GIS and Remote Sensing

Geographic Information Systems and Remote Sensing

2020 - UK - Using GIS to Measure the Rural Access Indicator New!
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The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It is now the key rural access indicator for the UN Sustainable Development Goals (SDGs) and has been incorporated as SDG 9.1.1. This measures the proportion of the rural population living within 2 km of an all-season road, using GIS layers and relying on three data sources: population, road network location and condition.There is potential to use open source GIS data for population and road location, but the most challenging aspect of the RAI is to define the all-season status of the road network.

The World Bank defines the term all-season as ‘a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration’. Every country measures its road condition in a different way and against different parameters, for example some countries use visual assessment, some use speed and some use road roughness. Similarly countries use different levels of condition, typically between three and five levels, for example Good, Fair and Poor. This makes finding consistency for the assessment of an all-season road between countries very challenging.

The UKAid funded programme Research for Community Access Partnership (ReCAP) has commissioned research to refine the methodology for assessing SDG 9.1.1 to make it more sustainable, repeatable and consistent by using geospatial data and tools. This is an important aspect of refining the RAI and has been trialled in four countries, Ghana, Malawi, Myanmar and Nepal; selected for their diversity of environment and data. Where existing condition data exists, paved roads have been considered as ‘all-season’ if they are in Good or Fair condition, whereas unpaved roads would need to be in Good condition to be considered as all-season. Whilst this provides an initial coarse estimate of all-season access, it ignores a number of important issues with rural road networks, where for example poor condition paved roads and fair condition unpaved roads could provide all-season access, which could significantly affect the measurement of RAI.

TRL have developed a method of using ‘Accessibility Factors’ to determine the allseason status of road networks, using GIS tools. These factors are applied to the population and network location layers and substitute the need to measure the road condition, which can be an onerous and expensive process for low income countries.

Road geometric design data are a vital input for diverse transportation studies. This information is usually obtained from the road design project. However, these are not always available and the as-built course of the road may diverge considerably from its projected one, rendering subsequent studies inaccurate or impossible. Moreover, the systematic acquisition of this data for the entire road network of a country or even a state represents a very challenging and laborious task. This study’s goal was the extraction of geometric design data for the paved segments of the Brazilian federal highway network, containing more than 47,000 km of highways. It presents the details of the method’s adoption process, the particularities of its application to the dataset and the obtained geometric design information. Additionally, it provides a first overview of the Brazilian federal highway network composition (curves and tangents) and geometry.

MSc thesis submitted by Ron Dalumpines to the International Institute for Geo-information Science and Earth Observation in the Netherlands.

The research explores the extraction of urban form/land use information in developing indicators to support TEF analysis using RS and GIS. Remotely sensed imagery provides a global information resource that when compared to traditional methods of data collection has the ability to provide data of an entire area, of areas that are difficult to access, at a greater frequency in acquiring data, reusable for different projects and can be cost efficient. GIS supports handling of spatial data from remotely sensed imagery and integrates it with other images and ancillary data from different sources. Recent RS and GIS applications can handle various spatial analyses and other data manipulation techniques considered useful for data mining, such as indicator extraction and quantification. In this study, urban RS plays a key role in providing thematic classifications (i.e., residential, commercial, institutional, and industrial classes) based on IRS-P6 satellite imagery. Perpixel classification methods, supervised and unsupervised, grey-level co-occurrence matrix texture measures and spatial metrics are explored in the extraction of four urban land uses for indicator quantification. The utility of freely available high-resolution Google Earth images supported by global positioning systems (GPS) are also explored in the process. The utilization of RS and GIS applications is further illustrated in the extraction and quantification of TEF-related indicators,   namely, density, proximity, trip distance estimate, and land-use mix.

Example results for the case study city of Ahmedabad in India provide preliminary insights into the challenges in deriving indicators from RS imagery for transport ecological footprint. This study shows that supervised classification method (overall accuracy = 54.87%, Kappa = 0.0706) have limited capability in extracting residential, commercial, institutional, and industrial building classes. Incorporation of optimal texture measures (Kappa = 0.1137) has a potential in improving per-pixel classification results. Applications of basic statistics, categorical analysis, and spatial metrics in quantifying RS-derived indicators demonstrate inconclusive (R2 = 0.007) links between urban form and TEF proxy, i.e. total number of trips. Hence, these methods are considered limited in assessing the transport ecological footprint of the city. For more insightful results, it is recommended that better image classification methods together with more sophisticated, model-enhanced indicators be employed. RS and GIS are highly applicable in this kind of endeavour.


Keywords: Remote sensing; GIS; Transport ecological footprint; Urban form; Image classification; GPS; Google Earth; IRS-P6; Spatial analysis; Spatial metrics

2007 - China - Urumqi Digital Road Data Review
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A report prepared for the city of Urumqi in China detailing their experiences in developing a digital map for their road management system. A series of different issues were encountered and the report serves as a good example of the types of problems that may arise in similar projects.

The National Road Network (NRN) conceptual model was elaborated in collaboration with interested data providers and is adopted by the Canadian Council on Geomatics (CCOG). The standard ISO 14825 — Intelligent transport systems — Geographic Data Files (GDF) — Overall data specification served as a guide for the elaboration of the NRN conceptual model. The NRN vocabulary used (class names and attribute names) largely conforms to the ISO 14825.

This document describes the NRN conceptual model according to a segmented view where segmentation is created on linear elements where an attribute value changes.

It also identifies classes and attributes that are part of the NRN, Edition 2.0 product distributed on the GeoBase portal ( The conceptual model is represented using UML (Unified Modeling Language) notation.

The following reports provide additional information:

The objective of this pilot was to create a simple participatory methodology capable of linking local level information and perspectives on mobility and access in the Senqu and Senqunyane River Valleys in southern Lesotho to the enhanced GIS at the Ministry of Public Works and Transport (MoPWT).
Paper on lessons learned and challenges encountered in the enhancement of an integrated GIS for the Ministry of Public Works and Transport (MoPWT) in Lesotho. The note briefly shares other innovative ways in which GIS is being used in transport programs in Latin America (Guatemala), South Asia (Mumbai), MENA (Yemen), and Africa, Madagascar, Ethiopia).
AS a follow-up to the needs assessment for enhancement of the GIS carried out in 2003, two workshops were held with the consultants from Landmat. This paper documents the discussion and outcomes from these workshops.
The component of the ITP that this report covers falls directly under this recommendation of databases, monitoring and evaluation systems for poverty reduction and is therefore of importance for the success of future IDA assistance in Lesotho, and is also likely to benefit other donor agencies supporting the transport sector in Lesotho.
This report is recommended to administrators and managers of transportation gencies as well as other agency personnel having responsibility for gathering, processing, and maintaining the information used by the agency. Geographic Information Systems (GIS) represent a powerful new means to efficiently manage and integrate the myriad types of information necessary for the planning, design, construction, analysis, operation, maintenance, and administration of transportation systems and facilities.