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Report No: AUS0000817

Rural Accessibility Mapping

COMPLETION REPORT FOR THE EAST ASIA AND PACIFIC (EAP) UMBRELLA FACILITY FOR GENDER EQUALITY (UFGE)

MAY 2019 Team: Holly Krambeck, Li Qu, Sarah Antos, Charles Fox

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© 2019 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org

Some rights reserved

This work is a product of the staff of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given.

Attribution—Please cite the work as follows: “World Bank. 2019. Rural Accessibility Map Completion Report © World Bank.”

All queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org.

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CONTENTS

1 Challenge ............................................................................................................................................... 4

2 Rural Accessibility Map ......................................................................................................................... 4

3 How It Works ........................................................................................................................................ 4

3.1 Calculating Accessibility ................................................................................................................ 4

3.1.1 A Better Indicator .................................................................................................................. 4

3.1.2 Data Requirements ............................................................................................................... 5

3.1.3 Model Formulation ............................................................................................................... 5

3.2 Rural Accessibility Map Platform .................................................................................................. 6

3.2.1 OPen-Source Software .......................................................................................................... 6

3.2.2 Documentation ................................................................................................................... 10

3.2.3 Integration with the World Bank Development Data Catalogue ........................................ 11

4 Case Studies ........................................................................................................................................ 11

4.1 Women’s Access to Key Services in Qianxinan, China ................................................................ 11

4.1.1 Challenge ............................................................................................................................. 12

4.1.2 Data and Model Assumptions ............................................................................................. 12

4.1.3 Results ................................................................................................................................. 14

4.2 Rural Accessibility in Vietnam ..................................................................................................... 29

4.2.1 Challenge ............................................................................................................................. 29

4.2.2 Results ................................................................................................................................. 30

5 Next Steps ........................................................................................................................................... 34

Annex 1: Speed Limit Assumptions ............................................................................................................. 35

Annex 2: Technical Details for Qianxinan Analysis ..................................................................................... 36

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1 CHALLENGE

Traditionally, improvements to accessibility for rural women through World Bank transport programs have been measured by the change in the number of villagers who live within 2 km of an all-weather or paved road. Unfortunately, this indicator is not helpful for rural road maintenance or service planning, because the objective of our programs is not to maximize the number of persons living next to paved roads. Rather, it is to reduce travel times for the most villagers to the places they need to go – markets, schools, health clinics, etc. For example, if all roads traversing villages were paved, while the rest of the network is unpaved, we could achieve 100% success with the traditional indicator, without having actually improved the lives of the persons for which the project is intended. How can we implement a more meaningful measure of accessibility, in environments where data and technical capacity are scarce?

2 RURAL ACCESSIBILITY MAP

With advances in GIS technology and open-source code libraries, the Task Team, with support from the EAP UFGE, has developed a multi-lingual, web-based tool for both task teams and their counterparts to cheaply and quickly estimate rural women’s accessibility in terms of the percent of village women who can access a place or service within X minutes by road, before and after an investment program.

3 HOW IT WORKS

3.1 CALCULATING ACCESSIBILITY

3.1.1 A BETTER INDICATOR Rather than think of rural accessibility as a measure of the percentage of a population who lives within 2 kilometers of a road, we can think of, rural accessibility as a percentage of population that can access a specified destination within a given time frame (see formula, below).

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖,𝑋𝑋 =∑ 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝐴𝐴𝑝𝑝𝐴𝐴𝐴𝐴𝑝𝑝𝑝𝑝𝑗𝑗1

∑ 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝐴𝐴𝑝𝑝𝐴𝐴𝐴𝐴𝑝𝑝𝑝𝑝𝑘𝑘1

where:

• 𝐴𝐴 denotes region 𝐴𝐴 • 𝑋𝑋 denotes the travel time threshold for reach their nearest destination • 𝑗𝑗 denotes all the villagers in region 𝐴𝐴 that can reach their nearest destination within time 𝑋𝑋 • 𝑘𝑘 denotes all the villagers in region 𝐴𝐴

For example, we can consider the percentage of population in a district who can access the nearest town/market/bank/school within 60 minutes by road, or the percentage of women in a province who

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can access a maternal health facility within 30 mins. These indicators can be used to prioritize investments in roads and facilities to expand service coverage to the most people.

3.1.2 DATA REQUIREMENTS The advantage of this new indicator is that while it more meaningfully informs road and facility planning, the data required to calculate the indicator are within reach of most counterparts. To estimate rural accessibility the traditional way (2-kilometer metric), the following information is required:

• Geospatial population / demographic data; and • Road network data, with road type classifications.

To estimate rural accessibility using our travel time metric, the above is required, plus:

• Location of destination to be queried (towns, hospitals, markets, etc.)1 While collecting these data have not necessarily been a barrier to using travel time-based metrics, our counterparts’ ability to compute these metrics have been a barrier, requiring specialized, proprietary software and/or programming skills. To overcome this barrier the team has developed a methodology that relies entirely on open-source algorithms and methods. The team’s methodology can be implemented directly by a programmer (see model formulation, below), and using the User Interface (UI) developed by the team, by a non-programmer with only basic geographic information systems (GIS) skills (see Section 3.2).

