# Analysis Process

Generic cost distance and cost time raster travel surfaces enable powerful analysis of accessibility and remoteness in relation to specific individuals, areas or types of features. Travel times to specific locations and services can be quickly computed and updated as new data become available. Because of its fine scale the model generates useful information at almost any scale. However, it is important to stress that this model should be understood as an overview, a means of generating estimates and comparing them across areas. For any small‐scale area, we recommend validating these estimates locally.

**_Table 8: Service facilities_**

|**Category**|**Sub‐category**|
|---|---|
|Health facilities|_All_|
||Health posts and sub‐health posts<br>All hospitals<br>Government /Privatehospitals|
|Financial institutions|_All_|
||Class A ‐ B|
|District headquarters|_All_|

Below we briefly summarize the process for and results from a descriptive analysis of model indicators at various administrative levels for the services listed in Table 8. We produced two types of analysis: summary statistics and population breakdowns within travel time categories. We discuss the methodologies for producing these in turn, then provide descriptive analysis.

## Summary statistics

Within a GIS software point vector, data for each facility type were overlaid onto the generic cost time surface to generate facility‐specific least cost travel surfaces (see Figure 10). Further layers were created for relevant sub‐categories of facilities, e.g. health posts and hospitals or primary and secondary schools (see Table 8). This analysis was repeated for the monsoon season using the monsoon travel modifiers described above, and for both normal and monsoon seasons at walking speed. The latter analysis is included in the annex for use by researchers but not described in this report.

For each combination of service type, season and travel modality we calculated two summary statistics:

**1.** The cumulative person‐hours to reach that service, aggregated across that administrative area.

**2.** The average travel time for the average resident

We first calculated the cumulative person‐hours of travel by downsampling the cost time rasters from 30 meters resolution to WorldPop’s 100m resolution, then multiplying the WorldPop gridded population density data set by the cost time raster for the service and season in question. This yielded the total travel hours to reach the given service for each grid cell. The results were then summarized for each administrative level using a Zonal Statistics function. In producing this metric, we closely followed the methodology of the Department of Roads’ 2007 Priority Investment Plan (PIP) (DHV _et al._ 2007).

Cumulative hours of travel strike an effective, easily interpreted balance between inaccessibility indicators and population density. For example, multiplying 10 hours times 1 person will produce the same results as 1 hour times 10 persons. However, an area with a population density of 0.001 times 100 hours will only yield 0. 1 cumulative hours. The formula thus mitigates the impact of largely unpopulated areas, common to hill and