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Rural Accessibility Mapping Public Disclosure Authorized Report No: AUS0000817 Public Disclosure Authorized Rural Accessibility Mapping Public Disclosure Authorized Public Disclosure Authorized 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 Public Disclosure Authorized 1 © 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: [email protected]. 2 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 3 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). , = ∑1 ∑1 where: • denotes region • denotes the travel time threshold for reach their nearest destination • denotes all the villagers in region that can reach their nearest destination within time • denotes all the villagers in region 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 4 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 5 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
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