3.1.3 MODEL FORMULATION To calculate the shortest path between villages and destinations, the team uses the Open Source Routing Machine (OSRM) – a C++ routing engine for solving road network shortest path problems.2 OSRM deploys an algorithm called Contraction Hierarchy to connect villages to the nearest points of interest in the shortest time. Contraction Hierarchy, originated from Graph Theory, finds the shortest path between nodes in a graph and is more efficient than the famous Dijkstra's algorithm.3 With the method of Contraction Hierarchy, OSRM is powerful enough to handle continental-sized networks within milliseconds.

1 When actual travel speed data are not available, we can use speed estimates based on road type and classification. 2 Detailed documentation on OSRM can be found at http://project-osrm.org/ 3 http://wiki.openstreetmap.org/wiki/Open_Source_Routing_Machine

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OSRM works with map data from the OpenStreetMap (OSM)4 and supports three modes of travel: car, bicycle and walk. With a tool called OSRM-extract along with the profile file that sets the rule of routing, the road network is parsed and extracted from OSM data. Then, OSRM-contract generates the Hierarchy, which is essentially precomputed data (i.e., nearest neighbor structure and a node map) that enables the routing engine to find shortest path within a certain amount of time.5 With a routable network ready, a query request needs to be sent to OSRM’s routing engine to find the shortest path between places. Further details about this algorithm and how it functions in our model may be found in Annex 2.

3.2 RURAL ACCESSIBILITY MAP PLATFORM

3.2.1 OPEN-SOURCE SOFTWARE The Rural Accessibility Platform is an open-source and web-based analytical tool developed by the World Bank Transport Global Practice, with support from the EAP UFGE. The platform simulates the shortest paths from villages (or population centers) to different points of interest (POIs) and outputs accessibility indicators (defined as the percentage of population that can access the nearest POI in X amount of time). The accessibility indicator, produced before and after road rehabilitation/upgrade projects, can be used to determine the impact of the intervention. To use the platform, a set of geospatial data are required, which include road network, population at the village level, POIs, and administrative boundaries. While the input the data could be uploaded by the user manually in .geojson format (for population, administrative boundaries and POI) or .osm format (for road network), users can also query the OpenStreetMap (OSM) directly from the platform for road network and POI input data.

4 OpenStreetMap (www.openstreetmap.org) is a map of the world, created by people like you and free to use under an open license. 5 https://github.com/Project-OSRM/osrm-backend/wiki/Running-OSRM

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Figure 1: Screenshot of Project Data Upload Screen

With all the input data uploaded to the platform, it then generates accessibility indicators within a pre-selected administrative boundary, based on the routing results. From this baseline, users can then edit the input data – location of POIs and roads, as well as default and custom surface/speed assumptions for roads.

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Figure 2: Screenshot of a Baseline Scenario

Figure 3: Screenshot of Default Speed Parameter Assumptions Editor

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After accessibility results are calculated, the platform generates a geospatial visualization and table with results. These are exportable as .geojson and CSV files.

Figure 4: Screenshot of Project Results Visualization

The CSV file contains all the villages within the selected administrative boundary and each row stores the village’s shortest travel time (in seconds) to their nearest POIs as well as other attributes such as coordinates and population headcount (man and woman). A filter can quickly sort out the villages that are able to reach POI in a given timeframe based on the travel time.

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The full software repository of the Rural Accessibility Platform can be found on World Bank Transport Github account at https://github.com/WorldBank-Transport. The open source platform can be installed in a cloud server and accessed through a web page. The CloudFormation (CF) templates for creating new instances of RAM limit the number of manual steps required. The templates and instructions can be found here: https://github.com/WorldBank-Transport/ram-datapipeline. The platform also has an offline version that can be installed directly on a PC.

3.2.2 DOCUMENTATION

Documentation of the Rural Accessibility Platform are presented in an interactive webpage that include the detailed user’s manual: http://ruralaccess.info/

Figure 5: Screenshot of Application Documentation Page

The platform also has documented the administrator’s manual on GitHub (https://github.com/WorldBank-Transport) together with the code repository, which will guide the administer to manage the platform and grant the permit to the users to publish the result to the online repository.

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Finally, an online repository or completed Rural Accessibility Mapping projects was set up through GitHub to include brief description and results of each project (indicators and isochrone maps), together with the option of uploading the input data of the project.

Figure 6: Screenshot of Rural Accessibility Hub for Project Sharing

3.2.3 INTEGRATION WITH THE WORLD BANK DEVELOPMENT DATA CATALOGUE

A prototype GUI function to allow upload of the complete input data set for RAM projects to the world Bank Data Catalog (https://datacatalog.worldbank.org/), as well as pull the data from Data Catalog to run analysis on RAM has been developed. The team tested the description tags for uploaded data (“metadata”), including “RAM population”, “RAM road network”, “RAM point of interest”, and “RAM boundary”. This function would enable teams to share their datasets with others directly through the World Bank Data Catalog, reducing duplication of effort.

