Training Material for UN Open GIS OpenData
Introduction
Scope
The following educational material has been drafted within the framework of the OSGeo UN Committee Educational Challenge - Open Geospatial Data and software for UN sustainable development goals. The overarching goal is to show that at this time, the combination of open (geo)data globally available and the significant developments of the free and open source solutions for geospatial is sufficient to initiate geospatial analysis, at worldwide level, at small and intermediate scales, to better understand our ecosystem. In that respect, we have employed OSGeo software solutions to process global open geospatial datasets to answer one selected indicator for a sustainable development goal. The selected indicator is 9.1.1 Proportion of the rural population who live within 2 km of an all-season road (C0901010) which supports the target of developing quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all. The indicator has been chosen after a close analysis of all SGDs and the corresponding indicators as to comply with the following:
- to have a spatial dimension;
- to not be an indicator that is already addressed through another initiative, such as the GEO Wetlands Initiative, WHO Interactive Air Pollution Maps, GEO AquaWatch, ESA CoastColour etc.;
- if possible, to not be yet the subject of a published methodology.
Audience
We have prepared this educational material for researchers, educators and professionals in local, regional, national or international agencies with minimal to intermediate geospatial information knowledge. We assume our audience has already basic knowledge of geospatial data structures, formats and that they have already used a GIS software, as to have basic skills and understanding of how to work with geospatial and tabular data. In that respect, we have limited the interactions with the command line, however we have inserted references to it.
Data and software used
Datasets and software used For the calculation of the SDG indicator, we have only used QGIS 3.4. We have also taken into consideration that changes that might occur from one version to another and thus focused on the functions used more than on a step-by-step guide. The datasets used for our exercise are the following:
Acquired knowledge
After going through the entire educational material, one will be able to:
- Have a broader view on what are the types of geospatial data open at global scale, as well as what are their limitations.
- Have a more deeper understanding of working with geospatial data using a dedicated software
- Consistent knowledge of QGIS software fundamentals
- Learn how to create cartographic representations of the obtained results
Educational Material
Open geospatial information and its role in answering UN Sustainable Development Goals
Population dataset description
Global Administrative Units Dataset description
For road related data, we have decided to use OpenStreetMap data as it is the only homogeneously designed globally available dataset. Without doubt, the amount and the quality of the available data for various regions around the world can vary consistently. However, given the clear and consistent definition of each map element and tag, this exercise should be reproducible in any other part of the world.
Yet, given our area of interest, the Tabora county from Tanzania, we must take into consideration specific developments for Africa, more precisely, the Highway Tag Africa - Topology of Road Network in African countries, and furthermore, the East Africa Tagging Guidelines.
However, with consideration to the global replicability of our educational material, we will also insert specifications on a more general scale. Of course, it must be acknowledged that the workflow presented here could require other adjustments with respect to the specificity of the road dataset used in calculation.
Preparing the geospatial data
For the scope of this exercise we have chosen the Tabora county of Tanzania. As we strive to create an educational material that can be applied no matter the region of interest, a decision was made to use the available datasets, on a global level.
The following table presents the datasets used:
| Topic | Name collection/dataset | Abstract | Indicators | Produce/collector | Owner | License | Type of data | Format | Scale/spatial resolution | Edition | CRS | URL | Other URL | Header text |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Administrative units | Database of Global Administrative Areas | GADM provides maps and spatial data for all countries and their sub-divisions. | University of California, Berkeley,Museum of Vertebrate Zoology, and theInternational Rice Research Institute (Global Administrative Areas 2009) | GDAM | The data are freely available for academic use and other non-commercial use. Redistribution, or commercial use is not allowed without prior permission. | vector | Geopackage, shapefile, geodatabase. KMZ, R formats | n/a | April 2018 | Geographic WGS84 | https://gadm.org/index.html | https://gadm.org/metadata.html | Example | Example |
| World Population | WorldPop | Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example |
| World Population | Global Rural-Urban Mapping project (GRUMP), v1 | To provide a polygon representation of urban areas with city or agglomeration name and time series population estimates. | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example |
| World Population | Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat, v1 (2010) | To provide high spatial resolution estimates of global urban extent derived from global 30m Landsat satellite data for the target year 2010 and a companion dataset to the Global Man-made Impervious Surface (GMIS) dataset. | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example | Example |