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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 3
AGILE GIScience Ser., 3, 47, 2022
AGILE GIScience Ser., 3, 47, 2022
11 Jun 2022
11 Jun 2022

Geospatial Analysis of Safe Delivery App Events Based on Geographically Weighted Regression Tool

Masoume Mahboubi1, Astrid Grønbæk2, Ana Fernandes3, Casper Fibæk3,4, and Paolo Dabove1 Masoume Mahboubi et al.
  • 1Department of Environmental Engineering (DIATI) - Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
  • 2Maternity Foundation, Forbindelsesvej 3, 2. floor 2100, Copenhagen Ø, Denmark
  • 3NIRAS Consulting, Sortemosevej 19, 3450 Allerød, Denmark
  • 4Aalborg University, Department of Planning, A.C. Meyers Vænge 15, 2450 Copenhagen SV, Denmark

Keywords: Geographically Weighted Regression, Safe Delivery app, Ghana, Spatial Relationship

Abstract. This study explores the spatial relationship between the number of recorded events of the Safe Delivery App, which is a mobile learning tool to train midwives in developing countries, during three months, and several independent variables, including the number of health facilities, pregnancies, number of women of childbearing age, number of infants aged 0–1 years, mobile network coverage data and total population density. The study aims to identify and analyse the reach of the Safe Delivery App at the district level in Ghana country, considering the correlation between dependent and independent variables. The geospatial analysis of App usage events layered with several related explanatory variables is based on Geographically Weighted Regression. The explanatory variables were able to predict and explain the number of events with of accuracy score of 90 % at the district level. The results have provided valuable insights into the further roll-out of the App and helped to highlight the districts that need further support to roll out the Safe Delivery App considering the analysed independent variables.

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