Download high-quality, up-to-date The Netherlands shapefile boundaries (SHP, projection system SRID 4326). Our The Netherlands Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Sint Maarten administrative level 0 (constituent country) and 1 (district) boundary polygon and line shapefiles, and geodatabase, and gazetteer

The administrative level 0 and 1 shapefiles and geodatabase layers are suitable for database or GIS linkage to the Sint Maarten administrative level 0-2 population statistics CSV population statistics tables.

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High) Background research data The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis. LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment. Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment. Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague. Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022. Climate Change: Computations of climate-related parameters based on KNMI climate scenarios. Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid. Input data Geographic divisions

The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service. District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague. The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file. The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague. These layers have been combined in the GeographicDivisions_TheHague GeoPackage. BAG data

BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page. In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline. The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage. 3D BAG

Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website. These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash. Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage. Ground elevation data was obtained from the Pand layer. Both of these layers were clipped to match The Hague's outline. Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building. The resulting data can be found in the BAG_buildings_TheHague GeoPackage. Energy labels

Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv. UHI effect data

A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff. Output data

The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage. The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:

SQ: Status Quo, covering the 2018-2022 reference period. 2030: An average scenario projected for the year 2030. 2050_L: A low-impact, best-case scenario for 2050. 2050_M: A medium-impact, moderate scenario for 2050. 2050_H: A high-impact, worst-case scenario for 2050.