| Title: |
A dataset of logistics sites in England and Wales: Location, size, type and loading bays. |
| Authors: |
de Saxe, Christopher; Ainalis, Daniel; Cebon, David |
| Publisher Information: |
Elsevier; Department of Engineering; //doi.org/10.1016/j.dib.2024.110399 |
| Publication Year: |
2024 |
| Collection: |
Apollo - University of Cambridge Repository |
| Subject Terms: |
Electrification; Factories; Freight; Heavy goods vehicles; Retail; Transport; Warehouses |
| Description: |
Data on the location and size of logistics sites is essential for the accurate system-level modelling of transport and logistics operations. This is becoming increasingly important to support governments and industry transition to a net zero future which will feature new operating models and vehicle technologies, particularly for electric vehicle operations. In this work we present a dataset of logistics sites across England and Wales categorised into warehouses, retail sites, and factories. There are 47,683 rows of data in total, comprising 27,691 warehouse sites, 6,441 retail sites, and 13,551 factory sites. Each row contains the site's category, location (latitude and longitude), size (in square meters), and modelled number of heavy goods vehicle loading bays. Raw data on non-domestic properties in England and Wales were sourced from the UK's Valuation Agency Office database. Addresses were geocoded to determine the coordinates of each site, floor area was determined for each site via a web crawler script, and the type of site was derived using a keyword-based categorisation process. The size of the site gives an indication of the expected transport activity (i.e. volume of goods handled) and is a useful proxy to estimate the number of loading bays which, in turn, is a useful proxy for the number of electric heavy goods vehicle charging points the site may have to accommodate to support electric vehicle operations. Models relating the floor area to the number of loading bays were developed using satellite imagery of a sample of data from each category. Uncertainty in the geolocation, category and floor area data is deemed to be very low |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://www.repository.cam.ac.uk/handle/1810/367271; https://doi.org/10.17863/CAM.107914 |
| DOI: |
10.17863/CAM.107914 |
| Availability: |
https://www.repository.cam.ac.uk/handle/1810/367271; https://doi.org/10.17863/CAM.107914 |
| Rights: |
Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.F26C064F |
| Database: |
BASE |