# 3. Data and methods

# 3.1. Inputs

The conditions of data production in Nepal mirror the physical conditions of the roads themselves: fragmented, incomplete, and least comprehensive for the most remote areas (Bikas Udhyami, 2018). In this environment, gathering sufficient primary data to accurately assess travel times and accessibility is difficult. Our model manages these issues by using global and freely available datasets – Shuttle Radar Topology Mission (SRTM) 30 m terrain elevation model, High Resolution Settlement Layer (HRSL) raster population data sets, OpenStreetMap, national land cover products (ICIMOD 2013) – and core governmental datasets like roads, health care, government offices, and financial institutions that a partner government agency can be reasonably expected to possess. We employ the HRSL data set instead of the WorldPop data set used in Banick and Kawasoe (2019) because it represents uninhabited areas as “Null” instead of fractional values as in WorldPop, and thus better capture Karnali’s unpopulated expanses, illustrated in Figure 3(b). In all cases OpenStreetMap data can provide a suitable “backstop” data set for missing or inaccurate governmental datasets. The data sources that were used to model accessibility to services are summarized in Table 1.

Table 1: Data sources used in this research.

|Data|Source|Date|Resolution<br>or Type|
|---|---|---|---|
|Ofcial road network of Nepal|Survey Department of Nepal|2019|Vector (Lines)|
|Road and path network of Nepal|OpenStreetMap|Dec 2019|Vector (Lines)|
|Trail bridges|Trail Bridge Infrastructure<br>Support Program (TBIS)|Aug 2019|Vector (Points)|
|Proposed roads in Karnali province|Karnali provincial government<br>(via RAP)|Aug 2019|Vector (Lines)|
|Terrain|NASA Shuttle Radar<br>Topology Mission|Global|1 arc-second<br>(≈30m × 30m)|
|Landcover of Nepal|Integrated Center for Mountain<br>Development (ICIMOD)|2020|1km × 1km|
|Rivers of Nepal|OpenStreetMap|Dec 2019|Vector (Lines)|
|Health facilities|Nepal Ministry of Health|2016|Vector (Points)|
|Karnali municipal headquarters|World Bank|2019|Vector (Points)|
|Formal fnancial institutions<br>(classes A - D)|Nepal Rastra Bank<br>(central bank)|2017|Vector (Points)|
|High Resolution Settlement|CIESINdFbk|2018|1 arc-second|
|Layer Maps (HRSL)|an aceoo||(≈30m × 30m)|

Geospatial data for health services and formal financial institutions were sourced from the Ministry of Health (2016) and Nepal Rastra Bank (2017), respectively, while municipal headquarters were compiled from assorted Nepali-language policy documents and then geolocated with reference to OpenStreetMap. This basket of services was selected as a representative of a cross-section of developmental, economic, and political needs; Figure 3(a) shows their current distribution versus existing roads. Effective health care is an essential input to human development, particularly in the region of Nepal most afflicted by food insecurity and consequent malnutrition. Formal financial institutions (hereafter referred to as financial institutions) contribute to economic development by enabling the growth of local small to medium enterprises (SMEs) – unfortunately, no suitably comprehensive data set of informal financial institutions exists. Finally, municipal headquarters not only provide essential administrative services but are acceptable proxies for principal local markets. Education was considered for inclusion but a suitably comprehensive geospatial data set could not be located.

The Rural Access Programme (RAP), a donor-funded infrastructure support program active in Karnali, shared shapefiles for the 92 individual proposed road segments specified in Karnali’s draft Provincial Transport Master Plan as of early 2019 and the average per-kilometer budget charge associated with the different 9