Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

Autor: Rhiannan Price, Amalia Mendes, Sidney Brown, Noha H. Farag, Julie Espey, Apoorva Mallya, Maureen Martinez, Andrew S Berens, Brian Kaplan, Tess Palmer
Rok vydání: 2021
Předmět:
Adult
Geographic information system
Geospatial analysis
Building footprints
010504 meteorology & atmospheric sciences
General Computer Science
Computer science
Computer applications to medicine. Medical informatics
Feature extraction
R858-859.7
0211 other engineering and technologies
02 engineering and technology
computer.software_genre
01 natural sciences
Mapathon
Statistics
Humans
Leverage (statistics)
Satellite imagery
Microplanning
Child
Population estimates
Digitization
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Family Characteristics
business.industry
Research
Vaccination
Public Health
Environmental and Occupational Health
Immunization (finance)
General Business
Management and Accounting
Identification (information)
Essential immunization
Geographic Information Systems
Immunization
business
computer
Zdroj: International Journal of Health Geographics, Vol 20, Iss 1, Pp 1-13 (2021)
International Journal of Health Geographics
ISSN: 1476-072X
DOI: 10.1186/s12942-021-00277-x
Popis: Background Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. Methods The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as “befriended” and disagreements as “lonely”; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. Results The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = − 2.09) (alpha = 0.05). Conclusions Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.
Databáze: OpenAIRE
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