4 CASE STUDIES

4.1 WOMEN’S ACCESS TO KEY SERVICES IN QIANXINAN, CHINA

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4.1.1 CHALLENGE

Guizhou Province (pop. 35 million), located in mountainous southwestern China, is the second poorest province in the country. Guizhou’s GDP per capita was US$ 4,856 in 2016, which is only 65 percent of the national average and about 28 percent of the GDP per capita in Beijing. Within Guizhou Province, the Qianxinan Buyi and Miao Autonomous Prefecture (pop. 3.4 million, 40 percent of which are Buyi, Miao, and Yao ethnic minority people) has been designated as an Extreme Poverty Area by the national government. More than 95 percent of Qianxinan’s 16,804 square-km area comprises hilly and mountainous terrain, resulting in a highly dispersed and isolated population.

In Qianxinan, only 43 percent of roads are paved and as a result, only 59 percent of administrative villages in Qianxinan are connected by paved road, 5 percent less than the provincial average and 33 percent less than the national average. As a result, accessibility to key public services – government, banking, health, schools – is very limited.

Under the Qianxinan Rural Transport Program for Results (P158545), the Task Team provided technical advisory to the Qianxinan Prefecture Transport Bureau on how to prioritize road improvements. Traditional World Bank advisory on improving rural accessibility is based on the percentage of a rural population that lives within 2 km of a paved or all-season road. However, this measure tells us very little about the actual utility of the road, its relationship with the existing network, and whether it supports specific socio-economic objectives for reducing travel times between rural areas and markets and services.

As an experiment, the Team leveraged the UFGE to develop and implement a new methodology for understanding and prioritizing road improvements for accessibility, based on travel time between villages and key public services.

For example, in 2009, the Chinese Ministry of Finance began a subsidy program to help rural women access professional prenatal, birth, and postpartum care. In 2016 in Qianxinan Prefecture, there were 35,372 rural women who received this subsidy, totaling CNY 14 million. The subsidy supported the provision of folic acid to 30,800 pregnant women to prevent birth deficits, as well as prenatal and delivery services.

The Team used the new Rural Accessibility Map platform to determine how the road investment program could increase participation in the subsidy program by expanding women’s access to certified health clinics. In addition, the team looked at how roads could improve women and girls’ access to schools and county seats. The platform has been translated into Mandarin and has been used by the Qianxinan Project Management Office (PMO) to update accessibility impacts of different project alternatives. The following are platform-generated results on how women's accessibility will be impacted by the World Bank investment in Qianxinan.

4.1.2 DATA AND MODEL ASSUMPTIONS

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To use the platform, a set of geospatial data are required, which include road network, population at the village level, points of interest (POIs), and administrative boundaries.

The underlying road network and POIs for Guizhou were purchased through Taobao, a China-based on-line marketplace (www.taobao.com). While the purchased proprietary road network data (dated 2010) is representative of highways, national roads, provincial roads and to some extent rural roads, the team has supplemented these data with recently completed transport bureau rural road survey results (including county, township and village roads), which include surface pavement information.

In addition to the underlying road network, point locations of villages and POIs are needed to establish the origins and destinations for routing. For the accessibility calculation, population headcount of each village also needs to be included in the village location file. In Guizhou, the most recently available population census data were collected for each township and disaggregated to each village in the township homogeneously. Administrative boundaries at various levels—admin1 (province), admin2 (prefecture), and admin3 (county)—define the geographic extent of routing and accessibility calculation. To use the open source software, the point data of villages and POIs, as well as administrative boundaries, need to be in a GeoJSON format, which can be converted from a shapefile in common GIS software packages.

This analysis models the movement of people along the road transport network in private vehicles. This involves a number of assumptions:

• Vehicles are assumed to travel at the speed limit of the road at all times, with few ‘friction’ costs (e.g., acceleration or deceleration). Different road highway types have different speed limits. These are defined in Annex 1.

• When a road is upgraded, its travel speed limit is raised, decreasing travel times along that segment.

The team has analyzed women’s access to four different classes of ‘points of service’: Health Facilities, County Seats, Financial Services and Schools.

For each service type, we estimate women’s access to these services both before the improvement to the road network (‘baseline’ scenario) and after upgrades to the road network, which are assumed to increase travel speed across those segments (‘upgrade’ scenario). We then compare the baseline and upgrade scenarios to identify the areas of greatest improvement.

Results are visualized through maps. In these maps, the size of the bubbles represents the population size of a village. The color of each bubble represents the travel time to the nearest point of service; dark green indicates a travel time of less than 10 minutes, whilst a dark grey symbol indicates a travel time of more than 2 hours.

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For each scenario, we provide a visualization of access from each of the villages to their nearest point of service in each of the 4 districts. On each map, the villages are sized according to their population. At a fully zoomed-out scale, the maps appear disorganized, with several overlapping icons. However, these are fully interactive visuals: zooming in using the controls on the left-hand-side affords the user much greater clarity, as demonstrated below. Here, we look at the access of the female population to their nearest County Seat at three different zoom levels. At the most zoomed in (largest image), we can clearly and quickly identify those villages with the greatest population, but the worst access (grey region, bottom right of the image).

