Building and Neighborhood Data Considering Energy in Dublin, Ireland

Description

Central to our investigation is the Building Energy Rating (BER) dataset of Ireland, sourced from GeoDirectory data. This dataset provides a foundational resource, encompassing a comprehensive range of building-scale parameters such as detailed addresses, geocode data (latitude and longitude), area, height, number of floors, roof type, construction age, radon emission, HVAC system details, and bedroom and bathroom counts. To enrich this dataset, we incorporated additional features from the Digital Landscape Models (DLM) Core Data from Tailte Éireann Surveying (PRIME2 Dataset). These enhancements include metrics such as nearest neighbor buildings, the density of the built environment surrounding each building, and district attributes like the ratio of green area to non-green area in the urban vicinity.

Steps to reproduce

Utilizing datasets like the GeoDirectory Building Energy Ratings (BER) dataset of Ireland, supplemented by data of Digital Landscape Models (DLM) Core Data from Tailte Éireann Surveying (PRIME2 Dataset), land use map of Dublin, we acquire both geometric and non-geometric data related to buildings in Dublin at both building and neighborhood scales. These datasets enable us to perform effective neighborhood-scale analysis and built environment analysis within a geospatial context. Our methodology employs a diverse array of tools and software, including programming languages such as MATLAB and Python ( in the Jupyter Notebook interface), with libraries such as Geopandas, Pandas, NumPy, Seaborn, and Scikit-learn were used for data processing and analysing. Additionally, we conduct geospatial analyses using toolbox and plugins of the ArcGIS and QGIS software. Our approach involves data collection encompassing various parameters such as building attributes, neighborhood characteristics, and urban-scale built environment metrics at both building and neighborhood scales.

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