Difference between revisions of "Training Material for UN Open GIS OpenData"

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(Created page with "== Introduction == === Scope === The following educational material has been drafted within the framework of the OSGeo UN Committee Educational Challenge - Open Geospatial Dat...")
 
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== Introduction ==
 
== Introduction ==
 
=== Scope ===
 
=== 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 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 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:  
 
The indicator has been chosen after a close analysis of all SGDs and the corresponding indicators as to comply with the following:  
(1) to have a spatial dimension;
+
# to have a spatial dimension;  
(2) 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.;
+
# 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.;  
(3) if possible, to not be yet the subject of a published methodology.  
+
# if possible, to not be yet the subject of a published methodology.  
 
===Audience ===
 
===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 have prepared this educational material for researchers, educators and professionals in local, regional, national or international agencies with minimal to intermediate geospatial information knowledge.  
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=== Data and software used ===
 
=== Data and software used ===
 
Datasets and software used
 
Datasets and software used
For the calculation of the SDG indicator, we have only used QGIS 3. 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.  
+
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:
 
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''<br />
 +
''Global Administrative Units Dataset description''<br />
 +
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.<br/>
 +
Yet, given our area of interest, the Tabora county from Tanzania, we must take into consideration specific developments for Africa, more precisely, the [https://wiki.openstreetmap.org/wiki/Highway_Tag_Africa Highway Tag Africa - Topology of Road Network in African countries], and furthermore, the [https://wiki.openstreetmap.org/wiki/East_Africa_Tagging_Guidelines East Africa Tagging Guidelines]. <br/>
 +
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.

Revision as of 01:25, 20 January 2019

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:

  1. to have a spatial dimension;
  2. 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.;
  3. 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.