4.1.3 RESULTS

4.1.3.1 BASELINE

The team used the prototype Rural Accessibility Map platform (RAM) to establish a baseline for rural women’s accessibility to health facilities, as well as county seats, financial services (ATMs and banks), and schools. The analysis uncovered significant service gaps in all four of the participating project counties, where less than half of women have access to an eligible health facility within 30 minutes by road, and only 13% in Wangmo County.

Table 1: Baseline Percentage of Women Who Can Access Nearest Service within 30 Minutes by Road

4.1.3.2 WOMEN’S ACCESS TO MATERNAL HEALTH FACILITIES The first analysis was conducted to determine baseline level of women’s accessibility to maternal health clinics for residents of the four project counties in Qianxinan. Note that while only origins in these counties were considered in the analysis, the destinations – health facilities – cover all surrounding areas. The following table summarizes the baseline accessibility levels.

Health Facility County Seat Financial Services School Ceheng 33% 13% 33% 37% Qinglong 32.94% 10.59% 30.30% 39.55% Wangmo 12.54% 7.73% 11.68% 18.49% Zhenfeng 46.31% 27.40% 44.23% 47.75%

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Table 2: Baseline Scenario: Percent of Women Who Can Access Maternal Health Facilities by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 10.38% 22.98% 33% 52.76% 58.60% 64.44% Qinglong 9.21% 22.82% 32.94% 47.60% 57.60% 61.53% Wangmo 4.66% 8.24% 12.54% 24.00% 32.68% 36.85% Zhenfeng 20.74% 37.41% 46.31% 56.71% 64.73% 69.97% Total 11.77% 23.52% 31.83% 45.27% 53.52% 58.28%

Figure 7: Baseline Scenario: Percent of Women Who Can Access Maternal Health Facilities by Travel Time

To understand the four-county baseline image, following is a zoomed-in perspective of accessibility to the women’s maternal health clinic in Zhelou, Ceheng County (shown with road map and satellite image base layers).

These images show, intuitively, that the villages close to Zhelou have larger populations (indicated by circle size) and can most easily access the clinic (indicated by the color). Where we want to focus our attention with this type of visualization are the villages rendered in gray, with the largest circles, representing larger populations of villagers that are least accessible.

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Figure 8: Baseline Travel Time to Maternal Health Facilities in Ceheng County (Road Base Map)

Figure 9: Baseline Travel Time to Maternal Health Facilities in Ceheng County (Satellite Image Base Map)

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Further, through the user interface, we can query and export specific travel time figures, by village:

After establishing a baseline, the Team applied different project improvement scenarios under consideration by the Qianxinan Project Management Office (PMO). The following results are from the most likely implementation scenario, based upon these accessibility results, as well as other factors with regards to current condition, average daily traffic, and road safety.

Table 3: Road Upgrade Scenario: Percent of Women Who Can Access Maternal Health Facilities by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 10.55% 24.15% 37% 63.43% 71.81% 77.30% Qinglong 9.47% 25.99% 37.95% 56.34% 66.75% 72.06% Wangmo 5.00% 8.57% 13.27% 27.23% 38.79% 45.22% Zhenfeng 21.65% 42.28% 57.48% 69.44% 76.10% 80.12% Total 12.23% 26.09% 37.38% 54.17% 63.34% 68.59%

Figure 10: Sample Baseline Travel Time Table for Four Project Counties

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Figure 11: Road Upgrade Scenario: Percent of Women Who Can Access Maternal Health Facilities by Travel Time

Finally, the rural access map (RAM) platform allows users to compare one scenario to another. Below we use this functionality to identify the areas where access will improve for women as a direct result of road network upgrading. Here, the color of the patches relates not to the actual travel time, but to the delta in travel time between the two scenarios. A green patch represents travel times decreasing, dark grey is no change, whilst red represents an increase in travel times.

Figure 12: Scenario Comparison: Change in Travel Time to Maternal Health Facilities

The following table highlights which districts are expected to see improved travel times (the values themselves are identical to the ‘Upgrade’ Scenario):

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Table 4: Scenario Comparison: Change in Travel Time to Maternal Health Facilities

Clearly, access for most time thresholds will improve in all four districts.

To show the impact of this change at a local level, again, here is an image of Zhelou, Ceheng County, showing how the proposed World Bank investment in road improvement would affect accessibility to the maternal health clinic. While travel times in the villages shown in gray will not change, those shown in green will improve.

Figure 13: Comparison of Travel Time to Maternal Health Facilities in Ceheng County

We can also quantify this improvement in percentage terms.

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Table 5: Absolute Additional Share of Women Population with Improved Access at Each Time Threshold

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 0.17% 1.17% 3.84% 10.67% 13.21% 12.86% Qinglong 0.26% 3.17% 5.01% 8.74% 9.15% 10.53% Wangmo 0.34% 0.33% 0.73% 3.23% 6.11% 8.37% Zhenfeng 0.91% 4.87% 11.17% 12.73% 11.37% 10.15% Total 0.46% 2.57% 5.55% 8.90% 9.82% 10.31%

As the table above shows, the benefits of the network upgrade are more significant in some districts than others.

