Last updated: December 26, 2019

Authors: Gabriel Carrasco-Escobar*, Edgar Manrique, Kelly Tello-Lizarraga, J. Jaime Miranda

* corresponding author


This study sought to estimate the travel time to the most proximate health facility in rural and urban areas across heterogeneous land coverage types in Peru as a means to help resources prioritization, disease surveillance, as well as prevention and control strategies.

Methods summary

  • Multiple sources of geospatial data were fitted with a novel cloud-based geospatial modeling approach (Weiss et al., 2018) to produce high-resolution (30 m) estimates of travel time to the most proximate health facility across the country. These estimates were then compared between urban and rural settings and across 16 major land coverage types in Peru.

  • The estimation of travel time were conducted in Google Earth Engine (GEE) (Gorelick et al., 2017). A surface grid was constructed using the information about land coverage, road infrastructure, and river network.

  • To calculate the travel time from the villages to the most proximate health facility, the cumulative cost function was used in GEE to generate the accessibility map.

  • The minimum travel time to the most proximate health facility was computed for each pixel in the grid, then the median travel time was summarized in a 500m-radius from the geolocation of each city or village.

  • The computed travel time was then summarized per district, province or department; by urban/rural areas; and across 16 major land coverage types defined by the Ministry of Environment. Urban/rural status was defined based on the MODIS land coverage satellite images.

Figure 1

Figure 1. Study area. A) Major ecological areas (coast, andes, and jungle) in Peru. Solid lines represent the 25 Departments (administrative level 1). B) Spatial location of primary, secondary, and tertiary health facilities. C) Spatial location of villages. Maps were produced using QGIS, and the base map was obtained OpenTopoMap (, under CC BY427 SA 3.0.