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 The aim of **PyHeatDemand** is to provide processing tools for heat demand input data of various categories on various scales. This includes heat demand input data provided as rasters or gridded polygons, heat demand input data associated with administrative areas (points or polygons), with building footprints (polygons), with street segments (lines), or with addresses directly provided in kWh or MWh but also as gas usage, district heating usage, or other sources of heat. It is also possible to calculate the heat demand based on a set of cultural data sets (building footprints, height of the buildings, population density, building type, etc.). The study area is first divided into a coarse mask before heat demands are calculated and harmonized for each cell with the size of the target resolution (e.g. 100 m x 100 m for states). We hereby make use of different spatial operations implemented in the GeoPandas and Shapely packages. The final heat demand map will be created utilizing the Rasterio package. Next to processing tools for the heat demand input data, workflows for analyzing the final heat demand map through the Rasterstats package are provided. 
 
-**PyHeatDemand** was developed as a result of works carried out within the Interreg NWE project DGE Rollout (Rollout of Deep Geothermal Energy).
+**PyHeatDemand** was developed as a result of works carried out within the Interreg NWE project DGE Rollout (Rollout of Deep Geothermal Energy). The development and maintenance of **PyHeatDemand** will continue in the future beyond the duration of the project. This will include adding bottom-up workflows based on building specifics to calculate the heat demand.
 
 
 # Statement of need 
 Space and water heating for residential and commercial buildings amount to a third of the European Union’s total final energy consumption. Approximately 75% of the primary energy and 50% of the thermal energy are still produced by burning fossil fuels, leading to high greenhouse gas emissions in the heating sector. The transition from centralized fossil-fueled district heating systems such as coal or gas power plants to district heating systems sourced by renewable energies such as geothermal energy or more decentralized individual solutions for city districts makes it necessary to map the heat demand for a more accurate planning of power plant capacities. In addition, heating and cooling plans become necessary according to directives of the European Union regarding energy efficiency to reach its aim of reducing greenhouse gas emissions by 55% of the 1990-levels by 2030. 
 
-Evaluating the annual heat demand (HD, usually in MWh = megawatt Hours) on a national or regional scale, including space and water heating for each apartment or each building for every day of a year separately is from a perspective of resolution (spatial and temporal scale) and computing power not feasible. Therefore, heat demand maps summarize the heat demand on a lower spatial resolution (e.g. 100 m x 100 m raster) cumulated for one year (lower temporal resolution) for different sectors such as the residential and tertiary sectors. Maps for the industrial heat demand are not available as the input data is not publicly available or can be deduced from cultural data. Customized solutions are therefore necessary for this branch to reduce greenhouse gas emissions. Heat demand input values for the residential and commercial sectors are easily accessible and assessable. With the new directives regarding energy efficiency, it becomes necessary for every city or commune to evaluate their heat demand. And this is where **PyHeatDemand** comes into place. Combining the functionality of well-known geospatial Python libraries, the open-source package **PyHeatDemand** provides tools for public entities, researchers, or students for processing heat demand input data associated with an administrative area (point or polygon), with a building footprint (polygon), with a street segment (line), or with an address directly provided in MWh but also as gas usage, district heating usage, or other sources of heat. The resulting heat demand map data can be analyzed using zonal statistics and can be compared to other administrative areas when working on regional or national scales. If heat demand maps already exist for a specific region, they can be analyzed using tools within **PyHeatDemand**. With **PyHeatDemand**, it has never been easier to create and analyze heat demand maps.  
+Evaluating the annual heat demand (HD, usually in MWh = megawatt Hours) on a national or regional scale, including space and water heating for each apartment or each building for every day of a year separately is from a perspective of resolution (spatial and temporal scale) and computing power not feasible. Therefore, heat demand maps summarize the heat demand on a lower spatial resolution (e.g. 100 m x 100 m raster) cumulated for one year (lower temporal resolution) for different sectors such as the residential and tertiary sectors. Maps for the industrial heat demand are not available as the input data is not publicly available or can be deduced from cultural data. Customized solutions are therefore necessary for this branch to reduce greenhouse gas emissions. Heat demand input values for the residential and commercial sectors are easily accessible and assessable. With the new directives regarding energy efficiency, it becomes necessary for every city or commune to evaluate their heat demand. And this is where **PyHeatDemand** comes into place. Combining the functionality of well-known geospatial Python libraries, the open-source package **PyHeatDemand** provides tools for public entities, researchers, or students for processing heat demand input data associated with an administrative area (point or polygon), with a building footprint (polygon), with a street segment (line), or with an address directly provided in MWh but also as gas usage, district heating usage, or other sources of heat. The resulting heat demand map data can be analyzed using zonal statistics and can be compared to other administrative areas when working on regional or national scales. If heat demand maps already exist for a specific region, they can be analyzed using tools within **PyHeatDemand**. With **PyHeatDemand**, it has never been easier to create and analyze heat demand maps. 
+
+Python libraries for calculating heat demands are sparse, especially for aggregating heat demand on various scales and categories. While UrbanHeatPro [@urbanheatpro] utilizes a bottom-up approach to calculate heat demand profiles for urban areas, the Heat package by Malcolm Peacock [@heat] generates heat demand time series from weather for EU countries. Repositories containing processing code for larger transnational heat demand projects like Hotmaps and Heat Roadmap Europe are unknown.
+
 
 # PyHeatDemand Functionality 
 
@@ -85,13 +88,6 @@ If no heat demand input data is available, the heat demand can be estimated usin
 
 Heat demand maps may contain millions of cells. Evaluating each cell would not be feasible. Therefore, **PyHeatDemand** utilizes the rasterstats package [@rasterstats] returning statistical values of the heat demand map for further analysis and results reporting.
 
-# State of the field
-
-Python libraries for calculating heat demands are sparse, especially for aggregating heat demand on various scales and categories. While UrbanHeatPro [@urbanheatpro] utilizes a bottom-up approach to calculate heat demand profiles for urban areas, the Heat package by Malcolm Peacock [@heat] generates heat demand time series from weather for EU countries. Repositories containing processing code for larger transnational heat demand projects like Hotmaps and Heat Roadmap Europe are unknown.
-
-# PyHeatDemand Outlook
-The development and maintenance of **PyHeatDemand** will continue in the future. This will include adding bottom-up workflows based on building specifics to calculate the heat demand. In addition, we welcome contributions of users in the form of questions on how to use **PyHeatDemand**, bug reports, and feature requests. 
-
 # PyHeatDemand Resources 
 
 The following resources are available for **PyHeatDemand**:
@@ -101,6 +97,8 @@ The following resources are available for **PyHeatDemand**:
 * Application of **PyHeatDemand** for transnational heat demand mapping [@Juestel2024_HeatDemand]
 * [DGE Rollout Webviewer](https://data.geus.dk/egdi/?mapname=dgerolloutwebtool#baslay=baseMapGEUS&extent=39620,-1581250,8465360,8046630&layers=dge_heat_final) 
 
+We welcome contributions of users in the form of questions on how to use **PyHeatDemand**, bug reports, and feature requests. 
+
 # Acknowledgements
 
 We would like to thank the open-source community for providing and constantly developing and maintaining great tools that can be combined and utilized for specific tasks such as working with heat demand data. The original codebase was developed within the framework of the Interreg NWE project DGE Rollout (Rollout for Deep Geothermal Energy) by Eileen Herbst and Elias Khashfe [@herbst]. It was rewritten and optimized for **PyHeatDemand**.