For example, the fraction of Ceheng’s female population that can access a maternal health facility within 2 hours increased by 12.86% - from under 65% before the works to over 77% due to the road network improvements. Likewise, Zhenfeng saw a very large improvement in access at the 1-hour threshold. By contrast, Wangmo district saw a comparatively less dramatic uplift. Taken together across all four districts, 10.31% of the female population will now have access to a health center in under 2 hours that didn’t previously, a strong indication of the project’s impact on women’s health.

4.1.3.3 WOMEN’S ACCESS TO COUNTY SEATS

Once again, we begin by calculating and visualizing the situation for women’s access to County Seats before any road improvements. In general, the following graphics look redder / grayer than the maps above, largely due to the fact that there are fewer County Seats than health facilities (one per county); as such, the average travel time to such seats is commensurately longer.

Table 6: Baseline Scenario: Percent of Women Who Can Access County Seats by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 2.54% 7.34% 13% 31.34% 47.99% 57.37% Qinglong 0.21% 2.50% 10.59% 25.94% 31.97% 42.28% Wangmo 3.60% 6.20% 7.73% 15.11% 26.16% 31.46% Zhenfeng 8.88% 19.10% 27.40% 44.12% 54.26% 61.48% Total 4.17% 9.44% 15.51% 29.71% 40.34% 48.23%

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Figure 14: Baseline Scenario: Percent of Women Who Can Access County Seats by Travel Time

We then recalculate travel time to the nearest county seat for every origin village, adjusted for the anticipated improvement in travel times for the road network upgrade:

Table 7: Upgrade Scenario: Percent of Women Who Can Access County Seats by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 2.54% 7.68% 15% 38.02% 59.38% 70.21%

Qinglong 0.21% 2.50% 11.02% 28.56% 38.13% 51.97% Wangmo 3.60% 6.20% 7.79% 16.78% 29.18% 37.76% Zhenfeng 8.88% 20.12% 31.03% 54.46% 66.77% 73.58% Total 4.17% 9.81% 16.99% 35.21% 48.62% 58.38%

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Figure 15: Upgrade Scenario: Percent of Women Who Can Access County Seats by Travel Time

Comparing the baseline and upgrade scenarios, we observe anticipated improvements in access across all districts, and at most travel time thresholds:

Table 8: Comparison: Percent of Women Who Can Access County Seats by Travel Time

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Figure 16: Comparison: Percent of Women Who Can Access County Seats by Travel Time

Further analysis suggests that the effects will be felt heterogeneously, with Ceheng and Zhenfeng the biggest winners, and Wangmo and Qinglong proportionally less affected.

Table 9: Absolute Additional Share of Women Population with Improved Access at Each Time Threshold

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 0.00% 0.34% 1.33% 6.68% 11.39% 12.84% Qinglong 0.00% 0.00% 0.43% 2.62% 6.16% 9.69% Wangmo 0.00% 0.00% 0.06% 1.67% 3.02% 6.30% Zhenfeng 0.00% 1.02% 3.63% 10.34% 12.51% 12.10% Total 0.00% 0.37% 1.48% 5.50% 8.28% 10.15%

Given there are many fewer county seats compared to health facilities, access is not much affected at the lower time thresholds, as most journeys to county seats are longer journeys of an hour or more.

4.1.3.4 WOEMN’S ACCESS TO FINANCIAL SERVICES

This time, we appraise the change in accessibility for the female population to financial points of service.

Table 10: Baseline Scenario: Percent of Women Who Can Access Financial Services by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 9.50% 22.47% 33% 52.71% 58.66% 64.39% Qinglong 6.53% 18.16% 30.30% 47.11% 56.96% 61.59%

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Wangmo 4.96% 8.36% 11.68% 24.25% 33.89% 39.05% Zhenfeng 19.15% 35.62% 44.23% 55.67% 63.34% 68.49% Total 10.53% 21.77% 30.30% 44.89% 53.27% 58.41%

Figure 17: Baseline Scenario: Percent of Women Who Can Access Financial Services by Travel Time

Table 11: Upgrade Scenario: Percent of Women Who Can Access Financial Services by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 9.67% 23.64% 37% 63.39% 71.78% 77.30% Qinglong 6.79% 20.79% 35.19% 56.76% 66.81% 72.33% Wangmo 4.96% 8.58% 12.19% 26.58% 38.99% 46.94% Zhenfeng 19.98% 40.32% 55.48% 68.25% 74.86% 79.58% Total 10.88% 24.13% 35.79% 53.73% 63.03% 68.93%

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Figure 18: Upgrade Scenario: Percent of Women Who Can Access Financial Services by Travel Time

Table 12: Comparison: Percent of Women Who Can Access Financial Services by Travel Time

Figure 19: Comparison: Percent of Women Who Can Access Financial Services by Travel Time

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Table 13: Absolute Additional Share of Women Population with Improved Access at Each Time Threshold

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 0.17% 1.17% 3.80% 10.68% 13.12% 12.91% Qinglong 0.26% 2.63% 4.89% 9.65% 9.85% 10.74% Wangmo 0.00% 0.22% 0.51% 2.33% 5.10% 7.89% Zhenfeng 0.83% 4.70% 11.25% 12.58% 11.52% 11.09% Total 0.35% 2.36% 5.49% 8.84% 9.76% 10.52%

The female population of Ceheng and Zhenfeng continue to be the biggest relative winners from the road upgrade program, with the most sizeable uplift in the share of their population having access to financial services within a given travel time. Zhenfeng adding an additional 11.25% of its female population to access within 30 minutes is particularly noticeable.

