<p>We share dataset of all general elections held in Poland between 1990 and 2024. These datasets correspond to two degrees of spatial aggregation: 41 constituencies for the Sejm and 94 for the Senate. In this dataset, for the first time, it was possible to collate voting results for electoral committees in unified territorial units. Such a compilation is unique and difficult to compile due to the multiple changes of administrative boundaries in Poland. In this dataset, we also organize more than 300 unique election committees into a uniform division into 5 basic political options. This data is publicly available but the way they are presented makes it impossible to create maps and comparisons, subject to time-consuming processing into any of the GIS systems. Therefore, we attach to the data a vector (SHP) map of electoral districts for the both chambers of the Polish parliament, to which it is easy to attach the developed relational dataset in XLS format in any year and aggregation.</p>

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General elections in Poland (1990–2024) in the unified territorial aggregation: GeoElections Poland 1.0

  • Przemysław Śleszyński,
  • Mariusz Kowalski

摘要

We share dataset of all general elections held in Poland between 1990 and 2024. These datasets correspond to two degrees of spatial aggregation: 41 constituencies for the Sejm and 94 for the Senate. In this dataset, for the first time, it was possible to collate voting results for electoral committees in unified territorial units. Such a compilation is unique and difficult to compile due to the multiple changes of administrative boundaries in Poland. In this dataset, we also organize more than 300 unique election committees into a uniform division into 5 basic political options. This data is publicly available but the way they are presented makes it impossible to create maps and comparisons, subject to time-consuming processing into any of the GIS systems. Therefore, we attach to the data a vector (SHP) map of electoral districts for the both chambers of the Polish parliament, to which it is easy to attach the developed relational dataset in XLS format in any year and aggregation.