables that lessen water demand. In Group III, water consumption was not linked to any component. The k-means method was used to identify clusters of similar days. In terms of PC1, the most numerous days were Saturdays, and in terms of PC2 Sundays and holidays. It was determined that further research aimed at explaining the specificity of water consumption on particular days of the week is appropriate.