4.1.3.5 WOMEN’S ACCESS TO SCHOOLS

Table 14: Baseline Scenario: Percent of Women Who Can Access Schools by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 15.28% 27.73% 37% 51.72% 58.44% 64.33% Qinglong 13.23% 27.20% 39.55% 54.65% 59.79% 62.77% Wangmo 8.26% 14.36% 18.49% 29.11% 36.05% 41.08% Zhenfeng 25.22% 39.88% 47.75% 59.18% 66.65% 70.92% Total 15.98% 27.84% 36.20% 48.87% 55.48% 59.95%

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Figure 20: Baseline Scenario: Percent of Women Who Can Access Schools by Travel Time

Table 15: Upgrade Scenario: Percent of Women Who Can Access Schools by Travel Time

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 15.75% 29.81% 43% 63.19% 72.31% 77.08% Qinglong 14.06% 30.65% 45.12% 63.46% 69.44% 73.34% Wangmo 8.46% 14.83% 19.38% 32.62% 42.58% 48.43% Zhenfeng 27.75% 47.05% 59.70% 71.84% 77.41% 80.68% Total 17.10% 31.39% 42.45% 57.99% 65.48% 69.86%

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Figure 21: Upgrade Scenario: Percent of Women Who Can Access Schools by Travel Time

Table 16: Comparison: Percent of Women Who Can Access Schools by Travel Time

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Figure 22: Comparison: Percent of Women Who Can Access Schools by Travel Time

Table 17: Absolute Additional Share of Women Population with Improved Access at Each Time Threshold

ADMIN AREA 10 MIN 20 MIN 30 MIN 60 MIN 90 MIN 120 MIN Ceheng 0.47% 2.08% 5.30% 11.47% 13.87% 12.75% Qinglong 0.83% 3.45% 5.57% 8.81% 9.65% 10.57% Wangmo 0.20% 0.47% 0.89% 3.51% 6.53% 7.35% Zhenfeng 2.53% 7.17% 11.95% 12.66% 10.76% 9.76% Total 1.12% 3.55% 6.25% 9.12% 10.00% 9.91%

The anticipated improvements to the road network see an additional 6.25% of the female population gain access to schools within 1 hour, and a further 9.9% of the population of the four districts achieve access to a school in under 2 hours. Specific districts show even more pronounced improvement, with Ceheng and Zhenfeng again leading the way at the 1 hour access threshold, and Ceheng standing out by adding 12.75% to the share of its female population that has access to a school within 2 hours.

It may be that the 1 hour threshold is more appropriate as a measure of reasonable access than 2 hours, as trips to school often need to be made daily. However, as the platform provides statistics for the fraction of the population able to access a point of interest at many different time thresholds, we are well covered in either eventuality.

4.2 RURAL ACCESSIBILITY IN VIETNAM

4.2.1 CHALLENGE

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While the Rural Accessibility Map platform can facilitate gender-specific accessibility analytics, the platform has also been piloted in other countries to determine accessibility related to Bank lending engagements. Following is an example of the Task Team’s use of the platform in Vietnam, through which the platform could be tested to run at a national scale, as opposed to the four-county scale used in the Qianxinan project.

The team estimated accessibility from every commune in Vietnam to a range of points of interest and key social services, including airports, major cities, railway stations, health facilities, and commune centers.

4.2.2 RESULTS

4.2.2.1 AIRPORTS

Results suggest that accessibility to airports is good near Hanoi and Ho chi Minh city, but poor elsewhere in country. On average, we estimate that 20% of the population can reach an airport in 30 minutes, 40% within 1 hour, 61% within 1.5 hours and 78% within 2 hours. There were large swathes of the country which did not have reasonable airport access, including in the north, and some areas of the central east and south.

4.2.2.2 MAJOR CITIES

Figure 23: Commune Access to Airports in Vietnam by Travel Time (Hanoi and Ho Chi Minh Cities Zoomed In)

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Here we define major cities as: Hanoi, Haiphong, Da Nang, Ho Chi Minh City, and Can Tho. As expected, access follows a semi-concentric circular pattern around each city.

Figure 24: Commune Access to Major Cities by Road, by Travel Time

Interestingly for Da Nang, given the shape of the road network, access extends along a north:south corridor either side of the city, rather than inland:

Figure 25: Commune Access to Da Nang by Road, by Travel Time

Similarly, a corridor of accessibility exists between Ho Chi Minh City and Can Tho, as well-developed transport links (primary / secondary roads) connect the two. We can also see from these visualizations just how densely populated this region is:

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Figure 26: Commune Access to Ho Chi Minh and Can Tho by Road, by Travel Time

Approximately 16% of the population has access to one of these 5 urban centers within 30 minutes, 32% within an hour, 50% within 90 minutes and 62% within 2 hours.

