Abstract
A significant obstacle to spatial epidemiology in healthcare facilities is the absence of computationally amenable maps of the underlying space. Spatial data for built spaces are typically stored in computer aided design (CAD) architectural files which are difficult to parse, query, and combine with other data sources. To alleviate this difficulty, we design a tool, cad2graph, which automatically extracts spatial maps from CAD files. To ensure that the spatial map is easily amenable to computation, we represent it as a graph whose vertices represent spatial units of a uniform size and whose edges represent obstacle-free, walkable paths of uniform length connecting adjacent pairs of spatial units. cad2graph extracts key information such as walls, doors, and room labels from the CAD file and through a series of geometric transformations, extracts a spatial graph.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adhikari, B., Lewis, B., Vullikanti, A., Jiménez, J.M., Prakash, B.A.: Fast and near-optimal monitoring for healthcare acquired infection outbreaks. PLoS Comput. Biol. 15(9), e1007284 (2019)
Cruz-Correia, R., et al.: Integration of hospital data using agent technologies-a case study. Artif. Intell. Commun. 18(3), 191–200 (2005)
Curtis, D.E., Hlady, C.S., Kanade, G., Pemmaraju, S.V., Polgreen, P.M., Segre, A.M.: Healthcare worker contact networks and the prevention of hospital-acquired infections. PLoS ONE 8(12), e79906 (2013)
Curtis, D.E., Hlady, C.S., Pemmaraju, S.V., Polgreen, P.M., Segre, A.M.: Modeling and estimating the spatial distribution of healthcare workers. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 287–296 (2010)
Hasan, D.H., et al.: Modeling and evaluation of clustering patient care into bubbles. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 73–82. IEEE (2021)
Jang, H., Pai, S., Adhikari, B., Pemmaraju, S.V.: Risk-aware temporal cascade reconstruction to detect asymptomatic cases: for the CDC mind healthcare network. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 240–249. IEEE (2021)
Monsalve, M.N., Pemmaraju, S.V., Thomas, G.W., Herman, T., Segre, A.M., Polgreen, P.M.: Do peer effects improve hand hygiene adherence among healthcare workers? Infect. Control Hosp. Epidemiol. 35(10), 1277–1285 (2014)
Pai, S., Polgreen, P.M., Segre, A.M., Sewell, D.K., Pemmaraju, S.V., et al.: Spatiotemporal clustering of in-hospital clostridioides difficile infection. Infect. Control Hosp. Epidemiol. 41(4), 418–424 (2020)
Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Maitra, P. et al. (2023). Cad2graph: Automated Extraction of Spatial Graphs from Architectural Drawings. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-43430-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43429-7
Online ISBN: 978-3-031-43430-3
eBook Packages: Computer ScienceComputer Science (R0)
