Geographically corrected clustering applied to establish medical service areas

Standard

Geographically corrected clustering applied to establish medical service areas. / Полицинский, Никита Сергеевич ; Кузнецов, Илья Сергеевич ; Паниди, Евгений Александрович.

In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLVIII-5-2024, 12.11.2024, p. 83-88.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{00001cbe9aa94b78a5215fcc7cfd3fb8,

title = "Geographically corrected clustering applied to establish medical service areas",

abstract = "In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.",

keywords = "Geospatial Data Management, Medical Geospatial Data, Medical Service Areas, QGIS",

author = "Полицинский, {Никита Сергеевич} and Кузнецов, {Илья Сергеевич} and Паниди, {Евгений Александрович}",

year = "2024",

month = nov,

day = "12",

doi = "10.5194/isprs-archives-XLVIII-5-2024-83-2024",

language = "English",

volume = "XLVIII-5-2024",

pages = "83--88",

journal = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

issn = "1682-1750",

publisher = "International Society for Photogrammetry and Remote Sensing",

note = "null ; Conference date: 06-08-2024 Through 08-08-2024",

url = "https://www.isprs.org/tc5-symposium2024/index.html",

}

RIS

TY - JOUR

T1 - Geographically corrected clustering applied to establish medical service areas

AU - Полицинский, Никита Сергеевич

AU - Кузнецов, Илья Сергеевич

AU - Паниди, Евгений Александрович

PY - 2024/11/12

Y1 - 2024/11/12

N2 - In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.

AB - In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.

KW - Geospatial Data Management

KW - Medical Geospatial Data

KW - Medical Service Areas

KW - QGIS

UR - https://www.mendeley.com/catalogue/8b54ab95-9bb0-3cb8-94b8-c43441c67a05/

U2 - 10.5194/isprs-archives-XLVIII-5-2024-83-2024

DO - 10.5194/isprs-archives-XLVIII-5-2024-83-2024

M3 - Article

VL - XLVIII-5-2024

SP - 83

EP - 88

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

Y2 - 6 August 2024 through 8 August 2024

ER -

ID: 127652097