4.2.2.3 HEALTH FACILITIES

As an upper-medium income country heading rapidly towards high-income status, Vietnam has a very comprehensive network of health facilities. As such, accessibility for the majority of communes to their nearest health facility is excellent, as depicted by the nearly universally green access map:

Figure 27: Commune Access to Health Facilities by Road, by Travel Time

This picture is reinforced by the aggregate statistics: 87% of the population has access to a health facility within 30 minutes, 95% have access in under an hour, and 99% have access inside 2 hours. The only region with mixed levels of coverage is in the mountainous north west of the country:

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Figure 28: Commune Access to Health Facilities by Road, by Travel Time near Urban Center

However, given the distribution of population, the lower access areas (grey dots) invariably are less populated than regions closer to urban centers, minimizing the impact at the aggregate level.

4.2.2.4 RAILWAY STATIONS

To understand the railway station access analytics undertaken for Vietnam, it is important to first visualize the position of the railway stations themselves, with no other data layers present.

On the figure to the left, railway stations are represented by yellow point markets. The railway network for the most part runs along the East coast, terminating in Ho Chi Minh City.

It is therefore not surprising that railway station access also follows a similar pattern, with poor accessibility south of Ho Chi Minh City, and regions of poor accessibility to the north and north-west.

This has practical application for businesses – businesses aiming to ship bulky freight to the major cities of Hanoi or Ho Chi Minh should consider their proximity to the rail network to minimize time and cost to market.

From a public transport perspective, 42% of the population of Vietnam has access to a railway station in under 30 minutes, and only 59% within an hour – indicating considerable scope for expansion of the rail network into under-served areas.

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Figure 29: Commune Access to Railway Stations by Road, by Travel Time

5 NEXT STEPS

Having tested the platform in a variety of countries and contexts, the next step is to support scaling of its use in World Bank and client projects, through a hosted demo instance and training activities for staff.

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ANNEX 1: SPEED LIMIT ASSUMPTIONS All speeds are expressed in km/h

• Expressway : 120, • National : 80, • Provincial : 60, • County : 20, • Township : 20, • Rural : 20, • ["4"] : 30, • ["5"] : 20, • ["6"] : 15, • project : 0.5, • motorway : 90, • motorway_link : 45, • trunk : 85, • trunk_link : 40, • primary : 65, • primary_link : 30, • secondary : 55, • secondary_link : 25, • tertiary : 40, • tertiary_link : 20, • unclassified : 25, • residential : 25, • living_street : 10, • service : 15

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ANNEX 2: TECHNICAL DETAILS FOR QIANXINAN ANALYSIS OSRM. The routing algorithm of the Rural Accessibility Platform is powered by the Open Source Routing Machine (OSRM) and is a modern C++ routing engine for solving road network shortest path problems.6 OSRM deploys an algorithm called Contraction Hierarchy to connect villages to the nearest POIs in the shortest time. Contraction Hierarchy, originated from Graph Theory, finds the shortest path between nodes in a graph and is more efficient than the famous Dijkstra's algorithm. 7 With the method of Contraction Hierarchy, OSRM is powerful enough to handle continental-sized networks within milliseconds. OSRM works with map data from OpenStreetMap (OSM)8 and supports three modes of travel: car, bicycle and walk. With a tool called OSRM-extract along with the profile file that sets the rule of routing, the road network is parsed and extracted from OSM data. Then, OSRM-contract generates the Hierarchy, which is essentially precomputed data (i.e., nearest neighbor structure and a node map) that enables the routing engine to find shortest path within a certain amount of time.9 With a routable network ready, a query request needs to be sent to OSRM’s routing engine to find the shortest path between places. There are five parameters to be specified as listed in Table A10-1. The service parameter is the solver plugins provided by OSRM and the profile file sets the rules of routing (e.g. speeds of roads by class and surface type). The coordinates inform the routing engine of the location of origins and destinations. The final parameter is the format of the request, which can only be a .json file as of time of writing.

OSRM Query Parameters Parameter Description

service One of the following values: route, nearest, table, match, trip, tile

version Version of the protocol implemented by the service. v1 for all OSRM 5.x installations

profile Mode of transportation, is determined statically by the Lua profile that is used to prepare the data using osrm-extract

coordinates String of format {longitude},{latitude};{longitude},{latitude}[;{longitude},{latitude}...]

format Only json is supported at the moment. This parameter is optional and defaults to json

A total of six routing plugins are available through OSRM. The Rural Accessibility Platform uses the table plugin, which computes the fastest route between all pairs of supplied coordinates. For the table plugin, there is additional parameters supported under coordinates—sources and destinations, which tells OSRM that I want to go from A (sources) to B (destinations). A sample code of the request can look like:

6 Detailed documentation on OSRM can be found at http://project-osrm.org/ 7 http://wiki.openstreetmap.org/wiki/Open_Source_Routing_Machine 8 OpenStreetMap (www.openstreetmap.org) is a map of the world, created by people like you and free to use under an open license. 9 https://github.com/Project-OSRM/osrm-backend/wiki/Running-OSRM

