|
|
Fig1_Heat_Consumers_Siechnice.jpg
JPEG Image - 1.1 MB - Jun 29, 2026 - 0 DownloadsMD5: 58328c93abb1a19b4905984283c0f075
License: CC BY - Creative Commons Attribution 4.0
This dataset contains JPG figures created for the publication “Least-Cost-Path and Closest Facility Analysis for Generating District Heating Networks on a Communal Level”. The files document the spatial analyses and cartographic outputs used to evaluate the potential development of district heating networks at the municipal scale.
The dataset consists of map figures exported in JPG format from ArcGIS Pro. The maps present the results of geospatial analyses, including the spatial distribution of heat consumers, heat sources, transportation infrastructure, and proposed district heating network routes generated using Least-Cost-Path (LCP) and Closest Facility (CF) methods. Building categories shown in the maps include single-family, multi-family, commercial, industrial, and other buildings. The figures also contain map elements such as legends, scale bars, and north arrows.
One of the figures presents the spatial distribution of heat consumers in Siechnice, Poland. Buildings connected to or considered for connection to the district heating system are classified according to their function, and the location of the heat source and street network are shown to provide spatial context for network planning and optimization.
All figures were generated using ArcGIS Pro software. The source data used to create the maps included geospatial information on buildings, road networks, and heat supply infrastructure. The JPG files are organized as individual figures corresponding to specific stages of the analysis and results presented in the publication.
Abbreviations used in the study include:
* LCP – Least-Cost Path,
* CF – Closest Facility,
* DH – District Heating,
* GIS – Geographic Information System.
The dataset is intended to support the interpretation and reproducibility of the analyses presented in the publication and may be useful for researchers, planners, and engineers working on district heating network design and municipal energy planning.
| |
|
|
Fig2_Cost_Surface_LCPA_Method.jpg
JPEG Image - 370.2 KB - Jun 29, 2026 - 0 DownloadsMD5: d871b71442f6494b81adf0b2722af5b1
License: CC BY - Creative Commons Attribution 4.0
This dataset contains JPG map figures created for the publication “Least-Cost-Path and Closest Facility Analysis for Generating District Heating Networks on a Communal Level”. The figures document the methodology, intermediate processing steps, and final results of GIS-based analyses performed to identify optimal district heating network routes at the municipal scale, using the town of Siechnice, Poland, as a case study.
All files are provided in JPG format and were created using ArcGIS Pro. The figures are organized as individual map outputs illustrating different stages of the spatial analysis workflow. Each file includes cartographic elements such as legends, scale bars, north arrows, and thematic symbology used to visualize the results.
The dataset includes, among others:
• Figures presenting the spatial distribution of heat consumers classified into building categories, including single-family, multi-family, commercial, industrial, and other buildings. These maps also show the street network and the location of the heat source used in the analysis.
• Figures presenting cost-surface analyses generated for the Least-Cost-Path Analysis (LCPA) method. Cost-surface maps visualize the accumulated routing cost from the heat source to surrounding areas and identify locations with lower or higher connection costs. These layers constitute the basis for determining optimal district heating network corridors.
• Figures illustrating the outputs of Least-Cost-Path (LCP) and Closest Facility (CF) analyses used to design and evaluate potential district heating network layouts.
The maps were produced using geospatial datasets describing buildings, transportation infrastructure, land use, and heat supply infrastructure. The analyses were performed within a GIS environment to support municipal-scale energy planning and district heating network development.
Abbreviations used in the dataset include:
• GIS – Geographic Information System
• DH – District Heating
• LCP – Least-Cost Path
• LCPA – Least-Cost-Path Analysis
• CF – Closest Facility
The dataset is intended to support the interpretation, transparency, and reproducibility of the analyses presented in the associated publication. It may be useful for researchers, municipal planners, energy analysts, and engineers involved in district heating system planning and optimization.
| |
|
|
Fig4_Generated_DH_Network_Closest_Facility_Method.jpg
JPEG Image - 1021.1 KB - Jun 29, 2026 - 0 DownloadsMD5: f2b8f9a50da11479a25b1f4a52483e29
License: CC BY - Creative Commons Attribution 4.0
A map showing the district heating network generated using the Closest Facility (CF) method. The figure presents the network routes connecting heat consumers (represented by building centroids) to the heat source through the shortest available paths within the transportation network. Blue lines represent the proposed district heating network, while red points indicate heat consumer locations. The map illustrates the final network configuration obtained using the Closest Facility approach and enables comparison with the network generated using the Least-Cost-Path Analysis (LCPA) method in terms of spatial layout, connectivity, and potential infrastructure requirements. | |
|
|
Fig5_Scheme_LCPA_Method.jpg
JPEG Image - 56.1 KB - Jun 29, 2026 - 0 DownloadsMD5: 63787ce95cc584bfccd8740d310299f3
License: CC BY - Creative Commons Attribution 4.0
Methodological workflow of the Least-Cost-Path Analysis implemented in ArcGIS Pro, showing the sequence of geoprocessing tools and intermediate datasets used to generate the district heating network.
workflow diagram illustrating the GIS processing steps used in the Least-Cost-Path Analysis (LCPA) method for district heating network generation. The scheme presents the sequence of geoprocessing operations performed in ArcGIS Pro, including conversion of building footprints to centroid points, snapping demand points to the street network, rasterization of the transportation network, cost-distance calculation from the heat source, generation of backlink and cost-distance rasters, and creation of the final least-cost network routes using the Cost Path as Polyline tool. The diagram documents the methodological workflow applied in the study and supports the reproducibility of the analysis. | |
|
|
Fig7_siechnice_PETA2.jpg
JPEG Image - 610.9 KB - Jun 29, 2026 - 0 DownloadsMD5: f4d139a5dbf444c4ec71b613868be7f6
License: CC BY - Creative Commons Attribution 4.0
The data used in this study are derived from the Pan-European Thermal Atlas 4.2 (PETA 4.2), an interactive mapping tool developed within the Heat Roadmap Europe project. The atlas provides a spatially explicit representation of Europe’s thermal energy system, including detailed information on heating and cooling demand, renewable energy potentials, and industrial waste heat sources.
PETA 4.2 offers high-resolution spatial data (down to hectare-scale resolution), enabling detailed analysis of local heat demand distribution. The dataset includes, among others:
spatial distribution of heating and cooling demand,
potential for renewable heat sources,
availability of industrial waste heat,
existing and potential district heating areas,
identification of heat synergy regions where demand and supply can be efficiently integrated.
By combining demand and supply-side information, PETA 4.2 supports the identification of spatial energy system interactions and provides a valuable basis for the planning and optimization of district heating networks and local energy transitions. | |