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curl 'http://router.project-

osrm.org/table/v1/driving/13.388860,52.517037;13.397634,52529407;13.428555,52.523219?sources=0;1&destinations=2'

The code is asking OSRM to find the fastest routes between the first two points and the third point and returns the results in a 2x1 matrix using the table plugin and the driving profile file. More detailed documentation, including use of other plugins can be found: http://project-osrm.org/docs/v5.7.0/api/#general-options. The demo interface by OSRM is similar to Google Map’s navigation (Figure A10-3). Moreover, since the project of OSRM is open-source, a developed can add other (analytical) features to it and create different interfaces based on their preference as is the case with the Rural Accessibility Platform developed by the World Bank. The entire code repository of OSRM is hosted on Github at https://github.com/Project-OSRM.

Demo of Routing from A to B in OSRM

Rule of Routing (profile). Profiles files, written in the “Lua” scripting language (www.lua.org), determine the rule of routing (e.g., what roads can be routed along; what sections of the roads are impassable; and what is the speed of driving on a particular road). Lua scripting offers a powerful way of coping with the complexity of different node, way, relation, tag combinations of OSM data. It is also in profile file where we specify the changes in speeds to the project roads before and after an intervention.

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Profile Speeds of Roads of Different Class and Surface before PforR Intervention

In OSM, the tag describes specific features of map elements. A tag consists of two items: key and value. As shown in the figure above, the tag that describes road type is: key = “highway” and value = “motorway”, “trunk”, “primary”, “secondary”, or “tertiary”. As OSRM runs on OSM data, some of the speed parameters are based on OSM defaults (i.e. “motorway” = 90 km/hour). The profile file can also be customized to account for additional road types that are not originally from OSM data such as “Expressway”, “National”, “Township”, “Rural”, “project” etc. The “project” class specifically refers to the identified project roads that are to be rehabilitated or upgraded. To account for the fact that most of the selected project roads are unclassified, unpaved and in bad condition, it is unlikely they can provide safe, reliable access to villagers all year-round, especially in the rainy season. For that reason, they are not considered as real functional roads that meet any technical standard and, hence, are set at very low speeds (0.5 km/hour). Another useful parameter is “surface”, which sets the maximum speed for a given type of surface, regardless of the road class. As illustrated in the profile file above, there is no restriction on roads paved with “asphalt” or “concrete” but if a road is “Unpaved”, the speed is capped at 0.5 km/hour, which is equal to walking given the challenging terrain in the region. As seen in the speed profile after a project intervention in the figure, the “project” class disappears, which means they are upgraded to either a class IV ([“5”]) or class III ([“4”]) road and would adhere to the default speed of the respective classes. Also, the tag value of “surface” also need to be updated to “paved” in the attributes of the road network to remove the speed restriction on unpaved roads. The examples illustrated here is only a portion of the full profile file but highlights the most important elements to enable new users to navigate around and move forward with setting up the project.

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Profile Speeds of Roads of Different Class after PforR Intervention

Travel Time Routing Speed Classification Class

Km/hour Expressway National Provincial County Township Class III

Class IV Unclassified Unpaved

Surface Walking

Base line

Non-project 120 80 60 20 20

30 20 15 0.5 0.5

project 0.5 0.5 0.5

Upgrade Non-

project 120 80 60 20 20 30 20 15

0.5 0.5 project 30 20 15

After reviewing the Technical Standard of Highway Engineering (JTG B01-2014)10 and consulting with local engineers, the above speeds \ are determined for all the routing simulations under the Qianxinan Rural Transport project and agreed upon with the PCO. Taking in account the mountainous terrain and the poor condition of the rural roads in the region, it is assumed that the driving speed of project roads before the intervention as well as all unpaved roads is capped at 0.5 km/hr. Translate to OSM. In most cases, road network in GIS are created and stored in a shapefile, as is the case with the one for Guizhou. Hence, to allow OSRM to read the network and route it, there is a prior step to convert shapefile to OSM. The Rural Accessibility Mapping Platform has a built-in function (a python script

10 http://www.moc.gov.cn/zizhan/siju/gonglusi/

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called org2osm)11 that converts uploaded road network from shapefile to OSM. Also, OSM has its unique tag key and value that define map feature and attributes. For instance, the key “highway” is used to describe all roads and footpath, and different types of roads are further classified by the value “motorway”, “primary”, and etc. The complete tags for OSM map features can be referred at http://wiki.openstreetmap.org/wiki/Map_Features. For this reason, the shapefile attributes need to be renamed to match the OSM key and value. The user has the option to either edit the attributes in GIS software to conform to the OSM format before the upload or attach a python script that guides the org2osm tool in renaming during the conversion process. In the example below, the script is informing the conversion tool that if the attribute name in the shapefile is “Class”, update it to “highway” and, also it is worth noting the naming convention in OSM is all letters are lowercase.

Conversion File to Standardize Attributes to OSM Style

11 http://wiki.openstreetmap.org/wiki/Ogr2osm

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